From 4a60b94a767b80b489d7916b0f3712b75bca30d7 Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Wed, 26 Nov 2025 05:33:03 +0000
Subject: [PATCH 001/100] Rebased

---
 example.py         | 27 ++++++++++++++++++++++++++
 pyproject.toml     | 26 +++++++++++++++++++++++++
 requirements.txt   | 48 +++++++++++++++++++++++++++-------------------
 src/piper.py       |  4 ++--
 src/piper_actor.py |  2 +-
 src/piper_exec.py  |  2 +-
 test/test_llama.py | 20 +++++++++++--------
 7 files changed, 97 insertions(+), 32 deletions(-)
 create mode 100644 example.py
 create mode 100644 pyproject.toml

diff --git a/example.py b/example.py
new file mode 100644
index 0000000..cc35729
--- /dev/null
+++ b/example.py
@@ -0,0 +1,27 @@
+import torch
+
+class Model(torch.nn.Module):
+   def __init__(self):
+      super().__init__()
+      self.linear = torch.nn.Linear(10, 10)
+
+   def forward(self, x):
+      return self.linear(x)
+
+def custom_backend(gm: torch.fx.GraphModule, *args, **kwargs):
+    print(gm.graph)
+    return gm
+
+torch._dynamo.config.compiled_autograd = True
+@torch.compile(backend=custom_backend)
+def train(model, x):
+   loss = model(x).sum()
+   loss.backward()
+
+def main():
+    model = Model()
+    x = torch.randn(10)
+    train(model, x)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..299661f
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,26 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "piper"
+version = "0.0.1"
+description = "Distributed pipeline parallelism with Ray"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "torch",
+    "ray"
+]
+
+[tool.setuptools.package-dir]
+"" = "src"
+
+[tool.setuptools]
+py-modules = [
+    "piper",
+    "piper_actor",
+    "piper_compile",
+    "piper_exec",
+    "piper_utils"
+]
diff --git a/requirements.txt b/requirements.txt
index 614eeb4..d4a210c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,42 +1,50 @@
 attrs==25.4.0
+bytecode==0.17.0
 certifi==2025.11.12
 charset-normalizer==3.4.4
 click==8.2.1
-filelock==3.19.1
-fsspec==2025.9.0
+cuda-bindings==12.9.4
+cuda-pathfinder==1.2.2
+filelock==3.20.0
+fsspec==2025.10.0
 idna==3.11
 jinja2==3.1.6
 jsonschema==4.25.1
 jsonschema-specifications==2025.9.1
-markupsafe==2.1.5
+markupsafe==3.0.2
 mpmath==1.3.0
 msgpack==1.1.2
-networkx==3.3
+networkx==3.4.2
 numpy==2.1.2
-nvidia-cublas-cu12==12.1.3.1
-nvidia-cuda-cupti-cu12==12.1.105
-nvidia-cuda-nvrtc-cu12==12.1.105
-nvidia-cuda-runtime-cu12==12.1.105
-nvidia-cudnn-cu12==9.1.0.70
-nvidia-cufft-cu12==11.0.2.54
-nvidia-curand-cu12==10.3.2.106
-nvidia-cusolver-cu12==11.4.5.107
-nvidia-cusparse-cu12==12.1.0.106
-nvidia-nccl-cu12==2.21.5
+nvidia-cublas-cu12==12.9.1.4
+nvidia-cuda-cupti-cu12==12.9.79
+nvidia-cuda-nvrtc-cu12==12.9.86
+nvidia-cuda-runtime-cu12==12.9.79
+nvidia-cudnn-cu12==9.10.2.21
+nvidia-cufft-cu12==11.4.1.4
+nvidia-cufile-cu12==1.14.1.1
+nvidia-curand-cu12==10.3.10.19
+nvidia-cusolver-cu12==11.7.5.82
+nvidia-cusparse-cu12==12.5.10.65
+nvidia-cusparselt-cu12==0.7.1
+nvidia-nccl-cu12==2.27.5
 nvidia-nvjitlink-cu12==12.9.86
-nvidia-nvtx-cu12==12.1.105
+nvidia-nvshmem-cu12==3.4.5
+nvidia-nvtx-cu12==12.9.79
 packaging==25.0
-pillow==11.3.0
+pillow==12.0.0
+-e file:///m-coriander/coriander/pgusto34/piper
 protobuf==6.33.1
+pytorch-triton==3.5.1+gitbfeb0668
 pyyaml==6.0.3
 ray==2.52.0
 referencing==0.37.0
 requests==2.32.5
 rpds-py==0.29.0
-sympy==1.13.1
-torch==2.5.1+cu121
-torchaudio==2.5.1+cu121
-torchvision==0.20.1+cu121
+sympy==1.14.0
+torch==2.10.0.dev20251123+cu129
+torchaudio==2.10.0.dev20251124+cu129
+torchvision==0.25.0.dev20251124+cu129
 triton==3.1.0
 typing-extensions==4.15.0
 urllib3==2.5.0
diff --git a/src/piper.py b/src/piper.py
index fbce940..3c804b4 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -1,9 +1,9 @@
 import ray
 import torch
-from .piper_actor import StageActor
+from piper_actor import StageActor
 from torch._dynamo.backends.registry import register_backend
 from torch._dynamo.decorators import _disallow_in_graph_helper
-from .piper_utils import RemoteTensor, serialize_graphmodule, piper_metadata
+from piper_utils import RemoteTensor, serialize_graphmodule, piper_metadata
 
 @_disallow_in_graph_helper(throw_if_not_allowed=False)
 def distributed_stage(stage_id, actor_id=None, mb=None, optim=None):
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 0fe368e..4b3ff0e 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -6,7 +6,7 @@
 from torch._guards import CompileId
 from torch.nn import Parameter
 from collections import defaultdict
-from .piper_utils import deserialize_graphmodule, piper_metadata
+from piper_utils import deserialize_graphmodule, piper_metadata
 
 torch.set_float32_matmul_precision('high')
 
diff --git a/src/piper_exec.py b/src/piper_exec.py
index 55ca1b0..023eea9 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -1,7 +1,7 @@
 import torch
 from typing import NamedTuple
 
-from .piper_utils import piper_metadata
+from piper_utils import piper_metadata
 
 class Task(NamedTuple):
     device_id: int
diff --git a/test/test_llama.py b/test/test_llama.py
index 9b41c25..2eb0a4b 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -5,10 +5,10 @@
 from torch import nn, optim
 from torch.profiler import profile, record_function, ProfilerActivity
 
-from src.piper_exec import Task, piper_exec
-from src.piper_compile import piper_setup
-from src.piper import piper
-from src.piper_utils import piper_metadata
+from piper_exec import Task, piper_exec
+from piper_compile import piper_setup
+from piper import piper
+from piper_utils import piper_metadata
 
 from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B
 from .schedule_helpers import build_1f1b_schedule, build_gpipe_schedule, print_schedule
@@ -105,10 +105,14 @@ def main(args):
     ray.get(actors[num_actors-1].send_truth.remote(y))
 
     schedule = None
-    if args.schedule == "interleaved-1f1b":
-        from .interleaved_grid_schedules import pp2_interleaved_1f1b_grid_schedule, pp4_interleaved_1f1b_grid_schedule
-        schedule = pp2_interleaved_1f1b_grid_schedule if num_devices == 2 else pp4_interleaved_1f1b_grid_schedule
-    elif args.schedule == "1f1b":
+    # if args.schedule == "interleaved-1f1b":
+    #     from .interleaved_grid_schedules import pp2_interleaved_1f1b_grid_schedule, pp4_interleaved_1f1b_grid_schedule
+    #     schedule = pp2_interleaved_1f1b_grid_schedule if num_devices == 2 else pp4_interleaved_1f1b_grid_schedule
+    # elif args.schedule == "1f1b":
+    #     schedule = build_1f1b_schedule(num_mbs, num_devices)
+    # elif args.schedule == "gpipe":
+    #     schedule = build_gpipe_schedule(num_mbs, num_devices)
+    if args.schedule == "1f1b":
         schedule = build_1f1b_schedule(num_mbs, num_devices)
     elif args.schedule == "gpipe":
         schedule = build_gpipe_schedule(num_mbs, num_devices)

From ebee722e127609d3203fa0b8e57f2cbe2e249b6b Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Wed, 26 Nov 2025 08:25:28 +0000
Subject: [PATCH 002/100] WIP

---
 example.py                               |  11 +-
 piper/__init__.py                        |   0
 src/piper_actor.py => piper/actor.py     |   7 +-
 src/piper_compile.py => piper/compile.py |   2 +-
 src/piper_exec.py => piper/exec.py       |   2 +-
 {src => piper}/piper.py                  |   4 +-
 src/piper_utils.py => piper/utils.py     |   8 ++
 pyproject.toml                           |  14 +--
 requirements.txt                         | 137 +++++++++++++++--------
 test/models/llama.py                     |   2 +-
 test/schedule_helpers.py                 |   2 +-
 test/test_llama.py                       |   8 +-
 test/test_simple.py                      |   5 +-
 13 files changed, 130 insertions(+), 72 deletions(-)
 create mode 100644 piper/__init__.py
 rename src/piper_actor.py => piper/actor.py (99%)
 rename src/piper_compile.py => piper/compile.py (96%)
 rename src/piper_exec.py => piper/exec.py (99%)
 rename {src => piper}/piper.py (97%)
 rename src/piper_utils.py => piper/utils.py (97%)

diff --git a/example.py b/example.py
index cc35729..92ee357 100644
--- a/example.py
+++ b/example.py
@@ -1,5 +1,13 @@
 import torch
 
+def split_backward_graph(gm: torch.fx.GraphModule):
+    """
+    Given a GraphModule representing a full forward+backward pass,
+    split it into two GraphModules: one for the backward weight pass and one for the backward input pass
+    """
+
+    return gm
+
 class Model(torch.nn.Module):
    def __init__(self):
       super().__init__()
@@ -24,4 +32,5 @@ def main():
     train(model, x)
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
+
diff --git a/piper/__init__.py b/piper/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/piper_actor.py b/piper/actor.py
similarity index 99%
rename from src/piper_actor.py
rename to piper/actor.py
index 4b3ff0e..279787c 100644
--- a/src/piper_actor.py
+++ b/piper/actor.py
@@ -6,7 +6,7 @@
 from torch._guards import CompileId
 from torch.nn import Parameter
 from collections import defaultdict
-from piper_utils import deserialize_graphmodule, piper_metadata
+from piper.utils import deserialize_graphmodule, piper_metadata, split_backward_graph
 
 torch.set_float32_matmul_precision('high')
 
@@ -22,6 +22,8 @@ def backward_backend(gm, example_inputs, **kwargs):
                 print(f"Arg {ex.shape} | parameter: {isinstance(ex, Parameter)} | requires_grad: {ex.requires_grad}")
     gm.print_readable()
 
+
+
     return gm
 
 
@@ -230,7 +232,8 @@ def unwrap(x):
         #     print(arg.shape, arg.requires_grad)
         # save first input as previous activation
         if stage_id != 0:
-            assert args[0].requires_grad
+            args[0].requires_grad_()
+            # assert args[0].requires_grad 
             self.prev_activation[stage_id][mb_idx] = args[0]
 
         out = self.compiled_fns[stage_id](*self.parameters[stage_id])
diff --git a/src/piper_compile.py b/piper/compile.py
similarity index 96%
rename from src/piper_compile.py
rename to piper/compile.py
index 3b62e0a..9217439 100644
--- a/src/piper_compile.py
+++ b/piper/compile.py
@@ -2,7 +2,7 @@
 import torch
 from torch._dynamo.backends.debugging import eager
 
-from .piper_utils import piper_metadata
+from piper.utils import piper_metadata
 
 def piper_setup(model, example_inputs, dynamic=False, backend=None):
     """
diff --git a/src/piper_exec.py b/piper/exec.py
similarity index 99%
rename from src/piper_exec.py
rename to piper/exec.py
index 023eea9..d6f8756 100644
--- a/src/piper_exec.py
+++ b/piper/exec.py
@@ -1,7 +1,7 @@
 import torch
 from typing import NamedTuple
 
-from piper_utils import piper_metadata
+from piper.utils import piper_metadata
 
 class Task(NamedTuple):
     device_id: int
diff --git a/src/piper.py b/piper/piper.py
similarity index 97%
rename from src/piper.py
rename to piper/piper.py
index 3c804b4..bcc774c 100644
--- a/src/piper.py
+++ b/piper/piper.py
@@ -1,9 +1,9 @@
 import ray
 import torch
-from piper_actor import StageActor
+from piper.actor import StageActor
 from torch._dynamo.backends.registry import register_backend
 from torch._dynamo.decorators import _disallow_in_graph_helper
-from piper_utils import RemoteTensor, serialize_graphmodule, piper_metadata
+from piper.utils import RemoteTensor, serialize_graphmodule, piper_metadata
 
 @_disallow_in_graph_helper(throw_if_not_allowed=False)
 def distributed_stage(stage_id, actor_id=None, mb=None, optim=None):
diff --git a/src/piper_utils.py b/piper/utils.py
similarity index 97%
rename from src/piper_utils.py
rename to piper/utils.py
index c2ac217..82c1ff5 100644
--- a/src/piper_utils.py
+++ b/piper/utils.py
@@ -292,4 +292,12 @@ def deserialize_graphmodule(s: str) -> fx.GraphModule:
     gm = fx.GraphModule(torch.nn.Module(), g)
     state = {k: torch.tensor(v) for k, v in data["state_dict"].items()}
     gm.load_state_dict(state, strict=False)
+    return gm
+
+def split_backward_graphs(gm: fx.GraphModule):
+    """
+    Given a GraphModule representing a full forward+backward pass,
+    split it into two GraphModules: one for the backward weight pass and one for the backward input pass
+    """
+
     return gm
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 299661f..6b1f300 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,14 +13,6 @@ dependencies = [
     "ray"
 ]
 
-[tool.setuptools.package-dir]
-"" = "src"
-
-[tool.setuptools]
-py-modules = [
-    "piper",
-    "piper_actor",
-    "piper_compile",
-    "piper_exec",
-    "piper_utils"
-]
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["piper*"]
diff --git a/requirements.txt b/requirements.txt
index d4a210c..cbce940 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,50 +1,95 @@
-attrs==25.4.0
-bytecode==0.17.0
-certifi==2025.11.12
-charset-normalizer==3.4.4
-click==8.2.1
-cuda-bindings==12.9.4
-cuda-pathfinder==1.2.2
-filelock==3.20.0
-fsspec==2025.10.0
-idna==3.11
-jinja2==3.1.6
-jsonschema==4.25.1
-jsonschema-specifications==2025.9.1
-markupsafe==3.0.2
+ansible-core==2.14.18
+argcomplete==1.12.0
+bcc==0.32.0
+Brlapi==0.8.2
+cffi==1.14.5
+chardet==4.0.0
+cockpit @ file:///builddir/build/BUILD/cockpit-334.2/tmp/wheel/cockpit-334.2-py3-none-any.whl
+configshell-fb==1.1.30
+cryptography==36.0.1
+cupshelpers==1.0
+dasbus==1.4
+dbus-python==1.2.18
+decorator==4.4.2
+distro==1.5.0
+file-magic==0.4.0
+filelock==3.13.1
+fsspec==2024.6.1
+gpg==1.15.1
+idna==2.10
+importlib_metadata==8.7.0
+iniparse==0.4
+iotop==0.6
+isc==2.0
+Jinja2==3.1.4
+jsonpointer==2.0
+kmod==0.1
+libcomps==0.1.18
+libvirt-python==10.10.0
+lxml==4.6.5
+Mako==1.1.4.dev0
+Markdown==3.3.4
+MarkupSafe==2.1.5
 mpmath==1.3.0
-msgpack==1.1.2
-networkx==3.4.2
-numpy==2.1.2
-nvidia-cublas-cu12==12.9.1.4
-nvidia-cuda-cupti-cu12==12.9.79
-nvidia-cuda-nvrtc-cu12==12.9.86
-nvidia-cuda-runtime-cu12==12.9.79
+netaddr==0.10.1
+networkx==3.2.1
+nftables==0.1
+numpy==2.0.2
+nvidia-cublas-cu12==12.8.4.1
+nvidia-cuda-cupti-cu12==12.8.90
+nvidia-cuda-nvrtc-cu12==12.8.93
+nvidia-cuda-runtime-cu12==12.8.90
 nvidia-cudnn-cu12==9.10.2.21
-nvidia-cufft-cu12==11.4.1.4
-nvidia-cufile-cu12==1.14.1.1
-nvidia-curand-cu12==10.3.10.19
-nvidia-cusolver-cu12==11.7.5.82
-nvidia-cusparse-cu12==12.5.10.65
+nvidia-cufft-cu12==11.3.3.83
+nvidia-cufile-cu12==1.13.1.3
+nvidia-curand-cu12==10.3.9.90
+nvidia-cusolver-cu12==11.7.3.90
+nvidia-cusparse-cu12==12.5.8.93
 nvidia-cusparselt-cu12==0.7.1
-nvidia-nccl-cu12==2.27.5
-nvidia-nvjitlink-cu12==12.9.86
-nvidia-nvshmem-cu12==3.4.5
-nvidia-nvtx-cu12==12.9.79
-packaging==25.0
-pillow==12.0.0
--e file:///m-coriander/coriander/pgusto34/piper
-protobuf==6.33.1
-pytorch-triton==3.5.1+gitbfeb0668
-pyyaml==6.0.3
-ray==2.52.0
-referencing==0.37.0
-requests==2.32.5
-rpds-py==0.29.0
+nvidia-nccl-cu12==2.27.3
+nvidia-nvjitlink-cu12==12.8.93
+nvidia-nvtx-cu12==12.8.90
+packaging==20.9
+pcp==5.0
+perf==0.1
+pexpect==4.8.0
+ply==3.11
+psutil==5.8.0
+psycopg2==2.8.6
+ptyprocess==0.6.0
+py-spy==0.4.0
+pycairo==1.20.1
+pycparser==2.20
+pycups==2.0.1
+pycurl==7.43.0.6
+pyelftools==0.27
+PyGObject==3.40.1
+pyinotify==0.9.6
+pyodbc===4.0.0-unsupported
+pyparsing==2.4.7
+PySocks==1.7.1
+python-dateutil==2.8.1
+python-linux-procfs==0.7.3
+pyudev==0.22.0
+PyYAML==5.4.1
+requests==2.25.1
+resolvelib==0.5.4
+rpm==4.16.1.3
+rtslib-fb==2.1.76
+scapy==2.6.1
+selinux==3.6
+sepolicy==3.6
+setools==4.4.4
+setroubleshoot @ file:///builddir/build/BUILD/setroubleshoot-3.3.32/src
+six==1.15.0
+sos==4.10.0
+subscription-manager==1.29.45.1
 sympy==1.14.0
-torch==2.10.0.dev20251123+cu129
-torchaudio==2.10.0.dev20251124+cu129
-torchvision==0.25.0.dev20251124+cu129
-triton==3.1.0
-typing-extensions==4.15.0
-urllib3==2.5.0
+systemd-python==234
+targetcli-fb==2.1.57
+torch==2.8.0
+triton==3.4.0
+typing_extensions==4.15.0
+urllib3==1.26.5
+urwid==2.1.2
+zipp==3.23.0
diff --git a/test/models/llama.py b/test/models/llama.py
index e4812f6..0a11fb4 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -14,7 +14,7 @@
 from torch import Tensor, nn
 from torch.nn.utils import parameters_to_vector
 
-from src.piper import distributed_stage
+from piper.piper import distributed_stage
 
 # import fairscale.nn.model_parallel.initialize as fs_init
 # from fairscale.nn.model_parallel.layers import (
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index 64bc54e..5fa9f65 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -1,4 +1,4 @@
-from src.piper_exec import Task
+from piper.exec import Task
 
 
 def print_schedule(schedule):
diff --git a/test/test_llama.py b/test/test_llama.py
index 2eb0a4b..b28d633 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -5,10 +5,10 @@
 from torch import nn, optim
 from torch.profiler import profile, record_function, ProfilerActivity
 
-from piper_exec import Task, piper_exec
-from piper_compile import piper_setup
-from piper import piper
-from piper_utils import piper_metadata
+from piper.exec import Task, piper_exec
+from piper.compile import piper_setup
+from piper.piper import piper
+from piper.utils import piper_metadata
 
 from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B
 from .schedule_helpers import build_1f1b_schedule, build_gpipe_schedule, print_schedule
diff --git a/test/test_simple.py b/test/test_simple.py
index 94f1dfe..595ef24 100644
--- a/test/test_simple.py
+++ b/test/test_simple.py
@@ -1,7 +1,8 @@
 import torch
 import ray
 from torch import nn
-from src.piper import piper, distributed_stage
+from torch.export import export, ExportedProgram
+from piper.piper import piper, distributed_stage
 from collections.abc import Iterable
 
 class MyModel(nn.Module):
@@ -30,7 +31,7 @@ def forward(self, x):
 
 # Manually run a training step on a single batch
 
-from src.piper_utils import piper_metadata
+from piper.utils import piper_metadata
 actors = piper_metadata['actors']
 
 # forward pass (executing with fused forwards, for now)

From 88a2eafeb38a1bfe08092adf645182648804aa9f Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Wed, 26 Nov 2025 08:36:08 +0000
Subject: [PATCH 003/100] Updated requirements.txt

---
 requirements.txt | 177 +++++++++++++++++++++++------------------------
 1 file changed, 87 insertions(+), 90 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index cbce940..b41f0ae 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,95 +1,92 @@
-ansible-core==2.14.18
-argcomplete==1.12.0
-bcc==0.32.0
-Brlapi==0.8.2
-cffi==1.14.5
-chardet==4.0.0
-cockpit @ file:///builddir/build/BUILD/cockpit-334.2/tmp/wheel/cockpit-334.2-py3-none-any.whl
-configshell-fb==1.1.30
-cryptography==36.0.1
-cupshelpers==1.0
-dasbus==1.4
-dbus-python==1.2.18
-decorator==4.4.2
-distro==1.5.0
-file-magic==0.4.0
-filelock==3.13.1
-fsspec==2024.6.1
-gpg==1.15.1
-idna==2.10
-importlib_metadata==8.7.0
-iniparse==0.4
-iotop==0.6
-isc==2.0
-Jinja2==3.1.4
-jsonpointer==2.0
-kmod==0.1
-libcomps==0.1.18
-libvirt-python==10.10.0
-lxml==4.6.5
-Mako==1.1.4.dev0
-Markdown==3.3.4
-MarkupSafe==2.1.5
+aiohappyeyeballs==2.6.1
+aiohttp==3.13.2
+aiohttp-cors==0.8.1
+aiosignal==1.4.0
+annotated-types==0.7.0
+async-timeout==5.0.1
+attrs==25.4.0
+bytecode==0.17.0
+cachetools==6.2.2
+certifi==2025.11.12
+charset-normalizer==3.4.4
+click==8.2.1
+colorful==0.5.8
+cuda-bindings==12.9.4
+cuda-pathfinder==1.2.2
+cupy-cuda12x==13.6.0
+distlib==0.4.0
+fastrlock==0.8.3
+filelock==3.20.0
+frozenlist==1.8.0
+fsspec==2025.10.0
+google-api-core==2.28.1
+google-auth==2.43.0
+googleapis-common-protos==1.72.0
+grpcio==1.76.0
+idna==3.11
+importlib-metadata==8.7.0
+jinja2==3.1.6
+jsonschema==4.25.1
+jsonschema-specifications==2025.9.1
+markupsafe==3.0.2
 mpmath==1.3.0
-netaddr==0.10.1
-networkx==3.2.1
-nftables==0.1
-numpy==2.0.2
-nvidia-cublas-cu12==12.8.4.1
-nvidia-cuda-cupti-cu12==12.8.90
-nvidia-cuda-nvrtc-cu12==12.8.93
-nvidia-cuda-runtime-cu12==12.8.90
+msgpack==1.1.2
+multidict==6.7.0
+networkx==3.4.2
+numpy==2.1.2
+nvidia-cublas-cu12==12.9.1.4
+nvidia-cuda-cupti-cu12==12.9.79
+nvidia-cuda-nvrtc-cu12==12.9.86
+nvidia-cuda-runtime-cu12==12.9.79
 nvidia-cudnn-cu12==9.10.2.21
-nvidia-cufft-cu12==11.3.3.83
-nvidia-cufile-cu12==1.13.1.3
-nvidia-curand-cu12==10.3.9.90
-nvidia-cusolver-cu12==11.7.3.90
-nvidia-cusparse-cu12==12.5.8.93
+nvidia-cufft-cu12==11.4.1.4
+nvidia-cufile-cu12==1.14.1.1
+nvidia-curand-cu12==10.3.10.19
+nvidia-cusolver-cu12==11.7.5.82
+nvidia-cusparse-cu12==12.5.10.65
 nvidia-cusparselt-cu12==0.7.1
-nvidia-nccl-cu12==2.27.3
-nvidia-nvjitlink-cu12==12.8.93
-nvidia-nvtx-cu12==12.8.90
-packaging==20.9
-pcp==5.0
-perf==0.1
-pexpect==4.8.0
-ply==3.11
-psutil==5.8.0
-psycopg2==2.8.6
-ptyprocess==0.6.0
-py-spy==0.4.0
-pycairo==1.20.1
-pycparser==2.20
-pycups==2.0.1
-pycurl==7.43.0.6
-pyelftools==0.27
-PyGObject==3.40.1
-pyinotify==0.9.6
-pyodbc===4.0.0-unsupported
-pyparsing==2.4.7
-PySocks==1.7.1
-python-dateutil==2.8.1
-python-linux-procfs==0.7.3
-pyudev==0.22.0
-PyYAML==5.4.1
-requests==2.25.1
-resolvelib==0.5.4
-rpm==4.16.1.3
-rtslib-fb==2.1.76
-scapy==2.6.1
-selinux==3.6
-sepolicy==3.6
-setools==4.4.4
-setroubleshoot @ file:///builddir/build/BUILD/setroubleshoot-3.3.32/src
-six==1.15.0
-sos==4.10.0
-subscription-manager==1.29.45.1
+nvidia-nccl-cu12==2.27.5
+nvidia-nvjitlink-cu12==12.9.86
+nvidia-nvshmem-cu12==3.4.5
+nvidia-nvtx-cu12==12.9.79
+opencensus==0.11.4
+opencensus-context==0.1.3
+opentelemetry-api==1.38.0
+opentelemetry-exporter-prometheus==0.59b0
+opentelemetry-proto==1.38.0
+opentelemetry-sdk==1.38.0
+opentelemetry-semantic-conventions==0.59b0
+packaging==25.0
+pillow==12.0.0
+-e file:///m-coriander/coriander/pgusto34/piper
+platformdirs==4.5.0
+prometheus-client==0.23.1
+propcache==0.4.1
+proto-plus==1.26.1
+protobuf==6.33.1
+py-spy==0.4.1
+pyasn1==0.6.1
+pyasn1-modules==0.4.2
+pydantic==2.12.4
+pydantic-core==2.41.5
+pytorch-triton==3.5.1+gitbfeb0668
+pyyaml==6.0.3
+ray==2.52.0
+referencing==0.37.0
+requests==2.32.5
+rpds-py==0.29.0
+rsa==4.9.1
+six==1.17.0
+smart-open==7.5.0
 sympy==1.14.0
-systemd-python==234
-targetcli-fb==2.1.57
-torch==2.8.0
-triton==3.4.0
-typing_extensions==4.15.0
-urllib3==1.26.5
-urwid==2.1.2
+torch==2.10.0.dev20251123+cu129
+torchaudio==2.10.0.dev20251124+cu129
+torchvision==0.25.0.dev20251124+cu129
+triton==3.1.0
+typing-extensions==4.15.0
+typing-inspection==0.4.2
+urllib3==2.5.0
+virtualenv==20.35.4
+wrapt==2.0.1
+yarl==1.22.0
 zipp==3.23.0

From c766869b945555da285206fa4cfad0e9c8c33d50 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Tue, 25 Nov 2025 21:36:51 +0000
Subject: [PATCH 004/100] stencil backwards_compile task on StageActor captures
 a partial backward graph

---
 src/piper.py         |   8 +--
 src/piper_actor.py   |  62 +++++++++++++++++++--
 src/piper_compile.py | 128 +------------------------------------------
 src/piper_utils.py   |   2 +-
 test/test_simple.py  |  32 +++++++++--
 5 files changed, 92 insertions(+), 140 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index 413e8cb..fbce940 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -75,7 +75,7 @@ def int_to_tensor(x):
 
     # return a wrapper function that runs the fx.Graph on the actor and 
     # returns remote futures for each graph output
-    def overwrite_compiled_fn(*args):
+    def run_remote_subgraph(*args):
         # ignore model parameter arguments (stored on the actor)
         input_tensors_only = []
         for i in input_idxs:
@@ -95,10 +95,10 @@ def unwrap(x):
 
         if piper_metadata['currently_compiling']:
             # dispatch task without nccl transport
-            refs = actor.call_cpu.options(num_returns=len(fakes)).remote(stage_id, mb_idx, *args)
+            refs = actor.forward_no_nccl.options(num_returns=len(fakes)).remote(stage_id, mb_idx, *args)
         else:
             # dispatch with nccl transport
-            refs = actor.call.options(num_returns=len(fakes)).remote(stage_id, mb_idx, *args)
+            refs = actor.forward.options(num_returns=len(fakes)).remote(stage_id, mb_idx, *args)
 
         # wrap the remote futures with RemoteTensor
         if isinstance(refs, list):
@@ -107,4 +107,4 @@ def unwrap(x):
         else:
             assert len(fakes) == 1
             return [RemoteTensor(fakes[0], refs, stage_id)]
-    return overwrite_compiled_fn
\ No newline at end of file
+    return run_remote_subgraph
\ No newline at end of file
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 2177cbd..0fe368e 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -4,11 +4,27 @@
 import os
 import time
 from torch._guards import CompileId
+from torch.nn import Parameter
 from collections import defaultdict
 from .piper_utils import deserialize_graphmodule, piper_metadata
 
 torch.set_float32_matmul_precision('high')
 
+torch._dynamo.config.compiled_autograd = True
+def backward_backend(gm, example_inputs, **kwargs):
+    print("BACKWARD GRAPH")
+
+    placeholders = gm.graph.find_nodes(op="placeholder")
+    graphargs = [node.meta["grapharg"] for node in placeholders]
+    for arg in graphargs:
+        if isinstance(arg._example, list) or isinstance(arg._example, tuple):
+            for ex in arg._example:
+                print(f"Arg {ex.shape} | parameter: {isinstance(ex, Parameter)} | requires_grad: {ex.requires_grad}")
+    gm.print_readable()
+
+    return gm
+
+
 @ray.remote
 class StageActor:
     # def __init__(self, id, compiler_fn, example_inputs, parameters, optim_fn=None):
@@ -113,7 +129,7 @@ def compile_graph(self, stage_id, gm_data, compiler_fn, graphargs, input_idxs):
         return "Finished compiling"
 
     @ray.method(tensor_transport="nccl")
-    def call(self, stage_id: int, mb_idx: int, *args):
+    def forward(self, stage_id: int, mb_idx: int, *args):
         self.log.info(f"Calling forward {stage_id} mb {mb_idx} on actor {self.actor_id} with {len(args)} args")
         
         if self.tracing:
@@ -186,7 +202,7 @@ def unwrap(x):
                 self.fwd_objs[stage_id] = torch.ones_like(out)
         return out
 
-    def call_cpu(self, stage_id: int, mb_idx: int, *args):
+    def forward_no_nccl(self, stage_id: int, mb_idx: int, *args):
         self.log.info(f"Calling cpu forward {stage_id} mb {mb_idx} on actor {self.actor_id} with {len(args)} args")
 
         def pre_loaded_input(param):
@@ -273,9 +289,9 @@ def backward(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
         del self.activation[stage_id][mb_idx]
         del activation
 
+        # propagate the gradient backwards if not the first stage
         if stage_id != 0:
-            ret = [self.prev_activation[stage_id][mb_idx].grad.clone()]
-            del self.prev_activation[stage_id][mb_idx]
+            ret = [self.prev_activation[stage_id][mb_idx]]
         else:
             ret = ["done"]
         
@@ -298,6 +314,44 @@ def backward(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
                 self.bwd_objs[stage_id] = torch.ones_like(ret[0])
         return ret + ret
 
+    def backward_compile(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
+        self.log.info(f"Calling backward {stage_id} mb {mb_idx} on actor {self.actor_id}", inp)
+        
+        # get the activation for the current stage
+        activation = self.activation[stage_id][mb_idx]
+
+        # Compile the backward pass
+        @torch.compile(backend=backward_backend)
+        def backward_loss(loss_fn, activation, labels):
+            loss = loss_fn(activation, labels)
+            loss.backward()
+            return loss
+        @torch.compile(backend=backward_backend)
+        def backward_activation(activation, inp):
+            activation.backward(gradient=inp)
+
+        # compute loss in the last stage. use the saved activation rather
+        # remembers the computation graph, inp should be the labels
+        if loss_fn is not None:
+            labels = inp[0]
+            loss = backward_loss(loss_fn, activation, labels)
+            self.loss.append(loss)
+
+        # if not the last stage, backprop on the stored activation given 
+        # the input gradient from the subsequent stage
+        else:
+            grad = inp[0]
+            assert activation.shape == grad.shape
+            backward_activation(activation, grad)
+
+        # propagate the gradient backwards if not the first stage
+        if stage_id != 0:
+            ret = [self.prev_activation[stage_id][mb_idx]]
+        else:
+            ret = ["done"]
+
+        return ret
+
     def update(self, *done_mbs):
         self.log.debug(f"Calling update on actor {self.actor_id}")
         
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 46e92a2..3b62e0a 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -1,97 +1,9 @@
-
-"""
-Split forward functions at __resume_at_* globals.
-This is to allow for interleaving forward tasks with other tasks.
-"""
-
-import dis, functools
-from dataclasses import dataclass
-from typing import Optional, Callable
-from bytecode import Bytecode, Instr, Label
-
 import ray
-
-from .piper_utils import piper_metadata, RemoteTensor
-
-@dataclass(frozen=True)
-class CallMarker:
-    next_call: str
-    args: tuple
-    kwargs: dict
-
-def _make_stub(name: str) -> Callable:
-    def _stub(*args, **kwargs):
-        return CallMarker(name, args, kwargs)
-    _stub.__name__ = f"__stub_{name}"
-    return _stub
-
-def _find_resume_name(fn: Callable) -> Optional[str]:
-    instrs = list(dis.get_instructions(fn))
-    for i, ins in enumerate(instrs):
-        if isinstance(ins.argval, str) and ins.argval.startswith("__resume_at_"):
-            return ins.argval
-    return None
-
-def _should_split(code: Bytecode) -> bool:
-    """
-    Return True iff there is a LOAD_ATTR distributed_stage before
-    a call to a __resume_at_* global.
-    """
-    for instr in code:
-        if hasattr(instr, "name") and hasattr(instr, "arg"):
-            if instr.name == "LOAD_ATTR" and instr.arg == "distributed_stage":
-                return True
-    return False
-
-def split_before(fn: Callable):
-    """
-    Wrap `fn`. If `fn` contains a single call to a name starting with "__resume_at_",
-    the wrapper replaces that global with a stub that returns CallMarker(args, kwargs).
-    The wrapper accepts either:
-      - normal calling form: wrapper(*args, **kwargs)
-      - chained form: wrapper(marker) where marker is CallMarker
-    In chained form the marker's args/kwargs are used as the call to `fn`.
-    """
-    code = Bytecode.from_code(fn.__code__)
-    g = fn.__globals__
-    resume_name = _find_resume_name(fn)
-    should_split = _should_split(code)
-
-    @functools.wraps(fn)
-    def wrapper(*args, **kwargs):
-        # allow chained form: single CallMarker positional argument
-        if len(args) == 1 and not kwargs and isinstance(args[0], CallMarker):
-            call_args, call_kwargs = args[0].args, args[0].kwargs
-        else:
-            call_args, call_kwargs = args, kwargs
-
-        # if no resume_name found, just invoke the function (still accept marker)
-        if not should_split:
-            return fn(*call_args, **call_kwargs)
-        
-        # print(f"Stubbing {fn}")
-
-        assert resume_name is not None
-        
-        # stub the target resume global, call, then restore
-        saved = {}
-        if resume_name in g:
-            # print("STUBBING", resume_name)
-            saved[resume_name] = g[resume_name]
-            g[resume_name] = _make_stub(resume_name)
-        try:
-            return fn(*call_args, **call_kwargs)
-        finally:
-            # restore original binding if we changed it
-            if resume_name in saved:
-                g[resume_name] = saved[resume_name]
-
-    return wrapper
-
-
 import torch
 from torch._dynamo.backends.debugging import eager
 
+from .piper_utils import piper_metadata
+
 def piper_setup(model, example_inputs, dynamic=False, backend=None):
     """
     Compile a model with the piper backend.
@@ -114,43 +26,9 @@ def piper_setup(model, example_inputs, dynamic=False, backend=None):
 
     print("[PIPER] Compiling...")
 
-    def make_remote(x):
-        return RemoteTensor(x, ray.put(x))
-    # example_inputs = list(map(make_remote, example_inputs))
-    out = compiled(*example_inputs).get()
+    out = compiled(*example_inputs)
 
     print("[PIPER] Compiled...")
-    
-    # import inspect
-    # output_codes = torch._dynamo.convert_frame.output_codes
-    # transformed_fns = {}
-    # for name, fn in output_codes.piper_fns.items():
-    #     transformed_fns[name] = split_before(fn)
-
-    # out = (model, *example_inputs)
-    # next_call = "forward"
-    # fwd_fns = []
-    # while next_call:
-    #     fn = transformed_fns[next_call]
-    #     fwd_fns.append(fn)
-    #     if next_call == "forward":
-    #         out = fn(*out, dynamo_mb=999)
-    #     elif isinstance(out, list) or isinstance(out, tuple):
-    #         out = fn(*out)
-    #     else:
-    #         out = fn(out)
-    #     if isinstance(out, CallMarker):
-    #         next_call = out.next_call
-    #     else:
-    #         next_call = None
-
-    # setattr(compiled, "_piper_fwd_fns", fwd_fns)
-
-    # # MEMORY CLEANUP
-    # torch._dynamo.convert_frame.output_codes.piper_fns.clear()
-    # torch._dynamo.convert_frame.output_codes.name_map.clear()
-    # import gc
-    # gc.collect()
 
     piper_metadata['currently_compiling'] = False
 
diff --git a/src/piper_utils.py b/src/piper_utils.py
index 002e264..c2ac217 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -17,7 +17,7 @@
 piper_metadata['actors'] = dict()
 piper_metadata['stage_fns'] = dict()
 piper_metadata['dag'] = set()
-
+piper_metadata['currently_compiling'] = True
 
 """
 Remote tensors wrap Ray ObjectRefs
diff --git a/test/test_simple.py b/test/test_simple.py
index fddf50d..94f1dfe 100644
--- a/test/test_simple.py
+++ b/test/test_simple.py
@@ -1,6 +1,8 @@
 import torch
+import ray
 from torch import nn
 from src.piper import piper, distributed_stage
+from collections.abc import Iterable
 
 class MyModel(nn.Module):
     def __init__(self):
@@ -11,17 +13,35 @@ def __init__(self):
         self.relu = nn.ReLU()
 
     def forward(self, x):
-        distributed_stage(0)
+        distributed_stage(0, optim=torch.optim.Adam)
         x = self.relu(self.fc1(x))
-        distributed_stage(1)
+        distributed_stage(1, optim=torch.optim.Adam)
         x = self.relu(self.fc2(x))
-        distributed_stage(2)
+        distributed_stage(2, optim=torch.optim.Adam)
         x = self.fc3(x)
         return x
 
-model = torch.compile(MyModel(), backend=piper)
+# Compile the model
 
-x = torch.randn(10, 1024)
+model = torch.compile(MyModel().to('cuda'), backend=piper)
+x = torch.randn(10, 1024).to('cuda')
+y = torch.randn(10, 10).to('cuda')
 out = model(x).get()
-print(out)
 
+# Manually run a training step on a single batch
+
+from src.piper_utils import piper_metadata
+actors = piper_metadata['actors']
+
+# forward pass (executing with fused forwards, for now)
+model(x).get()
+
+# backwards pass, compiling the backward graph for each stage
+grad_ref = actors[2].backward_compile.remote(stage_id=2, mb_idx=None, inp=[y], loss_fn=torch.nn.functional.mse_loss)
+grad_ref = actors[1].backward_compile.remote(stage_id=1, mb_idx=None, inp=grad_ref)
+grad_ref = actors[0].backward_compile.remote(stage_id=0, mb_idx=None, inp=grad_ref)
+
+ray.wait([grad_ref])
+
+import time
+time.sleep(2) # wait for logs to print
\ No newline at end of file

From 30401643e5082d8ab527b6e8cdd0edcc33d07ec4 Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Wed, 26 Nov 2025 05:33:03 +0000
Subject: [PATCH 005/100] Rebased

---
 example.py         | 27 ++++++++++++++++++++++++++
 pyproject.toml     | 26 +++++++++++++++++++++++++
 requirements.txt   | 48 +++++++++++++++++++++++++++-------------------
 src/piper.py       |  4 ++--
 src/piper_actor.py |  2 +-
 src/piper_exec.py  |  2 +-
 test/test_llama.py | 20 +++++++++++--------
 7 files changed, 97 insertions(+), 32 deletions(-)
 create mode 100644 example.py
 create mode 100644 pyproject.toml

diff --git a/example.py b/example.py
new file mode 100644
index 0000000..cc35729
--- /dev/null
+++ b/example.py
@@ -0,0 +1,27 @@
+import torch
+
+class Model(torch.nn.Module):
+   def __init__(self):
+      super().__init__()
+      self.linear = torch.nn.Linear(10, 10)
+
+   def forward(self, x):
+      return self.linear(x)
+
+def custom_backend(gm: torch.fx.GraphModule, *args, **kwargs):
+    print(gm.graph)
+    return gm
+
+torch._dynamo.config.compiled_autograd = True
+@torch.compile(backend=custom_backend)
+def train(model, x):
+   loss = model(x).sum()
+   loss.backward()
+
+def main():
+    model = Model()
+    x = torch.randn(10)
+    train(model, x)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..299661f
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,26 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "piper"
+version = "0.0.1"
+description = "Distributed pipeline parallelism with Ray"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "torch",
+    "ray"
+]
+
+[tool.setuptools.package-dir]
+"" = "src"
+
+[tool.setuptools]
+py-modules = [
+    "piper",
+    "piper_actor",
+    "piper_compile",
+    "piper_exec",
+    "piper_utils"
+]
diff --git a/requirements.txt b/requirements.txt
index 614eeb4..d4a210c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,42 +1,50 @@
 attrs==25.4.0
+bytecode==0.17.0
 certifi==2025.11.12
 charset-normalizer==3.4.4
 click==8.2.1
-filelock==3.19.1
-fsspec==2025.9.0
+cuda-bindings==12.9.4
+cuda-pathfinder==1.2.2
+filelock==3.20.0
+fsspec==2025.10.0
 idna==3.11
 jinja2==3.1.6
 jsonschema==4.25.1
 jsonschema-specifications==2025.9.1
-markupsafe==2.1.5
+markupsafe==3.0.2
 mpmath==1.3.0
 msgpack==1.1.2
-networkx==3.3
+networkx==3.4.2
 numpy==2.1.2
-nvidia-cublas-cu12==12.1.3.1
-nvidia-cuda-cupti-cu12==12.1.105
-nvidia-cuda-nvrtc-cu12==12.1.105
-nvidia-cuda-runtime-cu12==12.1.105
-nvidia-cudnn-cu12==9.1.0.70
-nvidia-cufft-cu12==11.0.2.54
-nvidia-curand-cu12==10.3.2.106
-nvidia-cusolver-cu12==11.4.5.107
-nvidia-cusparse-cu12==12.1.0.106
-nvidia-nccl-cu12==2.21.5
+nvidia-cublas-cu12==12.9.1.4
+nvidia-cuda-cupti-cu12==12.9.79
+nvidia-cuda-nvrtc-cu12==12.9.86
+nvidia-cuda-runtime-cu12==12.9.79
+nvidia-cudnn-cu12==9.10.2.21
+nvidia-cufft-cu12==11.4.1.4
+nvidia-cufile-cu12==1.14.1.1
+nvidia-curand-cu12==10.3.10.19
+nvidia-cusolver-cu12==11.7.5.82
+nvidia-cusparse-cu12==12.5.10.65
+nvidia-cusparselt-cu12==0.7.1
+nvidia-nccl-cu12==2.27.5
 nvidia-nvjitlink-cu12==12.9.86
-nvidia-nvtx-cu12==12.1.105
+nvidia-nvshmem-cu12==3.4.5
+nvidia-nvtx-cu12==12.9.79
 packaging==25.0
-pillow==11.3.0
+pillow==12.0.0
+-e file:///m-coriander/coriander/pgusto34/piper
 protobuf==6.33.1
+pytorch-triton==3.5.1+gitbfeb0668
 pyyaml==6.0.3
 ray==2.52.0
 referencing==0.37.0
 requests==2.32.5
 rpds-py==0.29.0
-sympy==1.13.1
-torch==2.5.1+cu121
-torchaudio==2.5.1+cu121
-torchvision==0.20.1+cu121
+sympy==1.14.0
+torch==2.10.0.dev20251123+cu129
+torchaudio==2.10.0.dev20251124+cu129
+torchvision==0.25.0.dev20251124+cu129
 triton==3.1.0
 typing-extensions==4.15.0
 urllib3==2.5.0
diff --git a/src/piper.py b/src/piper.py
index fbce940..3c804b4 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -1,9 +1,9 @@
 import ray
 import torch
-from .piper_actor import StageActor
+from piper_actor import StageActor
 from torch._dynamo.backends.registry import register_backend
 from torch._dynamo.decorators import _disallow_in_graph_helper
-from .piper_utils import RemoteTensor, serialize_graphmodule, piper_metadata
+from piper_utils import RemoteTensor, serialize_graphmodule, piper_metadata
 
 @_disallow_in_graph_helper(throw_if_not_allowed=False)
 def distributed_stage(stage_id, actor_id=None, mb=None, optim=None):
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 0fe368e..4b3ff0e 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -6,7 +6,7 @@
 from torch._guards import CompileId
 from torch.nn import Parameter
 from collections import defaultdict
-from .piper_utils import deserialize_graphmodule, piper_metadata
+from piper_utils import deserialize_graphmodule, piper_metadata
 
 torch.set_float32_matmul_precision('high')
 
diff --git a/src/piper_exec.py b/src/piper_exec.py
index 55ca1b0..023eea9 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -1,7 +1,7 @@
 import torch
 from typing import NamedTuple
 
-from .piper_utils import piper_metadata
+from piper_utils import piper_metadata
 
 class Task(NamedTuple):
     device_id: int
diff --git a/test/test_llama.py b/test/test_llama.py
index 9b41c25..2eb0a4b 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -5,10 +5,10 @@
 from torch import nn, optim
 from torch.profiler import profile, record_function, ProfilerActivity
 
-from src.piper_exec import Task, piper_exec
-from src.piper_compile import piper_setup
-from src.piper import piper
-from src.piper_utils import piper_metadata
+from piper_exec import Task, piper_exec
+from piper_compile import piper_setup
+from piper import piper
+from piper_utils import piper_metadata
 
 from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B
 from .schedule_helpers import build_1f1b_schedule, build_gpipe_schedule, print_schedule
@@ -105,10 +105,14 @@ def main(args):
     ray.get(actors[num_actors-1].send_truth.remote(y))
 
     schedule = None
-    if args.schedule == "interleaved-1f1b":
-        from .interleaved_grid_schedules import pp2_interleaved_1f1b_grid_schedule, pp4_interleaved_1f1b_grid_schedule
-        schedule = pp2_interleaved_1f1b_grid_schedule if num_devices == 2 else pp4_interleaved_1f1b_grid_schedule
-    elif args.schedule == "1f1b":
+    # if args.schedule == "interleaved-1f1b":
+    #     from .interleaved_grid_schedules import pp2_interleaved_1f1b_grid_schedule, pp4_interleaved_1f1b_grid_schedule
+    #     schedule = pp2_interleaved_1f1b_grid_schedule if num_devices == 2 else pp4_interleaved_1f1b_grid_schedule
+    # elif args.schedule == "1f1b":
+    #     schedule = build_1f1b_schedule(num_mbs, num_devices)
+    # elif args.schedule == "gpipe":
+    #     schedule = build_gpipe_schedule(num_mbs, num_devices)
+    if args.schedule == "1f1b":
         schedule = build_1f1b_schedule(num_mbs, num_devices)
     elif args.schedule == "gpipe":
         schedule = build_gpipe_schedule(num_mbs, num_devices)

From 0b16c7b7dbdafa2364dd3d6d839633bfd45c3eda Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Wed, 26 Nov 2025 08:25:28 +0000
Subject: [PATCH 006/100] WIP

---
 example.py                               |  11 +-
 piper/__init__.py                        |   0
 src/piper_actor.py => piper/actor.py     |   7 +-
 src/piper_compile.py => piper/compile.py |   2 +-
 src/piper_exec.py => piper/exec.py       |   2 +-
 {src => piper}/piper.py                  |   4 +-
 src/piper_utils.py => piper/utils.py     |   8 ++
 pyproject.toml                           |  14 +--
 requirements.txt                         | 137 +++++++++++++++--------
 test/models/llama.py                     |   2 +-
 test/schedule_helpers.py                 |   2 +-
 test/test_llama.py                       |   8 +-
 test/test_simple.py                      |   5 +-
 13 files changed, 130 insertions(+), 72 deletions(-)
 create mode 100644 piper/__init__.py
 rename src/piper_actor.py => piper/actor.py (99%)
 rename src/piper_compile.py => piper/compile.py (96%)
 rename src/piper_exec.py => piper/exec.py (99%)
 rename {src => piper}/piper.py (97%)
 rename src/piper_utils.py => piper/utils.py (97%)

diff --git a/example.py b/example.py
index cc35729..92ee357 100644
--- a/example.py
+++ b/example.py
@@ -1,5 +1,13 @@
 import torch
 
+def split_backward_graph(gm: torch.fx.GraphModule):
+    """
+    Given a GraphModule representing a full forward+backward pass,
+    split it into two GraphModules: one for the backward weight pass and one for the backward input pass
+    """
+
+    return gm
+
 class Model(torch.nn.Module):
    def __init__(self):
       super().__init__()
@@ -24,4 +32,5 @@ def main():
     train(model, x)
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
+
diff --git a/piper/__init__.py b/piper/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/piper_actor.py b/piper/actor.py
similarity index 99%
rename from src/piper_actor.py
rename to piper/actor.py
index 4b3ff0e..279787c 100644
--- a/src/piper_actor.py
+++ b/piper/actor.py
@@ -6,7 +6,7 @@
 from torch._guards import CompileId
 from torch.nn import Parameter
 from collections import defaultdict
-from piper_utils import deserialize_graphmodule, piper_metadata
+from piper.utils import deserialize_graphmodule, piper_metadata, split_backward_graph
 
 torch.set_float32_matmul_precision('high')
 
@@ -22,6 +22,8 @@ def backward_backend(gm, example_inputs, **kwargs):
                 print(f"Arg {ex.shape} | parameter: {isinstance(ex, Parameter)} | requires_grad: {ex.requires_grad}")
     gm.print_readable()
 
+
+
     return gm
 
 
@@ -230,7 +232,8 @@ def unwrap(x):
         #     print(arg.shape, arg.requires_grad)
         # save first input as previous activation
         if stage_id != 0:
-            assert args[0].requires_grad
+            args[0].requires_grad_()
+            # assert args[0].requires_grad 
             self.prev_activation[stage_id][mb_idx] = args[0]
 
         out = self.compiled_fns[stage_id](*self.parameters[stage_id])
diff --git a/src/piper_compile.py b/piper/compile.py
similarity index 96%
rename from src/piper_compile.py
rename to piper/compile.py
index 3b62e0a..9217439 100644
--- a/src/piper_compile.py
+++ b/piper/compile.py
@@ -2,7 +2,7 @@
 import torch
 from torch._dynamo.backends.debugging import eager
 
-from .piper_utils import piper_metadata
+from piper.utils import piper_metadata
 
 def piper_setup(model, example_inputs, dynamic=False, backend=None):
     """
diff --git a/src/piper_exec.py b/piper/exec.py
similarity index 99%
rename from src/piper_exec.py
rename to piper/exec.py
index 023eea9..d6f8756 100644
--- a/src/piper_exec.py
+++ b/piper/exec.py
@@ -1,7 +1,7 @@
 import torch
 from typing import NamedTuple
 
-from piper_utils import piper_metadata
+from piper.utils import piper_metadata
 
 class Task(NamedTuple):
     device_id: int
diff --git a/src/piper.py b/piper/piper.py
similarity index 97%
rename from src/piper.py
rename to piper/piper.py
index 3c804b4..bcc774c 100644
--- a/src/piper.py
+++ b/piper/piper.py
@@ -1,9 +1,9 @@
 import ray
 import torch
-from piper_actor import StageActor
+from piper.actor import StageActor
 from torch._dynamo.backends.registry import register_backend
 from torch._dynamo.decorators import _disallow_in_graph_helper
-from piper_utils import RemoteTensor, serialize_graphmodule, piper_metadata
+from piper.utils import RemoteTensor, serialize_graphmodule, piper_metadata
 
 @_disallow_in_graph_helper(throw_if_not_allowed=False)
 def distributed_stage(stage_id, actor_id=None, mb=None, optim=None):
diff --git a/src/piper_utils.py b/piper/utils.py
similarity index 97%
rename from src/piper_utils.py
rename to piper/utils.py
index c2ac217..82c1ff5 100644
--- a/src/piper_utils.py
+++ b/piper/utils.py
@@ -292,4 +292,12 @@ def deserialize_graphmodule(s: str) -> fx.GraphModule:
     gm = fx.GraphModule(torch.nn.Module(), g)
     state = {k: torch.tensor(v) for k, v in data["state_dict"].items()}
     gm.load_state_dict(state, strict=False)
+    return gm
+
+def split_backward_graphs(gm: fx.GraphModule):
+    """
+    Given a GraphModule representing a full forward+backward pass,
+    split it into two GraphModules: one for the backward weight pass and one for the backward input pass
+    """
+
     return gm
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 299661f..6b1f300 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,14 +13,6 @@ dependencies = [
     "ray"
 ]
 
-[tool.setuptools.package-dir]
-"" = "src"
-
-[tool.setuptools]
-py-modules = [
-    "piper",
-    "piper_actor",
-    "piper_compile",
-    "piper_exec",
-    "piper_utils"
-]
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["piper*"]
diff --git a/requirements.txt b/requirements.txt
index d4a210c..cbce940 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,50 +1,95 @@
-attrs==25.4.0
-bytecode==0.17.0
-certifi==2025.11.12
-charset-normalizer==3.4.4
-click==8.2.1
-cuda-bindings==12.9.4
-cuda-pathfinder==1.2.2
-filelock==3.20.0
-fsspec==2025.10.0
-idna==3.11
-jinja2==3.1.6
-jsonschema==4.25.1
-jsonschema-specifications==2025.9.1
-markupsafe==3.0.2
+ansible-core==2.14.18
+argcomplete==1.12.0
+bcc==0.32.0
+Brlapi==0.8.2
+cffi==1.14.5
+chardet==4.0.0
+cockpit @ file:///builddir/build/BUILD/cockpit-334.2/tmp/wheel/cockpit-334.2-py3-none-any.whl
+configshell-fb==1.1.30
+cryptography==36.0.1
+cupshelpers==1.0
+dasbus==1.4
+dbus-python==1.2.18
+decorator==4.4.2
+distro==1.5.0
+file-magic==0.4.0
+filelock==3.13.1
+fsspec==2024.6.1
+gpg==1.15.1
+idna==2.10
+importlib_metadata==8.7.0
+iniparse==0.4
+iotop==0.6
+isc==2.0
+Jinja2==3.1.4
+jsonpointer==2.0
+kmod==0.1
+libcomps==0.1.18
+libvirt-python==10.10.0
+lxml==4.6.5
+Mako==1.1.4.dev0
+Markdown==3.3.4
+MarkupSafe==2.1.5
 mpmath==1.3.0
-msgpack==1.1.2
-networkx==3.4.2
-numpy==2.1.2
-nvidia-cublas-cu12==12.9.1.4
-nvidia-cuda-cupti-cu12==12.9.79
-nvidia-cuda-nvrtc-cu12==12.9.86
-nvidia-cuda-runtime-cu12==12.9.79
+netaddr==0.10.1
+networkx==3.2.1
+nftables==0.1
+numpy==2.0.2
+nvidia-cublas-cu12==12.8.4.1
+nvidia-cuda-cupti-cu12==12.8.90
+nvidia-cuda-nvrtc-cu12==12.8.93
+nvidia-cuda-runtime-cu12==12.8.90
 nvidia-cudnn-cu12==9.10.2.21
-nvidia-cufft-cu12==11.4.1.4
-nvidia-cufile-cu12==1.14.1.1
-nvidia-curand-cu12==10.3.10.19
-nvidia-cusolver-cu12==11.7.5.82
-nvidia-cusparse-cu12==12.5.10.65
+nvidia-cufft-cu12==11.3.3.83
+nvidia-cufile-cu12==1.13.1.3
+nvidia-curand-cu12==10.3.9.90
+nvidia-cusolver-cu12==11.7.3.90
+nvidia-cusparse-cu12==12.5.8.93
 nvidia-cusparselt-cu12==0.7.1
-nvidia-nccl-cu12==2.27.5
-nvidia-nvjitlink-cu12==12.9.86
-nvidia-nvshmem-cu12==3.4.5
-nvidia-nvtx-cu12==12.9.79
-packaging==25.0
-pillow==12.0.0
--e file:///m-coriander/coriander/pgusto34/piper
-protobuf==6.33.1
-pytorch-triton==3.5.1+gitbfeb0668
-pyyaml==6.0.3
-ray==2.52.0
-referencing==0.37.0
-requests==2.32.5
-rpds-py==0.29.0
+nvidia-nccl-cu12==2.27.3
+nvidia-nvjitlink-cu12==12.8.93
+nvidia-nvtx-cu12==12.8.90
+packaging==20.9
+pcp==5.0
+perf==0.1
+pexpect==4.8.0
+ply==3.11
+psutil==5.8.0
+psycopg2==2.8.6
+ptyprocess==0.6.0
+py-spy==0.4.0
+pycairo==1.20.1
+pycparser==2.20
+pycups==2.0.1
+pycurl==7.43.0.6
+pyelftools==0.27
+PyGObject==3.40.1
+pyinotify==0.9.6
+pyodbc===4.0.0-unsupported
+pyparsing==2.4.7
+PySocks==1.7.1
+python-dateutil==2.8.1
+python-linux-procfs==0.7.3
+pyudev==0.22.0
+PyYAML==5.4.1
+requests==2.25.1
+resolvelib==0.5.4
+rpm==4.16.1.3
+rtslib-fb==2.1.76
+scapy==2.6.1
+selinux==3.6
+sepolicy==3.6
+setools==4.4.4
+setroubleshoot @ file:///builddir/build/BUILD/setroubleshoot-3.3.32/src
+six==1.15.0
+sos==4.10.0
+subscription-manager==1.29.45.1
 sympy==1.14.0
-torch==2.10.0.dev20251123+cu129
-torchaudio==2.10.0.dev20251124+cu129
-torchvision==0.25.0.dev20251124+cu129
-triton==3.1.0
-typing-extensions==4.15.0
-urllib3==2.5.0
+systemd-python==234
+targetcli-fb==2.1.57
+torch==2.8.0
+triton==3.4.0
+typing_extensions==4.15.0
+urllib3==1.26.5
+urwid==2.1.2
+zipp==3.23.0
diff --git a/test/models/llama.py b/test/models/llama.py
index e4812f6..0a11fb4 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -14,7 +14,7 @@
 from torch import Tensor, nn
 from torch.nn.utils import parameters_to_vector
 
-from src.piper import distributed_stage
+from piper.piper import distributed_stage
 
 # import fairscale.nn.model_parallel.initialize as fs_init
 # from fairscale.nn.model_parallel.layers import (
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index 64bc54e..5fa9f65 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -1,4 +1,4 @@
-from src.piper_exec import Task
+from piper.exec import Task
 
 
 def print_schedule(schedule):
diff --git a/test/test_llama.py b/test/test_llama.py
index 2eb0a4b..b28d633 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -5,10 +5,10 @@
 from torch import nn, optim
 from torch.profiler import profile, record_function, ProfilerActivity
 
-from piper_exec import Task, piper_exec
-from piper_compile import piper_setup
-from piper import piper
-from piper_utils import piper_metadata
+from piper.exec import Task, piper_exec
+from piper.compile import piper_setup
+from piper.piper import piper
+from piper.utils import piper_metadata
 
 from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B
 from .schedule_helpers import build_1f1b_schedule, build_gpipe_schedule, print_schedule
diff --git a/test/test_simple.py b/test/test_simple.py
index 94f1dfe..595ef24 100644
--- a/test/test_simple.py
+++ b/test/test_simple.py
@@ -1,7 +1,8 @@
 import torch
 import ray
 from torch import nn
-from src.piper import piper, distributed_stage
+from torch.export import export, ExportedProgram
+from piper.piper import piper, distributed_stage
 from collections.abc import Iterable
 
 class MyModel(nn.Module):
@@ -30,7 +31,7 @@ def forward(self, x):
 
 # Manually run a training step on a single batch
 
-from src.piper_utils import piper_metadata
+from piper.utils import piper_metadata
 actors = piper_metadata['actors']
 
 # forward pass (executing with fused forwards, for now)

From 8157cd7545bbcba26b95362c7868536009f62758 Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Wed, 26 Nov 2025 08:36:08 +0000
Subject: [PATCH 007/100] Updated requirements.txt

---
 requirements.txt | 177 +++++++++++++++++++++++------------------------
 1 file changed, 87 insertions(+), 90 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index cbce940..b41f0ae 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,95 +1,92 @@
-ansible-core==2.14.18
-argcomplete==1.12.0
-bcc==0.32.0
-Brlapi==0.8.2
-cffi==1.14.5
-chardet==4.0.0
-cockpit @ file:///builddir/build/BUILD/cockpit-334.2/tmp/wheel/cockpit-334.2-py3-none-any.whl
-configshell-fb==1.1.30
-cryptography==36.0.1
-cupshelpers==1.0
-dasbus==1.4
-dbus-python==1.2.18
-decorator==4.4.2
-distro==1.5.0
-file-magic==0.4.0
-filelock==3.13.1
-fsspec==2024.6.1
-gpg==1.15.1
-idna==2.10
-importlib_metadata==8.7.0
-iniparse==0.4
-iotop==0.6
-isc==2.0
-Jinja2==3.1.4
-jsonpointer==2.0
-kmod==0.1
-libcomps==0.1.18
-libvirt-python==10.10.0
-lxml==4.6.5
-Mako==1.1.4.dev0
-Markdown==3.3.4
-MarkupSafe==2.1.5
+aiohappyeyeballs==2.6.1
+aiohttp==3.13.2
+aiohttp-cors==0.8.1
+aiosignal==1.4.0
+annotated-types==0.7.0
+async-timeout==5.0.1
+attrs==25.4.0
+bytecode==0.17.0
+cachetools==6.2.2
+certifi==2025.11.12
+charset-normalizer==3.4.4
+click==8.2.1
+colorful==0.5.8
+cuda-bindings==12.9.4
+cuda-pathfinder==1.2.2
+cupy-cuda12x==13.6.0
+distlib==0.4.0
+fastrlock==0.8.3
+filelock==3.20.0
+frozenlist==1.8.0
+fsspec==2025.10.0
+google-api-core==2.28.1
+google-auth==2.43.0
+googleapis-common-protos==1.72.0
+grpcio==1.76.0
+idna==3.11
+importlib-metadata==8.7.0
+jinja2==3.1.6
+jsonschema==4.25.1
+jsonschema-specifications==2025.9.1
+markupsafe==3.0.2
 mpmath==1.3.0
-netaddr==0.10.1
-networkx==3.2.1
-nftables==0.1
-numpy==2.0.2
-nvidia-cublas-cu12==12.8.4.1
-nvidia-cuda-cupti-cu12==12.8.90
-nvidia-cuda-nvrtc-cu12==12.8.93
-nvidia-cuda-runtime-cu12==12.8.90
+msgpack==1.1.2
+multidict==6.7.0
+networkx==3.4.2
+numpy==2.1.2
+nvidia-cublas-cu12==12.9.1.4
+nvidia-cuda-cupti-cu12==12.9.79
+nvidia-cuda-nvrtc-cu12==12.9.86
+nvidia-cuda-runtime-cu12==12.9.79
 nvidia-cudnn-cu12==9.10.2.21
-nvidia-cufft-cu12==11.3.3.83
-nvidia-cufile-cu12==1.13.1.3
-nvidia-curand-cu12==10.3.9.90
-nvidia-cusolver-cu12==11.7.3.90
-nvidia-cusparse-cu12==12.5.8.93
+nvidia-cufft-cu12==11.4.1.4
+nvidia-cufile-cu12==1.14.1.1
+nvidia-curand-cu12==10.3.10.19
+nvidia-cusolver-cu12==11.7.5.82
+nvidia-cusparse-cu12==12.5.10.65
 nvidia-cusparselt-cu12==0.7.1
-nvidia-nccl-cu12==2.27.3
-nvidia-nvjitlink-cu12==12.8.93
-nvidia-nvtx-cu12==12.8.90
-packaging==20.9
-pcp==5.0
-perf==0.1
-pexpect==4.8.0
-ply==3.11
-psutil==5.8.0
-psycopg2==2.8.6
-ptyprocess==0.6.0
-py-spy==0.4.0
-pycairo==1.20.1
-pycparser==2.20
-pycups==2.0.1
-pycurl==7.43.0.6
-pyelftools==0.27
-PyGObject==3.40.1
-pyinotify==0.9.6
-pyodbc===4.0.0-unsupported
-pyparsing==2.4.7
-PySocks==1.7.1
-python-dateutil==2.8.1
-python-linux-procfs==0.7.3
-pyudev==0.22.0
-PyYAML==5.4.1
-requests==2.25.1
-resolvelib==0.5.4
-rpm==4.16.1.3
-rtslib-fb==2.1.76
-scapy==2.6.1
-selinux==3.6
-sepolicy==3.6
-setools==4.4.4
-setroubleshoot @ file:///builddir/build/BUILD/setroubleshoot-3.3.32/src
-six==1.15.0
-sos==4.10.0
-subscription-manager==1.29.45.1
+nvidia-nccl-cu12==2.27.5
+nvidia-nvjitlink-cu12==12.9.86
+nvidia-nvshmem-cu12==3.4.5
+nvidia-nvtx-cu12==12.9.79
+opencensus==0.11.4
+opencensus-context==0.1.3
+opentelemetry-api==1.38.0
+opentelemetry-exporter-prometheus==0.59b0
+opentelemetry-proto==1.38.0
+opentelemetry-sdk==1.38.0
+opentelemetry-semantic-conventions==0.59b0
+packaging==25.0
+pillow==12.0.0
+-e file:///m-coriander/coriander/pgusto34/piper
+platformdirs==4.5.0
+prometheus-client==0.23.1
+propcache==0.4.1
+proto-plus==1.26.1
+protobuf==6.33.1
+py-spy==0.4.1
+pyasn1==0.6.1
+pyasn1-modules==0.4.2
+pydantic==2.12.4
+pydantic-core==2.41.5
+pytorch-triton==3.5.1+gitbfeb0668
+pyyaml==6.0.3
+ray==2.52.0
+referencing==0.37.0
+requests==2.32.5
+rpds-py==0.29.0
+rsa==4.9.1
+six==1.17.0
+smart-open==7.5.0
 sympy==1.14.0
-systemd-python==234
-targetcli-fb==2.1.57
-torch==2.8.0
-triton==3.4.0
-typing_extensions==4.15.0
-urllib3==1.26.5
-urwid==2.1.2
+torch==2.10.0.dev20251123+cu129
+torchaudio==2.10.0.dev20251124+cu129
+torchvision==0.25.0.dev20251124+cu129
+triton==3.1.0
+typing-extensions==4.15.0
+typing-inspection==0.4.2
+urllib3==2.5.0
+virtualenv==20.35.4
+wrapt==2.0.1
+yarl==1.22.0
 zipp==3.23.0

From c272be429b480955ca32cf57f68491c12a3d4824 Mon Sep 17 00:00:00 2001
From: pgusto34 <pgusto34@gmail.com>
Date: Wed, 26 Nov 2025 13:29:46 -0800
Subject: [PATCH 008/100] Split backwards example script

---
 example.py         |  36 --------
 requirements.txt   |  92 -------------------
 split_example.py   | 217 +++++++++++++++++++++++++++++++++++++++++++++
 visualize_graph.py |  35 ++++++++
 4 files changed, 252 insertions(+), 128 deletions(-)
 delete mode 100644 example.py
 delete mode 100644 requirements.txt
 create mode 100644 split_example.py
 create mode 100644 visualize_graph.py

diff --git a/example.py b/example.py
deleted file mode 100644
index 92ee357..0000000
--- a/example.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import torch
-
-def split_backward_graph(gm: torch.fx.GraphModule):
-    """
-    Given a GraphModule representing a full forward+backward pass,
-    split it into two GraphModules: one for the backward weight pass and one for the backward input pass
-    """
-
-    return gm
-
-class Model(torch.nn.Module):
-   def __init__(self):
-      super().__init__()
-      self.linear = torch.nn.Linear(10, 10)
-
-   def forward(self, x):
-      return self.linear(x)
-
-def custom_backend(gm: torch.fx.GraphModule, *args, **kwargs):
-    print(gm.graph)
-    return gm
-
-torch._dynamo.config.compiled_autograd = True
-@torch.compile(backend=custom_backend)
-def train(model, x):
-   loss = model(x).sum()
-   loss.backward()
-
-def main():
-    model = Model()
-    x = torch.randn(10)
-    train(model, x)
-
-if __name__ == "__main__":
-    main()
-
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index b41f0ae..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,92 +0,0 @@
-aiohappyeyeballs==2.6.1
-aiohttp==3.13.2
-aiohttp-cors==0.8.1
-aiosignal==1.4.0
-annotated-types==0.7.0
-async-timeout==5.0.1
-attrs==25.4.0
-bytecode==0.17.0
-cachetools==6.2.2
-certifi==2025.11.12
-charset-normalizer==3.4.4
-click==8.2.1
-colorful==0.5.8
-cuda-bindings==12.9.4
-cuda-pathfinder==1.2.2
-cupy-cuda12x==13.6.0
-distlib==0.4.0
-fastrlock==0.8.3
-filelock==3.20.0
-frozenlist==1.8.0
-fsspec==2025.10.0
-google-api-core==2.28.1
-google-auth==2.43.0
-googleapis-common-protos==1.72.0
-grpcio==1.76.0
-idna==3.11
-importlib-metadata==8.7.0
-jinja2==3.1.6
-jsonschema==4.25.1
-jsonschema-specifications==2025.9.1
-markupsafe==3.0.2
-mpmath==1.3.0
-msgpack==1.1.2
-multidict==6.7.0
-networkx==3.4.2
-numpy==2.1.2
-nvidia-cublas-cu12==12.9.1.4
-nvidia-cuda-cupti-cu12==12.9.79
-nvidia-cuda-nvrtc-cu12==12.9.86
-nvidia-cuda-runtime-cu12==12.9.79
-nvidia-cudnn-cu12==9.10.2.21
-nvidia-cufft-cu12==11.4.1.4
-nvidia-cufile-cu12==1.14.1.1
-nvidia-curand-cu12==10.3.10.19
-nvidia-cusolver-cu12==11.7.5.82
-nvidia-cusparse-cu12==12.5.10.65
-nvidia-cusparselt-cu12==0.7.1
-nvidia-nccl-cu12==2.27.5
-nvidia-nvjitlink-cu12==12.9.86
-nvidia-nvshmem-cu12==3.4.5
-nvidia-nvtx-cu12==12.9.79
-opencensus==0.11.4
-opencensus-context==0.1.3
-opentelemetry-api==1.38.0
-opentelemetry-exporter-prometheus==0.59b0
-opentelemetry-proto==1.38.0
-opentelemetry-sdk==1.38.0
-opentelemetry-semantic-conventions==0.59b0
-packaging==25.0
-pillow==12.0.0
--e file:///m-coriander/coriander/pgusto34/piper
-platformdirs==4.5.0
-prometheus-client==0.23.1
-propcache==0.4.1
-proto-plus==1.26.1
-protobuf==6.33.1
-py-spy==0.4.1
-pyasn1==0.6.1
-pyasn1-modules==0.4.2
-pydantic==2.12.4
-pydantic-core==2.41.5
-pytorch-triton==3.5.1+gitbfeb0668
-pyyaml==6.0.3
-ray==2.52.0
-referencing==0.37.0
-requests==2.32.5
-rpds-py==0.29.0
-rsa==4.9.1
-six==1.17.0
-smart-open==7.5.0
-sympy==1.14.0
-torch==2.10.0.dev20251123+cu129
-torchaudio==2.10.0.dev20251124+cu129
-torchvision==0.25.0.dev20251124+cu129
-triton==3.1.0
-typing-extensions==4.15.0
-typing-inspection==0.4.2
-urllib3==2.5.0
-virtualenv==20.35.4
-wrapt==2.0.1
-yarl==1.22.0
-zipp==3.23.0
diff --git a/split_example.py b/split_example.py
new file mode 100644
index 0000000..c499305
--- /dev/null
+++ b/split_example.py
@@ -0,0 +1,217 @@
+import torch
+import torch.nn as nn
+import torch.fx as fx
+import copy
+from visualize_graph import fx_to_graphviz
+
+torch._dynamo.config.compiled_autograd = True
+
+SPLIT_GRADS = None
+
+class OneLayerToy(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear = nn.Linear(10, 10)
+
+    def forward(self, x):
+        return self.linear(x)
+
+class TwoLayerToy(nn.Module):
+    def __init__(self, in_dim=8, hidden_dim=8, out_dim=1):
+        super().__init__()
+        self.fc1 = nn.Linear(in_dim, hidden_dim)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(hidden_dim, out_dim)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        return x
+
+
+def extract_subgraph(gm: fx.GraphModule, output_nodes):
+    graph = gm.graph
+
+    needed = set(output_nodes)
+    queue = list(output_nodes)
+
+    while queue:
+        node = queue.pop()
+        for inp in node.all_input_nodes:
+            if inp not in needed:
+                needed.add(inp)
+                queue.append(inp)
+
+    for node in graph.nodes:
+        if node.op == "placeholder":
+            needed.add(node)
+
+    new_graph = fx.Graph()
+    node_copies = {}
+
+    for node in graph.nodes:
+        if node.op == "output":
+            continue
+        if node in needed:
+            new_node = new_graph.node_copy(node, lambda n: node_copies[n])
+            node_copies[node] = new_node
+
+    new_outputs = [node_copies[n] for n in output_nodes]
+    if len(new_outputs) == 1:
+        new_graph.output((new_outputs[0],))
+    else:
+        new_graph.output(tuple(new_outputs))
+
+    new_gm = fx.GraphModule(gm, new_graph)
+    new_gm.recompile()
+    return new_gm
+
+
+def split_backward_graph(
+    gm: fx.GraphModule,
+    input_grad_indices,
+    param_grad_indices,
+):
+    graph = gm.graph
+    output_node = None
+    for n in graph.nodes:
+        if n.op == "output":
+            output_node = n
+            break
+    assert output_node is not None, "No output node in FX graph?"
+
+    outputs_tuple = output_node.args[0]
+    assert isinstance(outputs_tuple, (tuple, list)), "Expected tuple of outputs"
+
+    input_grad_nodes = [outputs_tuple[i] for i in input_grad_indices]
+    param_grad_nodes = [outputs_tuple[i] for i in param_grad_indices]
+
+    gm_input = extract_subgraph(gm, input_grad_nodes)
+    gm_weight = extract_subgraph(gm, param_grad_nodes)
+    return gm_input, gm_weight
+
+
+def is_backward_graph(gm: fx.GraphModule) -> bool:
+    for node in gm.graph.nodes:
+        if node.op == "call_function":
+            mod = getattr(node.target, "__module__", "")
+            if "torch._dynamo.compiled_autograd" in mod:
+                return True
+    return False
+
+
+def custom_backend(gm: fx.GraphModule, example_inputs):
+    if not is_backward_graph(gm):
+        print("FORWARD GRAPH")
+        gm.print_readable()
+        return gm
+
+    print("FULL BACKWARD GRAPH")
+    gm.print_readable()
+
+    fx_to_graphviz(gm, filename="full_backward_graph")
+    print("Saved full backward graph visualization to file.")
+
+    input_grad_indices = [0]
+    param_grad_indices = [1,2]
+
+    gm_input, gm_weight = split_backward_graph(
+        gm,
+        input_grad_indices=input_grad_indices,
+        param_grad_indices=param_grad_indices,
+    )
+
+    print("BACKWARD INPUT SUBGRAPH")
+    gm_input.print_readable()
+
+    print("BACKWARD WEIGHT SUBGRAPH")
+    gm_weight.print_readable()
+
+    with torch.no_grad():
+        full_out = gm(*example_inputs)
+        in_out = gm_input(*example_inputs)
+        w_out = gm_weight(*example_inputs)
+
+    print("BACKWARD OUTPUT CHECK")
+    print("full_out shapes:", [t.shape for t in full_out])
+    print("input_out shapes:", [t.shape for t in in_out])
+    print("weight_out shapes:", [t.shape for t in w_out])
+
+    full_out_list = flatten_grads(full_out)
+    split_out_list = flatten_grads(in_out) + flatten_grads(w_out)
+
+    global SPLIT_GRADS
+    SPLIT_GRADS = split_out_list
+
+    print("SPLIT vs FULL GRAPH CHECK")
+    compare_grad_lists(full_out_list, split_out_list)
+
+
+    fx_to_graphviz(gm_input, filename="backward_input_subgraph")
+    fx_to_graphviz(gm_weight, filename="backward_weight_subgraph")
+    print("Saved subgraph visualizations to files.")
+
+    return gm
+
+def flatten_grads(grads):
+    if isinstance(grads, dict):
+        return list(grads.values())
+    if isinstance(grads, (list, tuple)):
+        return list(grads)
+    return [grads]
+
+
+def compare_grad_lists(a, b, atol=1e-6, rtol=1e-6):
+    assert len(a) == len(b), f"Different #grads: {len(a)} vs {len(b)}"
+
+    for i, (g1, g2) in enumerate(zip(a, b)):
+        if g1 is None or g2 is None:
+            print(f"grad[{i}] is None")
+            continue
+
+        if not torch.allclose(g1, g2, atol=atol, rtol=rtol):
+            print(f"grad[{i}] mismatch — max diff: {(g1 - g2).abs().max().item():.6f}")
+        else:
+            print(f"grad[{i}] match")
+
+
+def autograd_grads(model, x):
+    with torch.enable_grad():
+        copy.deepcopy(model)
+        model.zero_grad(set_to_none=True)
+
+        x = x.clone().detach().requires_grad_(True)
+        y = model(x).sum()
+        y.backward()
+
+        grads = [x.grad]
+        grads += [p.grad for _, p in model.named_parameters()]
+
+    return grads
+
+
+@torch.compile(backend=custom_backend)
+def train(model, x):
+    x = x.clone().detach().requires_grad_(True)
+    y = model(x).sum()
+    y.backward()
+    return y
+
+
+def main():
+    global MODEL, X
+    torch.manual_seed(0)
+    model = OneLayerToy()
+    x = torch.randn(1, 10)
+
+    loss = train(model, x)
+    print("Loss:", loss.item())
+
+    true_grads = autograd_grads(model, x)
+
+    print("SPLIT vs PYTORCH AUTOGRAD CHECK")
+    compare_grad_lists(SPLIT_GRADS, true_grads)
+
+if __name__ == "__main__":
+    main()
diff --git a/visualize_graph.py b/visualize_graph.py
new file mode 100644
index 0000000..b81607a
--- /dev/null
+++ b/visualize_graph.py
@@ -0,0 +1,35 @@
+import torch
+import torch.fx as fx
+import graphviz
+
+
+def fx_to_graphviz(
+    gm: fx.GraphModule,
+    filename: str = "fx_graph",
+    format: str = "png",
+    show_attrs: bool = True,
+):
+    """
+    Convert a torch.fx.GraphModule to a Graphviz visualization.
+
+    Args:
+        gm (fx.GraphModule): The traced model
+        filename (str): Output file name (no extension)
+        format (str): 'png', 'pdf', 'svg', etc.
+        show_attrs (bool): Show full node target names
+    """
+    dot = graphviz.Digraph(format=format, graph_attr={"rankdir": "LR"})
+
+    for node in gm.graph.nodes:
+        label = f"{node.op}\n{node.name}"
+
+        if show_attrs and node.target not in ("output", "placeholder"):
+            label += f"\n{node.target}"
+
+        dot.node(node.name, label)
+
+        for user in node.users:
+            dot.edge(node.name, user.name)
+
+    dot.render(filename, cleanup=True)
+    return dot

From ea2139e0f3c15b5e5dc5a816f2f7c288ef8ae441 Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Wed, 26 Nov 2025 09:35:52 +0000
Subject: [PATCH 009/100] Removed requirements.txt file and updated README in
 place of it

---
 README.md | 34 +++++++++++++++++++++++++++++++++-
 1 file changed, 33 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 0f6d636..3cd944e 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,39 @@
 
 Piper is a PyTorch library for training large models with flexible pipeline parallel schedules.
 
-## Dependencies
+## Set-up Directions
+We assume a Linux-based environment
+
+1. Install UV
+Follow the [Installing uv instructions](https://docs.astral.sh/uv/getting-started/installation/) from the uv documentation, or run
+```
+curl -LsSf https://astral.sh/uv/install.sh | sh
+```
+
+Validate the installation by running
+```
+uv --version
+```
+
+2. Create venv
+```
+uv venv .venv
+source .venv/bin/activate
+```
+
+3. Install PyTorch (Nightly)
+```
+uv pip install --pre \
+  torch torchvision torchaudio \
+  --extra-index-url https://download.pytorch.org/whl/nightly/cu129
+```
+
+4. Install Remaining Dependencies from `pyproject.toml`
+```
+uv pip install -e .
+```
+
+5. Modify dependencies located in `.venv/lib/`
 
 PyTorch
 

From 65952d59ee759360e75dafde65b181710336f065 Mon Sep 17 00:00:00 2001
From: pgusto34 <pgusto34@gmail.com>
Date: Wed, 26 Nov 2025 14:37:14 -0800
Subject: [PATCH 010/100] Graph PNGs

---
 backward_input_subgraph.png  | Bin 0 -> 335961 bytes
 backward_weight_subgraph.png | Bin 0 -> 491281 bytes
 full_backward_graph.png      | Bin 0 -> 564263 bytes
 3 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 backward_input_subgraph.png
 create mode 100644 backward_weight_subgraph.png
 create mode 100644 full_backward_graph.png

diff --git a/backward_input_subgraph.png b/backward_input_subgraph.png
new file mode 100644
index 0000000000000000000000000000000000000000..f24f6cf341fc680d6dc1820f46cb9f395b7259a1
GIT binary patch
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HcmV?d00001


From c3f6979fa16adcef7bea195abae908e2222f948b Mon Sep 17 00:00:00 2001
From: pgusto34 <pgusto34@gmail.com>
Date: Mon, 1 Dec 2025 15:01:24 -0800
Subject: [PATCH 011/100] Commented out NCCL

---
 piper/actor.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/piper/actor.py b/piper/actor.py
index 279787c..eaf0c13 100644
--- a/piper/actor.py
+++ b/piper/actor.py
@@ -130,7 +130,7 @@ def compile_graph(self, stage_id, gm_data, compiler_fn, graphargs, input_idxs):
         self.log.debug(f"compile_graph took {(end-start)*1000:.2f}ms")
         return "Finished compiling"
 
-    @ray.method(tensor_transport="nccl")
+    # @ray.method(tensor_transport="nccl")
     def forward(self, stage_id: int, mb_idx: int, *args):
         self.log.info(f"Calling forward {stage_id} mb {mb_idx} on actor {self.actor_id} with {len(args)} args")
         
@@ -247,7 +247,7 @@ def unwrap(x):
         self.activation[stage_id][mb_idx] = out[0]
         return out
 
-    @ray.method(tensor_transport="nccl")
+    # @ray.method(tensor_transport="nccl")
     def backward(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
         self.log.info(f"Calling backward {stage_id} mb {mb_idx} on actor {self.actor_id}", inp)
         
@@ -444,7 +444,7 @@ def stop_mem_tracing(self) -> None:
         torch.cuda.memory._record_memory_history(enabled=None)
         return "done"
     
-    @ray.method(tensor_transport="nccl")
+    # @ray.method(tensor_transport="nccl")
     def get_object(self, is_fwd, stage_id):
         if is_fwd:
             return self.fwd_objs[stage_id]

From 9330880d7d0a682351ebd7b28cc669ad85fe1e3a Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Mon, 1 Dec 2025 23:07:30 +0000
Subject: [PATCH 012/100] Merged changes

---
 README.md                    |  47 +++++---
 backward_input_subgraph.png  | Bin 0 -> 335961 bytes
 backward_weight_subgraph.png | Bin 0 -> 491281 bytes
 full_backward_graph.png      | Bin 0 -> 564263 bytes
 piper/actor.py               |   6 +-
 requirements.txt             |  92 ---------------
 split_example.py             | 217 +++++++++++++++++++++++++++++++++++
 visualize_graph.py           |  35 ++++++
 8 files changed, 288 insertions(+), 109 deletions(-)
 create mode 100644 backward_input_subgraph.png
 create mode 100644 backward_weight_subgraph.png
 create mode 100644 full_backward_graph.png
 delete mode 100644 requirements.txt
 create mode 100644 split_example.py
 create mode 100644 visualize_graph.py

diff --git a/README.md b/README.md
index 930273e..3cd944e 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,39 @@
 
 Piper is a PyTorch library for training large models with flexible pipeline parallel schedules.
 
-## Dependencies
+## Set-up Directions
+We assume a Linux-based environment
+
+1. Install UV
+Follow the [Installing uv instructions](https://docs.astral.sh/uv/getting-started/installation/) from the uv documentation, or run
+```
+curl -LsSf https://astral.sh/uv/install.sh | sh
+```
+
+Validate the installation by running
+```
+uv --version
+```
+
+2. Create venv
+```
+uv venv .venv
+source .venv/bin/activate
+```
+
+3. Install PyTorch (Nightly)
+```
+uv pip install --pre \
+  torch torchvision torchaudio \
+  --extra-index-url https://download.pytorch.org/whl/nightly/cu129
+```
+
+4. Install Remaining Dependencies from `pyproject.toml`
+```
+uv pip install -e .
+```
+
+5. Modify dependencies located in `.venv/lib/`
 
 PyTorch
 
@@ -19,19 +51,6 @@ for k, v in locals.items():
         locals[k] = v._fake
 ####### PIPER MODIFICATION END #######
 ```
-
-The torch.compile backend interface is too limiting. 
-Compiler backends accept a graph module and example inputs.
-`graphargs` is datastructure describing the arguments to a grpah module, including their source in the top-level module and an example.
-The list of example inputs required by the torch.compile backend interface are derived from the graph args datastructure. 
-The Piper backend requires source information to differentiate model parameters from inputs. 
-- Modification: Change the invocation of [self.call_user_compiler in output_graph.py](https://github.com/pytorch/pytorch/blob/f9724db4921288a096e331cee835abd43257fbd6/torch/_dynamo/output_graph.py#L2217):
-```
-####### PIPER MODIFICATION START #######
-# compiled_fn = self.call_user_compiler(gm, self.example_inputs())
-compiled_fn = self.call_user_compiler(gm, self.graphargs)
-####### PIPER MODIFICATION END #######
-```
  
 Ray: Tensor transport backends currently only support 1 return value per task.
 - WIP: Upstream this into Ray.
diff --git a/backward_input_subgraph.png b/backward_input_subgraph.png
new file mode 100644
index 0000000000000000000000000000000000000000..f24f6cf341fc680d6dc1820f46cb9f395b7259a1
GIT binary patch
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AzyJUM

literal 0
HcmV?d00001

diff --git a/piper/actor.py b/piper/actor.py
index 279787c..eaf0c13 100644
--- a/piper/actor.py
+++ b/piper/actor.py
@@ -130,7 +130,7 @@ def compile_graph(self, stage_id, gm_data, compiler_fn, graphargs, input_idxs):
         self.log.debug(f"compile_graph took {(end-start)*1000:.2f}ms")
         return "Finished compiling"
 
-    @ray.method(tensor_transport="nccl")
+    # @ray.method(tensor_transport="nccl")
     def forward(self, stage_id: int, mb_idx: int, *args):
         self.log.info(f"Calling forward {stage_id} mb {mb_idx} on actor {self.actor_id} with {len(args)} args")
         
@@ -247,7 +247,7 @@ def unwrap(x):
         self.activation[stage_id][mb_idx] = out[0]
         return out
 
-    @ray.method(tensor_transport="nccl")
+    # @ray.method(tensor_transport="nccl")
     def backward(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
         self.log.info(f"Calling backward {stage_id} mb {mb_idx} on actor {self.actor_id}", inp)
         
@@ -444,7 +444,7 @@ def stop_mem_tracing(self) -> None:
         torch.cuda.memory._record_memory_history(enabled=None)
         return "done"
     
-    @ray.method(tensor_transport="nccl")
+    # @ray.method(tensor_transport="nccl")
     def get_object(self, is_fwd, stage_id):
         if is_fwd:
             return self.fwd_objs[stage_id]
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index b41f0ae..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,92 +0,0 @@
-aiohappyeyeballs==2.6.1
-aiohttp==3.13.2
-aiohttp-cors==0.8.1
-aiosignal==1.4.0
-annotated-types==0.7.0
-async-timeout==5.0.1
-attrs==25.4.0
-bytecode==0.17.0
-cachetools==6.2.2
-certifi==2025.11.12
-charset-normalizer==3.4.4
-click==8.2.1
-colorful==0.5.8
-cuda-bindings==12.9.4
-cuda-pathfinder==1.2.2
-cupy-cuda12x==13.6.0
-distlib==0.4.0
-fastrlock==0.8.3
-filelock==3.20.0
-frozenlist==1.8.0
-fsspec==2025.10.0
-google-api-core==2.28.1
-google-auth==2.43.0
-googleapis-common-protos==1.72.0
-grpcio==1.76.0
-idna==3.11
-importlib-metadata==8.7.0
-jinja2==3.1.6
-jsonschema==4.25.1
-jsonschema-specifications==2025.9.1
-markupsafe==3.0.2
-mpmath==1.3.0
-msgpack==1.1.2
-multidict==6.7.0
-networkx==3.4.2
-numpy==2.1.2
-nvidia-cublas-cu12==12.9.1.4
-nvidia-cuda-cupti-cu12==12.9.79
-nvidia-cuda-nvrtc-cu12==12.9.86
-nvidia-cuda-runtime-cu12==12.9.79
-nvidia-cudnn-cu12==9.10.2.21
-nvidia-cufft-cu12==11.4.1.4
-nvidia-cufile-cu12==1.14.1.1
-nvidia-curand-cu12==10.3.10.19
-nvidia-cusolver-cu12==11.7.5.82
-nvidia-cusparse-cu12==12.5.10.65
-nvidia-cusparselt-cu12==0.7.1
-nvidia-nccl-cu12==2.27.5
-nvidia-nvjitlink-cu12==12.9.86
-nvidia-nvshmem-cu12==3.4.5
-nvidia-nvtx-cu12==12.9.79
-opencensus==0.11.4
-opencensus-context==0.1.3
-opentelemetry-api==1.38.0
-opentelemetry-exporter-prometheus==0.59b0
-opentelemetry-proto==1.38.0
-opentelemetry-sdk==1.38.0
-opentelemetry-semantic-conventions==0.59b0
-packaging==25.0
-pillow==12.0.0
--e file:///m-coriander/coriander/pgusto34/piper
-platformdirs==4.5.0
-prometheus-client==0.23.1
-propcache==0.4.1
-proto-plus==1.26.1
-protobuf==6.33.1
-py-spy==0.4.1
-pyasn1==0.6.1
-pyasn1-modules==0.4.2
-pydantic==2.12.4
-pydantic-core==2.41.5
-pytorch-triton==3.5.1+gitbfeb0668
-pyyaml==6.0.3
-ray==2.52.0
-referencing==0.37.0
-requests==2.32.5
-rpds-py==0.29.0
-rsa==4.9.1
-six==1.17.0
-smart-open==7.5.0
-sympy==1.14.0
-torch==2.10.0.dev20251123+cu129
-torchaudio==2.10.0.dev20251124+cu129
-torchvision==0.25.0.dev20251124+cu129
-triton==3.1.0
-typing-extensions==4.15.0
-typing-inspection==0.4.2
-urllib3==2.5.0
-virtualenv==20.35.4
-wrapt==2.0.1
-yarl==1.22.0
-zipp==3.23.0
diff --git a/split_example.py b/split_example.py
new file mode 100644
index 0000000..c499305
--- /dev/null
+++ b/split_example.py
@@ -0,0 +1,217 @@
+import torch
+import torch.nn as nn
+import torch.fx as fx
+import copy
+from visualize_graph import fx_to_graphviz
+
+torch._dynamo.config.compiled_autograd = True
+
+SPLIT_GRADS = None
+
+class OneLayerToy(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear = nn.Linear(10, 10)
+
+    def forward(self, x):
+        return self.linear(x)
+
+class TwoLayerToy(nn.Module):
+    def __init__(self, in_dim=8, hidden_dim=8, out_dim=1):
+        super().__init__()
+        self.fc1 = nn.Linear(in_dim, hidden_dim)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(hidden_dim, out_dim)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        return x
+
+
+def extract_subgraph(gm: fx.GraphModule, output_nodes):
+    graph = gm.graph
+
+    needed = set(output_nodes)
+    queue = list(output_nodes)
+
+    while queue:
+        node = queue.pop()
+        for inp in node.all_input_nodes:
+            if inp not in needed:
+                needed.add(inp)
+                queue.append(inp)
+
+    for node in graph.nodes:
+        if node.op == "placeholder":
+            needed.add(node)
+
+    new_graph = fx.Graph()
+    node_copies = {}
+
+    for node in graph.nodes:
+        if node.op == "output":
+            continue
+        if node in needed:
+            new_node = new_graph.node_copy(node, lambda n: node_copies[n])
+            node_copies[node] = new_node
+
+    new_outputs = [node_copies[n] for n in output_nodes]
+    if len(new_outputs) == 1:
+        new_graph.output((new_outputs[0],))
+    else:
+        new_graph.output(tuple(new_outputs))
+
+    new_gm = fx.GraphModule(gm, new_graph)
+    new_gm.recompile()
+    return new_gm
+
+
+def split_backward_graph(
+    gm: fx.GraphModule,
+    input_grad_indices,
+    param_grad_indices,
+):
+    graph = gm.graph
+    output_node = None
+    for n in graph.nodes:
+        if n.op == "output":
+            output_node = n
+            break
+    assert output_node is not None, "No output node in FX graph?"
+
+    outputs_tuple = output_node.args[0]
+    assert isinstance(outputs_tuple, (tuple, list)), "Expected tuple of outputs"
+
+    input_grad_nodes = [outputs_tuple[i] for i in input_grad_indices]
+    param_grad_nodes = [outputs_tuple[i] for i in param_grad_indices]
+
+    gm_input = extract_subgraph(gm, input_grad_nodes)
+    gm_weight = extract_subgraph(gm, param_grad_nodes)
+    return gm_input, gm_weight
+
+
+def is_backward_graph(gm: fx.GraphModule) -> bool:
+    for node in gm.graph.nodes:
+        if node.op == "call_function":
+            mod = getattr(node.target, "__module__", "")
+            if "torch._dynamo.compiled_autograd" in mod:
+                return True
+    return False
+
+
+def custom_backend(gm: fx.GraphModule, example_inputs):
+    if not is_backward_graph(gm):
+        print("FORWARD GRAPH")
+        gm.print_readable()
+        return gm
+
+    print("FULL BACKWARD GRAPH")
+    gm.print_readable()
+
+    fx_to_graphviz(gm, filename="full_backward_graph")
+    print("Saved full backward graph visualization to file.")
+
+    input_grad_indices = [0]
+    param_grad_indices = [1,2]
+
+    gm_input, gm_weight = split_backward_graph(
+        gm,
+        input_grad_indices=input_grad_indices,
+        param_grad_indices=param_grad_indices,
+    )
+
+    print("BACKWARD INPUT SUBGRAPH")
+    gm_input.print_readable()
+
+    print("BACKWARD WEIGHT SUBGRAPH")
+    gm_weight.print_readable()
+
+    with torch.no_grad():
+        full_out = gm(*example_inputs)
+        in_out = gm_input(*example_inputs)
+        w_out = gm_weight(*example_inputs)
+
+    print("BACKWARD OUTPUT CHECK")
+    print("full_out shapes:", [t.shape for t in full_out])
+    print("input_out shapes:", [t.shape for t in in_out])
+    print("weight_out shapes:", [t.shape for t in w_out])
+
+    full_out_list = flatten_grads(full_out)
+    split_out_list = flatten_grads(in_out) + flatten_grads(w_out)
+
+    global SPLIT_GRADS
+    SPLIT_GRADS = split_out_list
+
+    print("SPLIT vs FULL GRAPH CHECK")
+    compare_grad_lists(full_out_list, split_out_list)
+
+
+    fx_to_graphviz(gm_input, filename="backward_input_subgraph")
+    fx_to_graphviz(gm_weight, filename="backward_weight_subgraph")
+    print("Saved subgraph visualizations to files.")
+
+    return gm
+
+def flatten_grads(grads):
+    if isinstance(grads, dict):
+        return list(grads.values())
+    if isinstance(grads, (list, tuple)):
+        return list(grads)
+    return [grads]
+
+
+def compare_grad_lists(a, b, atol=1e-6, rtol=1e-6):
+    assert len(a) == len(b), f"Different #grads: {len(a)} vs {len(b)}"
+
+    for i, (g1, g2) in enumerate(zip(a, b)):
+        if g1 is None or g2 is None:
+            print(f"grad[{i}] is None")
+            continue
+
+        if not torch.allclose(g1, g2, atol=atol, rtol=rtol):
+            print(f"grad[{i}] mismatch — max diff: {(g1 - g2).abs().max().item():.6f}")
+        else:
+            print(f"grad[{i}] match")
+
+
+def autograd_grads(model, x):
+    with torch.enable_grad():
+        copy.deepcopy(model)
+        model.zero_grad(set_to_none=True)
+
+        x = x.clone().detach().requires_grad_(True)
+        y = model(x).sum()
+        y.backward()
+
+        grads = [x.grad]
+        grads += [p.grad for _, p in model.named_parameters()]
+
+    return grads
+
+
+@torch.compile(backend=custom_backend)
+def train(model, x):
+    x = x.clone().detach().requires_grad_(True)
+    y = model(x).sum()
+    y.backward()
+    return y
+
+
+def main():
+    global MODEL, X
+    torch.manual_seed(0)
+    model = OneLayerToy()
+    x = torch.randn(1, 10)
+
+    loss = train(model, x)
+    print("Loss:", loss.item())
+
+    true_grads = autograd_grads(model, x)
+
+    print("SPLIT vs PYTORCH AUTOGRAD CHECK")
+    compare_grad_lists(SPLIT_GRADS, true_grads)
+
+if __name__ == "__main__":
+    main()
diff --git a/visualize_graph.py b/visualize_graph.py
new file mode 100644
index 0000000..b81607a
--- /dev/null
+++ b/visualize_graph.py
@@ -0,0 +1,35 @@
+import torch
+import torch.fx as fx
+import graphviz
+
+
+def fx_to_graphviz(
+    gm: fx.GraphModule,
+    filename: str = "fx_graph",
+    format: str = "png",
+    show_attrs: bool = True,
+):
+    """
+    Convert a torch.fx.GraphModule to a Graphviz visualization.
+
+    Args:
+        gm (fx.GraphModule): The traced model
+        filename (str): Output file name (no extension)
+        format (str): 'png', 'pdf', 'svg', etc.
+        show_attrs (bool): Show full node target names
+    """
+    dot = graphviz.Digraph(format=format, graph_attr={"rankdir": "LR"})
+
+    for node in gm.graph.nodes:
+        label = f"{node.op}\n{node.name}"
+
+        if show_attrs and node.target not in ("output", "placeholder"):
+            label += f"\n{node.target}"
+
+        dot.node(node.name, label)
+
+        for user in node.users:
+            dot.edge(node.name, user.name)
+
+    dot.render(filename, cleanup=True)
+    return dot

From f5d1b6386fdd57b7a04c50a04f97d0b662520c35 Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Tue, 9 Dec 2025 04:56:14 +0000
Subject: [PATCH 013/100] Initial ZeroBubble implementation

---
 backward_input_fx.png        | Bin 0 -> 494991 bytes
 backward_input_subgraph.png  | Bin 335961 -> 455217 bytes
 backward_weight_fx.png       | Bin 0 -> 674110 bytes
 backward_weight_subgraph.png | Bin 491281 -> 988447 bytes
 full_backward_graph.png      | Bin 564263 -> 1169825 bytes
 piper/actor.py               | 162 ++++++++++++++-
 piper/exec.py                | 110 ++++++++--
 piper/utils.py               |   8 -
 split_baseline_experiment.py |   0
 split_example.py             | 217 --------------------
 split_example_FX.py          | 385 +++++++++++++++++++++++++++++++++++
 split_example_autograd.py    | 267 ++++++++++++++++++++++++
 test/schedule_helpers.py     |  54 ++++-
 test/test_backward_split.py  | 144 +++++++++++++
 test/test_llama.py           |   7 +-
 test/test_simple.py          |   1 +
 16 files changed, 1101 insertions(+), 254 deletions(-)
 create mode 100644 backward_input_fx.png
 create mode 100644 backward_weight_fx.png
 create mode 100644 split_baseline_experiment.py
 delete mode 100644 split_example.py
 create mode 100644 split_example_FX.py
 create mode 100644 split_example_autograd.py
 create mode 100644 test/test_backward_split.py

diff --git a/backward_input_fx.png b/backward_input_fx.png
new file mode 100644
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diff --git a/piper/actor.py b/piper/actor.py
index eaf0c13..e17eea0 100644
--- a/piper/actor.py
+++ b/piper/actor.py
@@ -6,7 +6,7 @@
 from torch._guards import CompileId
 from torch.nn import Parameter
 from collections import defaultdict
-from piper.utils import deserialize_graphmodule, piper_metadata, split_backward_graph
+from piper.utils import deserialize_graphmodule, piper_metadata
 
 torch.set_float32_matmul_precision('high')
 
@@ -21,9 +21,7 @@ def backward_backend(gm, example_inputs, **kwargs):
             for ex in arg._example:
                 print(f"Arg {ex.shape} | parameter: {isinstance(ex, Parameter)} | requires_grad: {ex.requires_grad}")
     gm.print_readable()
-
-
-
+    
     return gm
 
 
@@ -65,6 +63,9 @@ def __init__(self, id, optim_fn=None):
         # accumuate loss for each microbatch
         self.loss = []
 
+        # save upstream gradients for split backward
+        # self.saved_upstream = defaultdict(dict)
+
         # Timing infrastructure
         self.tracing = False  # Toggle for timing and memory tracing
         self.trace_data = {'update': {'total': [], 'peak_memory_delta': [], 'peak_memory': []}}
@@ -119,6 +120,8 @@ def compile_graph(self, stage_id, gm_data, compiler_fn, graphargs, input_idxs):
         self.parameters[stage_id] = graphargs
 
         # initialize the optimizer for this stage
+        ## Added this manual_seed for debugging
+        torch.manual_seed(0)
         if [param for param in self.parameters[stage_id] if param is not None]:
             self.optims[stage_id] = self.optim_fn([param for param in self.parameters[stage_id] if param is not None])
 
@@ -166,6 +169,7 @@ def unwrap(x):
             args[0].requires_grad_()
             self.prev_activation[stage_id][mb_idx] = args[0]
 
+
         if self.tracing:
             pre_forward_event.record()
 
@@ -329,6 +333,7 @@ def backward_loss(loss_fn, activation, labels):
             loss = loss_fn(activation, labels)
             loss.backward()
             return loss
+        
         @torch.compile(backend=backward_backend)
         def backward_activation(activation, inp):
             activation.backward(gradient=inp)
@@ -356,7 +361,8 @@ def backward_activation(activation, inp):
         return ret
 
     def update(self, *done_mbs):
-        self.log.debug(f"Calling update on actor {self.actor_id}")
+        self.log.info(f"Calling update on actor {self.actor_id}")
+        # print("UPDATE FOR STAGE ", self.actor_id, " MBS ", done_mbs)
         
         if self.tracing:
             start_event = torch.cuda.Event(enable_timing=True)
@@ -368,9 +374,12 @@ def update(self, *done_mbs):
             start_event.record()
         
         assert self.optim_fn
+        # print("len(self.optims): ", len(self.optims))
         for _, optim in self.optims.items():
+            # print("GRAD STEP")
+            # print(optim)
             optim.step()
-            optim.zero_grad()
+            optim.zero_grad(set_to_none=True)
         losses = self.loss
         self.loss.clear()
         
@@ -458,4 +467,143 @@ def time_object_retrieval(self, obj) -> dict:
         else:
             objs = [obj + 1]
         self.objs = objs
-        return "done"
\ No newline at end of file
+        return "done"
+    
+    # Debugging param dump function #
+    def get_param_grads(self, stage_id: int):
+        stage_params = [p for p in self.parameters[stage_id] if isinstance(p, Parameter)]
+        return [p.grad.detach().clone().cpu() if p.grad is not None else None
+                for p in stage_params]
+    
+    def get_params(self, stage_id: int):
+        stage_params = [p for p in self.parameters[stage_id] if isinstance(p, Parameter)]
+        return [p.detach().clone().cpu() for p in stage_params]
+    ######
+
+    def backward_input(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
+        self.log.info(f"Calling backward_input {stage_id} mb {mb_idx} on actor {self.actor_id}")
+        # print("BACKWARD INPUT FOR STAGE ", stage_id, " MB ", mb_idx)
+        is_last_stage = loss_fn is not None
+        activation = self.activation[stage_id][mb_idx]
+
+        if stage_id == 0:
+            if is_last_stage:
+                labels = self.truth if self.truth is not None else inp[0]
+                loss = loss_fn(activation, labels)
+                self.loss.append(loss)
+                upstream = torch.ones_like(loss)
+                # print("UPSTREAM FROM LAST STAGE: ", upstream)
+                # print("UPSTREAM SHAPE FROM LAST STAGE: ", upstream.shape)
+                # self.saved_upstream[stage_id][mb_idx] = upstream
+            else:
+                # upstream = inp[0]
+                upstream = inp
+                # self.saved_upstream[stage_id][mb_idx] = upstream
+            gx = torch.zeros_like(activation)
+            # print(f"INPUT GX on stage {stage_id} mb {mb_idx}: {gx}")
+            # print(f"INPUT GX SHAPE on stage {stage_id} mb {mb_idx}: {gx.shape}")
+            # print(f"INPUT UPSTREAM on stage {stage_id} mb {mb_idx}: {upstream}")
+            # print(f"INPUT UPSTREAM SHAPE on stage {stage_id} mb {mb_idx}: {upstream.shape}")
+            return [gx, upstream]
+
+        stage_input = self.prev_activation[stage_id][mb_idx]
+
+        if is_last_stage:
+            labels = self.truth if self.truth is not None else inp[0]
+            loss = loss_fn(activation, labels)
+            self.loss.append(loss)
+
+            upstream = torch.ones_like(loss)
+
+            gx = torch.autograd.grad(
+                outputs=loss,
+                inputs=stage_input,
+                grad_outputs=upstream,
+                retain_graph=True,
+            )[0]
+
+        else:
+            # upstream = inp[0]
+            upstream = inp
+
+            gx = torch.autograd.grad(
+                outputs=activation,
+                inputs=stage_input,
+                grad_outputs=upstream,
+                retain_graph=True,
+            )[0]
+
+        # self.saved_upstream[stage_id][mb_idx] = upstream
+
+        # Change to also return upstream for temporal dependency
+        # print(f"INPUT GX on stage {stage_id} mb {mb_idx}: {gx}")
+        # print(f"INPUT GX SHAPE on stage {stage_id} mb {mb_idx}: {gx.shape}")
+        # print(f"INPUT UPSTREAM on stage {stage_id} mb {mb_idx}: {upstream}")
+        # print(f"INPUT UPSTREAM SHAPE on stage {stage_id} mb {mb_idx}: {upstream.shape}")
+
+        return [gx, upstream]
+
+
+    def backward_weight(self, stage_id: int, mb_idx: int, upstream_ref, loss_fn=None):
+        # print("BACKWARD WEIGHT FOR STAGE ", stage_id, " MB ", mb_idx)
+        self.log.info(f"Calling backward_weight {stage_id} mb {mb_idx} on actor {self.actor_id}")
+
+        is_last_stage = loss_fn is not None
+        activation = self.activation[stage_id][mb_idx]
+
+        stage_params = [p for p in self.parameters[stage_id] if isinstance(p, Parameter)]
+
+        # print(f"UPSTREAM: {upstream_ref}")
+        # print(f"UPSTREAM[0]: {upstream_ref[0]}")
+        # print(f"UPSTREAM[1]: {upstream_ref[1]}")
+
+        gx = ray.get(upstream_ref[0])
+        upstream = ray.get(upstream_ref[1])
+        # print(f"RESOLVED UPSTREAM on stage {stage_id} mb {mb_idx}: {upstream}")
+        # print(f"RESOLVED UPSTREAM SHAPE on stage {stage_id} mb {mb_idx}: {upstream.shape}")
+        # print(f"RESOLVED GX on stage {stage_id} mb {mb_idx}: {gx}")
+        # print(f"RESOLVED GX SHAPE on stage {stage_id} mb {mb_idx}: {gx.shape}")
+
+        if is_last_stage:
+            labels = self.truth if self.truth is not None else None
+            loss = loss_fn(activation, labels)
+
+            # upstream = self.saved_upstream[stage_id][mb_idx]
+
+            gparams = torch.autograd.grad(
+                outputs=loss,
+                inputs=stage_params,
+                # grad_outputs=upstream,
+                retain_graph=False,
+            )
+
+        else:
+            # upstream = self.saved_upstream[stage_id][mb_idx]
+
+            gparams = torch.autograd.grad(
+                outputs=activation,
+                inputs=stage_params,
+                grad_outputs=upstream,
+                retain_graph=False,
+            )
+
+        assert len(gparams) == len(stage_params), (
+            f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(stage_params)}"
+        )
+        
+        for p, g in zip(stage_params, gparams):
+            if p.grad is None:
+                p.grad = g.clone()
+            else:
+                p.grad.add_(g)
+
+        del self.activation[stage_id][mb_idx]
+        # del self.saved_upstream[stage_id][mb_idx]
+        # del self.prev_activation[stage_id][mb_idx]
+
+        return [gx] + ["done"]
+
+
+
+
+
diff --git a/piper/exec.py b/piper/exec.py
index d6f8756..dadc3fe 100644
--- a/piper/exec.py
+++ b/piper/exec.py
@@ -1,14 +1,21 @@
 import torch
 from typing import NamedTuple
+from strenum import StrEnum
 
 from piper.utils import piper_metadata
 
+class TaskType(StrEnum):
+    FORWARD = "f"
+    BACKWARD = "b"
+    UPDATE = "u"
+    BACKWARD_INPUT = "i"
+    BACKWARD_WEIGHT = "w"
+
 class Task(NamedTuple):
     device_id: int
     stage_id: int
     mb_idx: int
-    is_fwd: bool
-    upd: bool
+    task: TaskType
 
 class DAGEdge(NamedTuple):
     from_stage: int
@@ -49,18 +56,18 @@ def validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdge
                     )
                 
                 # Check for duplicates
-                task_key = (task.stage_id, task.mb_idx, task.is_fwd, task.upd)
+                task_key = (task.stage_id, task.mb_idx, task.task)
                 if task_key in all_tasks:
                     raise ValueError(
                         f"Duplicate task found: stage_id={task.stage_id}, "
-                        f"mb_idx={task.mb_idx}, is_fwd={task.is_fwd}, upd={task.upd}"
+                        f"mb_idx={task.mb_idx}, task={task.task}"
                     )
                 all_tasks.add(task_key)
                 
                 # Track tasks by microbatch
                 if task.mb_idx not in microbatch_tasks:
                     microbatch_tasks[task.mb_idx] = set()
-                microbatch_tasks[task.mb_idx].add((task.stage_id, task.is_fwd, task.upd))
+                microbatch_tasks[task.mb_idx].add((task.stage_id, task.task == TaskType.FORWARD, task.task == TaskType.UPDATE))
     
     # Get all required stages from DAG edges
     all_required_stages = set()
@@ -98,9 +105,9 @@ def validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdge
             for time_step in range(num_steps):
                 task = schedule[stage_id][time_step]
                 if task is not None and task.mb_idx == mb_idx:
-                    if task.is_fwd and not task.upd:
+                    if task.task == TaskType.FORWARD:
                         fwd_times[task.stage_id] = time_step
-                    elif not task.is_fwd and not task.upd:
+                    elif not task.task == TaskType.FORWARD and not task.task == TaskType.UPDATE:
                         bwd_times[task.stage_id] = time_step
         
         # Check forward stage ordering: if A -> B, then fwd(A) < fwd(B)
@@ -158,7 +165,7 @@ def piper_exec(model, schedule, inputs, truth, loss_fn, num_mbs, split_fwd_fns:
     dag_edges = list(map(lambda e: (DAGEdge(e[0], e[1])), list(piper_metadata['dag'])))
     
     # Validate the schedule before execution
-    validate_schedule(schedule, dag_edges, num_mbs)
+    # validate_schedule(schedule, dag_edges, num_mbs)
 
     # maps mb_idx to the ref resulting from a forward call on the microbatch
     fwd_refs = dict()
@@ -172,10 +179,10 @@ def piper_exec(model, schedule, inputs, truth, loss_fn, num_mbs, split_fwd_fns:
         for j in range(num_stages-1, -1, -1):
             task = schedule[j][i]
             if task:
-                device_id,stage_id, mb_idx, is_fwd, upd = task
+                device_id, stage_id, mb_idx, task_type = task
                 piper_metadata['current_mb'] = mb_idx
                 actor_id = j
-                if upd:
+                if task_type == TaskType.UPDATE:
                     num_bwd_targets = len(get_backward_targets(stage_id, dag_edges))
                     if num_bwd_targets == 0:
                         num_bwd_targets = 1
@@ -189,10 +196,9 @@ def piper_exec(model, schedule, inputs, truth, loss_fn, num_mbs, split_fwd_fns:
                         else:
                             done_refs.add(bwd_refs)
                     done_refs = list(done_refs)
-                    # print(f"[PIPER] Update stage {stage_id}")
                     upd = actors[actor_id].update.remote(*done_refs)
                     ret.append(upd)
-                elif is_fwd:
+                elif task_type == TaskType.FORWARD:
                     if split_fwd_fns:
                         # print(f"[PIPER] Forward stage {stage_id} mb {mb_idx}")
                         # Set the current microbatch index in thread-local storage
@@ -213,7 +219,7 @@ def piper_exec(model, schedule, inputs, truth, loss_fn, num_mbs, split_fwd_fns:
                         if mb_idx not in fwd_refs:
                             # print(f"Fwd mb {mb_idx}")
                             fwd_refs[mb_idx] = model(*inputs, dynamo_mb=mb_idx)
-                else:
+                elif task_type == TaskType.BACKWARD:
                     # log order of task dispatch by printing
                     # also see output_graph.py:1785 where we log forward dispatch
                     # print(f"Calling backward stage {stage_id} mb {mb_idx}")
@@ -276,4 +282,82 @@ def piper_exec(model, schedule, inputs, truth, loss_fn, num_mbs, split_fwd_fns:
                             .backward.options(num_returns=num_bwd_targets*2)
                             .remote(stage_id, mb_idx, bwd_ref)
                         )
+
+                ############ ZB Modification ###########
+                elif task_type == TaskType.BACKWARD_INPUT:
+                    num_bwd_targets = len(get_backward_targets(stage_id, dag_edges))
+                    if num_bwd_targets == 0:
+                        num_bwd_targets = 1
+
+                    if mb_idx not in bwd_ref_dicts:
+                        fwd_ref = fwd_refs[mb_idx]
+                        bwd_ref_dicts[mb_idx] = {}
+
+                        bwd_ref_dicts[mb_idx][stage_id] = (
+                            actors[actor_id]
+                            .backward_input.options(num_returns=2*num_bwd_targets)
+                            .remote(
+                                stage_id,
+                                mb_idx,
+                                fwd_ref.get_ref(),
+                                loss_fn=loss_fn,
+                            )
+                        )
+
+                    else:
+                        to_stage = [
+                            edge.to_stage
+                            for edge in dag_edges
+                            if edge.from_stage == stage_id
+                        ]
+                        
+                        assert len(to_stage) == 1
+                        to_stage = to_stage[0]
+
+                        targets = get_backward_targets(to_stage, dag_edges)
+                        idx = targets.index(DAGEdge(stage_id, to_stage))
+
+                        assert to_stage in bwd_ref_dicts[mb_idx]
+
+                        successor_upstreams = bwd_ref_dicts[mb_idx][to_stage]
+                        upstream_ref = successor_upstreams[idx]
+
+                        bwd_ref_dicts[mb_idx][stage_id] = (
+                            actors[actor_id]
+                            .backward_input.options(num_returns=2*num_bwd_targets)
+                            .remote(
+                                stage_id,
+                                mb_idx,
+                                upstream_ref,
+                                loss_fn=None,
+                            )
+                        )
+
+                elif task_type == TaskType.BACKWARD_WEIGHT:
+                    num_bwd_targets = len(get_backward_targets(stage_id, dag_edges))
+                    if num_bwd_targets == 0:
+                        num_bwd_targets = 1
+
+                    is_last_stage = len(
+                        [e for e in dag_edges if e.from_stage == stage_id]
+                    ) == 0
+
+                    # Ensures the stage's backward_input was called before backward_weight for this mb
+                    assert mb_idx in bwd_ref_dicts and stage_id in bwd_ref_dicts[mb_idx]
+
+                    upstream_ref = bwd_ref_dicts[mb_idx][stage_id]
+
+                    bwd_ref_dicts[mb_idx][stage_id] = (
+                            actors[actor_id]
+                            .backward_weight.options(num_returns=2*num_bwd_targets)
+                            .remote(
+                            stage_id,
+                            mb_idx,
+                            upstream_ref,
+                            loss_fn=loss_fn if is_last_stage else None,
+                        )
+                    )
+                ###########################################
+                else:
+                    raise ValueError(f"Unknown task type: {task_type}")
     return ret
\ No newline at end of file
diff --git a/piper/utils.py b/piper/utils.py
index 82c1ff5..52b7d39 100644
--- a/piper/utils.py
+++ b/piper/utils.py
@@ -293,11 +293,3 @@ def deserialize_graphmodule(s: str) -> fx.GraphModule:
     state = {k: torch.tensor(v) for k, v in data["state_dict"].items()}
     gm.load_state_dict(state, strict=False)
     return gm
-
-def split_backward_graphs(gm: fx.GraphModule):
-    """
-    Given a GraphModule representing a full forward+backward pass,
-    split it into two GraphModules: one for the backward weight pass and one for the backward input pass
-    """
-
-    return gm
\ No newline at end of file
diff --git a/split_baseline_experiment.py b/split_baseline_experiment.py
new file mode 100644
index 0000000..e69de29
diff --git a/split_example.py b/split_example.py
deleted file mode 100644
index c499305..0000000
--- a/split_example.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.fx as fx
-import copy
-from visualize_graph import fx_to_graphviz
-
-torch._dynamo.config.compiled_autograd = True
-
-SPLIT_GRADS = None
-
-class OneLayerToy(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.linear = nn.Linear(10, 10)
-
-    def forward(self, x):
-        return self.linear(x)
-
-class TwoLayerToy(nn.Module):
-    def __init__(self, in_dim=8, hidden_dim=8, out_dim=1):
-        super().__init__()
-        self.fc1 = nn.Linear(in_dim, hidden_dim)
-        self.relu = nn.ReLU()
-        self.fc2 = nn.Linear(hidden_dim, out_dim)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.relu(x)
-        x = self.fc2(x)
-        return x
-
-
-def extract_subgraph(gm: fx.GraphModule, output_nodes):
-    graph = gm.graph
-
-    needed = set(output_nodes)
-    queue = list(output_nodes)
-
-    while queue:
-        node = queue.pop()
-        for inp in node.all_input_nodes:
-            if inp not in needed:
-                needed.add(inp)
-                queue.append(inp)
-
-    for node in graph.nodes:
-        if node.op == "placeholder":
-            needed.add(node)
-
-    new_graph = fx.Graph()
-    node_copies = {}
-
-    for node in graph.nodes:
-        if node.op == "output":
-            continue
-        if node in needed:
-            new_node = new_graph.node_copy(node, lambda n: node_copies[n])
-            node_copies[node] = new_node
-
-    new_outputs = [node_copies[n] for n in output_nodes]
-    if len(new_outputs) == 1:
-        new_graph.output((new_outputs[0],))
-    else:
-        new_graph.output(tuple(new_outputs))
-
-    new_gm = fx.GraphModule(gm, new_graph)
-    new_gm.recompile()
-    return new_gm
-
-
-def split_backward_graph(
-    gm: fx.GraphModule,
-    input_grad_indices,
-    param_grad_indices,
-):
-    graph = gm.graph
-    output_node = None
-    for n in graph.nodes:
-        if n.op == "output":
-            output_node = n
-            break
-    assert output_node is not None, "No output node in FX graph?"
-
-    outputs_tuple = output_node.args[0]
-    assert isinstance(outputs_tuple, (tuple, list)), "Expected tuple of outputs"
-
-    input_grad_nodes = [outputs_tuple[i] for i in input_grad_indices]
-    param_grad_nodes = [outputs_tuple[i] for i in param_grad_indices]
-
-    gm_input = extract_subgraph(gm, input_grad_nodes)
-    gm_weight = extract_subgraph(gm, param_grad_nodes)
-    return gm_input, gm_weight
-
-
-def is_backward_graph(gm: fx.GraphModule) -> bool:
-    for node in gm.graph.nodes:
-        if node.op == "call_function":
-            mod = getattr(node.target, "__module__", "")
-            if "torch._dynamo.compiled_autograd" in mod:
-                return True
-    return False
-
-
-def custom_backend(gm: fx.GraphModule, example_inputs):
-    if not is_backward_graph(gm):
-        print("FORWARD GRAPH")
-        gm.print_readable()
-        return gm
-
-    print("FULL BACKWARD GRAPH")
-    gm.print_readable()
-
-    fx_to_graphviz(gm, filename="full_backward_graph")
-    print("Saved full backward graph visualization to file.")
-
-    input_grad_indices = [0]
-    param_grad_indices = [1,2]
-
-    gm_input, gm_weight = split_backward_graph(
-        gm,
-        input_grad_indices=input_grad_indices,
-        param_grad_indices=param_grad_indices,
-    )
-
-    print("BACKWARD INPUT SUBGRAPH")
-    gm_input.print_readable()
-
-    print("BACKWARD WEIGHT SUBGRAPH")
-    gm_weight.print_readable()
-
-    with torch.no_grad():
-        full_out = gm(*example_inputs)
-        in_out = gm_input(*example_inputs)
-        w_out = gm_weight(*example_inputs)
-
-    print("BACKWARD OUTPUT CHECK")
-    print("full_out shapes:", [t.shape for t in full_out])
-    print("input_out shapes:", [t.shape for t in in_out])
-    print("weight_out shapes:", [t.shape for t in w_out])
-
-    full_out_list = flatten_grads(full_out)
-    split_out_list = flatten_grads(in_out) + flatten_grads(w_out)
-
-    global SPLIT_GRADS
-    SPLIT_GRADS = split_out_list
-
-    print("SPLIT vs FULL GRAPH CHECK")
-    compare_grad_lists(full_out_list, split_out_list)
-
-
-    fx_to_graphviz(gm_input, filename="backward_input_subgraph")
-    fx_to_graphviz(gm_weight, filename="backward_weight_subgraph")
-    print("Saved subgraph visualizations to files.")
-
-    return gm
-
-def flatten_grads(grads):
-    if isinstance(grads, dict):
-        return list(grads.values())
-    if isinstance(grads, (list, tuple)):
-        return list(grads)
-    return [grads]
-
-
-def compare_grad_lists(a, b, atol=1e-6, rtol=1e-6):
-    assert len(a) == len(b), f"Different #grads: {len(a)} vs {len(b)}"
-
-    for i, (g1, g2) in enumerate(zip(a, b)):
-        if g1 is None or g2 is None:
-            print(f"grad[{i}] is None")
-            continue
-
-        if not torch.allclose(g1, g2, atol=atol, rtol=rtol):
-            print(f"grad[{i}] mismatch — max diff: {(g1 - g2).abs().max().item():.6f}")
-        else:
-            print(f"grad[{i}] match")
-
-
-def autograd_grads(model, x):
-    with torch.enable_grad():
-        copy.deepcopy(model)
-        model.zero_grad(set_to_none=True)
-
-        x = x.clone().detach().requires_grad_(True)
-        y = model(x).sum()
-        y.backward()
-
-        grads = [x.grad]
-        grads += [p.grad for _, p in model.named_parameters()]
-
-    return grads
-
-
-@torch.compile(backend=custom_backend)
-def train(model, x):
-    x = x.clone().detach().requires_grad_(True)
-    y = model(x).sum()
-    y.backward()
-    return y
-
-
-def main():
-    global MODEL, X
-    torch.manual_seed(0)
-    model = OneLayerToy()
-    x = torch.randn(1, 10)
-
-    loss = train(model, x)
-    print("Loss:", loss.item())
-
-    true_grads = autograd_grads(model, x)
-
-    print("SPLIT vs PYTORCH AUTOGRAD CHECK")
-    compare_grad_lists(SPLIT_GRADS, true_grads)
-
-if __name__ == "__main__":
-    main()
diff --git a/split_example_FX.py b/split_example_FX.py
new file mode 100644
index 0000000..49d1d97
--- /dev/null
+++ b/split_example_FX.py
@@ -0,0 +1,385 @@
+import torch
+import torch.nn as nn
+import torch.fx as fx
+from torch.utils.weak import TensorWeakRef
+import copy
+from visualize_graph import fx_to_graphviz
+from test.models.llama import Transformer, LLAMA_DEBUG
+from torch.nn import Parameter
+
+PRINT_FX_GRAPHS = False
+
+torch._dynamo.config.compiled_autograd = True
+
+placeholder_types = []
+
+SPLIT_GRADS = None
+
+class OneLayerToy(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear = nn.Linear(10, 10)
+
+    def forward(self, x):
+        return self.linear(x)
+
+class TwoLayerToy(nn.Module):
+    def __init__(self, in_dim=10, hidden_dim=10, out_dim=2):
+        super().__init__()
+        self.fc1 = nn.Linear(in_dim, hidden_dim)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(hidden_dim, out_dim)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        return x
+    
+def unwrap_example(ex):
+    # If it's a list/tuple, recurse
+    if isinstance(ex, (list, tuple)):
+        return [unwrap_example(e) for e in ex]
+
+    # Already a tensor or parameter
+    if isinstance(ex, torch.Tensor):
+        return ex
+
+    # TensorWeakRef case
+    if isinstance(ex, TensorWeakRef):
+        # Be defensive about method names across versions
+        if hasattr(ex, "get_tensor"):
+            t = ex.get_tensor()
+        elif hasattr(ex, "get"):
+            t = ex.get()
+        elif callable(ex):
+            # some weakref-like objects are callable
+            t = ex()
+        else:
+            t = None
+        return t
+
+    # Anything else / underlying tensor GC'd
+    return None
+
+
+
+def extract_subgraph(gm: fx.GraphModule, output_nodes):
+    graph = gm.graph
+
+    needed = set(output_nodes)
+    queue = list(output_nodes)
+
+    while queue:
+        node = queue.pop()
+        for inp in node.all_input_nodes:
+            if inp not in needed:
+                needed.add(inp)
+                queue.append(inp)
+
+    for node in graph.nodes:
+        if node.op == "placeholder":
+            needed.add(node)
+
+    new_graph = fx.Graph()
+    node_copies = {}
+
+    for node in graph.nodes:
+        if node.op == "output":
+            continue
+        if node in needed:
+            new_node = new_graph.node_copy(node, lambda n: node_copies[n])
+            node_copies[node] = new_node
+
+    new_outputs = [node_copies[n] for n in output_nodes]
+    if len(new_outputs) == 1:
+        new_graph.output((new_outputs[0],))
+    else:
+        new_graph.output(tuple(new_outputs))
+
+    new_gm = fx.GraphModule(gm, new_graph)
+    new_gm.recompile()
+    return new_gm
+
+
+def split_backward_graph(
+    gm: fx.GraphModule,
+    input_grad_indices,
+    param_grad_indices,
+):
+    graph = gm.graph
+    output_node = None
+    for n in graph.nodes:
+        if n.op == "output":
+            output_node = n
+            break
+    assert output_node is not None, "No output node in FX graph?"
+
+    outputs_tuple = output_node.args[0]
+    assert isinstance(outputs_tuple, (tuple, list)), "Expected tuple of outputs"
+
+    input_grad_nodes = [outputs_tuple[i] for i in input_grad_indices]
+    param_grad_nodes = [outputs_tuple[i] for i in param_grad_indices]
+
+    gm_input = extract_subgraph(gm, input_grad_nodes)
+    gm_weight = extract_subgraph(gm, param_grad_nodes)
+    return gm_input, gm_weight
+
+
+def is_backward_graph(gm: fx.GraphModule) -> bool:
+    for node in gm.graph.nodes:
+        if node.op == "call_function":
+            mod = getattr(node.target, "__module__", "")
+            if "torch._dynamo.compiled_autograd" in mod:
+                return True
+    return False
+
+def forward_backend(gm: fx.GraphModule, example_inputs):
+    global placeholder_types
+
+    print("FORWARD GRAPH")
+    if PRINT_FX_GRAPHS:
+        gm.print_readable()
+
+    placeholders = gm.graph.find_nodes(op="placeholder", sort=True)
+    print("placeholders:", placeholders)
+
+    placeholder_types = []
+    graphargs = [node.meta["grapharg"] for node in placeholders]
+    print("graphargs:", graphargs)
+    print("len(graphargs):", len(graphargs))
+    for arg in graphargs:
+        arg_ex = arg._example
+        arg_ex = arg_ex if isinstance(arg_ex, (list, tuple)) else [arg_ex]
+        for ex in arg_ex:
+            print(f"example input: {ex}")
+            ex = unwrap_example(ex)
+            print(f"unwrapped: {ex}")
+
+            if ex is None:
+                continue
+
+            is_param = any(isinstance(t, torch.nn.Parameter) for t in (ex if isinstance(ex, (list, tuple)) else [ex]))
+            requires_grad = any(getattr(t, "requires_grad", False) for t in (ex if isinstance(ex, (list, tuple)) else [ex]))
+            print(f"parameter: {is_param} | requires_grad: {requires_grad} | shape: {[t.shape for t in (ex if isinstance(ex, (list, tuple)) else [ex]) if isinstance(t, torch.Tensor)]}")
+            placeholder_types.append("param" if is_param else "input")
+
+    print("placeholder_types:", placeholder_types)
+    return gm
+
+
+def backward_backend(gm: fx.GraphModule, example_inputs):
+    if not is_backward_graph(gm):
+        global placeholder_types
+
+        print("FORWARD GRAPH")
+        if PRINT_FX_GRAPHS:
+            gm.print_readable()
+
+        placeholders = gm.graph.find_nodes(op="placeholder", sort=True)
+        print("placeholders:", placeholders)
+
+        placeholder_types = []
+        graphargs = [node.meta["grapharg"] for node in placeholders]
+        print("graphargs:", graphargs)
+        print("len(graphargs):", len(graphargs))
+        for arg in graphargs:
+            arg_ex = arg._example
+            arg_ex = arg_ex if isinstance(arg_ex, (list, tuple)) else [arg_ex]
+            for ex in arg_ex:
+                print(f"example input: {ex}")
+                ex = unwrap_example(ex)
+                print(f"unwrapped: {ex}")
+
+                if ex is None:
+                    continue
+
+                is_param = any(isinstance(t, torch.nn.Parameter) for t in (ex if isinstance(ex, (list, tuple)) else [ex]))
+                requires_grad = any(getattr(t, "requires_grad", False) for t in (ex if isinstance(ex, (list, tuple)) else [ex]))
+                print(f"parameter: {is_param} | requires_grad: {requires_grad} | shape: {[t.shape for t in (ex if isinstance(ex, (list, tuple)) else [ex]) if isinstance(t, torch.Tensor)]}")
+                placeholder_types.append("param" if is_param else "input")
+
+        print("placeholder_types:", placeholder_types)
+        return gm
+
+    print("FULL BACKWARD GRAPH")
+    if PRINT_FX_GRAPHS:
+        gm.print_readable()
+    # print("placeholder_types:", placeholder_types)
+
+    placeholders = gm.graph.find_nodes(op="placeholder", sort=True)
+    print("placeholders:", placeholders)
+    print("len(placeholders):", len(placeholders))
+
+    placeholder_types = []
+    graphargs = [node.meta["grapharg"] for node in placeholders]
+    print("graphargs:", graphargs)
+    print("len(graphargs):", len(graphargs))
+    for arg in graphargs:
+        arg_ex = arg._example
+        arg_ex = arg_ex if isinstance(arg_ex, (list, tuple)) else [arg_ex]
+        for ex in arg_ex:
+            print(f"example input: {ex}")
+            ex = unwrap_example(ex)
+            print(f"unwrapped: {ex}")
+
+            if ex is None:
+                continue
+
+            is_param = any(isinstance(t, torch.nn.Parameter) for t in (ex if isinstance(ex, (list, tuple)) else [ex]))
+            requires_grad = any(getattr(t, "requires_grad", False) for t in (ex if isinstance(ex, (list, tuple)) else [ex]))
+            print(f"parameter: {is_param} | requires_grad: {requires_grad} | shape: {[t.shape for t in (ex if isinstance(ex, (list, tuple)) else [ex]) if isinstance(t, torch.Tensor)]}")
+            placeholder_types.append("param" if is_param else "input")
+
+    print("placeholder_types:", placeholder_types)
+
+    return gm
+
+    fx_to_graphviz(gm, filename="full_backward_graph")
+    print("Saved full backward graph visualization to file.")
+
+    graph = gm.graph
+    output_node = next(n for n in graph.nodes if n.op == "output")
+    outputs_tuple = output_node.args[0]
+
+    if not isinstance(outputs_tuple, (tuple, list)):
+        outputs_tuple = (outputs_tuple,)
+
+    num_outputs = len(outputs_tuple)
+
+    print(outputs_tuple)
+    for out in outputs_tuple:
+        print("output:", type(out), out.name, out.target, out.op, out.args, out.kwargs)
+
+    assert num_outputs == len(placeholder_types), (
+        f"Mismatch: backward outputs ({num_outputs}) "
+        f"vs forward grad targets ({len(placeholder_types)})"
+    )
+
+    num_inputs_grads = placeholder_types.count("input")
+    num_parameters_grads = placeholder_types.count("param")
+    input_grad_indices = range(num_inputs_grads)
+    param_grad_indices = range(num_inputs_grads, num_inputs_grads + num_parameters_grads)
+
+    print("input_grad_indices:", input_grad_indices)
+    print("param_grad_indices:", param_grad_indices)
+    
+
+    # input_grad_indices = [1]
+    # param_grad_indices = [0, 2, 3, 4]
+
+    gm_input, gm_weight = split_backward_graph(
+        gm,
+        input_grad_indices=input_grad_indices,
+        param_grad_indices=param_grad_indices,
+    )
+
+    print("BACKWARD INPUT SUBGRAPH")
+    if PRINT_FX_GRAPHS:
+        gm.print_readable()
+
+    print("BACKWARD WEIGHT SUBGRAPH")
+    if PRINT_FX_GRAPHS:
+        gm.print_readable()
+
+    with torch.no_grad():
+        full_out = gm(*example_inputs)
+        in_out = gm_input(*example_inputs)
+        w_out = gm_weight(*example_inputs)
+
+    print("BACKWARD OUTPUT CHECK")
+    print("full_out shapes:", [t.shape for t in full_out])
+    print("input_out shapes:", [t.shape for t in in_out])
+    print("weight_out shapes:", [t.shape for t in w_out])
+
+    full_out_list = flatten_grads(full_out)
+    split_out_list = flatten_grads(in_out) + flatten_grads(w_out)
+
+    global SPLIT_GRADS
+    SPLIT_GRADS = split_out_list
+
+    print("SPLIT vs FULL GRAPH CHECK")
+    compare_grad_lists(full_out_list, split_out_list)
+
+
+    fx_to_graphviz(gm_input, filename="backward_input_subgraph")
+    fx_to_graphviz(gm_weight, filename="backward_weight_subgraph")
+    print("Saved subgraph visualizations to files.")
+
+    return gm
+
+def flatten_grads(grads):
+    if isinstance(grads, dict):
+        return list(grads.values())
+    if isinstance(grads, (list, tuple)):
+        return list(grads)
+    return [grads]
+
+
+def compare_grad_lists(a, b, atol=1e-6, rtol=1e-6):
+    assert len(a) == len(b), f"Different #grads: {len(a)} vs {len(b)}"
+
+    for i, (g1, g2) in enumerate(zip(a, b)):
+        if g1 is None or g2 is None:
+            print(f"grad[{i}] is None")
+            continue
+
+        if not torch.allclose(g1, g2, atol=atol, rtol=rtol):
+            print(f"grad[{i}] mismatch — max diff: {(g1 - g2).abs().max().item():.6f}")
+        else:
+            print(f"grad[{i}] match")
+
+
+def autograd_grads(model, x):
+    with torch.enable_grad():
+        copy.deepcopy(model)
+        model.zero_grad(set_to_none=True)
+
+        x = x.clone().detach().requires_grad_(True)
+        y = model(x).sum()
+        y.backward()
+
+        grads = [x.grad]
+        grads += [p.grad for _, p in model.named_parameters()]
+
+    return grads
+
+
+@torch.compile(backend=backward_backend)
+def train(model, x):
+    x = x.clone().detach().requires_grad_(True)
+    y = model(x).sum()
+    y.backward()
+    return y
+
+
+
+def main():
+    global MODEL, X, Y
+    torch.manual_seed(0)
+
+    # llama_config = LLAMA_DEBUG
+    # loss_fn = torch.nn.CrossEntropyLoss()
+    # # device = 'cuda'
+    
+    # batch_size = 8
+    # seq_len = 512
+    # x = torch.randint(0, llama_config.vocab_size, (batch_size, seq_len))
+    # y = torch.zeros((batch_size, llama_config.vocab_size), dtype=torch.long)
+
+    # print_cuda_memory_stats(device, "after loading data")
+
+    model = TwoLayerToy()
+    x = torch.randn(1, 10)
+
+
+    loss = train(model, x)
+    print("Loss:", loss.item())
+
+    # true_grads = autograd_grads(model, x)
+
+    # print("SPLIT vs PYTORCH AUTOGRAD CHECK")
+    # compare_grad_lists(SPLIT_GRADS, true_grads)
+
+if __name__ == "__main__":
+    main()
diff --git a/split_example_autograd.py b/split_example_autograd.py
new file mode 100644
index 0000000..25cc8ba
--- /dev/null
+++ b/split_example_autograd.py
@@ -0,0 +1,267 @@
+import torch
+import torch.nn as nn
+import torch.fx as fx
+import copy
+from torch.utils.weak import TensorWeakRef
+
+from visualize_graph import fx_to_graphviz
+from test.models.llama import Transformer, LLAMA_DEBUG  # kept for later use
+from torch.nn import Parameter
+
+PRINT_FX_GRAPHS = True
+
+torch._dynamo.config.compiled_autograd = True
+
+# Global storage for captured backward graphs
+#   CAPTURED["bwd_input"]  = (gm_in, example_inputs_in)
+#   CAPTURED["bwd_weight"] = (gm_wt, example_inputs_wt)
+CAPTURED = {}
+
+
+class OneLayerToy(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear = nn.Linear(10, 10)
+
+    def forward(self, x):
+        return self.linear(x)
+
+
+class TwoLayerToy(nn.Module):
+    def __init__(self, in_dim=10, hidden_dim=10, out_dim=2):
+        super().__init__()
+        self.fc1 = nn.Linear(in_dim, hidden_dim)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(hidden_dim, out_dim)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        return x
+
+
+def is_backward_graph(gm: fx.GraphModule) -> bool:
+    """
+    Heuristic: compiled-autograd backward graphs usually have call_function
+    nodes whose targets live in 'torch._dynamo.compiled_autograd.*'.
+    """
+    for node in gm.graph.nodes:
+        if node.op == "call_function":
+            mod = getattr(node.target, "__module__", "")
+            if "torch._dynamo.compiled_autograd" in mod:
+                return True
+    return False
+
+def unwrap_example(ex):
+    # If it's a list/tuple, recurse
+    if isinstance(ex, (list, tuple)):
+        return [unwrap_example(e) for e in ex]
+
+    # Already a tensor or parameter
+    if isinstance(ex, torch.Tensor):
+        return ex
+
+    # TensorWeakRef case
+    if isinstance(ex, TensorWeakRef):
+        # Be defensive about method names across versions
+        if hasattr(ex, "get_tensor"):
+            t = ex.get_tensor()
+        elif hasattr(ex, "get"):
+            t = ex.get()
+        elif callable(ex):
+            # some weakref-like objects are callable
+            t = ex()
+        else:
+            t = None
+        return t
+
+    # Anything else / underlying tensor GC'd
+    return None
+
+
+def make_capture_backend(tag: str):
+    """
+    Backend factory that captures the FIRST backward FX graph seen for a given tag.
+    It returns the graph unchanged so TorchDynamo / AOTAutograd can still run.
+    """
+    def backend(gm: fx.GraphModule, example_inputs):
+        if is_backward_graph(gm):
+            print(f"\n=== CAPTURED BACKWARD GRAPH: {tag} ===")
+            if PRINT_FX_GRAPHS:
+                gm.print_readable()
+
+            if tag not in CAPTURED:
+                CAPTURED[tag] = (gm, example_inputs)
+                print(
+                    f"Stored backward graph for tag='{tag}' with "
+                    f"{len(example_inputs)} example_inputs."
+                )
+                placeholders = gm.graph.find_nodes(op="placeholder", sort=True)
+                print("placeholders:", placeholders)
+                print("len(placeholders):", len(placeholders))
+
+                placeholder_types = []
+                graphargs = [node.meta["grapharg"] for node in placeholders]
+                print("graphargs:", graphargs)
+                print("len(graphargs):", len(graphargs))
+                for arg in graphargs:
+                    arg_ex = arg._example
+                    arg_ex = arg_ex if isinstance(arg_ex, (list, tuple)) else [arg_ex]
+                    for ex in arg_ex:
+                        print(f"example input: {ex}")
+                        ex = unwrap_example(ex)
+                        print(f"unwrapped: {ex}")
+
+                        if ex is None:
+                            continue
+
+                        is_param = any(isinstance(t, torch.nn.Parameter) for t in (ex if isinstance(ex, (list, tuple)) else [ex]))
+                        requires_grad = any(getattr(t, "requires_grad", False) for t in (ex if isinstance(ex, (list, tuple)) else [ex]))
+                        print(f"parameter: {is_param} | requires_grad: {requires_grad} | shape: {[t.shape for t in (ex if isinstance(ex, (list, tuple)) else [ex]) if isinstance(t, torch.Tensor)]}")
+                        placeholder_types.append("param" if is_param else "input")
+
+        else:
+            if PRINT_FX_GRAPHS:
+                print(f"\n=== FORWARD GRAPH seen in backend '{tag}' ===")
+                gm.print_readable()
+
+        return gm
+
+    return backend
+
+
+# ====== Pure helper: forward prep ======
+
+def forward_prepare(model, x):
+    """
+    Run a forward pass producing:
+      - scalar loss y
+      - leaf input x_leaf with requires_grad=True
+      - tuple of parameters
+    """
+    x_leaf = x.clone().detach().requires_grad_(True)
+    y = model(x_leaf).sum()
+    params = tuple(p for p in model.parameters())
+    return y, x_leaf, params
+
+
+# ====== Compiled backward helpers that call autograd.grad ======
+
+@torch.compile(backend=make_capture_backend("bwd_input"))
+def bwd_input_fn(y, x):
+    # grads w.r.t. input only
+    (gx,) = torch.autograd.grad(y, x)
+    return gx
+
+
+@torch.compile(backend=make_capture_backend("bwd_weight"))
+def bwd_weight_fn(y, params):
+    # grads w.r.t. parameters only
+    gparams = torch.autograd.grad(y, params)
+    return gparams
+
+
+# ====== Utilities for checking correctness ======
+
+def flatten_grads(grads):
+    if isinstance(grads, dict):
+        return list(grads.values())
+    if isinstance(grads, (list, tuple)):
+        return list(grads)
+    return [grads]
+
+
+def compare_grad_lists(a, b, atol=1e-6, rtol=1e-6):
+    assert len(a) == len(b), f"Different #grads: {len(a)} vs {len(b)}"
+
+    for i, (g1, g2) in enumerate(zip(a, b)):
+        if g1 is None or g2 is None:
+            print(f"grad[{i}] is None")
+            continue
+
+        if not torch.allclose(g1, g2, atol=atol, rtol=rtol):
+            max_diff = (g1 - g2).abs().max().item()
+            print(f"grad[{i}] mismatch — max diff: {max_diff:.6f}")
+        else:
+            print(f"grad[{i}] match")
+
+
+def autograd_grads(model, x):
+    """
+    Vanilla PyTorch autograd: return [grad_x, grad_param_0, grad_param_1, ...]
+    so we can compare against the compiled-backward FX graphs.
+    """
+    with torch.enable_grad():
+        model = copy.deepcopy(model)  # avoid contaminating original model's .grad
+        model.zero_grad(set_to_none=True)
+
+        x_leaf = x.clone().detach().requires_grad_(True)
+        y = model(x_leaf).sum()
+        y.backward()
+
+        grads = [x_leaf.grad]
+        grads += [p.grad for _, p in model.named_parameters()]
+
+    return grads
+
+
+# ====== Main demo: capture & compare backward FX graphs ======
+
+def main():
+    torch.manual_seed(0)
+
+    model = TwoLayerToy()
+    x = torch.randn(1, 10)
+
+    # 1) Ground truth grads via vanilla autograd
+    true_grads = autograd_grads(model, x)
+    print("\n=== Vanilla autograd grads collected ===")
+
+    # 2) Run forward + compiled backward for inputs ONCE to capture 'bwd_input' backward FX graph
+    y1, x1, params1 = forward_prepare(model, x)
+    _ = bwd_input_fn(y1, x1)   # triggers capture for "bwd_input"
+
+    # 3) Run forward + compiled backward for weights ONCE to capture 'bwd_weight' backward FX graph
+    y2, x2, params2 = forward_prepare(model, x)
+    _ = bwd_weight_fn(y2, params2)  # triggers capture for "bwd_weight"
+
+    assert "bwd_input" in CAPTURED, "Did not capture bwd_input backward graph"
+    assert "bwd_weight" in CAPTURED, "Did not capture bwd_weight backward graph"
+
+    gm_in, ex_in = CAPTURED["bwd_input"]
+    gm_wt, ex_wt = CAPTURED["bwd_weight"]
+
+    print("\nCaptured backward FX graphs:")
+    print("BWD_Input Placeholders:", [n for n in gm_in.graph.nodes if n.op == "placeholder"])
+    print("len(BWD_Input Placeholders):", len([n for n in gm_in.graph.nodes if n.op == "placeholder"]))
+    print("BWD_Weight Placeholders:", [n for n in gm_wt.graph.nodes if n.op == "placeholder"])
+    print("len(BWD_Weight Placeholders):", len([n for n in gm_wt.graph.nodes if n.op == "placeholder"]))
+
+
+    print("\n=== Using captured backward FX graphs directly ===")
+    with torch.no_grad():
+        out_in = gm_in(*ex_in)     # grads w.r.t. input
+        out_wt = gm_wt(*ex_wt)     # grads w.r.t. params
+
+    out_in_list = flatten_grads(out_in)
+    out_wt_list = flatten_grads(out_wt)
+
+    print("bwd_input outputs:", [g.shape for g in out_in_list if isinstance(g, torch.Tensor)])
+    print("bwd_weight outputs:", [g.shape for g in out_wt_list if isinstance(g, torch.Tensor)])
+
+    # 4) Compare combined grads vs vanilla autograd
+    compiled_grads = out_in_list + out_wt_list
+
+    print("\n=== SPLIT vs PYTORCH AUTOGRAD CHECK ===")
+    compare_grad_lists(compiled_grads, true_grads)
+
+    # 5) (Optional) visualize graphs
+    if PRINT_FX_GRAPHS:
+        fx_to_graphviz(gm_in, filename="backward_input_fx")
+        fx_to_graphviz(gm_wt, filename="backward_weight_fx")
+        print("Saved backward_input_fx and backward_weight_fx graphs to files.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index 5fa9f65..f65de6b 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -1,11 +1,11 @@
-from piper.exec import Task
+from piper.exec import Task, TaskType
 
 
 def print_schedule(schedule):
     for stage in schedule:
         for step in stage:
             if step:
-                string = f"{step.stage_id}:{step.mb_idx}:{'u' if step.upd else 'f' if step.is_fwd else 'b'}"
+                string = f"{step.stage_id}:{step.mb_idx}:{step.task}"
             else:
                 string = " -- "
             print(string, end="\t")
@@ -18,15 +18,15 @@ def build_gpipe_schedule(n_mbs: int, n_stages: int):
         for stage_id in range(n_stages):
             mb_idx = step - stage_id
             if mb_idx >= 0 and mb_idx < n_mbs:
-                schedule[stage_id][step] = Task(stage_id, stage_id, mb_idx, True, False)
+                schedule[stage_id][step] = Task(stage_id, stage_id, mb_idx, TaskType.FORWARD)
 
     for step in range(steps, steps * 2):
         for stage_id in reversed(range(n_stages)):
             mb_idx = (step - steps) - (n_stages - stage_id - 1)
             if mb_idx >= 0 and mb_idx < n_mbs:
-                schedule[stage_id][step] = Task(stage_id, stage_id, mb_idx, False, False)
+                schedule[stage_id][step] = Task(stage_id, stage_id, mb_idx, TaskType.BACKWARD)
     for i, stage in enumerate(range(n_stages)):
-        schedule[stage][-i-1] = Task(stage_id=stage, device_id=stage, mb_idx=0, is_fwd=False, upd=True)
+        schedule[stage][-i-1] = Task(stage_id=stage, device_id=stage, mb_idx=0, task=TaskType.UPDATE)
     return schedule
 
 def build_1f1b_schedule(n_mbs: int, n_stages: int):
@@ -40,7 +40,7 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int):
                 if mb_idx >= 0 and mb_idx < n_mbs:
                     schedule[stage_id][step] = Task(
                         device_id=stage_id, stage_id=stage_id, 
-                        mb_idx=mb_idx, is_fwd=True, upd=False
+                        mb_idx=mb_idx, task=TaskType.FORWARD
                     )
                     stage_mb[stage_id][0] += 1
     for step in range(n_stages, 2 * steps):
@@ -54,7 +54,47 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int):
                 if mb_idx >= 0 and mb_idx < n_mbs:
                     schedule[stage_id][step] = Task(
                         device_id=stage_id, stage_id=stage_id,
-                        mb_idx=mb_idx, is_fwd=task_type, upd=False
+                        mb_idx=mb_idx, task=TaskType.FORWARD if task_type else TaskType.BACKWARD
                     )
                     stage_mb[stage_id][fwd_or_bwd] += 1
     return schedule
+
+## ZB: Finish schedule function ##
+def build_zb1p_schedule(n_mbs: int, n_stages: int):
+    if n_stages != 2 or n_mbs != 4:
+        raise NotImplementedError("ZB1P schedule is only implemented for 2 stages and 4 microbatches.")
+    schedule = [
+        [
+            Task(device_id=0, stage_id=0, mb_idx=0, task=TaskType.FORWARD),         # t0:  F0
+            None,                                                                   # t1:   -
+            Task(device_id=0, stage_id=0, mb_idx=1, task=TaskType.FORWARD),         # t2:  F1
+            Task(device_id=0, stage_id=0, mb_idx=0, task=TaskType.BACKWARD_INPUT),  # t3:  I0
+            Task(device_id=0, stage_id=0, mb_idx=0, task=TaskType.BACKWARD_WEIGHT), # t4:  W0
+            Task(device_id=0, stage_id=0, mb_idx=2, task=TaskType.FORWARD),         # t5:  F2
+            Task(device_id=0, stage_id=0, mb_idx=1, task=TaskType.BACKWARD_INPUT),  # t6:  I1
+            Task(device_id=0, stage_id=0, mb_idx=1, task=TaskType.BACKWARD_WEIGHT), # t7:  W1
+            Task(device_id=0, stage_id=0, mb_idx=3, task=TaskType.FORWARD),         # t8:  F3
+            Task(device_id=0, stage_id=0, mb_idx=2, task=TaskType.BACKWARD_INPUT),  # t9:  I2
+            Task(device_id=0, stage_id=0, mb_idx=2, task=TaskType.BACKWARD_WEIGHT), # t10: W2
+            Task(device_id=0, stage_id=0, mb_idx=3, task=TaskType.BACKWARD_INPUT),  # t11: I3
+            Task(device_id=0, stage_id=0, mb_idx=3, task=TaskType.BACKWARD_WEIGHT), # t12: W3
+            Task(device_id=0, stage_id=0, mb_idx=0, task=TaskType.UPDATE),         # t13: U0
+        ],
+        [
+            None,                                                                   # t0:  -
+            Task(device_id=1, stage_id=1, mb_idx=0, task=TaskType.FORWARD),         # t1:  F0
+            Task(device_id=1, stage_id=1, mb_idx=0, task=TaskType.BACKWARD_INPUT),  # t2:  I0
+            Task(device_id=1, stage_id=1, mb_idx=1, task=TaskType.FORWARD),         # t3:  F1
+            Task(device_id=1, stage_id=1, mb_idx=1, task=TaskType.BACKWARD_INPUT),  # t4:  I1
+            Task(device_id=1, stage_id=1, mb_idx=0, task=TaskType.BACKWARD_WEIGHT), # t5:  W0
+            Task(device_id=1, stage_id=1, mb_idx=2, task=TaskType.FORWARD),         # t6:  F2
+            Task(device_id=1, stage_id=1, mb_idx=2, task=TaskType.BACKWARD_INPUT),  # t7:  I2
+            Task(device_id=1, stage_id=1, mb_idx=1, task=TaskType.BACKWARD_WEIGHT), # t8:  W1
+            Task(device_id=1, stage_id=1, mb_idx=3, task=TaskType.FORWARD),         # t9:  F3
+            Task(device_id=1, stage_id=1, mb_idx=3, task=TaskType.BACKWARD_INPUT),  # t10: I3
+            Task(device_id=1, stage_id=1, mb_idx=2, task=TaskType.BACKWARD_WEIGHT), # t11: W2
+            Task(device_id=1, stage_id=1, mb_idx=3, task=TaskType.BACKWARD_WEIGHT), # t12: W3
+            Task(device_id=1, stage_id=1, mb_idx=0, task=TaskType.UPDATE),         # t13: U0
+        ],
+        ]
+    return schedule
\ No newline at end of file
diff --git a/test/test_backward_split.py b/test/test_backward_split.py
new file mode 100644
index 0000000..dc3feae
--- /dev/null
+++ b/test/test_backward_split.py
@@ -0,0 +1,144 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import ray
+
+from piper.piper import piper, distributed_stage
+from piper.exec import piper_exec, Task, TaskType
+from piper.utils import piper_metadata
+from piper.compile import piper_setup 
+from .schedule_helpers import build_zb1p_schedule, print_schedule
+
+
+class MyModelVanilla(nn.Module):
+    """
+    Simple 2-layer MLP: 1024 -> 512 -> 512
+    Used as the baseline (non-pipelined) model.
+    """
+    def __init__(self, in_dim=1024, hidden_dim=512, out_dim=512):
+        super().__init__()
+        self.fc1 = nn.Linear(in_dim, hidden_dim)
+        self.fc2 = nn.Linear(hidden_dim, out_dim)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        return x
+
+
+class MyModelPipelined(nn.Module):
+    def __init__(self, in_dim=1024, hidden_dim=512, out_dim=512):
+        super().__init__()
+        self.fc1 = nn.Linear(in_dim, hidden_dim)
+        self.fc2 = nn.Linear(hidden_dim, out_dim)
+
+    def forward(self, x, dynamo_mb: int = 0):
+        distributed_stage(0, actor_id=0, mb=dynamo_mb, optim=torch.optim.Adam)
+        x = F.relu(self.fc1(x))
+
+        distributed_stage(1, actor_id=1, mb=dynamo_mb, optim=torch.optim.Adam)
+        x = F.relu(self.fc2(x))
+
+        return x
+
+
+def main():
+    torch.manual_seed(0)
+    ray.init(ignore_reinit_error=True)
+
+    device = "cuda"
+
+    init_model = MyModelVanilla().to(device)
+    init_state = init_model.state_dict()
+
+    model_base = MyModelVanilla().to(device)
+    model_base.load_state_dict(init_state)
+    opt_base = torch.optim.Adam(model_base.parameters(), lr=1e-3)
+
+    model_pipe = MyModelPipelined().to(device)
+    model_pipe.load_state_dict(init_state)
+
+    batch_size = 8
+    in_dim = 1024
+    out_dim = 512
+    num_mbs = 4
+
+    x = torch.randn(batch_size, in_dim, device=device)
+    y = torch.randn(batch_size, out_dim, device=device)
+    loss_fn = nn.MSELoss(reduction="mean")
+
+    compiled = piper_setup(
+        model_pipe,
+        example_inputs=[x],
+        backend=piper,
+    )
+
+    actors = piper_metadata["actors"]
+    num_actors = len(actors)
+    assert num_actors == 2, f"Expected 2 actors, got {num_actors}"
+
+    ray.get(actors[0].send_input.remote(x))
+    ray.get(actors[num_actors - 1].send_truth.remote(y))
+
+    schedule = build_zb1p_schedule(n_mbs=num_mbs, n_stages=2)
+    print("Schedule:")
+    print_schedule(schedule)
+
+    def iter_schedule():
+        out = piper_exec(compiled, schedule, [x], y, loss_fn, num_mbs)
+        return ray.get(out)
+
+    iter_schedule()
+
+    piper_grads = []
+    for stage_id in range(num_actors):
+        stage_grads = ray.get(actors[stage_id].get_param_grads.remote(stage_id))
+        piper_grads.extend(stage_grads)
+
+    print(piper_grads)
+
+    piper_params_after = []
+    for stage_id in range(num_actors):
+        stage_params = ray.get(actors[stage_id].get_params.remote(stage_id))
+        piper_params_after.extend(stage_params)
+
+    opt_base.zero_grad(set_to_none=True)
+
+    for _ in range(num_mbs):
+        out_base = model_base(x)
+        loss_base = loss_fn(out_base, y)
+        loss_base.backward()
+
+    print(opt_base)
+    opt_base.step()
+
+    base_params_after = [
+        p.detach().clone().cpu() for p in model_base.parameters()
+    ]
+    base_grads = [p.grad.detach().clone().cpu() for p in model_base.parameters()]
+
+    assert len(piper_params_after) == len(base_params_after), \
+        "Mismatch in # of parameters between pipeline and baseline models"
+
+    for i, (g_pipe, g_base) in enumerate(zip(piper_grads, base_grads)):
+        if g_pipe is None and g_base is None:
+            print(f"Grad {i}: both None")
+            continue
+        if g_pipe is None or g_base is None:
+            print(f"Grad {i}: one is None, one not")
+            continue
+        diff = (g_pipe - g_base).abs().max().item()
+        print(f"Grad {i}: max |Δ| = {diff:.6e}")
+
+
+    max_abs_diff = 0.0
+    for i, (p_pipe, p_base) in enumerate(zip(piper_params_after, base_params_after)):
+        diff = (p_pipe - p_base).abs().max().item()
+        max_abs_diff = max(max_abs_diff, diff)
+        print(f"Param {i}: max |Δ| = {diff:.6e}")
+
+    print(f"\n[RESULT] Max param diff after one step: {max_abs_diff:.6e}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/test/test_llama.py b/test/test_llama.py
index b28d633..33eb555 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -11,13 +11,13 @@
 from piper.utils import piper_metadata
 
 from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B
-from .schedule_helpers import build_1f1b_schedule, build_gpipe_schedule, print_schedule
+from .schedule_helpers import build_1f1b_schedule, build_gpipe_schedule, build_zb1p_schedule, print_schedule
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
     parser.add_argument('--model', choices=['LLAMA_DEBUG', 'LLAMA_1B', 'LLAMA_3B', 'LLAMA_8B'], default='LLAMA_DEBUG',
                         help='Model configuration: LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, or LLAMA_8B (default: LLAMA_DEBUG)')
-    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b'], default='1f1b',
+    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'zb1p'], default='1f1b',
                         help='Schedule type: gpipe, 1f1b, or interleaved-1f1b (default: 1f1b)')
     parser.add_argument('--devices', type=int, choices=[2, 4], default=2,
                         help='Number of devices/stages (default: 2)')
@@ -116,6 +116,9 @@ def main(args):
         schedule = build_1f1b_schedule(num_mbs, num_devices)
     elif args.schedule == "gpipe":
         schedule = build_gpipe_schedule(num_mbs, num_devices)
+    elif args.schedule == "zb1p":
+        schedule = build_zb1p_schedule(num_mbs, num_devices)
+
 
     print("SCHEDULE:")
     print_schedule(schedule)
diff --git a/test/test_simple.py b/test/test_simple.py
index 595ef24..c2fe17f 100644
--- a/test/test_simple.py
+++ b/test/test_simple.py
@@ -3,6 +3,7 @@
 from torch import nn
 from torch.export import export, ExportedProgram
 from piper.piper import piper, distributed_stage
+from piper.utils import piper_metadata
 from collections.abc import Iterable
 
 class MyModel(nn.Module):

From 832385cae596e332f4ab20c529f7171c44964e5a Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Wed, 14 Jan 2026 06:38:54 +0000
Subject: [PATCH 014/100] Added graph pruning to optimize split backward pass
 for ZB, including a backward_utils.py file

---
 piper/actor.py          | 273 +++++++++++++++++++++++++---------------
 piper/backward_utils.py | 155 +++++++++++++++++++++++
 piper/exec.py           |   3 -
 3 files changed, 324 insertions(+), 107 deletions(-)
 create mode 100644 piper/backward_utils.py

diff --git a/piper/actor.py b/piper/actor.py
index e17eea0..a887654 100644
--- a/piper/actor.py
+++ b/piper/actor.py
@@ -3,10 +3,13 @@
 import logging
 import os
 import time
+from typing import Any, Dict, Iterator, List, Optional, Set, Tuple, Union
 from torch._guards import CompileId
 from torch.nn import Parameter
+from torch.autograd.graph import GradientEdge, Node
 from collections import defaultdict
 from piper.utils import deserialize_graphmodule, piper_metadata
+from piper.backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
 
 torch.set_float32_matmul_precision('high')
 
@@ -62,9 +65,8 @@ def __init__(self, id, optim_fn=None):
         self.activation = defaultdict(dict)
         # accumuate loss for each microbatch
         self.loss = []
-
-        # save upstream gradients for split backward
-        # self.saved_upstream = defaultdict(dict)
+        # stage_id -> mb_idx -> param_groups
+        self._bw_param_groups = defaultdict(dict)
 
         # Timing infrastructure
         self.tracing = False  # Toggle for timing and memory tracing
@@ -362,7 +364,6 @@ def backward_activation(activation, inp):
 
     def update(self, *done_mbs):
         self.log.info(f"Calling update on actor {self.actor_id}")
-        # print("UPDATE FOR STAGE ", self.actor_id, " MBS ", done_mbs)
         
         if self.tracing:
             start_event = torch.cuda.Event(enable_timing=True)
@@ -374,10 +375,7 @@ def update(self, *done_mbs):
             start_event.record()
         
         assert self.optim_fn
-        # print("len(self.optims): ", len(self.optims))
         for _, optim in self.optims.items():
-            # print("GRAD STEP")
-            # print(optim)
             optim.step()
             optim.zero_grad(set_to_none=True)
         losses = self.loss
@@ -469,141 +467,208 @@ def time_object_retrieval(self, obj) -> dict:
         self.objs = objs
         return "done"
     
-    # Debugging param dump function #
-    def get_param_grads(self, stage_id: int):
-        stage_params = [p for p in self.parameters[stage_id] if isinstance(p, Parameter)]
-        return [p.grad.detach().clone().cpu() if p.grad is not None else None
-                for p in stage_params]
-    
-    def get_params(self, stage_id: int):
-        stage_params = [p for p in self.parameters[stage_id] if isinstance(p, Parameter)]
-        return [p.detach().clone().cpu() for p in stage_params]
-    ######
 
     def backward_input(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
-        self.log.info(f"Calling backward_input {stage_id} mb {mb_idx} on actor {self.actor_id}")
-        # print("BACKWARD INPUT FOR STAGE ", stage_id, " MB ", mb_idx)
         is_last_stage = loss_fn is not None
         activation = self.activation[stage_id][mb_idx]
 
-        if stage_id == 0:
-            if is_last_stage:
-                labels = self.truth if self.truth is not None else inp[0]
-                loss = loss_fn(activation, labels)
-                self.loss.append(loss)
-                upstream = torch.ones_like(loss)
-                # print("UPSTREAM FROM LAST STAGE: ", upstream)
-                # print("UPSTREAM SHAPE FROM LAST STAGE: ", upstream.shape)
-                # self.saved_upstream[stage_id][mb_idx] = upstream
-            else:
-                # upstream = inp[0]
-                upstream = inp
-                # self.saved_upstream[stage_id][mb_idx] = upstream
-            gx = torch.zeros_like(activation)
-            # print(f"INPUT GX on stage {stage_id} mb {mb_idx}: {gx}")
-            # print(f"INPUT GX SHAPE on stage {stage_id} mb {mb_idx}: {gx.shape}")
-            # print(f"INPUT UPSTREAM on stage {stage_id} mb {mb_idx}: {upstream}")
-            # print(f"INPUT UPSTREAM SHAPE on stage {stage_id} mb {mb_idx}: {upstream.shape}")
-            return [gx, upstream]
-
-        stage_input = self.prev_activation[stage_id][mb_idx]
-
         if is_last_stage:
             labels = self.truth if self.truth is not None else inp[0]
             loss = loss_fn(activation, labels)
             self.loss.append(loss)
 
-            upstream = torch.ones_like(loss)
-
-            gx = torch.autograd.grad(
-                outputs=loss,
-                inputs=stage_input,
-                grad_outputs=upstream,
-                retain_graph=True,
-            )[0]
+            activation_or_loss = loss
+            upstream_grads = torch.ones_like(loss)
 
         else:
-            # upstream = inp[0]
-            upstream = inp
+            activation_or_loss = activation
+            upstream_grads = inp
 
-            gx = torch.autograd.grad(
-                outputs=activation,
-                inputs=stage_input,
-                grad_outputs=upstream,
-                retain_graph=True,
-            )[0]
+        # In first stage, we treat the backward_input call like a NOOP
+        if stage_id == 0:
+            gx = torch.zeros_like(activation)
+            return [gx, upstream_grads]
 
-        # self.saved_upstream[stage_id][mb_idx] = upstream
+        stage_input = self.prev_activation[stage_id][mb_idx]
+        stage_params = [p for p in self.parameters[stage_id] if p is not None]
+
+        output_nodes = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [activation_or_loss]) if n is not None]
+        input_nodes  = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [stage_input]) if n is not None]
+        param_nodes   = [n for n in (_get_grad_fn_or_grad_acc(p) for p in stage_params) if n is not None]
+
+        reverse_edges = construct_reverse_graph(output_nodes)
+        param_groups = get_param_groups(input_nodes, param_nodes, reverse_edges)
+
+        # Hooks to capture grads at intermediate nodes. For use in backward_weight
+        handles = []
+        for pg in param_groups:
+            inters = pg["intermediates"]
+            if not inters:
+                continue
+
+            pg["grads"] = [None] * len(inters)
+
+            for i, inter in enumerate(inters):
+                def make_hook(group: Dict[str, Any], idx: int):
+                    def hook(grad_inputs):
+                        group["grads"][idx] = grad_inputs
+                    return hook
+                handles.append(inter.register_prehook(make_hook(pg, i)))
+
+        gx = torch.autograd.grad(
+            outputs=activation_or_loss,
+            inputs=stage_input,
+            grad_outputs=upstream_grads,
+            retain_graph=True,
+            allow_unused=True,
+        )
 
-        # Change to also return upstream for temporal dependency
-        # print(f"INPUT GX on stage {stage_id} mb {mb_idx}: {gx}")
-        # print(f"INPUT GX SHAPE on stage {stage_id} mb {mb_idx}: {gx.shape}")
-        # print(f"INPUT UPSTREAM on stage {stage_id} mb {mb_idx}: {upstream}")
-        # print(f"INPUT UPSTREAM SHAPE on stage {stage_id} mb {mb_idx}: {upstream.shape}")
+        gx = gx[0] # Take the gx gradient out of the tuple returned by autograd.grad
+        if gx is not None and stage_input.requires_grad:
+            if stage_input.grad is None:
+                stage_input.grad = gx
+            else:
+                stage_input.grad.add_(gx)
 
-        return [gx, upstream]
+        # Free output tensors between the output nodes and the intermediate nodes
+        if not isinstance(activation_or_loss, list):
+            activation_or_loss = [activation_or_loss]
 
+        for t in activation_or_loss:
+            t.detach_()
 
-    def backward_weight(self, stage_id: int, mb_idx: int, upstream_ref, loss_fn=None):
-        # print("BACKWARD WEIGHT FOR STAGE ", stage_id, " MB ", mb_idx)
-        self.log.info(f"Calling backward_weight {stage_id} mb {mb_idx} on actor {self.actor_id}")
+        for h in handles:
+            h.remove()
 
-        is_last_stage = loss_fn is not None
-        activation = self.activation[stage_id][mb_idx]
+        self._bw_param_groups[stage_id][mb_idx] = param_groups
+
+        return [gx, upstream_grads]
 
-        stage_params = [p for p in self.parameters[stage_id] if isinstance(p, Parameter)]
 
-        # print(f"UPSTREAM: {upstream_ref}")
-        # print(f"UPSTREAM[0]: {upstream_ref[0]}")
-        # print(f"UPSTREAM[1]: {upstream_ref[1]}")
+    def backward_weight(self, stage_id: int, mb_idx: int, upstream_ref, loss_fn=None):
+        stage_params = [p for p in self.parameters[stage_id] if p is not None]
 
+        # right now, we're only keeping gx to enforce temporal dependency
+        # upstream is only required for the stage 0 case
         gx = ray.get(upstream_ref[0])
         upstream = ray.get(upstream_ref[1])
-        # print(f"RESOLVED UPSTREAM on stage {stage_id} mb {mb_idx}: {upstream}")
-        # print(f"RESOLVED UPSTREAM SHAPE on stage {stage_id} mb {mb_idx}: {upstream.shape}")
-        # print(f"RESOLVED GX on stage {stage_id} mb {mb_idx}: {gx}")
-        # print(f"RESOLVED GX SHAPE on stage {stage_id} mb {mb_idx}: {gx.shape}")
 
-        if is_last_stage:
-            labels = self.truth if self.truth is not None else None
-            loss = loss_fn(activation, labels)
+        # Special case to handle stage 0 since we don't create param_groups
+        if stage_id == 0:
+            is_last_stage = loss_fn is not None
+            activation = self.activation[stage_id][mb_idx]
+
+            if is_last_stage:
+                labels = self.truth if self.truth is not None else None
+                loss = loss_fn(activation, labels)
 
-            # upstream = self.saved_upstream[stage_id][mb_idx]
+                gparams = torch.autograd.grad(
+                    outputs=loss,
+                    inputs=stage_params,
+                    retain_graph=False,
+                )
 
-            gparams = torch.autograd.grad(
-                outputs=loss,
-                inputs=stage_params,
-                # grad_outputs=upstream,
-                retain_graph=False,
+            else:
+                gparams = torch.autograd.grad(
+                    outputs=activation,
+                    inputs=stage_params,
+                    grad_outputs=upstream,
+                    retain_graph=False,
+                )
+
+            assert len(gparams) == len(stage_params), (
+                f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(stage_params)}"
             )
+            
+            for p, g in zip(stage_params, gparams):
+                if p.grad is None:
+                    p.grad = g.clone()
+                else:
+                    p.grad.add_(g)
 
-        else:
-            # upstream = self.saved_upstream[stage_id][mb_idx]
+            del self.activation[stage_id][mb_idx]
+
+            return [None] + ["done"]
+
+        grad_acc_to_weight: Dict[Node, Tuple[Parameter, int]] = {}
+        for param in stage_params:
+            node = _get_grad_fn_or_grad_acc(param)
+            grad_acc_to_weight[node] = param
+
+        param_groups = self._bw_param_groups[stage_id][mb_idx]
+
+        # Perform the weight updates separately for each param_group
+        for g in param_groups:
+            inters: List[Node] = g.get("intermediates", [])
+            grads_list = g.get("grads", None)
+
+            # Groups with no intermediates: nothing to do via this mechanism
+            if not inters or grads_list is None:
+                continue
+
+            valid_edges: List[GradientEdge] = []
+            valid_grad_outs: List[torch.Tensor] = []
+
+            for inter, grad_inputs in zip(inters, grads_list):
+                if grad_inputs is None:
+                    continue
+
+                gs = [x for x in grad_inputs if x is not None]
+                if not gs:
+                    continue
+                
+                summed = sum(gs)
+
+                valid_edges.append(GradientEdge(inter, 0))
+                valid_grad_outs.append(summed)
+
+            del g["intermediates"]
+
+            if not valid_edges:
+                continue
+
+            mapped_param_nodes = [p for p in g["params"] if p in grad_acc_to_weight]
+            if not mapped_param_nodes:
+                continue
+
+            weight_edges = tuple(GradientEdge(p, 0) for p in mapped_param_nodes)
 
             gparams = torch.autograd.grad(
-                outputs=activation,
-                inputs=stage_params,
-                grad_outputs=upstream,
+                outputs=valid_edges,
+                inputs=weight_edges,
+                grad_outputs=valid_grad_outs,
                 retain_graph=False,
             )
 
-        assert len(gparams) == len(stage_params), (
-            f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(stage_params)}"
-        )
-        
-        for p, g in zip(stage_params, gparams):
-            if p.grad is None:
-                p.grad = g.clone()
-            else:
-                p.grad.add_(g)
+            del g["grads"]
 
-        del self.activation[stage_id][mb_idx]
-        # del self.saved_upstream[stage_id][mb_idx]
-        # del self.prev_activation[stage_id][mb_idx]
+            assert len(gparams) == len(mapped_param_nodes), (
+                f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(mapped_param_nodes)}"
+            )
+            
+            for param_node, dw in zip(g["params"], gparams):
+                if dw is None:
+                    continue
+
+                weight = grad_acc_to_weight[param_node]
 
-        return [gx] + ["done"]
+                if weight.grad is None:
+                    weight.grad = dw
+                else:
+                    weight.grad.add_(dw)
 
+        return [None] + ["done"]
 
 
+    ##### Debugging param dump function #####
+    def get_param_grads(self, stage_id: int):
+        stage_params = [p for p in self.parameters[stage_id] if isinstance(p, Parameter)]
+        return [p.grad.detach().clone().cpu() if p.grad is not None else None
+                for p in stage_params]
+    
+    def get_params(self, stage_id: int):
+        stage_params = [p for p in self.parameters[stage_id] if isinstance(p, Parameter)]
+        return [p.detach().clone().cpu() for p in stage_params]
+    ########
 
 
diff --git a/piper/backward_utils.py b/piper/backward_utils.py
new file mode 100644
index 0000000..0ae27a8
--- /dev/null
+++ b/piper/backward_utils.py
@@ -0,0 +1,155 @@
+from __future__ import annotations
+
+import collections
+from typing import Any, Dict, Iterator, List, Optional, Set, Tuple
+
+import torch
+from torch.autograd.graph import GradientEdge, Node
+from torch.nn import Parameter
+
+
+def _get_grad_fn_or_grad_acc(t: torch.Tensor) -> Optional[Node]:
+    """
+    Return the autograd Node for a tensor:
+      - non-leaf tensors: t.grad_fn
+      - leaf tensors requiring grad (Parameters): AccumulateGrad node (created lazily)
+    """
+    if not t.requires_grad:
+        return None
+    if t.grad_fn is not None:
+        return t.grad_fn
+
+    # If t is a leaf tensor, we create a view to generate its AccumulateGrad node
+    viewed = t.view_as(t)
+    grad_fn = viewed.grad_fn
+    if grad_fn is None:
+        raise RuntimeError(
+            "Tried to get grad accumulator but got None. "
+            "Are you in a no-grad context?"
+        )
+    return grad_fn.next_functions[0][0]
+
+
+def reverse_closure(
+    roots: List[Node],
+    target_nodes: Set[Node],
+    reverse_edges: Dict[Node, List[Node]],
+) -> Tuple[Set[Node], Set[Node]]:
+    """
+    From roots, follow reverse_edges to collect reachable nodes, but stop traversal
+    when hitting any node in target_nodes. Returns the closure (set of reachable nodes) and
+    the set of target nodes that were able to be visited.
+    """
+    closure: Set[Node] = set()
+    visited_targets: Set[Node] = set()
+    q: collections.deque[Node] = collections.deque()
+
+    for node in roots:
+        if node is None or node in closure:
+            continue
+        closure.add(node)
+        q.append(node)
+
+    while q:
+        currNode = q.popleft()
+        for node in reverse_edges.get(currNode, []):
+            if node is None or node in closure:
+                continue
+            if node in target_nodes:
+                visited_targets.add(node)
+                continue
+            closure.add(node)
+            q.append(node)
+
+    return closure, visited_targets
+
+
+def construct_reverse_graph(roots: List[Node]) -> Dict[Node, List[Node]]:
+    """
+    Builds a reverse graph. In a backward graph, for each node X, 
+    reverse_edges[X] is the list of nodes that X sends gradients to.
+    """
+    q: collections.deque[Node] = collections.deque()
+    seen: Set[Node] = set()
+    reverse_edges: Dict[Node, List[Node]] = collections.defaultdict(list)
+
+    for node in roots:
+        if node is not None and node not in seen:
+            seen.add(node)
+            q.append(node)
+
+    while q:
+        node = q.popleft()
+        for fn, _ in node.next_functions:
+            if fn is None:
+                continue
+
+            # fn -> node in reverse graph
+            reverse_edges[fn].append(node)
+            if fn not in seen:
+                seen.add(fn)
+                q.append(fn)
+
+    return reverse_edges
+
+
+def get_param_groups(
+    inputs: List[Node],
+    params: List[Node],
+    reverse_edges: Dict[Node, List[Node]],
+) -> List[Dict[str, Any]]:
+    """
+    Returns a list of parameter groups. Parameters are in the same group if they share the same 
+    intermediate boundary nodes where the 'param-side' graph intersects the input closure.
+    """
+    inputs_closure, _ = reverse_closure(inputs, set(), reverse_edges)
+
+    intermediate_to_param_group: Dict[Node, Dict[str, Any]] = {}
+    solo_groups: List[Dict[str, Any]] = []
+
+    for p in params:
+        if p is None:
+            continue
+
+        _, intersected_nodes = reverse_closure([p], inputs_closure, reverse_edges)
+
+        if not intersected_nodes:
+            # if the parameter doesn't intersect inputs (unused or disconnected), then it 
+            # we place it in its own group
+            solo_groups.append({"params": {p}, "intermediates": []})
+            continue
+
+        # Merge into an existing group if any intersected intermediate node already has one
+        group: Optional[Dict[str, Any]] = None
+        for inter_node in intersected_nodes:
+            if inter_node in intermediate_to_param_group:
+                group = intermediate_to_param_group[inter_node]
+                break
+
+        if group is None:
+            group = {"params": set(), "intermediates": []}
+
+        group["params"].add(p)
+
+        # remap all intersected intermediates to this group
+        for inter_node in intersected_nodes:
+            intermediate_to_param_group[inter_node] = group
+
+    # add unique groups to param_groups
+    param_groups: List[Dict[str, Any]] = []
+    seen_ids: Set[int] = set()
+    for params in intermediate_to_param_group.values():
+        if id(params) in seen_ids:
+            continue
+        seen_ids.add(id(params))
+        param_groups.append(params)
+
+    for params in param_groups:
+        inters = [k for k, v in intermediate_to_param_group.items() if v is params]
+        # sort nodes to maintain deterministic order when iterating over them in backward_weight
+        inters.sort(key=id)
+        params["intermediates"] = inters
+
+    param_groups.extend(solo_groups)
+
+    return param_groups
diff --git a/piper/exec.py b/piper/exec.py
index dadc3fe..c46fe8b 100644
--- a/piper/exec.py
+++ b/piper/exec.py
@@ -282,8 +282,6 @@ def piper_exec(model, schedule, inputs, truth, loss_fn, num_mbs, split_fwd_fns:
                             .backward.options(num_returns=num_bwd_targets*2)
                             .remote(stage_id, mb_idx, bwd_ref)
                         )
-
-                ############ ZB Modification ###########
                 elif task_type == TaskType.BACKWARD_INPUT:
                     num_bwd_targets = len(get_backward_targets(stage_id, dag_edges))
                     if num_bwd_targets == 0:
@@ -357,7 +355,6 @@ def piper_exec(model, schedule, inputs, truth, loss_fn, num_mbs, split_fwd_fns:
                             loss_fn=loss_fn if is_last_stage else None,
                         )
                     )
-                ###########################################
                 else:
                     raise ValueError(f"Unknown task type: {task_type}")
     return ret
\ No newline at end of file

From 955d7b23c5be5af8ff1e8f7bda1978f358dbbcb8 Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Wed, 21 Jan 2026 00:36:07 +0000
Subject: [PATCH 015/100] Dockerfile and run.sh to deploy Piper in a container

---
 Dockerfile | 38 ++++++++++++++++++++++++++++++++++++++
 run.sh     | 19 +++++++++++++++++++
 2 files changed, 57 insertions(+)
 create mode 100755 Dockerfile
 create mode 100755 run.sh

diff --git a/Dockerfile b/Dockerfile
new file mode 100755
index 0000000..3331361
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,38 @@
+FROM nvidia/cuda:12.9.1-cudnn-devel-ubuntu22.04
+
+WORKDIR /workspace/piper
+
+# Install system deps and NVSHMEM (Ray dependency)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    python3 python3-venv python3-pip \
+    git ca-certificates \
+    libnvshmem3-cuda-12 \
+ && rm -rf /var/lib/apt/lists/*
+
+# Make NVSHMEM visible to dynamic linker
+RUN echo "/usr/lib/x86_64-linux-gnu/nvshmem/12" > /etc/ld.so.conf.d/nvshmem.conf && ldconfig
+
+RUN python3 -m venv /opt/venv
+ENV PATH=/opt/venv/bin:$PATH \
+    PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1
+
+# Python tooling and remaining dependencies that aren't Ray or Torch
+# (we mount and use the patched Ray and Torch installs from our .venv/lib/python3.10/site-packages directory instead)
+RUN pip install --upgrade pip setuptools wheel
+
+RUN pip install nvidia-cusparselt-cu12
+
+RUN pip install \
+    numpy \
+    cupy-cuda12x \
+    msgpack \
+    grpcio \
+    protobuf \
+    filelock \
+    requests \
+    pyyaml \
+    jsonschema \
+    packaging
+
+CMD ["/bin/bash"]
\ No newline at end of file
diff --git a/run.sh b/run.sh
new file mode 100755
index 0000000..2d25823
--- /dev/null
+++ b/run.sh
@@ -0,0 +1,19 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+IMAGE="${IMAGE:-piper-container}"
+GPU_DEVICES="${GPU_DEVICES:-0}"
+
+docker run --rm -it \
+  --gpus "\"device=${GPU_DEVICES}\"" \
+  -e CUDA_VISIBLE_DEVICES="${GPU_DEVICES}" \
+  --network host \
+  --shm-size=32g \
+  -v "$PWD:/workspace/piper" \
+  -v "$PWD/.venv/lib/python3.10/site-packages:/opt/venv/lib/python3.10/site-packages" \
+  -w /workspace/piper \
+  "${IMAGE}" \
+  "${@:-bash}"
+
+# Example of using this script
+# GPU_DEVICES=0,1,2 ./run.sh python -m test.test_llama
\ No newline at end of file

From 931bb45f4d118ab31826cda94fe12a5b54cd68c8 Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Wed, 28 Jan 2026 06:24:46 +0000
Subject: [PATCH 016/100] Code cleanup and updated validate schedule to account
 for split backward passes

---
 backward_input_fx.png        | Bin 494991 -> 0 bytes
 backward_input_subgraph.png  | Bin 455217 -> 0 bytes
 backward_weight_fx.png       | Bin 674110 -> 0 bytes
 backward_weight_subgraph.png | Bin 988447 -> 0 bytes
 example.py                   |  36 ----
 full_backward_graph.png      | Bin 1169825 -> 0 bytes
 piper/actor.py               | 136 ++++++-------
 piper/backward_utils.py      |  14 ++
 piper/compile.py             |   7 +-
 piper/exec.py                | 310 +++++++++++++++++-----------
 piper/piper.py               |   6 +-
 piper/piper_coordinator.py   |   4 +-
 split_baseline_experiment.py |   0
 split_example_FX.py          | 385 -----------------------------------
 split_example_autograd.py    | 267 ------------------------
 test/models/llama.py         |  42 ++--
 test/schedule_helpers.py     | 248 ++++++++++------------
 test/test_backward_split.py  |  18 +-
 test/test_llama.py           |   6 +-
 visualize_graph.py           |  35 ----
 20 files changed, 425 insertions(+), 1089 deletions(-)
 delete mode 100644 backward_input_fx.png
 delete mode 100644 backward_input_subgraph.png
 delete mode 100644 backward_weight_fx.png
 delete mode 100644 backward_weight_subgraph.png
 delete mode 100644 example.py
 delete mode 100644 full_backward_graph.png
 delete mode 100644 split_baseline_experiment.py
 delete mode 100644 split_example_FX.py
 delete mode 100644 split_example_autograd.py
 delete mode 100644 visualize_graph.py

diff --git a/backward_input_fx.png b/backward_input_fx.png
deleted file mode 100644
index aaf3a39e7a00ea8d9242169b71fc7148e234a7d5..0000000000000000000000000000000000000000
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diff --git a/example.py b/example.py
deleted file mode 100644
index 92ee357..0000000
--- a/example.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import torch
-
-def split_backward_graph(gm: torch.fx.GraphModule):
-    """
-    Given a GraphModule representing a full forward+backward pass,
-    split it into two GraphModules: one for the backward weight pass and one for the backward input pass
-    """
-
-    return gm
-
-class Model(torch.nn.Module):
-   def __init__(self):
-      super().__init__()
-      self.linear = torch.nn.Linear(10, 10)
-
-   def forward(self, x):
-      return self.linear(x)
-
-def custom_backend(gm: torch.fx.GraphModule, *args, **kwargs):
-    print(gm.graph)
-    return gm
-
-torch._dynamo.config.compiled_autograd = True
-@torch.compile(backend=custom_backend)
-def train(model, x):
-   loss = model(x).sum()
-   loss.backward()
-
-def main():
-    model = Model()
-    x = torch.randn(10)
-    train(model, x)
-
-if __name__ == "__main__":
-    main()
-
diff --git a/full_backward_graph.png b/full_backward_graph.png
deleted file mode 100644
index a6a992b08c20011a111da8d01f8e46338a71960f..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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diff --git a/piper/actor.py b/piper/actor.py
index 159220f..2097d90 100644
--- a/piper/actor.py
+++ b/piper/actor.py
@@ -14,27 +14,14 @@
 from piper.backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
 
 torch.set_float32_matmul_precision('high')
-torch._dynamo.config.compiled_autograd = True
+torch._dynamo.config.compiled_autograd = False
 
 CLEANUP_MEMORY = False
 
-def backward_backend(gm, example_inputs, **kwargs):
-    print("BACKWARD GRAPH")
-
-    placeholders = gm.graph.find_nodes(op="placeholder")
-    graphargs = [node.meta["grapharg"] for node in placeholders]
-    for arg in graphargs:
-        if isinstance(arg._example, list) or isinstance(arg._example, tuple):
-            for ex in arg._example:
-                print(f"Arg {ex.shape} | parameter: {isinstance(ex, Parameter)} | requires_grad: {ex.requires_grad}")
-    gm.print_readable()
-    
-    return gm
-
 @ray.remote
 class PiperActor:
     def __init__(self, actor_id, world_size, dp_rank=0, dp_degree=1, pp_degree=1, optim_fn=None):
-        self.logger = create_logger("piper_actor", "INFO")
+        self.logger = create_logger("piper_actor", "DEBUG")
         
         start = time.perf_counter()
 
@@ -110,7 +97,7 @@ def reset_peak_memory(self):
     def get_peak_memory(self):
         return torch.cuda.max_memory_allocated() / (1024**3)
 
-    @ray.method
+    # @ray.method
     def join_process_groups(self):
         master_addr = os.environ.get('PIPER_MASTER_ADDR', "127.0.0.1")
         master_port = os.environ.get('PIPER_MASTER_PORT', "10000")
@@ -182,7 +169,7 @@ def compile_graph(self, stage_id, gm_data, compiler_fn, graphargs, input_idxs):
         self.logger.debug(f"compile_graph took {(end-start)*1000:.2f}ms")
         return "Finished compiling"
 
-    # @ray.method(tensor_transport="nccl")
+    @ray.method(tensor_transport="nccl")
     def forward(self, stage_id: int, mb_idx: int, *args):
         self.logger.debug(f"Calling forward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
 
@@ -308,8 +295,8 @@ def unwrap(x):
 
         # save first input as previous activation
         if stage_id != 0:
-            args[0].requires_grad_()
-            # assert args[0].requires_grad 
+            # args[0].requires_grad_()
+            assert args[0].requires_grad 
             self.prev_activation[stage_id][mb_idx] = args[0]
 
         out = self.compiled_fns[stage_id](*self.parameters[stage_id])
@@ -332,7 +319,7 @@ def unwrap(x):
         
         return out
 
-    # @ray.method(tensor_transport="nccl")
+    @ray.method(tensor_transport="nccl")
     def backward(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
         self.logger.debug(f"Calling backward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
 
@@ -441,7 +428,7 @@ def update(self, *done_mbs):
         assert self.optim_fn
         for _, optim in self.optims.items():
             optim.step()
-            optim.zero_grad(set_to_none=True)
+            optim.zero_grad()
         losses = self.loss
         self.loss.clear()
 
@@ -518,7 +505,7 @@ def stop_mem_tracing(self) -> None:
         torch.cuda.memory._record_memory_history(enabled=None)
         return "done"
     
-    # @ray.method(tensor_transport="nccl")
+    @ray.method(tensor_transport="nccl")
     def get_object(self, is_fwd, stage_id):
         if is_fwd:
             return self.fwd_objs[stage_id]
@@ -534,11 +521,12 @@ def time_object_retrieval(self, obj) -> dict:
         self.objs = objs
         return "done"
     
-
+    @ray.method(tensor_transport="nccl")
     def backward_input(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
         is_last_stage = loss_fn is not None
         activation = self.activation[stage_id][mb_idx]
 
+        # Use loss instead of activation if this is the backward_input call for the last stage
         if is_last_stage:
             labels = self.truth if self.truth is not None else inp[0]
             loss = loss_fn(activation, labels)
@@ -553,34 +541,40 @@ def backward_input(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
 
         # In first stage, we treat the backward_input call like a NOOP
         if stage_id == 0:
-            gx = torch.zeros_like(activation)
+            gx = torch.zeros_like(activation) # May get slight performance boost if we don't send the full ones tensor
             return [gx, upstream_grads]
 
         stage_input = self.prev_activation[stage_id][mb_idx]
-        stage_params = [p for p in self.parameters[stage_id] if p is not None]
+        stage_params = [p for p in self.parameters[stage_id] if p is not None and p.requires_grad]
 
+        # Lists of autograd nodes for the layer outputs, inputs, and parameters
         output_nodes = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [activation_or_loss]) if n is not None]
         input_nodes  = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [stage_input]) if n is not None]
         param_nodes   = [n for n in (_get_grad_fn_or_grad_acc(p) for p in stage_params) if n is not None]
 
+        # Use the autograd graph with edges reversed to compute parameter groups, which are groups 
+        # of parameters that share the same intermediate nodes. Intermediate nodes are the nodes that
+        # lie on both (1) a backward path from the output node(s) to the stage input nodes and 
+        # (2) in a path from the output node(s) a parameter node/gradient accumulator
         reverse_edges = construct_reverse_graph(output_nodes)
         param_groups = get_param_groups(input_nodes, param_nodes, reverse_edges)
 
-        # Hooks to capture grads at intermediate nodes. For use in backward_weight
+        # Hooks to capture grads at intermediate nodes. In backward_weight,
+        # we'll backprop from these intermediate values
         handles = []
         for pg in param_groups:
-            inters = pg["intermediates"]
-            if not inters:
+            intermediates = pg["intermediates"]
+            if not intermediates:
                 continue
 
-            pg["grads"] = [None] * len(inters)
+            pg["grads"] = [None] * len(intermediates)
 
-            for i, inter in enumerate(inters):
+            for i, intermediate_node in enumerate(intermediates):
                 def make_hook(group: Dict[str, Any], idx: int):
                     def hook(grad_inputs):
                         group["grads"][idx] = grad_inputs
                     return hook
-                handles.append(inter.register_prehook(make_hook(pg, i)))
+                handles.append(intermediate_node.register_prehook(make_hook(pg, i)))
 
         gx = torch.autograd.grad(
             outputs=activation_or_loss,
@@ -607,20 +601,22 @@ def hook(grad_inputs):
         for h in handles:
             h.remove()
 
+        # Save parameter groups for use in backward_weight
         self._bw_param_groups[stage_id][mb_idx] = param_groups
 
         return [gx, upstream_grads]
 
+    @ray.method(tensor_transport="nccl")
+    def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, loss_fn=None):
+        stage_params = [p for p in self.parameters[stage_id] if p is not None and p.requires_grad]
 
-    def backward_weight(self, stage_id: int, mb_idx: int, upstream_ref, loss_fn=None):
-        stage_params = [p for p in self.parameters[stage_id] if p is not None]
-
-        # right now, we're only keeping gx to enforce temporal dependency
+        # We only pass to enforce temporal dependency.
         # upstream is only required for the stage 0 case
-        gx = ray.get(upstream_ref[0])
-        upstream = ray.get(upstream_ref[1])
+        gx = gx
+        upstream = upstream
 
-        # Special case to handle stage 0 since we don't create param_groups
+        # Special case to handle stage 0 since backward_input is a NOOP, 
+        # meaning no parameter groups are created
         if stage_id == 0:
             is_last_stage = loss_fn is not None
             activation = self.activation[stage_id][mb_idx]
@@ -647,16 +643,17 @@ def backward_weight(self, stage_id: int, mb_idx: int, upstream_ref, loss_fn=None
                 f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(stage_params)}"
             )
             
-            for p, g in zip(stage_params, gparams):
+            for p, pg in zip(stage_params, gparams):
                 if p.grad is None:
-                    p.grad = g.clone()
+                    p.grad = pg.clone()
                 else:
-                    p.grad.add_(g)
+                    p.grad.add_(pg)
 
             del self.activation[stage_id][mb_idx]
 
             return [None] + ["done"]
 
+        # Create mapping from autograd nodes -> parameters
         grad_acc_to_weight: Dict[Node, Tuple[Parameter, int]] = {}
         for param in stage_params:
             node = _get_grad_fn_or_grad_acc(param)
@@ -664,19 +661,21 @@ def backward_weight(self, stage_id: int, mb_idx: int, upstream_ref, loss_fn=None
 
         param_groups = self._bw_param_groups[stage_id][mb_idx]
 
-        # Perform the weight updates separately for each param_group
-        for g in param_groups:
-            inters: List[Node] = g.get("intermediates", [])
-            grads_list = g.get("grads", None)
+        # Perform the weight updates separately for each param_group, beginning
+        # backprop from each the intermediate node(s) of each group
+        for pg in param_groups:
+            intermediates: List[Node] = pg.get("intermediates", [])
+            intermediate_grads = pg.get("grads", None) # List of intermediate node gradients, captured by the hooks
 
-            # Groups with no intermediates: nothing to do via this mechanism
-            if not inters or grads_list is None:
+            # Skip groups without intermediate nodes (could happen in weird cases
+            # where one node is disconnected from the rest of the autograd graph for some reason)
+            if not intermediates or intermediate_grads is None:
                 continue
 
-            valid_edges: List[GradientEdge] = []
-            valid_grad_outs: List[torch.Tensor] = []
+            intermediate_edges: List[GradientEdge] = []
+            intermediate_edge_grads: List[torch.Tensor] = []
 
-            for inter, grad_inputs in zip(inters, grads_list):
+            for intermediate_node, grad_inputs in zip(intermediates, intermediate_grads):
                 if grad_inputs is None:
                     continue
 
@@ -684,36 +683,43 @@ def backward_weight(self, stage_id: int, mb_idx: int, upstream_ref, loss_fn=None
                 if not gs:
                     continue
                 
+                # Sum all gradients arriving at the current intermediate node
+                # in case the node has multiple source of gradients
                 summed = sum(gs)
 
-                valid_edges.append(GradientEdge(inter, 0))
-                valid_grad_outs.append(summed)
+                # Create a GradientEdge for each intermediate node (we can backprop with respect to these)
+                # and store the summed gradient for that node
+                intermediate_edges.append(GradientEdge(intermediate_node, 0))
+                intermediate_edge_grads.append(summed)
 
-            del g["intermediates"]
+            del pg["intermediates"]
 
-            if not valid_edges:
+            if not intermediate_edges:
                 continue
 
-            mapped_param_nodes = [p for p in g["params"] if p in grad_acc_to_weight]
+            # Grab params for the param_nodes in this param group using our grad_acc_to_weight map from earlier
+            mapped_param_nodes = [p for p in pg["params"] if p in grad_acc_to_weight]
             if not mapped_param_nodes:
                 continue
 
+            # Use these parameters to create a GradientEdge that we'll use as our input to autograd.grad
             weight_edges = tuple(GradientEdge(p, 0) for p in mapped_param_nodes)
 
             gparams = torch.autograd.grad(
-                outputs=valid_edges,
+                outputs=intermediate_edges,
                 inputs=weight_edges,
-                grad_outputs=valid_grad_outs,
+                grad_outputs=intermediate_edge_grads,
                 retain_graph=False,
             )
 
-            del g["grads"]
+            del pg["grads"]
 
             assert len(gparams) == len(mapped_param_nodes), (
                 f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(mapped_param_nodes)}"
             )
             
-            for param_node, dw in zip(g["params"], gparams):
+            # Finally, update gradients for the params in this param_group
+            for param_node, dw in zip(pg["params"], gparams):
                 if dw is None:
                     continue
 
@@ -727,15 +733,3 @@ def backward_weight(self, stage_id: int, mb_idx: int, upstream_ref, loss_fn=None
         return [None] + ["done"]
 
 
-    ##### Debugging param dump function #####
-    def get_param_grads(self, stage_id: int):
-        stage_params = [p for p in self.parameters[stage_id] if isinstance(p, Parameter)]
-        return [p.grad.detach().clone().cpu() if p.grad is not None else None
-                for p in stage_params]
-    
-    def get_params(self, stage_id: int):
-        stage_params = [p for p in self.parameters[stage_id] if isinstance(p, Parameter)]
-        return [p.detach().clone().cpu() for p in stage_params]
-    ########
-
-
diff --git a/piper/backward_utils.py b/piper/backward_utils.py
index 0ae27a8..fb5bf56 100644
--- a/piper/backward_utils.py
+++ b/piper/backward_utils.py
@@ -1,3 +1,17 @@
+# ===========================================================================================
+# This file contains helper functions to implement the split backward pass.
+#
+# These helper functions were copied from the PyTorch pipelining backward implementation.
+#
+#   PyTorch Team,
+#   "torch.distributed.pipelining._backward",
+#   https://github.com/pytorch/pytorch/blob/main/torch/distributed/pipelining/_backward.py
+#
+# There are some minor implementation differences, but these functions are nearly identical.
+#
+# ===========================================================================================
+
+
 from __future__ import annotations
 
 import collections
diff --git a/piper/compile.py b/piper/compile.py
index c2e697a..c8e31bc 100644
--- a/piper/compile.py
+++ b/piper/compile.py
@@ -1,12 +1,13 @@
 import ray
 import torch
-from piper_utils import piper_metadata, RemoteTensor, create_logger
+from piper.utils import piper_metadata, RemoteTensor, create_logger
 from torch._dynamo.backends.debugging import eager
+import torch.distributed as dist
 import threading
 import os
 import gc
 
-logger = create_logger("piper_compile", "INFO")
+logger = create_logger("piper_compile", "DEBUG")
 
 def setup_data_parallel(local_rank, data_parallel):
     """ Just adds every rank to the same process group"""
@@ -34,7 +35,7 @@ def piper_setup(model, example_inputs, num_stages, pp_size, dynamic=False, backe
 
     compiled = torch.compile(model, dynamic=dynamic, backend=backend)
 
-    from piper_utils import events_tls
+    from piper.utils import events_tls
     events_tls.actor_mutexes = dict([(actor_id, threading.Lock()) for actor_id in range(pp_size)])
     events_tls.events = [threading.Event() for _ in range(num_stages)]
     for event in events_tls.events:
diff --git a/piper/exec.py b/piper/exec.py
index 904a907..52459f3 100644
--- a/piper/exec.py
+++ b/piper/exec.py
@@ -46,35 +46,66 @@ def validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdge
     """
     num_devices, num_steps = len(schedule), len(schedule[0]) if schedule else 0
     
-    # Check well-formedness: no duplicates, device_id matches row, and all stages present
+    # Check well-formedness: no duplicates, device_id matches row, and track task times
     all_tasks = set()
-    microbatch_tasks = {}  # mb_idx -> set of (stage_id, is_fwd, upd)
+    mb_fwd_times = {} # mb_idx -> set of (stage_id, time)
+    mb_bwd_times = {}
+    mb_bwd_input_times = {}
+    mb_bwd_weight_times = {}
     
-    for stage_id in range(num_devices):
+    for device_row in range(num_devices):
         for time_step in range(num_steps):
-            task = schedule[stage_id][time_step]
-            if task is not None:
-                # Check device_id matches row
-                if task.device_id != stage_id:
-                    raise ValueError(
-                        f"Task device_id {task.device_id} does not match row {stage_id} "
-                        f"at time step {time_step}"
-                    )
-                
-                # Check for duplicates
-                task_key = (task.stage_id, task.mb_idx, task.task)
-                if task_key in all_tasks:
-                    raise ValueError(
-                        f"Duplicate task found: stage_id={task.stage_id}, "
-                        f"mb_idx={task.mb_idx}, task={task.task}"
-                    )
-                all_tasks.add(task_key)
-                
-                # Track tasks by microbatch
-                if task.mb_idx not in microbatch_tasks:
-                    microbatch_tasks[task.mb_idx] = set()
-                microbatch_tasks[task.mb_idx].add((task.stage_id, task.task == TaskType.FORWARD, task.task == TaskType.UPDATE))
-    
+            task = schedule[device_row][time_step]
+            if task is None:
+                continue
+            
+            # Check device_id matches row
+            if task.device_id != device_row:
+                raise ValueError(
+                    f"Task device_id {task.device_id} does not match row {device_row} "
+                    f"at time step {time_step}"
+                )
+            
+            # Check for duplicates
+            task_key = (task.stage_id, task.mb_idx, task.task)
+            if task_key in all_tasks:
+                raise ValueError(
+                    f"Duplicate task found: stage_id={task.stage_id}, "
+                    f"mb_idx={task.mb_idx}, task={task.task}"
+                )
+            all_tasks.add(task_key)
+
+            # Record time for each task type
+            mb_idx = task.mb_idx
+            if task.task == TaskType.FORWARD:
+                if mb_idx not in mb_fwd_times:
+                    mb_fwd_times[mb_idx] = {}
+                mb_fwd_times[mb_idx][task.stage_id] = time_step
+            elif task.task == TaskType.BACKWARD:
+                if mb_idx not in mb_bwd_times:
+                    mb_bwd_times[mb_idx] = {}
+                mb_bwd_times[mb_idx][task.stage_id] = time_step
+            elif task.task == TaskType.BACKWARD_INPUT:
+                if mb_idx not in mb_bwd_input_times:
+                    mb_bwd_input_times[mb_idx] = {}
+                mb_bwd_input_times[mb_idx][task.stage_id] = time_step
+            elif task.task == TaskType.BACKWARD_WEIGHT:
+                if mb_idx not in mb_bwd_weight_times:
+                    mb_bwd_weight_times[mb_idx] = {}
+                mb_bwd_weight_times[mb_idx][task.stage_id] = time_step
+            elif task.task == TaskType.UPDATE:
+                # In this loop, updates are validated only for duplication/device placement 
+                pass
+            else:
+                raise ValueError(f"Unknown task type: {task.task}")
+
+    has_combined_backward = any(mb_bwd_times.values())
+    has_split_backward = any(mb_bwd_input_times.values()) or any(mb_bwd_weight_times.values())
+
+    # Schedules cannot mix combined backward with split backward
+    if has_combined_backward and has_split_backward:
+        raise ValueError("Schedule has both backward and backward_input/backward_weight tasks.")
+
     # Get all required stages from DAG edges
     all_required_stages = set()
     for edge in dag_edges:
@@ -82,72 +113,109 @@ def validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdge
         all_required_stages.add(edge.to_stage)
     
     # Check that each microbatch has all required forward and backward stages
-    for mb_idx, tasks in microbatch_tasks.items():
-        # Find all stages that have forward/backward tasks for this microbatch
-        fwd_stages = {stage_id for stage_id, is_fwd, upd in tasks if is_fwd and not upd}
-        bwd_stages = {stage_id for stage_id, is_fwd, upd in tasks if not is_fwd and not upd}
-        
+    for mb_idx in range(num_mbs):
+        fwd_times = mb_fwd_times.get(mb_idx, {})
+
         # Check that all required stages have forward tasks
-        missing_fwd = all_required_stages - fwd_stages
+        missing_fwd = all_required_stages - set(fwd_times.keys())
         if missing_fwd:
-            raise ValueError(
-                f"Microbatch {mb_idx} missing forward stages: {missing_fwd}"
-            )
-        
+            raise ValueError(f"Microbatch {mb_idx} missing forward stages: {missing_fwd}")
+
         # Check that all required stages have backward tasks
-        missing_bwd = all_required_stages - bwd_stages
-        if missing_bwd:
-            raise ValueError(
-                f"Microbatch {mb_idx} missing backward stages: {missing_bwd}"
-            )
-    
-    # Check pipeline stage dependencies
-    for mb_idx in range(num_mbs):
-        # Find all tasks for this microbatch
-        fwd_times = {}  # stage_id -> time_step
-        bwd_times = {}  # stage_id -> time_step
-        
-        for device_id in range(num_devices):
-            for time_step in range(num_steps):
-                task = schedule[device_id][time_step]
-                if task is not None and task.mb_idx == mb_idx:
-                    if task.task == TaskType.FORWARD:
-                        fwd_times[task.stage_id] = time_step
-                    elif not task.task == TaskType.FORWARD and not task.task == TaskType.UPDATE:
-                        bwd_times[task.stage_id] = time_step
-        
-        # Check forward stage ordering: if A -> B, then fwd(A) < fwd(B)
-        for edge in dag_edges:
-            from_stage, to_stage = edge.from_stage, edge.to_stage
-            if from_stage in fwd_times and to_stage in fwd_times:
-                if fwd_times[from_stage] >= fwd_times[to_stage]:
+        if has_combined_backward:
+            bwd_times = mb_bwd_times.get(mb_idx, {})
+            missing_bwd = all_required_stages - set(bwd_times.keys())
+            if missing_bwd:
+                raise ValueError(f"Microbatch {mb_idx} missing backward stages: {missing_bwd}")
+
+            # fwd ordering: if A -> B then fwd(A) < fwd(B)
+            for edge in dag_edges:
+                from_stage, to_stage = edge.from_stage, edge.to_stage
+                if from_stage in fwd_times and to_stage in fwd_times:
+                    if fwd_times[from_stage] >= fwd_times[to_stage]:
+                        raise ValueError(
+                            f"Forward stage ordering violation for microbatch {mb_idx}: "
+                            f"forward stage {from_stage} (time {fwd_times[from_stage]}) must come "
+                            f"before forward stage {to_stage} (time {fwd_times[to_stage]})"
+                        )
+
+            # Check forward-backward ordering: fwd(A) < bwd(A)
+            for stage_id in all_required_stages:
+                if stage_id in fwd_times and stage_id in bwd_times:
+                    if fwd_times[stage_id] >= bwd_times[stage_id]:
+                        raise ValueError(
+                            f"Forward-backward ordering violation for microbatch {mb_idx}, "
+                            f"stage {stage_id}: forward (time {fwd_times[stage_id]}) must come "
+                            f"before backward (time {bwd_times[stage_id]})"
+                        )
+
+            # Check backward stage ordering: if A -> B, then bwd(B) < bwd(A)
+            for edge in dag_edges:
+                from_stage, to_stage = edge.from_stage, edge.to_stage
+                if from_stage in bwd_times and to_stage in bwd_times:
+                    if bwd_times[to_stage] >= bwd_times[from_stage]:
+                        raise ValueError(
+                            f"Backward stage ordering violation for microbatch {mb_idx}: "
+                            f"backward stage {to_stage} (time {bwd_times[to_stage]}) must come "
+                            f"before backward stage {from_stage} (time {bwd_times[from_stage]})"
+                        )
+
+        # Split backward validation
+        if has_split_backward:
+            bwd_input_times = mb_bwd_input_times.get(mb_idx, {})
+            bwd_weight_times = mb_bwd_weight_times.get(mb_idx, {})
+            missing_input = all_required_stages - set(bwd_input_times.keys())
+            if missing_input:
+                raise ValueError(f"Microbatch {mb_idx} missing backward_input stages: {missing_input}")
+
+            missing_weight = all_required_stages - set(bwd_weight_times.keys())
+            if missing_weight:
+                raise ValueError(f"Microbatch {mb_idx} missing backward_weight stages: {missing_weight}")
+
+            # fwd ordering: if A -> B then fwd(A) < fwd(B)
+            for edge in dag_edges:
+                from_stage, to_stage = edge.from_stage, edge.to_stage
+                if from_stage in fwd_times and to_stage in fwd_times:
+                    if fwd_times[from_stage] >= fwd_times[to_stage]:
+                        raise ValueError(
+                            f"Forward stage ordering violation for microbatch {mb_idx}: "
+                            f"forward stage {from_stage} (time {fwd_times[from_stage]}) must come "
+                            f"before forward stage {to_stage} (time {fwd_times[to_stage]})"
+                        )
+
+            # Per-stage ordering: fwd(A) < bwd_input(A) < bwd_weight(A)
+            for stage_id in all_required_stages:
+                fwd_time = fwd_times[stage_id]
+                bwd_i_time = bwd_input_times[stage_id]
+                bwd_w_time = bwd_weight_times[stage_id]
+
+                if fwd_time >= bwd_i_time:
                     raise ValueError(
-                        f"Forward stage ordering violation for microbatch {mb_idx}: "
-                        f"forward stage {from_stage} (time {fwd_times[from_stage]}) must come "
-                        f"before forward stage {to_stage} (time {fwd_times[to_stage]})"
+                        f"Ordering violation for microbatch {mb_idx}, stage {stage_id}: "
+                        f"forward (time {fwd_time}) must come before backward_input (time {bwd_i_time})."
                     )
-        
-        # Check forward-backward ordering: fwd(A) < bwd(A)
-        for stage_id in fwd_times:
-            if stage_id in bwd_times:
-                if fwd_times[stage_id] >= bwd_times[stage_id]:
+                if bwd_i_time >= bwd_w_time:
                     raise ValueError(
-                        f"Forward-backward ordering violation for microbatch {mb_idx}, "
-                        f"stage {stage_id}: forward (time {fwd_times[stage_id]}) must come "
-                        f"before backward (time {bwd_times[stage_id]})"
+                        f"Ordering violation for microbatch {mb_idx}, stage {stage_id}: "
+                        f"backward_input (time {bwd_i_time}) must come before backward_weight (time {bwd_w_time})."
                     )
-        
-        # Check backward stage ordering: if A -> B, then bwd(B) < bwd(A)
-        for edge in dag_edges:
-            from_stage, to_stage = edge.from_stage, edge.to_stage
-            if from_stage in bwd_times and to_stage in bwd_times:
-                if bwd_times[to_stage] >= bwd_times[from_stage]:
+                if fwd_time >= bwd_w_time:
                     raise ValueError(
-                        f"Backward stage ordering violation for microbatch {mb_idx}: "
-                        f"backward stage {to_stage} (time {bwd_times[to_stage]}) must come "
-                        f"before backward stage {from_stage} (time {bwd_times[from_stage]})"
+                        f"Ordering violation for microbatch {mb_idx}, stage {stage_id}: "
+                        f"forward (time {fwd_time}) must come before backward_weight (time {bwd_w_time})."
                     )
 
+            # Check backward stage ordering: if A -> B then bwd_input(B) < bwd_input(A)
+            for edge in dag_edges:
+                from_stage, to_stage = edge.from_stage, edge.to_stage
+                if from_stage in bwd_input_times and to_stage in bwd_input_times:
+                    if bwd_input_times[to_stage] >= bwd_input_times[from_stage]:
+                        raise ValueError(
+                            f"Backward-input stage ordering violation for microbatch {mb_idx}: "
+                            f"backward_input stage {to_stage} (time {bwd_input_times[to_stage]}) must come "
+                            f"before backward_input stage {from_stage} (time {bwd_input_times[from_stage]})."
+                        )
+
 def piper_exec(model, schedule, inputs, truth, loss_fn, num_mbs, num_stages):
     """
     Execute one step of the pipeline schedule on the distributed model.
@@ -170,7 +238,7 @@ def piper_exec(model, schedule, inputs, truth, loss_fn, num_mbs, num_stages):
     dag_edges = list(map(lambda e: (DAGEdge(e[0], e[1])), list(piper_metadata.dag)))
     
     # Validate the schedule before execution
-    # validate_schedule(schedule, dag_edges, num_mbs)
+    validate_schedule(schedule, dag_edges, num_mbs)
 
     # maps mb_idx to the ref resulting from a forward call on the microbatch
     fwd_refs = dict()
@@ -184,7 +252,7 @@ def piper_exec(model, schedule, inputs, truth, loss_fn, num_mbs, num_stages):
     threads = dict()
     
     def run_model(inputs, mb_idx, events, actor_mutexes):
-        from piper_utils import events_tls
+        from piper.utils import events_tls
         events_tls.events = events
         events_tls.mb_idx = mb_idx
         events_tls.actor_mutexes = actor_mutexes
@@ -197,7 +265,6 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
             task = schedule[j][i]
             if task:
                 device_id, stage_id, mb_idx, task_type = task
-                piper_metadata['current_mb'] = mb_idx
                 actor_id = j
                 if task_type == TaskType.UPDATE:
                     logger.debug(f"Controller updating stage {stage_id} mb {mb_idx}")
@@ -214,8 +281,8 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
                         else:
                             done_refs.add(bwd_refs)
                     done_refs = list(done_refs)
-                    upd = actors[actor_id].update.remote(*done_refs)
-                    ret.append(upd)
+                    with actor_mutexes[actor_id]:
+                        ret.append(actors[actor_id].update.remote(*done_refs))
                 elif task_type == TaskType.FORWARD:
                     if stage_id == 0:
                         thread = threading.Thread(target=run_model, args=(inputs, mb_idx, events[mb_idx], actor_mutexes))
@@ -305,19 +372,24 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
                         num_bwd_targets = 1
 
                     if mb_idx not in bwd_ref_dicts:
+                        logger.debug(f"Controller waiting for thread {mb_idx} to join")
+                        threads[mb_idx].join()
+                        logger.debug(f"Controller thread {mb_idx} joined")
                         fwd_ref = fwd_refs[mb_idx]
                         bwd_ref_dicts[mb_idx] = {}
-
-                        bwd_ref_dicts[mb_idx][stage_id] = (
-                            actors[actor_id]
-                            .backward_input.options(num_returns=2*num_bwd_targets)
-                            .remote(
-                                stage_id,
-                                mb_idx,
-                                fwd_ref.get_ref(),
-                                loss_fn=loss_fn,
+                        logger.debug(f"Controller waiting for actor {actor_id} mutex")
+                        with actor_mutexes[actor_id]:
+                            logger.debug(f"Controller got actor {actor_id} mutex, launching backward input stage {stage_id} mb {mb_idx}")
+                            bwd_ref_dicts[mb_idx][stage_id] = (
+                                actors[actor_id]
+                                .backward_input.options(num_returns=2*num_bwd_targets)
+                                .remote(
+                                    stage_id,
+                                    mb_idx,
+                                    fwd_ref.get_ref(),
+                                    loss_fn=loss_fn,
+                                )
                             )
-                        )
 
                     else:
                         to_stage = [
@@ -337,16 +409,18 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
                         successor_upstreams = bwd_ref_dicts[mb_idx][to_stage]
                         upstream_ref = successor_upstreams[idx]
 
-                        bwd_ref_dicts[mb_idx][stage_id] = (
-                            actors[actor_id]
-                            .backward_input.options(num_returns=2*num_bwd_targets)
-                            .remote(
-                                stage_id,
-                                mb_idx,
-                                upstream_ref,
-                                loss_fn=None,
+                        logger.debug(f"Controller waiting for actor {actor_id} mutex")
+                        with actor_mutexes[actor_id]:
+                            bwd_ref_dicts[mb_idx][stage_id] = (
+                                actors[actor_id]
+                                .backward_input.options(num_returns=2*num_bwd_targets)
+                                .remote(
+                                    stage_id,
+                                    mb_idx,
+                                    upstream_ref,
+                                    loss_fn=None,
+                                )
                             )
-                        )
 
                 elif task_type == TaskType.BACKWARD_WEIGHT:
                     num_bwd_targets = len(get_backward_targets(stage_id, dag_edges))
@@ -361,18 +435,22 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
                     assert mb_idx in bwd_ref_dicts and stage_id in bwd_ref_dicts[mb_idx]
 
                     upstream_ref = bwd_ref_dicts[mb_idx][stage_id]
+                    gx = upstream_ref[0]
+                    upstream_grad = upstream_ref[1]
 
-                    bwd_ref_dicts[mb_idx][stage_id] = (
-                            actors[actor_id]
-                            .backward_weight.options(num_returns=2*num_bwd_targets)
-                            .remote(
-                            stage_id,
-                            mb_idx,
-                            upstream_ref,
-                            loss_fn=loss_fn if is_last_stage else None,
+                    with actor_mutexes[actor_id]:
+                        bwd_ref_dicts[mb_idx][stage_id] = (
+                                actors[actor_id]
+                                .backward_weight.options(num_returns=2*num_bwd_targets)
+                                .remote(
+                                stage_id,
+                                mb_idx,
+                                gx,
+                                upstream_grad,
+                                loss_fn=loss_fn if is_last_stage else None,
+                            )
                         )
-                    )
                 else:
                     raise ValueError(f"Unknown task type: {task_type}")
 
-    return ret
\ No newline at end of file
+    return ret
diff --git a/piper/piper.py b/piper/piper.py
index 30e4f17..847f09e 100644
--- a/piper/piper.py
+++ b/piper/piper.py
@@ -1,13 +1,13 @@
 import ray
 import torch
-from piper.actor import StageActor
+from piper.actor import PiperActor
 from torch._dynamo.backends.registry import register_backend
 from torch._dynamo.decorators import _disallow_in_graph_helper
 from piper.utils import RemoteTensor, serialize_graphmodule, piper_metadata, create_logger
 import os
 import threading
 
-logger = create_logger("piper_backend", "INFO")
+logger = create_logger("piper_backend", "DEBUG")
 
 """
 Annotation for stage boundaries, causes torch.compile graph break
@@ -114,7 +114,7 @@ def int_to_tensor(x):
     # returns remote futures for each graph output
     def run_remote_subgraph(*args):
 
-        from .piper_utils import events_tls
+        from piper.utils import events_tls
 
         mb_idx = events_tls.mb_idx
 
diff --git a/piper/piper_coordinator.py b/piper/piper_coordinator.py
index ab71ae6..14bc47d 100644
--- a/piper/piper_coordinator.py
+++ b/piper/piper_coordinator.py
@@ -4,9 +4,9 @@
 import os
 import socket
 
-from piper_utils import create_logger
+from piper.utils import create_logger
 
-logger = create_logger("piper_coordinator", "INFO")
+logger = create_logger("piper_coordinator", "DEBUG")
 
 @ray.remote(num_gpus=0.1)
 def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, master_addr, master_port, training_func: Callable, *args, **kwargs):
diff --git a/split_baseline_experiment.py b/split_baseline_experiment.py
deleted file mode 100644
index e69de29..0000000
diff --git a/split_example_FX.py b/split_example_FX.py
deleted file mode 100644
index 49d1d97..0000000
--- a/split_example_FX.py
+++ /dev/null
@@ -1,385 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.fx as fx
-from torch.utils.weak import TensorWeakRef
-import copy
-from visualize_graph import fx_to_graphviz
-from test.models.llama import Transformer, LLAMA_DEBUG
-from torch.nn import Parameter
-
-PRINT_FX_GRAPHS = False
-
-torch._dynamo.config.compiled_autograd = True
-
-placeholder_types = []
-
-SPLIT_GRADS = None
-
-class OneLayerToy(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.linear = nn.Linear(10, 10)
-
-    def forward(self, x):
-        return self.linear(x)
-
-class TwoLayerToy(nn.Module):
-    def __init__(self, in_dim=10, hidden_dim=10, out_dim=2):
-        super().__init__()
-        self.fc1 = nn.Linear(in_dim, hidden_dim)
-        self.relu = nn.ReLU()
-        self.fc2 = nn.Linear(hidden_dim, out_dim)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.relu(x)
-        x = self.fc2(x)
-        return x
-    
-def unwrap_example(ex):
-    # If it's a list/tuple, recurse
-    if isinstance(ex, (list, tuple)):
-        return [unwrap_example(e) for e in ex]
-
-    # Already a tensor or parameter
-    if isinstance(ex, torch.Tensor):
-        return ex
-
-    # TensorWeakRef case
-    if isinstance(ex, TensorWeakRef):
-        # Be defensive about method names across versions
-        if hasattr(ex, "get_tensor"):
-            t = ex.get_tensor()
-        elif hasattr(ex, "get"):
-            t = ex.get()
-        elif callable(ex):
-            # some weakref-like objects are callable
-            t = ex()
-        else:
-            t = None
-        return t
-
-    # Anything else / underlying tensor GC'd
-    return None
-
-
-
-def extract_subgraph(gm: fx.GraphModule, output_nodes):
-    graph = gm.graph
-
-    needed = set(output_nodes)
-    queue = list(output_nodes)
-
-    while queue:
-        node = queue.pop()
-        for inp in node.all_input_nodes:
-            if inp not in needed:
-                needed.add(inp)
-                queue.append(inp)
-
-    for node in graph.nodes:
-        if node.op == "placeholder":
-            needed.add(node)
-
-    new_graph = fx.Graph()
-    node_copies = {}
-
-    for node in graph.nodes:
-        if node.op == "output":
-            continue
-        if node in needed:
-            new_node = new_graph.node_copy(node, lambda n: node_copies[n])
-            node_copies[node] = new_node
-
-    new_outputs = [node_copies[n] for n in output_nodes]
-    if len(new_outputs) == 1:
-        new_graph.output((new_outputs[0],))
-    else:
-        new_graph.output(tuple(new_outputs))
-
-    new_gm = fx.GraphModule(gm, new_graph)
-    new_gm.recompile()
-    return new_gm
-
-
-def split_backward_graph(
-    gm: fx.GraphModule,
-    input_grad_indices,
-    param_grad_indices,
-):
-    graph = gm.graph
-    output_node = None
-    for n in graph.nodes:
-        if n.op == "output":
-            output_node = n
-            break
-    assert output_node is not None, "No output node in FX graph?"
-
-    outputs_tuple = output_node.args[0]
-    assert isinstance(outputs_tuple, (tuple, list)), "Expected tuple of outputs"
-
-    input_grad_nodes = [outputs_tuple[i] for i in input_grad_indices]
-    param_grad_nodes = [outputs_tuple[i] for i in param_grad_indices]
-
-    gm_input = extract_subgraph(gm, input_grad_nodes)
-    gm_weight = extract_subgraph(gm, param_grad_nodes)
-    return gm_input, gm_weight
-
-
-def is_backward_graph(gm: fx.GraphModule) -> bool:
-    for node in gm.graph.nodes:
-        if node.op == "call_function":
-            mod = getattr(node.target, "__module__", "")
-            if "torch._dynamo.compiled_autograd" in mod:
-                return True
-    return False
-
-def forward_backend(gm: fx.GraphModule, example_inputs):
-    global placeholder_types
-
-    print("FORWARD GRAPH")
-    if PRINT_FX_GRAPHS:
-        gm.print_readable()
-
-    placeholders = gm.graph.find_nodes(op="placeholder", sort=True)
-    print("placeholders:", placeholders)
-
-    placeholder_types = []
-    graphargs = [node.meta["grapharg"] for node in placeholders]
-    print("graphargs:", graphargs)
-    print("len(graphargs):", len(graphargs))
-    for arg in graphargs:
-        arg_ex = arg._example
-        arg_ex = arg_ex if isinstance(arg_ex, (list, tuple)) else [arg_ex]
-        for ex in arg_ex:
-            print(f"example input: {ex}")
-            ex = unwrap_example(ex)
-            print(f"unwrapped: {ex}")
-
-            if ex is None:
-                continue
-
-            is_param = any(isinstance(t, torch.nn.Parameter) for t in (ex if isinstance(ex, (list, tuple)) else [ex]))
-            requires_grad = any(getattr(t, "requires_grad", False) for t in (ex if isinstance(ex, (list, tuple)) else [ex]))
-            print(f"parameter: {is_param} | requires_grad: {requires_grad} | shape: {[t.shape for t in (ex if isinstance(ex, (list, tuple)) else [ex]) if isinstance(t, torch.Tensor)]}")
-            placeholder_types.append("param" if is_param else "input")
-
-    print("placeholder_types:", placeholder_types)
-    return gm
-
-
-def backward_backend(gm: fx.GraphModule, example_inputs):
-    if not is_backward_graph(gm):
-        global placeholder_types
-
-        print("FORWARD GRAPH")
-        if PRINT_FX_GRAPHS:
-            gm.print_readable()
-
-        placeholders = gm.graph.find_nodes(op="placeholder", sort=True)
-        print("placeholders:", placeholders)
-
-        placeholder_types = []
-        graphargs = [node.meta["grapharg"] for node in placeholders]
-        print("graphargs:", graphargs)
-        print("len(graphargs):", len(graphargs))
-        for arg in graphargs:
-            arg_ex = arg._example
-            arg_ex = arg_ex if isinstance(arg_ex, (list, tuple)) else [arg_ex]
-            for ex in arg_ex:
-                print(f"example input: {ex}")
-                ex = unwrap_example(ex)
-                print(f"unwrapped: {ex}")
-
-                if ex is None:
-                    continue
-
-                is_param = any(isinstance(t, torch.nn.Parameter) for t in (ex if isinstance(ex, (list, tuple)) else [ex]))
-                requires_grad = any(getattr(t, "requires_grad", False) for t in (ex if isinstance(ex, (list, tuple)) else [ex]))
-                print(f"parameter: {is_param} | requires_grad: {requires_grad} | shape: {[t.shape for t in (ex if isinstance(ex, (list, tuple)) else [ex]) if isinstance(t, torch.Tensor)]}")
-                placeholder_types.append("param" if is_param else "input")
-
-        print("placeholder_types:", placeholder_types)
-        return gm
-
-    print("FULL BACKWARD GRAPH")
-    if PRINT_FX_GRAPHS:
-        gm.print_readable()
-    # print("placeholder_types:", placeholder_types)
-
-    placeholders = gm.graph.find_nodes(op="placeholder", sort=True)
-    print("placeholders:", placeholders)
-    print("len(placeholders):", len(placeholders))
-
-    placeholder_types = []
-    graphargs = [node.meta["grapharg"] for node in placeholders]
-    print("graphargs:", graphargs)
-    print("len(graphargs):", len(graphargs))
-    for arg in graphargs:
-        arg_ex = arg._example
-        arg_ex = arg_ex if isinstance(arg_ex, (list, tuple)) else [arg_ex]
-        for ex in arg_ex:
-            print(f"example input: {ex}")
-            ex = unwrap_example(ex)
-            print(f"unwrapped: {ex}")
-
-            if ex is None:
-                continue
-
-            is_param = any(isinstance(t, torch.nn.Parameter) for t in (ex if isinstance(ex, (list, tuple)) else [ex]))
-            requires_grad = any(getattr(t, "requires_grad", False) for t in (ex if isinstance(ex, (list, tuple)) else [ex]))
-            print(f"parameter: {is_param} | requires_grad: {requires_grad} | shape: {[t.shape for t in (ex if isinstance(ex, (list, tuple)) else [ex]) if isinstance(t, torch.Tensor)]}")
-            placeholder_types.append("param" if is_param else "input")
-
-    print("placeholder_types:", placeholder_types)
-
-    return gm
-
-    fx_to_graphviz(gm, filename="full_backward_graph")
-    print("Saved full backward graph visualization to file.")
-
-    graph = gm.graph
-    output_node = next(n for n in graph.nodes if n.op == "output")
-    outputs_tuple = output_node.args[0]
-
-    if not isinstance(outputs_tuple, (tuple, list)):
-        outputs_tuple = (outputs_tuple,)
-
-    num_outputs = len(outputs_tuple)
-
-    print(outputs_tuple)
-    for out in outputs_tuple:
-        print("output:", type(out), out.name, out.target, out.op, out.args, out.kwargs)
-
-    assert num_outputs == len(placeholder_types), (
-        f"Mismatch: backward outputs ({num_outputs}) "
-        f"vs forward grad targets ({len(placeholder_types)})"
-    )
-
-    num_inputs_grads = placeholder_types.count("input")
-    num_parameters_grads = placeholder_types.count("param")
-    input_grad_indices = range(num_inputs_grads)
-    param_grad_indices = range(num_inputs_grads, num_inputs_grads + num_parameters_grads)
-
-    print("input_grad_indices:", input_grad_indices)
-    print("param_grad_indices:", param_grad_indices)
-    
-
-    # input_grad_indices = [1]
-    # param_grad_indices = [0, 2, 3, 4]
-
-    gm_input, gm_weight = split_backward_graph(
-        gm,
-        input_grad_indices=input_grad_indices,
-        param_grad_indices=param_grad_indices,
-    )
-
-    print("BACKWARD INPUT SUBGRAPH")
-    if PRINT_FX_GRAPHS:
-        gm.print_readable()
-
-    print("BACKWARD WEIGHT SUBGRAPH")
-    if PRINT_FX_GRAPHS:
-        gm.print_readable()
-
-    with torch.no_grad():
-        full_out = gm(*example_inputs)
-        in_out = gm_input(*example_inputs)
-        w_out = gm_weight(*example_inputs)
-
-    print("BACKWARD OUTPUT CHECK")
-    print("full_out shapes:", [t.shape for t in full_out])
-    print("input_out shapes:", [t.shape for t in in_out])
-    print("weight_out shapes:", [t.shape for t in w_out])
-
-    full_out_list = flatten_grads(full_out)
-    split_out_list = flatten_grads(in_out) + flatten_grads(w_out)
-
-    global SPLIT_GRADS
-    SPLIT_GRADS = split_out_list
-
-    print("SPLIT vs FULL GRAPH CHECK")
-    compare_grad_lists(full_out_list, split_out_list)
-
-
-    fx_to_graphviz(gm_input, filename="backward_input_subgraph")
-    fx_to_graphviz(gm_weight, filename="backward_weight_subgraph")
-    print("Saved subgraph visualizations to files.")
-
-    return gm
-
-def flatten_grads(grads):
-    if isinstance(grads, dict):
-        return list(grads.values())
-    if isinstance(grads, (list, tuple)):
-        return list(grads)
-    return [grads]
-
-
-def compare_grad_lists(a, b, atol=1e-6, rtol=1e-6):
-    assert len(a) == len(b), f"Different #grads: {len(a)} vs {len(b)}"
-
-    for i, (g1, g2) in enumerate(zip(a, b)):
-        if g1 is None or g2 is None:
-            print(f"grad[{i}] is None")
-            continue
-
-        if not torch.allclose(g1, g2, atol=atol, rtol=rtol):
-            print(f"grad[{i}] mismatch — max diff: {(g1 - g2).abs().max().item():.6f}")
-        else:
-            print(f"grad[{i}] match")
-
-
-def autograd_grads(model, x):
-    with torch.enable_grad():
-        copy.deepcopy(model)
-        model.zero_grad(set_to_none=True)
-
-        x = x.clone().detach().requires_grad_(True)
-        y = model(x).sum()
-        y.backward()
-
-        grads = [x.grad]
-        grads += [p.grad for _, p in model.named_parameters()]
-
-    return grads
-
-
-@torch.compile(backend=backward_backend)
-def train(model, x):
-    x = x.clone().detach().requires_grad_(True)
-    y = model(x).sum()
-    y.backward()
-    return y
-
-
-
-def main():
-    global MODEL, X, Y
-    torch.manual_seed(0)
-
-    # llama_config = LLAMA_DEBUG
-    # loss_fn = torch.nn.CrossEntropyLoss()
-    # # device = 'cuda'
-    
-    # batch_size = 8
-    # seq_len = 512
-    # x = torch.randint(0, llama_config.vocab_size, (batch_size, seq_len))
-    # y = torch.zeros((batch_size, llama_config.vocab_size), dtype=torch.long)
-
-    # print_cuda_memory_stats(device, "after loading data")
-
-    model = TwoLayerToy()
-    x = torch.randn(1, 10)
-
-
-    loss = train(model, x)
-    print("Loss:", loss.item())
-
-    # true_grads = autograd_grads(model, x)
-
-    # print("SPLIT vs PYTORCH AUTOGRAD CHECK")
-    # compare_grad_lists(SPLIT_GRADS, true_grads)
-
-if __name__ == "__main__":
-    main()
diff --git a/split_example_autograd.py b/split_example_autograd.py
deleted file mode 100644
index 25cc8ba..0000000
--- a/split_example_autograd.py
+++ /dev/null
@@ -1,267 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.fx as fx
-import copy
-from torch.utils.weak import TensorWeakRef
-
-from visualize_graph import fx_to_graphviz
-from test.models.llama import Transformer, LLAMA_DEBUG  # kept for later use
-from torch.nn import Parameter
-
-PRINT_FX_GRAPHS = True
-
-torch._dynamo.config.compiled_autograd = True
-
-# Global storage for captured backward graphs
-#   CAPTURED["bwd_input"]  = (gm_in, example_inputs_in)
-#   CAPTURED["bwd_weight"] = (gm_wt, example_inputs_wt)
-CAPTURED = {}
-
-
-class OneLayerToy(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.linear = nn.Linear(10, 10)
-
-    def forward(self, x):
-        return self.linear(x)
-
-
-class TwoLayerToy(nn.Module):
-    def __init__(self, in_dim=10, hidden_dim=10, out_dim=2):
-        super().__init__()
-        self.fc1 = nn.Linear(in_dim, hidden_dim)
-        self.relu = nn.ReLU()
-        self.fc2 = nn.Linear(hidden_dim, out_dim)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.relu(x)
-        x = self.fc2(x)
-        return x
-
-
-def is_backward_graph(gm: fx.GraphModule) -> bool:
-    """
-    Heuristic: compiled-autograd backward graphs usually have call_function
-    nodes whose targets live in 'torch._dynamo.compiled_autograd.*'.
-    """
-    for node in gm.graph.nodes:
-        if node.op == "call_function":
-            mod = getattr(node.target, "__module__", "")
-            if "torch._dynamo.compiled_autograd" in mod:
-                return True
-    return False
-
-def unwrap_example(ex):
-    # If it's a list/tuple, recurse
-    if isinstance(ex, (list, tuple)):
-        return [unwrap_example(e) for e in ex]
-
-    # Already a tensor or parameter
-    if isinstance(ex, torch.Tensor):
-        return ex
-
-    # TensorWeakRef case
-    if isinstance(ex, TensorWeakRef):
-        # Be defensive about method names across versions
-        if hasattr(ex, "get_tensor"):
-            t = ex.get_tensor()
-        elif hasattr(ex, "get"):
-            t = ex.get()
-        elif callable(ex):
-            # some weakref-like objects are callable
-            t = ex()
-        else:
-            t = None
-        return t
-
-    # Anything else / underlying tensor GC'd
-    return None
-
-
-def make_capture_backend(tag: str):
-    """
-    Backend factory that captures the FIRST backward FX graph seen for a given tag.
-    It returns the graph unchanged so TorchDynamo / AOTAutograd can still run.
-    """
-    def backend(gm: fx.GraphModule, example_inputs):
-        if is_backward_graph(gm):
-            print(f"\n=== CAPTURED BACKWARD GRAPH: {tag} ===")
-            if PRINT_FX_GRAPHS:
-                gm.print_readable()
-
-            if tag not in CAPTURED:
-                CAPTURED[tag] = (gm, example_inputs)
-                print(
-                    f"Stored backward graph for tag='{tag}' with "
-                    f"{len(example_inputs)} example_inputs."
-                )
-                placeholders = gm.graph.find_nodes(op="placeholder", sort=True)
-                print("placeholders:", placeholders)
-                print("len(placeholders):", len(placeholders))
-
-                placeholder_types = []
-                graphargs = [node.meta["grapharg"] for node in placeholders]
-                print("graphargs:", graphargs)
-                print("len(graphargs):", len(graphargs))
-                for arg in graphargs:
-                    arg_ex = arg._example
-                    arg_ex = arg_ex if isinstance(arg_ex, (list, tuple)) else [arg_ex]
-                    for ex in arg_ex:
-                        print(f"example input: {ex}")
-                        ex = unwrap_example(ex)
-                        print(f"unwrapped: {ex}")
-
-                        if ex is None:
-                            continue
-
-                        is_param = any(isinstance(t, torch.nn.Parameter) for t in (ex if isinstance(ex, (list, tuple)) else [ex]))
-                        requires_grad = any(getattr(t, "requires_grad", False) for t in (ex if isinstance(ex, (list, tuple)) else [ex]))
-                        print(f"parameter: {is_param} | requires_grad: {requires_grad} | shape: {[t.shape for t in (ex if isinstance(ex, (list, tuple)) else [ex]) if isinstance(t, torch.Tensor)]}")
-                        placeholder_types.append("param" if is_param else "input")
-
-        else:
-            if PRINT_FX_GRAPHS:
-                print(f"\n=== FORWARD GRAPH seen in backend '{tag}' ===")
-                gm.print_readable()
-
-        return gm
-
-    return backend
-
-
-# ====== Pure helper: forward prep ======
-
-def forward_prepare(model, x):
-    """
-    Run a forward pass producing:
-      - scalar loss y
-      - leaf input x_leaf with requires_grad=True
-      - tuple of parameters
-    """
-    x_leaf = x.clone().detach().requires_grad_(True)
-    y = model(x_leaf).sum()
-    params = tuple(p for p in model.parameters())
-    return y, x_leaf, params
-
-
-# ====== Compiled backward helpers that call autograd.grad ======
-
-@torch.compile(backend=make_capture_backend("bwd_input"))
-def bwd_input_fn(y, x):
-    # grads w.r.t. input only
-    (gx,) = torch.autograd.grad(y, x)
-    return gx
-
-
-@torch.compile(backend=make_capture_backend("bwd_weight"))
-def bwd_weight_fn(y, params):
-    # grads w.r.t. parameters only
-    gparams = torch.autograd.grad(y, params)
-    return gparams
-
-
-# ====== Utilities for checking correctness ======
-
-def flatten_grads(grads):
-    if isinstance(grads, dict):
-        return list(grads.values())
-    if isinstance(grads, (list, tuple)):
-        return list(grads)
-    return [grads]
-
-
-def compare_grad_lists(a, b, atol=1e-6, rtol=1e-6):
-    assert len(a) == len(b), f"Different #grads: {len(a)} vs {len(b)}"
-
-    for i, (g1, g2) in enumerate(zip(a, b)):
-        if g1 is None or g2 is None:
-            print(f"grad[{i}] is None")
-            continue
-
-        if not torch.allclose(g1, g2, atol=atol, rtol=rtol):
-            max_diff = (g1 - g2).abs().max().item()
-            print(f"grad[{i}] mismatch — max diff: {max_diff:.6f}")
-        else:
-            print(f"grad[{i}] match")
-
-
-def autograd_grads(model, x):
-    """
-    Vanilla PyTorch autograd: return [grad_x, grad_param_0, grad_param_1, ...]
-    so we can compare against the compiled-backward FX graphs.
-    """
-    with torch.enable_grad():
-        model = copy.deepcopy(model)  # avoid contaminating original model's .grad
-        model.zero_grad(set_to_none=True)
-
-        x_leaf = x.clone().detach().requires_grad_(True)
-        y = model(x_leaf).sum()
-        y.backward()
-
-        grads = [x_leaf.grad]
-        grads += [p.grad for _, p in model.named_parameters()]
-
-    return grads
-
-
-# ====== Main demo: capture & compare backward FX graphs ======
-
-def main():
-    torch.manual_seed(0)
-
-    model = TwoLayerToy()
-    x = torch.randn(1, 10)
-
-    # 1) Ground truth grads via vanilla autograd
-    true_grads = autograd_grads(model, x)
-    print("\n=== Vanilla autograd grads collected ===")
-
-    # 2) Run forward + compiled backward for inputs ONCE to capture 'bwd_input' backward FX graph
-    y1, x1, params1 = forward_prepare(model, x)
-    _ = bwd_input_fn(y1, x1)   # triggers capture for "bwd_input"
-
-    # 3) Run forward + compiled backward for weights ONCE to capture 'bwd_weight' backward FX graph
-    y2, x2, params2 = forward_prepare(model, x)
-    _ = bwd_weight_fn(y2, params2)  # triggers capture for "bwd_weight"
-
-    assert "bwd_input" in CAPTURED, "Did not capture bwd_input backward graph"
-    assert "bwd_weight" in CAPTURED, "Did not capture bwd_weight backward graph"
-
-    gm_in, ex_in = CAPTURED["bwd_input"]
-    gm_wt, ex_wt = CAPTURED["bwd_weight"]
-
-    print("\nCaptured backward FX graphs:")
-    print("BWD_Input Placeholders:", [n for n in gm_in.graph.nodes if n.op == "placeholder"])
-    print("len(BWD_Input Placeholders):", len([n for n in gm_in.graph.nodes if n.op == "placeholder"]))
-    print("BWD_Weight Placeholders:", [n for n in gm_wt.graph.nodes if n.op == "placeholder"])
-    print("len(BWD_Weight Placeholders):", len([n for n in gm_wt.graph.nodes if n.op == "placeholder"]))
-
-
-    print("\n=== Using captured backward FX graphs directly ===")
-    with torch.no_grad():
-        out_in = gm_in(*ex_in)     # grads w.r.t. input
-        out_wt = gm_wt(*ex_wt)     # grads w.r.t. params
-
-    out_in_list = flatten_grads(out_in)
-    out_wt_list = flatten_grads(out_wt)
-
-    print("bwd_input outputs:", [g.shape for g in out_in_list if isinstance(g, torch.Tensor)])
-    print("bwd_weight outputs:", [g.shape for g in out_wt_list if isinstance(g, torch.Tensor)])
-
-    # 4) Compare combined grads vs vanilla autograd
-    compiled_grads = out_in_list + out_wt_list
-
-    print("\n=== SPLIT vs PYTORCH AUTOGRAD CHECK ===")
-    compare_grad_lists(compiled_grads, true_grads)
-
-    # 5) (Optional) visualize graphs
-    if PRINT_FX_GRAPHS:
-        fx_to_graphviz(gm_in, filename="backward_input_fx")
-        fx_to_graphviz(gm_wt, filename="backward_weight_fx")
-        print("Saved backward_input_fx and backward_weight_fx graphs to files.")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/test/models/llama.py b/test/models/llama.py
index 91052bb..6ee75ad 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -443,25 +443,25 @@ def log_size(layer, indent=0):
     # - 2 stages
     # - n_layers is divisible by 2
     # """
-    # def forward(self, tokens: torch.Tensor):
+    def forward(self, tokens: torch.Tensor):
 
-    #     distributed_stage(0, actor_id=0, optim=torch.optim.Adam)
+        distributed_stage(0, actor_id=0, optim=torch.optim.Adam)
 
-    #     h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-    #     start_pos = 0
+        h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
+        start_pos = 0
         
-    #     for layer in self.layers[:self.n_layers//2]:
-    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
+        for layer in self.layers[:self.n_layers//2]:
+            h = layer(h, start_pos, self.freqs_cis, self.mask)
 
-    #     distributed_stage(1, actor_id=1, optim=torch.optim.Adam)
+        distributed_stage(1, actor_id=1, optim=torch.optim.Adam)
 
-    #     for layer in self.layers[self.n_layers//2:]:
-    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
+        for layer in self.layers[self.n_layers//2:]:
+            h = layer(h, start_pos, self.freqs_cis, self.mask)
 
-    #     h = self.norm(h) if self.norm else h
-    #     output = self.output(h).float() if self.output else h
+        h = self.norm(h) if self.norm else h
+        output = self.output(h).float() if self.output else h
 
-    #     return output
+        return output
 
     """
     forward method for no pp
@@ -469,17 +469,17 @@ def log_size(layer, indent=0):
     - 1 device
     - 1 stages
     """
-    def forward(self, tokens: torch.Tensor):
+    # def forward(self, tokens: torch.Tensor):
 
-        distributed_stage(0, actor_id=0, optim=torch.optim.Adam)
+    #     distributed_stage(0, actor_id=0, optim=torch.optim.Adam)
 
-        h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-        start_pos = 0
+    #     h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
+    #     start_pos = 0
         
-        for layer in self.layers:
-            h = layer(h, start_pos, self.freqs_cis, self.mask)
+    #     for layer in self.layers:
+    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
 
-        h = self.norm(h) if self.norm else h
-        output = self.output(h).float() if self.output else h
+    #     h = self.norm(h) if self.norm else h
+    #     output = self.output(h).float() if self.output else h
 
-        return output
\ No newline at end of file
+    #     return output
\ No newline at end of file
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index 31a2868..8cb1223 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -29,35 +29,6 @@ def build_gpipe_schedule(n_mbs: int, n_stages: int):
         schedule[stage][-i-1] = Task(stage_id=stage, device_id=stage, mb_idx=0, task=TaskType.UPDATE)
     return schedule
 
-# def build_1f1b_schedule(n_mbs: int, n_stages: int):
-#     steps = n_mbs + n_stages - 1
-#     schedule = [[None] * (steps * 2 + 1) for _ in range(n_stages)]
-#     stage_mb = [[0, 0] for _ in range(n_stages)]
-#     for step in range(n_stages):
-#         for stage_id in range(n_stages):
-#             if step >= stage_id:
-#                 mb_idx = stage_mb[stage_id][0]
-#                 if mb_idx >= 0 and mb_idx < n_mbs:
-#                     schedule[stage_id][step] = Task(
-#                         device_id=stage_id, stage_id=stage_id, 
-#                         mb_idx=mb_idx, is_fwd=True, upd=False
-#                     )
-#                     stage_mb[stage_id][0] += 1
-#     for step in range(n_stages, 2 * steps):
-#         relative_step = step - n_stages
-#         for stage_id in range(n_stages):
-#             inv_stage = n_stages - stage_id - 1
-#             if relative_step >= inv_stage:
-#                 fwd_or_bwd = 1 - (relative_step + inv_stage) % 2
-#                 task_type = True if fwd_or_bwd == 0 else False
-#                 mb_idx = stage_mb[stage_id][fwd_or_bwd]
-#                 if mb_idx >= 0 and mb_idx < n_mbs:
-#                     schedule[stage_id][step] = Task(
-#                         device_id=stage_id, stage_id=stage_id,
-#                         mb_idx=mb_idx, is_fwd=task_type, upd=False
-#                     )
-#                     stage_mb[stage_id][fwd_or_bwd] += 1
-#     return schedule
 def build_1f1b_schedule(n_mbs: int, n_stages: int):
     steps = n_mbs + n_stages - 1
     schedule = [[None] * (steps * 2 + 1) for _ in range(n_stages)]
@@ -87,10 +58,10 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int):
                     )
                     stage_mb[stage_id][fwd_or_bwd] += 1
     for i, stage in enumerate(range(n_stages)):
-        schedule[stage][-i-1] = Task(stage_id=stage, device_id=stage, mb_idx=n_mbs-1, is_fwd=False, upd=True)
+        schedule[stage][-i-1] = Task(stage_id=stage, device_id=stage, mb_idx=n_mbs-1, task=TaskType.UPDATE)
     return schedule
 
-## ZB: Finish schedule function ##
+## TODO: Finish zb1p schedule function to produce schedules for arbitrary n_mbs and n_stages ##
 def build_zb1p_schedule(n_mbs: int, n_stages: int):
     if n_stages != 2 or n_mbs != 4:
         raise NotImplementedError("ZB1P schedule is only implemented for 2 stages and 4 microbatches.")
@@ -109,7 +80,7 @@ def build_zb1p_schedule(n_mbs: int, n_stages: int):
             Task(device_id=0, stage_id=0, mb_idx=2, task=TaskType.BACKWARD_WEIGHT), # t10: W2
             Task(device_id=0, stage_id=0, mb_idx=3, task=TaskType.BACKWARD_INPUT),  # t11: I3
             Task(device_id=0, stage_id=0, mb_idx=3, task=TaskType.BACKWARD_WEIGHT), # t12: W3
-            Task(device_id=0, stage_id=0, mb_idx=0, task=TaskType.UPDATE),         # t13: U0
+            Task(device_id=0, stage_id=0, mb_idx=0, task=TaskType.UPDATE),          # t13: U0
         ],
         [
             None,                                                                   # t0:  -
@@ -125,136 +96,137 @@ def build_zb1p_schedule(n_mbs: int, n_stages: int):
             Task(device_id=1, stage_id=1, mb_idx=3, task=TaskType.BACKWARD_INPUT),  # t10: I3
             Task(device_id=1, stage_id=1, mb_idx=2, task=TaskType.BACKWARD_WEIGHT), # t11: W2
             Task(device_id=1, stage_id=1, mb_idx=3, task=TaskType.BACKWARD_WEIGHT), # t12: W3
-            Task(device_id=1, stage_id=1, mb_idx=0, task=TaskType.UPDATE),         # t13: U0
+            Task(device_id=1, stage_id=1, mb_idx=0, task=TaskType.UPDATE),          # t13: U0
         ],
         ]
     return schedule
+
 no_pp_schedule = [
     [
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=False, upd=True),
+        Task(device_id=0, stage_id=0, mb_idx=0, task=TaskType.FORWARD),
+        Task(device_id=0, stage_id=0, mb_idx=0, task=TaskType.BACKWARD),
+        Task(device_id=0, stage_id=0, mb_idx=0, task=TaskType.UPDATE),
     ]
 ]
 
 pp2_interleaved_1f1b_grid_schedule = [
     [
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=2, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=2, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=2, mb_idx=0, is_fwd=False, upd=False),
+        Task(device_id=0, stage_id=0, mb_idx=0, task=TaskType.FORWARD),
+        Task(device_id=0, stage_id=0, mb_idx=1, task=TaskType.FORWARD),
+        Task(device_id=0, stage_id=2, mb_idx=0, task=TaskType.FORWARD),
+        Task(device_id=0, stage_id=2, mb_idx=1, task=TaskType.FORWARD),
+        Task(device_id=0, stage_id=0, mb_idx=2, task=TaskType.FORWARD),
+        Task(device_id=0, stage_id=0, mb_idx=3, task=TaskType.FORWARD),
+        Task(device_id=0, stage_id=2, mb_idx=0, task=TaskType.BACKWARD),
         None,
-        Task(device_id=0, stage_id=2, mb_idx=1, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=2, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=2, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=1, is_fwd=False, upd=False),
+        Task(device_id=0, stage_id=2, mb_idx=1, task=TaskType.BACKWARD),
+        Task(device_id=0, stage_id=2, mb_idx=2, task=TaskType.FORWARD),
+        Task(device_id=0, stage_id=0, mb_idx=0, task=TaskType.BACKWARD),
+        Task(device_id=0, stage_id=2, mb_idx=3, task=TaskType.FORWARD),
+        Task(device_id=0, stage_id=0, mb_idx=1, task=TaskType.BACKWARD),
         None,
-        Task(device_id=0, stage_id=2, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=2, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=False, upd=True),
+        Task(device_id=0, stage_id=2, mb_idx=2, task=TaskType.BACKWARD),
+        Task(device_id=0, stage_id=2, mb_idx=3, task=TaskType.BACKWARD),
+        Task(device_id=0, stage_id=0, mb_idx=2, task=TaskType.BACKWARD),
+        Task(device_id=0, stage_id=0, mb_idx=3, task=TaskType.BACKWARD),
+        Task(device_id=0, stage_id=0, mb_idx=0, task=TaskType.UPDATE),
     ],
     [
         None,
-        Task(device_id=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=3, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=3, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=3, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=3, mb_idx=1, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=1, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=3, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=3, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=3, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=3, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=0, is_fwd=False, upd=True),
+        Task(device_id=1, stage_id=1, mb_idx=0, task=TaskType.FORWARD),
+        Task(device_id=1, stage_id=1, mb_idx=1, task=TaskType.FORWARD),
+        Task(device_id=1, stage_id=3, mb_idx=0, task=TaskType.FORWARD),
+        Task(device_id=1, stage_id=3, mb_idx=0, task=TaskType.BACKWARD),
+        Task(device_id=1, stage_id=3, mb_idx=1, task=TaskType.FORWARD),
+        Task(device_id=1, stage_id=3, mb_idx=1, task=TaskType.BACKWARD),
+        Task(device_id=1, stage_id=1, mb_idx=2, task=TaskType.FORWARD),
+        Task(device_id=1, stage_id=1, mb_idx=0, task=TaskType.BACKWARD),
+        Task(device_id=1, stage_id=1, mb_idx=3, task=TaskType.FORWARD),
+        Task(device_id=1, stage_id=1, mb_idx=1, task=TaskType.BACKWARD),
+        Task(device_id=1, stage_id=3, mb_idx=2, task=TaskType.FORWARD),
+        Task(device_id=1, stage_id=3, mb_idx=2, task=TaskType.BACKWARD),
+        Task(device_id=1, stage_id=3, mb_idx=3, task=TaskType.FORWARD),
+        Task(device_id=1, stage_id=3, mb_idx=3, task=TaskType.BACKWARD),
+        Task(device_id=1, stage_id=1, mb_idx=2, task=TaskType.BACKWARD),
+        Task(device_id=1, stage_id=1, mb_idx=3, task=TaskType.BACKWARD),
+        Task(device_id=1, stage_id=1, mb_idx=0, task=TaskType.UPDATE),
         None,
     ],
 ]
 
 pp4_interleaved_1f1b_grid_schedule = [
     [
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=4, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=4, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=4, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=4, mb_idx=3, is_fwd=True, upd=False),
+        Task(device_id=0, stage_id=0, mb_idx=0, task=TaskType.FORWARD),
+        Task(device_id=0, stage_id=0, mb_idx=1, task=TaskType.FORWARD),
+        Task(device_id=0, stage_id=0, mb_idx=2, task=TaskType.FORWARD),
+        Task(device_id=0, stage_id=0, mb_idx=3, task=TaskType.FORWARD),
+        Task(device_id=0, stage_id=4, mb_idx=0, task=TaskType.FORWARD),
+        Task(device_id=0, stage_id=4, mb_idx=1, task=TaskType.FORWARD),
+        Task(device_id=0, stage_id=4, mb_idx=2, task=TaskType.FORWARD),
+        Task(device_id=0, stage_id=4, mb_idx=3, task=TaskType.FORWARD),
         None,
         None,
         None,
-        Task(device_id=0, stage_id=4, mb_idx=0, is_fwd=False, upd=False),
+        Task(device_id=0, stage_id=4, mb_idx=0, task=TaskType.BACKWARD),
         None,
-        Task(device_id=0, stage_id=4, mb_idx=1, is_fwd=False, upd=False),
+        Task(device_id=0, stage_id=4, mb_idx=1, task=TaskType.BACKWARD),
         None,
-        Task(device_id=0, stage_id=4, mb_idx=2, is_fwd=False, upd=False),
+        Task(device_id=0, stage_id=4, mb_idx=2, task=TaskType.BACKWARD),
         None,
-        Task(device_id=0, stage_id=4, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=1, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=False, upd=True),
+        Task(device_id=0, stage_id=4, mb_idx=3, task=TaskType.BACKWARD),
+        Task(device_id=0, stage_id=0, mb_idx=0, task=TaskType.BACKWARD),
+        Task(device_id=0, stage_id=0, mb_idx=1, task=TaskType.BACKWARD),
+        Task(device_id=0, stage_id=0, mb_idx=2, task=TaskType.BACKWARD),
+        Task(device_id=0, stage_id=0, mb_idx=3, task=TaskType.BACKWARD),
+        Task(device_id=0, stage_id=0, mb_idx=0, task=TaskType.UPDATE),
     ],
     [
         None,
-        Task(device_id=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=5, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=5, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=5, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=5, mb_idx=3, is_fwd=True, upd=False),
+        Task(device_id=1, stage_id=1, mb_idx=0, task=TaskType.FORWARD),
+        Task(device_id=1, stage_id=1, mb_idx=1, task=TaskType.FORWARD),
+        Task(device_id=1, stage_id=1, mb_idx=2, task=TaskType.FORWARD),
+        Task(device_id=1, stage_id=1, mb_idx=3, task=TaskType.FORWARD),
+        Task(device_id=1, stage_id=5, mb_idx=0, task=TaskType.FORWARD),
+        Task(device_id=1, stage_id=5, mb_idx=1, task=TaskType.FORWARD),
+        Task(device_id=1, stage_id=5, mb_idx=2, task=TaskType.FORWARD),
+        Task(device_id=1, stage_id=5, mb_idx=3, task=TaskType.FORWARD),
         None,
-        Task(device_id=1, stage_id=5, mb_idx=0, is_fwd=False, upd=False),
+        Task(device_id=1, stage_id=5, mb_idx=0, task=TaskType.BACKWARD),
         None,
-        Task(device_id=1, stage_id=5, mb_idx=1, is_fwd=False, upd=False),
+        Task(device_id=1, stage_id=5, mb_idx=1, task=TaskType.BACKWARD),
         None,
-        Task(device_id=1, stage_id=5, mb_idx=2, is_fwd=False, upd=False),
+        Task(device_id=1, stage_id=5, mb_idx=2, task=TaskType.BACKWARD),
         None,
-        Task(device_id=1, stage_id=5, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=1, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=0, is_fwd=False, upd=True),
+        Task(device_id=1, stage_id=5, mb_idx=3, task=TaskType.BACKWARD),
+        Task(device_id=1, stage_id=1, mb_idx=0, task=TaskType.BACKWARD),
+        Task(device_id=1, stage_id=1, mb_idx=1, task=TaskType.BACKWARD),
+        Task(device_id=1, stage_id=1, mb_idx=2, task=TaskType.BACKWARD),
+        Task(device_id=1, stage_id=1, mb_idx=3, task=TaskType.BACKWARD),
+        Task(device_id=1, stage_id=1, mb_idx=0, task=TaskType.UPDATE),
         None,
     ],
     [
         None,
         None,
-        Task(device_id=2, stage_id=2, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=2, stage_id=2, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=2, stage_id=2, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=2, stage_id=2, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=2, stage_id=6, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=2, stage_id=6, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=2, stage_id=6, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=2, stage_id=6, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=2, stage_id=6, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=2, stage_id=6, mb_idx=1, is_fwd=False, upd=False),
+        Task(device_id=2, stage_id=2, mb_idx=0, task=TaskType.FORWARD),
+        Task(device_id=2, stage_id=2, mb_idx=1, task=TaskType.FORWARD),
+        Task(device_id=2, stage_id=2, mb_idx=2, task=TaskType.FORWARD),
+        Task(device_id=2, stage_id=2, mb_idx=3, task=TaskType.FORWARD),
+        Task(device_id=2, stage_id=6, mb_idx=0, task=TaskType.FORWARD),
+        Task(device_id=2, stage_id=6, mb_idx=1, task=TaskType.FORWARD),
+        Task(device_id=2, stage_id=6, mb_idx=2, task=TaskType.FORWARD),
+        Task(device_id=2, stage_id=6, mb_idx=0, task=TaskType.BACKWARD),
+        Task(device_id=2, stage_id=6, mb_idx=3, task=TaskType.FORWARD),
+        Task(device_id=2, stage_id=6, mb_idx=1, task=TaskType.BACKWARD),
         None,
-        Task(device_id=2, stage_id=6, mb_idx=2, is_fwd=False, upd=False),
+        Task(device_id=2, stage_id=6, mb_idx=2, task=TaskType.BACKWARD),
         None,
-        Task(device_id=2, stage_id=6, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=2, stage_id=2, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=2, stage_id=2, mb_idx=1, is_fwd=False, upd=False),
-        Task(device_id=2, stage_id=2, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=2, stage_id=2, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=2, stage_id=2, mb_idx=0, is_fwd=False, upd=True),
+        Task(device_id=2, stage_id=6, mb_idx=3, task=TaskType.BACKWARD),
+        Task(device_id=2, stage_id=2, mb_idx=0, task=TaskType.BACKWARD),
+        Task(device_id=2, stage_id=2, mb_idx=1, task=TaskType.BACKWARD),
+        Task(device_id=2, stage_id=2, mb_idx=2, task=TaskType.BACKWARD),
+        Task(device_id=2, stage_id=2, mb_idx=3, task=TaskType.BACKWARD),
+        Task(device_id=2, stage_id=2, mb_idx=0, task=TaskType.UPDATE),
         None,
         None,
     ],
@@ -262,23 +234,23 @@ def build_zb1p_schedule(n_mbs: int, n_stages: int):
         None,
         None,
         None,
-        Task(device_id=3, stage_id=3, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=3, stage_id=3, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=3, stage_id=3, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=3, stage_id=3, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=3, stage_id=7, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=3, stage_id=7, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=3, stage_id=7, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=3, stage_id=7, mb_idx=1, is_fwd=False, upd=False),
-        Task(device_id=3, stage_id=7, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=3, stage_id=7, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=3, stage_id=7, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=3, stage_id=7, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=3, stage_id=3, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=3, stage_id=3, mb_idx=1, is_fwd=False, upd=False),
-        Task(device_id=3, stage_id=3, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=3, stage_id=3, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=3, stage_id=3, mb_idx=0, is_fwd=False, upd=True),
+        Task(device_id=3, stage_id=3, mb_idx=0, task=TaskType.FORWARD),
+        Task(device_id=3, stage_id=3, mb_idx=1, task=TaskType.FORWARD),
+        Task(device_id=3, stage_id=3, mb_idx=2, task=TaskType.FORWARD),
+        Task(device_id=3, stage_id=3, mb_idx=3, task=TaskType.FORWARD),
+        Task(device_id=3, stage_id=7, mb_idx=0, task=TaskType.FORWARD),
+        Task(device_id=3, stage_id=7, mb_idx=0, task=TaskType.BACKWARD),
+        Task(device_id=3, stage_id=7, mb_idx=1, task=TaskType.FORWARD),
+        Task(device_id=3, stage_id=7, mb_idx=1, task=TaskType.BACKWARD),
+        Task(device_id=3, stage_id=7, mb_idx=2, task=TaskType.FORWARD),
+        Task(device_id=3, stage_id=7, mb_idx=2, task=TaskType.BACKWARD),
+        Task(device_id=3, stage_id=7, mb_idx=3, task=TaskType.FORWARD),
+        Task(device_id=3, stage_id=7, mb_idx=3, task=TaskType.BACKWARD),
+        Task(device_id=3, stage_id=3, mb_idx=0, task=TaskType.BACKWARD),
+        Task(device_id=3, stage_id=3, mb_idx=1, task=TaskType.BACKWARD),
+        Task(device_id=3, stage_id=3, mb_idx=2, task=TaskType.BACKWARD),
+        Task(device_id=3, stage_id=3, mb_idx=3, task=TaskType.BACKWARD),
+        Task(device_id=3, stage_id=3, mb_idx=0, task=TaskType.UPDATE),
         None,
         None,
         None,
diff --git a/test/test_backward_split.py b/test/test_backward_split.py
index dc3feae..edc0890 100644
--- a/test/test_backward_split.py
+++ b/test/test_backward_split.py
@@ -120,15 +120,15 @@ def iter_schedule():
     assert len(piper_params_after) == len(base_params_after), \
         "Mismatch in # of parameters between pipeline and baseline models"
 
-    for i, (g_pipe, g_base) in enumerate(zip(piper_grads, base_grads)):
-        if g_pipe is None and g_base is None:
-            print(f"Grad {i}: both None")
-            continue
-        if g_pipe is None or g_base is None:
-            print(f"Grad {i}: one is None, one not")
-            continue
-        diff = (g_pipe - g_base).abs().max().item()
-        print(f"Grad {i}: max |Δ| = {diff:.6e}")
+    # for i, (g_pipe, g_base) in enumerate(zip(piper_grads, base_grads)):
+    #     if g_pipe is None and g_base is None:
+    #         print(f"Grad {i}: both None")
+    #         continue
+    #     if g_pipe is None or g_base is None:
+    #         print(f"Grad {i}: one is None, one not")
+    #         continue
+    #     diff = (g_pipe - g_base).abs().max().item()
+    #     print(f"Grad {i}: max |Δ| = {diff:.6e}")
 
 
     max_abs_diff = 0.0
diff --git a/test/test_llama.py b/test/test_llama.py
index 4b52cbd..1b594c9 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -13,7 +13,7 @@
 
 from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B
 from .schedule_helpers import build_1f1b_schedule, build_gpipe_schedule, build_zb1p_schedule, print_schedule
-from piper_coordinator import PiperProgramCoordinator
+from piper.piper_coordinator import PiperProgramCoordinator
 
 from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B
 from .schedule_helpers import (
@@ -115,8 +115,7 @@ def main(args):
     elif args.model == 'LLAMA_8B':
         llama_config = LLAMA_8B
     print(args) 
-    #local_rank = int(os.environ['LOCAL_RANK'])
-    #print(f"Got local_rank={local_rank}")
+
     loss_fn = torch.nn.CrossEntropyLoss()
     device = 'cuda'
     
@@ -125,6 +124,7 @@ def main(args):
     seq_len = args.seq_len
     warmup = args.warmup
     iters = args.iters
+    
     # creating the training data; might make this a new method to get the dataloader to work properly
     x = torch.randint(0, llama_config.vocab_size, (batch_size, seq_len)).to(device)
     y = torch.zeros((batch_size, llama_config.vocab_size), dtype=torch.long).to(device)
diff --git a/visualize_graph.py b/visualize_graph.py
deleted file mode 100644
index b81607a..0000000
--- a/visualize_graph.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import torch
-import torch.fx as fx
-import graphviz
-
-
-def fx_to_graphviz(
-    gm: fx.GraphModule,
-    filename: str = "fx_graph",
-    format: str = "png",
-    show_attrs: bool = True,
-):
-    """
-    Convert a torch.fx.GraphModule to a Graphviz visualization.
-
-    Args:
-        gm (fx.GraphModule): The traced model
-        filename (str): Output file name (no extension)
-        format (str): 'png', 'pdf', 'svg', etc.
-        show_attrs (bool): Show full node target names
-    """
-    dot = graphviz.Digraph(format=format, graph_attr={"rankdir": "LR"})
-
-    for node in gm.graph.nodes:
-        label = f"{node.op}\n{node.name}"
-
-        if show_attrs and node.target not in ("output", "placeholder"):
-            label += f"\n{node.target}"
-
-        dot.node(node.name, label)
-
-        for user in node.users:
-            dot.edge(node.name, user.name)
-
-    dot.render(filename, cleanup=True)
-    return dot

From e4e8b5b5d49134dc63db5c10a392211bd07f5f45 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Mon, 2 Feb 2026 20:04:24 +0000
Subject: [PATCH 017/100] add centralized comm op scheduling and implement comm
 ops for DP gradient sync. llama experiment with PP=1 and DP=2 shows that comm
 is entirely overlapped with backward

---
 src/piper.py                 |  43 +++++-----
 src/piper_actor.py           | 147 +++++++++++++++++++++++++++--------
 src/piper_compile.py         |  18 +----
 src/piper_graph_transform.py |  14 ++++
 src/piper_utils.py           |   3 +
 test/models/llama.py         |  72 ++++++++---------
 test/test_llama.py           |  32 +++++---
 7 files changed, 218 insertions(+), 111 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index 1c5eea3..007d15c 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -7,7 +7,7 @@
 from torch._dynamo.decorators import _disallow_in_graph_helper
 
 from .piper_utils import RemoteTensor, serialize_graphmodule, piper_metadata, create_logger, print_backward_graph, LOG_LEVEL
-from .piper_graph_transform import split_gm_by_experts
+from .piper_graph_transform import split_gm_by_experts, get_dp_comm_ops
 from .piper_actor import get_actor
 
 logger = create_logger("piper_backend", LOG_LEVEL)
@@ -51,35 +51,33 @@ def piper(gm, example_inputs, **kwargs):
     original_gm = gm
     gm = split_gm_by_experts(gm, stage_id, pp_degree)
 
+    # Get the data parallel communication operations
+    comm_ops, ids = get_dp_comm_ops(example_inputs)
+
     # For the top-level graph, log which arguments are input tensors
     # vs parameter tensors and make sure all example inputs are serializable
 
     placeholders = gm.graph.find_nodes(op="placeholder")
     graphargs = [node.meta["grapharg"] for node in placeholders]
 
-    # make sure example inputs are serializable by turning symbolic
-    # ints and fake tensors into concrete values
-    serializable_examples = []
+    # get the indices of input vs parameters tensors
     input_idxs = []
     param_idxs = []
-    for i, (arg, ex) in enumerate(zip(graphargs, example_inputs)):
-        # save indices of input tensors and model parameters
-        if 'self' not in str(arg):
-            input_idxs.append(i)
-        else:
+    for i, arg in enumerate(example_inputs):
+        if isinstance(arg, torch.nn.Parameter):
             param_idxs.append(i)
+        else:
+            input_idxs.append(i)
+
+    # make sure example inputs are serializable by turning symbolic
+    # ints and fake tensors into concrete values
+    serializable_examples = []
+    for ex in example_inputs:
         # convert symbolic ints and fake tensors to concrete values
         if isinstance(ex, torch.SymInt):
             serializable_examples.append(int(ex))
         elif isinstance(ex, torch._subclasses.fake_tensor.FakeTensor):
-            new = torch.full(
-                ex.shape,
-                0,
-                dtype=ex.dtype,
-                device=ex.device,
-                layout=ex.layout,
-                requires_grad=ex.requires_grad,
-            )
+            new = torch.full(ex.shape, 0, dtype=ex.dtype, device=ex.device, layout=ex.layout, requires_grad=ex.requires_grad)
             serializable_examples.append(new)
         else:
             serializable_examples.append(ex)
@@ -91,28 +89,37 @@ def piper(gm, example_inputs, **kwargs):
     stage_id = piper_metadata.current_stage
     actor_id = piper_metadata.current_actor
     actor = get_actor(actor_id)
+    naive_gradient_sync = piper_metadata.naive_gradient_sync
 
     dp_rank = int(os.environ['PIPER_DP_RANK'])
     dp_degree = int(os.environ['PIPER_DP_DEGREE'])
     global_rank = dp_rank * dp_degree + actor_id
     
+    # load the stage graph and params on the actor
     ray.get(
         actor.load_graph.remote(
             stage_id,
             payload,
-            torch._dynamo.backends.debugging.eager,
+            comm_ops,
             serializable_examples,
+            ids,
             input_idxs,
         )
     )
 
+    # start the communication loop on the actor
+    if dp_degree > 1 and not naive_gradient_sync:
+        actor.comm_loop.remote(stage_id)
+
     # Get fake tensor representations of the graph output(s)
     def symint_to_int(x):
         return int(x) if isinstance(x, torch.SymInt) else x
     def int_to_tensor(x):
         return torch.tensor(x) if isinstance(x, int) else x
     example_inputs = list(map(symint_to_int, serializable_examples))
+    example_inputs = [t.to('cuda') for t in example_inputs]
     fakes = original_gm(*example_inputs)
+    fakes = [t.to('cpu') for t in fakes]
     fakes = list(map(int_to_tensor, fakes))
 
     # wait for a signal to run this graph if it's the first graph of the stage
diff --git a/src/piper_actor.py b/src/piper_actor.py
index a91f7a4..272d6de 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -14,11 +14,10 @@
 from .piper_utils import deserialize_graphmodule, create_logger, RemoteTensor, print_backward_graph, LOG_LEVEL
 
 CLEANUP_MEMORY = False
-torch.autograd.set_detect_anomaly(True)
 
 logger = create_logger("piper_actor", LOG_LEVEL)
 
-def create_actors(num_actors, optim_class):
+def create_actors(num_actors, optim_class, num_mbs, naive_gradient_sync=False):
     dp_rank = int(os.environ['PIPER_DP_RANK'])
     world_size = int(os.environ['PIPER_WORLD_SIZE'])
     dp_degree = int(os.environ['PIPER_DP_DEGREE'])
@@ -27,7 +26,7 @@ def create_actors(num_actors, optim_class):
     from .piper_utils import piper_metadata
     for actor_id in range(num_actors):
         global_rank = dp_rank * dp_degree + actor_id
-        actor = PiperActor.options(num_gpus=0.9, max_concurrency=2).remote(actor_id, optim_class, world_size, dp_rank=dp_rank, dp_degree=dp_degree, pp_degree=pp_degree)
+        actor = PiperActor.options(num_gpus=1, max_concurrency=3).remote(actor_id, optim_class, world_size, num_mbs, naive_gradient_sync, dp_rank=dp_rank, dp_degree=dp_degree, pp_degree=pp_degree)
         piper_metadata.actors[actor_id] = actor
 
     ray.get([actor.load_actor_handles.remote(piper_metadata.actors) for actor in piper_metadata.actors.values()])
@@ -123,17 +122,19 @@ def dispatch_expert_ray(global_expert_id: int, local_expert_id: int, batch_idx:
 
 @ray.remote
 class PiperActor:
-    def __init__(self, actor_id, optim_class, world_size, dp_rank=0, dp_degree=1, pp_degree=1):
+    def __init__(self, actor_id, optim_class, world_size, num_mbs, naive_gradient_sync=False, dp_rank=0, dp_degree=1, pp_degree=1):
         self.logger = create_logger("piper_actor", LOG_LEVEL)
 
         self.actor_id = actor_id
         self.optim_class = optim_class
+        self.naive_gradient_sync = naive_gradient_sync
         
         self.dp_rank = dp_rank
         self.dp_degree = dp_degree
         self.pp_degree = pp_degree
         self.world_size = world_size
 
+        self.num_mbs = num_mbs
         self.dp_group = None
         self.device = 'cuda'
 
@@ -177,10 +178,22 @@ def __init__(self, actor_id, optim_class, world_size, dp_rank=0, dp_degree=1, pp
         self.expert_input_activations = defaultdict(dict)
         # map (expert_id, batch_idx) -> output activation for backward
         self.expert_output_activations = defaultdict(dict)
-
+        # map stage id -> data parallel communication operations
+        self.comm_ops = dict()
+        # map stage id -> tensor id -> comm op status
+        self.comm_op_status = defaultdict(lambda: defaultdict(int))
+        # map stage id -> tensor id -> comm op handle
+        self.comm_op_handles = defaultdict(dict)
         # Timing infrastructure
         self.tracing = False  # Toggle for timing and memory tracing
-        self.trace_data = {'update': {'total': [], 'peak_memory_delta': [], 'peak_memory': []}}
+        self.trace_data = {
+            'update': {
+                'total': [], 
+                'sync': [], 
+                'optim': [], 
+                'peak_memory_delta': [], 
+                'peak_memory': []
+            }}
 
         from .piper_utils import piper_metadata
         piper_metadata.actor_self = self
@@ -221,8 +234,63 @@ def join_dp_process_group(self):
                 self.dp_group = process_group
                 self.logger.info(f"Global rank {self.global_rank} joined its dp group {dp_group_id} along with ranks {group_ranks}")
 
-    def load_graph(self, stage_id: int, gm_data, compiler_fn, graphargs, input_idxs):
-        self.logger.debug(f"Compiling graph on actor {self.actor_id} for stage id: {stage_id} with inputs: {len(graphargs)} and input indices: {input_idxs}")
+    def prepare_comm_ops(self, stage_id, comm_ops, graphargs, ids):
+        def hook_maker(tensor_id):
+            def post_backward_hook(grad):
+                self.comm_op_status[stage_id][tensor_id] += 1
+                self.logger.debug(f"post_accumulate_grad_hook called: dp_rank: {self.dp_rank}, tensor_id={tensor_id}, shape={grad.shape}, status={self.comm_op_status[stage_id][tensor_id]}")
+                return grad
+            return post_backward_hook
+        
+        comm_op_ids = [op.tensor_id for op in comm_ops]
+        for (t, i) in zip(graphargs, ids):
+            if i in comm_op_ids:
+               comm_op = comm_ops[comm_op_ids.index(i)]
+               comm_op.tensor = t
+               match comm_op.op:
+                   case "allreduce":
+                        if comm_op.pass_type == "backward" and comm_op.dep == "post":
+                            tensor_id = i
+                            tensor_shape = t.shape
+                            self.logger.debug(f"Registering post_accumulate_grad_hook on dp_rank: {self.dp_rank}, tensor_id={tensor_id}, shape={tensor_shape}")
+                            t.register_post_accumulate_grad_hook(hook_maker(tensor_id))
+                        else:
+                            raise ValueError(f"Unknown comm op type or dependency: {comm_op.op} {comm_op.dep}")
+                   case _:
+                       raise ValueError(f"Unknown communication operation: {comm_op.op}")
+
+    def comm_loop(self, stage_id):
+        comm_stream = torch.cuda.Stream()
+        self.logger.info(f"Running comm loop for stage {stage_id} on actor {self.actor_id} global rank {self.global_rank}")
+        for comm_op in self.comm_ops[stage_id]:
+            done = False
+            while not done:
+                if comm_op.op == "allreduce" and comm_op.pass_type == "backward" and comm_op.dep == "post" and comm_op.group == "dp":
+                    # perform the all reduce when all gradients have been accumulated
+                    if self.comm_op_status[stage_id][comm_op.tensor_id] == self.num_mbs:
+                        with torch.cuda.stream(comm_stream):
+                            handle = dist.all_reduce(comm_op.tensor, op=dist.ReduceOp.AVG, group=self.dp_group, async_op=True)
+                        # -1 indicates that the all reduce has been performed
+                        self.comm_op_status[stage_id][comm_op.tensor_id] = 0
+                        self.comm_op_handles[stage_id][comm_op.tensor_id] = handle
+                        done = True
+                else:
+                    raise ValueError(f"Unknown comm op: {comm_op}")
+                # time.sleep(0.001)
+
+    def wait_for_comm_ops(self):
+        self.logger.debug(f"Actor {self.actor_id} global rank {self.global_rank} waiting for comm ops")
+        for stage_id, comm_ops in self.comm_ops.items():
+            for comm_op in comm_ops:
+                done = False
+                while not done:
+                    if comm_op.tensor_id in self.comm_op_handles[stage_id]:
+                        self.comm_op_handles[stage_id][comm_op.tensor_id].wait()
+                        done = True
+                    # time.sleep(0.001)
+
+    def load_graph(self, stage_id: int, gm_data, comm_ops, graphargs, ids, input_idxs):
+        self.logger.info(f"Loading stage {stage_id} graph on actor {self.actor_id} global rank {self.global_rank}")
 
         # set up tracing data structure
         if stage_id not in self.trace_data:
@@ -247,40 +315,38 @@ def load_graph(self, stage_id: int, gm_data, compiler_fn, graphargs, input_idxs)
         # Store GraphModule reference
         self.graph_modules[stage_id] = gm
         self.forward_fns[stage_id] = gm.forward
-        
-        # save the parameters and initialize the optimizer
-        self.add_param_group(stage_id, graphargs, input_idxs)
-
-        del gm_data
 
-    def add_param_group(self, stage_id: int, params, input_idxs):
-        self.logger.debug(f"Adding param group for stage {stage_id}")
-
-        if stage_id in self.parameters:
-            self.logger.debug(f"Param group already exists for stage {stage_id}")
-            return
-        
         # place parameters on the device
         def move_to_device(idx, arg):
             if idx not in input_idxs:
                 return arg.to(self.device).detach().requires_grad_(True)
             else:
                 return arg.to(self.device)
+        params = graphargs
         params = list(map(move_to_device, range(len(params)), params))
 
         # discard the graphargs that correspond to input tensors
         for i in input_idxs:
             params[i] = None
 
-        # save the parameters
+        # prepare tensors with comm ops
+        if not self.naive_gradient_sync:
+            self.prepare_comm_ops(stage_id, comm_ops, params, ids)
+        self.comm_ops[stage_id] = list(reversed(comm_ops))
+
+        # save parameters
         self.input_idxs[stage_id] = input_idxs
         self.parameters[stage_id] = params
 
         # add the parameters to the optimizer for this stage
+        all_params = [param for param in params if param is not None]
         if stage_id not in self.optims:
-            self.optims[stage_id] = self.optim_class([param for param in params if param is not None])
+            self.optims[stage_id] = self.optim_class(all_params)
         else:
-            self.optims[stage_id].add_param_group({'params': [param for param in params if param is not None]})
+            self.optims[stage_id].add_param_group({'params': all_params})
+
+        del gm_data
+
 
     def load_expert(self, stage_id, expert_id, local_expert_id, batch_idx, expert_module, input_idxs, param_idxs, params):
         self.logger.debug(f"Loading expert {expert_id} on actor {self.actor_id} global rank {self.global_rank}")
@@ -377,7 +443,7 @@ def backward_expert(self, expert_id, batch_idx, grad_output):
         
         return grad_inputs
     
-    # @ray.method(tensor_transport="nccl")
+    @ray.method(tensor_transport="nccl")
     def forward(self, stage_id: int, mb_idx: int, *args):
         self.logger.debug(f"Calling forward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
 
@@ -516,7 +582,7 @@ def place(arg):
         
         return out
 
-    # @ray.method(tensor_transport="nccl")
+    @ray.method(tensor_transport="nccl")
     def backward(self, stage_id: int, mb_idx: int, inp, truth=None, loss_fn=None):
         self.logger.debug(f"Calling backward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
 
@@ -596,21 +662,19 @@ def backward(self, stage_id: int, mb_idx: int, inp, truth=None, loss_fn=None):
         return [ret] * 2
 
     def synchronize_gradients(self):
-        """Synchronize gradients across all DP ranks for this stage using all-reduce."""     
         self.logger.debug(f"Actor {self.actor_id} global rank {self.global_rank} synchronizing gradients")
-        
         # Iterate over all stages on this actor and synchronize their parameters
         for stage_id, parameters in self.parameters.items():
             for param in parameters:
                 if param is not None and param.grad is not None:
                     dist.all_reduce(param.grad, op=dist.ReduceOp.AVG, group=self.dp_group)
-        self.logger.info(f"Actor {self.actor_id} global rank {self.global_rank} finished synchronizing gradients")
 
     def update(self, *done_mbs):
-        self.logger.debug(f"Calling update on actor {self.actor_id} global rank {self.global_rank}")
-        
         if self.tracing:
             start_event = torch.cuda.Event(enable_timing=True)
+            start_sync = torch.cuda.Event(enable_timing=True)
+            end_sync = torch.cuda.Event(enable_timing=True)
+            start_optim = torch.cuda.Event(enable_timing=True)
             end_event = torch.cuda.Event(enable_timing=True)
 
             torch.cuda.reset_peak_memory_stats()
@@ -618,9 +682,20 @@ def update(self, *done_mbs):
 
             start_event.record()
         
-        # if dp degree > 1, synchronize the gradients
+        if self.tracing:
+            start_sync.record()
+
+        # if dp degree > 1, make sure all gradients are synchronized before optimizer step
+        # TODO: this does not allow overlapping with the optimizer step
         if self.dp_degree > 1:
-            self.synchronize_gradients()
+            if self.naive_gradient_sync:
+                self.synchronize_gradients()
+            else:
+                self.wait_for_comm_ops()
+        
+        if self.tracing:
+            end_sync.record()
+            start_optim.record()
         
         # step the optimizer for each stage
         for _, optim in self.optims.items():
@@ -633,10 +708,14 @@ def update(self, *done_mbs):
             end_event.record()
             torch.cuda.synchronize()
             total_time = start_event.elapsed_time(end_event)
+            sync_time = start_sync.elapsed_time(end_sync)
+            optim_time = start_optim.elapsed_time(end_event)
             update_peak_memory_delta_gb = (torch.cuda.max_memory_allocated() - update_start_memory) / (1024**3)
             update_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
 
             self.trace_data['update']['total'].append(total_time)
+            self.trace_data['update']['sync'].append(sync_time)
+            self.trace_data['update']['optim'].append(optim_time)
             self.trace_data['update']['peak_memory_delta'].append(update_peak_memory_delta_gb)
             self.trace_data['update']['peak_memory'].append(update_peak_memory_gb)
 
@@ -663,10 +742,12 @@ def clear_trace_data(self) -> None:
             }
         self.trace_data['update'] = {
             'total': [],
+            'sync': [],
+            'optim': [],
             'peak_memory_delta': [],
             'peak_memory': []
         }
-    
+
     def get_trace_data(self) -> dict:
         return self.trace_data
 
diff --git a/src/piper_compile.py b/src/piper_compile.py
index df29e27..16e54f2 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -12,29 +12,19 @@
 logger = create_logger("piper_compile", LOG_LEVEL)
 
 
-def piper_setup(model_class, model_args, optim_fn, example_inputs, num_stages, pp_degree, dynamic=False, check_correct=False):
+def piper_setup(model_class, model_args, optim_fn, example_inputs, num_stages, pp_degree, num_mbs, naive_gradient_sync=False, dynamic=False, check_correct=False):
     """
     Compile a model with the piper backend.
-
-    Args:
-        model: A model to compile.
-        example_inputs: Example inputs to the model.
-        dynamic: Whether to compile in dynamic mode.
-        backend: The backend to use for compilation.
-
-    Returns:
-        A tuple of (compiled_model, piper_metadata) where piper_metadata contains
-        the actors, stage_fns, and other state populated during compilation.
     """
-    create_actors(pp_degree, optim_fn)
+    piper_metadata.currently_compiling = True
+    piper_metadata.naive_gradient_sync = naive_gradient_sync
+    create_actors(pp_degree, optim_fn, num_mbs, naive_gradient_sync)
 
     model = model_class(*model_args)
 
     if check_correct:
         model_nocompile = copy.deepcopy(model)
 
-    piper_metadata.currently_compiling = True
-
     compiled = torch.compile(model, dynamic=dynamic, backend=piper)
 
     from .piper_utils import events_tls
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index 7a610c0..29ab3ae 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -9,6 +9,20 @@
 
 logger = create_logger("piper_graph_transform", LOG_LEVEL)
 
+class CommOp:
+    def __init__(self, tensor_id: int, dep: str, pass_type: str, op: str, group: str):
+        self.tensor = None # this gets set on the actor
+        self.tensor_id = tensor_id
+        self.dep = dep # "pre" or "post"
+        self.pass_type = pass_type # "forward" or "backward"
+        self.op = op # "allreduce", "allgather", "scatter", "alltoall"
+        self.group = group # "dp" or "pp"
+
+def get_dp_comm_ops(example_inputs):
+    dp_comm_ops = [CommOp(id(t), "post", "backward", "allreduce", "dp") for t in example_inputs if isinstance(t, torch.nn.Parameter)]
+    ids = [id(t) for t in example_inputs]
+    return [op for op in dp_comm_ops if op is not None], ids
+
 def split_gm_by_experts(gm, stage_id, pp_degree):
     """
     Transform a graph module with expert annotations by extracting
diff --git a/src/piper_utils.py b/src/piper_utils.py
index 2bcbab4..06546eb 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -10,6 +10,8 @@
 from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
 from typing import Any, Optional
 
+import src.piper_patches
+
 LOG_LEVEL = "INFO"
 
 """ 
@@ -85,6 +87,7 @@ class PiperMetadata:
     actor_self = None
     first_graph_of_stage = None
     parallelism_configs = {'dp': 1}
+    naive_gradient_sync = False
 
 piper_metadata = PiperMetadata()
 
diff --git a/test/models/llama.py b/test/models/llama.py
index 04ce362..0e4e744 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -408,7 +408,7 @@ def log_size(layer, indent=0):
     # """
     # def forward(self, tokens: torch.Tensor):
 
-    #     distributed_stage(0, actor_id=0, optim=torch.optim.Adam)
+    #     distributed_stage(0, actor_id=0)
 
     #     h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
     #     start_pos = 0
@@ -416,17 +416,17 @@ def log_size(layer, indent=0):
     #     for layer in self.layers[:self.n_layers//4]:
     #         h = layer(h, start_pos, self.freqs_cis, self.mask)
 
-    #     distributed_stage(1, actor_id=1, optim=torch.optim.Adam)
+    #     distributed_stage(1, actor_id=1)
 
     #     for layer in self.layers[self.n_layers//4:self.n_layers//2]:
     #         h = layer(h, start_pos, self.freqs_cis, self.mask)
 
-    #     distributed_stage(2, actor_id=0, optim=torch.optim.Adam)
+    #     distributed_stage(2, actor_id=0)
 
     #     for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
     #         h = layer(h, start_pos, self.freqs_cis, self.mask)
 
-    #     distributed_stage(3, actor_id=1, optim=torch.optim.Adam)
+    #     distributed_stage(3, actor_id=1)
 
     #     for layer in self.layers[3*self.n_layers//4:]:
     #         h = layer(h, start_pos, self.freqs_cis, self.mask)
@@ -436,12 +436,38 @@ def log_size(layer, indent=0):
 
     #     return output
 
+    # """
+    # forward method for 1f1b schedule
+    # requires:
+    # - 2 devices
+    # - 2 stages
+    # - n_layers is divisible by 2
+    # """
+    # def forward(self, tokens: torch.Tensor):
+
+    #     distributed_stage(0, actor_id=0)
+
+    #     h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
+    #     start_pos = 0
+        
+    #     for layer in self.layers[:self.n_layers//2]:
+    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     distributed_stage(1, actor_id=1)
+
+    #     for layer in self.layers[self.n_layers//2:]:
+    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     h = self.norm(h) if self.norm else h
+    #     output = self.output(h).float() if self.output else h
+
+    #     return output
+
     """
-    forward method for 1f1b schedule
+    forward method for no pp
     requires:
-    - 2 devices
-    - 2 stages
-    - n_layers is divisible by 2
+    - 1 device
+    - 1 stages
     """
     def forward(self, tokens: torch.Tensor):
 
@@ -450,36 +476,10 @@ def forward(self, tokens: torch.Tensor):
         h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
         start_pos = 0
         
-        for layer in self.layers[:self.n_layers//2]:
-            h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-        distributed_stage(1, actor_id=1)
-
-        for layer in self.layers[self.n_layers//2:]:
+        for layer in self.layers:
             h = layer(h, start_pos, self.freqs_cis, self.mask)
 
         h = self.norm(h) if self.norm else h
         output = self.output(h).float() if self.output else h
 
-        return output
-
-    # """
-    # forward method for no pp
-    # requires:
-    # - 1 device
-    # - 1 stages
-    # """
-    # def forward(self, tokens: torch.Tensor):
-
-    #     distributed_stage(0, actor_id=0, optim=torch.optim.Adam)
-
-    #     h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-    #     start_pos = 0
-        
-    #     for layer in self.layers:
-    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     h = self.norm(h) if self.norm else h
-    #     output = self.output(h).float() if self.output else h
-
-    #     return output
\ No newline at end of file
+        return output
\ No newline at end of file
diff --git a/test/test_llama.py b/test/test_llama.py
index ee5e76a..ff9c824 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -47,6 +47,8 @@ def parse_args():
                         help='Number of timing iterations (default: 20)')
     parser.add_argument('--tracing', action='store_true', default=False,
                         help='Enable tracing')
+    parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
+                        help='Enable naive gradient sync')
     return parser.parse_args()
 
 
@@ -131,7 +133,16 @@ def main(args):
         torch.manual_seed(0)
 
     num_stages = args.num_stages
-    compiled = piper_setup(Transformer, (llama_config, seq_len), torch.optim.Adam, [x], num_stages, args.pp_degree)
+    compiled = piper_setup(
+        Transformer, 
+        (llama_config, seq_len), 
+        torch.optim.Adam, 
+        [x], 
+        num_stages, 
+        args.pp_degree,
+        num_mbs,
+        naive_gradient_sync=args.naive_gradient_sync,
+    )
     
     assert num_stages == len(piper_metadata.dag) + 1
 
@@ -143,9 +154,6 @@ def main(args):
             schedule = pp2_interleaved_1f1b_grid_schedule if args.pp_degree == 2 else pp4_interleaved_1f1b_grid_schedule
         case "1f1b":
             schedule = build_1f1b_schedule(num_mbs, num_stages)
-            schedule[0][2] = schedule[0][4]
-            schedule[0][4] = schedule[0][6]
-            schedule[0][6] = None
         case "gpipe":
             schedule = build_gpipe_schedule(num_mbs, num_stages)
     
@@ -159,8 +167,6 @@ def main(args):
     # ray.get(actors[0].send_input.remote(x))
     # ray.get(actors[num_actors-1].send_truth.remote(y))
 
-    ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
-
     # Definte one iteration of the schedule
     def iter_schedule():
         losses = piper_exec(compiled, schedule, [x], y, loss_fn, num_mbs, num_stages)
@@ -171,8 +177,7 @@ def iter_schedule():
         iter_schedule()
 
     # Clear tracing data
-    ray.get([actor.clear_trace_data.remote() for actor in actors.values()])
-    ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
+    ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
 
     # Time training steps
     start = time.perf_counter()
@@ -198,12 +203,19 @@ def iter_schedule():
             trace_data = ray.get(actor.get_trace_data.remote())
             actor_id = ray.get(actor.id.remote())
             print_mean_timing_data(trace_data, actor_id)
-        
-    ray.timeline(f"out/{args.model}-pp{args.pp_degree}-dp{args.dp_degree}-{args.schedule}.json")
+    
+    if args.naive_gradient_sync:
+        suffix = "-naive-sync"
+    else:
+        suffix = ""
+    timeline_filename = f"out/{args.model}-pp{args.pp_degree}-dp{args.dp_degree}-{args.schedule}{suffix}.json"
+    ray.timeline(timeline_filename)
+    print(f"\nRay timeline saved to: {timeline_filename}")
 
 if __name__ == "__main__":
     ray.init(include_dashboard=False, log_to_driver=True, namespace="llama")
     args = parse_args()
     piper_coordinator = PiperProgramCoordinator.remote(dp_degree=args.dp_degree, pp_degree=args.pp_degree)
     ray.get(piper_coordinator.run_program.remote(main, args))
+    time.sleep(3)
     ray.shutdown()

From 11691b2c6bb675c9c9a25d1ebdf359fbd42c8685 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Tue, 10 Feb 2026 06:15:16 +0000
Subject: [PATCH 018/100] a2a support for EP in Piper. Implemented with a graph
 transformation _insert_comm_ops that inserts a2a comm ops detected by user
 annotations with 'collective'='all_to_all_single'. Also changed stage
 boundary API to be another graph annotation (possible under new assumption
 that there are no graph breaks). Now, entire model is distributed in one
 invocation of the piper backend, which transforms the graph using
 _inser_comm_ops and _split_gm_by_stages. Need to implement priority
 scheduling to avoid interference between synchronous a2a EP ops and
 asynchronous allreduce DP ops.

---
 src/piper.py                 | 201 ++-------
 src/piper_actor.py           | 483 +++++---------------
 src/piper_compile.py         |  49 +-
 src/piper_coordinator.py     |   1 +
 src/piper_exec.py            |  83 ++--
 src/piper_graph_transform.py | 834 +++++++++++++++++++++++++++++------
 src/piper_utils.py           | 235 ++++------
 test/models/llama.py         |  63 ++-
 test/models/mixtral.py       | 326 ++++++++++++++
 test/models/moe.py           | 163 -------
 test/test_graph_break.py     |  62 ---
 test/test_llama.py           |  38 +-
 test/test_llama_single.py    | 274 ------------
 test/test_mixtral.py         |  52 +++
 test/test_moe.py             |  89 ----
 15 files changed, 1394 insertions(+), 1559 deletions(-)
 create mode 100644 test/models/mixtral.py
 delete mode 100644 test/models/moe.py
 delete mode 100644 test/test_graph_break.py
 delete mode 100644 test/test_llama_single.py
 create mode 100644 test/test_mixtral.py
 delete mode 100644 test/test_moe.py

diff --git a/src/piper.py b/src/piper.py
index 007d15c..77a19a2 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -4,187 +4,50 @@
 import torch
 import os
 from torch._dynamo.backends.registry import register_backend
-from torch._dynamo.decorators import _disallow_in_graph_helper
 
-from .piper_utils import RemoteTensor, serialize_graphmodule, piper_metadata, create_logger, print_backward_graph, LOG_LEVEL
-from .piper_graph_transform import split_gm_by_experts, get_dp_comm_ops
-from .piper_actor import get_actor
+from .piper_utils import _serialize_graphmodule, piper_metadata, create_logger, LOG_LEVEL
+from .piper_graph_transform import _get_dp_comm_ops, _split_gm_by_stages, _insert_comm_ops
+from .piper_actor import _get_actor
 
 logger = create_logger("piper_backend", LOG_LEVEL)
 
 
-@torch.compiler.disable
-def distributed_stage(stage_id, actor_id=None):
-    """
-    Annotation for stage boundaries, causes torch.compile graph break
-    and sets metadata appropriately at compile time
-    """
-    dp_rank = int(os.environ['PIPER_DP_RANK'])
-    world_size = int(os.environ['PIPER_WORLD_SIZE'])
-    dp_degree = int(os.environ['PIPER_DP_DEGREE'])
-    pp_degree = int(os.environ['PIPER_PP_DEGREE'])
-
-    if actor_id is None:
-        actor_id = stage_id
-
-    piper_metadata.current_stage = stage_id
-    piper_metadata.current_actor = actor_id
-    piper_metadata.first_graph_of_stage = True
-
-
 @register_backend
 def piper(gm, example_inputs, **kwargs):
-    """
-    torch.compile backend loads the graph module on 
-    a Ray actor and returns a callback that remotely
-    runs the graph module. 
-    """
-    logger.debug(f"Compiling subgraph {id(gm)}")
-
-    if not piper_metadata.currently_compiling:
-        gm.print_readable()
-        assert False, "Piper backend called outside of compilation"
 
-    # Distribute expert submodules to actors if there are expert annotations
-    stage_id = piper_metadata.current_stage
-    pp_degree = int(os.environ['PIPER_PP_DEGREE'])
     original_gm = gm
-    gm = split_gm_by_experts(gm, stage_id, pp_degree)
-
-    # Get the data parallel communication operations
-    comm_ops, ids = get_dp_comm_ops(example_inputs)
-
-    # For the top-level graph, log which arguments are input tensors
-    # vs parameter tensors and make sure all example inputs are serializable
-
-    placeholders = gm.graph.find_nodes(op="placeholder")
-    graphargs = [node.meta["grapharg"] for node in placeholders]
-
-    # get the indices of input vs parameters tensors
-    input_idxs = []
-    param_idxs = []
-    for i, arg in enumerate(example_inputs):
-        if isinstance(arg, torch.nn.Parameter):
-            param_idxs.append(i)
-        else:
-            input_idxs.append(i)
-
-    # make sure example inputs are serializable by turning symbolic
-    # ints and fake tensors into concrete values
-    serializable_examples = []
-    for ex in example_inputs:
-        # convert symbolic ints and fake tensors to concrete values
-        if isinstance(ex, torch.SymInt):
-            serializable_examples.append(int(ex))
-        elif isinstance(ex, torch._subclasses.fake_tensor.FakeTensor):
-            new = torch.full(ex.shape, 0, dtype=ex.dtype, device=ex.device, layout=ex.layout, requires_grad=ex.requires_grad)
-            serializable_examples.append(new)
-        else:
-            serializable_examples.append(ex)
-
-    # serialize the fx.Graph
-    payload = serialize_graphmodule(gm)
-
-    # send the fx.Graph and model attributes to the actor
-    stage_id = piper_metadata.current_stage
-    actor_id = piper_metadata.current_actor
-    actor = get_actor(actor_id)
-    naive_gradient_sync = piper_metadata.naive_gradient_sync
-
-    dp_rank = int(os.environ['PIPER_DP_RANK'])
-    dp_degree = int(os.environ['PIPER_DP_DEGREE'])
-    global_rank = dp_rank * dp_degree + actor_id
-    
-    # load the stage graph and params on the actor
-    ray.get(
-        actor.load_graph.remote(
-            stage_id,
-            payload,
+    top_level_gm, submodules = _split_gm_by_stages(gm)
+
+    refs = []
+    actor_stages = []
+    for (stage_id, stage_gm, input_idxs, params_with_holes, placeholders) in submodules:
+        stage_gm = _insert_comm_ops(stage_gm)
+        actor_id = piper_metadata.stage_to_device[stage_id]
+        comm_ops, tids = _get_dp_comm_ops(params_with_holes, placeholders)
+        actor = _get_actor(actor_id)
+        actor_stages.append((actor, stage_id))
+        stage_gm_data = _serialize_graphmodule(stage_gm)
+        logger.debug(f"Loading stage {stage_id} on actor {actor_id}")
+        refs.append(actor._load_stage.remote(
+            stage_id, 
+            stage_gm_data, 
             comm_ops,
-            serializable_examples,
-            ids,
+            params_with_holes,
+            tids,
             input_idxs,
-        )
-    )
-
-    # start the communication loop on the actor
-    if dp_degree > 1 and not naive_gradient_sync:
-        actor.comm_loop.remote(stage_id)
-
-    # Get fake tensor representations of the graph output(s)
-    def symint_to_int(x):
-        return int(x) if isinstance(x, torch.SymInt) else x
-    def int_to_tensor(x):
-        return torch.tensor(x) if isinstance(x, int) else x
-    example_inputs = list(map(symint_to_int, serializable_examples))
-    example_inputs = [t.to('cuda') for t in example_inputs]
-    fakes = original_gm(*example_inputs)
-    fakes = [t.to('cpu') for t in fakes]
-    fakes = list(map(int_to_tensor, fakes))
-
-    # wait for a signal to run this graph if it's the first graph of the stage
-    first_graph_of_stage = piper_metadata.first_graph_of_stage
-    if first_graph_of_stage:
-        piper_metadata.first_graph_of_stage = False
-    
-    # return a wrapper function that runs the fx.Graph on the actor and 
-    # returns remote futures for each graph output
-    def run_remote_subgraph(*args):
-
-        logger.debug(f"Running subgraph {id(gm)}")
-
-        from .piper_utils import events_tls
-
-        mb_idx = events_tls.mb_idx
-
-        # wait for a signal to run the partial graph
-        if first_graph_of_stage:
-            logger.debug(f"Thread {mb_idx} global rank {global_rank} waiting for stage {stage_id}")
-            events_tls.events[stage_id].wait()
-            logger.debug(f"Thread {mb_idx} global rank {global_rank} running stage {stage_id}")
-
-        # Mutex ensures that only one thread submits a task to this actor at a time
-        logger.debug(f"Thread {mb_idx} global rank {global_rank} waiting for actor mutex {actor_id}")
-        with events_tls.actor_mutexes[actor_id]:
-            logger.debug(f"Thread {mb_idx} global rank {global_rank} got actor mutex {actor_id}")
-
-            # clear the event for the next stage
-            if first_graph_of_stage:
-                events_tls.events[stage_id].clear()
-
-            # ignore model parameter arguments (stored on the actor)
-            input_tensors_only = []
-            for i in input_idxs:
-                input_tensors_only.append(args[i])
-            args = input_tensors_only
-            
-            # track stage dependencies
-            for arg in args:
-                if isinstance(arg, RemoteTensor):
-                    prev_stage = arg.get_stage_id()
-                    if prev_stage != stage_id:
-                        piper_metadata.dag.add((prev_stage, stage_id))
+        ))
+    ray.get(refs)
 
-            # get Ray ObjectRefs from RemoteTensors
-            def unwrap(x):
-                return x.get_ref() if isinstance(x, RemoteTensor) else x
-            args = list(map(unwrap, args))
+    [actor._comm_loop.remote(stage_id) for (actor, stage_id) in actor_stages]
 
-            if piper_metadata.currently_compiling:
-                # dispatch task without nccl transport
-                refs = actor.forward_cpu.options(num_returns=len(fakes)).remote(stage_id, mb_idx, *args)
-            else:
-                # dispatch with nccl transport
-                refs = actor.forward.options(num_returns=len(fakes)).remote(stage_id, mb_idx, *args)
+    # TODO: build dag by analyzing stage dependencies
+    # this only supports sequential pipelines
+    for stage_id in piper_metadata.stage_to_device.keys():
+        if stage_id < len(piper_metadata.stage_to_device.keys()) - 1:
+            piper_metadata.dag.add((stage_id, stage_id+1))
 
-            # wrap the remote futures with RemoteTensor
-            # if piper_metadata.currently_compiling:
-            #     return [t.to('cpu') for t in ray.get(refs)]
+    def callback(*args):
+        logger.warning("Should not directly call compiled module, running non-distributed execution")
+        return original_gm(*args)
 
-            if isinstance(refs, list):
-                assert len(fakes) == len(refs)
-                return [RemoteTensor(fake, ref, stage_id) for fake, ref in zip(fakes, refs)]
-            else:
-                assert len(fakes) == 1
-                return [RemoteTensor(fakes[0], refs, stage_id)]
-    return run_remote_subgraph
\ No newline at end of file
+    return callback
\ No newline at end of file
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 272d6de..74d3569 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -11,13 +11,13 @@
 import threading
 from typing import Callable
 
-from .piper_utils import deserialize_graphmodule, create_logger, RemoteTensor, print_backward_graph, LOG_LEVEL
+from .piper_utils import _deserialize_graphmodule, create_logger, LOG_LEVEL
 
 CLEANUP_MEMORY = False
 
 logger = create_logger("piper_actor", LOG_LEVEL)
 
-def create_actors(num_actors, optim_class, num_mbs, naive_gradient_sync=False):
+def _create_actors(num_actors, optim_class, num_mbs, naive_gradient_sync=False):
     dp_rank = int(os.environ['PIPER_DP_RANK'])
     world_size = int(os.environ['PIPER_WORLD_SIZE'])
     dp_degree = int(os.environ['PIPER_DP_DEGREE'])
@@ -28,98 +28,12 @@ def create_actors(num_actors, optim_class, num_mbs, naive_gradient_sync=False):
         global_rank = dp_rank * dp_degree + actor_id
         actor = PiperActor.options(num_gpus=1, max_concurrency=3).remote(actor_id, optim_class, world_size, num_mbs, naive_gradient_sync, dp_rank=dp_rank, dp_degree=dp_degree, pp_degree=pp_degree)
         piper_metadata.actors[actor_id] = actor
+        logger.debug(f"DP rank {dp_rank} created actor {actor} global rank {global_rank}")
 
-    ray.get([actor.load_actor_handles.remote(piper_metadata.actors) for actor in piper_metadata.actors.values()])
-
-def get_actor(actor_id):
+def _get_actor(actor_id):
     from .piper_utils import piper_metadata
     return piper_metadata.actors[actor_id]
 
-class ExpertRayFunction(torch.autograd.Function):
-    """
-    Custom autograd Function that dispatches expert calls to Ray actors.
-    """
-    
-    @staticmethod
-    def forward(ctx, global_expert_id: int, local_expert_id: int, batch_idx: int, pp_degree: int, *args):
-        """
-        Dispatch a forward call to the expert `global_expert_id` 
-        on the actor `local_expert_id % pp_degree`. 
-        Future work will allow custom actor placement. 
-        """
-        # Get the actor for this expert
-        actor_id = local_expert_id % pp_degree
-        actor = get_actor(actor_id)
-        current_actor = ray.get_runtime_context().current_actor
-        
-        # Store metadata for backward pass
-        ctx.global_expert_id = global_expert_id
-        ctx.local_expert_id = local_expert_id
-        ctx.batch_idx = batch_idx
-        ctx.pp_degree = pp_degree
-        ctx.actor_id = actor_id
-
-        from .piper_utils import piper_metadata
-        actor_self = piper_metadata.actor_self
-        
-        # Store which inputs require gradients
-        ctx.input_requires_grad = [arg.requires_grad if isinstance(arg, torch.Tensor) else False for arg in args]
-        ctx.num_inputs = len(args)
-        
-        # Dispatch a remote call if actor is not current actor
-        if actor == current_actor:
-            output = actor_self.run_expert(global_expert_id, batch_idx, *args)
-        else:
-            ref = actor.run_expert.remote(global_expert_id, batch_idx, *args)
-            output = ray.get(ref)
-
-        # Detach the output so the graph is not carried over when 
-        # the expert runs on the current actor. 
-        if isinstance(output, (list, tuple)):
-            output = [t.detach() for t in output]
-        else:
-            output = output.detach()
-        
-        return output
-    
-    @staticmethod
-    def backward(ctx, grad_output):
-        """
-        Dispatch a backward expert call corresponding to the 
-        forward expert call from the ctx. 
-        """
-        # Get the actor for this expert
-        actor = get_actor(ctx.actor_id)
-        current_actor = ray.get_runtime_context().current_actor
-        logger.debug(f"Expert {ctx.global_expert_id} {ctx.batch_idx} backward on actor: {current_actor} calling actor: {actor}")
-        num_inputs = ctx.num_inputs
-
-        from .piper_utils import piper_metadata
-        actor_self = piper_metadata.actor_self
-        
-        # Dispatch a remote call if actor is not current actor
-        if actor == current_actor:
-            grad_inputs = actor_self.backward_expert(ctx.global_expert_id, ctx.batch_idx, grad_output)
-        else:
-            refs = actor.backward_expert.options(num_returns=num_inputs).remote(ctx.global_expert_id, ctx.batch_idx, grad_output)
-            grad_inputs = ray.get(refs)
-        
-        # Return gradients for inputs (None for metadata arguments)
-        result = [None, None, None, None] + list(grad_inputs)
-        
-        return tuple(result)
-
-def dispatch_expert_ray(global_expert_id: int, local_expert_id: int, batch_idx: int, pp_degree: int, *args):
-    """
-    Dispatch a Ray remote call to run an expert GraphModule
-    using a custom autograd Function. This function is called
-    from the FX graph, so it needs to be allowed in the graph.
-    """
-    return ExpertRayFunction.apply(global_expert_id, local_expert_id, batch_idx, pp_degree, *args)
-
-# Allow the dispatch function in the graph
-torch.compiler.allow_in_graph(dispatch_expert_ray)
-
 @ray.remote
 class PiperActor:
     def __init__(self, actor_id, optim_class, world_size, num_mbs, naive_gradient_sync=False, dp_rank=0, dp_degree=1, pp_degree=1):
@@ -166,18 +80,6 @@ def __init__(self, actor_id, optim_class, world_size, num_mbs, naive_gradient_sy
         self.out_activation = defaultdict(dict)
         # accumuate loss for each microbatch
         self.loss = []
-        # map expert id -> expert
-        self.experts = dict()
-        # map expert id -> expert parameters
-        self.expert_parameters = dict()
-        # map expert id -> expert input indices
-        self.expert_input_idxs = dict()
-        # map expert id -> expert module
-        self.expert_modules = dict()
-        # map expert id -> expert input activations
-        self.expert_input_activations = defaultdict(dict)
-        # map (expert_id, batch_idx) -> output activation for backward
-        self.expert_output_activations = defaultdict(dict)
         # map stage id -> data parallel communication operations
         self.comm_ops = dict()
         # map stage id -> tensor id -> comm op status
@@ -198,22 +100,61 @@ def __init__(self, actor_id, optim_class, world_size, num_mbs, naive_gradient_sy
         from .piper_utils import piper_metadata
         piper_metadata.actor_self = self
 
-    def load_actor_handles(self, actor_handles):
-        from .piper_utils import piper_metadata
-        piper_metadata.actors = actor_handles
+    def clear_trace_data(self) -> None:
+        """
+        Clear all collected timing data.
+        """
+        for stage_id in self.trace_data:
+            self.trace_data[stage_id] = {
+                'forward': {
+                    'forward': [],
+                    'total': [],
+                    'peak_memory_delta': [],
+                    'peak_memory': []
+                },
+                'backward': {
+                    'backward': [],
+                    'total': [],
+                    'peak_memory_delta': [],
+                    'peak_memory': []
+                },
+            }
+        self.trace_data['update'] = {
+            'total': [],
+            'sync': [],
+            'optim': [],
+            'peak_memory_delta': [],
+            'peak_memory': []
+        }
+
+    def get_trace_data(self) -> dict:
+        return self.trace_data
+
+    def set_tracing(self, enabled: bool) -> None:
+        self.tracing = enabled
+        self.logger.info(f"Actor {self.actor_id}: Tracing {'enabled' if enabled else 'disabled'}")
+
+    def start_mem_tracing(self) -> None:
+        torch.cuda.memory._record_memory_history()
     
-    def id(self):
-        return self.actor_id
+    def stop_mem_tracing(self) -> None:
+        torch.cuda.memory._dump_snapshot(f"actor{self.actor_id}_memory_snapshot_mb4_gpipe.pickle")
+        self.logger.info(f"Saved memory snapshot to actor{self.actor_id}_memory_snapshot_mb4_gpipe.pickle")
+        torch.cuda.memory._record_memory_history(enabled=None)
+
+    def reset_peak_memory(self):
+        torch.cuda.reset_peak_memory_stats()
+
+    def get_peak_memory(self):
+        return torch.cuda.max_memory_allocated() / (1024**3)
 
-    def send_input(self, tensor):
+    def load_input(self, tensor):
         self.input = tensor.to(self.device)
-        return "done"
     
-    def send_truth(self, tensor):
+    def load_labels(self, tensor):
         self.truth = tensor.to(self.device)
-        return "done"
 
-    def join_process_groups(self):
+    def _join_process_groups(self):
         master_addr = os.environ.get('PIPER_MASTER_ADDR', "127.0.0.1")
         master_port = os.environ.get('PIPER_MASTER_PORT', "10000")
         init_method = f"tcp://{master_addr}:{master_port}"
@@ -222,10 +163,9 @@ def join_process_groups(self):
         self.logger.debug(f"Actor {self.actor_id} global rank {self.global_rank} has GPU {os.environ['CUDA_VISIBLE_DEVICES']}, joined the global process group")
 
         if self.dp_degree > 1:
-            self.join_dp_process_group()
+            self._join_dp_process_group()
     
-    def join_dp_process_group(self):
-        # Every process needs to participate in every subgroup creation
+    def _join_dp_process_group(self):
         num_dp_groups = self.world_size // self.dp_degree
         for dp_group_id in range(num_dp_groups):
             group_ranks = [(dp_group_id + num_dp_groups * i) for i in range(self.dp_degree)]
@@ -234,11 +174,11 @@ def join_dp_process_group(self):
                 self.dp_group = process_group
                 self.logger.info(f"Global rank {self.global_rank} joined its dp group {dp_group_id} along with ranks {group_ranks}")
 
-    def prepare_comm_ops(self, stage_id, comm_ops, graphargs, ids):
+    def _prepare_comm_ops(self, stage_id, comm_ops, graphargs, ids):
         def hook_maker(tensor_id):
             def post_backward_hook(grad):
                 self.comm_op_status[stage_id][tensor_id] += 1
-                self.logger.debug(f"post_accumulate_grad_hook called: dp_rank: {self.dp_rank}, tensor_id={tensor_id}, shape={grad.shape}, status={self.comm_op_status[stage_id][tensor_id]}")
+                self.logger.debug(f"Updating tensor status dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={grad.shape}, status={self.comm_op_status[stage_id][tensor_id]}")
                 return grad
             return post_backward_hook
         
@@ -252,14 +192,14 @@ def post_backward_hook(grad):
                         if comm_op.pass_type == "backward" and comm_op.dep == "post":
                             tensor_id = i
                             tensor_shape = t.shape
-                            self.logger.debug(f"Registering post_accumulate_grad_hook on dp_rank: {self.dp_rank}, tensor_id={tensor_id}, shape={tensor_shape}")
+                            self.logger.debug(f"Registering update hook dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={tensor_shape}")
                             t.register_post_accumulate_grad_hook(hook_maker(tensor_id))
                         else:
                             raise ValueError(f"Unknown comm op type or dependency: {comm_op.op} {comm_op.dep}")
                    case _:
                        raise ValueError(f"Unknown communication operation: {comm_op.op}")
 
-    def comm_loop(self, stage_id):
+    def _comm_loop(self, stage_id):
         comm_stream = torch.cuda.Stream()
         self.logger.info(f"Running comm loop for stage {stage_id} on actor {self.actor_id} global rank {self.global_rank}")
         for comm_op in self.comm_ops[stage_id]:
@@ -270,26 +210,26 @@ def comm_loop(self, stage_id):
                     if self.comm_op_status[stage_id][comm_op.tensor_id] == self.num_mbs:
                         with torch.cuda.stream(comm_stream):
                             handle = dist.all_reduce(comm_op.tensor, op=dist.ReduceOp.AVG, group=self.dp_group, async_op=True)
-                        # -1 indicates that the all reduce has been performed
+                            self.logger.debug(f"Allreduce on dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={comm_op.tensor.shape}")
                         self.comm_op_status[stage_id][comm_op.tensor_id] = 0
                         self.comm_op_handles[stage_id][comm_op.tensor_id] = handle
                         done = True
                 else:
                     raise ValueError(f"Unknown comm op: {comm_op}")
-                # time.sleep(0.001)
 
-    def wait_for_comm_ops(self):
+    def _wait_for_comm_ops(self):
         self.logger.debug(f"Actor {self.actor_id} global rank {self.global_rank} waiting for comm ops")
         for stage_id, comm_ops in self.comm_ops.items():
             for comm_op in comm_ops:
+                self.logger.debug(f"Waiting for comm op to be launched dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={comm_op.tensor.shape}")
                 done = False
                 while not done:
                     if comm_op.tensor_id in self.comm_op_handles[stage_id]:
+                        self.logger.debug(f"Waiting for comm op to be finished dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={comm_op.tensor.shape}")
                         self.comm_op_handles[stage_id][comm_op.tensor_id].wait()
                         done = True
-                    # time.sleep(0.001)
 
-    def load_graph(self, stage_id: int, gm_data, comm_ops, graphargs, ids, input_idxs):
+    def _load_stage(self, stage_id: int, gm_data, comm_ops, params_with_holes, ids, input_idxs):
         self.logger.info(f"Loading stage {stage_id} graph on actor {self.actor_id} global rank {self.global_rank}")
 
         # set up tracing data structure
@@ -310,7 +250,7 @@ def load_graph(self, stage_id: int, gm_data, comm_ops, graphargs, ids, input_idx
             }
 
         # compile the graph with the given graphargs
-        gm = deserialize_graphmodule(gm_data)
+        gm = _deserialize_graphmodule(gm_data)
 
         # Store GraphModule reference
         self.graph_modules[stage_id] = gm
@@ -319,132 +259,34 @@ def load_graph(self, stage_id: int, gm_data, comm_ops, graphargs, ids, input_idx
         # place parameters on the device
         def move_to_device(idx, arg):
             if idx not in input_idxs:
-                return arg.to(self.device).detach().requires_grad_(True)
-            else:
-                return arg.to(self.device)
-        params = graphargs
-        params = list(map(move_to_device, range(len(params)), params))
+                if arg.requires_grad:
+                    return arg.to(self.device).detach().requires_grad_(True)
+                else:
+                    return arg.to(self.device)
+            return arg
+        params_with_holes = list(map(move_to_device, range(len(params_with_holes)), params_with_holes))
 
-        # discard the graphargs that correspond to input tensors
-        for i in input_idxs:
-            params[i] = None
 
         # prepare tensors with comm ops
-        if not self.naive_gradient_sync:
-            self.prepare_comm_ops(stage_id, comm_ops, params, ids)
+        if not self.naive_gradient_sync and self.dp_degree > 1:
+            self._prepare_comm_ops(stage_id, comm_ops, params_with_holes, ids)
         self.comm_ops[stage_id] = list(reversed(comm_ops))
 
         # save parameters
         self.input_idxs[stage_id] = input_idxs
-        self.parameters[stage_id] = params
+        self.parameters[stage_id] = params_with_holes
 
         # add the parameters to the optimizer for this stage
-        all_params = [param for param in params if param is not None]
+        only_params = [param for param in params_with_holes if param is not None]
         if stage_id not in self.optims:
-            self.optims[stage_id] = self.optim_class(all_params)
+            self.optims[stage_id] = self.optim_class(only_params)
         else:
-            self.optims[stage_id].add_param_group({'params': all_params})
+            self.optims[stage_id].add_param_group({'params': only_params})
 
         del gm_data
 
-
-    def load_expert(self, stage_id, expert_id, local_expert_id, batch_idx, expert_module, input_idxs, param_idxs, params):
-        self.logger.debug(f"Loading expert {expert_id} on actor {self.actor_id} global rank {self.global_rank}")
-        self.experts[expert_id] = expert_module
-
-        # place parameters on the device
-        def move_to_device(idx, arg):
-            if arg is None:
-                return None
-            if idx not in input_idxs:
-                return arg.to(self.device).detach().requires_grad_(True)
-            else:
-                return arg.to(self.device)
-        params = list(map(move_to_device, range(len(params)), params))
-
-        self.expert_input_idxs[expert_id] = input_idxs
-        self.expert_parameters[expert_id] = params
-        self.expert_modules[expert_id] = expert_module
-
-        # add the parameters to the optimizer for this stage
-        if stage_id not in self.optims:
-            self.optims[stage_id] = self.optim_class([param for param in params if param is not None])
-        else:
-            self.optims[stage_id].add_param_group({'params': [param for param in params if param is not None]})
-
-    def run_expert(self, expert_id, batch_idx, *args):
-        logger.debug(f"Actor {self.actor_id} global rank {self.global_rank} forward expert {expert_id} batch element {batch_idx}")
-
-        # Place args on the device and detach existing grad_fn
-        def place(arg):
-            if isinstance(arg, torch.Tensor):
-                if arg.device == torch.device('cpu'):
-                    out = arg.to(self.device)
-                else:
-                    out = arg
-                if out.grad_fn is not None:
-                    out = out.detach().requires_grad_(True)
-            return out
-        args = list(map(place, args))
-
-        # place input tensors in the correct indices
-        for i, arg in zip(self.expert_input_idxs[expert_id], args):
-            self.expert_parameters[expert_id][i] = arg
-
-        # save input activations
-        self.expert_input_activations[expert_id][batch_idx] = args
-        
-        # run expert module, track grad fn
-        with torch.set_grad_enabled(True):
-            out = self.expert_modules[expert_id](*self.expert_parameters[expert_id])
-        
-        # save output activation
-        if isinstance(out, (list, tuple)):
-            self.expert_output_activations[expert_id][batch_idx] = out[0]
-        else:
-            self.expert_output_activations[expert_id][batch_idx] = out
-        
-        # clear the input tensors
-        for i in self.expert_input_idxs[expert_id]:
-            self.expert_parameters[expert_id][i] = None
-        del args
-
-        return out
-    
-    def backward_expert(self, expert_id, batch_idx, grad_output):
-        logger.debug(f"Actor {self.actor_id} global rank {self.global_rank} backward expert {expert_id} batch element {batch_idx}")
-
-        if expert_id not in self.expert_output_activations or batch_idx not in self.expert_output_activations[expert_id]:
-            raise ValueError(f"No output activation found for expert {expert_id} batch {batch_idx}")
-        
-        # Get the stored output activation
-        output_activation = self.expert_output_activations[expert_id][batch_idx]
-        
-        self.logger.debug(f"Expert {expert_id} backward graph:")
-        print_backward_graph(self.logger.debug, output_activation)
-
-        if isinstance(grad_output, torch.Tensor) and grad_output.device != self.device:
-            grad_output = grad_output.to(self.device)
-
-        output_activation.backward(gradient=grad_output)
-
-        # Get the gradients for the input activations
-        input_activations = self.expert_input_activations[expert_id][batch_idx]
-        grad_inputs = []
-        for i, input_activation in enumerate(input_activations):
-            if input_activation.requires_grad:
-                grad_inputs.append(input_activation.grad)
-            else:
-                grad_inputs.append(None)
-        
-        # Clean up
-        del output_activation
-        del input_activations
-        
-        return grad_inputs
-    
-    @ray.method(tensor_transport="nccl")
-    def forward(self, stage_id: int, mb_idx: int, *args):
+    # @ray.method(tensor_transport="nccl")
+    def _forward(self, stage_id: int, mb_idx: int, args):
         self.logger.debug(f"Calling forward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
 
         if self.tracing:
@@ -454,6 +296,9 @@ def forward(self, stage_id: int, mb_idx: int, *args):
             end_event = torch.cuda.Event(enable_timing=True)
             beginning_event.record()
         
+        if not (isinstance(args, list) or isinstance(args, tuple)):
+            args = [args]
+
         def pre_loaded_input(param):
             if param is None:
                 return self.input
@@ -476,13 +321,19 @@ def place(arg):
         args = list(map(place, args))
 
         # place input tensors in the correct indices
+        self.logger.debug(f"Stage {stage_id} inputs: {[arg.shape for arg in args]}, input_idxs: {self.input_idxs[stage_id]}, parameters: {[p.shape if p is not None else None for p in self.parameters[stage_id]]}")
+        assert len(args) == len([p for p in self.parameters[stage_id] if p is None]), "Number of arguments must match number of holes in the parameters"
         for i, arg in zip(self.input_idxs[stage_id], args):
             self.parameters[stage_id][i] = arg
 
-        # save first input as input activation
-        if args[0].dtype == torch.float:
-            args[0].requires_grad_().retain_grad()
-        self.inp_activation[stage_id][mb_idx] = args[0]
+        # save first input that requires grad as input activation
+        if stage_id > 0:
+            inp_with_grad = [inp for inp in args if inp.requires_grad]
+            assert len(inp_with_grad) == 1, "Piper only supports one input per subgraph with requires_grad"
+            if inp_with_grad[0].dtype == torch.float:
+                inp_with_grad[0].requires_grad_().retain_grad()
+            self.logger.debug(f"Saving input activation {inp_with_grad[0].shape} for stage {stage_id} mb {mb_idx}")
+            self.inp_activation[stage_id][mb_idx] = inp_with_grad[0]
 
         # Record start event for forward timing
         if self.tracing:
@@ -492,8 +343,6 @@ def place(arg):
 
         # Call compiled function
         output = self.forward_fns[stage_id](*self.parameters[stage_id])
-        assert isinstance(output, (list, tuple)) and len(output) == 1, "Piper only supports one output per subgraph"
-        output = output[0]
 
         # Record end event and calculate forward timing
         if self.tracing:
@@ -504,9 +353,12 @@ def place(arg):
             forward_peak_memory_delta_gb = (torch.cuda.max_memory_allocated() - forward_start_memory) / (1024**3)
             forward_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
 
-        # save output as output activation
-        self.logger.debug(f"Saving output activation {output.shape} for stage {stage_id} mb {mb_idx}")
-        self.out_activation[stage_id][mb_idx] = output
+        # save first output that requires grad as output activation
+        # TODO: support multiple outputs
+        out_with_grad = [out for out in output if out.requires_grad]
+        assert len(out_with_grad) == 1, "Piper only supports one output per subgraph with requires_grad"
+        self.logger.debug(f"Saving output activation {out_with_grad[0].shape} for stage {stage_id} mb {mb_idx}")
+        self.out_activation[stage_id][mb_idx] = out_with_grad[0]
 
         # clear the input tensors
         for i in self.input_idxs[stage_id]:
@@ -527,63 +379,11 @@ def place(arg):
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
         
+        self.logger.debug(f"Forward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank} returning {[out.shape for out in output]}")
         return output
 
-    def forward_cpu(self, stage_id: int, mb_idx: int, *args):
-        self.logger.debug(f"Calling cpu forward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
-
-        def pre_loaded_input(param):
-            if param is None:
-                return self.input
-            else:
-                return param
-        args = list(map(pre_loaded_input, args))
-
-        # Ray object refs resolve to a single element list
-        def unwrap(x):
-            if isinstance(x, list) or isinstance(x, tuple):
-                assert len(x) == 1
-                return x[0]
-            else:
-                return x
-        args = list(map(unwrap, args))
-        
-        def place(arg):
-            if arg.device == torch.device('cpu'):
-                return arg.to(self.device)
-            else:
-                return arg
-        args = list(map(place, args))
-
-        # place input tensors in the correct indices
-        for i, arg in zip(self.input_idxs[stage_id], args):
-            self.parameters[stage_id][i] = arg
-
-        # save first input as input activation
-        if args[0].dtype == torch.float:
-            args[0].requires_grad_().retain_grad()
-        self.inp_activation[stage_id][mb_idx] = args[0]
-
-        out = self.forward_fns[stage_id](*self.parameters[stage_id])
-
-        # save output as output activation
-        activation_tensor = out[0] if isinstance(out, (list, tuple)) else out
-        self.out_activation[stage_id][mb_idx] = activation_tensor
-        
-        # clear the input tensors
-        for i in self.input_idxs[stage_id]:
-            self.parameters[stage_id][i] = None
-        del args
-        
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-        
-        return out
-
-    @ray.method(tensor_transport="nccl")
-    def backward(self, stage_id: int, mb_idx: int, inp, truth=None, loss_fn=None):
+    # @ray.method(tensor_transport="nccl")
+    def _backward(self, stage_id: int, mb_idx: int, inp, truth=None, loss_fn=None):
         self.logger.debug(f"Calling backward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
 
         if self.tracing:
@@ -597,14 +397,14 @@ def backward(self, stage_id: int, mb_idx: int, inp, truth=None, loss_fn=None):
             backward_start_event.record()
 
         if isinstance(inp, list) or isinstance(inp, tuple):
+            self.logger.debug(f"Backward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank} receiving input {[inp.shape for inp in inp]}")
             assert len(inp) == 1
             inp = inp[0]
 
         # Get the activations for this stage and microbatch
         out_activation = self.out_activation[stage_id][mb_idx]
-        inp_activation = self.inp_activation[stage_id][mb_idx]
-        self.logger.debug(f"Stage {stage_id} backward graph:")
-        print_backward_graph(self.logger.debug, out_activation)
+        if stage_id > 0:
+            inp_activation = self.inp_activation[stage_id][mb_idx]
 
         # compute loss with the final activation of the final stage. 
         # use the saved activation rather than inp because the saved activation stores the computation graph
@@ -622,18 +422,18 @@ def backward(self, stage_id: int, mb_idx: int, inp, truth=None, loss_fn=None):
         # the input gradient from the subsequent stage
         else:
             assert inp is not None
+            self.logger.debug(f"activation: {out_activation.shape}, input: {inp.shape}")
             assert out_activation.shape == inp.shape
             out_activation.backward(gradient=inp)
 
         del out_activation
 
         # propagate the gradient backwards if not the first stage
-        if stage_id == 0:
-            ret = "done"
-        else:
+        if stage_id > 0:
             ret = inp_activation.grad
-        
-        del inp_activation
+            del inp_activation
+        else:
+            ret = "done"        
 
         # Record end event and calculate backward timing
         if self.tracing:
@@ -661,7 +461,7 @@ def backward(self, stage_id: int, mb_idx: int, inp, truth=None, loss_fn=None):
         
         return [ret] * 2
 
-    def synchronize_gradients(self):
+    def _synchronize_gradients(self):
         self.logger.debug(f"Actor {self.actor_id} global rank {self.global_rank} synchronizing gradients")
         # Iterate over all stages on this actor and synchronize their parameters
         for stage_id, parameters in self.parameters.items():
@@ -669,7 +469,7 @@ def synchronize_gradients(self):
                 if param is not None and param.grad is not None:
                     dist.all_reduce(param.grad, op=dist.ReduceOp.AVG, group=self.dp_group)
 
-    def update(self, *done_mbs):
+    def _update(self, *done_mbs):
         if self.tracing:
             start_event = torch.cuda.Event(enable_timing=True)
             start_sync = torch.cuda.Event(enable_timing=True)
@@ -689,9 +489,9 @@ def update(self, *done_mbs):
         # TODO: this does not allow overlapping with the optimizer step
         if self.dp_degree > 1:
             if self.naive_gradient_sync:
-                self.synchronize_gradients()
+                self._synchronize_gradients()
             else:
-                self.wait_for_comm_ops()
+                self._wait_for_comm_ops()
         
         if self.tracing:
             end_sync.record()
@@ -720,60 +520,3 @@ def update(self, *done_mbs):
             self.trace_data['update']['peak_memory'].append(update_peak_memory_gb)
 
         return losses
-
-    def clear_trace_data(self) -> None:
-        """
-        Clear all collected timing data.
-        """
-        for stage_id in self.trace_data:
-            self.trace_data[stage_id] = {
-                'forward': {
-                    'forward': [],
-                    'total': [],
-                    'peak_memory_delta': [],
-                    'peak_memory': []
-                },
-                'backward': {
-                    'backward': [],
-                    'total': [],
-                    'peak_memory_delta': [],
-                    'peak_memory': []
-                },
-            }
-        self.trace_data['update'] = {
-            'total': [],
-            'sync': [],
-            'optim': [],
-            'peak_memory_delta': [],
-            'peak_memory': []
-        }
-
-    def get_trace_data(self) -> dict:
-        return self.trace_data
-
-    def set_tracing(self, enabled: bool) -> None:
-        """
-        Enable or disable timing and memory tracing.
-        
-        Args:
-            enabled (bool): True to enable tracing, False to disable.
-        """
-        self.tracing = enabled
-        self.logger.info(f"Actor {self.actor_id}: Tracing {'enabled' if enabled else 'disabled'}")
-
-    def start_mem_tracing(self) -> None:
-        torch.cuda.memory._record_memory_history()
-        return "done"
-    
-    def stop_mem_tracing(self) -> None:
-        torch.cuda.memory._dump_snapshot(f"actor{self.actor_id}_memory_snapshot_mb4_gpipe.pickle")
-        self.logger.info(f"Saved memory snapshot to actor{self.actor_id}_memory_snapshot_mb4_gpipe.pickle")
-        torch.cuda.memory._record_memory_history(enabled=None)
-        return "done"
-
-    def reset_peak_memory(self):
-        torch.cuda.reset_peak_memory_stats()
-        return "done"
-
-    def get_peak_memory(self):
-        return torch.cuda.max_memory_allocated() / (1024**3)
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 16e54f2..3e209a7 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -5,57 +5,36 @@
 import gc
 import copy
 
-from .piper_actor import create_actors
-from .piper_utils import piper_metadata, RemoteTensor, create_logger, LOG_LEVEL
+from .piper_actor import _create_actors
+from .piper_utils import piper_metadata, create_logger, LOG_LEVEL
 from .piper import piper
 
 logger = create_logger("piper_compile", LOG_LEVEL)
 
 
-def piper_setup(model_class, model_args, optim_fn, example_inputs, num_stages, pp_degree, num_mbs, naive_gradient_sync=False, dynamic=False, check_correct=False):
+def piper_setup(model_class, model_args, optim_fn, example_inputs, schedule, naive_gradient_sync=False):
     """
     Compile a model with the piper backend.
     """
-    piper_metadata.currently_compiling = True
     piper_metadata.naive_gradient_sync = naive_gradient_sync
-    create_actors(pp_degree, optim_fn, num_mbs, naive_gradient_sync)
 
-    model = model_class(*model_args)
+    num_devices = len(schedule)
+    num_mbs = len(set([task.mb_idx for row in schedule for task in row if task is not None]))
+    piper_metadata.stage_to_device = {task.stage_id: task.device_id for row in schedule for task in row if task is not None}
 
-    if check_correct:
-        model_nocompile = copy.deepcopy(model)
+    _create_actors(num_devices, optim_fn, num_mbs, naive_gradient_sync)
+    ray.get([actor._join_process_groups.remote() for actor in piper_metadata.actors.values()])
 
-    compiled = torch.compile(model, dynamic=dynamic, backend=piper)
+    model = model_class(*model_args)
 
-    from .piper_utils import events_tls
-    events_tls.actor_mutexes = dict([(actor_id, threading.Lock()) for actor_id in range(pp_degree)])
-    events_tls.events = [threading.Event() for _ in range(num_stages)]
-    for event in events_tls.events:
-        event.set()
+    compiled = torch.compile(model, backend=piper)
 
     dp_rank = int(os.environ['PIPER_DP_RANK'])
-    logger.info(f"DP rank {dp_rank+1} compiling {num_stages} stages...")
-
-    output = compiled(*example_inputs).get()
-
-    if check_correct:
-        correct_output = model_nocompile(*example_inputs)
-        if not torch.allclose(output, correct_output):
-            logger.error(f"Model output is not correct")
-            logger.error(f"Compiled output: {output}")
-            logger.error(f"Correct output: {correct_output}")
-        else:
-            logger.info(f"Model output is correct")
-
-    logger.info(f"DP rank {dp_rank+1} finished compiling model. DAG: {piper_metadata.dag}")
-    
-    piper_metadata.currently_compiling = False
-
-    logger.info(f"DP rank {dp_rank} joining process groups")
+    logger.info(f"DP rank {dp_rank+1} compiling...")
 
-    ray.get([actor.join_process_groups.remote() for actor in piper_metadata.actors.values()])
+    output = compiled(*example_inputs)
 
-    logger.info(f"Completed DP rank {dp_rank+1} setup for {len(piper_metadata.actors)} actors")
+    logger.info(f"DP rank {dp_rank+1} done.")
 
     from ray.experimental.collective import create_collective_group
     
@@ -63,6 +42,6 @@ def piper_setup(model_class, model_args, optim_fn, example_inputs, num_stages, p
         list(piper_metadata.actors.values()),
         backend="nccl")
     
-    logger.info(f"DP rank {dp_rank} Started NCCL group with {len(piper_metadata.actors)} actors")
+    logger.info(f"DP rank {dp_rank} Started NCCL group with {len(piper_metadata.actors)} actors and DAG {piper_metadata.dag}")
     
     return compiled
diff --git a/src/piper_coordinator.py b/src/piper_coordinator.py
index 17c0f91..ed71060 100644
--- a/src/piper_coordinator.py
+++ b/src/piper_coordinator.py
@@ -18,6 +18,7 @@ def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, master_addr, master_p
     os.environ['PIPER_WORLD_SIZE'] = str(world_size)
     os.environ['PIPER_MASTER_ADDR'] = str(master_addr)
     os.environ['PIPER_MASTER_PORT'] = str(master_port)
+    os.environ['TORCH_LOGS'] = "+graph_breaks"
     return training_func(*args, **kwargs)
 
 def find_free_port():
diff --git a/src/piper_exec.py b/src/piper_exec.py
index 33e1afd..34afd0a 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -22,11 +22,11 @@ class DAGEdge(NamedTuple):
     to_stage: int
 
 
-def get_backward_targets(stage_id: int, dag_edges: list[DAGEdge]):
+def _get_backward_targets(stage_id: int, dag_edges: list[DAGEdge]):
     return [edge for edge in dag_edges if edge.to_stage == stage_id]
 
 
-def validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdge], num_mbs: int) -> None:
+def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdge], num_mbs: int) -> None:
     """
     Validate that the schedule respects well-formedness rules and DAG dependencies.
     
@@ -142,7 +142,7 @@ def validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdge
                         f"before backward stage {from_stage} (time {bwd_times[from_stage]})"
                     )
 
-def piper_exec(model, schedule, inputs, truth, loss_fn, num_mbs, num_stages):
+def piper_exec(model, schedule, inputs, truth, loss_fn):
     """
     Execute one step of the pipeline schedule on the distributed model.
 
@@ -159,12 +159,13 @@ def piper_exec(model, schedule, inputs, truth, loss_fn, num_mbs, num_stages):
     """
     num_steps, num_devices = len(schedule[0]), len(schedule)
     actors = piper_metadata.actors
+    num_mbs = len(set([task.mb_idx for row in schedule for task in row if task is not None]))
 
     dag_edges = piper_metadata.dag
     dag_edges = list(map(lambda e: (DAGEdge(e[0], e[1])), list(piper_metadata.dag)))
     
     # Validate the schedule before execution
-    validate_schedule(schedule, dag_edges, num_mbs)
+    _validate_schedule(schedule, dag_edges, num_mbs)
 
     # maps mb_idx to the ref resulting from a forward call on the microbatch
     fwd_refs = dict()
@@ -172,32 +173,20 @@ def piper_exec(model, schedule, inputs, truth, loss_fn, num_mbs, num_stages):
     # maps mb_idx to a dict that maps stage_id to the refs output from the stage's backward on that microbatch
     bwd_ref_dicts = dict()
 
-    # create events for each microbatch
-    events = dict([(mb_idx, [threading.Event() for _ in range(num_stages)]) for mb_idx in range(num_mbs)])
-    actor_mutexes = dict([(actor_id, threading.Lock()) for actor_id in actors.keys()])
-    threads = dict()
-    
-    def run_model(inputs, mb_idx, events, actor_mutexes):
-        logger.debug(f"Controller launching thread {mb_idx}")
-        from .piper_utils import events_tls
-        events_tls.events = events
-        events_tls.mb_idx = mb_idx
-        events_tls.actor_mutexes = actor_mutexes
-        fwd_refs[mb_idx] = model(*inputs)
-
     # iterate over evrery task in the schedule
     ret = []
     for i in range(num_steps):
         for j in range(num_devices-1, -1, -1):
             task = schedule[j][i]
             if task:
-                device_id,stage_id, mb_idx, is_fwd, upd = task
+                device_id, stage_id, mb_idx, is_fwd, upd = task
                 actor_id = j
-                num_bwd_targets = len(get_backward_targets(stage_id, dag_edges))
+                actor = actors[actor_id]
+                num_bwd_targets = len(_get_backward_targets(stage_id, dag_edges))
                 if num_bwd_targets == 0:
                     num_bwd_targets = 1
                 if upd:
-                    logger.debug(f"Controller updating stage {stage_id} mb {mb_idx}")
+                    logger.debug(f"Launching update stage {stage_id} mb {mb_idx} on actor {actor}")
                     done_refs = set()
                     for _, bwd_ref_dict in bwd_ref_dicts.items():
                         bwd_refs = bwd_ref_dict[stage_id]
@@ -207,41 +196,27 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
                         else:
                             done_refs.add(bwd_refs)
                     done_refs = list(done_refs)
-                    with actor_mutexes[actor_id]:
-                        ret.append(actors[actor_id].update.remote(*done_refs))
+                    ret.append(actor._update.remote(*done_refs))
                 elif is_fwd:
+                    logger.debug(f"Launching forward stage {stage_id} mb {mb_idx} on actor {actor}")
                     if stage_id == 0:
-                        thread = threading.Thread(target=run_model, args=(inputs, mb_idx, events[mb_idx], actor_mutexes))
-                        threads[mb_idx] = thread
-                        thread.start()
-                    events[mb_idx][stage_id].set()
-                    logger.debug(f"Controller set event for thread {mb_idx} stage {stage_id}: waiting for thread to grab lock")
-                    # Wait for the thread to grab a lock on the actor mutex,
-                    # signalled by unsetting the event
-                    while events[mb_idx][stage_id].is_set():
-                        time.sleep(0.001)
-                    logger.debug(f"Controller: thread {mb_idx} grabbed lock")
+                        fwd_refs[mb_idx] = actor._forward.remote(stage_id, mb_idx, inputs)
+                    else:
+                        fwd_refs[mb_idx] = actor._forward.remote(stage_id, mb_idx, fwd_refs[mb_idx])
                 else:
-                    # log order of task dispatch by printing
-                    # also see output_graph.py:1785 where we log forward dispatch
                     if mb_idx not in bwd_ref_dicts:
                         # if this is the first backward task for a microbatch, dispatch the
                         # backward task and cache the resulting ref(s)
-                        logger.debug(f"Controller waiting for thread {mb_idx} to join")
-                        threads[mb_idx].join()
-                        logger.debug(f"Controller thread {mb_idx} joined")
                         fwd_ref = fwd_refs[mb_idx]
                         bwd_ref_dicts[mb_idx] = dict()
-                        logger.debug(f"Controller waiting for actor {actor_id} mutex")
-                        with actor_mutexes[actor_id]:
-                            logger.debug(f"Controller got actor {actor_id} mutex, launching backward stage {stage_id} mb {mb_idx}")
-                            bwd_ref_dicts[mb_idx][stage_id] = (
-                                actors[actor_id]
-                                .backward.options(num_returns=num_bwd_targets*2)
-                                .remote(
-                                    stage_id, mb_idx, fwd_ref.get_ref(), truth=truth, loss_fn=loss_fn
-                                )
+                        logger.debug(f"Launching backward stage {stage_id} mb {mb_idx} on actor {actor}")
+                        bwd_ref_dicts[mb_idx][stage_id] = (
+                            actor
+                            ._backward.options(num_returns=num_bwd_targets*2)
+                            .remote(
+                                stage_id, mb_idx, fwd_ref, truth=truth, loss_fn=loss_fn
                             )
+                        )
                     else:
                         # if this is not the first backward task for a microbatch, look up
                         # the input ref which represents a gradient from the backward call
@@ -260,7 +235,7 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
                         to_stage = to_stage[0]
 
                         # get the refs resulting from the subsequent stage's backward
-                        targets = get_backward_targets(to_stage, dag_edges)
+                        targets = _get_backward_targets(to_stage, dag_edges)
 
                         # get the idx of the current stage in the subsequent stage's output list
                         # LIMITATION: this logic assumes that the user's dag_edges list is ordered according
@@ -278,12 +253,10 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
                         bwd_refs = bwd_ref_dicts[mb_idx][to_stage]
                         bwd_ref = bwd_refs[idx]
                         # dispatch the current stage's backward and cache the resulting ref(s)
-                        logger.debug(f"Controller waiting for actor {actor_id} mutex")
-                        with actor_mutexes[actor_id]:
-                            logger.debug(f"Controller got actor {actor_id} mutex, launching backward stage {stage_id} mb {mb_idx}")
-                            bwd_ref_dicts[mb_idx][stage_id] = (
-                                actors[actor_id]
-                                .backward.options(num_returns=num_bwd_targets*2)
-                                .remote(stage_id, mb_idx, bwd_ref)
-                            )
+                        logger.debug(f"Launching backward stage {stage_id} mb {mb_idx} on actor {actor}")
+                        bwd_ref_dicts[mb_idx][stage_id] = (
+                            actor
+                            ._backward.options(num_returns=num_bwd_targets*2)
+                            .remote(stage_id, mb_idx, bwd_ref)
+                        )
     return ray.get(ret)
\ No newline at end of file
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index 29ab3ae..7e42c27 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -1,164 +1,454 @@
 import ray
 import torch
 import torch.fx as fx
+import torch.distributed as dist
 from collections import defaultdict
+from typing import Optional
 import operator
+import os
+from torch.autograd import Function
 
-from .piper_actor import get_actor, dispatch_expert_ray
-from .piper_utils import create_logger, LOG_LEVEL
+from .piper_utils import create_logger, LOG_LEVEL, piper_metadata
 
 logger = create_logger("piper_graph_transform", LOG_LEVEL)
 
+
 class CommOp:
-    def __init__(self, tensor_id: int, dep: str, pass_type: str, op: str, group: str):
+    def __init__(self, tensor_id: int, name: str, dep: str, pass_type: str, op: str, group: str):
         self.tensor = None # this gets set on the actor
         self.tensor_id = tensor_id
+        self.name = name
         self.dep = dep # "pre" or "post"
         self.pass_type = pass_type # "forward" or "backward"
         self.op = op # "allreduce", "allgather", "scatter", "alltoall"
         self.group = group # "dp" or "pp"
 
-def get_dp_comm_ops(example_inputs):
-    dp_comm_ops = [CommOp(id(t), "post", "backward", "allreduce", "dp") for t in example_inputs if isinstance(t, torch.nn.Parameter)]
-    ids = [id(t) for t in example_inputs]
-    return [op for op in dp_comm_ops if op is not None], ids
+def _get_dp_comm_ops(example_inputs, placeholders):
+    dp_comm_ops = []
+    ids = []
+    for p, t in zip(placeholders, example_inputs):
+        ids.append(id(t))
+        if isinstance(t, torch.nn.Parameter) and t.requires_grad:
+            dp_comm_ops.append(CommOp(id(t), p.name, "post", "backward", "allreduce", "dp"))
+    logger.debug(f"Got {len(dp_comm_ops)} dp comm ops for {len(example_inputs)} example inputs")
+    return dp_comm_ops, ids
+
+
+class AllToAllSingleFunction(torch.autograd.Function):
+    """
+    Custom autograd function for all_to_all_single that ensures gradients flow correctly.
+    
+    This wraps torch.distributed.all_to_all_single to ensure that gradients
+    from the input tensor properly flow to the output buffer.
+    The backward pass performs another all_to_all_single to reverse the communication.
+    """
+    @staticmethod
+    def forward(ctx, output, input_tensor):
+        """
+        Forward pass: performs all_to_all_single communication.
+        
+        Args:
+            ctx: Context for storing information for backward pass
+            output: Output buffer (will be modified in-place)
+            input_tensor: Input tensor to communicate
+            group: Process group (optional)
+        """
+        # Store group for backward pass
+        from .piper_utils import piper_metadata
+        actor_self = piper_metadata.actor_self
+        ctx.group = actor_self.dp_group
+        
+        # Ensure input_tensor is contiguous (all_to_all_single requires contiguous tensors)
+        # TODO: performance cost?
+        input_tensor = input_tensor.contiguous()
+        
+        # Perform the communication (modifies output in-place)
+        dist.all_to_all_single(output, input_tensor, group=ctx.group)
+        
+        return output
+    
+    @staticmethod
+    def backward(ctx, grad_output):
+        """
+        Backward pass: performs reverse all_to_all_single to propagate gradients.
+        
+        The backward of all_to_all_single is another all_to_all_single operation
+        that reverses the communication pattern.
+        """
+        if grad_output is None:
+            return None, None, None
+        
+        # Ensure grad_output is contiguous (all_to_all_single requires contiguous tensors)
+        # TODO: performance cost?
+        grad_output = grad_output.contiguous()
+        
+        # Create a buffer for the gradient input
+        grad_input = torch.empty_like(grad_output)
+        
+        # Reverse the communication: all_to_all_single in backward
+        # This propagates gradients from output back to input
+        if ctx.group is None:
+            dist.all_to_all_single(grad_input, grad_output)
+        else:
+            dist.all_to_all_single(grad_input, grad_output, group=ctx.group)
+        
+        # Return gradients: grad_output flows to grad_input, None for group
+        return grad_input, grad_input, None
+
+def _dispatch_a2a_single(output: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+    """
+    Dispatch a Ray remote call to run an all_to_all_single operation
+    using a custom autograd Function. This function is called
+    from the FX graph, so it needs to be allowed in the graph.
+    """
+    return AllToAllSingleFunction.apply(output, input_tensor)
+
+# Allow the dispatch function in the graph
+torch.compiler.allow_in_graph(_dispatch_a2a_single)
 
-def split_gm_by_experts(gm, stage_id, pp_degree):
+def _split_gm_by_stages(gm) -> tuple[fx.GraphModule, list[tuple[int, fx.GraphModule, list[int], list]]]:
     """
-    Transform a graph module with expert annotations by extracting
-    expert computations into submodules and distributing the expert
+    Transform a graph module with stage annotations by extracting
+    stage computations into submodules and distributing the stage
     submodules to Ray actors.
+    
+    Returns:
+        A tuple of (new_graph_module, list_of_stage_info) where:
+        - new_graph_module: The transformed top-level graph module
+        - list_of_stage_info: List of (stage_id, submodule, input_idxs, all_inputs) tuples for each extracted stage
     """
     # Collect all nodes with custom metadata
     nodes_with_metadata = []
-    expert_metadata = {}
     for node in gm.graph.nodes:
         if 'custom' in node.meta:
             custom_meta = node.meta['custom']
-            # Extract expert and batch_idx from metadata
+            # Extract stage from metadata
             if isinstance(custom_meta, dict):
-                global_expert_id = custom_meta.get('global_expert_id')
-                local_expert_id = custom_meta.get('local_expert_id')
-                batch_idx = custom_meta.get('batch_idx')
-                if global_expert_id is not None:
-                    expert_metadata[global_expert_id] = (local_expert_id, batch_idx)
-                    nodes_with_metadata.append((node, global_expert_id, local_expert_id, batch_idx))
+                stage_annotation_id = custom_meta.get('stage')
+                if stage_annotation_id is not None:
+                    nodes_with_metadata.append((node, stage_annotation_id))
     
     if not nodes_with_metadata:
-        logger.info("No expert nodes found in graph")
-        return gm
+        logger.info("No stage nodes found in graph")
+        return gm, []
     else:
-        logger.info(f"Found expert nodes in graph")
+        logger.info(f"Found stage nodes in graph")
     
-    # Group nodes by expert ID for creating expert modules
-    expert_code = defaultdict(list)  # expert_id -> list of all nodes for this expert
+    # Group nodes by stage ID for creating stage modules
+    stage_code = defaultdict(list)  # stage_id -> list of all nodes for this stage
     
-    for node, global_expert_id, _, _ in nodes_with_metadata:
-        expert_code[global_expert_id].append(node)
+    for node, stage_annotation_id in nodes_with_metadata:
+        stage_code[stage_annotation_id].append(node)
     
-    expert_modules = {}
+    stage_modules = {}
     
-    for expert_id, expert_nodes in expert_code.items():
-        expert_graph = fx.Graph()
-        expert_node_mapping = {}
+    # Process stages in sorted order to track outputs from previous stages
+    for stage_annotation_id, stage_nodes in sorted(stage_code.items(), key=lambda x: x[0]):
+        stage_graph = fx.Graph()
+        stage_node_mapping = {}
+        
+        stage_node_set = set(stage_nodes)
         
-        expert_node_set = set(expert_nodes)
+        # Collect all nodes from other stages to identify stage boundaries
+        other_stage_nodes = set()
+        for other_stage_id, other_stage_nodes_list in stage_code.items():
+            if other_stage_id != stage_annotation_id:
+                other_stage_nodes.update(other_stage_nodes_list)
         
-        # Find all inputs needed by this expert (nodes that are not in the expert set)
-        expert_inputs = set()
-        for node in expert_nodes:
+        # Check if any nodes are outputs from previous stages
+        prev_stage_outputs = set()
+        for prev_stage_id in range(stage_annotation_id):
+            if prev_stage_id in stage_modules:
+                _, _, _, prev_outputs, _, _, _, _, _ = stage_modules[prev_stage_id]
+                prev_stage_outputs.update(prev_outputs)
+        
+        # Find all inputs needed by this stage (nodes that are not in the stage set)
+        # Check both args and kwargs to find all dependencies
+        stage_inputs = set()
+        get_attr_nodes = set()  # Track get_attr nodes separately
+        
+        def extract_nodes_from_arg(arg):
+            """Recursively extract all fx.Node objects from an argument"""
+            nodes = []
+            if isinstance(arg, fx.Node):
+                nodes.append(arg)
+            elif isinstance(arg, (list, tuple)):
+                for item in arg:
+                    nodes.extend(extract_nodes_from_arg(item))
+            elif isinstance(arg, dict):
+                for value in arg.values():
+                    nodes.extend(extract_nodes_from_arg(value))
+            return nodes
+        
+        for node in stage_nodes:
+            # Check args - use both map_arg and explicit extraction for robustness
             for arg in fx.graph.map_arg(node.args, lambda n: n):
-                if isinstance(arg, fx.Node) and arg not in expert_node_set:
-                    expert_inputs.add(arg)
+                if isinstance(arg, fx.Node) and arg not in stage_node_set:
+                    if arg.op == "get_attr":
+                        get_attr_nodes.add(arg)
+                    elif arg.op == "placeholder":
+                        # Placeholders are always inputs
+                        stage_inputs.add(arg)
+                    elif arg in other_stage_nodes or arg in prev_stage_outputs:
+                        # Nodes from other stages or outputs from previous stages are inputs
+                        stage_inputs.add(arg)
+                    else:
+                        # Could be computed in this stage or an input - will determine later
+                        stage_inputs.add(arg)
+            
+            # Check kwargs - explicitly extract nodes from dict values
+            # fx.graph.map_arg should work, but let's also manually check kwargs dict
+            if node.kwargs:
+                # Use map_arg first (should handle most cases)
+                for arg in fx.graph.map_arg(node.kwargs, lambda n: n):
+                    if isinstance(arg, fx.Node) and arg not in stage_node_set:
+                        if arg.op == "get_attr":
+                            get_attr_nodes.add(arg)
+                        elif arg.op == "placeholder":
+                            # Placeholders are always inputs
+                            stage_inputs.add(arg)
+                        elif arg in other_stage_nodes or arg in prev_stage_outputs:
+                            # Nodes from other stages or outputs from previous stages are inputs
+                            stage_inputs.add(arg)
+                        else:
+                            # Could be computed in this stage or an input - will determine later
+                            stage_inputs.add(arg)
+                
+                # Also explicitly extract nodes from kwargs dict values as a fallback
+                for kwarg_value in extract_nodes_from_arg(node.kwargs):
+                    if kwarg_value not in stage_node_set:
+                        if kwarg_value.op == "get_attr":
+                            get_attr_nodes.add(kwarg_value)
+                        elif kwarg_value.op == "placeholder":
+                            stage_inputs.add(kwarg_value)
+                        elif kwarg_value in other_stage_nodes or kwarg_value in prev_stage_outputs:
+                            stage_inputs.add(kwarg_value)
+                        else:
+                            stage_inputs.add(kwarg_value)
+        
+        # Copy get_attr nodes (parameters) to the stage graph first
+        # These should be available directly in the stage, not passed as inputs
+        for node in gm.graph.nodes:
+            if node in get_attr_nodes:
+                new_get_attr = stage_graph.node_copy(node, lambda n: n)
+                stage_node_mapping[node] = new_get_attr
         
-        # Create placeholders for inputs
+        # Create placeholders for inputs in original graph order to ensure correct argument ordering
+        # Preserve all metadata from original nodes
         input_placeholders = {}
-        for input_node in expert_inputs:
-            placeholder = expert_graph.placeholder(input_node.name)
-            expert_node_mapping[input_node] = placeholder
-            input_placeholders[input_node] = placeholder
-
-        # Find outputs of this expert (nodes used outside the expert set)
-        expert_outputs = []
-        for node in expert_nodes:
+        input_order = []  # Track order of inputs as they appear in original graph
+        for node in gm.graph.nodes:
+            if node in stage_inputs and node not in input_placeholders:
+                # If the input is already a placeholder, use node_copy to preserve all metadata
+                if node.op == "placeholder":
+                    placeholder = stage_graph.node_copy(node, lambda n: n)
+                else:
+                    # For computed nodes, create a placeholder but preserve metadata
+                    placeholder = stage_graph.placeholder(node.name)
+                    # Copy all metadata from the original node
+                    placeholder.meta.update(node.meta)
+                stage_node_mapping[node] = placeholder
+                input_placeholders[node] = placeholder
+                input_order.append(node)
+        
+        # Find outputs of this stage (nodes used outside the stage set)
+        # First, collect all nodes from other stages to identify stage boundaries
+        other_stage_nodes = set()
+        for other_stage_id, other_stage_nodes_list in stage_code.items():
+            if other_stage_id != stage_annotation_id:
+                other_stage_nodes.update(other_stage_nodes_list)
+        
+        # Check if any nodes are outputs from previous stages
+        prev_stage_outputs = set()
+        for prev_stage_id in range(stage_annotation_id):
+            if prev_stage_id in stage_modules:
+                _, _, _, prev_outputs, _, _, _, _, _ = stage_modules[prev_stage_id]
+                prev_stage_outputs.update(prev_outputs)
+        
+        # Find all nodes computed in this stage (dependencies of stage_nodes)
+        # These are nodes that are arguments to stage_nodes but not inputs from other stages
+        # We need to recursively collect all dependencies
+        stage_computed_nodes = set(stage_nodes)
+        visited = set()
+        
+        def collect_dependencies(node):
+            """Recursively collect all node dependencies that are computed in this stage"""
+            if node in visited:
+                return
+            visited.add(node)
+            
+            # Skip if already in computed nodes
+            if node in stage_computed_nodes:
+                return
+            
+            # Skip placeholders, get_attr, nodes from other stages, and outputs from previous stages
+            if (node.op == "placeholder" or node.op == "get_attr" or 
+                node in other_stage_nodes or node in prev_stage_outputs):
+                return
+            
+            # This node is computed in this stage (it's a dependency of a stage node)
+            if node in stage_inputs:
+                # Remove from inputs if it's computed in this stage
+                stage_inputs.discard(node)
+            stage_computed_nodes.add(node)
+            
+            # Recursively collect dependencies of this node from args
+            for arg in fx.graph.map_arg(node.args, lambda n: n):
+                if isinstance(arg, fx.Node):
+                    collect_dependencies(arg)
+            
+            # Recursively collect dependencies from kwargs - use both map_arg and explicit extraction
+            if node.kwargs:
+                # Use map_arg first
+                for arg in fx.graph.map_arg(node.kwargs, lambda n: n):
+                    if isinstance(arg, fx.Node):
+                        collect_dependencies(arg)
+                
+                # Also explicitly extract nodes from kwargs dict values as a fallback
+                for kwarg_node in extract_nodes_from_arg(node.kwargs):
+                    collect_dependencies(kwarg_node)
+        
+        # Collect all dependencies of stage nodes
+        for stage_node in stage_nodes:
+            # Collect from args
+            for arg in fx.graph.map_arg(stage_node.args, lambda n: n):
+                if isinstance(arg, fx.Node):
+                    collect_dependencies(arg)
+            
+            # Collect from kwargs - use both map_arg and explicit extraction
+            if stage_node.kwargs:
+                for arg in fx.graph.map_arg(stage_node.kwargs, lambda n: n):
+                    if isinstance(arg, fx.Node):
+                        collect_dependencies(arg)
+                
+                # Also explicitly extract nodes from kwargs dict values
+                for kwarg_node in extract_nodes_from_arg(stage_node.kwargs):
+                    collect_dependencies(kwarg_node)
+        
+        # Find outputs: nodes computed in this stage that are used by other stages
+        # Check if users have stage annotations from other stages, or if they're in already-processed stages
+        stage_outputs = []
+        for node in stage_computed_nodes:
             for user in node.users:
-                if user not in expert_node_set:
-                    if node not in expert_outputs:
-                        expert_outputs.append(node)
+                # Check if user has a stage annotation from a different stage
+                user_in_other_stage = False
+                if 'custom' in user.meta:
+                    custom_meta = user.meta['custom']
+                    if isinstance(custom_meta, dict):
+                        user_stage = custom_meta.get('stage')
+                        if user_stage is not None and user_stage != stage_annotation_id:
+                            user_in_other_stage = True
+                
+                # Also check if user is in an already-processed stage's computed nodes
+                if not user_in_other_stage:
+                    for other_stage_id in range(stage_annotation_id):
+                        if other_stage_id in stage_modules:
+                            _, _, _, _, _, other_ordered_nodes, _, _, _ = stage_modules[other_stage_id]
+                            if user in other_ordered_nodes:
+                                user_in_other_stage = True
+                                break
+                
+                # Check if user is in another stage (annotated nodes)
+                if not user_in_other_stage and user in other_stage_nodes:
+                    user_in_other_stage = True
+                
+                if user_in_other_stage:
+                    if node not in stage_outputs:
+                        stage_outputs.append(node)
+                # Also check if user is the output node (which uses stage outputs)
+                elif user.op == "output":
+                    if node not in stage_outputs:
+                        stage_outputs.append(node)
         
         # If no external users, use the last node(s) as output
-        if not expert_outputs:
-            expert_outputs = [expert_nodes[-1]] if expert_nodes else []
-
-        # Topological sort of expert nodes
-        def get_dependencies(node):
-            deps = set()
-            for arg in fx.graph.map_arg(node.args, lambda n: n):
-                if isinstance(arg, fx.Node) and arg in expert_node_set:
-                    deps.add(arg)
-            return deps
-        remaining = set(expert_nodes)
-        ordered_expert_nodes = []
-        while remaining:
-            for node in list(remaining):
-                deps = get_dependencies(node)
-                if deps.issubset(set(ordered_expert_nodes)):
-                    ordered_expert_nodes.append(node)
-                    remaining.remove(node)
-                    break
-        
-        # Copy expert nodes to the expert graph in topological order
-        for node in ordered_expert_nodes:
-            new_expert_node = expert_graph.node_copy(
+        if not stage_outputs:
+            # Use the last computed node in topological order
+            ordered_computed = [n for n in gm.graph.nodes if n in stage_computed_nodes]
+            stage_outputs = [ordered_computed[-1]] if ordered_computed else []
+        
+        # Preserve original graph order by iterating through nodes in original order
+        # This ensures correctness of computation order
+        # Include ALL computed nodes, not just annotated ones
+        ordered_stage_nodes = []
+        for node in gm.graph.nodes:
+            if node in stage_computed_nodes:
+                ordered_stage_nodes.append(node)
+        
+        # Copy stage nodes to the stage graph in original graph order
+        for node in ordered_stage_nodes:
+            new_stage_node = stage_graph.node_copy(
                 node,
-                lambda n: expert_node_mapping.get(n, input_placeholders.get(n))
+                lambda n: stage_node_mapping.get(n, input_placeholders.get(n))
             )
-            expert_node_mapping[node] = new_expert_node
+            stage_node_mapping[node] = new_stage_node
 
         # Create output node
-        if expert_outputs:
-            output_values = [expert_node_mapping[node] for node in expert_outputs]
+        if stage_outputs:
+            print(f"Stage {stage_annotation_id} outputs: {[node.name for node in stage_outputs]}")
+            output_values = [stage_node_mapping[node] for node in stage_outputs]
             if len(output_values) == 1:
-                expert_graph.output(output_values[0])
+                stage_graph.output(output_values[0])
             else:
-                expert_graph.output(tuple(output_values))
+                stage_graph.output(tuple(output_values))
         else:
             # No outputs, create a dummy output
-            expert_graph.output(expert_graph.placeholder('dummy'))
+            stage_graph.output(stage_graph.placeholder('dummy'))
         
-        expert_gm = fx.GraphModule(torch.nn.Module(), expert_graph)
+        stage_gm = fx.GraphModule(torch.nn.Module(), stage_graph)
         
-        # Store the expert module and related info
-        for node in ordered_expert_nodes:
+        # Store the stage module and related info
+        for node in ordered_stage_nodes:
             if node.op == "placeholder":
                 input_placeholders[node] = node
 
         # Load the module on the corresponding actor
-        input_idxs, param_idxs = [], []
+        # Track which inputs come from previous stage outputs
+        input_idxs = []
         params = []
-        placeholders = expert_gm.graph.find_nodes(op="placeholder")
+        placeholders = stage_gm.graph.find_nodes(op="placeholder")
+        
+        # Create reverse mapping from placeholder to original node
+        placeholder_to_original = {placeholder: orig_node for orig_node, placeholder in input_placeholders.items()}
+        
+        # Get outputs from previous stage (if it exists)
+        prev_stage_outputs = set()
+        prev_stage_id = stage_annotation_id - 1
+        if prev_stage_id in stage_modules:
+            _, _, _, prev_stage_outputs_list, _, _, _, _, _ = stage_modules[prev_stage_id]
+            prev_stage_outputs = set(prev_stage_outputs_list)
+        
         for i, placeholder in enumerate(placeholders):
-            if "grapharg" in placeholder.meta:
-                if 'self' in str(placeholder.meta["grapharg"]):
-                    param_idxs.append(i)
+            # For the first stage, the input indices are everything that's not an attribute
+            if stage_annotation_id == 0:
+                if 'self' in placeholder.name:
                     params.append(placeholder.meta["grapharg"]._example())
                 else:
                     input_idxs.append(i)
                     params.append(None)
+            # For subsequent stages, check if this placeholder corresponds to an output from the previous stage
             else:
-                input_idxs.append(i)
-                params.append(None)
-
-        # logger.debug(f"Submodule {expert_id} input_idxs: {input_idxs} param_idxs: {param_idxs}, params: {[p.shape if p is not None else None for p in params]}")
-        local_expert_id, batch_idx = expert_metadata[expert_id]
-        expert_metadata[expert_id] = (local_expert_id, batch_idx, input_idxs)
-        actor_id = local_expert_id % pp_degree
-        actor = get_actor(actor_id)
-        ray.get(actor.load_expert.remote(stage_id, expert_id, local_expert_id, batch_idx, expert_gm, input_idxs, param_idxs, params))
-
-        expert_modules[expert_id] = (expert_gm, input_placeholders, expert_outputs, expert_nodes, ordered_expert_nodes)
+                orig_node = placeholder_to_original.get(placeholder)
+                is_from_prev_stage = orig_node is not None and orig_node in prev_stage_outputs
+                # Check if this is from previous stage output
+                if is_from_prev_stage:
+                    input_idxs.append(i)
+                    params.append(None)
+                else:
+                    params.append(placeholder.meta["grapharg"]._example())
+        logger.debug(f"Stage {stage_annotation_id} inputs: {input_idxs}, params: {[param.shape if param is not None else None for param in params]}")
+        stage_modules[stage_annotation_id] = (
+            stage_gm,
+            input_placeholders,
+            input_order,
+            stage_outputs,
+            stage_nodes,
+            ordered_stage_nodes,
+            stage_annotation_id,
+            input_idxs,
+            params,
+        )
 
-    # Create a new top-level graph and replace expert nodes with call_module
+    # Create a new top-level graph and replace stage nodes with call_module
     new_graph = fx.Graph()
     node_mapping = {}
     
@@ -168,10 +458,10 @@ def get_dependencies(node):
             new_node = new_graph.node_copy(node, lambda n: node_mapping.get(n, n))
             node_mapping[node] = new_node
     
-    # Track which expert calls have been replaced
-    expert_call_replaced = {}
+    # Track which stage calls have been replaced
+    stage_call_replaced = {}
     
-    # Copy all get_attr nodes (parameters) so they're available for expert calls
+    # Copy all get_attr nodes (parameters) so they're available for stage calls
     for node in gm.graph.nodes:
         if node.op == "get_attr":
             if node not in node_mapping:
@@ -196,31 +486,30 @@ def get_dependencies(node):
             # Already processed
             continue
         
-        # Check if this node belongs to an expert and find which expert call it's part of
-        in_expert = False
-        for expert_id, expert_nodes in expert_code.items():
-            if node in expert_nodes:
-                in_expert = True
+        # Check if this node belongs to a stage and find which stage call it's part of
+        in_stage = False
+        for stage_annotation_id, stage_nodes in stage_code.items():
+            if node in stage_nodes:
+                in_stage = True
                 
-                # Get the expert module info
-                expert_gm, input_placeholders, expert_outputs, expert_nodes, ordered_expert_nodes = expert_modules[expert_id]
+                # Get the stage module info
+                stage_gm, input_placeholders, input_order, stage_outputs, stage_nodes, ordered_stage_nodes, _, _, _ = stage_modules[stage_annotation_id]
                 
-                # Check if we've already created the call_module for this expert call
-                if expert_id not in expert_call_replaced:
-                    # Find first node in this expert's nodes (in original graph order)
+                # Check if we've already created the call_module for this stage call
+                if stage_annotation_id not in stage_call_replaced:
+                    # Find first node in this stage's nodes (in original graph order)
                     first_node = None
                     for n in gm.graph.nodes:
-                        if n in expert_nodes:
+                        if n in stage_nodes:
                             first_node = n
                             break
                     
                     if first_node is None:
-                        first_node = expert_nodes[0]
+                        first_node = stage_nodes[0]
                     
-                    # Map inputs in the same order as placeholders
-                    # Use the inputs that the first node of this expert needs
+                    # Map inputs in the same order as placeholders (original graph order)
                     mapped_args = []
-                    for input_node in input_placeholders.keys():
+                    for input_node in input_order:
                         if input_node in node_mapping:
                             mapped_args.append(node_mapping[input_node])
                         else:
@@ -229,28 +518,22 @@ def get_dependencies(node):
                             node_mapping[input_node] = new_input_node
                             mapped_args.append(new_input_node)
                     
-                    # Dispatch expert module to a Ray actor at the position of the first expert node for this expert
-                    module_name = f"expert_{expert_id}"
-                    local_expert_id, batch_idx, input_idxs = expert_metadata[expert_id]
-                    input_tensors_only = []
-                    for i in input_idxs:
-                        input_tensors_only.append(mapped_args[i])
-                    # expert_module_node = new_graph.get_attr(module_name)
-                    ray_call_args = [expert_id, local_expert_id, batch_idx, pp_degree] + input_tensors_only
-                    call_node = new_graph.call_function(dispatch_expert_ray, tuple(ray_call_args))
-                    expert_call_replaced[expert_id] = (call_node, expert_outputs)
+                    # Create call_module node for the stage
+                    module_name = f"stage_{stage_annotation_id}"
+                    call_node = new_graph.call_module(module_name, tuple(mapped_args), {})
+                    stage_call_replaced[stage_annotation_id] = (call_node, stage_outputs)
                 
-                # Map expert nodes to the call_module output
-                call_node, expert_outputs = expert_call_replaced[expert_id]
+                # Map stage nodes to the call_module output
+                call_node, stage_outputs = stage_call_replaced[stage_annotation_id]
                 
-                # Only create output mapping for output nodes (nodes used outside expert)
-                if node in expert_outputs:
-                    if len(expert_outputs) == 1:
+                # Only create output mapping for output nodes (nodes used outside stage)
+                if node in stage_outputs:
+                    if len(stage_outputs) == 1:
                         # Single output, use call_node directly
                         node_mapping[node] = call_node
                     else:
                         # Multiple outputs, need to getitem
-                        output_idx = expert_outputs.index(node)
+                        output_idx = stage_outputs.index(node)
                         getitem_node = new_graph.call_function(
                             operator.getitem,
                             (call_node, output_idx)
@@ -260,7 +543,7 @@ def get_dependencies(node):
                     node_mapping[node] = call_node
                 break
         
-        if not in_expert:
+        if not in_stage:
             # Regular node, copy it
             new_node = new_graph.node_copy(node, lambda n: node_mapping.get(n, n))
             node_mapping[node] = new_node
@@ -280,11 +563,13 @@ def get_dependencies(node):
         else:
             new_graph.output(output_args)
     
-    # Create root module and add expert modules as submodules
+    # Create root module and add stage modules as submodules
     root_module = torch.nn.Module()
-    for expert_id, (expert_gm, _, _, _, _) in expert_modules.items():
-        module_name = f"expert_{expert_id}"
-        root_module.add_module(module_name, expert_gm)
+    submodule_list = []
+    for stage_annotation_id, (stage_gm, placeholders, _, _, _, _, _, input_idxs, params) in stage_modules.items():
+        module_name = f"stage_{stage_annotation_id}"
+        root_module.add_module(module_name, stage_gm)
+        submodule_list.append((stage_annotation_id, stage_gm, input_idxs, params, placeholders))
     
     new_gm = fx.GraphModule(root_module, new_graph)
     
@@ -298,9 +583,284 @@ def get_dependencies(node):
             new_gm.register_buffer(name, buffer)
     
     for name, module in gm.named_children():
-        if name not in [f"expert_{expert_id}" for expert_id in expert_modules.keys()]:
+        if name not in [f"stage_{stage_annotation_id}" for stage_annotation_id in stage_modules.keys()]:
             new_gm.add_module(name, module)
     
     new_gm.recompile()
     
+    return new_gm, submodule_list
+
+
+
+def _insert_comm_ops(gm: fx.GraphModule) -> fx.GraphModule:
+    """
+    Transform a graph module by inserting communication operations on annotated nodes.
+    
+    This function finds nodes annotated with torch.fx.traceback.annotate containing
+    "collective": "all_to_all_single" and inserts the corresponding communication
+    operations into the graph.
+    
+    For each annotated node, it inserts:
+    1. (Optional) Reshape input if "reshape": ("input", shape) is specified
+    2. buf = torch.empty_like(annotated_tensor)
+    3. torch.distributed.all_to_all_single(buf, annotated_tensor)  # Uses default process group
+    4. (Optional) Reshape output if "reshape": ("output", shape) is specified
+    5. Replaces all uses of annotated_tensor with the result
+    
+    The communication uses the default distributed process group (no group parameter specified).
+    
+    The reshape annotation format is: ("input"|"output", shape_tuple)
+    where shape_tuple is a tuple of dimensions (can include -1 for inferred dimension).
+    
+    Args:
+        gm: The graph module to transform
+        
+    Returns:
+        A new graph module with communication operations inserted
+    """
+    # Find all nodes with collective communication annotations
+    # We need to identify contiguous blocks of annotations, not just group by metadata
+    # Each contiguous block should get its own communication operation
+    
+    # First, collect all annotated nodes with their metadata
+    annotated_nodes = []
+    node_list = list(gm.graph.nodes)
+    for idx, node in enumerate(node_list):
+        if 'custom' in node.meta:
+            custom_meta = node.meta['custom']
+            if isinstance(custom_meta, dict):
+                collective = custom_meta.get('collective')
+                if collective == 'all_to_all_single':
+                    reshape = custom_meta.get('reshape')
+                    annotation_key = tuple(sorted(custom_meta.items()))
+                    annotated_nodes.append((idx, node, annotation_key, reshape))
+    
+    if not annotated_nodes:
+        logger.info("No communication annotations found in graph")
+        return gm
+    
+    # Group annotated nodes into contiguous blocks
+    # A contiguous block is a sequence of nodes with the same annotation_key
+    # that appear sequentially in the graph. Each block gets its own communication operation.
+    # We split blocks when:
+    # 1. The annotation_key changes (different annotation pattern)
+    # 2. The same annotation_key appears again after being interrupted (new instance of same pattern)
+    #    This handles the case where the same annotation appears in different layers
+    annotation_blocks = []
+    current_block = None
+    MAX_GAP_FOR_CONTIGUOUS = 50  # Maximum number of non-annotated nodes allowed in a block
+    
+    for idx, node, annotation_key, reshape in annotated_nodes:
+        if current_block is None:
+            # Start a new block
+            current_block = {
+                'annotation_key': annotation_key,
+                'nodes': [(node, reshape)],
+                'last_idx': idx
+            }
+        elif current_block['annotation_key'] == annotation_key:
+            # Same annotation - check if it's still part of the same contiguous block
+            gap = idx - current_block['last_idx'] - 1
+            if gap <= MAX_GAP_FOR_CONTIGUOUS:
+                # Contiguous (or close enough), add to current block
+                current_block['nodes'].append((node, reshape))
+                current_block['last_idx'] = idx
+            else:
+                # Too large a gap - this is a new instance of the same annotation pattern
+                # (e.g., a different layer). Finish current block and start new one.
+                annotation_blocks.append(current_block)
+                logger.debug(f"Split block due to gap of {gap} nodes for annotation {annotation_key}")
+                current_block = {
+                    'annotation_key': annotation_key,
+                    'nodes': [(node, reshape)],
+                    'last_idx': idx
+                }
+        else:
+            # Different annotation, finish current block and start new one
+            annotation_blocks.append(current_block)
+            current_block = {
+                'annotation_key': annotation_key,
+                'nodes': [(node, reshape)],
+                'last_idx': idx
+            }
+    
+    # Don't forget the last block
+    if current_block is not None:
+        annotation_blocks.append(current_block)
+    
+    logger.info(f"Found {len(annotation_blocks)} contiguous annotation blocks")
+    
+    # For each contiguous block, find the output node (the one used outside the annotation)
+    # This is the node that should have communication applied to it
+    annotated_output_nodes = []
+    for block_idx, block in enumerate(annotation_blocks):
+        nodes_in_block = block['nodes']
+        nodes_set = {node for node, _ in nodes_in_block}
+        
+        if len(nodes_in_block) == 1:
+            # Single node, use it directly
+            annotated_output_nodes.append(nodes_in_block[0])
+        else:
+            # Multiple nodes in the same block
+            # Find the one that is used by nodes outside the annotation block
+            # This is the output of the annotated block
+            output_candidates = []
+            for node, reshape in nodes_in_block:
+                # Check if this node is used by any node not in the annotation block
+                for user in node.users:
+                    if user not in nodes_set:
+                        output_candidates.append((node, reshape))
+                        break
+            
+            if output_candidates:
+                # Use the first output candidate (should be the output of the annotated block)
+                annotated_output_nodes.append(output_candidates[0])
+                logger.debug(f"Block {block_idx}: Using output node {output_candidates[0][0].name}")
+            else:
+                # No external users found, use the last node in topological order within the block
+                # This happens if the annotated block's output isn't used yet
+                last_node = max(nodes_in_block, key=lambda x: node_list.index(x[0]))
+                annotated_output_nodes.append(last_node)
+                logger.debug(f"Block {block_idx}: No external users found, using last node: {last_node[0].name}")
+    
+    logger.info(f"Found {len(annotated_output_nodes)} annotation blocks requiring communication operations")
+    
+    # Create a new graph to build the transformed version
+    new_graph = fx.Graph()
+    node_mapping = {}
+    
+    # Copy placeholders first
+    for node in gm.graph.nodes:
+        if node.op == "placeholder":
+            new_node = new_graph.node_copy(node, lambda n: node_mapping.get(n, n))
+            node_mapping[node] = new_node
+    
+    # Copy all get_attr nodes (parameters) so they're available for use
+    for node in gm.graph.nodes:
+        if node.op == "get_attr":
+            if node not in node_mapping:
+                new_node = new_graph.node_copy(node, lambda n: node_mapping.get(n, n))
+                node_mapping[node] = new_node
+    
+    # Process nodes in topological order
+    for node in gm.graph.nodes:
+        if node.op == "placeholder":
+            # Already handled
+            continue
+        
+        if node.op == "output":
+            # Handle output separately at the end
+            continue
+        
+        if node.op == "get_attr":
+            # Already handled above
+            continue
+        
+        # Check if this node needs communication inserted
+        # This should be the output node of an annotated block
+        needs_comm = False
+        reshape_info = None
+        for annotated_node, reshape in annotated_output_nodes:
+            if node == annotated_node:
+                needs_comm = True
+                reshape_info = reshape
+                break
+        
+        if needs_comm:
+            # First, copy the node to get its output
+            new_node = new_graph.node_copy(
+                node,
+                lambda n: node_mapping.get(n, n)
+            )
+            
+            # Handle input reshape if specified
+            # reshape_info format: ("input"|"output", shape_tuple)
+            if reshape_info is not None:
+                reshape_type, reshape_shape = reshape_info
+                if reshape_type == "input":
+                    # Reshape before communication
+                    new_node = new_graph.call_function(
+                        torch.reshape,
+                        (new_node, reshape_shape)
+                    )
+                    logger.debug(f"Applied input reshape {reshape_shape} for node {node.name}")
+            
+            # Insert communication operations after this node
+            # Following the exact pattern from the comments in mixtral.py:
+            # buf = torch.empty_like(expert_inputs)
+            # torch.distributed.all_to_all_single(buf, expert_inputs, group=device_mesh.get_group())
+            # buf = buf.reshape(...)  # if output reshape
+            # expert_inputs = buf
+            
+            # 1. Create buffer: buf = torch.empty_like(new_node)
+            buf_node = new_graph.call_function(
+                torch.empty_like,
+                (new_node,)
+            )
+            
+            # 2. Call all_to_all_single using custom autograd function
+            # Use AllToAllSingleFunction to ensure gradients flow correctly
+            # This wraps all_to_all_single with proper backward pass
+            buf_node = new_graph.call_function(
+                _dispatch_a2a_single,
+                (buf_node, new_node)
+            )
+            # After all_to_all_single, buf_node contains the result
+            # and maintains gradient connection from new_node through the custom Function
+            
+            # Handle output reshape if specified
+            if reshape_info is not None:
+                reshape_type, reshape_shape = reshape_info
+                if reshape_type == "output":
+                    # Reshape after communication
+                    buf_node = new_graph.call_function(
+                        torch.reshape,
+                        (buf_node, reshape_shape)
+                    )
+                    logger.debug(f"Applied output reshape {reshape_shape} for node {node.name}")
+            
+            # 3. Replace all uses of the original node with the buffer
+            # The buffer now contains the result after communication (and optional reshape)
+            node_mapping[node] = buf_node
+            
+            logger.debug(f"Inserted all_to_all_single communication for node {node.name}")
+        else:
+            # Regular node, just copy it
+            new_node = new_graph.node_copy(
+                node,
+                lambda n: node_mapping.get(n, n)
+            )
+            node_mapping[node] = new_node
+    
+    # Handle output node
+    output_node = None
+    for node in gm.graph.nodes:
+        if node.op == "output":
+            output_node = node
+            break
+    
+    if output_node:
+        output_args = fx.graph.map_arg(output_node.args, lambda n: node_mapping.get(n, n))
+        if isinstance(output_args, tuple) and len(output_args) == 1:
+            new_graph.output(output_args[0])
+        else:
+            new_graph.output(output_args)
+    
+    # Create new graph module with a fresh root module
+    root_module = torch.nn.Module()
+    
+    # Copy parameters, buffers, and modules from original module
+    for name, param in gm.named_parameters(recurse=False):
+        root_module.register_parameter(name, param)
+    
+    for name, buffer in gm.named_buffers(recurse=False):
+        root_module.register_buffer(name, buffer)
+    
+    for name, module in gm.named_children():
+        root_module.add_module(name, module)
+    
+    new_gm = fx.GraphModule(root_module, new_graph)
+    
+    new_gm.recompile()
+    
     return new_gm
\ No newline at end of file
diff --git a/src/piper_utils.py b/src/piper_utils.py
index 06546eb..b489a56 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -39,6 +39,7 @@ def _print(t, indent=0):
                 printer(" " * (indent + 2) + f"{prefix}None")
     _print(tensor, 0)
 
+
 """
 Logger utility
 """
@@ -66,133 +67,25 @@ def create_logger(name: str, log_level: str):
     
     return logger
 
+
 """
 Piper thread local storage for tracking Piper actors, stages, and microbatches
 """
 
-class ThreadLocal(threading.local):
-    events = None
-    mb_idx = None
-    actor_mutexes = None
-
-events_tls = ThreadLocal()
-
 class PiperMetadata:
     actors = dict()
     dag = set()
-    world_size = None
-    currently_compiling = True
-    current_stage = None
-    current_actor = None
-    actor_self = None
-    first_graph_of_stage = None
-    parallelism_configs = {'dp': 1}
+    stage_to_device = dict()
     naive_gradient_sync = False
 
 piper_metadata = PiperMetadata()
 
-"""
-Remote tensors wrap Ray ObjectRefs
-"""
-
-_fake_tensor_mode = FakeTensorMode()
-_fake_tensor_converter = _fake_tensor_mode.fake_tensor_converter
-
-class RemoteTensorKey:
-    def __init__(self):
-        self.key = str(uuid.uuid4())
-
-class RemoteTensor(torch.Tensor):
-    _fake: torch.Tensor
-    _stage_id: Optional[int]
-    _obj_ref: ray._raylet.ObjectRef
-    _resolved: Any
-
-    def __new__(cls, 
-                fake: FakeTensor, 
-                obj_ref: ray._raylet.ObjectRef,
-                stage_id: Optional[int] = None):
-        instance = torch.Tensor._make_wrapper_subclass(
-            cls,
-            fake.size(),
-            strides=fake.stride(),
-            storage_offset=fake.storage_offset(),
-            device=fake.device,  # This is the device of of either input tensor or first tensor of a list
-            dtype=fake.dtype,
-            layout=fake.layout,
-            requires_grad=fake.requires_grad,
-        )
-        instance._obj_ref = obj_ref
-        instance._fake = fake
-        instance._stage_id = stage_id
-        instance._resolved = None
-        instance.key = RemoteTensorKey()
-        return instance
-
-    def get_stage_id(self):
-        return self._stage_id
-
-    def get(self):
-        # return fake tensor during compilation
-        if self._obj_ref is None:
-            return self._fake
-
-        if self._resolved is None:
-            obj = ray.get(self._obj_ref)
-            if isinstance(obj, list) or isinstance(obj, tuple):
-                assert len(obj) == 1
-                self._resolved = obj[0]
-            else:
-                self._resolved = obj
-        self._resolved = self._resolved.to('cpu')
-        return self._resolved
-    
-    def get_ref(self):
-        if self._obj_ref is None:
-            raise RuntimeError("Cannot get ObjectRef from a compile-time RemoteTensor")
-        return self._obj_ref
-
-    def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
-        def unwrap(x):
-            if isinstance(x, RemoteTensor):
-                return x.get()
-            elif isinstance(x, (list, tuple)):
-                return type(x)(unwrap(v) for v in x)
-            elif isinstance(x, dict):
-                return {k: unwrap(v) for k, v in x.items()}
-            else:
-                return x
-
-        args = torch.utils._pytree.tree_map(unwrap, args)
-        kwargs = torch.utils._pytree.tree_map(unwrap, kwargs or {})
-
-        out = func(*args, **kwargs)
-        return out
-    
-    def __tensor_flatten__(self):
-        return (["_fake"], {})
-
-    @classmethod
-    def __tensor_unflatten__(cls, inner_tensors, metadata, outer_size, outer_stride):
-        fake = inner_tensors["_fake"]
-        return cls(fake, None, None)
-    
-    def __repr__(self):
-        """Custom repr that avoids triggering masked_select from tensor formatting."""
-        if self._resolved is not None:
-            return self._resolved.__repr__()
-        return (
-            f"RemoteTensor(shape={tuple(self.shape)}, dtype={self.dtype}, "
-            f"device={self.device}, stage_id={self._stage_id})"
-        )
-        
-torch._dynamo.config.traceable_tensor_subclasses.add(RemoteTensor)
 
 """
 Serialize/deserialize an fx.GraphModule
 """
 
-def encode_arg(a):
+def _encode_arg(a):
     if isinstance(a, fx.Node):
         return {"__node__": a.name}
     if isinstance(a, torch.device):
@@ -201,20 +94,20 @@ def encode_arg(a):
         return {"__dtype__": str(a).replace("torch.", "")}
     if isinstance(a, slice):
         return {"__slice__": True,
-                "start": encode_arg(a.start),
-                "stop": encode_arg(a.stop),
-                "step": encode_arg(a.step)}
+                "start": _encode_arg(a.start),
+                "stop": _encode_arg(a.stop),
+                "step": _encode_arg(a.step)}
     if a is Ellipsis:
         return {"__ellipsis__": True}
     if isinstance(a, tuple):  # <-- preserve tuples
-        return {"__tuple__": [encode_arg(x) for x in a]}
+        return {"__tuple__": [_encode_arg(x) for x in a]}
     if isinstance(a, list):
-        return [encode_arg(x) for x in a]
+        return [_encode_arg(x) for x in a]
     if isinstance(a, dict):
-        return {k: encode_arg(v) for k, v in a.items()}
+        return {k: _encode_arg(v) for k, v in a.items()}
     return a
 
-def decode_arg(a, name_to_node):
+def _decode_arg(a, name_to_node):
     if isinstance(a, dict):
         if "__node__" in a:
             return name_to_node[a["__node__"]]
@@ -224,26 +117,26 @@ def decode_arg(a, name_to_node):
             return getattr(torch, a["__dtype__"])
         if "__slice__" in a:
             return slice(
-                decode_arg(a["start"], name_to_node),
-                decode_arg(a["stop"], name_to_node),
-                decode_arg(a["step"], name_to_node),
+                _decode_arg(a["start"], name_to_node),
+                _decode_arg(a["stop"], name_to_node),
+                _decode_arg(a["step"], name_to_node),
             )
         if "__ellipsis__" in a:
             return Ellipsis
         if "__tuple__" in a:  # <-- reconstruct tuples
-            return tuple(decode_arg(x, name_to_node) for x in a["__tuple__"])
+            return tuple(_decode_arg(x, name_to_node) for x in a["__tuple__"])
         # generic dict
-        return {k: decode_arg(v, name_to_node) for k, v in a.items()}
+        return {k: _decode_arg(v, name_to_node) for k, v in a.items()}
     if isinstance(a, list):
-        return [decode_arg(x, name_to_node) for x in a]
+        return [_decode_arg(x, name_to_node) for x in a]
     return a
 
 def _is_op_overload(obj):
     # Works across PyTorch versions without importing private types directly
     return obj.__class__.__module__.startswith("torch._ops") or obj.__class__.__name__.startswith("OpOverload")
 
-def serialize_target(t):
-    # print("SERIALIZING", t)
+def _serialize_target(t):
+    # print("SERIALIZING", t, type(t))
     # call_method uses a string method name, pass through
     if isinstance(t, str):
         return {"kind": "string", "value": t}
@@ -262,6 +155,49 @@ def serialize_target(t):
 
     # regular python function or built-in
     if inspect.isfunction(t) or inspect.isbuiltin(t):
+        # Special handling for torch.autograd.Function.apply methods
+        # These are static methods that may have the wrong module in inspect.getmodule
+        if t.__name__ == "apply":
+            qualname = getattr(t, "__qualname__", "")
+            mod = inspect.getmodule(t)
+            
+            # If qualname contains a class name (e.g., "AllToAllSingleFunction.apply")
+            if "." in qualname:
+                class_name = qualname.split(".")[0]
+                # Try to find the class by searching common modules
+                # First try the module where the function was found
+                if mod and hasattr(mod, class_name):
+                    func_class = getattr(mod, class_name)
+                    if isinstance(func_class, type) and issubclass(func_class, torch.autograd.Function):
+                        # Serialize as the Function class with .apply
+                        return {"kind": "py_obj", "module": func_class.__module__, "qualname": func_class.__qualname__ + ".apply"}
+                
+                # If not found, search sys.modules for the class
+                # This handles the case where inspect.getmodule returns torch.autograd.function
+                # instead of the actual module where the Function class is defined
+                import sys
+                for module_name, module_obj in sys.modules.items():
+                    if module_obj is not None and hasattr(module_obj, class_name):
+                        try:
+                            func_class = getattr(module_obj, class_name)
+                            if isinstance(func_class, type) and issubclass(func_class, torch.autograd.Function):
+                                # Serialize as the Function class with .apply
+                                return {"kind": "py_obj", "module": func_class.__module__, "qualname": func_class.__qualname__ + ".apply"}
+                        except (TypeError, AttributeError):
+                            continue
+                
+                # If still not found and we have a qualname, try to import the module directly
+                # based on common patterns (e.g., if class is AllToAllSingleFunction, try src.piper_graph_transform)
+                if class_name.startswith("AllToAll"):
+                    try:
+                        from src import piper_graph_transform
+                        if hasattr(piper_graph_transform, class_name):
+                            func_class = getattr(piper_graph_transform, class_name)
+                            if isinstance(func_class, type) and issubclass(func_class, torch.autograd.Function):
+                                return {"kind": "py_obj", "module": func_class.__module__, "qualname": func_class.__qualname__ + ".apply"}
+                    except (ImportError, AttributeError):
+                        pass
+        
         mod = inspect.getmodule(t)
         if mod is None:
             raise ValueError(f"Cannot serialize function without module: {t}")
@@ -290,8 +226,7 @@ def _resolve_qualname(mod, qualname):
         obj = getattr(obj, part)
     return obj
 
-def deserialize_target(payload):
-    # print("DESERIALIZING", payload)
+def _deserialize_target(payload):
     kind = payload["kind"]
 
     if kind == "string":
@@ -303,7 +238,13 @@ def deserialize_target(payload):
 
     if kind == "py_obj":
         mod = importlib.import_module(payload["module"])
-        return _resolve_qualname(mod, payload["qualname"])
+        try:
+            return _resolve_qualname(mod, payload["qualname"])
+        except AttributeError as e:
+            # Provide better error message for debugging
+            raise AttributeError(
+                f"Failed to resolve qualname '{payload['qualname']}' in module '{payload['module']}': {e}"
+            ) from e
 
     if kind == "torch_op":
         obj = torch.ops
@@ -313,15 +254,15 @@ def deserialize_target(payload):
 
     raise NotImplementedError(f"Unknown target kind: {kind}")
 
-def serialize_graphmodule(gm: fx.GraphModule) -> str:
+def _serialize_graphmodule(gm: fx.GraphModule) -> str:
     nodes = []
     for n in gm.graph.nodes:
         nodes.append({
             "name": n.name,
             "op": n.op,
-            "target": serialize_target(n.target) if n.op in ("call_function", "call_method", "call_module", "get_attr") else None,
-            "args": encode_arg(n.args),
-            "kwargs": encode_arg(n.kwargs),
+            "target": _serialize_target(n.target) if n.op in ("call_function", "call_method", "call_module", "get_attr") else None,
+            "args": _encode_arg(n.args),
+            "kwargs": _encode_arg(n.kwargs),
         })
 
     # Serialize all direct child modules that are GraphModules
@@ -329,7 +270,7 @@ def serialize_graphmodule(gm: fx.GraphModule) -> str:
     for name, module in gm.named_children():
         if isinstance(module, fx.GraphModule):
             # Recursively serialize GraphModule submodules
-            submodules[name] = serialize_graphmodule(module)
+            submodules[name] = _serialize_graphmodule(module)
 
     data = {
         "nodes": nodes,
@@ -352,7 +293,7 @@ def _unwrap_output_arg(decoded):
         return decoded[0]
     return decoded
 
-def deserialize_graphmodule(s: str) -> fx.GraphModule:
+def _deserialize_graphmodule(s: str) -> fx.GraphModule:
     data = json.loads(s)
     g = fx.Graph()
     name_to_node = {}
@@ -362,25 +303,25 @@ def deserialize_graphmodule(s: str) -> fx.GraphModule:
         if op == "placeholder":
             node = g.placeholder(n["name"])
         elif op == "output":
-            decoded = decode_arg(n["args"], name_to_node)
+            decoded = _decode_arg(n["args"], name_to_node)
             node = g.output(_unwrap_output_arg(decoded))
         elif op == "call_function":
-            target = deserialize_target(n["target"])
-            args = decode_arg(n["args"], name_to_node)
-            kwargs = decode_arg(n["kwargs"], name_to_node)
+            target = _deserialize_target(n["target"])
+            args = _decode_arg(n["args"], name_to_node)
+            kwargs = _decode_arg(n["kwargs"], name_to_node)
             node = g.call_function(target, tuple(args), kwargs)
         elif op == "call_method":
-            target = deserialize_target(n["target"])
-            args = decode_arg(n["args"], name_to_node)
-            kwargs = decode_arg(n["kwargs"], name_to_node)
+            target = _deserialize_target(n["target"])
+            args = _decode_arg(n["args"], name_to_node)
+            kwargs = _decode_arg(n["kwargs"], name_to_node)
             node = g.call_method(target, tuple(args), kwargs)
         elif op == "call_module":
-            target = deserialize_target(n["target"])
-            args = decode_arg(n["args"], name_to_node)
-            kwargs = decode_arg(n["kwargs"], name_to_node)
+            target = _deserialize_target(n["target"])
+            args = _decode_arg(n["args"], name_to_node)
+            kwargs = _decode_arg(n["kwargs"], name_to_node)
             node = g.call_module(target, tuple(args), kwargs)
         elif op == "get_attr":
-            target = deserialize_target(n["target"])
+            target = _deserialize_target(n["target"])
             node = g.get_attr(target)
         else:
             raise NotImplementedError(f"op {op} not handled")
@@ -393,7 +334,7 @@ def deserialize_graphmodule(s: str) -> fx.GraphModule:
     submodules = data.get("submodules", {})
     for module_name, serialized_submodule in submodules.items():
         # Recursively deserialize submodule
-        submodule_gm = deserialize_graphmodule(serialized_submodule)
+        submodule_gm = _deserialize_graphmodule(serialized_submodule)
         # Add as direct child module (handles both simple names and nested paths)
         # For nested paths like "layer.expert_0", we need to create intermediate modules
         parts = module_name.split(".")
diff --git a/test/models/llama.py b/test/models/llama.py
index 0e4e744..8ce4f13 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -14,8 +14,6 @@
 from torch import Tensor, nn
 from torch.nn.utils import parameters_to_vector
 
-from src.piper import distributed_stage
-
 # import fairscale.nn.model_parallel.initialize as fs_init
 # from fairscale.nn.model_parallel.layers import (
 #     ColumnParallelLinear,
@@ -436,12 +434,35 @@ def log_size(layer, indent=0):
 
     #     return output
 
+    """
+    forward method for 1f1b schedule
+    requires:
+    - 2 devices
+    - 2 stages
+    - n_layers is divisible by 2
+    """
+    def forward(self, tokens: torch.Tensor):
+
+        with torch.fx.traceback.annotate({"stage": 0}):
+            tokens += 0
+            h = self.tok_embeddings(tokens)
+            start_pos = 0
+            for layer in self.layers[:self.n_layers//2]:
+                h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+        with torch.fx.traceback.annotate({"stage": 1}):
+            for layer in self.layers[self.n_layers//2:]:
+                h = layer(h, start_pos, self.freqs_cis, self.mask)
+            h = self.norm(h)
+            output = self.output(h).float()
+
+        return output
+
     # """
-    # forward method for 1f1b schedule
+    # forward method for no pp
     # requires:
-    # - 2 devices
-    # - 2 stages
-    # - n_layers is divisible by 2
+    # - 1 device
+    # - 1 stages
     # """
     # def forward(self, tokens: torch.Tensor):
 
@@ -450,36 +471,10 @@ def log_size(layer, indent=0):
     #     h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
     #     start_pos = 0
         
-    #     for layer in self.layers[:self.n_layers//2]:
-    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     distributed_stage(1, actor_id=1)
-
-    #     for layer in self.layers[self.n_layers//2:]:
+    #     for layer in self.layers:
     #         h = layer(h, start_pos, self.freqs_cis, self.mask)
 
     #     h = self.norm(h) if self.norm else h
     #     output = self.output(h).float() if self.output else h
 
-    #     return output
-
-    """
-    forward method for no pp
-    requires:
-    - 1 device
-    - 1 stages
-    """
-    def forward(self, tokens: torch.Tensor):
-
-        distributed_stage(0, actor_id=0)
-
-        h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-        start_pos = 0
-        
-        for layer in self.layers:
-            h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-        h = self.norm(h) if self.norm else h
-        output = self.output(h).float() if self.output else h
-
-        return output
\ No newline at end of file
+    #     return output
\ No newline at end of file
diff --git a/test/models/mixtral.py b/test/models/mixtral.py
new file mode 100644
index 0000000..ccd965f
--- /dev/null
+++ b/test/models/mixtral.py
@@ -0,0 +1,326 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+from dataclasses import dataclass
+from typing import Optional
+import math
+import torch.cuda.nvtx as nvtx
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+from torch.nn import functional as F
+from torch.distributed import get_world_size
+
+def find_multiple(n: int, k: int) -> int:
+    if n % k == 0:
+        return n
+    return n + k - (n % k)
+
+
+@dataclass
+class ModelArgs:
+    block_size: int = 2048
+    vocab_size: int = 32000
+    n_layer: int = 32
+    n_head: int = 32
+    dim: int = 4096
+    intermediate_size: int = None
+    n_local_heads: int = -1
+    head_dim: int = 64
+    rope_base: float = 10000
+    norm_eps: float = 1e-5
+    num_experts: int = 8
+    num_activated_experts: int = 2
+
+    def __post_init__(self):
+        if self.n_local_heads == -1:
+            self.n_local_heads = self.n_head
+        if self.intermediate_size is None:
+            hidden_dim = 4 * self.dim
+            n_hidden = int(2 * hidden_dim / 3)
+            self.intermediate_size = find_multiple(n_hidden, 256)
+        self.head_dim = self.dim // self.n_head
+
+    @classmethod
+    def from_name(cls, name: str):
+        if name in transformer_configs:
+            return cls(**transformer_configs[name])
+        # fuzzy search
+        config = [config for config in transformer_configs if config in str(name).upper() or config in str(name)]
+        assert len(config) == 1, name
+        return cls(**transformer_configs[config[0]])
+
+
+transformer_configs = {
+    "tiny": dict(block_size=128, n_layer=2, n_head=2, n_local_heads=1, dim=256, intermediate_size=512, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "small": dict(block_size=512, n_layer=8, n_head=8, n_local_heads=2, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "medium": dict(block_size=512, n_layer=24, n_head=32, n_local_heads=8, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "Mixtral-8x7B-v0.1": dict(block_size=32768, n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+}
+
+class KVCache(nn.Module):
+    def __init__(self, max_batch_size, max_seq_length, n_heads, head_dim, dtype=torch.bfloat16):
+        super().__init__()
+        cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
+        self.register_buffer('k_cache', torch.zeros(cache_shape, dtype=dtype))
+        self.register_buffer('v_cache', torch.zeros(cache_shape, dtype=dtype))
+
+    def update(self, input_pos, k_val, v_val):
+        # input_pos: [S], k_val: [B, H, S, D]
+        assert input_pos.shape[0] == k_val.shape[2]
+
+        k_out = self.k_cache
+        v_out = self.v_cache
+        k_out[:, :, input_pos] = k_val
+        v_out[:, :, input_pos] = v_val
+
+        return k_out, v_out
+
+class Transformer(nn.Module):
+    def __init__(self, config: ModelArgs) -> None:
+        super().__init__()
+        self.config = config
+
+        # Initialize layers normally
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
+        self.layers = nn.ModuleList(TransformerBlock(config) for _ in range(config.n_layer))
+        self.norm = RMSNorm(config.dim, eps=config.norm_eps)
+        self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
+
+        self.freqs_cis: Optional[Tensor] = precompute_freqs_cis(
+            self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base
+        )
+        self.causal_mask: Optional[Tensor] = torch.tril(
+            torch.ones(self.config.block_size, self.config.block_size, dtype=torch.bool)
+        )
+        
+        self.mask_cache: Optional[Tensor] = None
+        self.max_batch_size = -1
+        self.max_seq_length = -1
+
+    def setup_caches(self, max_batch_size, max_seq_length):
+        assert False, "Should not be called"
+        if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
+            return
+        head_dim = self.config.dim // self.config.n_head
+        max_seq_length = find_multiple(max_seq_length, 8)
+        self.max_seq_length = max_seq_length
+        self.max_batch_size = max_batch_size
+        for b in self.layers:
+            b.attention.kv_cache = KVCache(max_batch_size, max_seq_length, self.config.n_local_heads, head_dim)
+
+        self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base)
+        self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
+
+    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        with torch.fx.traceback.annotate({"stage": 0}):
+            idx += 0
+            input_pos += 0
+            mask = self.causal_mask[None, None, input_pos]
+            freqs_cis = self.freqs_cis[input_pos]
+            x = self.tok_embeddings(idx)
+
+            for layer in self.layers[:len(self.layers)//2]:
+                x = layer(x, input_pos, freqs_cis, mask)
+        
+        with torch.fx.traceback.annotate({"stage": 1}):
+            for layer in self.layers[len(self.layers)//2:]:
+                x = layer(x, input_pos, freqs_cis, mask)
+            x = self.norm(x)
+            logits = self.output(x)
+        return logits
+
+    @classmethod
+    def from_name(cls, name: str):
+        return cls(ModelArgs.from_name(name))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, config: ModelArgs) -> None:
+        super().__init__()
+        self.attention = Attention(config)
+        self.block_sparse_moe = MOEFeedForward(config)
+        self.ffn_norm = RMSNorm(config.dim, config.norm_eps)
+        self.attention_norm = RMSNorm(config.dim, config.norm_eps)
+
+    def forward(self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor) -> Tensor:
+        h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos)
+        out = h + self.block_sparse_moe(self.ffn_norm(h))
+        return out
+
+
+class Attention(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        assert config.dim % config.n_head == 0
+
+        total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
+        # key, query, value projections for all heads, but in a batch
+        # Initialize layers normally
+        self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
+        self.wo = nn.Linear(config.dim, config.dim, bias=False)
+        
+        self.kv_cache = None
+
+        self.n_head = config.n_head
+        self.head_dim = config.head_dim
+        self.n_local_heads = config.n_local_heads
+        self.dim = config.dim
+        self._register_load_state_dict_pre_hook(self.load_hook)
+
+    def load_hook(self, state_dict, prefix, *args):
+        if prefix + "wq.weight" in state_dict:
+            wq = state_dict.pop(prefix + "wq.weight")
+            wk = state_dict.pop(prefix + "wk.weight")
+            wv = state_dict.pop(prefix + "wv.weight")
+            state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
+
+    def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        bsz, seqlen, _ = x.shape
+
+        kv_size = self.n_local_heads * self.head_dim
+        q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
+
+        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
+        k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+        v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+
+        q = apply_rotary_emb(q, freqs_cis)
+        k = apply_rotary_emb(k, freqs_cis)
+
+        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
+
+        if self.kv_cache is not None:
+            k, v = self.kv_cache.update(input_pos, k, v)
+
+        k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+        v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
+
+        y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
+
+        y = self.wo(y)
+        return y
+
+
+class ConditionalFeedForward(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+
+        self.w1 = nn.Parameter(torch.randn(config.num_experts, config.intermediate_size, config.dim))
+        self.w2 = nn.Parameter(torch.randn(config.num_experts, config.dim, config.intermediate_size))
+        self.w3 = nn.Parameter(torch.randn(config.num_experts, config.intermediate_size, config.dim))
+
+    def forward(self, x: Tensor, dispatch_mask: Tensor, combine_weights: Tensor, capacity: int) -> Tensor:
+        """
+        x: [T, D]
+        dispatch_mask: [T, E, C]
+        combine_weights: [T, E, C]
+        capacity: int
+        """
+        with torch.fx.traceback.annotate({
+            "collective": "all_to_all_single",
+            "group": "ep",
+            "reshape": ("output", (2, self.config.num_experts, -1, self.config.dim))
+        }):
+            expert_inputs = torch.einsum("tec,tm->ecm", dispatch_mask.type_as(x[0]), x) # [E, C, D]
+            # Annotation should insert this code into the graph:
+            # buf = torch.empty_like(expert_inputs)
+            # torch.distributed.all_to_all_single(buf, expert_inputs, group=device_mesh.get_group())
+            # buf = buf.reshape(get_world_size(), self.config.num_experts, -1, self.config.dim) # [..., E, C, D]
+            # expert_inputs = buf
+
+        x1 = F.silu(torch.einsum('...ecd, emd -> ...ecm', expert_inputs, self.w1))
+        x3 = torch.einsum('...ecd, emd -> ...ecm', expert_inputs, self.w3)
+
+        with torch.fx.traceback.annotate({
+            "collective": "all_to_all_single",
+            "group": "ep",
+            "reshape": ("input", (self.config.num_experts, -1, self.config.dim))
+        }):
+            expert_output =  torch.einsum('...ecm, edm -> ...ecd', (x1 * x3), self.w2) # [..., E, C, D]
+            # Annotation should insert this code into the graph:
+            # expert_output = expert_output.reshape(self.config.num_experts, -1, self.config.dim) # [E, C, D]
+            # buf = torch.empty_like(expert_output)
+            # torch.distributed.all_to_all_single(buf, expert_output, group=device_mesh.get_group())
+            # expert_output = buf
+        
+        combined_output = torch.einsum("tec,ecd->td", combine_weights, expert_output)
+        return combined_output
+
+class MOEFeedForward(nn.Module):
+    def __init__(self, config) -> None:
+        super().__init__()
+        # Initialize layers normally
+        self.gate = nn.Linear(config.dim, config.num_experts, bias=False)
+        self.cond_ffn = ConditionalFeedForward(config)
+        
+        self.dim = config.dim
+        self.num_activated_experts = config.num_activated_experts
+        
+    def forward(self, x: Tensor) -> Tensor:
+        # Save original shape to reshape output back
+        original_shape = x.shape  # [batch_size, seq_len, dim] or [seq_len, dim]
+        x = x.view(-1, self.dim)
+        # T = num_tokens, E = num_experts, D = hidden dim, A = activated experts
+        # x: [T, D]
+        scores = self.gate(x) # [T, E]
+        gates = F.softmax(scores, dim=-1)
+        expert_weights, expert_indices = torch.topk(gates, self.num_activated_experts, dim=-1) # [T, A], [T, A]
+        expert_weights /= expert_weights.sum(dim=-1, keepdim=True) # [T, A]
+
+        num_experts = scores.shape[-1]
+        mask = F.one_hot(expert_indices, num_classes=num_experts).sum(dim=1).bool()  # [T, E]
+        capacity = x.shape[0] # // num_experts * 2
+        locations = torch.cumsum(mask, dim=0) - 1 # [T, E]
+        locations_sc = F.one_hot(locations * mask, capacity).float() # [T, E, C]
+        combine_weights = torch.einsum("se,sec->sec", gates * mask, locations_sc)
+        dispatch_mask = combine_weights.bool()
+
+        output = self.cond_ffn(x, dispatch_mask, combine_weights, capacity)
+        return output.view(original_shape)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-5):
+        super().__init__()
+        self.eps = eps
+        # Initialize weight normally (ones for RMSNorm)
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
+
+    def forward(self, x: Tensor) -> Tensor:
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+def precompute_freqs_cis(
+    seq_len: int, n_elem: int, base: int = 10000
+) -> Tensor:
+    freqs = 1.0 / (base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem))
+    t = torch.arange(seq_len, device=freqs.device)
+    freqs = torch.outer(t, freqs)
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+    cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
+    return cache.to(dtype=torch.bfloat16)
+
+
+def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
+    xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
+    freqs_cis = freqs_cis.reshape(1, xshaped.size(1), 1, xshaped.size(3), 2)
+    x_out2 = torch.stack(
+        [
+            xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
+            xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
+        ],
+        -1,
+    )
+
+    x_out2 = x_out2.flatten(3)
+    return x_out2.type_as(x)
\ No newline at end of file
diff --git a/test/models/moe.py b/test/models/moe.py
deleted file mode 100644
index 9339635..0000000
--- a/test/models/moe.py
+++ /dev/null
@@ -1,163 +0,0 @@
-"""
-MoE implementation: https://github.com/peytontolbert/simple-moe
-"""
-
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from typing import Optional, Dict, Any, Tuple
-
-class ExpertBase(nn.Module):
-    """Base class for expert networks in the Mixture of Experts model."""
-    
-    def __init__(self, input_dim: int, output_dim: int, hidden_dim: Optional[int] = None):
-        """
-        Initialize the expert network.
-        
-        Args:
-            input_dim: Dimension of input features
-            output_dim: Dimension of output features
-            hidden_dim: Dimension of hidden layer (if None, uses 4x input_dim)
-        """
-        super().__init__()
-        self.input_dim = input_dim
-        self.output_dim = output_dim
-        self.hidden_dim = hidden_dim or 4 * input_dim
-        
-        self.network = nn.Sequential(
-            nn.Linear(input_dim, self.hidden_dim),
-            nn.ReLU(),
-            nn.Linear(self.hidden_dim, output_dim)
-        )
-        
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """Forward pass through the expert."""
-        return self.network(x)
-    
-    def get_config(self) -> Dict[str, Any]:
-        """Get expert configuration for serialization."""
-        return {
-            'input_dim': self.input_dim,
-            'output_dim': self.output_dim,
-            'hidden_dim': self.hidden_dim
-        }
-
-
-class FFNExpert(ExpertBase):
-    """Feed-forward neural network expert implementation."""
-    
-    def __init__(self, input_dim: int, output_dim: int, hidden_dim: Optional[int] = None,
-                 num_layers: int = 2, dropout: float = 0.1):
-        """
-        Initialize the FFN expert.
-        
-        Args:
-            input_dim: Dimension of input features
-            output_dim: Dimension of output features
-            hidden_dim: Dimension of hidden layers
-            num_layers: Number of hidden layers
-            dropout: Dropout probability
-        """
-        super().__init__(input_dim, output_dim, hidden_dim)
-        
-        layers = []
-        current_dim = input_dim
-        
-        for _ in range(num_layers - 1):
-            layers.extend([
-                nn.Linear(current_dim, self.hidden_dim),
-                nn.ReLU(),
-                nn.Dropout(dropout)
-            ])
-            current_dim = self.hidden_dim
-            
-        layers.append(nn.Linear(current_dim, output_dim))
-        self.network = nn.Sequential(*layers)
-        
-    def get_config(self) -> Dict[str, Any]:
-        config = super().get_config()
-        config.update({
-            'num_layers': len(self.network) // 3 + 1,
-            'dropout': self.network[2].p if len(self.network) > 2 else 0.0
-        })
-        return config 
-
-
-class MixtureOfExperts(nn.Module):
-    """Mixture of Experts model implementation."""
-    
-    def __init__(self, 
-                 input_dim: int,
-                 output_dim: int,
-                 num_experts: int,
-                 expert_class: type = FFNExpert,
-                 expert_kwargs: Optional[Dict[str, Any]] = None,
-                 k: int = 1,
-                 capacity_factor: float = 1.0,
-                 router_noise_epsilon: float = 1e-2):
-        """
-        Initialize the MoE model.
-        
-        Args:
-            input_dim: Input dimension
-            output_dim: Output dimension
-            num_experts: Number of experts
-            expert_class: Expert class to use
-            expert_kwargs: Additional arguments for expert initialization
-            k: Number of experts to route to
-            capacity_factor: Expert capacity multiplier
-            router_noise_epsilon: Noise factor for router load balancing
-        """
-        super().__init__()
-        self.input_dim = input_dim
-        self.output_dim = output_dim
-        self.num_experts = num_experts
-        self.k = k
-
-        assert k == 1
-        
-        # Initialize experts
-        expert_kwargs = expert_kwargs or {}
-        self.experts = nn.ModuleList([
-            expert_class(input_dim=input_dim, output_dim=output_dim, **expert_kwargs)
-            for _ in range(num_experts)
-        ])
-        
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
-        """
-        Forward pass through the MoE model.
-        
-        Args:
-            x: Input tensor of shape [batch_size, input_dim]
-            
-        Returns:
-            Tuple of:
-            - output: Model output of shape [batch_size, output_dim]
-            - aux_loss: Auxiliary load balancing loss (None if not training)
-        """
-
-        batch_size = x.shape[0]
-        
-        outputs = []
-        for batch_idx in range(batch_size):
-            expert_idx = batch_idx % self.num_experts
-            expert_id = id(self.experts[expert_idx])
-            batch = x[batch_idx:batch_idx+1]
-            with torch.fx.traceback.annotate({"global_expert_id": expert_id, "local_expert_id": expert_idx, "batch_idx": batch_idx}):
-                expert_output = self.experts[expert_idx](batch)
-            outputs.append(expert_output)
-        output = torch.stack(outputs, dim=-1).squeeze()
-            
-        return output
-
-    
-    def get_config(self) -> Dict[str, Any]:
-        """Get model configuration for serialization."""
-        return {
-            'input_dim': self.input_dim,
-            'output_dim': self.output_dim,
-            'num_experts': self.num_experts,
-            'k': self.k,
-            'expert_class': self.experts[0].__class__.__name__,
-            'expert_config': self.experts[0].get_config()
-        } 
\ No newline at end of file
diff --git a/test/test_graph_break.py b/test/test_graph_break.py
deleted file mode 100644
index 2807769..0000000
--- a/test/test_graph_break.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import torch
-import torch.nn as nn
-import ray
-import time
-
-from src.piper_compile import piper_setup
-from src.piper_exec import piper_exec
-from src.piper import distributed_stage
-from src.piper_coordinator import PiperProgramCoordinator
-
-from .schedule_helpers import build_1f1b_schedule, print_schedule
-
-def main():
-    class TwoLayerNN(nn.Module):
-        def __init__(self, input_dim, hidden_dim, output_dim):
-            super().__init__()
-            self.layer1 = nn.Linear(input_dim, hidden_dim)
-            self.layer2 = nn.Linear(hidden_dim, output_dim)
-            self.layer3 = nn.Linear(output_dim, output_dim)
-
-        def forward(self, x):
-            distributed_stage(0)
-            x = self.layer1(x)
-            print(f"graph break: {x.data} {x + 1} {x[0]}")
-            x = self.layer2(x)
-            
-            distributed_stage(1)
-            x = self.layer3(x)
-            return x
-
-    x = torch.randn(2)
-    y = torch.randn(2)
-
-    model = piper_setup(TwoLayerNN, (2, 2, 2), torch.optim.Adam, [x], num_stages=2, num_devices=2)
-
-    return
-
-    num_mbs = 1
-    num_stages = 2
-    schedule = build_1f1b_schedule(num_mbs, num_stages)
-    print_schedule(schedule)
-    loss_fn = torch.nn.CrossEntropyLoss()
-
-    from src.piper_utils import piper_metadata
-    stage1_weights = ray.get(piper_metadata.actors[0].get_weights.remote(0))
-    stage2_weights = ray.get(piper_metadata.actors[1].get_weights.remote(1))
-    print("stage 1 weights:", stage1_weights)
-    print("stage 2 weights:", stage2_weights)
-
-    piper_exec(model, schedule, [x], y, loss_fn, num_mbs, num_stages)
-
-    stage1_weights = ray.get(piper_metadata.actors[0].get_weights.remote(0))
-    stage2_weights = ray.get(piper_metadata.actors[1].get_weights.remote(1))
-    print("stage 1 weights:", stage1_weights)
-    print("stage 2 weights:", stage2_weights)
-
-if __name__ == "__main__":
-    ray.init(include_dashboard=False, log_to_driver=True, namespace="llama")
-    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=2, dp_degree=1, world_size=2)
-    ray.get(piper_coordinator.run_program.remote(main))
-    time.sleep(3)
-    ray.shutdown()
\ No newline at end of file
diff --git a/test/test_llama.py b/test/test_llama.py
index ff9c824..787d821 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -28,9 +28,6 @@ def parse_args():
                         help='Model configuration: LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, or LLAMA_8B (default: LLAMA_DEBUG)')
     parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'no-pp'], default='1f1b',
                         help='Schedule type: gpipe, 1f1b, or interleaved-1f1b (default: 1f1b)')
-    # num_stages should be able to be inferred from the model code
-    parser.add_argument('--num_stages', type=int, default=2,
-                        help='Number of stages (default: 2)')
     parser.add_argument('--dp_degree', type=int, default=1,
                         help='Number of data parallel degrees (default: 1)')
     parser.add_argument('--pp_degree', type=int, default=2,
@@ -132,20 +129,6 @@ def main(args):
         x = torch.randint(0, llama_config.vocab_size, (batch_size, seq_len))
         torch.manual_seed(0)
 
-    num_stages = args.num_stages
-    compiled = piper_setup(
-        Transformer, 
-        (llama_config, seq_len), 
-        torch.optim.Adam, 
-        [x], 
-        num_stages, 
-        args.pp_degree,
-        num_mbs,
-        naive_gradient_sync=args.naive_gradient_sync,
-    )
-    
-    assert num_stages == len(piper_metadata.dag) + 1
-
     schedule = None
     match args.schedule:
         case "no-pp":
@@ -153,23 +136,30 @@ def main(args):
         case "interleaved-1f1b":
             schedule = pp2_interleaved_1f1b_grid_schedule if args.pp_degree == 2 else pp4_interleaved_1f1b_grid_schedule
         case "1f1b":
-            schedule = build_1f1b_schedule(num_mbs, num_stages)
+            schedule = build_1f1b_schedule(num_mbs, args.pp_degree)
         case "gpipe":
-            schedule = build_gpipe_schedule(num_mbs, num_stages)
-    
-    print("SCHEDULE:")
+            schedule = build_gpipe_schedule(num_mbs, args.pp_degree)
+    print("Schedule:")
     print_schedule(schedule)
 
+    compiled = piper_setup(
+        Transformer, 
+        (llama_config, seq_len), 
+        torch.optim.Adam, 
+        [x], 
+        schedule
+    )
+    
     actors = piper_metadata.actors
 
     # Send data to actors ahead of time
     # num_actors = len(actors)
-    # ray.get(actors[0].send_input.remote(x))
-    # ray.get(actors[num_actors-1].send_truth.remote(y))
+    # ray.get(actors[0].load_input.remote(x))
+    # ray.get(actors[num_actors-1].load_labels.remote(y))
 
     # Definte one iteration of the schedule
     def iter_schedule():
-        losses = piper_exec(compiled, schedule, [x], y, loss_fn, num_mbs, num_stages)
+        losses = piper_exec(compiled, schedule, [x], y, loss_fn)
 
     # Warmup
     print(f"Running {warmup} warmup iterations...")
diff --git a/test/test_llama_single.py b/test/test_llama_single.py
deleted file mode 100644
index 6f36db6..0000000
--- a/test/test_llama_single.py
+++ /dev/null
@@ -1,274 +0,0 @@
-import ray
-import torch
-import time
-import argparse
-from torch import nn, optim
-from torch.profiler import profile, record_function, ProfilerActivity
-
-from src.piper_exec import Task, piper_exec
-from src.piper_compile import piper_setup
-from src.piper import piper
-from src.piper_utils import piper_metadata
-
-from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B
-from .schedule_helpers import print_schedule
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='Run LLaMA model on single device (no pipeline parallelism)')
-    parser.add_argument('--model', choices=['LLAMA_DEBUG', 'LLAMA_1B', 'LLAMA_3B', 'LLAMA_8B'], default='LLAMA_DEBUG',
-                        help='Model configuration: LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, or LLAMA_8B (default: LLAMA_DEBUG)')
-    parser.add_argument('--batch_size', type=int, default=8,
-                        help='Batch size (default: 8)')
-    parser.add_argument('--num_mbs', type=int, default=4,
-                        help='Number of microbatches (default: 4)')
-    parser.add_argument('--seq_len', type=int, default=512,
-                        help='Sequence length (default: 512)')
-    parser.add_argument('--warmup', type=int, default=5,
-                        help='Number of warmup iterations (default: 5)')
-    parser.add_argument('--iters', type=int, default=20,
-                        help='Number of timing iterations (default: 20)')
-    parser.add_argument('--tracing', action='store_true', default=False,
-                        help='Enable tracing')
-    return parser.parse_args()
-
-
-def build_single_device_schedule(n_mbs: int):
-    """
-    Build a simple schedule for single device (no pipeline parallelism).
-    
-    For single device, we just do:
-    - All forward passes for all microbatches
-    - All backward passes for all microbatches
-    - Single update step
-    
-    Args:
-        n_mbs: Number of microbatches
-    
-    Returns:
-        A 1xN schedule (1 device, N time steps)
-    """
-    # Calculate number of time steps: n_mbs forwards + n_mbs backwards + 1 update
-    num_steps = n_mbs * 2 + 1
-    
-    # Single device (stage 0), multiple time steps
-    schedule = [[None] * num_steps]
-    
-    # Forward passes for all microbatches
-    for mb_idx in range(n_mbs):
-        schedule[0][mb_idx] = Task(
-            device_id=0, 
-            stage_id=0, 
-            mb_idx=mb_idx, 
-            is_fwd=True, 
-            upd=False
-        )
-    
-    # Backward passes for all microbatches
-    for mb_idx in range(n_mbs):
-        schedule[0][n_mbs + mb_idx] = Task(
-            device_id=0, 
-            stage_id=0, 
-            mb_idx=mb_idx, 
-            is_fwd=False, 
-            upd=False
-        )
-    
-    # Single update step at the end
-    schedule[0][-1] = Task(
-        device_id=0, 
-        stage_id=0, 
-        mb_idx=0, 
-        is_fwd=False, 
-        upd=True
-    )
-    
-    return schedule
-
-
-def print_cuda_memory_stats(device: str, message: str = "") -> None:
-    """Prints CUDA memory usage statistics for the specified device, including available memory.
-
-    Args:
-        device (str): The CUDA device identifier (e.g., 'cuda', 'cuda:0').
-        message (str): Optional message to describe what is being logged (e.g., "loaded model", "loaded batch").
-    """
-    torch.cuda.synchronize()
-    device_obj = torch.device(device)
-    device_idx = device_obj.index if device_obj.index is not None else 0
-    BYTES_IN_GB: int = 1024 ** 3
-
-    used_gb: float = torch.cuda.memory_allocated(device_idx) / BYTES_IN_GB
-    reserved_gb: float = torch.cuda.memory_reserved(device_idx) / BYTES_IN_GB
-    peak_gb: float = torch.cuda.max_memory_allocated(device_idx) / BYTES_IN_GB
-
-    # Get total memory using torch.cuda.get_device_properties
-    total_gb: float = torch.cuda.get_device_properties(device_idx).total_memory / BYTES_IN_GB
-    available_gb: float = total_gb - reserved_gb
-
-    message_prefix = f":{message}" if message else ""
-    print(
-        f"[CUDA:{device_idx}] Memory stats{message_prefix} | "
-        f"used={used_gb:.1f} GiB, reserved={reserved_gb:.1f} GiB, "
-        f"peak={peak_gb:.1f} GiB, available={available_gb:.1f} GiB, total={total_gb:.1f} GiB"
-    )
-
-
-def main(args):
-    
-    # Set model configuration based on argument
-    if args.model == 'LLAMA_DEBUG':
-        llama_config = LLAMA_DEBUG
-    elif args.model == 'LLAMA_1B':
-        llama_config = LLAMA_1B
-    elif args.model == 'LLAMA_3B':
-        llama_config = LLAMA_3B
-    elif args.model == 'LLAMA_8B':
-        llama_config = LLAMA_8B
-        
-    loss_fn = torch.nn.CrossEntropyLoss()
-    device = 'cuda'
-    
-    batch_size = args.batch_size
-    num_mbs = args.num_mbs
-    seq_len = args.seq_len
-    warmup = args.warmup
-    iters = args.iters
-
-    x = torch.randint(0, llama_config.vocab_size, (batch_size, seq_len)).to(device)
-    y = torch.zeros((batch_size, llama_config.vocab_size), dtype=torch.long).to(device)
-
-    # print_cuda_memory_stats(device, "after loading data")
-
-    model = Transformer(llama_config)
-    model.to(device)
-
-    # print_cuda_memory_stats(device, "after loading model")
-    compiled, compilation_metadata = piper_setup(model, [x], backend=piper)
-    # print_cuda_memory_stats(device, "after compiling model")
-    
-    # Update the global piper_metadata with the compilation metadata
-    # This is necessary because piper_exec and other functions expect the global metadata to be populated
-    piper_metadata.update(compilation_metadata)
-    
-    actors = compilation_metadata['actors']
-    num_actors = len(actors)
-    
-    print(f"Number of actors (devices): {num_actors}")
-    
-    # For single device, we expect exactly 1 actor
-    if num_actors != 1:
-        print(f"WARNING: Expected 1 actor for single-device execution, but got {num_actors} actors.")
-        print(f"This may indicate that the model has distributed_stage annotations configured for {num_actors} stages.")
-        print(f"Proceeding with execution on actor 0 only.")
-    
-    # Send input and truth to the first (and only) actor
-    ray.get(actors[0].send_input.remote(x))
-    ray.get(actors[0].send_truth.remote(y))
-
-    # Build simple single-device schedule
-    schedule = build_single_device_schedule(num_mbs)
-
-    print("\nSCHEDULE:")
-    print_schedule(schedule)
-    print(f"Schedule details: {num_mbs} forward passes, {num_mbs} backward passes, 1 update\n")
-
-    def iter_schedule():
-        out = piper_exec(compiled, schedule, [x], y, loss_fn, num_mbs)
-        ray.get(out)
-
-    # Set tracing
-    ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
-
-    # Warmup iterations
-    print(f"Running {warmup} warmup iterations...")
-    for _ in range(warmup):
-        iter_schedule()
-
-    # Clear timing data after warmup
-    ray.get([actor.clear_trace_data.remote() for actor in actors.values()])
-
-    # Timed iterations
-    print(f"Running {iters} timed iterations...")
-    start = time.perf_counter()
-    for _ in range(iters):
-        iter_schedule()
-    end = time.perf_counter()
-
-    def print_mean_timing_data(trace_data: dict, actor_id: int) -> None:
-        """Prints mean timing and memory statistics from trace_data for a given actor.
-
-        Args:
-            trace_data (dict): Trace data dictionary from the actor containing timing and memory metrics.
-            actor_id (int): The ID of the actor.
-        """
-        import numpy as np
-
-        print(f"\nTiming and Memory statistics for Actor {actor_id}:")
-        
-        for stage_id, stage_data in trace_data.items():
-            if stage_id == 'update':
-                # Handle update data (global optimizer step)
-                print(f"  Update:")
-                for metric, values in stage_data.items():
-                    if not values:
-                        mean_val = float('nan')
-                    else:
-                        mean_val = float(np.mean(values))
-                    
-                    if 'memory' in metric:
-                        if 'delta' in metric:
-                            print(f"    {metric}: {mean_val:.3f} GB")
-                        else:
-                            print(f"    {metric}: {mean_val:.3f} GB")
-                    else:
-                        print(f"    {metric}: {mean_val:.3f} ms")
-            else:
-                # Handle stage data (forward/backward passes)
-                print(f"  Stage {stage_id}:")
-                for phase, phase_data in stage_data.items():
-                    print(f"    {phase.capitalize()}:")
-                    for metric, values in phase_data.items():
-                        if not values:
-                            mean_val = float('nan')
-                        else:
-                            mean_val = float(np.mean(values))
-                        
-                        if 'memory' in metric:
-                            if 'delta' in metric:
-                                print(f"      {metric}: {mean_val:.3f} GB")
-                            else:
-                                print(f"      {metric}: {mean_val:.3f} GB")
-                        else:
-                            print(f"      {metric}: {mean_val:.3f} ms")
-
-    print("\n" + "="*60)
-    print("THROUGHPUT RESULTS:")
-    print("="*60)
-    print(
-        f"Throughput: {(iters * batch_size * num_mbs * seq_len)/(end - start):.0f} tokens/sec"
-    )
-    print(
-        f"Average iteration time: {(end - start)*1000/iters:.2f} ms"
-    )
-    print(
-        f"Total time for {iters} iterations: {(end - start):.2f} seconds"
-    )
-    print("="*60)
-
-    if args.tracing:
-        for actor in actors.values():
-            trace_data = ray.get(actor.get_trace_data.remote())
-            actor_id = ray.get(actor.id.remote())
-            print_mean_timing_data(trace_data, actor_id)
-        
-        timeline_filename = f"out/{args.model}-single-device.json"
-        ray.timeline(timeline_filename)
-        print(f"\nRay timeline saved to: {timeline_filename}")
-
-if __name__ == "__main__":
-    ray.init(include_dashboard=True, log_to_driver=True, namespace="llama-single")
-    torch.manual_seed(0)
-    args = parse_args()
-    main(args)
-
-
diff --git a/test/test_mixtral.py b/test/test_mixtral.py
new file mode 100644
index 0000000..cbd12f3
--- /dev/null
+++ b/test/test_mixtral.py
@@ -0,0 +1,52 @@
+import ray
+import torch
+import torch.nn as nn
+import argparse
+import time
+import os
+
+from src.piper_coordinator import PiperProgramCoordinator
+from src.piper_compile import piper_setup
+from src.piper_exec import piper_exec
+
+from .models.mixtral import Transformer, ModelArgs
+from .schedule_helpers import build_1f1b_schedule, print_schedule
+from torch._dynamo.backends.debugging import eager
+
+def main(dp_degree, pp_degree):
+
+    world_size = dp_degree * pp_degree
+    num_mbs = 1
+    batch_size = 8
+
+    config = ModelArgs.from_name("tiny")
+    dp_rank = int(os.environ['PIPER_DP_RANK'])
+
+    x = torch.randint(0, config.vocab_size, (batch_size, config.block_size))
+    input_pos = torch.arange(config.block_size)
+    y = torch.randn(batch_size, config.block_size, config.vocab_size)
+
+    schedule = build_1f1b_schedule(num_mbs, pp_degree)
+    print_schedule(schedule)
+    model = piper_setup(
+        Transformer, 
+        [config], 
+        torch.optim.Adam, 
+        [x, input_pos], 
+        schedule,
+    )
+
+    loss_fn = torch.nn.CrossEntropyLoss()
+    for _ in range(5):
+        piper_exec(model, schedule, [x, input_pos], y, loss_fn)
+        time.sleep(1)
+    
+    ray.timeline(f"out/mixtral.json")
+
+if __name__ == "__main__":
+    ray.init(include_dashboard=False, log_to_driver=True, namespace="llama", _temp_dir="/m-coriander/coriander/mfris/tmp/ray")
+    dp_degree, pp_degree = 2, 2
+    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=pp_degree, dp_degree=dp_degree)
+    handles = piper_coordinator.run_program.remote(main, dp_degree, pp_degree)
+    ray.get(handles)
+    ray.shutdown()
diff --git a/test/test_moe.py b/test/test_moe.py
deleted file mode 100644
index 2647b8f..0000000
--- a/test/test_moe.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import ray
-import torch
-import torch.nn as nn
-import argparse
-import time
-
-from src.piper_coordinator import PiperProgramCoordinator
-from src.piper_compile import piper_setup
-from src.piper_exec import piper_exec
-from src.piper import distributed_stage, piper
-from src.piper_actor import get_actor
-
-from .schedule_helpers import no_pp_schedule, print_schedule, build_1f1b_schedule
-
-from .models.moe import MixtureOfExperts, FFNExpert
-
-class MoETransformer(nn.Module):
-    def __init__(self, vocab_size, hidden_dim, experts_per_layer, k, world_size):
-        super().__init__()
-
-        self.embedding = nn.Embedding(vocab_size, hidden_dim)
-        self.moe1 = MixtureOfExperts(hidden_dim, hidden_dim, experts_per_layer, FFNExpert, k=k)
-        self.moe2 = MixtureOfExperts(hidden_dim, hidden_dim, experts_per_layer, FFNExpert, k=k)
-        self.output = nn.Linear(hidden_dim, vocab_size)
-
-    def forward(self, x):
-        distributed_stage(0)
-        x = self.embedding(x)
-        x = self.moe1(x)
-
-        distributed_stage(1)
-        x = self.moe2(x)
-        return self.output(x)
-    
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='Run MoE model with pipeline and data parallelism')
-    parser.add_argument('--pp_degree', type=int, default=2,
-                        help='Pipeline parallel degree (default: 2)')
-    parser.add_argument('--dp_degree', type=int, default=1,
-                        help='Data parallel degree (default: 1)')
-    parser.add_argument('--num_mbs', type=int, default=1,
-                        help='Number of microbatches (default: 1)')
-    return parser.parse_args()
-
-
-def main(args):
-
-    # Create MoE model
-    world_size = args.dp_degree * args.pp_degree
-    vocab_size = 2
-    batch_size = 2
-    seq_len = 2
-
-    x = torch.randint(0, vocab_size, (batch_size,))
-    y = torch.randn(batch_size, vocab_size)
-
-    pp_degree = args.pp_degree
-    world_size = args.dp_degree * args.pp_degree
-    num_mbs = args.num_mbs
-
-    model = piper_setup(
-        MoETransformer, 
-        (vocab_size, 2, 2, 1, world_size), 
-        torch.optim.Adam, 
-        [x], 
-        num_stages=pp_degree, 
-        pp_degree=pp_degree,
-        check_correct=True)
-    
-    if args.pp_degree == 1:
-        schedule = no_pp_schedule
-    else:
-        schedule = build_1f1b_schedule(num_mbs, pp_degree)
-    loss_fn = torch.nn.CrossEntropyLoss()
-
-    print_schedule(schedule)
-    
-    losses = piper_exec(model, schedule, [x], y, loss_fn, num_mbs, pp_degree)
-    
-    ray.timeline(f"out/moe.json")
-
-if __name__ == "__main__":
-    ray.init(include_dashboard=False, log_to_driver=True, namespace="llama")
-    args = parse_args()
-    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp_degree, dp_degree=args.dp_degree)
-    handles = piper_coordinator.run_program.remote(main, args)
-    ray.get(handles)
-    ray.shutdown()

From d6afed60616ff84f7159ee6c06b93e7e49432a7c Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Thu, 12 Feb 2026 03:48:05 +0000
Subject: [PATCH 019/100] piper manages p2p comm. committing code as it stands
 with potential bug: a2a and p2p comm in test_mixtral.py is slower than
 expected, also running multiple iterations triggers a CUDA device side assert

---
 src/piper.py                 |  20 +-
 src/piper_actor.py           | 384 +++++++++++++++--------------------
 src/piper_compile.py         |  15 +-
 src/piper_exec.py            | 101 ++-------
 src/piper_graph_transform.py | 258 +++++++++++++++++------
 src/piper_utils.py           |   2 +-
 test/models/llama.py         |   1 -
 test/models/mixtral.py       |  13 --
 test/schedule_helpers.py     | 222 ++++++++++----------
 test/test_llama.py           |   7 +-
 test/test_mixtral.py         |  16 +-
 11 files changed, 518 insertions(+), 521 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index 77a19a2..30bbf6c 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -6,7 +6,7 @@
 from torch._dynamo.backends.registry import register_backend
 
 from .piper_utils import _serialize_graphmodule, piper_metadata, create_logger, LOG_LEVEL
-from .piper_graph_transform import _get_dp_comm_ops, _split_gm_by_stages, _insert_comm_ops
+from .piper_graph_transform import _get_dp_comm_ops, _split_gm_by_stages, _insert_a2a_ops, _insert_p2p_ops
 from .piper_actor import _get_actor
 
 logger = create_logger("piper_backend", LOG_LEVEL)
@@ -17,29 +17,35 @@ def piper(gm, example_inputs, **kwargs):
 
     original_gm = gm
     top_level_gm, submodules = _split_gm_by_stages(gm)
+    num_stages = len(piper_metadata.stage_to_device.keys())
+    dp_rank = int(os.environ['PIPER_DP_RANK'])
+    pp_degree = int(os.environ['PIPER_PP_DEGREE'])
+    dp_degree = int(os.environ['PIPER_DP_DEGREE'])
 
     refs = []
     actor_stages = []
-    for (stage_id, stage_gm, input_idxs, params_with_holes, placeholders) in submodules:
-        stage_gm = _insert_comm_ops(stage_gm)
+    for (stage_id, stage_gm, input_idxs, graphargs, placeholders) in submodules:
+        stage_gm = _insert_a2a_ops(stage_gm)
+        # if dp_rank == 0:
+        #     stage_gm.print_readable()
+        comm_ops, tids = _get_dp_comm_ops(graphargs, placeholders)
+
         actor_id = piper_metadata.stage_to_device[stage_id]
-        comm_ops, tids = _get_dp_comm_ops(params_with_holes, placeholders)
         actor = _get_actor(actor_id)
         actor_stages.append((actor, stage_id))
+
         stage_gm_data = _serialize_graphmodule(stage_gm)
         logger.debug(f"Loading stage {stage_id} on actor {actor_id}")
         refs.append(actor._load_stage.remote(
             stage_id, 
             stage_gm_data, 
             comm_ops,
-            params_with_holes,
+            graphargs,
             tids,
             input_idxs,
         ))
     ray.get(refs)
 
-    [actor._comm_loop.remote(stage_id) for (actor, stage_id) in actor_stages]
-
     # TODO: build dag by analyzing stage dependencies
     # this only supports sequential pipelines
     for stage_id in piper_metadata.stage_to_device.keys():
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 74d3569..00e15cc 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -11,35 +11,42 @@
 import threading
 from typing import Callable
 
-from .piper_utils import _deserialize_graphmodule, create_logger, LOG_LEVEL
+from .piper_utils import _deserialize_graphmodule, create_logger, LOG_LEVEL, piper_metadata
 
 CLEANUP_MEMORY = False
 
 logger = create_logger("piper_actor", LOG_LEVEL)
 
-def _create_actors(num_actors, optim_class, num_mbs, naive_gradient_sync=False):
+def _get_rank(pp_rank, dp_rank, pp_degree):
+    return pp_rank + dp_rank * pp_degree
+
+def _create_actors(num_actors, optim_class, num_mbs, num_stages, naive_gradient_sync=False):
     dp_rank = int(os.environ['PIPER_DP_RANK'])
     world_size = int(os.environ['PIPER_WORLD_SIZE'])
     dp_degree = int(os.environ['PIPER_DP_DEGREE'])
     pp_degree = int(os.environ['PIPER_PP_DEGREE'])
 
     from .piper_utils import piper_metadata
-    for actor_id in range(num_actors):
-        global_rank = dp_rank * dp_degree + actor_id
-        actor = PiperActor.options(num_gpus=1, max_concurrency=3).remote(actor_id, optim_class, world_size, num_mbs, naive_gradient_sync, dp_rank=dp_rank, dp_degree=dp_degree, pp_degree=pp_degree)
-        piper_metadata.actors[actor_id] = actor
+    for pp_rank in range(num_actors):
+        global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
+        if dp_degree > 1:
+            max_concurrency = 2
+        else:
+            max_concurrency = 1
+        actor = PiperActor.options(num_gpus=1, max_concurrency=max_concurrency).remote(pp_rank, optim_class, world_size, num_mbs, num_stages, naive_gradient_sync, dp_rank=dp_rank, dp_degree=dp_degree, pp_degree=pp_degree)
+        piper_metadata.actors[pp_rank] = actor
         logger.debug(f"DP rank {dp_rank} created actor {actor} global rank {global_rank}")
 
-def _get_actor(actor_id):
+def _get_actor(pp_rank):
     from .piper_utils import piper_metadata
-    return piper_metadata.actors[actor_id]
+    return piper_metadata.actors[pp_rank]
 
 @ray.remote
 class PiperActor:
-    def __init__(self, actor_id, optim_class, world_size, num_mbs, naive_gradient_sync=False, dp_rank=0, dp_degree=1, pp_degree=1):
+    def __init__(self, pp_rank, optim_class, world_size, num_mbs, num_stages, naive_gradient_sync=False, dp_rank=0, dp_degree=1, pp_degree=1):
         self.logger = create_logger("piper_actor", LOG_LEVEL)
 
-        self.actor_id = actor_id
+        self.pp_rank = pp_rank
         self.optim_class = optim_class
         self.naive_gradient_sync = naive_gradient_sync
         
@@ -49,27 +56,29 @@ def __init__(self, actor_id, optim_class, world_size, num_mbs, naive_gradient_sy
         self.world_size = world_size
 
         self.num_mbs = num_mbs
+        self.num_stages = num_stages
         self.dp_group = None
+        self.pp_group = None
         self.device = 'cuda'
 
-        if dp_degree == 1:
-            self.global_rank = actor_id
-        elif pp_degree == 1:
-            self.global_rank = dp_rank
-        else:
-            self.global_rank = dp_rank * dp_degree + actor_id
+        self.global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
 
-        self.logger.info(f"Initializing Ray actor {actor_id} global rank {self.global_rank} GPU {os.environ['CUDA_VISIBLE_DEVICES']}")
+        self.logger.info(f"Initializing Ray actor {pp_rank} global rank {self.global_rank} GPU {os.environ['CUDA_VISIBLE_DEVICES']}")
 
         self.input = None
-        self.truth = None
+        self.labels = None
+
+        self.comp_stream = torch.cuda.Stream()
+        self.comm_stream = torch.cuda.Stream()
+        self.a2a_stream = torch.cuda.Stream()
+        self.p2p_stream = torch.cuda.Stream()
 
         # map stage id -> compiled fx.Graph function
         self.forward_fns = dict()
         # map stage id -> original GraphModule (for hook registration)
         self.graph_modules = dict()
         # map stage id -> model parameters used by the fx.Graph with holes (None values) for input tensors
-        self.parameters = dict()
+        self.forward_args = dict()
         # map stage id -> indices of the input tensors (as opposed to model parameters) used by the fx.Graph
         self.input_idxs = dict()
         # map stage id -> optimizer for the fx.Graph
@@ -86,6 +95,8 @@ def __init__(self, actor_id, optim_class, world_size, num_mbs, naive_gradient_sy
         self.comm_op_status = defaultdict(lambda: defaultdict(int))
         # map stage id -> tensor id -> comm op handle
         self.comm_op_handles = defaultdict(dict)
+        # map stage id -> p2p op handle
+        self.p2p_op_handles = defaultdict(list)
         # Timing infrastructure
         self.tracing = False  # Toggle for timing and memory tracing
         self.trace_data = {
@@ -132,14 +143,14 @@ def get_trace_data(self) -> dict:
 
     def set_tracing(self, enabled: bool) -> None:
         self.tracing = enabled
-        self.logger.info(f"Actor {self.actor_id}: Tracing {'enabled' if enabled else 'disabled'}")
+        self.logger.info(f"Actor {self.pp_rank}: Tracing {'enabled' if enabled else 'disabled'}")
 
     def start_mem_tracing(self) -> None:
         torch.cuda.memory._record_memory_history()
     
     def stop_mem_tracing(self) -> None:
-        torch.cuda.memory._dump_snapshot(f"actor{self.actor_id}_memory_snapshot_mb4_gpipe.pickle")
-        self.logger.info(f"Saved memory snapshot to actor{self.actor_id}_memory_snapshot_mb4_gpipe.pickle")
+        torch.cuda.memory._dump_snapshot(f"actor{self.pp_rank}_memory_snapshot_mb4_gpipe.pickle")
+        self.logger.info(f"Saved memory snapshot to actor{self.pp_rank}_memory_snapshot_mb4_gpipe.pickle")
         torch.cuda.memory._record_memory_history(enabled=None)
 
     def reset_peak_memory(self):
@@ -148,11 +159,11 @@ def reset_peak_memory(self):
     def get_peak_memory(self):
         return torch.cuda.max_memory_allocated() / (1024**3)
 
-    def load_input(self, tensor):
-        self.input = tensor.to(self.device)
+    def load_input(self, inputs):
+        self.inputs = [inp.to(self.device) for inp in inputs]
     
-    def load_labels(self, tensor):
-        self.truth = tensor.to(self.device)
+    def load_labels(self, labels):
+        self.labels = labels.to(self.device)
 
     def _join_process_groups(self):
         master_addr = os.environ.get('PIPER_MASTER_ADDR', "127.0.0.1")
@@ -160,10 +171,12 @@ def _join_process_groups(self):
         init_method = f"tcp://{master_addr}:{master_port}"
 
         dist.init_process_group("nccl", init_method=init_method, rank=self.global_rank, world_size=self.world_size)
-        self.logger.debug(f"Actor {self.actor_id} global rank {self.global_rank} has GPU {os.environ['CUDA_VISIBLE_DEVICES']}, joined the global process group")
+        self.logger.debug(f"Actor {self.pp_rank} global rank {self.global_rank} has GPU {os.environ['CUDA_VISIBLE_DEVICES']}, joined the global process group")
 
         if self.dp_degree > 1:
             self._join_dp_process_group()
+        if self.pp_degree > 1:
+            self._join_pp_process_group()
     
     def _join_dp_process_group(self):
         num_dp_groups = self.world_size // self.dp_degree
@@ -173,6 +186,15 @@ def _join_dp_process_group(self):
             if self.global_rank % num_dp_groups == dp_group_id:
                 self.dp_group = process_group
                 self.logger.info(f"Global rank {self.global_rank} joined its dp group {dp_group_id} along with ranks {group_ranks}")
+    
+    def _join_pp_process_group(self):
+        num_pp_groups = self.world_size // self.pp_degree
+        for pp_group_id in range(num_pp_groups):
+            group_ranks = [(pp_group_id * self.pp_degree + i) for i in range(self.pp_degree)]
+            process_group = dist.new_group(ranks=group_ranks, backend='nccl')
+            if self.global_rank // self.pp_degree == pp_group_id:
+                self.pp_group = process_group
+                self.logger.info(f"Global rank {self.global_rank} joined its pp group {pp_group_id} along with ranks {group_ranks}")
 
     def _prepare_comm_ops(self, stage_id, comm_ops, graphargs, ids):
         def hook_maker(tensor_id):
@@ -192,33 +214,34 @@ def post_backward_hook(grad):
                         if comm_op.pass_type == "backward" and comm_op.dep == "post":
                             tensor_id = i
                             tensor_shape = t.shape
-                            self.logger.debug(f"Registering update hook dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={tensor_shape}")
                             t.register_post_accumulate_grad_hook(hook_maker(tensor_id))
                         else:
                             raise ValueError(f"Unknown comm op type or dependency: {comm_op.op} {comm_op.dep}")
                    case _:
                        raise ValueError(f"Unknown communication operation: {comm_op.op}")
 
-    def _comm_loop(self, stage_id):
-        comm_stream = torch.cuda.Stream()
-        self.logger.info(f"Running comm loop for stage {stage_id} on actor {self.actor_id} global rank {self.global_rank}")
+    def _comm_loop(self):
+        stage_id = self.stage_id
+        # look into: nccl priority for sync vs async ops, stream priority
+        self.logger.info(f"Running comm loop for stage {stage_id} on actor {self.pp_rank} global rank {self.global_rank}")
         for comm_op in self.comm_ops[stage_id]:
             done = False
             while not done:
                 if comm_op.op == "allreduce" and comm_op.pass_type == "backward" and comm_op.dep == "post" and comm_op.group == "dp":
                     # perform the all reduce when all gradients have been accumulated
                     if self.comm_op_status[stage_id][comm_op.tensor_id] == self.num_mbs:
-                        with torch.cuda.stream(comm_stream):
+                        with torch.cuda.stream(self.comm_stream):
                             handle = dist.all_reduce(comm_op.tensor, op=dist.ReduceOp.AVG, group=self.dp_group, async_op=True)
-                            self.logger.debug(f"Allreduce on dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={comm_op.tensor.shape}")
+                        self.logger.debug(f"Allreduce on dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={comm_op.tensor.shape}")
                         self.comm_op_status[stage_id][comm_op.tensor_id] = 0
                         self.comm_op_handles[stage_id][comm_op.tensor_id] = handle
                         done = True
                 else:
                     raise ValueError(f"Unknown comm op: {comm_op}")
+        self.logger.info(f"Completed comm loop for stage {stage_id} on actor {self.pp_rank} global rank {self.global_rank}")
 
     def _wait_for_comm_ops(self):
-        self.logger.debug(f"Actor {self.actor_id} global rank {self.global_rank} waiting for comm ops")
+        self.logger.debug(f"Actor {self.pp_rank} global rank {self.global_rank} waiting for comm ops")
         for stage_id, comm_ops in self.comm_ops.items():
             for comm_op in comm_ops:
                 self.logger.debug(f"Waiting for comm op to be launched dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={comm_op.tensor.shape}")
@@ -229,8 +252,14 @@ def _wait_for_comm_ops(self):
                         self.comm_op_handles[stage_id][comm_op.tensor_id].wait()
                         done = True
 
-    def _load_stage(self, stage_id: int, gm_data, comm_ops, params_with_holes, ids, input_idxs):
-        self.logger.info(f"Loading stage {stage_id} graph on actor {self.actor_id} global rank {self.global_rank}")
+    def _wait_for_p2p_ops(self):
+        self.logger.debug(f"Actor {self.pp_rank} global rank {self.global_rank} waiting for p2p ops")
+        for stage_id, handles in self.p2p_op_handles.items():
+            for handle in handles:
+                handle.wait()
+
+    def _load_stage(self, stage_id: int, gm_data, comm_ops, forward_args, ids, input_idxs):
+        self.logger.info(f"Loading stage {stage_id} graph on actor {self.pp_rank} global rank {self.global_rank}")
 
         # set up tracing data structure
         if stage_id not in self.trace_data:
@@ -258,102 +287,68 @@ def _load_stage(self, stage_id: int, gm_data, comm_ops, params_with_holes, ids,
 
         # place parameters on the device
         def move_to_device(idx, arg):
-            if idx not in input_idxs:
-                if arg.requires_grad:
-                    return arg.to(self.device).detach().requires_grad_(True)
-                else:
-                    return arg.to(self.device)
-            return arg
-        params_with_holes = list(map(move_to_device, range(len(params_with_holes)), params_with_holes))
+            # if idx not in input_idxs:
+            if arg.requires_grad:
+                return arg.to(self.device).detach().requires_grad_(True)
+            else:
+                return arg.to(self.device)
+            # return arg.to(self.device)
+        forward_args = list(map(move_to_device, range(len(forward_args)), forward_args))
 
 
         # prepare tensors with comm ops
         if not self.naive_gradient_sync and self.dp_degree > 1:
-            self._prepare_comm_ops(stage_id, comm_ops, params_with_holes, ids)
+            self._prepare_comm_ops(stage_id, comm_ops, forward_args, ids)
         self.comm_ops[stage_id] = list(reversed(comm_ops))
 
         # save parameters
         self.input_idxs[stage_id] = input_idxs
-        self.parameters[stage_id] = params_with_holes
+        self.forward_args[stage_id] = forward_args
+        self.stage_id = stage_id
 
         # add the parameters to the optimizer for this stage
-        only_params = [param for param in params_with_holes if param is not None]
+        params = [param for param in forward_args if param.requires_grad]
         if stage_id not in self.optims:
-            self.optims[stage_id] = self.optim_class(only_params)
+            self.optims[stage_id] = self.optim_class(params)
         else:
-            self.optims[stage_id].add_param_group({'params': only_params})
+            self.optims[stage_id].add_param_group({'params': params})
 
         del gm_data
 
     # @ray.method(tensor_transport="nccl")
-    def _forward(self, stage_id: int, mb_idx: int, args):
-        self.logger.debug(f"Calling forward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
+    def _forward(self, stage_id: int, mb_idx: int):
 
-        if self.tracing:
-            beginning_event = torch.cuda.Event(enable_timing=True)
-            forward_start_event = torch.cuda.Event(enable_timing=True)
-            forward_end_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-            beginning_event.record()
-        
-        if not (isinstance(args, list) or isinstance(args, tuple)):
-            args = [args]
-
-        def pre_loaded_input(param):
-            if param is None:
-                return self.input
-            else:
-                return param
-        args = list(map(pre_loaded_input, args))
-
-        # Ray object refs resolve to a single element list
-        def unwrap(x):
-            if isinstance(x, list) or isinstance(x, tuple):
-                assert len(x) == 1
-            return x[0] if isinstance(x, list) or isinstance(x, tuple) else x
-        args = list(map(unwrap, args))
-
-        def place(arg):
-            if arg.device == torch.device('cpu'):
-                return arg.to(self.device)
-            else:
-                return arg
-        args = list(map(place, args))
+        self.logger.debug(f"Calling forward {stage_id} mb {mb_idx} on actor {self.pp_rank} global rank {self.global_rank}")
 
-        # place input tensors in the correct indices
-        self.logger.debug(f"Stage {stage_id} inputs: {[arg.shape for arg in args]}, input_idxs: {self.input_idxs[stage_id]}, parameters: {[p.shape if p is not None else None for p in self.parameters[stage_id]]}")
-        assert len(args) == len([p for p in self.parameters[stage_id] if p is None]), "Number of arguments must match number of holes in the parameters"
-        for i, arg in zip(self.input_idxs[stage_id], args):
-            self.parameters[stage_id][i] = arg
+        if stage_id == 0:
+            # For the first stage, load input tensors from self.inputs
+            for i, inp in zip(self.input_idxs[stage_id], self.inputs):
+                self.forward_args[stage_id][i] = inp
+        else:
+            # For non-first stages, receive input tensors from the previous stage
+            start_event = torch.cuda.Event(enable_timing=True)
+            end_event = torch.cuda.Event(enable_timing=True)
+            start_event.record(self.p2p_stream)
+            for i in self.input_idxs[stage_id]:
+                pp_rank = piper_metadata.stage_to_device[stage_id - 1]
+                global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+                dist.recv(self.forward_args[stage_id][i], src=global_src_rank, group=self.pp_group)
+            end_event.record(self.p2p_stream)
+            torch.cuda.synchronize()
+            time = start_event.elapsed_time(end_event)
+            logger.info(f"Completed fwd p2p recv rank={self.global_rank} time={time:.2f} ms")
 
-        # save first input that requires grad as input activation
-        if stage_id > 0:
-            inp_with_grad = [inp for inp in args if inp.requires_grad]
-            assert len(inp_with_grad) == 1, "Piper only supports one input per subgraph with requires_grad"
-            if inp_with_grad[0].dtype == torch.float:
-                inp_with_grad[0].requires_grad_().retain_grad()
+            # save first input that requires grad as input activation
+            self.forward_args[stage_id][self.input_idxs[stage_id][-1]].requires_grad_()
+            inp_with_grad = [self.forward_args[stage_id][i] for i in self.input_idxs[stage_id] if self.forward_args[stage_id][i].requires_grad]
+            assert len(inp_with_grad) == 1, "Exactly one input per stage should require a gradient"
             self.logger.debug(f"Saving input activation {inp_with_grad[0].shape} for stage {stage_id} mb {mb_idx}")
             self.inp_activation[stage_id][mb_idx] = inp_with_grad[0]
 
-        # Record start event for forward timing
-        if self.tracing:
-            torch.cuda.reset_peak_memory_stats()
-            forward_start_memory = torch.cuda.memory_allocated()
-            forward_start_event.record()
+        # Run the forward pass
+        output = self.forward_fns[stage_id](*self.forward_args[stage_id])
 
-        # Call compiled function
-        output = self.forward_fns[stage_id](*self.parameters[stage_id])
-
-        # Record end event and calculate forward timing
-        if self.tracing:
-            forward_end_event.record()
-            torch.cuda.synchronize()
-            # Calculate total forward time
-            forward_time = forward_start_event.elapsed_time(forward_end_event)
-            forward_peak_memory_delta_gb = (torch.cuda.max_memory_allocated() - forward_start_memory) / (1024**3)
-            forward_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
-
-        # save first output that requires grad as output activation
+        # Save first output that requires grad as output activation
         # TODO: support multiple outputs
         out_with_grad = [out for out in output if out.requires_grad]
         assert len(out_with_grad) == 1, "Piper only supports one output per subgraph with requires_grad"
@@ -362,141 +357,107 @@ def place(arg):
 
         # clear the input tensors
         for i in self.input_idxs[stage_id]:
-            self.parameters[stage_id][i] = None
-        del args
+            self.forward_args[stage_id][i] = torch.empty_like(self.forward_args[stage_id][i])
 
-        if self.tracing:
-            end_event.record()
+        if stage_id < self.num_stages - 1:
+            start_event = torch.cuda.Event(enable_timing=True)
+            end_event = torch.cuda.Event(enable_timing=True)
+            start_event.record(self.p2p_stream)
+            torch.cuda.synchronize()
+            # For non-final stages, send output tensors to the next stage
+            for i in range(len(output)):
+                pp_rank = piper_metadata.stage_to_device[stage_id + 1]
+                global_dst_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+                dist.send(output[i], dst=global_dst_rank, group=self.pp_group)
+            end_event.record(self.p2p_stream)
             torch.cuda.synchronize()
-            total_time = forward_start_event.elapsed_time(end_event)
-            self.trace_data[stage_id]['forward']['forward'].append(forward_time)
-            self.trace_data[stage_id]['forward']['total'].append(forward_time)
-            self.trace_data[stage_id]['forward']['peak_memory_delta'].append(forward_peak_memory_delta_gb)
-            self.trace_data[stage_id]['forward']['peak_memory'].append(forward_peak_memory_gb)
+            time = start_event.elapsed_time(end_event)
+            logger.info(f"Completed fwd p2p send rank={self.global_rank} time={time:.2f} ms")
 
         if CLEANUP_MEMORY:
             gc.collect()
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
         
-        self.logger.debug(f"Forward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank} returning {[out.shape for out in output]}")
-        return output
+        self.logger.debug(f"Forward {stage_id} mb {mb_idx} on actor {self.pp_rank} global rank {self.global_rank} returning {[out.shape for out in output]}")
 
     # @ray.method(tensor_transport="nccl")
-    def _backward(self, stage_id: int, mb_idx: int, inp, truth=None, loss_fn=None):
-        self.logger.debug(f"Calling backward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
-
-        if self.tracing:
-            beginning_event = torch.cuda.Event(enable_timing=True)
-            backward_start_event = torch.cuda.Event(enable_timing=True)
-            backward_end_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-            beginning_event.record()
-            torch.cuda.reset_peak_memory_stats()
-            backward_start_memory = torch.cuda.memory_allocated()
-            backward_start_event.record()
-
-        if isinstance(inp, list) or isinstance(inp, tuple):
-            self.logger.debug(f"Backward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank} receiving input {[inp.shape for inp in inp]}")
-            assert len(inp) == 1
-            inp = inp[0]
+    def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
 
-        # Get the activations for this stage and microbatch
+        self.logger.debug(f"Calling backward {stage_id} mb {mb_idx} on actor {self.pp_rank} global rank {self.global_rank}")
+        
         out_activation = self.out_activation[stage_id][mb_idx]
-        if stage_id > 0:
-            inp_activation = self.inp_activation[stage_id][mb_idx]
-
-        # compute loss with the final activation of the final stage. 
-        # use the saved activation rather than inp because the saved activation stores the computation graph
-        if loss_fn is not None:
-            assert out_activation.shape == inp.shape
-            if self.truth is not None:
-                labels = self.truth
-            else:
-                labels = truth.to(self.device)
+
+        if stage_id < self.num_stages - 1:
+            # For non-final stages, recieve input gradients from the subsequent backward pass
+            start_event = torch.cuda.Event(enable_timing=True)
+            end_event = torch.cuda.Event(enable_timing=True)
+            start_event.record(self.p2p_stream)
+            input_grad = torch.empty_like(out_activation)
+            pp_rank = piper_metadata.stage_to_device[stage_id + 1]
+            global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+            with torch.cuda.stream(self.p2p_stream):
+                dist.recv(input_grad, src=global_src_rank, group=self.pp_group)
+            end_event.record(self.p2p_stream)
+            torch.cuda.synchronize()
+            time = start_event.elapsed_time(end_event)
+            logger.info(f"Completed bwd p2p recv rank={self.global_rank} time={time:.2f} ms")
+            out_activation.backward(gradient=input_grad)
+        else:
+            # For the final stage, wait for the forward pass to complete
+            assert loss_fn is not None
+            labels = self.labels
             assert out_activation.shape == labels.shape
             loss = loss_fn(out_activation, labels)
             loss.backward()
             self.loss.append(loss.item())
-        # if not the last stage, backprop on the stored activation given 
-        # the input gradient from the subsequent stage
-        else:
-            assert inp is not None
-            self.logger.debug(f"activation: {out_activation.shape}, input: {inp.shape}")
-            assert out_activation.shape == inp.shape
-            out_activation.backward(gradient=inp)
 
         del out_activation
 
-        # propagate the gradient backwards if not the first stage
         if stage_id > 0:
-            ret = inp_activation.grad
-            del inp_activation
-        else:
-            ret = "done"        
-
-        # Record end event and calculate backward timing
-        if self.tracing:
-            backward_end_event.record()
+            # For non-first stages, send output gradients to the previous backward stage
+            start_event = torch.cuda.Event(enable_timing=True)
+            end_event = torch.cuda.Event(enable_timing=True)
+            start_event.record(self.p2p_stream)
+            output_grad = self.inp_activation[stage_id][mb_idx].grad
+            assert output_grad is not None
+            pp_rank = piper_metadata.stage_to_device[stage_id - 1]
+            global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+            with torch.cuda.stream(self.p2p_stream):
+                dist.send(output_grad, dst=global_src_rank, group=self.pp_group)
+            end_event.record(self.p2p_stream)
             torch.cuda.synchronize()
-            
-            # Calculate total backward time
-            backward_time = backward_start_event.elapsed_time(backward_end_event)
-            
-            backward_peak_memory_delta_gb = (torch.cuda.max_memory_allocated() - backward_start_memory) / (1024**3)
-            backward_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
+            time = start_event.elapsed_time(end_event)
+            logger.info(f"Completed bwd p2p send rank={self.global_rank} time={time:.2f} ms")
+            self.inp_activation[stage_id][mb_idx] = None
 
-            end_event.record()
-            torch.cuda.synchronize()
-            total_time = beginning_event.elapsed_time(end_event)
-            self.trace_data[stage_id]['backward']['backward'].append(backward_time)
-            self.trace_data[stage_id]['backward']['total'].append(total_time)
-            self.trace_data[stage_id]['backward']['peak_memory_delta'].append(backward_peak_memory_delta_gb)
-            self.trace_data[stage_id]['backward']['peak_memory'].append(backward_peak_memory_gb)
-        
         if CLEANUP_MEMORY:
             gc.collect()
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
         
-        return [ret] * 2
-
     def _synchronize_gradients(self):
-        self.logger.debug(f"Actor {self.actor_id} global rank {self.global_rank} synchronizing gradients")
+        self.logger.debug(f"Actor {self.pp_rank} global rank {self.global_rank} synchronizing gradients")
         # Iterate over all stages on this actor and synchronize their parameters
-        for stage_id, parameters in self.parameters.items():
+        for stage_id, parameters in self.forward_args.items():
             for param in parameters:
                 if param is not None and param.grad is not None:
-                    dist.all_reduce(param.grad, op=dist.ReduceOp.AVG, group=self.dp_group)
-
-    def _update(self, *done_mbs):
-        if self.tracing:
-            start_event = torch.cuda.Event(enable_timing=True)
-            start_sync = torch.cuda.Event(enable_timing=True)
-            end_sync = torch.cuda.Event(enable_timing=True)
-            start_optim = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-
-            torch.cuda.reset_peak_memory_stats()
-            update_start_memory = torch.cuda.memory_allocated()
-
-            start_event.record()
-        
-        if self.tracing:
-            start_sync.record()
+                    dist.all_reduce(param.grad, op=dist.ReduceOp.AVG, group=self.dp_group, stream=self.comm_stream)
 
+    def _update(self):
+        self.logger.debug(f"Actor {self.pp_rank} global rank {self.global_rank} waiting for backward sync events")
+            
         # if dp degree > 1, make sure all gradients are synchronized before optimizer step
         # TODO: this does not allow overlapping with the optimizer step
+        if self.pp_degree > 1:
+            self._wait_for_p2p_ops()
+            
         if self.dp_degree > 1:
             if self.naive_gradient_sync:
                 self._synchronize_gradients()
             else:
                 self._wait_for_comm_ops()
         
-        if self.tracing:
-            end_sync.record()
-            start_optim.record()
-        
         # step the optimizer for each stage
         for _, optim in self.optims.items():
             optim.step()
@@ -504,19 +465,4 @@ def _update(self, *done_mbs):
         losses = self.loss
         self.loss.clear()
 
-        if self.tracing:
-            end_event.record()
-            torch.cuda.synchronize()
-            total_time = start_event.elapsed_time(end_event)
-            sync_time = start_sync.elapsed_time(end_sync)
-            optim_time = start_optim.elapsed_time(end_event)
-            update_peak_memory_delta_gb = (torch.cuda.max_memory_allocated() - update_start_memory) / (1024**3)
-            update_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
-
-            self.trace_data['update']['total'].append(total_time)
-            self.trace_data['update']['sync'].append(sync_time)
-            self.trace_data['update']['optim'].append(optim_time)
-            self.trace_data['update']['peak_memory_delta'].append(update_peak_memory_delta_gb)
-            self.trace_data['update']['peak_memory'].append(update_peak_memory_gb)
-
         return losses
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 3e209a7..b702e62 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -20,9 +20,10 @@ def piper_setup(model_class, model_args, optim_fn, example_inputs, schedule, nai
 
     num_devices = len(schedule)
     num_mbs = len(set([task.mb_idx for row in schedule for task in row if task is not None]))
-    piper_metadata.stage_to_device = {task.stage_id: task.device_id for row in schedule for task in row if task is not None}
+    num_stages = len(set([task.stage_id for row in schedule for task in row if task is not None]))
+    piper_metadata.stage_to_device = {task.stage_id: task.pp_rank for row in schedule for task in row if task is not None}
 
-    _create_actors(num_devices, optim_fn, num_mbs, naive_gradient_sync)
+    _create_actors(num_devices, optim_fn, num_mbs, num_stages, naive_gradient_sync)
     ray.get([actor._join_process_groups.remote() for actor in piper_metadata.actors.values()])
 
     model = model_class(*model_args)
@@ -35,13 +36,5 @@ def piper_setup(model_class, model_args, optim_fn, example_inputs, schedule, nai
     output = compiled(*example_inputs)
 
     logger.info(f"DP rank {dp_rank+1} done.")
-
-    from ray.experimental.collective import create_collective_group
-    
-    create_collective_group(
-        list(piper_metadata.actors.values()),
-        backend="nccl")
-    
-    logger.info(f"DP rank {dp_rank} Started NCCL group with {len(piper_metadata.actors)} actors and DAG {piper_metadata.dag}")
-    
+ 
     return compiled
diff --git a/src/piper_exec.py b/src/piper_exec.py
index 34afd0a..5cd4850 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -11,7 +11,7 @@
 logger = create_logger("piper_exec", LOG_LEVEL)
 
 class Task(NamedTuple):
-    device_id: int
+    pp_rank: int
     stage_id: int
     mb_idx: int
     is_fwd: bool
@@ -40,7 +40,7 @@ def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdg
     """
     num_devices, num_steps = len(schedule), len(schedule[0]) if schedule else 0
     
-    # Check well-formedness: no duplicates, device_id matches row, and all stages present
+    # Check well-formedness: no duplicates, pp_rank matches row, and all stages present
     all_tasks = set()
     microbatch_tasks = {}  # mb_idx -> set of (stage_id, is_fwd, upd)
     
@@ -48,10 +48,10 @@ def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdg
         for time_step in range(num_steps):
             task = schedule[stage_id][time_step]
             if task is not None:
-                # Check device_id matches row
-                if task.device_id != stage_id:
+                # Check pp_rank matches row
+                if task.pp_rank != stage_id:
                     raise ValueError(
-                        f"Task device_id {task.device_id} does not match row {stage_id} "
+                        f"Task pp_rank {task.pp_rank} does not match row {stage_id} "
                         f"at time step {time_step}"
                     )
                 
@@ -101,9 +101,9 @@ def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdg
         fwd_times = {}  # stage_id -> time_step
         bwd_times = {}  # stage_id -> time_step
         
-        for device_id in range(num_devices):
+        for pp_rank in range(num_devices):
             for time_step in range(num_steps):
-                task = schedule[device_id][time_step]
+                task = schedule[pp_rank][time_step]
                 if task is not None and task.mb_idx == mb_idx:
                     if task.is_fwd and not task.upd:
                         fwd_times[task.stage_id] = time_step
@@ -164,99 +164,24 @@ def piper_exec(model, schedule, inputs, truth, loss_fn):
     dag_edges = piper_metadata.dag
     dag_edges = list(map(lambda e: (DAGEdge(e[0], e[1])), list(piper_metadata.dag)))
     
-    # Validate the schedule before execution
     _validate_schedule(schedule, dag_edges, num_mbs)
 
-    # maps mb_idx to the ref resulting from a forward call on the microbatch
-    fwd_refs = dict()
+    [actor._comm_loop.remote() for actor in actors.values()]
 
-    # maps mb_idx to a dict that maps stage_id to the refs output from the stage's backward on that microbatch
-    bwd_ref_dicts = dict()
+    time.sleep(3)
 
-    # iterate over evrery task in the schedule
     ret = []
     for i in range(num_steps):
         for j in range(num_devices-1, -1, -1):
             task = schedule[j][i]
             if task:
-                device_id, stage_id, mb_idx, is_fwd, upd = task
+                _, stage_id, mb_idx, is_fwd, upd = task
                 actor_id = j
                 actor = actors[actor_id]
-                num_bwd_targets = len(_get_backward_targets(stage_id, dag_edges))
-                if num_bwd_targets == 0:
-                    num_bwd_targets = 1
                 if upd:
-                    logger.debug(f"Launching update stage {stage_id} mb {mb_idx} on actor {actor}")
-                    done_refs = set()
-                    for _, bwd_ref_dict in bwd_ref_dicts.items():
-                        bwd_refs = bwd_ref_dict[stage_id]
-                        bwd_refs = bwd_refs[num_bwd_targets:]
-                        if isinstance(bwd_refs, list):
-                            done_refs = done_refs | set(bwd_refs)
-                        else:
-                            done_refs.add(bwd_refs)
-                    done_refs = list(done_refs)
-                    ret.append(actor._update.remote(*done_refs))
+                    ret.append(actor._update.remote())
                 elif is_fwd:
-                    logger.debug(f"Launching forward stage {stage_id} mb {mb_idx} on actor {actor}")
-                    if stage_id == 0:
-                        fwd_refs[mb_idx] = actor._forward.remote(stage_id, mb_idx, inputs)
-                    else:
-                        fwd_refs[mb_idx] = actor._forward.remote(stage_id, mb_idx, fwd_refs[mb_idx])
+                    actor._forward.remote(stage_id, mb_idx)
                 else:
-                    if mb_idx not in bwd_ref_dicts:
-                        # if this is the first backward task for a microbatch, dispatch the
-                        # backward task and cache the resulting ref(s)
-                        fwd_ref = fwd_refs[mb_idx]
-                        bwd_ref_dicts[mb_idx] = dict()
-                        logger.debug(f"Launching backward stage {stage_id} mb {mb_idx} on actor {actor}")
-                        bwd_ref_dicts[mb_idx][stage_id] = (
-                            actor
-                            ._backward.options(num_returns=num_bwd_targets*2)
-                            .remote(
-                                stage_id, mb_idx, fwd_ref, truth=truth, loss_fn=loss_fn
-                            )
-                        )
-                    else:
-                        # if this is not the first backward task for a microbatch, look up
-                        # the input ref which represents a gradient from the backward call
-                        # of a subsequent stage
-
-                        # get the result of the subsequent stage's backward call
-                        # LIMITATION: there cannot be more than one subsequent stage (e.g. the
-                        # forward of stage A cannot be inputs to both stage B and C)
-                        # this limitation should eventually be resolved by making the logic below more general
-                        to_stage = [
-                            edge.to_stage
-                            for edge in dag_edges
-                            if edge.from_stage == stage_id
-                        ]
-                        assert len(to_stage) == 1
-                        to_stage = to_stage[0]
-
-                        # get the refs resulting from the subsequent stage's backward
-                        targets = _get_backward_targets(to_stage, dag_edges)
-
-                        # get the idx of the current stage in the subsequent stage's output list
-                        # LIMITATION: this logic assumes that the user's dag_edges list is ordered according
-                        # to how the stages are ordered in the model file. e.g. in clip.py stage 0 comes
-                        # before stage 1, so dag_edges must look like
-                        # dag_edges = [DAGEdge(0, 2), DAGEdge(1, 2)] and NOT
-                        # dag_edges = [DAGEdge(1, 2), DAGEdge(0, 2)]
-                        idx = targets.index(DAGEdge(stage_id, to_stage))
-
-                        # make sure the subsequent stage's backward was already dispatched
-                        assert to_stage in bwd_ref_dicts[mb_idx]
-
-                        # if the subsequent stage's backward had more than one output, index the
-                        # list to get the ref for the current stage
-                        bwd_refs = bwd_ref_dicts[mb_idx][to_stage]
-                        bwd_ref = bwd_refs[idx]
-                        # dispatch the current stage's backward and cache the resulting ref(s)
-                        logger.debug(f"Launching backward stage {stage_id} mb {mb_idx} on actor {actor}")
-                        bwd_ref_dicts[mb_idx][stage_id] = (
-                            actor
-                            ._backward.options(num_returns=num_bwd_targets*2)
-                            .remote(stage_id, mb_idx, bwd_ref)
-                        )
+                    actor._backward.remote(stage_id, mb_idx, loss_fn=loss_fn)
     return ray.get(ret)
\ No newline at end of file
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index 7e42c27..c55d01c 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -6,6 +6,7 @@
 from typing import Optional
 import operator
 import os
+import time
 from torch.autograd import Function
 
 from .piper_utils import create_logger, LOG_LEVEL, piper_metadata
@@ -23,17 +24,15 @@ def __init__(self, tensor_id: int, name: str, dep: str, pass_type: str, op: str,
         self.op = op # "allreduce", "allgather", "scatter", "alltoall"
         self.group = group # "dp" or "pp"
 
-def _get_dp_comm_ops(example_inputs, placeholders):
+def _get_dp_comm_ops(inputs, placeholders):
     dp_comm_ops = []
     ids = []
-    for p, t in zip(placeholders, example_inputs):
+    for p, t in zip(placeholders, inputs):
         ids.append(id(t))
         if isinstance(t, torch.nn.Parameter) and t.requires_grad:
             dp_comm_ops.append(CommOp(id(t), p.name, "post", "backward", "allreduce", "dp"))
-    logger.debug(f"Got {len(dp_comm_ops)} dp comm ops for {len(example_inputs)} example inputs")
     return dp_comm_ops, ids
 
-
 class AllToAllSingleFunction(torch.autograd.Function):
     """
     Custom autograd function for all_to_all_single that ensures gradients flow correctly.
@@ -57,14 +56,28 @@ def forward(ctx, output, input_tensor):
         from .piper_utils import piper_metadata
         actor_self = piper_metadata.actor_self
         ctx.group = actor_self.dp_group
+        ctx.global_rank = actor_self.global_rank
+        ctx.stream = actor_self.a2a_stream
+
+        logger.info(f"Dispatch AllToAllSingleFunction forward rank={ctx.global_rank}, shape={input_tensor.shape}")
+        
+        start_event = torch.cuda.Event(enable_timing=True)
+        end_event = torch.cuda.Event(enable_timing=True)
+        start_event.record(ctx.stream)
         
         # Ensure input_tensor is contiguous (all_to_all_single requires contiguous tensors)
         # TODO: performance cost?
         input_tensor = input_tensor.contiguous()
         
         # Perform the communication (modifies output in-place)
-        dist.all_to_all_single(output, input_tensor, group=ctx.group)
+        with torch.cuda.stream(ctx.stream):
+            dist.all_to_all_single(output, input_tensor, group=ctx.group)
         
+        end_event.record(ctx.stream)
+        torch.cuda.synchronize()
+        time = start_event.elapsed_time(end_event)
+        logger.info(f"Completed AllToAllSingleFunction forward rank={ctx.global_rank} time={time:.2f} ms")
+
         return output
     
     @staticmethod
@@ -75,6 +88,12 @@ def backward(ctx, grad_output):
         The backward of all_to_all_single is another all_to_all_single operation
         that reverses the communication pattern.
         """
+        logger.info(f"Dispatch AllToAllSingleFunction backward rank={ctx.global_rank}, shape={grad_output.shape}")
+
+        start_event = torch.cuda.Event(enable_timing=True)
+        end_event = torch.cuda.Event(enable_timing=True)
+        start_event.record(ctx.stream)
+
         if grad_output is None:
             return None, None, None
         
@@ -87,10 +106,13 @@ def backward(ctx, grad_output):
         
         # Reverse the communication: all_to_all_single in backward
         # This propagates gradients from output back to input
-        if ctx.group is None:
-            dist.all_to_all_single(grad_input, grad_output)
-        else:
+        with torch.cuda.stream(ctx.stream):
             dist.all_to_all_single(grad_input, grad_output, group=ctx.group)
+
+        end_event.record(ctx.stream)
+        torch.cuda.synchronize()
+        time = start_event.elapsed_time(end_event)
+        logger.info(f"Completed AllToAllSingleFunction backward rank={ctx.global_rank} time={time:.2f} ms")
         
         # Return gradients: grad_output flows to grad_input, None for group
         return grad_input, grad_input, None
@@ -233,10 +255,39 @@ def extract_nodes_from_arg(arg):
                 new_get_attr = stage_graph.node_copy(node, lambda n: n)
                 stage_node_mapping[node] = new_get_attr
         
-        # Create placeholders for inputs in original graph order to ensure correct argument ordering
-        # Preserve all metadata from original nodes
+        # Create placeholders for inputs with proper ordering
+        # For stages > 0, inputs from previous stage outputs should be ordered
+        # according to their order in the previous stage's outputs
         input_placeholders = {}
-        input_order = []  # Track order of inputs as they appear in original graph
+        input_order = []  # Track order of inputs
+        
+        # Get outputs from previous stage (if it exists) to determine ordering
+        prev_stage_outputs_ordered = []
+        if stage_annotation_id > 0:
+            prev_stage_id = stage_annotation_id - 1
+            if prev_stage_id in stage_modules:
+                _, _, _, prev_stage_outputs_list, _, _, _, _, _ = stage_modules[prev_stage_id]
+                prev_stage_outputs_ordered = prev_stage_outputs_list
+        
+        # For stages > 0, first add inputs that come from previous stage outputs
+        # in the order they appear in the previous stage's outputs
+        if stage_annotation_id > 0 and prev_stage_outputs_ordered:
+            for prev_output_node in prev_stage_outputs_ordered:
+                if prev_output_node in stage_inputs and prev_output_node not in input_placeholders:
+                    # If the input is already a placeholder, use node_copy to preserve all metadata
+                    if prev_output_node.op == "placeholder":
+                        placeholder = stage_graph.node_copy(prev_output_node, lambda n: n)
+                    else:
+                        # For computed nodes, create a placeholder but preserve metadata
+                        placeholder = stage_graph.placeholder(prev_output_node.name)
+                        # Copy all metadata from the original node
+                        placeholder.meta.update(prev_output_node.meta)
+                    stage_node_mapping[prev_output_node] = placeholder
+                    input_placeholders[prev_output_node] = placeholder
+                    input_order.append(prev_output_node)
+        
+        # Then add any remaining inputs in original graph order
+        # (these are typically placeholders or other inputs not from previous stage)
         for node in gm.graph.nodes:
             if node in stage_inputs and node not in input_placeholders:
                 # If the input is already a placeholder, use node_copy to preserve all metadata
@@ -384,7 +435,6 @@ def collect_dependencies(node):
 
         # Create output node
         if stage_outputs:
-            print(f"Stage {stage_annotation_id} outputs: {[node.name for node in stage_outputs]}")
             output_values = [stage_node_mapping[node] for node in stage_outputs]
             if len(output_values) == 1:
                 stage_graph.output(output_values[0])
@@ -404,7 +454,7 @@ def collect_dependencies(node):
         # Load the module on the corresponding actor
         # Track which inputs come from previous stage outputs
         input_idxs = []
-        params = []
+        graphargs = []
         placeholders = stage_gm.graph.find_nodes(op="placeholder")
         
         # Create reverse mapping from placeholder to original node
@@ -418,24 +468,22 @@ def collect_dependencies(node):
             prev_stage_outputs = set(prev_stage_outputs_list)
         
         for i, placeholder in enumerate(placeholders):
+            if "grapharg" in placeholder.meta:
+                graphargs.append(placeholder.meta["grapharg"]._example())
+            else:
+                assert (stage_annotation_id == 0 and 'self' not in placeholder.name) or (stage_annotation_id > 0 and placeholder_to_original.get(placeholder) in prev_stage_outputs)
+                graphargs.append(torch.zeros(placeholder.meta["example_value"].shape, dtype=placeholder.meta["example_value"].dtype, requires_grad=placeholder.meta["example_value"].requires_grad))
             # For the first stage, the input indices are everything that's not an attribute
             if stage_annotation_id == 0:
-                if 'self' in placeholder.name:
-                    params.append(placeholder.meta["grapharg"]._example())
-                else:
+                if 'self' not in placeholder.name:
                     input_idxs.append(i)
-                    params.append(None)
             # For subsequent stages, check if this placeholder corresponds to an output from the previous stage
             else:
                 orig_node = placeholder_to_original.get(placeholder)
                 is_from_prev_stage = orig_node is not None and orig_node in prev_stage_outputs
-                # Check if this is from previous stage output
                 if is_from_prev_stage:
                     input_idxs.append(i)
-                    params.append(None)
-                else:
-                    params.append(placeholder.meta["grapharg"]._example())
-        logger.debug(f"Stage {stage_annotation_id} inputs: {input_idxs}, params: {[param.shape if param is not None else None for param in params]}")
+
         stage_modules[stage_annotation_id] = (
             stage_gm,
             input_placeholders,
@@ -445,7 +493,7 @@ def collect_dependencies(node):
             ordered_stage_nodes,
             stage_annotation_id,
             input_idxs,
-            params,
+            graphargs,
         )
 
     # Create a new top-level graph and replace stage nodes with call_module
@@ -592,7 +640,7 @@ def collect_dependencies(node):
 
 
 
-def _insert_comm_ops(gm: fx.GraphModule) -> fx.GraphModule:
+def _insert_a2a_ops(gm: fx.GraphModule) -> fx.GraphModule:
     """
     Transform a graph module by inserting communication operations on annotated nodes.
     
@@ -646,47 +694,23 @@ def _insert_comm_ops(gm: fx.GraphModule) -> fx.GraphModule:
     # 1. The annotation_key changes (different annotation pattern)
     # 2. The same annotation_key appears again after being interrupted (new instance of same pattern)
     #    This handles the case where the same annotation appears in different layers
-    annotation_blocks = []
-    current_block = None
-    MAX_GAP_FOR_CONTIGUOUS = 50  # Maximum number of non-annotated nodes allowed in a block
-    
+    # Group *all* nodes with the same annotation_key into a single block,
+    # regardless of how far apart they are in the FX graph.
+    # This ensures that every logically-related annotated region (e.g., the
+    # "input" or "output" side of a given MoE layer) is treated as one block.
+    blocks_by_key = {}
     for idx, node, annotation_key, reshape in annotated_nodes:
-        if current_block is None:
-            # Start a new block
-            current_block = {
-                'annotation_key': annotation_key,
-                'nodes': [(node, reshape)],
-                'last_idx': idx
-            }
-        elif current_block['annotation_key'] == annotation_key:
-            # Same annotation - check if it's still part of the same contiguous block
-            gap = idx - current_block['last_idx'] - 1
-            if gap <= MAX_GAP_FOR_CONTIGUOUS:
-                # Contiguous (or close enough), add to current block
-                current_block['nodes'].append((node, reshape))
-                current_block['last_idx'] = idx
-            else:
-                # Too large a gap - this is a new instance of the same annotation pattern
-                # (e.g., a different layer). Finish current block and start new one.
-                annotation_blocks.append(current_block)
-                logger.debug(f"Split block due to gap of {gap} nodes for annotation {annotation_key}")
-                current_block = {
-                    'annotation_key': annotation_key,
-                    'nodes': [(node, reshape)],
-                    'last_idx': idx
-                }
-        else:
-            # Different annotation, finish current block and start new one
-            annotation_blocks.append(current_block)
-            current_block = {
+        if annotation_key not in blocks_by_key:
+            blocks_by_key[annotation_key] = {
                 'annotation_key': annotation_key,
-                'nodes': [(node, reshape)],
-                'last_idx': idx
+                'nodes': [],
+                'last_idx': idx,
             }
-    
-    # Don't forget the last block
-    if current_block is not None:
-        annotation_blocks.append(current_block)
+        block = blocks_by_key[annotation_key]
+        block['nodes'].append((node, reshape))
+        block['last_idx'] = idx
+
+    annotation_blocks = list(blocks_by_key.values())
     
     logger.info(f"Found {len(annotation_blocks)} contiguous annotation blocks")
     
@@ -863,4 +887,114 @@ def _insert_comm_ops(gm: fx.GraphModule) -> fx.GraphModule:
     
     new_gm.recompile()
     
+    return new_gm
+
+def _insert_p2p_ops(
+    gm: fx.GraphModule,
+    input_idxs: list[int],
+    prev_stage_id: int,
+    next_stage_id: int,
+) -> fx.GraphModule:
+    """
+    Insert point-to-point recv/send ops into a stage graph.
+
+    For each placeholder index in input_idxs, this will:
+      1. Create an empty_like buffer from the corresponding placeholder.
+      2. Replace all uses of that placeholder with a tensor received from
+         prev_stage_id via torch.distributed.recv.
+
+    For each output tensor, this will:
+      1. Wrap the tensor in a call to torch.distributed.send with dst set
+         to next_stage_id.
+
+    Args:
+        gm: GraphModule to transform.
+        input_idxs: Indices of placeholders that should receive tensors
+            from the previous stage.
+        prev_stage_id: Rank used as src for recv operations.
+        next_stage_id: Rank used as dst for send operations.
+
+    Returns:
+        A new GraphModule with inserted P2P recv/send operations.
+    """
+    new_graph = fx.Graph()
+    node_mapping: dict[fx.Node, fx.Node] = {}
+
+    placeholders = [n for n in gm.graph.nodes if n.op == "placeholder"]
+
+    # Copy placeholders first so we preserve the original signature.
+    for idx, node in enumerate(placeholders):
+        new_ph = new_graph.node_copy(node, lambda n: node_mapping.get(n, n))
+        node_mapping[node] = new_ph
+
+        if idx in input_idxs:
+            # Directly recv into the placeholder tensor and use that value
+            # in place of the original placeholder.
+            recv_node = new_graph.call_function(
+                _dispatch_p2p_recv,
+                (new_ph, prev_stage_id),
+            )
+            node_mapping[node] = recv_node
+
+    # Copy get_attr nodes (parameters/buffers).
+    for node in gm.graph.nodes:
+        if node.op == "get_attr" and node not in node_mapping:
+            new_attr = new_graph.node_copy(node, lambda n: node_mapping.get(n, n))
+            node_mapping[node] = new_attr
+
+    # Copy all other nodes except output, rewiring to use P2P-recv inputs.
+    output_node: fx.Node | None = None
+    for node in gm.graph.nodes:
+        if node.op in ("placeholder", "get_attr"):
+            continue
+        if node.op == "output":
+            output_node = node
+            continue
+
+        new_node = new_graph.node_copy(
+            node,
+            lambda n: node_mapping.get(n, n),
+        )
+        node_mapping[node] = new_node
+
+    if output_node is None:
+        raise RuntimeError("GraphModule has no output node")
+
+    # Map original outputs into the new graph.
+    raw_outputs = fx.graph.map_arg(
+        output_node.args,
+        lambda n: node_mapping.get(n, n),
+    )
+
+    # For each tensor-valued node in the outputs, add a send op and
+    # return the result of the send so it is kept in the dataflow.
+    def _wrap_with_send(arg):
+        if isinstance(arg, fx.Node):
+            return new_graph.call_function(
+                _dispatch_p2p_send,
+                (arg, next_stage_id),
+            )
+        return arg
+
+    wrapped_outputs = fx.graph.map_arg(raw_outputs, _wrap_with_send)
+
+    if isinstance(wrapped_outputs, tuple) and len(wrapped_outputs) == 1:
+        new_graph.output(wrapped_outputs[0])
+    else:
+        new_graph.output(wrapped_outputs)
+
+    root_module = torch.nn.Module()
+
+    # Copy parameters, buffers, and submodules from the original module.
+    for name, param in gm.named_parameters(recurse=False):
+        root_module.register_parameter(name, param)
+
+    for name, buffer in gm.named_buffers(recurse=False):
+        root_module.register_buffer(name, buffer)
+
+    for name, module in gm.named_children():
+        root_module.add_module(name, module)
+
+    new_gm = fx.GraphModule(root_module, new_graph)
+    new_gm.recompile()
     return new_gm
\ No newline at end of file
diff --git a/src/piper_utils.py b/src/piper_utils.py
index b489a56..eb9eb25 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -12,7 +12,7 @@
 
 import src.piper_patches
 
-LOG_LEVEL = "INFO"
+LOG_LEVEL = "DEBUG"
 
 """ 
 Print the backward graph of a tensor
diff --git a/test/models/llama.py b/test/models/llama.py
index 8ce4f13..a8a6017 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -444,7 +444,6 @@ def log_size(layer, indent=0):
     def forward(self, tokens: torch.Tensor):
 
         with torch.fx.traceback.annotate({"stage": 0}):
-            tokens += 0
             h = self.tok_embeddings(tokens)
             start_pos = 0
             for layer in self.layers[:self.n_layers//2]:
diff --git a/test/models/mixtral.py b/test/models/mixtral.py
index ccd965f..353027b 100644
--- a/test/models/mixtral.py
+++ b/test/models/mixtral.py
@@ -228,27 +228,14 @@ def forward(self, x: Tensor, dispatch_mask: Tensor, combine_weights: Tensor, cap
             "reshape": ("output", (2, self.config.num_experts, -1, self.config.dim))
         }):
             expert_inputs = torch.einsum("tec,tm->ecm", dispatch_mask.type_as(x[0]), x) # [E, C, D]
-            # Annotation should insert this code into the graph:
-            # buf = torch.empty_like(expert_inputs)
-            # torch.distributed.all_to_all_single(buf, expert_inputs, group=device_mesh.get_group())
-            # buf = buf.reshape(get_world_size(), self.config.num_experts, -1, self.config.dim) # [..., E, C, D]
-            # expert_inputs = buf
-
         x1 = F.silu(torch.einsum('...ecd, emd -> ...ecm', expert_inputs, self.w1))
         x3 = torch.einsum('...ecd, emd -> ...ecm', expert_inputs, self.w3)
-
         with torch.fx.traceback.annotate({
             "collective": "all_to_all_single",
             "group": "ep",
             "reshape": ("input", (self.config.num_experts, -1, self.config.dim))
         }):
             expert_output =  torch.einsum('...ecm, edm -> ...ecd', (x1 * x3), self.w2) # [..., E, C, D]
-            # Annotation should insert this code into the graph:
-            # expert_output = expert_output.reshape(self.config.num_experts, -1, self.config.dim) # [E, C, D]
-            # buf = torch.empty_like(expert_output)
-            # torch.distributed.all_to_all_single(buf, expert_output, group=device_mesh.get_group())
-            # expert_output = buf
-        
         combined_output = torch.einsum("tec,ecd->td", combine_weights, expert_output)
         return combined_output
 
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index 09699bb..b50c70c 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -26,7 +26,7 @@ def build_gpipe_schedule(n_mbs: int, n_stages: int):
             if mb_idx >= 0 and mb_idx < n_mbs:
                 schedule[stage_id][step] = Task(stage_id, stage_id, mb_idx, False, False)
     for i, stage in enumerate(range(n_stages)):
-        schedule[stage][-i-1] = Task(stage_id=stage, device_id=stage, mb_idx=0, is_fwd=False, upd=True)
+        schedule[stage][-i-1] = Task(stage_id=stage, pp_rank=stage, mb_idx=0, is_fwd=False, upd=True)
     return schedule
 
 def build_1f1b_schedule(n_mbs: int, n_stages: int):
@@ -39,7 +39,7 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int):
                 mb_idx = stage_mb[stage_id][0]
                 if mb_idx >= 0 and mb_idx < n_mbs:
                     schedule[stage_id][step] = Task(
-                        device_id=stage_id, stage_id=stage_id, 
+                        pp_rank=stage_id, stage_id=stage_id, 
                         mb_idx=mb_idx, is_fwd=True, upd=False
                     )
                     stage_mb[stage_id][0] += 1
@@ -53,140 +53,140 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int):
                 mb_idx = stage_mb[stage_id][fwd_or_bwd]
                 if mb_idx >= 0 and mb_idx < n_mbs:
                     schedule[stage_id][step] = Task(
-                        device_id=stage_id, stage_id=stage_id,
+                        pp_rank=stage_id, stage_id=stage_id,
                         mb_idx=mb_idx, is_fwd=task_type, upd=False
                     )
                     stage_mb[stage_id][fwd_or_bwd] += 1
     for i, stage in enumerate(range(n_stages)):
-        schedule[stage][-i-1] = Task(stage_id=stage, device_id=stage, mb_idx=n_mbs-1, is_fwd=False, upd=True)
+        schedule[stage][-i-1] = Task(stage_id=stage, pp_rank=stage, mb_idx=n_mbs-1, is_fwd=False, upd=True)
     return schedule
 
 no_pp_schedule = [
     [
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=False, upd=True),
+        Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=True),
     ]
 ]
 
 pp2_interleaved_1f1b_grid_schedule = [
     [
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=2, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=2, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=2, mb_idx=0, is_fwd=False, upd=False),
-        None,
-        Task(device_id=0, stage_id=2, mb_idx=1, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=2, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=2, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=1, is_fwd=False, upd=False),
-        None,
-        Task(device_id=0, stage_id=2, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=2, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=False, upd=True),
+        Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=False, upd=False),
+        None,
+        Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=False, upd=False),
+        Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False),
+        Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=False, upd=False),
+        None,
+        Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=False, upd=False),
+        Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=False, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=False, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=False, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=True),
     ],
     [
         None,
-        Task(device_id=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=3, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=3, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=3, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=3, mb_idx=1, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=1, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=3, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=3, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=3, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=3, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=0, is_fwd=False, upd=True),
+        Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=True),
         None,
     ],
 ]
 
 pp4_interleaved_1f1b_grid_schedule = [
     [
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=4, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=4, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=4, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=0, stage_id=4, mb_idx=3, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=4, mb_idx=0, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=4, mb_idx=1, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=4, mb_idx=2, is_fwd=True, upd=False),
+        Task(pp_rank=0, stage_id=4, mb_idx=3, is_fwd=True, upd=False),
         None,
         None,
         None,
-        Task(device_id=0, stage_id=4, mb_idx=0, is_fwd=False, upd=False),
+        Task(pp_rank=0, stage_id=4, mb_idx=0, is_fwd=False, upd=False),
         None,
-        Task(device_id=0, stage_id=4, mb_idx=1, is_fwd=False, upd=False),
+        Task(pp_rank=0, stage_id=4, mb_idx=1, is_fwd=False, upd=False),
         None,
-        Task(device_id=0, stage_id=4, mb_idx=2, is_fwd=False, upd=False),
+        Task(pp_rank=0, stage_id=4, mb_idx=2, is_fwd=False, upd=False),
         None,
-        Task(device_id=0, stage_id=4, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=1, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=0, stage_id=0, mb_idx=0, is_fwd=False, upd=True),
+        Task(pp_rank=0, stage_id=4, mb_idx=3, is_fwd=False, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=False, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=False, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=False, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=True),
     ],
     [
         None,
-        Task(device_id=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=5, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=5, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=5, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=1, stage_id=5, mb_idx=3, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=5, mb_idx=0, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=5, mb_idx=1, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=5, mb_idx=2, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=5, mb_idx=3, is_fwd=True, upd=False),
         None,
-        Task(device_id=1, stage_id=5, mb_idx=0, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=5, mb_idx=0, is_fwd=False, upd=False),
         None,
-        Task(device_id=1, stage_id=5, mb_idx=1, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=5, mb_idx=1, is_fwd=False, upd=False),
         None,
-        Task(device_id=1, stage_id=5, mb_idx=2, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=5, mb_idx=2, is_fwd=False, upd=False),
         None,
-        Task(device_id=1, stage_id=5, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=1, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=1, stage_id=1, mb_idx=0, is_fwd=False, upd=True),
+        Task(pp_rank=1, stage_id=5, mb_idx=3, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=True),
         None,
     ],
     [
         None,
         None,
-        Task(device_id=2, stage_id=2, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=2, stage_id=2, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=2, stage_id=2, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=2, stage_id=2, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=2, stage_id=6, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=2, stage_id=6, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=2, stage_id=6, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=2, stage_id=6, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=2, stage_id=6, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=2, stage_id=6, mb_idx=1, is_fwd=False, upd=False),
+        Task(pp_rank=2, stage_id=2, mb_idx=0, is_fwd=True, upd=False),
+        Task(pp_rank=2, stage_id=2, mb_idx=1, is_fwd=True, upd=False),
+        Task(pp_rank=2, stage_id=2, mb_idx=2, is_fwd=True, upd=False),
+        Task(pp_rank=2, stage_id=2, mb_idx=3, is_fwd=True, upd=False),
+        Task(pp_rank=2, stage_id=6, mb_idx=0, is_fwd=True, upd=False),
+        Task(pp_rank=2, stage_id=6, mb_idx=1, is_fwd=True, upd=False),
+        Task(pp_rank=2, stage_id=6, mb_idx=2, is_fwd=True, upd=False),
+        Task(pp_rank=2, stage_id=6, mb_idx=0, is_fwd=False, upd=False),
+        Task(pp_rank=2, stage_id=6, mb_idx=3, is_fwd=True, upd=False),
+        Task(pp_rank=2, stage_id=6, mb_idx=1, is_fwd=False, upd=False),
         None,
-        Task(device_id=2, stage_id=6, mb_idx=2, is_fwd=False, upd=False),
+        Task(pp_rank=2, stage_id=6, mb_idx=2, is_fwd=False, upd=False),
         None,
-        Task(device_id=2, stage_id=6, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=2, stage_id=2, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=2, stage_id=2, mb_idx=1, is_fwd=False, upd=False),
-        Task(device_id=2, stage_id=2, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=2, stage_id=2, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=2, stage_id=2, mb_idx=0, is_fwd=False, upd=True),
+        Task(pp_rank=2, stage_id=6, mb_idx=3, is_fwd=False, upd=False),
+        Task(pp_rank=2, stage_id=2, mb_idx=0, is_fwd=False, upd=False),
+        Task(pp_rank=2, stage_id=2, mb_idx=1, is_fwd=False, upd=False),
+        Task(pp_rank=2, stage_id=2, mb_idx=2, is_fwd=False, upd=False),
+        Task(pp_rank=2, stage_id=2, mb_idx=3, is_fwd=False, upd=False),
+        Task(pp_rank=2, stage_id=2, mb_idx=0, is_fwd=False, upd=True),
         None,
         None,
     ],
@@ -194,23 +194,23 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int):
         None,
         None,
         None,
-        Task(device_id=3, stage_id=3, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=3, stage_id=3, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=3, stage_id=3, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=3, stage_id=3, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=3, stage_id=7, mb_idx=0, is_fwd=True, upd=False),
-        Task(device_id=3, stage_id=7, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=3, stage_id=7, mb_idx=1, is_fwd=True, upd=False),
-        Task(device_id=3, stage_id=7, mb_idx=1, is_fwd=False, upd=False),
-        Task(device_id=3, stage_id=7, mb_idx=2, is_fwd=True, upd=False),
-        Task(device_id=3, stage_id=7, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=3, stage_id=7, mb_idx=3, is_fwd=True, upd=False),
-        Task(device_id=3, stage_id=7, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=3, stage_id=3, mb_idx=0, is_fwd=False, upd=False),
-        Task(device_id=3, stage_id=3, mb_idx=1, is_fwd=False, upd=False),
-        Task(device_id=3, stage_id=3, mb_idx=2, is_fwd=False, upd=False),
-        Task(device_id=3, stage_id=3, mb_idx=3, is_fwd=False, upd=False),
-        Task(device_id=3, stage_id=3, mb_idx=0, is_fwd=False, upd=True),
+        Task(pp_rank=3, stage_id=3, mb_idx=0, is_fwd=True, upd=False),
+        Task(pp_rank=3, stage_id=3, mb_idx=1, is_fwd=True, upd=False),
+        Task(pp_rank=3, stage_id=3, mb_idx=2, is_fwd=True, upd=False),
+        Task(pp_rank=3, stage_id=3, mb_idx=3, is_fwd=True, upd=False),
+        Task(pp_rank=3, stage_id=7, mb_idx=0, is_fwd=True, upd=False),
+        Task(pp_rank=3, stage_id=7, mb_idx=0, is_fwd=False, upd=False),
+        Task(pp_rank=3, stage_id=7, mb_idx=1, is_fwd=True, upd=False),
+        Task(pp_rank=3, stage_id=7, mb_idx=1, is_fwd=False, upd=False),
+        Task(pp_rank=3, stage_id=7, mb_idx=2, is_fwd=True, upd=False),
+        Task(pp_rank=3, stage_id=7, mb_idx=2, is_fwd=False, upd=False),
+        Task(pp_rank=3, stage_id=7, mb_idx=3, is_fwd=True, upd=False),
+        Task(pp_rank=3, stage_id=7, mb_idx=3, is_fwd=False, upd=False),
+        Task(pp_rank=3, stage_id=3, mb_idx=0, is_fwd=False, upd=False),
+        Task(pp_rank=3, stage_id=3, mb_idx=1, is_fwd=False, upd=False),
+        Task(pp_rank=3, stage_id=3, mb_idx=2, is_fwd=False, upd=False),
+        Task(pp_rank=3, stage_id=3, mb_idx=3, is_fwd=False, upd=False),
+        Task(pp_rank=3, stage_id=3, mb_idx=0, is_fwd=False, upd=True),
         None,
         None,
         None,
diff --git a/test/test_llama.py b/test/test_llama.py
index 787d821..a13bdb4 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -150,12 +150,11 @@ def main(args):
         schedule
     )
     
-    actors = piper_metadata.actors
 
     # Send data to actors ahead of time
-    # num_actors = len(actors)
-    # ray.get(actors[0].load_input.remote(x))
-    # ray.get(actors[num_actors-1].load_labels.remote(y))
+    actors = piper_metadata.actors
+    ray.get(actors[0].load_input.remote([x]))
+    ray.get(actors[len(actors)-1].load_labels.remote(y))
 
     # Definte one iteration of the schedule
     def iter_schedule():
diff --git a/test/test_mixtral.py b/test/test_mixtral.py
index cbd12f3..6e2d1d0 100644
--- a/test/test_mixtral.py
+++ b/test/test_mixtral.py
@@ -8,6 +8,7 @@
 from src.piper_coordinator import PiperProgramCoordinator
 from src.piper_compile import piper_setup
 from src.piper_exec import piper_exec
+from src.piper_utils import piper_metadata
 
 from .models.mixtral import Transformer, ModelArgs
 from .schedule_helpers import build_1f1b_schedule, print_schedule
@@ -36,11 +37,18 @@ def main(dp_degree, pp_degree):
         schedule,
     )
 
+    # Send data to actors ahead of time
+    actors = piper_metadata.actors
+    ray.get(actors[0].load_input.remote([x]))
+    ray.get(actors[len(actors)-1].load_labels.remote(y))
+
     loss_fn = torch.nn.CrossEntropyLoss()
-    for _ in range(5):
-        piper_exec(model, schedule, [x, input_pos], y, loss_fn)
-        time.sleep(1)
-    
+
+    start = time.perf_counter()
+    piper_exec(model, schedule, [x, input_pos], y, loss_fn)
+    end = time.perf_counter()
+    print(f"Iter time: {end - start:.2f} s")
+
     ray.timeline(f"out/mixtral.json")
 
 if __name__ == "__main__":

From 6b27bd3b3df2135065540f08974b7bac436e56c6 Mon Sep 17 00:00:00 2001
From: Shubham Tiwari <shubhamtiwaribits@gmail.com>
Date: Thu, 12 Feb 2026 11:07:21 +0000
Subject: [PATCH 020/100] fix device-side assertion when running multiple iters

---
 src/piper_exec.py                   |  5 ++
 test/test_mixtral_multiple_iters.py | 78 +++++++++++++++++++++++++++++
 2 files changed, 83 insertions(+)
 create mode 100644 test/test_mixtral_multiple_iters.py

diff --git a/src/piper_exec.py b/src/piper_exec.py
index 5cd4850..766c5bf 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -159,6 +159,11 @@ def piper_exec(model, schedule, inputs, truth, loss_fn):
     """
     num_steps, num_devices = len(schedule[0]), len(schedule)
     actors = piper_metadata.actors
+
+    # ensure loading of inputs and labels onto actors 
+    ray.get(actors[0].load_input.remote(inputs))
+    ray.get(actors[len(actors)-1].load_labels.remote(truth))
+    
     num_mbs = len(set([task.mb_idx for row in schedule for task in row if task is not None]))
 
     dag_edges = piper_metadata.dag
diff --git a/test/test_mixtral_multiple_iters.py b/test/test_mixtral_multiple_iters.py
new file mode 100644
index 0000000..3a9f41c
--- /dev/null
+++ b/test/test_mixtral_multiple_iters.py
@@ -0,0 +1,78 @@
+import ray
+import torch
+import torch.nn as nn
+import argparse
+import time
+import os
+
+from src.piper_coordinator import PiperProgramCoordinator
+from src.piper_compile import piper_setup
+from src.piper_exec import piper_exec
+from src.piper_utils import piper_metadata
+
+from .models.mixtral import Transformer, ModelArgs
+from .schedule_helpers import build_1f1b_schedule, print_schedule
+from torch._dynamo.backends.debugging import eager
+
+def main(dp_degree, pp_degree, num_warmup=2, num_iters=5):
+
+    world_size = dp_degree * pp_degree
+    num_mbs = 1
+    batch_size = 8
+
+    config = ModelArgs.from_name("tiny")
+    dp_rank = int(os.environ['PIPER_DP_RANK'])
+
+    x = torch.randint(0, config.vocab_size, (batch_size, config.block_size))
+    input_pos = torch.arange(config.block_size)
+    y = torch.randn(batch_size, config.block_size, config.vocab_size)
+
+    schedule = build_1f1b_schedule(num_mbs, pp_degree)
+    print_schedule(schedule)
+    model = piper_setup(
+        Transformer, 
+        [config], 
+        torch.optim.Adam, 
+        [x, input_pos], 
+        schedule,
+    )
+
+    # Send data to actors ahead of time
+    actors = piper_metadata.actors
+    ray.get(actors[0].load_input.remote([x]))
+    ray.get(actors[len(actors)-1].load_labels.remote(y))
+
+    loss_fn = torch.nn.CrossEntropyLoss()
+
+    print(f"running {warmup} warmup iters...")
+    for i in range(num_warmup):
+        piper_exec(model, schedule, [x, input_pos], y, loss_fn)
+        print(f"warmup iter {i + 1}/{num_warmup} completed")
+
+    print(f"running {num_iters} iters...")
+    iter_times = []
+    for i in range(num_iters):
+        start = time.perf_counter()
+        piper_exec(model, schedule, [x, input_pos], y, loss_fn)
+        end = time.perf_counter()
+        iter_time = end - start
+        iter_times.append(iter_time)
+        print(f"iter {i + 1}/{num_iters} took {iter_time:.2f} s")
+    
+    if iter_times:
+        avg_time = sum(iter_times) / len(iter_times)
+        print(f"average iter time: {avg_time:.2f} s")
+        print(f"min iter time: {min(iter_times):.2f} s")
+        print(f"max iter time: {max(iter_times):.2f} s")
+
+    ray.timeline(f"out/mixtral.json")
+
+if __name__ == "__main__":
+    ray.init(include_dashboard=False, log_to_driver=True, namespace="llama", _temp_dir="/m-coriander/coriander/mfris/tmp/ray")
+    dp_degree, pp_degree = 2, 2
+    warmup = 2  
+    num_iterations = 10 
+    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=pp_degree, dp_degree=dp_degree)
+    handles = piper_coordinator.run_program.remote(main, dp_degree, pp_degree, warmup, num_iterations)
+    ray.get(handles)
+    ray.shutdown()
\ No newline at end of file

From c506131dc3161e4f3e1f3a8fd9e0a01a0cf55998 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Thu, 12 Feb 2026 19:30:53 +0000
Subject: [PATCH 021/100] remove input clear on actor

---
 src/piper_actor.py           |  18 +++--
 src/piper_exec.py            |   6 --
 src/piper_graph_transform.py |   8 +-
 src/piper_utils.py           |   2 +-
 test/test_llama.py           | 149 ++++++++++++++++++-----------------
 test/test_mixtral.py         |  49 +++++++++---
 6 files changed, 127 insertions(+), 105 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index 00e15cc..4c5b9a2 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -328,6 +328,7 @@ def _forward(self, stage_id: int, mb_idx: int):
             # For non-first stages, receive input tensors from the previous stage
             start_event = torch.cuda.Event(enable_timing=True)
             end_event = torch.cuda.Event(enable_timing=True)
+            torch.cuda.synchronize()
             start_event.record(self.p2p_stream)
             for i in self.input_idxs[stage_id]:
                 pp_rank = piper_metadata.stage_to_device[stage_id - 1]
@@ -336,7 +337,7 @@ def _forward(self, stage_id: int, mb_idx: int):
             end_event.record(self.p2p_stream)
             torch.cuda.synchronize()
             time = start_event.elapsed_time(end_event)
-            logger.info(f"Completed fwd p2p recv rank={self.global_rank} time={time:.2f} ms")
+            logger.debug(f"Completed fwd p2p recv rank={self.global_rank} time={time:.2f} ms")
 
             # save first input that requires grad as input activation
             self.forward_args[stage_id][self.input_idxs[stage_id][-1]].requires_grad_()
@@ -356,14 +357,14 @@ def _forward(self, stage_id: int, mb_idx: int):
         self.out_activation[stage_id][mb_idx] = out_with_grad[0]
 
         # clear the input tensors
-        for i in self.input_idxs[stage_id]:
-            self.forward_args[stage_id][i] = torch.empty_like(self.forward_args[stage_id][i])
+        # for i in self.input_idxs[stage_id]:
+        #     self.forward_args[stage_id][i] = torch.empty_like(self.forward_args[stage_id][i])
 
         if stage_id < self.num_stages - 1:
             start_event = torch.cuda.Event(enable_timing=True)
             end_event = torch.cuda.Event(enable_timing=True)
-            start_event.record(self.p2p_stream)
             torch.cuda.synchronize()
+            start_event.record(self.p2p_stream)
             # For non-final stages, send output tensors to the next stage
             for i in range(len(output)):
                 pp_rank = piper_metadata.stage_to_device[stage_id + 1]
@@ -372,7 +373,7 @@ def _forward(self, stage_id: int, mb_idx: int):
             end_event.record(self.p2p_stream)
             torch.cuda.synchronize()
             time = start_event.elapsed_time(end_event)
-            logger.info(f"Completed fwd p2p send rank={self.global_rank} time={time:.2f} ms")
+            logger.debug(f"Completed fwd p2p send rank={self.global_rank} time={time:.2f} ms")
 
         if CLEANUP_MEMORY:
             gc.collect()
@@ -392,6 +393,7 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
             # For non-final stages, recieve input gradients from the subsequent backward pass
             start_event = torch.cuda.Event(enable_timing=True)
             end_event = torch.cuda.Event(enable_timing=True)
+            torch.cuda.synchronize()
             start_event.record(self.p2p_stream)
             input_grad = torch.empty_like(out_activation)
             pp_rank = piper_metadata.stage_to_device[stage_id + 1]
@@ -401,7 +403,7 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
             end_event.record(self.p2p_stream)
             torch.cuda.synchronize()
             time = start_event.elapsed_time(end_event)
-            logger.info(f"Completed bwd p2p recv rank={self.global_rank} time={time:.2f} ms")
+            logger.debug(f"Completed bwd p2p recv rank={self.global_rank} time={time:.2f} ms")
             out_activation.backward(gradient=input_grad)
         else:
             # For the final stage, wait for the forward pass to complete
@@ -413,11 +415,13 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
             self.loss.append(loss.item())
 
         del out_activation
+        self.out_activation[stage_id][mb_idx] = None
 
         if stage_id > 0:
             # For non-first stages, send output gradients to the previous backward stage
             start_event = torch.cuda.Event(enable_timing=True)
             end_event = torch.cuda.Event(enable_timing=True)
+            torch.cuda.synchronize()
             start_event.record(self.p2p_stream)
             output_grad = self.inp_activation[stage_id][mb_idx].grad
             assert output_grad is not None
@@ -428,7 +432,7 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
             end_event.record(self.p2p_stream)
             torch.cuda.synchronize()
             time = start_event.elapsed_time(end_event)
-            logger.info(f"Completed bwd p2p send rank={self.global_rank} time={time:.2f} ms")
+            logger.debug(f"Completed bwd p2p send rank={self.global_rank} time={time:.2f} ms")
             self.inp_activation[stage_id][mb_idx] = None
 
         if CLEANUP_MEMORY:
diff --git a/src/piper_exec.py b/src/piper_exec.py
index 766c5bf..6489dcf 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -159,10 +159,6 @@ def piper_exec(model, schedule, inputs, truth, loss_fn):
     """
     num_steps, num_devices = len(schedule[0]), len(schedule)
     actors = piper_metadata.actors
-
-    # ensure loading of inputs and labels onto actors 
-    ray.get(actors[0].load_input.remote(inputs))
-    ray.get(actors[len(actors)-1].load_labels.remote(truth))
     
     num_mbs = len(set([task.mb_idx for row in schedule for task in row if task is not None]))
 
@@ -173,8 +169,6 @@ def piper_exec(model, schedule, inputs, truth, loss_fn):
 
     [actor._comm_loop.remote() for actor in actors.values()]
 
-    time.sleep(3)
-
     ret = []
     for i in range(num_steps):
         for j in range(num_devices-1, -1, -1):
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index c55d01c..d53eb8d 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -59,7 +59,7 @@ def forward(ctx, output, input_tensor):
         ctx.global_rank = actor_self.global_rank
         ctx.stream = actor_self.a2a_stream
 
-        logger.info(f"Dispatch AllToAllSingleFunction forward rank={ctx.global_rank}, shape={input_tensor.shape}")
+        logger.debug(f"Dispatch AllToAllSingleFunction forward rank={ctx.global_rank}, shape={input_tensor.shape}")
         
         start_event = torch.cuda.Event(enable_timing=True)
         end_event = torch.cuda.Event(enable_timing=True)
@@ -76,7 +76,7 @@ def forward(ctx, output, input_tensor):
         end_event.record(ctx.stream)
         torch.cuda.synchronize()
         time = start_event.elapsed_time(end_event)
-        logger.info(f"Completed AllToAllSingleFunction forward rank={ctx.global_rank} time={time:.2f} ms")
+        logger.debug(f"Completed AllToAllSingleFunction forward rank={ctx.global_rank} time={time:.2f} ms")
 
         return output
     
@@ -88,7 +88,7 @@ def backward(ctx, grad_output):
         The backward of all_to_all_single is another all_to_all_single operation
         that reverses the communication pattern.
         """
-        logger.info(f"Dispatch AllToAllSingleFunction backward rank={ctx.global_rank}, shape={grad_output.shape}")
+        logger.debug(f"Dispatch AllToAllSingleFunction backward rank={ctx.global_rank}, shape={grad_output.shape}")
 
         start_event = torch.cuda.Event(enable_timing=True)
         end_event = torch.cuda.Event(enable_timing=True)
@@ -112,7 +112,7 @@ def backward(ctx, grad_output):
         end_event.record(ctx.stream)
         torch.cuda.synchronize()
         time = start_event.elapsed_time(end_event)
-        logger.info(f"Completed AllToAllSingleFunction backward rank={ctx.global_rank} time={time:.2f} ms")
+        logger.debug(f"Completed AllToAllSingleFunction backward rank={ctx.global_rank} time={time:.2f} ms")
         
         # Return gradients: grad_output flows to grad_input, None for group
         return grad_input, grad_input, None
diff --git a/src/piper_utils.py b/src/piper_utils.py
index eb9eb25..b489a56 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -12,7 +12,7 @@
 
 import src.piper_patches
 
-LOG_LEVEL = "DEBUG"
+LOG_LEVEL = "INFO"
 
 """ 
 Print the backward graph of a tensor
diff --git a/test/test_llama.py b/test/test_llama.py
index a13bdb4..1acf5b5 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -22,80 +22,6 @@
     no_pp_schedule
 )
 
-def parse_args():
-    parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
-    parser.add_argument('--model', choices=['LLAMA_DEBUG', 'LLAMA_1B', 'LLAMA_3B', 'LLAMA_8B'], default='LLAMA_DEBUG',
-                        help='Model configuration: LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, or LLAMA_8B (default: LLAMA_DEBUG)')
-    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'no-pp'], default='1f1b',
-                        help='Schedule type: gpipe, 1f1b, or interleaved-1f1b (default: 1f1b)')
-    parser.add_argument('--dp_degree', type=int, default=1,
-                        help='Number of data parallel degrees (default: 1)')
-    parser.add_argument('--pp_degree', type=int, default=2,
-                        help='Number of pipeline parallel degrees (default: 2)')
-    parser.add_argument('--batch_size', type=int, default=16,
-                        help='Batch size (default: 16)')
-    parser.add_argument('--num_mbs', type=int, default=4,
-                        help='Number of microbatches (default: 4)')
-    parser.add_argument('--seq_len', type=int, default=256,
-                        help='Sequence length (default: 256)')
-    parser.add_argument('--warmup', type=int, default=5,
-                        help='Number of warmup iterations (default: 5)')
-    parser.add_argument('--iters', type=int, default=20,
-                        help='Number of timing iterations (default: 20)')
-    parser.add_argument('--tracing', action='store_true', default=False,
-                        help='Enable tracing')
-    parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
-                        help='Enable naive gradient sync')
-    return parser.parse_args()
-
-
-def print_mean_timing_data(trace_data: dict, actor_id: int) -> None:
-    """Prints mean timing and memory statistics from trace_data for a given actor.
-
-    Args:
-        trace_data (dict): Trace data dictionary from the actor containing timing and memory metrics.
-        actor_id (int): The ID of the actor.
-    """
-    import numpy as np
-
-    print(f"\nTiming and Memory statistics for Actor {actor_id}:")
-    
-    for stage_id, stage_data in trace_data.items():
-        if stage_id == 'update':
-            # Handle update data (global optimizer step)
-            print(f"  Update:")
-            for metric, values in stage_data.items():
-                if not values:
-                    mean_val = float('nan')
-                else:
-                    mean_val = float(np.mean(values))
-                
-                if 'memory' in metric:
-                    if 'delta' in metric:
-                        print(f"    {metric}: {mean_val:.3f} GB")
-                    else:
-                        print(f"    {metric}: {mean_val:.3f} GB")
-                else:
-                    print(f"    {metric}: {mean_val:.3f} ms")
-        else:
-            # Handle stage data (forward/backward passes)
-            print(f"  Stage {stage_id}:")
-            for phase, phase_data in stage_data.items():
-                print(f"    {phase.capitalize()}:")
-                for metric, values in phase_data.items():
-                    if not values:
-                        mean_val = float('nan')
-                    else:
-                        mean_val = float(np.mean(values))
-                    
-                    if 'memory' in metric:
-                        if 'delta' in metric:
-                            print(f"      {metric}: {mean_val:.3f} GB")
-                        else:
-                            print(f"      {metric}: {mean_val:.3f} GB")
-                    else:
-                        print(f"      {metric}: {mean_val:.3f} ms")
-
 
 def main(args):
     
@@ -201,6 +127,81 @@ def iter_schedule():
     ray.timeline(timeline_filename)
     print(f"\nRay timeline saved to: {timeline_filename}")
 
+
+def print_mean_timing_data(trace_data: dict, actor_id: int) -> None:
+    """Prints mean timing and memory statistics from trace_data for a given actor.
+
+    Args:
+        trace_data (dict): Trace data dictionary from the actor containing timing and memory metrics.
+        actor_id (int): The ID of the actor.
+    """
+    import numpy as np
+
+    print(f"\nTiming and Memory statistics for Actor {actor_id}:")
+    
+    for stage_id, stage_data in trace_data.items():
+        if stage_id == 'update':
+            # Handle update data (global optimizer step)
+            print(f"  Update:")
+            for metric, values in stage_data.items():
+                if not values:
+                    mean_val = float('nan')
+                else:
+                    mean_val = float(np.mean(values))
+                
+                if 'memory' in metric:
+                    if 'delta' in metric:
+                        print(f"    {metric}: {mean_val:.3f} GB")
+                    else:
+                        print(f"    {metric}: {mean_val:.3f} GB")
+                else:
+                    print(f"    {metric}: {mean_val:.3f} ms")
+        else:
+            # Handle stage data (forward/backward passes)
+            print(f"  Stage {stage_id}:")
+            for phase, phase_data in stage_data.items():
+                print(f"    {phase.capitalize()}:")
+                for metric, values in phase_data.items():
+                    if not values:
+                        mean_val = float('nan')
+                    else:
+                        mean_val = float(np.mean(values))
+                    
+                    if 'memory' in metric:
+                        if 'delta' in metric:
+                            print(f"      {metric}: {mean_val:.3f} GB")
+                        else:
+                            print(f"      {metric}: {mean_val:.3f} GB")
+                    else:
+                        print(f"      {metric}: {mean_val:.3f} ms")
+
+                        
+def parse_args():
+    parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
+    parser.add_argument('--model', choices=['LLAMA_DEBUG', 'LLAMA_1B', 'LLAMA_3B', 'LLAMA_8B'], default='LLAMA_DEBUG',
+                        help='Model configuration: LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, or LLAMA_8B (default: LLAMA_DEBUG)')
+    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'no-pp'], default='1f1b',
+                        help='Schedule type: gpipe, 1f1b, or interleaved-1f1b (default: 1f1b)')
+    parser.add_argument('--dp_degree', type=int, default=1,
+                        help='Number of data parallel degrees (default: 1)')
+    parser.add_argument('--pp_degree', type=int, default=2,
+                        help='Number of pipeline parallel degrees (default: 2)')
+    parser.add_argument('--batch_size', type=int, default=16,
+                        help='Batch size (default: 16)')
+    parser.add_argument('--num_mbs', type=int, default=4,
+                        help='Number of microbatches (default: 4)')
+    parser.add_argument('--seq_len', type=int, default=256,
+                        help='Sequence length (default: 256)')
+    parser.add_argument('--warmup', type=int, default=5,
+                        help='Number of warmup iterations (default: 5)')
+    parser.add_argument('--iters', type=int, default=20,
+                        help='Number of timing iterations (default: 20)')
+    parser.add_argument('--tracing', action='store_true', default=False,
+                        help='Enable tracing')
+    parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
+                        help='Enable naive gradient sync')
+    return parser.parse_args()
+
 if __name__ == "__main__":
     ray.init(include_dashboard=False, log_to_driver=True, namespace="llama")
     args = parse_args()
diff --git a/test/test_mixtral.py b/test/test_mixtral.py
index 6e2d1d0..513c59b 100644
--- a/test/test_mixtral.py
+++ b/test/test_mixtral.py
@@ -14,20 +14,18 @@
 from .schedule_helpers import build_1f1b_schedule, print_schedule
 from torch._dynamo.backends.debugging import eager
 
-def main(dp_degree, pp_degree):
+def main(args):
 
-    world_size = dp_degree * pp_degree
-    num_mbs = 1
-    batch_size = 8
+    world_size = args.dp * args.pp
 
-    config = ModelArgs.from_name("tiny")
+    config = ModelArgs.from_name(args.model)
     dp_rank = int(os.environ['PIPER_DP_RANK'])
 
-    x = torch.randint(0, config.vocab_size, (batch_size, config.block_size))
+    x = torch.randint(0, config.vocab_size, (args.batch_size, config.block_size))
     input_pos = torch.arange(config.block_size)
-    y = torch.randn(batch_size, config.block_size, config.vocab_size)
+    y = torch.randn(args.batch_size, config.block_size, config.vocab_size)
 
-    schedule = build_1f1b_schedule(num_mbs, pp_degree)
+    schedule = build_1f1b_schedule(args.mbs, args.pp)
     print_schedule(schedule)
     model = piper_setup(
         Transformer, 
@@ -35,6 +33,7 @@ def main(dp_degree, pp_degree):
         torch.optim.Adam, 
         [x, input_pos], 
         schedule,
+        naive_gradient_sync=args.naive_gradient_sync,
     )
 
     # Send data to actors ahead of time
@@ -44,17 +43,41 @@ def main(dp_degree, pp_degree):
 
     loss_fn = torch.nn.CrossEntropyLoss()
 
+    for _ in range(args.warmup):
+        piper_exec(model, schedule, [x, input_pos], y, loss_fn)
+
     start = time.perf_counter()
-    piper_exec(model, schedule, [x, input_pos], y, loss_fn)
+    for _ in range(args.iters):
+        piper_exec(model, schedule, [x, input_pos], y, loss_fn)
     end = time.perf_counter()
-    print(f"Iter time: {end - start:.2f} s")
+    print(f"Iter time: {(end - start)/args.iters:.2f} s")
 
     ray.timeline(f"out/mixtral.json")
 
+def parse_args():
+    parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
+    parser.add_argument('--model', choices=['tiny', 'small', 'medium', 'Mixtral-8x7B-v0.1'], default='tiny',
+                        help='Model configuration: tiny, small, medium, or Mixtral-8x7B-v0.1 (default: tiny)')
+    parser.add_argument('--dp', type=int, default=2,
+                        help='Number of data parallel degrees (default: 2)')
+    parser.add_argument('--pp', type=int, default=2,
+                        help='Number of pipeline parallel degrees (default: 2)')
+    parser.add_argument('--batch_size', type=int, default=16,
+                        help='Batch size (default: 16)')
+    parser.add_argument('--mbs', type=int, default=4,
+                        help='Number of microbatches (default: 4)')
+    parser.add_argument('--warmup', type=int, default=3,
+                        help='Number of warmup iterations (default: 3)')
+    parser.add_argument('--iters', type=int, default=5,
+                        help='Number of timing iterations (default: 5)')
+    parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
+                        help='Enable naive gradient sync')
+    return parser.parse_args()
+
 if __name__ == "__main__":
     ray.init(include_dashboard=False, log_to_driver=True, namespace="llama", _temp_dir="/m-coriander/coriander/mfris/tmp/ray")
-    dp_degree, pp_degree = 2, 2
-    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=pp_degree, dp_degree=dp_degree)
-    handles = piper_coordinator.run_program.remote(main, dp_degree, pp_degree)
+    args = parse_args()
+    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
+    handles = piper_coordinator.run_program.remote(main, args)
     ray.get(handles)
     ray.shutdown()

From cc6dda2c71f6824d56f2a67fe2679bdde8b00bfc Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Fri, 13 Feb 2026 02:13:43 +0000
Subject: [PATCH 022/100] Revert "fix device-side assertion when running
 multiple iters"

This reverts commit 6b27bd3b3df2135065540f08974b7bac436e56c6.
---
 src/piper_exec.py                   |  1 -
 test/test_mixtral_multiple_iters.py | 78 -----------------------------
 2 files changed, 79 deletions(-)
 delete mode 100644 test/test_mixtral_multiple_iters.py

diff --git a/src/piper_exec.py b/src/piper_exec.py
index 6489dcf..27713c1 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -159,7 +159,6 @@ def piper_exec(model, schedule, inputs, truth, loss_fn):
     """
     num_steps, num_devices = len(schedule[0]), len(schedule)
     actors = piper_metadata.actors
-    
     num_mbs = len(set([task.mb_idx for row in schedule for task in row if task is not None]))
 
     dag_edges = piper_metadata.dag
diff --git a/test/test_mixtral_multiple_iters.py b/test/test_mixtral_multiple_iters.py
deleted file mode 100644
index 3a9f41c..0000000
--- a/test/test_mixtral_multiple_iters.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import ray
-import torch
-import torch.nn as nn
-import argparse
-import time
-import os
-
-from src.piper_coordinator import PiperProgramCoordinator
-from src.piper_compile import piper_setup
-from src.piper_exec import piper_exec
-from src.piper_utils import piper_metadata
-
-from .models.mixtral import Transformer, ModelArgs
-from .schedule_helpers import build_1f1b_schedule, print_schedule
-from torch._dynamo.backends.debugging import eager
-
-def main(dp_degree, pp_degree, num_warmup=2, num_iters=5):
-
-    world_size = dp_degree * pp_degree
-    num_mbs = 1
-    batch_size = 8
-
-    config = ModelArgs.from_name("tiny")
-    dp_rank = int(os.environ['PIPER_DP_RANK'])
-
-    x = torch.randint(0, config.vocab_size, (batch_size, config.block_size))
-    input_pos = torch.arange(config.block_size)
-    y = torch.randn(batch_size, config.block_size, config.vocab_size)
-
-    schedule = build_1f1b_schedule(num_mbs, pp_degree)
-    print_schedule(schedule)
-    model = piper_setup(
-        Transformer, 
-        [config], 
-        torch.optim.Adam, 
-        [x, input_pos], 
-        schedule,
-    )
-
-    # Send data to actors ahead of time
-    actors = piper_metadata.actors
-    ray.get(actors[0].load_input.remote([x]))
-    ray.get(actors[len(actors)-1].load_labels.remote(y))
-
-    loss_fn = torch.nn.CrossEntropyLoss()
-
-    print(f"running {warmup} warmup iters...")
-    for i in range(num_warmup):
-        piper_exec(model, schedule, [x, input_pos], y, loss_fn)
-        print(f"warmup iter {i + 1}/{num_warmup} completed")
-
-    print(f"running {num_iters} iters...")
-    iter_times = []
-    for i in range(num_iters):
-        start = time.perf_counter()
-        piper_exec(model, schedule, [x, input_pos], y, loss_fn)
-        end = time.perf_counter()
-        iter_time = end - start
-        iter_times.append(iter_time)
-        print(f"iter {i + 1}/{num_iters} took {iter_time:.2f} s")
-    
-    if iter_times:
-        avg_time = sum(iter_times) / len(iter_times)
-        print(f"average iter time: {avg_time:.2f} s")
-        print(f"min iter time: {min(iter_times):.2f} s")
-        print(f"max iter time: {max(iter_times):.2f} s")
-
-    ray.timeline(f"out/mixtral.json")
-
-if __name__ == "__main__":
-    ray.init(include_dashboard=False, log_to_driver=True, namespace="llama", _temp_dir="/m-coriander/coriander/mfris/tmp/ray")
-    dp_degree, pp_degree = 2, 2
-    warmup = 2  
-    num_iterations = 10 
-    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=pp_degree, dp_degree=dp_degree)
-    handles = piper_coordinator.run_program.remote(main, dp_degree, pp_degree, warmup, num_iterations)
-    ray.get(handles)
-    ray.shutdown()
\ No newline at end of file

From b6b8f48eea948d836098a6c9e4ebaef9e16e4f21 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Fri, 13 Feb 2026 02:15:12 +0000
Subject: [PATCH 023/100] Reapply "fix device-side assertion when running
 multiple iters"

This reverts commit cc6dda2c71f6824d56f2a67fe2679bdde8b00bfc.
---
 src/piper_exec.py                   |  1 +
 test/test_mixtral_multiple_iters.py | 78 +++++++++++++++++++++++++++++
 2 files changed, 79 insertions(+)
 create mode 100644 test/test_mixtral_multiple_iters.py

diff --git a/src/piper_exec.py b/src/piper_exec.py
index 27713c1..6489dcf 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -159,6 +159,7 @@ def piper_exec(model, schedule, inputs, truth, loss_fn):
     """
     num_steps, num_devices = len(schedule[0]), len(schedule)
     actors = piper_metadata.actors
+    
     num_mbs = len(set([task.mb_idx for row in schedule for task in row if task is not None]))
 
     dag_edges = piper_metadata.dag
diff --git a/test/test_mixtral_multiple_iters.py b/test/test_mixtral_multiple_iters.py
new file mode 100644
index 0000000..3a9f41c
--- /dev/null
+++ b/test/test_mixtral_multiple_iters.py
@@ -0,0 +1,78 @@
+import ray
+import torch
+import torch.nn as nn
+import argparse
+import time
+import os
+
+from src.piper_coordinator import PiperProgramCoordinator
+from src.piper_compile import piper_setup
+from src.piper_exec import piper_exec
+from src.piper_utils import piper_metadata
+
+from .models.mixtral import Transformer, ModelArgs
+from .schedule_helpers import build_1f1b_schedule, print_schedule
+from torch._dynamo.backends.debugging import eager
+
+def main(dp_degree, pp_degree, num_warmup=2, num_iters=5):
+
+    world_size = dp_degree * pp_degree
+    num_mbs = 1
+    batch_size = 8
+
+    config = ModelArgs.from_name("tiny")
+    dp_rank = int(os.environ['PIPER_DP_RANK'])
+
+    x = torch.randint(0, config.vocab_size, (batch_size, config.block_size))
+    input_pos = torch.arange(config.block_size)
+    y = torch.randn(batch_size, config.block_size, config.vocab_size)
+
+    schedule = build_1f1b_schedule(num_mbs, pp_degree)
+    print_schedule(schedule)
+    model = piper_setup(
+        Transformer, 
+        [config], 
+        torch.optim.Adam, 
+        [x, input_pos], 
+        schedule,
+    )
+
+    # Send data to actors ahead of time
+    actors = piper_metadata.actors
+    ray.get(actors[0].load_input.remote([x]))
+    ray.get(actors[len(actors)-1].load_labels.remote(y))
+
+    loss_fn = torch.nn.CrossEntropyLoss()
+
+    print(f"running {warmup} warmup iters...")
+    for i in range(num_warmup):
+        piper_exec(model, schedule, [x, input_pos], y, loss_fn)
+        print(f"warmup iter {i + 1}/{num_warmup} completed")
+
+    print(f"running {num_iters} iters...")
+    iter_times = []
+    for i in range(num_iters):
+        start = time.perf_counter()
+        piper_exec(model, schedule, [x, input_pos], y, loss_fn)
+        end = time.perf_counter()
+        iter_time = end - start
+        iter_times.append(iter_time)
+        print(f"iter {i + 1}/{num_iters} took {iter_time:.2f} s")
+    
+    if iter_times:
+        avg_time = sum(iter_times) / len(iter_times)
+        print(f"average iter time: {avg_time:.2f} s")
+        print(f"min iter time: {min(iter_times):.2f} s")
+        print(f"max iter time: {max(iter_times):.2f} s")
+
+    ray.timeline(f"out/mixtral.json")
+
+if __name__ == "__main__":
+    ray.init(include_dashboard=False, log_to_driver=True, namespace="llama", _temp_dir="/m-coriander/coriander/mfris/tmp/ray")
+    dp_degree, pp_degree = 2, 2
+    warmup = 2  
+    num_iterations = 10 
+    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=pp_degree, dp_degree=dp_degree)
+    handles = piper_coordinator.run_program.remote(main, dp_degree, pp_degree, warmup, num_iterations)
+    ray.get(handles)
+    ray.shutdown()
\ No newline at end of file

From c9cbd3243f8ea00175498f5211d480514b0ec3b1 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Fri, 13 Feb 2026 02:15:41 +0000
Subject: [PATCH 024/100] Revert "remove input clear on actor"

This reverts commit c506131dc3161e4f3e1f3a8fd9e0a01a0cf55998.
---
 src/piper_actor.py           |  18 ++---
 src/piper_exec.py            |   6 ++
 src/piper_graph_transform.py |   8 +-
 src/piper_utils.py           |   2 +-
 test/test_llama.py           | 149 +++++++++++++++++------------------
 test/test_mixtral.py         |  49 +++---------
 6 files changed, 105 insertions(+), 127 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index 4c5b9a2..00e15cc 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -328,7 +328,6 @@ def _forward(self, stage_id: int, mb_idx: int):
             # For non-first stages, receive input tensors from the previous stage
             start_event = torch.cuda.Event(enable_timing=True)
             end_event = torch.cuda.Event(enable_timing=True)
-            torch.cuda.synchronize()
             start_event.record(self.p2p_stream)
             for i in self.input_idxs[stage_id]:
                 pp_rank = piper_metadata.stage_to_device[stage_id - 1]
@@ -337,7 +336,7 @@ def _forward(self, stage_id: int, mb_idx: int):
             end_event.record(self.p2p_stream)
             torch.cuda.synchronize()
             time = start_event.elapsed_time(end_event)
-            logger.debug(f"Completed fwd p2p recv rank={self.global_rank} time={time:.2f} ms")
+            logger.info(f"Completed fwd p2p recv rank={self.global_rank} time={time:.2f} ms")
 
             # save first input that requires grad as input activation
             self.forward_args[stage_id][self.input_idxs[stage_id][-1]].requires_grad_()
@@ -357,14 +356,14 @@ def _forward(self, stage_id: int, mb_idx: int):
         self.out_activation[stage_id][mb_idx] = out_with_grad[0]
 
         # clear the input tensors
-        # for i in self.input_idxs[stage_id]:
-        #     self.forward_args[stage_id][i] = torch.empty_like(self.forward_args[stage_id][i])
+        for i in self.input_idxs[stage_id]:
+            self.forward_args[stage_id][i] = torch.empty_like(self.forward_args[stage_id][i])
 
         if stage_id < self.num_stages - 1:
             start_event = torch.cuda.Event(enable_timing=True)
             end_event = torch.cuda.Event(enable_timing=True)
-            torch.cuda.synchronize()
             start_event.record(self.p2p_stream)
+            torch.cuda.synchronize()
             # For non-final stages, send output tensors to the next stage
             for i in range(len(output)):
                 pp_rank = piper_metadata.stage_to_device[stage_id + 1]
@@ -373,7 +372,7 @@ def _forward(self, stage_id: int, mb_idx: int):
             end_event.record(self.p2p_stream)
             torch.cuda.synchronize()
             time = start_event.elapsed_time(end_event)
-            logger.debug(f"Completed fwd p2p send rank={self.global_rank} time={time:.2f} ms")
+            logger.info(f"Completed fwd p2p send rank={self.global_rank} time={time:.2f} ms")
 
         if CLEANUP_MEMORY:
             gc.collect()
@@ -393,7 +392,6 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
             # For non-final stages, recieve input gradients from the subsequent backward pass
             start_event = torch.cuda.Event(enable_timing=True)
             end_event = torch.cuda.Event(enable_timing=True)
-            torch.cuda.synchronize()
             start_event.record(self.p2p_stream)
             input_grad = torch.empty_like(out_activation)
             pp_rank = piper_metadata.stage_to_device[stage_id + 1]
@@ -403,7 +401,7 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
             end_event.record(self.p2p_stream)
             torch.cuda.synchronize()
             time = start_event.elapsed_time(end_event)
-            logger.debug(f"Completed bwd p2p recv rank={self.global_rank} time={time:.2f} ms")
+            logger.info(f"Completed bwd p2p recv rank={self.global_rank} time={time:.2f} ms")
             out_activation.backward(gradient=input_grad)
         else:
             # For the final stage, wait for the forward pass to complete
@@ -415,13 +413,11 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
             self.loss.append(loss.item())
 
         del out_activation
-        self.out_activation[stage_id][mb_idx] = None
 
         if stage_id > 0:
             # For non-first stages, send output gradients to the previous backward stage
             start_event = torch.cuda.Event(enable_timing=True)
             end_event = torch.cuda.Event(enable_timing=True)
-            torch.cuda.synchronize()
             start_event.record(self.p2p_stream)
             output_grad = self.inp_activation[stage_id][mb_idx].grad
             assert output_grad is not None
@@ -432,7 +428,7 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
             end_event.record(self.p2p_stream)
             torch.cuda.synchronize()
             time = start_event.elapsed_time(end_event)
-            logger.debug(f"Completed bwd p2p send rank={self.global_rank} time={time:.2f} ms")
+            logger.info(f"Completed bwd p2p send rank={self.global_rank} time={time:.2f} ms")
             self.inp_activation[stage_id][mb_idx] = None
 
         if CLEANUP_MEMORY:
diff --git a/src/piper_exec.py b/src/piper_exec.py
index 6489dcf..766c5bf 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -159,6 +159,10 @@ def piper_exec(model, schedule, inputs, truth, loss_fn):
     """
     num_steps, num_devices = len(schedule[0]), len(schedule)
     actors = piper_metadata.actors
+
+    # ensure loading of inputs and labels onto actors 
+    ray.get(actors[0].load_input.remote(inputs))
+    ray.get(actors[len(actors)-1].load_labels.remote(truth))
     
     num_mbs = len(set([task.mb_idx for row in schedule for task in row if task is not None]))
 
@@ -169,6 +173,8 @@ def piper_exec(model, schedule, inputs, truth, loss_fn):
 
     [actor._comm_loop.remote() for actor in actors.values()]
 
+    time.sleep(3)
+
     ret = []
     for i in range(num_steps):
         for j in range(num_devices-1, -1, -1):
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index d53eb8d..c55d01c 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -59,7 +59,7 @@ def forward(ctx, output, input_tensor):
         ctx.global_rank = actor_self.global_rank
         ctx.stream = actor_self.a2a_stream
 
-        logger.debug(f"Dispatch AllToAllSingleFunction forward rank={ctx.global_rank}, shape={input_tensor.shape}")
+        logger.info(f"Dispatch AllToAllSingleFunction forward rank={ctx.global_rank}, shape={input_tensor.shape}")
         
         start_event = torch.cuda.Event(enable_timing=True)
         end_event = torch.cuda.Event(enable_timing=True)
@@ -76,7 +76,7 @@ def forward(ctx, output, input_tensor):
         end_event.record(ctx.stream)
         torch.cuda.synchronize()
         time = start_event.elapsed_time(end_event)
-        logger.debug(f"Completed AllToAllSingleFunction forward rank={ctx.global_rank} time={time:.2f} ms")
+        logger.info(f"Completed AllToAllSingleFunction forward rank={ctx.global_rank} time={time:.2f} ms")
 
         return output
     
@@ -88,7 +88,7 @@ def backward(ctx, grad_output):
         The backward of all_to_all_single is another all_to_all_single operation
         that reverses the communication pattern.
         """
-        logger.debug(f"Dispatch AllToAllSingleFunction backward rank={ctx.global_rank}, shape={grad_output.shape}")
+        logger.info(f"Dispatch AllToAllSingleFunction backward rank={ctx.global_rank}, shape={grad_output.shape}")
 
         start_event = torch.cuda.Event(enable_timing=True)
         end_event = torch.cuda.Event(enable_timing=True)
@@ -112,7 +112,7 @@ def backward(ctx, grad_output):
         end_event.record(ctx.stream)
         torch.cuda.synchronize()
         time = start_event.elapsed_time(end_event)
-        logger.debug(f"Completed AllToAllSingleFunction backward rank={ctx.global_rank} time={time:.2f} ms")
+        logger.info(f"Completed AllToAllSingleFunction backward rank={ctx.global_rank} time={time:.2f} ms")
         
         # Return gradients: grad_output flows to grad_input, None for group
         return grad_input, grad_input, None
diff --git a/src/piper_utils.py b/src/piper_utils.py
index b489a56..eb9eb25 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -12,7 +12,7 @@
 
 import src.piper_patches
 
-LOG_LEVEL = "INFO"
+LOG_LEVEL = "DEBUG"
 
 """ 
 Print the backward graph of a tensor
diff --git a/test/test_llama.py b/test/test_llama.py
index 1acf5b5..a13bdb4 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -22,6 +22,80 @@
     no_pp_schedule
 )
 
+def parse_args():
+    parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
+    parser.add_argument('--model', choices=['LLAMA_DEBUG', 'LLAMA_1B', 'LLAMA_3B', 'LLAMA_8B'], default='LLAMA_DEBUG',
+                        help='Model configuration: LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, or LLAMA_8B (default: LLAMA_DEBUG)')
+    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'no-pp'], default='1f1b',
+                        help='Schedule type: gpipe, 1f1b, or interleaved-1f1b (default: 1f1b)')
+    parser.add_argument('--dp_degree', type=int, default=1,
+                        help='Number of data parallel degrees (default: 1)')
+    parser.add_argument('--pp_degree', type=int, default=2,
+                        help='Number of pipeline parallel degrees (default: 2)')
+    parser.add_argument('--batch_size', type=int, default=16,
+                        help='Batch size (default: 16)')
+    parser.add_argument('--num_mbs', type=int, default=4,
+                        help='Number of microbatches (default: 4)')
+    parser.add_argument('--seq_len', type=int, default=256,
+                        help='Sequence length (default: 256)')
+    parser.add_argument('--warmup', type=int, default=5,
+                        help='Number of warmup iterations (default: 5)')
+    parser.add_argument('--iters', type=int, default=20,
+                        help='Number of timing iterations (default: 20)')
+    parser.add_argument('--tracing', action='store_true', default=False,
+                        help='Enable tracing')
+    parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
+                        help='Enable naive gradient sync')
+    return parser.parse_args()
+
+
+def print_mean_timing_data(trace_data: dict, actor_id: int) -> None:
+    """Prints mean timing and memory statistics from trace_data for a given actor.
+
+    Args:
+        trace_data (dict): Trace data dictionary from the actor containing timing and memory metrics.
+        actor_id (int): The ID of the actor.
+    """
+    import numpy as np
+
+    print(f"\nTiming and Memory statistics for Actor {actor_id}:")
+    
+    for stage_id, stage_data in trace_data.items():
+        if stage_id == 'update':
+            # Handle update data (global optimizer step)
+            print(f"  Update:")
+            for metric, values in stage_data.items():
+                if not values:
+                    mean_val = float('nan')
+                else:
+                    mean_val = float(np.mean(values))
+                
+                if 'memory' in metric:
+                    if 'delta' in metric:
+                        print(f"    {metric}: {mean_val:.3f} GB")
+                    else:
+                        print(f"    {metric}: {mean_val:.3f} GB")
+                else:
+                    print(f"    {metric}: {mean_val:.3f} ms")
+        else:
+            # Handle stage data (forward/backward passes)
+            print(f"  Stage {stage_id}:")
+            for phase, phase_data in stage_data.items():
+                print(f"    {phase.capitalize()}:")
+                for metric, values in phase_data.items():
+                    if not values:
+                        mean_val = float('nan')
+                    else:
+                        mean_val = float(np.mean(values))
+                    
+                    if 'memory' in metric:
+                        if 'delta' in metric:
+                            print(f"      {metric}: {mean_val:.3f} GB")
+                        else:
+                            print(f"      {metric}: {mean_val:.3f} GB")
+                    else:
+                        print(f"      {metric}: {mean_val:.3f} ms")
+
 
 def main(args):
     
@@ -127,81 +201,6 @@ def iter_schedule():
     ray.timeline(timeline_filename)
     print(f"\nRay timeline saved to: {timeline_filename}")
 
-
-def print_mean_timing_data(trace_data: dict, actor_id: int) -> None:
-    """Prints mean timing and memory statistics from trace_data for a given actor.
-
-    Args:
-        trace_data (dict): Trace data dictionary from the actor containing timing and memory metrics.
-        actor_id (int): The ID of the actor.
-    """
-    import numpy as np
-
-    print(f"\nTiming and Memory statistics for Actor {actor_id}:")
-    
-    for stage_id, stage_data in trace_data.items():
-        if stage_id == 'update':
-            # Handle update data (global optimizer step)
-            print(f"  Update:")
-            for metric, values in stage_data.items():
-                if not values:
-                    mean_val = float('nan')
-                else:
-                    mean_val = float(np.mean(values))
-                
-                if 'memory' in metric:
-                    if 'delta' in metric:
-                        print(f"    {metric}: {mean_val:.3f} GB")
-                    else:
-                        print(f"    {metric}: {mean_val:.3f} GB")
-                else:
-                    print(f"    {metric}: {mean_val:.3f} ms")
-        else:
-            # Handle stage data (forward/backward passes)
-            print(f"  Stage {stage_id}:")
-            for phase, phase_data in stage_data.items():
-                print(f"    {phase.capitalize()}:")
-                for metric, values in phase_data.items():
-                    if not values:
-                        mean_val = float('nan')
-                    else:
-                        mean_val = float(np.mean(values))
-                    
-                    if 'memory' in metric:
-                        if 'delta' in metric:
-                            print(f"      {metric}: {mean_val:.3f} GB")
-                        else:
-                            print(f"      {metric}: {mean_val:.3f} GB")
-                    else:
-                        print(f"      {metric}: {mean_val:.3f} ms")
-
-                        
-def parse_args():
-    parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
-    parser.add_argument('--model', choices=['LLAMA_DEBUG', 'LLAMA_1B', 'LLAMA_3B', 'LLAMA_8B'], default='LLAMA_DEBUG',
-                        help='Model configuration: LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, or LLAMA_8B (default: LLAMA_DEBUG)')
-    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'no-pp'], default='1f1b',
-                        help='Schedule type: gpipe, 1f1b, or interleaved-1f1b (default: 1f1b)')
-    parser.add_argument('--dp_degree', type=int, default=1,
-                        help='Number of data parallel degrees (default: 1)')
-    parser.add_argument('--pp_degree', type=int, default=2,
-                        help='Number of pipeline parallel degrees (default: 2)')
-    parser.add_argument('--batch_size', type=int, default=16,
-                        help='Batch size (default: 16)')
-    parser.add_argument('--num_mbs', type=int, default=4,
-                        help='Number of microbatches (default: 4)')
-    parser.add_argument('--seq_len', type=int, default=256,
-                        help='Sequence length (default: 256)')
-    parser.add_argument('--warmup', type=int, default=5,
-                        help='Number of warmup iterations (default: 5)')
-    parser.add_argument('--iters', type=int, default=20,
-                        help='Number of timing iterations (default: 20)')
-    parser.add_argument('--tracing', action='store_true', default=False,
-                        help='Enable tracing')
-    parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
-                        help='Enable naive gradient sync')
-    return parser.parse_args()
-
 if __name__ == "__main__":
     ray.init(include_dashboard=False, log_to_driver=True, namespace="llama")
     args = parse_args()
diff --git a/test/test_mixtral.py b/test/test_mixtral.py
index 513c59b..6e2d1d0 100644
--- a/test/test_mixtral.py
+++ b/test/test_mixtral.py
@@ -14,18 +14,20 @@
 from .schedule_helpers import build_1f1b_schedule, print_schedule
 from torch._dynamo.backends.debugging import eager
 
-def main(args):
+def main(dp_degree, pp_degree):
 
-    world_size = args.dp * args.pp
+    world_size = dp_degree * pp_degree
+    num_mbs = 1
+    batch_size = 8
 
-    config = ModelArgs.from_name(args.model)
+    config = ModelArgs.from_name("tiny")
     dp_rank = int(os.environ['PIPER_DP_RANK'])
 
-    x = torch.randint(0, config.vocab_size, (args.batch_size, config.block_size))
+    x = torch.randint(0, config.vocab_size, (batch_size, config.block_size))
     input_pos = torch.arange(config.block_size)
-    y = torch.randn(args.batch_size, config.block_size, config.vocab_size)
+    y = torch.randn(batch_size, config.block_size, config.vocab_size)
 
-    schedule = build_1f1b_schedule(args.mbs, args.pp)
+    schedule = build_1f1b_schedule(num_mbs, pp_degree)
     print_schedule(schedule)
     model = piper_setup(
         Transformer, 
@@ -33,7 +35,6 @@ def main(args):
         torch.optim.Adam, 
         [x, input_pos], 
         schedule,
-        naive_gradient_sync=args.naive_gradient_sync,
     )
 
     # Send data to actors ahead of time
@@ -43,41 +44,17 @@ def main(args):
 
     loss_fn = torch.nn.CrossEntropyLoss()
 
-    for _ in range(args.warmup):
-        piper_exec(model, schedule, [x, input_pos], y, loss_fn)
-
     start = time.perf_counter()
-    for _ in range(args.iters):
-        piper_exec(model, schedule, [x, input_pos], y, loss_fn)
+    piper_exec(model, schedule, [x, input_pos], y, loss_fn)
     end = time.perf_counter()
-    print(f"Iter time: {(end - start)/args.iters:.2f} s")
+    print(f"Iter time: {end - start:.2f} s")
 
     ray.timeline(f"out/mixtral.json")
 
-def parse_args():
-    parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
-    parser.add_argument('--model', choices=['tiny', 'small', 'medium', 'Mixtral-8x7B-v0.1'], default='tiny',
-                        help='Model configuration: tiny, small, medium, or Mixtral-8x7B-v0.1 (default: tiny)')
-    parser.add_argument('--dp', type=int, default=2,
-                        help='Number of data parallel degrees (default: 2)')
-    parser.add_argument('--pp', type=int, default=2,
-                        help='Number of pipeline parallel degrees (default: 2)')
-    parser.add_argument('--batch_size', type=int, default=16,
-                        help='Batch size (default: 16)')
-    parser.add_argument('--mbs', type=int, default=4,
-                        help='Number of microbatches (default: 4)')
-    parser.add_argument('--warmup', type=int, default=3,
-                        help='Number of warmup iterations (default: 3)')
-    parser.add_argument('--iters', type=int, default=5,
-                        help='Number of timing iterations (default: 5)')
-    parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
-                        help='Enable naive gradient sync')
-    return parser.parse_args()
-
 if __name__ == "__main__":
     ray.init(include_dashboard=False, log_to_driver=True, namespace="llama", _temp_dir="/m-coriander/coriander/mfris/tmp/ray")
-    args = parse_args()
-    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
-    handles = piper_coordinator.run_program.remote(main, args)
+    dp_degree, pp_degree = 2, 2
+    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=pp_degree, dp_degree=dp_degree)
+    handles = piper_coordinator.run_program.remote(main, dp_degree, pp_degree)
     ray.get(handles)
     ray.shutdown()

From 4e6054f2910499f74b60c1d5b1a2ca732b7f737f Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Fri, 13 Feb 2026 21:55:58 +0000
Subject: [PATCH 025/100] clean up test files and timing code

---
 Dockerfile                          |  10 +
 run.sh                              |   5 +
 src/piper.py                        |  12 +-
 src/piper_actor.py                  | 174 +++++---------
 src/piper_compile.py                |   7 +
 src/piper_exec.py                   |  14 +-
 src/piper_graph_transform.py        |  41 +---
 src/test_compile.py                 |   7 +
 test/models/mixtral-dtensor-a2a.py  | 354 ++++++++++++++++++++++++++++
 test/models/mixtral-dtensor.py      | 343 +++++++++++++++++++++++++++
 test/models/mixtral-orig.py         | 288 ++++++++++++++++++++++
 test/models/mixtral.py              |   4 +-
 test/models/mixtral_a2a.py          | 314 ++++++++++++++++++++++++
 test/test_llama.py                  | 191 ++++++---------
 test/test_mixtral.py                |  85 +++++--
 test/test_mixtral_multiple_iters.py |   4 +-
 test/test_mixtral_pippy.py          | 104 ++++++++
 test/test_mixtral_single.py         |  51 ++++
 test/test_simple.py                 |  50 ++++
 19 files changed, 1760 insertions(+), 298 deletions(-)
 create mode 100644 Dockerfile
 create mode 100755 run.sh
 create mode 100644 src/test_compile.py
 create mode 100644 test/models/mixtral-dtensor-a2a.py
 create mode 100644 test/models/mixtral-dtensor.py
 create mode 100644 test/models/mixtral-orig.py
 create mode 100644 test/models/mixtral_a2a.py
 create mode 100644 test/test_mixtral_pippy.py
 create mode 100644 test/test_mixtral_single.py
 create mode 100644 test/test_simple.py

diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..f24c0c1
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,10 @@
+# Use a minimal Python base image
+FROM python:3.10-slim
+
+# Set working directory inside the container
+WORKDIR /piper
+
+RUN pip install numpy ray==2.53.0 cupy-cuda12x
+RUN pip install torch==2.10.0 --index-url https://download.pytorch.org/whl/cu128
+
+
diff --git a/run.sh b/run.sh
new file mode 100755
index 0000000..85fdfab
--- /dev/null
+++ b/run.sh
@@ -0,0 +1,5 @@
+docker run --rm -it \
+  --shm-size=10.24gb \
+  -v "$(pwd)":/piper \
+  piper \
+  bash
\ No newline at end of file
diff --git a/src/piper.py b/src/piper.py
index 30bbf6c..89ac526 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -25,17 +25,17 @@ def piper(gm, example_inputs, **kwargs):
     refs = []
     actor_stages = []
     for (stage_id, stage_gm, input_idxs, graphargs, placeholders) in submodules:
-        stage_gm = _insert_a2a_ops(stage_gm)
-        # if dp_rank == 0:
-        #     stage_gm.print_readable()
-        comm_ops, tids = _get_dp_comm_ops(graphargs, placeholders)
-
+        if dp_degree > 1:
+            stage_gm = _insert_a2a_ops(stage_gm)
+            comm_ops, tids = _get_dp_comm_ops(graphargs, placeholders)
+        else:
+            comm_ops, tids = [], []
+            
         actor_id = piper_metadata.stage_to_device[stage_id]
         actor = _get_actor(actor_id)
         actor_stages.append((actor, stage_id))
 
         stage_gm_data = _serialize_graphmodule(stage_gm)
-        logger.debug(f"Loading stage {stage_id} on actor {actor_id}")
         refs.append(actor._load_stage.remote(
             stage_id, 
             stage_gm_data, 
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 00e15cc..33bcc8d 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -97,60 +97,30 @@ def __init__(self, pp_rank, optim_class, world_size, num_mbs, num_stages, naive_
         self.comm_op_handles = defaultdict(dict)
         # map stage id -> p2p op handle
         self.p2p_op_handles = defaultdict(list)
-        # Timing infrastructure
-        self.tracing = False  # Toggle for timing and memory tracing
-        self.trace_data = {
-            'update': {
-                'total': [], 
-                'sync': [], 
-                'optim': [], 
-                'peak_memory_delta': [], 
-                'peak_memory': []
-            }}
+        
+        self.tracing = False
+        self.trace_events = dict()
+        self.trace_data = defaultdict(list)
 
         from .piper_utils import piper_metadata
         piper_metadata.actor_self = self
 
-    def clear_trace_data(self) -> None:
-        """
-        Clear all collected timing data.
-        """
-        for stage_id in self.trace_data:
-            self.trace_data[stage_id] = {
-                'forward': {
-                    'forward': [],
-                    'total': [],
-                    'peak_memory_delta': [],
-                    'peak_memory': []
-                },
-                'backward': {
-                    'backward': [],
-                    'total': [],
-                    'peak_memory_delta': [],
-                    'peak_memory': []
-                },
-            }
-        self.trace_data['update'] = {
-            'total': [],
-            'sync': [],
-            'optim': [],
-            'peak_memory_delta': [],
-            'peak_memory': []
-        }
-
     def get_trace_data(self) -> dict:
-        return self.trace_data
+        return self.global_rank, self.trace_data
+    
+    def clear_trace_data(self) -> None:
+        self.trace_data.clear()
 
     def set_tracing(self, enabled: bool) -> None:
         self.tracing = enabled
-        self.logger.info(f"Actor {self.pp_rank}: Tracing {'enabled' if enabled else 'disabled'}")
+        self.logger.info(f"Actor {self.global_rank}: Tracing {'enabled' if enabled else 'disabled'}")
 
     def start_mem_tracing(self) -> None:
         torch.cuda.memory._record_memory_history()
     
     def stop_mem_tracing(self) -> None:
-        torch.cuda.memory._dump_snapshot(f"actor{self.pp_rank}_memory_snapshot_mb4_gpipe.pickle")
-        self.logger.info(f"Saved memory snapshot to actor{self.pp_rank}_memory_snapshot_mb4_gpipe.pickle")
+        torch.cuda.memory._dump_snapshot(f"actor{self.global_rank}_memory_snapshot_mb4_gpipe.pickle")
+        self.logger.info(f"Saved memory snapshot to actor{self.global_rank}_memory_snapshot_mb4_gpipe.pickle")
         torch.cuda.memory._record_memory_history(enabled=None)
 
     def reset_peak_memory(self):
@@ -161,9 +131,26 @@ def get_peak_memory(self):
 
     def load_input(self, inputs):
         self.inputs = [inp.to(self.device) for inp in inputs]
+        self.logger.debug(f"Actor {self.global_rank} loaded inputs {len(self.inputs)}")
     
     def load_labels(self, labels):
         self.labels = labels.to(self.device)
+        self.logger.debug(f"Actor {self.global_rank} loaded labels {self.labels.shape}")
+
+    def _start_timing(self, stream, label):
+        if self.tracing:
+            if label not in self.trace_events:
+                self.trace_events[label] = (torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True))
+            start, _ = self.trace_events[label]
+            start.record(stream)
+
+    def _stop_timing(self, stream, label):
+        if self.tracing:
+            if label in self.trace_events:
+                start, stop = self.trace_events[label]
+                stop.record(stream)
+                stream.synchronize()
+                self.trace_data[label].append(start.elapsed_time(stop))
 
     def _join_process_groups(self):
         master_addr = os.environ.get('PIPER_MASTER_ADDR', "127.0.0.1")
@@ -171,7 +158,7 @@ def _join_process_groups(self):
         init_method = f"tcp://{master_addr}:{master_port}"
 
         dist.init_process_group("nccl", init_method=init_method, rank=self.global_rank, world_size=self.world_size)
-        self.logger.debug(f"Actor {self.pp_rank} global rank {self.global_rank} has GPU {os.environ['CUDA_VISIBLE_DEVICES']}, joined the global process group")
+        self.logger.debug(f"Actor {self.global_rank} has GPU {os.environ['CUDA_VISIBLE_DEVICES']}, joined the global process group")
 
         if self.dp_degree > 1:
             self._join_dp_process_group()
@@ -223,7 +210,7 @@ def post_backward_hook(grad):
     def _comm_loop(self):
         stage_id = self.stage_id
         # look into: nccl priority for sync vs async ops, stream priority
-        self.logger.info(f"Running comm loop for stage {stage_id} on actor {self.pp_rank} global rank {self.global_rank}")
+        self.logger.info(f"Running comm loop for stage {stage_id} on actor {self.global_rank}")
         for comm_op in self.comm_ops[stage_id]:
             done = False
             while not done:
@@ -238,10 +225,10 @@ def _comm_loop(self):
                         done = True
                 else:
                     raise ValueError(f"Unknown comm op: {comm_op}")
-        self.logger.info(f"Completed comm loop for stage {stage_id} on actor {self.pp_rank} global rank {self.global_rank}")
+        self.logger.debug(f"Completed comm loop for stage {stage_id} on actor {self.global_rank}")
 
     def _wait_for_comm_ops(self):
-        self.logger.debug(f"Actor {self.pp_rank} global rank {self.global_rank} waiting for comm ops")
+        self.logger.debug(f"Actor {self.global_rank} waiting for comm ops")
         for stage_id, comm_ops in self.comm_ops.items():
             for comm_op in comm_ops:
                 self.logger.debug(f"Waiting for comm op to be launched dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={comm_op.tensor.shape}")
@@ -253,30 +240,13 @@ def _wait_for_comm_ops(self):
                         done = True
 
     def _wait_for_p2p_ops(self):
-        self.logger.debug(f"Actor {self.pp_rank} global rank {self.global_rank} waiting for p2p ops")
+        self.logger.debug(f"Actor {self.global_rank} waiting for p2p ops")
         for stage_id, handles in self.p2p_op_handles.items():
             for handle in handles:
                 handle.wait()
 
     def _load_stage(self, stage_id: int, gm_data, comm_ops, forward_args, ids, input_idxs):
-        self.logger.info(f"Loading stage {stage_id} graph on actor {self.pp_rank} global rank {self.global_rank}")
-
-        # set up tracing data structure
-        if stage_id not in self.trace_data:
-            self.trace_data[stage_id] = {
-                'forward': {
-                    'forward': [],
-                    'total': [],
-                    'peak_memory_delta': [],
-                    'peak_memory': []
-                },
-                'backward': {
-                    'backward': [],
-                    'total': [],
-                    'peak_memory_delta': [],
-                    'peak_memory': []
-                },
-            }
+        self.logger.info(f"Loading stage {stage_id} graph on actor {self.global_rank}")
 
         # compile the graph with the given graphargs
         gm = _deserialize_graphmodule(gm_data)
@@ -287,15 +257,12 @@ def _load_stage(self, stage_id: int, gm_data, comm_ops, forward_args, ids, input
 
         # place parameters on the device
         def move_to_device(idx, arg):
-            # if idx not in input_idxs:
             if arg.requires_grad:
                 return arg.to(self.device).detach().requires_grad_(True)
             else:
                 return arg.to(self.device)
-            # return arg.to(self.device)
         forward_args = list(map(move_to_device, range(len(forward_args)), forward_args))
 
-
         # prepare tensors with comm ops
         if not self.naive_gradient_sync and self.dp_degree > 1:
             self._prepare_comm_ops(stage_id, comm_ops, forward_args, ids)
@@ -315,10 +282,9 @@ def move_to_device(idx, arg):
 
         del gm_data
 
-    # @ray.method(tensor_transport="nccl")
     def _forward(self, stage_id: int, mb_idx: int):
 
-        self.logger.debug(f"Calling forward {stage_id} mb {mb_idx} on actor {self.pp_rank} global rank {self.global_rank}")
+        self.logger.debug(f"Calling forward {stage_id} mb {mb_idx} on actor {self.global_rank}")
 
         if stage_id == 0:
             # For the first stage, load input tensors from self.inputs
@@ -326,17 +292,13 @@ def _forward(self, stage_id: int, mb_idx: int):
                 self.forward_args[stage_id][i] = inp
         else:
             # For non-first stages, receive input tensors from the previous stage
-            start_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-            start_event.record(self.p2p_stream)
+            pp_rank = piper_metadata.stage_to_device[stage_id - 1]
+            global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+
+            self._start_timing(self.p2p_stream, "fwd_p2p_recv")
             for i in self.input_idxs[stage_id]:
-                pp_rank = piper_metadata.stage_to_device[stage_id - 1]
-                global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
                 dist.recv(self.forward_args[stage_id][i], src=global_src_rank, group=self.pp_group)
-            end_event.record(self.p2p_stream)
-            torch.cuda.synchronize()
-            time = start_event.elapsed_time(end_event)
-            logger.info(f"Completed fwd p2p recv rank={self.global_rank} time={time:.2f} ms")
+            self._stop_timing(self.p2p_stream, "fwd_p2p_recv")
 
             # save first input that requires grad as input activation
             self.forward_args[stage_id][self.input_idxs[stage_id][-1]].requires_grad_()
@@ -356,52 +318,45 @@ def _forward(self, stage_id: int, mb_idx: int):
         self.out_activation[stage_id][mb_idx] = out_with_grad[0]
 
         # clear the input tensors
-        for i in self.input_idxs[stage_id]:
-            self.forward_args[stage_id][i] = torch.empty_like(self.forward_args[stage_id][i])
+        # for i in self.input_idxs[stage_id]:
+        #     self.forward_args[stage_id][i] = torch.empty_like(self.forward_args[stage_id][i])
 
         if stage_id < self.num_stages - 1:
-            start_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-            start_event.record(self.p2p_stream)
-            torch.cuda.synchronize()
             # For non-final stages, send output tensors to the next stage
+            pp_rank = piper_metadata.stage_to_device[stage_id + 1]
+            global_dst_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+
+            self._start_timing(self.p2p_stream, "fwd_p2p_send")
             for i in range(len(output)):
-                pp_rank = piper_metadata.stage_to_device[stage_id + 1]
-                global_dst_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
                 dist.send(output[i], dst=global_dst_rank, group=self.pp_group)
-            end_event.record(self.p2p_stream)
-            torch.cuda.synchronize()
-            time = start_event.elapsed_time(end_event)
-            logger.info(f"Completed fwd p2p send rank={self.global_rank} time={time:.2f} ms")
+            self._stop_timing(self.p2p_stream, "fwd_p2p_send")
 
         if CLEANUP_MEMORY:
             gc.collect()
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
         
-        self.logger.debug(f"Forward {stage_id} mb {mb_idx} on actor {self.pp_rank} global rank {self.global_rank} returning {[out.shape for out in output]}")
+        self.logger.debug(f"Forward {stage_id} mb {mb_idx} on actor {self.global_rank} returning {[out.shape for out in output]}")
+        torch.cuda.synchronize()
 
     # @ray.method(tensor_transport="nccl")
     def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
 
-        self.logger.debug(f"Calling backward {stage_id} mb {mb_idx} on actor {self.pp_rank} global rank {self.global_rank}")
+        self.logger.debug(f"Calling backward {stage_id} mb {mb_idx} on actor {self.global_rank}")
         
         out_activation = self.out_activation[stage_id][mb_idx]
 
         if stage_id < self.num_stages - 1:
             # For non-final stages, recieve input gradients from the subsequent backward pass
-            start_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-            start_event.record(self.p2p_stream)
             input_grad = torch.empty_like(out_activation)
             pp_rank = piper_metadata.stage_to_device[stage_id + 1]
             global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+
+            self._start_timing(self.p2p_stream, "bwd_p2p_recv")
             with torch.cuda.stream(self.p2p_stream):
                 dist.recv(input_grad, src=global_src_rank, group=self.pp_group)
-            end_event.record(self.p2p_stream)
-            torch.cuda.synchronize()
-            time = start_event.elapsed_time(end_event)
-            logger.info(f"Completed bwd p2p recv rank={self.global_rank} time={time:.2f} ms")
+            self._stop_timing(self.p2p_stream, "bwd_p2p_recv")
+
             out_activation.backward(gradient=input_grad)
         else:
             # For the final stage, wait for the forward pass to complete
@@ -416,19 +371,16 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
 
         if stage_id > 0:
             # For non-first stages, send output gradients to the previous backward stage
-            start_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-            start_event.record(self.p2p_stream)
             output_grad = self.inp_activation[stage_id][mb_idx].grad
             assert output_grad is not None
             pp_rank = piper_metadata.stage_to_device[stage_id - 1]
             global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+
+            self._start_timing(self.p2p_stream, "bwd_p2p_send")
             with torch.cuda.stream(self.p2p_stream):
                 dist.send(output_grad, dst=global_src_rank, group=self.pp_group)
-            end_event.record(self.p2p_stream)
-            torch.cuda.synchronize()
-            time = start_event.elapsed_time(end_event)
-            logger.info(f"Completed bwd p2p send rank={self.global_rank} time={time:.2f} ms")
+            self._stop_timing(self.p2p_stream, "bwd_p2p_send")
+
             self.inp_activation[stage_id][mb_idx] = None
 
         if CLEANUP_MEMORY:
@@ -436,16 +388,18 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
         
+        torch.cuda.synchronize()
+        
     def _synchronize_gradients(self):
-        self.logger.debug(f"Actor {self.pp_rank} global rank {self.global_rank} synchronizing gradients")
+        self.logger.debug(f"Actor {self.global_rank} synchronizing gradients")
         # Iterate over all stages on this actor and synchronize their parameters
         for stage_id, parameters in self.forward_args.items():
             for param in parameters:
                 if param is not None and param.grad is not None:
-                    dist.all_reduce(param.grad, op=dist.ReduceOp.AVG, group=self.dp_group, stream=self.comm_stream)
+                    dist.all_reduce(param.grad, op=dist.ReduceOp.AVG, group=self.dp_group)
 
     def _update(self):
-        self.logger.debug(f"Actor {self.pp_rank} global rank {self.global_rank} waiting for backward sync events")
+        self.logger.debug(f"Actor {self.global_rank} waiting for backward sync events")
             
         # if dp degree > 1, make sure all gradients are synchronized before optimizer step
         # TODO: this does not allow overlapping with the optimizer step
@@ -465,4 +419,6 @@ def _update(self):
         losses = self.loss
         self.loss.clear()
 
+        torch.cuda.synchronize()
+
         return losses
diff --git a/src/piper_compile.py b/src/piper_compile.py
index b702e62..4a28e94 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -5,6 +5,8 @@
 import gc
 import copy
 
+from torch._dynamo.backends.debugging import eager
+
 from .piper_actor import _create_actors
 from .piper_utils import piper_metadata, create_logger, LOG_LEVEL
 from .piper import piper
@@ -28,6 +30,11 @@ def piper_setup(model_class, model_args, optim_fn, example_inputs, schedule, nai
 
     model = model_class(*model_args)
 
+    logger.debug("Compiling model with full graph")
+    dummy = torch.compile(model, backend=eager, fullgraph=True)
+    dummy(*example_inputs)
+    logger.debug("Model compiled with full graph")
+
     compiled = torch.compile(model, backend=piper)
 
     dp_rank = int(os.environ['PIPER_DP_RANK'])
diff --git a/src/piper_exec.py b/src/piper_exec.py
index 766c5bf..b76a7ac 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -142,7 +142,7 @@ def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdg
                         f"before backward stage {from_stage} (time {bwd_times[from_stage]})"
                     )
 
-def piper_exec(model, schedule, inputs, truth, loss_fn):
+def piper_exec(model, schedule, inputs, truth, loss_fn, dp_degree=1):
     """
     Execute one step of the pipeline schedule on the distributed model.
 
@@ -158,23 +158,17 @@ def piper_exec(model, schedule, inputs, truth, loss_fn):
         List of losses per microbatch.)
     """
     num_steps, num_devices = len(schedule[0]), len(schedule)
-    actors = piper_metadata.actors
 
-    # ensure loading of inputs and labels onto actors 
-    ray.get(actors[0].load_input.remote(inputs))
-    ray.get(actors[len(actors)-1].load_labels.remote(truth))
+    actors = piper_metadata.actors
+    if dp_degree > 1:
+        [actor._comm_loop.remote() for actor in actors.values()]
     
     num_mbs = len(set([task.mb_idx for row in schedule for task in row if task is not None]))
-
     dag_edges = piper_metadata.dag
     dag_edges = list(map(lambda e: (DAGEdge(e[0], e[1])), list(piper_metadata.dag)))
     
     _validate_schedule(schedule, dag_edges, num_mbs)
 
-    [actor._comm_loop.remote() for actor in actors.values()]
-
-    time.sleep(3)
-
     ret = []
     for i in range(num_steps):
         for j in range(num_devices-1, -1, -1):
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index c55d01c..3f04509 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -54,17 +54,15 @@ def forward(ctx, output, input_tensor):
         """
         # Store group for backward pass
         from .piper_utils import piper_metadata
-        actor_self = piper_metadata.actor_self
-        ctx.group = actor_self.dp_group
-        ctx.global_rank = actor_self.global_rank
-        ctx.stream = actor_self.a2a_stream
+        ctx.actor_self = piper_metadata.actor_self
+        ctx.group = ctx.actor_self.dp_group
+        ctx.global_rank = ctx.actor_self.global_rank
+        ctx.stream = ctx.actor_self.a2a_stream
 
-        logger.info(f"Dispatch AllToAllSingleFunction forward rank={ctx.global_rank}, shape={input_tensor.shape}")
-        
-        start_event = torch.cuda.Event(enable_timing=True)
-        end_event = torch.cuda.Event(enable_timing=True)
-        start_event.record(ctx.stream)
+        logger.debug(f"Dispatch AllToAllSingleFunction forward rank={ctx.global_rank}, shape={input_tensor.shape}")
         
+        ctx.actor_self._start_timing(ctx.stream, "fwd_a2a_single")
+
         # Ensure input_tensor is contiguous (all_to_all_single requires contiguous tensors)
         # TODO: performance cost?
         input_tensor = input_tensor.contiguous()
@@ -72,11 +70,8 @@ def forward(ctx, output, input_tensor):
         # Perform the communication (modifies output in-place)
         with torch.cuda.stream(ctx.stream):
             dist.all_to_all_single(output, input_tensor, group=ctx.group)
-        
-        end_event.record(ctx.stream)
-        torch.cuda.synchronize()
-        time = start_event.elapsed_time(end_event)
-        logger.info(f"Completed AllToAllSingleFunction forward rank={ctx.global_rank} time={time:.2f} ms")
+
+        ctx.actor_self._stop_timing(ctx.stream, "fwd_a2a_single")
 
         return output
     
@@ -88,11 +83,9 @@ def backward(ctx, grad_output):
         The backward of all_to_all_single is another all_to_all_single operation
         that reverses the communication pattern.
         """
-        logger.info(f"Dispatch AllToAllSingleFunction backward rank={ctx.global_rank}, shape={grad_output.shape}")
+        logger.debug(f"Dispatch AllToAllSingleFunction backward rank={ctx.global_rank}, shape={grad_output.shape}")
 
-        start_event = torch.cuda.Event(enable_timing=True)
-        end_event = torch.cuda.Event(enable_timing=True)
-        start_event.record(ctx.stream)
+        ctx.actor_self._start_timing(ctx.stream, "bwd_a2a_single")
 
         if grad_output is None:
             return None, None, None
@@ -109,10 +102,7 @@ def backward(ctx, grad_output):
         with torch.cuda.stream(ctx.stream):
             dist.all_to_all_single(grad_input, grad_output, group=ctx.group)
 
-        end_event.record(ctx.stream)
-        torch.cuda.synchronize()
-        time = start_event.elapsed_time(end_event)
-        logger.info(f"Completed AllToAllSingleFunction backward rank={ctx.global_rank} time={time:.2f} ms")
+        ctx.actor_self._stop_timing(ctx.stream, "bwd_a2a_single")
         
         # Return gradients: grad_output flows to grad_input, None for group
         return grad_input, grad_input, None
@@ -151,10 +141,7 @@ def _split_gm_by_stages(gm) -> tuple[fx.GraphModule, list[tuple[int, fx.GraphMod
                     nodes_with_metadata.append((node, stage_annotation_id))
     
     if not nodes_with_metadata:
-        logger.info("No stage nodes found in graph")
         return gm, []
-    else:
-        logger.info(f"Found stage nodes in graph")
     
     # Group nodes by stage ID for creating stage modules
     stage_code = defaultdict(list)  # stage_id -> list of all nodes for this stage
@@ -712,7 +699,7 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> fx.GraphModule:
 
     annotation_blocks = list(blocks_by_key.values())
     
-    logger.info(f"Found {len(annotation_blocks)} contiguous annotation blocks")
+    logger.debug(f"Found {len(annotation_blocks)} comm annotation blocks")
     
     # For each contiguous block, find the output node (the one used outside the annotation)
     # This is the node that should have communication applied to it
@@ -747,8 +734,6 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> fx.GraphModule:
                 annotated_output_nodes.append(last_node)
                 logger.debug(f"Block {block_idx}: No external users found, using last node: {last_node[0].name}")
     
-    logger.info(f"Found {len(annotated_output_nodes)} annotation blocks requiring communication operations")
-    
     # Create a new graph to build the transformed version
     new_graph = fx.Graph()
     node_mapping = {}
diff --git a/src/test_compile.py b/src/test_compile.py
new file mode 100644
index 0000000..da9256c
--- /dev/null
+++ b/src/test_compile.py
@@ -0,0 +1,7 @@
+from test.models.llama import Transformer, LLAMA_1B
+
+from torch._dynamo.backends.debugging import eager
+
+model = Transformer(LLAMA_1B, 1024)
+dummy = torch.compile(model, backend=eager, fullgraph=True)
+dummy(torch.randint(0, 1024, (1, 1024)))
\ No newline at end of file
diff --git a/test/models/mixtral-dtensor-a2a.py b/test/models/mixtral-dtensor-a2a.py
new file mode 100644
index 0000000..cfff4c5
--- /dev/null
+++ b/test/models/mixtral-dtensor-a2a.py
@@ -0,0 +1,354 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+from dataclasses import dataclass
+from typing import Optional
+import math
+import torch.cuda.nvtx as nvtx
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+from torch.nn import functional as F
+from torch.distributed.tensor import DTensor, Shard, Replicate, DeviceMesh, distribute_tensor
+from torch.distributed import get_rank, get_world_size
+from torch.distributed.tensor import Placement
+
+def distribute_module_parameters(mod: nn.Module, device_mesh: DeviceMesh, placement: list[Placement]) -> None:
+    """Distribute all parameters of a module with the given placement."""
+    for name, param in mod.named_parameters(recurse=False):
+        # Distribute the parameter
+        dist_param = nn.Parameter(
+            distribute_tensor(param, device_mesh, placement)
+        )
+        # Register the distributed parameter back to the module
+        mod.register_parameter(name, dist_param)
+
+def find_multiple(n: int, k: int) -> int:
+    if n % k == 0:
+        return n
+    return n + k - (n % k)
+
+
+@dataclass
+class ModelArgs:
+    block_size: int = 2048
+    vocab_size: int = 32000
+    n_layer: int = 32
+    n_head: int = 32
+    dim: int = 4096
+    intermediate_size: int = None
+    n_local_heads: int = -1
+    head_dim: int = 64
+    rope_base: float = 10000
+    norm_eps: float = 1e-5
+    num_experts: int = 8
+    num_activated_experts: int = 2
+
+    def __post_init__(self):
+        if self.n_local_heads == -1:
+            self.n_local_heads = self.n_head
+        if self.intermediate_size is None:
+            hidden_dim = 4 * self.dim
+            n_hidden = int(2 * hidden_dim / 3)
+            self.intermediate_size = find_multiple(n_hidden, 256)
+        self.head_dim = self.dim // self.n_head
+
+    @classmethod
+    def from_name(cls, name: str):
+        if name in transformer_configs:
+            return cls(**transformer_configs[name])
+        # fuzzy search
+        config = [config for config in transformer_configs if config in str(name).upper() or config in str(name)]
+        assert len(config) == 1, name
+        return cls(**transformer_configs[config[0]])
+
+
+transformer_configs = {
+    "small": dict(block_size=512, n_layer=8, n_head=8, n_local_heads=2, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "medium": dict(block_size=1024, n_layer=32, n_head=32, n_local_heads=8, dim=2048, intermediate_size=3584, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "Mixtral-8x7B-v0.1": dict(block_size=32768, n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+}
+
+class KVCache(nn.Module):
+    def __init__(self, max_batch_size, max_seq_length, n_heads, head_dim, dtype=torch.bfloat16):
+        super().__init__()
+        cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
+        self.register_buffer('k_cache', torch.zeros(cache_shape, dtype=dtype))
+        self.register_buffer('v_cache', torch.zeros(cache_shape, dtype=dtype))
+
+    def update(self, input_pos, k_val, v_val):
+        # input_pos: [S], k_val: [B, H, S, D]
+        assert input_pos.shape[0] == k_val.shape[2]
+
+        k_out = self.k_cache
+        v_out = self.v_cache
+        k_out[:, :, input_pos] = k_val
+        v_out[:, :, input_pos] = v_val
+
+        return k_out, v_out
+
+class Transformer(nn.Module):
+    def __init__(self, config: ModelArgs, device_mesh: DeviceMesh) -> None:
+        super().__init__()
+        self.config = config
+        self.device_mesh = device_mesh
+
+        # Initialize layers normally
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
+        self.layers = nn.ModuleList(TransformerBlock(config, device_mesh) for _ in range(config.n_layer))
+        self.norm = RMSNorm(config.dim, device_mesh, eps=config.norm_eps)
+        self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
+        
+        # Distribute parameters with Replicate placement
+        distribute_module_parameters(self.tok_embeddings, device_mesh, [Replicate()])
+        distribute_module_parameters(self.output, device_mesh, [Replicate()])
+        distribute_module_parameters(self.norm, device_mesh, [Replicate()])
+        for layer in self.layers:
+            distribute_module_parameters(layer, device_mesh, [Replicate()])
+
+        self.freqs_cis: Optional[DTensor] = distribute_tensor(
+            precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base),
+            device_mesh,
+            [Replicate()])
+        self.causal_mask: Optional[DTensor] = distribute_tensor(
+            torch.tril(torch.ones(self.config.block_size, self.config.block_size, dtype=torch.bool)),
+            device_mesh,
+            [Replicate()])
+        
+        self.mask_cache: Optional[Tensor] = None
+        self.max_batch_size = -1
+        self.max_seq_length = -1
+
+    def setup_caches(self, max_batch_size, max_seq_length):
+        assert False, "Should not be called"
+        if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
+            return
+        head_dim = self.config.dim // self.config.n_head
+        max_seq_length = find_multiple(max_seq_length, 8)
+        self.max_seq_length = max_seq_length
+        self.max_batch_size = max_batch_size
+        for b in self.layers:
+            b.attention.kv_cache = KVCache(max_batch_size, max_seq_length, self.config.n_local_heads, head_dim)
+
+        self.freqs_cis: Optional[Tensor] = distribute_tensor(
+            precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base),
+            device_mesh,
+            [Replicate()])        
+        self.causal_mask = distribute_tensor(
+            torch.tril(torch.ones(self.config.block_size, self.config.block_size, dtype=torch.bool)),
+            device_mesh,
+            [Replicate()])
+
+    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        assert self.freqs_cis is not None, "Caches must be initialized first"
+        mask = self.causal_mask[None, None, input_pos]
+        freqs_cis = self.freqs_cis[input_pos]
+        x = self.tok_embeddings(idx)
+
+        for i, layer in enumerate(self.layers):
+            x = layer(x, input_pos, freqs_cis, mask)
+        x = self.norm(x)
+        logits = self.output(x)
+        return logits
+
+    @classmethod
+    def from_name(cls, name: str, device_mesh: DeviceMesh):
+        return cls(ModelArgs.from_name(name), device_mesh)
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, config: ModelArgs, device_mesh: DeviceMesh) -> None:
+        super().__init__()
+        self.attention = Attention(config, device_mesh)
+        self.block_sparse_moe = MOEFeedForward(config, device_mesh)
+        self.ffn_norm = RMSNorm(config.dim, device_mesh, config.norm_eps)
+        self.attention_norm = RMSNorm(config.dim, device_mesh, config.norm_eps)
+
+    def forward(self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor) -> Tensor:
+        h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos)
+        out = h + self.block_sparse_moe(self.ffn_norm(h))
+        return out
+
+
+class Attention(nn.Module):
+    def __init__(self, config: ModelArgs, device_mesh: DeviceMesh):
+        super().__init__()
+        assert config.dim % config.n_head == 0
+
+        total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
+        # key, query, value projections for all heads, but in a batch
+        # Initialize layers normally
+        self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
+        self.wo = nn.Linear(config.dim, config.dim, bias=False)
+        
+        # Distribute parameters with Replicate placement
+        distribute_module_parameters(self.wqkv, device_mesh, [Replicate()])
+        distribute_module_parameters(self.wo, device_mesh, [Replicate()])
+        self.kv_cache = None
+
+        self.n_head = config.n_head
+        self.head_dim = config.head_dim
+        self.n_local_heads = config.n_local_heads
+        self.dim = config.dim
+        self._register_load_state_dict_pre_hook(self.load_hook)
+
+    def load_hook(self, state_dict, prefix, *args):
+        if prefix + "wq.weight" in state_dict:
+            wq = state_dict.pop(prefix + "wq.weight")
+            wk = state_dict.pop(prefix + "wk.weight")
+            wv = state_dict.pop(prefix + "wv.weight")
+            state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
+
+    def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        bsz, seqlen, _ = x.shape
+
+        kv_size = self.n_local_heads * self.head_dim
+        q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
+
+        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
+        k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+        v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+
+        q = apply_rotary_emb(q, freqs_cis)
+        k = apply_rotary_emb(k, freqs_cis)
+
+        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
+
+        if self.kv_cache is not None:
+            k, v = self.kv_cache.update(input_pos, k, v)
+
+        k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+        v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
+
+        y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
+
+        y = self.wo(y)
+        return y
+
+
+class ConditionalFeedForward(nn.Module):
+    def __init__(self, config, device_mesh: DeviceMesh):
+        super().__init__()
+        self.w1 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
+        self.w2 = nn.Parameter(torch.empty(config.num_experts, config.dim, config.intermediate_size))
+        self.w3 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
+        
+        self.num_experts = config.num_experts
+        self.device_mesh = device_mesh
+        self.ep_size = device_mesh.size(0)
+        self.num_local_experts = config.num_experts // self.ep_size
+        self.config = config
+        
+        # Distribute parameters with Shard(0) placement
+        distribute_module_parameters(self, device_mesh, [Shard(0)])
+        
+        self.rank = get_rank()
+
+    def forward(self, x: Tensor, dispatch_mask: Tensor, combine_weights: Tensor, capacity: int) -> Tensor:
+        # all to all
+        print(f"x: {x.shape}")
+        x_local = x.to_local()
+        print(f"x_local: {x_local.shape}")
+        expert_inputs = torch.einsum("tec,tm->ecm", dispatch_mask.type_as(x_local[0]).to_local(), x_local) # [E, C, D]
+        buf = torch.empty_like(expert_inputs)
+        torch.distributed.all_to_all_single(buf, expert_inputs, group=self.device_mesh.get_group())
+        expert_inputs = buf.reshape(self.ep_size, self.num_local_experts, -1, self.config.dim)
+
+        x1 = F.silu(torch.einsum('glcd, lmd -> glcm', expert_inputs, self.w1.to_local()))
+        x3 = torch.einsum('glcd, lmd -> glcm', expert_inputs, self.w3.to_local())
+        expert_output =  torch.einsum('glcm, ldm -> glcd', (x1 * x3), self.w2.to_local())
+
+        # all to all
+        expert_output = expert_output.reshape(self.num_experts, -1, self.config.dim)
+        buf = torch.empty_like(expert_output)
+        torch.distributed.all_to_all_single(buf, expert_output, group=self.device_mesh.get_group())
+        
+        combined_output = torch.einsum("tec,ecd->td", combine_weights.to_local(), buf)
+        combined_output = DTensor.from_local(combined_output, self.device_mesh, [Shard(0)])
+        return combined_output
+
+class MOEFeedForward(nn.Module):
+    def __init__(self, config, device_mesh: DeviceMesh) -> None:
+        super().__init__()
+        # Initialize layers normally
+        self.gate = nn.Linear(config.dim, config.num_experts, bias=False)
+        self.cond_ffn = ConditionalFeedForward(config, device_mesh)
+        
+        # Distribute gate parameters with Replicate placement
+        distribute_module_parameters(self.gate, device_mesh, [Replicate()])
+        self.dim = config.dim
+        self.num_activated_experts = config.num_activated_experts
+        self.rank = get_rank()
+        self.device_mesh = device_mesh
+        
+    def forward(self, x: Tensor) -> Tensor:
+        # Save original shape to reshape output back
+        shape = x.shape
+        x = x.view(-1, self.dim)
+        # T = num_tokens, E = num_experts, D = hidden dim, A = activated experts
+        # x: [T, D]
+        scores = self.gate(x) # [T, E]
+        gates = F.softmax(scores, dim=-1)
+        expert_weights, expert_indices = torch.topk(gates, self.num_activated_experts, dim=-1) # [T, A], [T, A]
+        expert_weights /= expert_weights.sum(dim=-1, keepdim=True) # [T, A]
+
+        num_experts = scores.shape[-1]
+        mask = F.one_hot(expert_indices, num_classes=num_experts).sum(dim=1).bool()  # [T, E]
+        capacity = x.shape[0] # // num_experts * 2
+        # print(f"capacity: {capacity}, expert_counts: {mask.sum(dim=0)}")
+        locations = torch.cumsum(mask.to_local(), dim=0) - 1 # [T, E]
+        locations = DTensor.from_local(locations, self.device_mesh, [Shard(0)])
+        locations_sc = F.one_hot(locations * mask, capacity).float() # [T, E, C]
+        combine_weights = torch.einsum("se,sec->sec", gates * mask, locations_sc)
+        dispatch_mask = combine_weights.bool()
+
+        output = self.cond_ffn(x, dispatch_mask, combine_weights, capacity)
+        return output.view(shape)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, device_mesh: DeviceMesh, eps: float = 1e-5):
+        super().__init__()
+        self.eps = eps
+        # Initialize weight normally (ones for RMSNorm)
+        self.weight = nn.Parameter(torch.ones(dim))
+        
+        # Distribute parameters with Replicate placement
+        distribute_module_parameters(self, device_mesh, [Replicate()])
+
+    def _norm(self, x):
+        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
+
+    def forward(self, x: Tensor) -> Tensor:
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+def precompute_freqs_cis(
+    seq_len: int, n_elem: int, base: int = 10000
+) -> Tensor:
+    freqs = 1.0 / (base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem))
+    t = torch.arange(seq_len, device=freqs.device)
+    freqs = torch.outer(t, freqs)
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+    cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
+    return cache.to(dtype=torch.bfloat16)
+
+
+def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
+    xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
+    freqs_cis = freqs_cis.reshape(1, xshaped.size(1), 1, xshaped.size(3), 2)
+    x_out2 = torch.stack(
+        [
+            xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
+            xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
+        ],
+        -1,
+    )
+
+    x_out2 = x_out2.flatten(3)
+    return x_out2.type_as(x)
\ No newline at end of file
diff --git a/test/models/mixtral-dtensor.py b/test/models/mixtral-dtensor.py
new file mode 100644
index 0000000..fbc1a8b
--- /dev/null
+++ b/test/models/mixtral-dtensor.py
@@ -0,0 +1,343 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+from dataclasses import dataclass
+from typing import Optional
+import math
+import torch.cuda.nvtx as nvtx
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+from torch.nn import functional as F
+from torch.distributed.tensor import DTensor, Shard, Replicate, DeviceMesh, distribute_tensor
+from torch.distributed import get_rank, get_world_size
+from torch.distributed.tensor import Placement
+
+def distribute_module_parameters(mod: nn.Module, device_mesh: DeviceMesh, placement: list[Placement]) -> None:
+    """Distribute all parameters of a module with the given placement."""
+    for name, param in mod.named_parameters(recurse=False):
+        # Distribute the parameter
+        dist_param = nn.Parameter(
+            distribute_tensor(param, device_mesh, placement)
+        )
+        # Register the distributed parameter back to the module
+        mod.register_parameter(name, dist_param)
+
+def find_multiple(n: int, k: int) -> int:
+    if n % k == 0:
+        return n
+    return n + k - (n % k)
+
+
+@dataclass
+class ModelArgs:
+    block_size: int = 2048
+    vocab_size: int = 32000
+    n_layer: int = 32
+    n_head: int = 32
+    dim: int = 4096
+    intermediate_size: int = None
+    n_local_heads: int = -1
+    head_dim: int = 64
+    rope_base: float = 10000
+    norm_eps: float = 1e-5
+    num_experts: int = 8
+    num_activated_experts: int = 2
+
+    def __post_init__(self):
+        if self.n_local_heads == -1:
+            self.n_local_heads = self.n_head
+        if self.intermediate_size is None:
+            hidden_dim = 4 * self.dim
+            n_hidden = int(2 * hidden_dim / 3)
+            self.intermediate_size = find_multiple(n_hidden, 256)
+        self.head_dim = self.dim // self.n_head
+
+    @classmethod
+    def from_name(cls, name: str):
+        if name in transformer_configs:
+            return cls(**transformer_configs[name])
+        # fuzzy search
+        config = [config for config in transformer_configs if config in str(name).upper() or config in str(name)]
+        assert len(config) == 1, name
+        return cls(**transformer_configs[config[0]])
+
+
+transformer_configs = {
+    "small": dict(block_size=512, n_layer=8, n_head=8, n_local_heads=2, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "medium": dict(block_size=1024, n_layer=32, n_head=32, n_local_heads=8, dim=2048, intermediate_size=3584, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "Mixtral-8x7B-v0.1": dict(block_size=32768, n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+}
+
+class KVCache(nn.Module):
+    def __init__(self, max_batch_size, max_seq_length, n_heads, head_dim, dtype=torch.bfloat16):
+        super().__init__()
+        cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
+        self.register_buffer('k_cache', torch.zeros(cache_shape, dtype=dtype))
+        self.register_buffer('v_cache', torch.zeros(cache_shape, dtype=dtype))
+
+    def update(self, input_pos, k_val, v_val):
+        # input_pos: [S], k_val: [B, H, S, D]
+        assert input_pos.shape[0] == k_val.shape[2]
+
+        k_out = self.k_cache
+        v_out = self.v_cache
+        k_out[:, :, input_pos] = k_val
+        v_out[:, :, input_pos] = v_val
+
+        return k_out, v_out
+
+class Transformer(nn.Module):
+    def __init__(self, config: ModelArgs, device_mesh: DeviceMesh) -> None:
+        super().__init__()
+        self.config = config
+        self.device_mesh = device_mesh
+
+        # Initialize layers normally
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
+        self.layers = nn.ModuleList(TransformerBlock(config, device_mesh) for _ in range(config.n_layer))
+        self.norm = RMSNorm(config.dim, device_mesh, eps=config.norm_eps)
+        self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
+        
+        # Distribute parameters with Replicate placement
+        distribute_module_parameters(self.tok_embeddings, device_mesh, [Replicate()])
+        distribute_module_parameters(self.output, device_mesh, [Replicate()])
+        distribute_module_parameters(self.norm, device_mesh, [Replicate()])
+        for layer in self.layers:
+            distribute_module_parameters(layer, device_mesh, [Replicate()])
+
+        self.freqs_cis: Optional[DTensor] = distribute_tensor(
+            precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base),
+            device_mesh,
+            [Replicate()])
+        self.causal_mask: Optional[DTensor] = distribute_tensor(
+            torch.tril(torch.ones(self.config.block_size, self.config.block_size, dtype=torch.bool)),
+            device_mesh,
+            [Replicate()])
+        
+        self.mask_cache: Optional[Tensor] = None
+        self.max_batch_size = -1
+        self.max_seq_length = -1
+
+    def setup_caches(self, max_batch_size, max_seq_length):
+        assert False, "Should not be called"
+        if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
+            return
+        head_dim = self.config.dim // self.config.n_head
+        max_seq_length = find_multiple(max_seq_length, 8)
+        self.max_seq_length = max_seq_length
+        self.max_batch_size = max_batch_size
+        for b in self.layers:
+            b.attention.kv_cache = KVCache(max_batch_size, max_seq_length, self.config.n_local_heads, head_dim)
+
+        self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base)
+        self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
+
+    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        assert self.freqs_cis is not None, "Caches must be initialized first"
+        mask = self.causal_mask[None, None, input_pos]
+        freqs_cis = self.freqs_cis[input_pos]
+        x = self.tok_embeddings(idx)
+
+        for i, layer in enumerate(self.layers):
+            x = layer(x, input_pos, freqs_cis, mask)
+        x = self.norm(x)
+        logits = self.output(x)
+        return logits
+
+    @classmethod
+    def from_name(cls, name: str, device_mesh: DeviceMesh):
+        return cls(ModelArgs.from_name(name), device_mesh)
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, config: ModelArgs, device_mesh: DeviceMesh) -> None:
+        super().__init__()
+        self.attention = Attention(config, device_mesh)
+        self.block_sparse_moe = MOEFeedForward(config, device_mesh)
+        self.ffn_norm = RMSNorm(config.dim, device_mesh, config.norm_eps)
+        self.attention_norm = RMSNorm(config.dim, device_mesh, config.norm_eps)
+
+    def forward(self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor) -> Tensor:
+        h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos)
+        out = h + self.block_sparse_moe(self.ffn_norm(h))
+        return out
+
+
+class Attention(nn.Module):
+    def __init__(self, config: ModelArgs, device_mesh: DeviceMesh):
+        super().__init__()
+        assert config.dim % config.n_head == 0
+
+        total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
+        # key, query, value projections for all heads, but in a batch
+        # Initialize layers normally
+        self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
+        self.wo = nn.Linear(config.dim, config.dim, bias=False)
+        
+        # Distribute parameters with Replicate placement
+        distribute_module_parameters(self.wqkv, device_mesh, [Replicate()])
+        distribute_module_parameters(self.wo, device_mesh, [Replicate()])
+        self.kv_cache = None
+
+        self.n_head = config.n_head
+        self.head_dim = config.head_dim
+        self.n_local_heads = config.n_local_heads
+        self.dim = config.dim
+        self._register_load_state_dict_pre_hook(self.load_hook)
+
+    def load_hook(self, state_dict, prefix, *args):
+        if prefix + "wq.weight" in state_dict:
+            wq = state_dict.pop(prefix + "wq.weight")
+            wk = state_dict.pop(prefix + "wk.weight")
+            wv = state_dict.pop(prefix + "wv.weight")
+            state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
+
+    def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        bsz, seqlen, _ = x.shape
+
+        kv_size = self.n_local_heads * self.head_dim
+        q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
+
+        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
+        k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+        v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+
+        q = apply_rotary_emb(q, freqs_cis)
+        k = apply_rotary_emb(k, freqs_cis)
+
+        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
+
+        if self.kv_cache is not None:
+            k, v = self.kv_cache.update(input_pos, k, v)
+
+        k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+        v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
+
+        y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
+
+        y = self.wo(y)
+        return y
+
+
+class ConditionalFeedForward(nn.Module):
+    def __init__(self, config, device_mesh: DeviceMesh):
+        super().__init__()
+        self.w1 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
+        self.w2 = nn.Parameter(torch.empty(config.num_experts, config.dim, config.intermediate_size))
+        self.w3 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
+        
+        self.num_experts = config.num_experts
+        self.device_mesh = device_mesh
+        self.ep_size = device_mesh.size(0)
+        self.num_local_experts = config.num_experts // self.ep_size
+        self.config = config
+        
+        # Distribute parameters with Shard(0) placement
+        distribute_module_parameters(self, device_mesh, [Shard(0)])
+        
+        self.rank = get_rank()
+
+    def forward(self, x: Tensor, dispatch_mask: Tensor, combine_weights: Tensor, capacity: int) -> Tensor:
+        """
+        x: [T, D]
+        dispatch_mask: [T, E, C]
+        combine_weights: [T, E, C]
+        capacity: int
+        """
+        # all gather
+        expert_inputs = torch.einsum("tec,td->ecd", dispatch_mask.type_as(x[0]), x) # [E, C, D]
+        # reduce scatter
+        x1 = F.silu(torch.einsum('ecd, emd -> ecm', expert_inputs, self.w1))
+        # reduce scatter
+        x3 = torch.einsum('ecd, emd -> ecm', expert_inputs, self.w3)
+        expert_output =  torch.einsum('ecm, edm -> ecd', (x1 * x3), self.w2)
+        # all gather
+        expert_output = expert_output.redistribute(self.device_mesh, [Replicate()])
+        combined_output = torch.einsum("tec,ecd->td", combine_weights, expert_output)
+        return combined_output
+
+class MOEFeedForward(nn.Module):
+    def __init__(self, config, device_mesh: DeviceMesh) -> None:
+        super().__init__()
+        # Initialize layers normally
+        self.gate = nn.Linear(config.dim, config.num_experts, bias=False)
+        self.cond_ffn = ConditionalFeedForward(config, device_mesh)
+        
+        # Distribute gate parameters with Replicate placement
+        distribute_module_parameters(self.gate, device_mesh, [Replicate()])
+        self.dim = config.dim
+        self.num_activated_experts = config.num_activated_experts
+        self.rank = get_rank()
+        self.device_mesh = device_mesh
+        
+    def forward(self, x: Tensor) -> Tensor:
+        # Save original shape to reshape output back
+        shape = x.shape
+        x = x.view(-1, self.dim)
+        # T = num_tokens, E = num_experts, D = hidden dim, A = activated experts
+        # x: [T, D]
+        scores = self.gate(x) # [T, E]
+        gates = F.softmax(scores, dim=-1)
+        expert_weights, expert_indices = torch.topk(gates, self.num_activated_experts, dim=-1) # [T, A], [T, A]
+        expert_weights /= expert_weights.sum(dim=-1, keepdim=True) # [T, A]
+
+        num_experts = scores.shape[-1]
+        mask = F.one_hot(expert_indices, num_classes=num_experts).sum(dim=1).bool()  # [T, E]
+        capacity = x.shape[0] #// num_experts * 2
+        locations = torch.cumsum(mask.to_local(), dim=0) - 1 # [T, E]
+        locations = DTensor.from_local(locations, self.device_mesh, [Shard(0)])
+        locations_sc = F.one_hot(locations * mask, capacity).float() # [T, E, C]
+        combine_weights = torch.einsum("se,sec->sec", gates * mask, locations_sc)
+        dispatch_mask = combine_weights.bool()
+
+        output = self.cond_ffn(x, dispatch_mask, combine_weights, capacity)
+        return output.view(shape)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, device_mesh: DeviceMesh, eps: float = 1e-5):
+        super().__init__()
+        self.eps = eps
+        # Initialize weight normally (ones for RMSNorm)
+        self.weight = nn.Parameter(torch.ones(dim))
+        
+        # Distribute parameters with Replicate placement
+        distribute_module_parameters(self, device_mesh, [Replicate()])
+
+    def _norm(self, x):
+        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
+
+    def forward(self, x: Tensor) -> Tensor:
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+def precompute_freqs_cis(
+    seq_len: int, n_elem: int, base: int = 10000
+) -> Tensor:
+    freqs = 1.0 / (base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem))
+    t = torch.arange(seq_len, device=freqs.device)
+    freqs = torch.outer(t, freqs)
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+    cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
+    return cache.to(dtype=torch.bfloat16)
+
+
+def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
+    xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
+    freqs_cis = freqs_cis.reshape(1, xshaped.size(1), 1, xshaped.size(3), 2)
+    x_out2 = torch.stack(
+        [
+            xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
+            xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
+        ],
+        -1,
+    )
+
+    x_out2 = x_out2.flatten(3)
+    return x_out2.type_as(x)
\ No newline at end of file
diff --git a/test/models/mixtral-orig.py b/test/models/mixtral-orig.py
new file mode 100644
index 0000000..b04324b
--- /dev/null
+++ b/test/models/mixtral-orig.py
@@ -0,0 +1,288 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+from dataclasses import dataclass
+from typing import Optional
+import math
+import torch.cuda.nvtx as nvtx
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+from torch.nn import functional as F
+from torch.distributed import get_rank, get_world_size
+
+def find_multiple(n: int, k: int) -> int:
+    if n % k == 0:
+        return n
+    return n + k - (n % k)
+
+
+@dataclass
+class ModelArgs:
+    block_size: int = 2048
+    vocab_size: int = 32000
+    n_layer: int = 32
+    n_head: int = 32
+    dim: int = 4096
+    intermediate_size: int = None
+    n_local_heads: int = -1
+    head_dim: int = 64
+    rope_base: float = 10000
+    norm_eps: float = 1e-5
+    num_experts: int = 8
+    num_activated_experts: int = 2
+
+    def __post_init__(self):
+        if self.n_local_heads == -1:
+            self.n_local_heads = self.n_head
+        if self.intermediate_size is None:
+            hidden_dim = 4 * self.dim
+            n_hidden = int(2 * hidden_dim / 3)
+            self.intermediate_size = find_multiple(n_hidden, 256)
+        self.head_dim = self.dim // self.n_head
+
+    @classmethod
+    def from_name(cls, name: str):
+        if name in transformer_configs:
+            return cls(**transformer_configs[name])
+        # fuzzy search
+        config = [config for config in transformer_configs if config in str(name).upper() or config in str(name)]
+        assert len(config) == 1, name
+        return cls(**transformer_configs[config[0]])
+
+
+transformer_configs = {
+    "small": dict(block_size=512, n_layer=8, n_head=8, n_local_heads=2, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "medium": dict(block_size=1024, n_layer=32, n_head=32, n_local_heads=8, dim=2048, intermediate_size=3584, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "Mixtral-8x7B-v0.1": dict(block_size=32768, n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+}
+
+class KVCache(nn.Module):
+    def __init__(self, max_batch_size, max_seq_length, n_heads, head_dim, dtype=torch.bfloat16):
+        super().__init__()
+        cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
+        self.register_buffer('k_cache', torch.zeros(cache_shape, dtype=dtype))
+        self.register_buffer('v_cache', torch.zeros(cache_shape, dtype=dtype))
+
+    def update(self, input_pos, k_val, v_val):
+        # input_pos: [S], k_val: [B, H, S, D]
+        assert input_pos.shape[0] == k_val.shape[2]
+
+        k_out = self.k_cache
+        v_out = self.v_cache
+        k_out[:, :, input_pos] = k_val
+        v_out[:, :, input_pos] = v_val
+
+        return k_out, v_out
+
+class Transformer(nn.Module):
+    def __init__(self, config: ModelArgs) -> None:
+        super().__init__()
+        self.config = config
+
+        # Initialize layers normally
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
+        self.layers = nn.ModuleList(TransformerBlock(config) for _ in range(config.n_layer))
+        self.norm = RMSNorm(config.dim, eps=config.norm_eps)
+        self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
+
+        self.freqs_cis: Optional[Tensor] = precompute_freqs_cis(
+            self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base
+        ).to(f"cuda:{get_rank()}")
+        self.causal_mask: Optional[Tensor] = torch.tril(
+            torch.ones(self.config.block_size, self.config.block_size, dtype=torch.bool)
+        ).to(f"cuda:{get_rank()}")
+        
+        self.mask_cache: Optional[Tensor] = None
+        self.max_batch_size = -1
+        self.max_seq_length = -1
+
+    def setup_caches(self, max_batch_size, max_seq_length):
+        assert False, "Should not be called"
+        if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
+            return
+        head_dim = self.config.dim // self.config.n_head
+        max_seq_length = find_multiple(max_seq_length, 8)
+        self.max_seq_length = max_seq_length
+        self.max_batch_size = max_batch_size
+        for b in self.layers:
+            b.attention.kv_cache = KVCache(max_batch_size, max_seq_length, self.config.n_local_heads, head_dim)
+
+        self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base)
+        self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
+
+    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        assert self.freqs_cis is not None, "Caches must be initialized first"
+        mask = self.causal_mask[None, None, input_pos]
+        freqs_cis = self.freqs_cis[input_pos]
+        x = self.tok_embeddings(idx)
+
+        for i, layer in enumerate(self.layers):
+            x = layer(x, input_pos, freqs_cis, mask)
+        x = self.norm(x)
+        logits = self.output(x)
+        return logits
+
+    @classmethod
+    def from_name(cls, name: str):
+        return cls(ModelArgs.from_name(name))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, config: ModelArgs) -> None:
+        super().__init__()
+        self.attention = Attention(config)
+        self.block_sparse_moe = MOEFeedForward(config)
+        self.ffn_norm = RMSNorm(config.dim, config.norm_eps)
+        self.attention_norm = RMSNorm(config.dim, config.norm_eps)
+
+    def forward(self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor) -> Tensor:
+        h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos)
+        out = h + self.block_sparse_moe(self.ffn_norm(h))
+        return out
+
+
+class Attention(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        assert config.dim % config.n_head == 0
+
+        total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
+        # key, query, value projections for all heads, but in a batch
+        # Initialize layers normally
+        self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
+        self.wo = nn.Linear(config.dim, config.dim, bias=False)
+        
+        self.kv_cache = None
+
+        self.n_head = config.n_head
+        self.head_dim = config.head_dim
+        self.n_local_heads = config.n_local_heads
+        self.dim = config.dim
+        self._register_load_state_dict_pre_hook(self.load_hook)
+
+    def load_hook(self, state_dict, prefix, *args):
+        if prefix + "wq.weight" in state_dict:
+            wq = state_dict.pop(prefix + "wq.weight")
+            wk = state_dict.pop(prefix + "wk.weight")
+            wv = state_dict.pop(prefix + "wv.weight")
+            state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
+
+    def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        bsz, seqlen, _ = x.shape
+
+        kv_size = self.n_local_heads * self.head_dim
+        q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
+
+        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
+        k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+        v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+
+        q = apply_rotary_emb(q, freqs_cis)
+        k = apply_rotary_emb(k, freqs_cis)
+
+        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
+
+        if self.kv_cache is not None:
+            k, v = self.kv_cache.update(input_pos, k, v)
+
+        k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+        v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
+
+        y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
+
+        y = self.wo(y)
+        return y
+
+
+class ConditionalFeedForward(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.w1 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
+        self.w2 = nn.Parameter(torch.empty(config.num_experts, config.dim, config.intermediate_size))
+        self.w3 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
+        
+        self.num_experts = config.num_experts
+        self.config = config
+        
+        self.rank = get_rank()
+
+    def forward(self, x: Tensor, expert_indices: Tensor, expert_weights: Tensor) -> Tensor:
+        w1_weights = self.w1[expert_indices] # [T, A, D, D]
+        w3_weights = self.w3[expert_indices] # [T, A, D, D]
+        w2_weights = self.w2[expert_indices]  # [T, A, D, D]
+        x1 = F.silu(torch.einsum('ti,taoi -> tao', x, w1_weights))
+        x3 = torch.einsum('ti, taoi -> tao', x, w3_weights)
+        expert_outs =  torch.einsum('tao, taio -> tai', (x1 * x3), w2_weights)
+        combined_output = torch.einsum('tai,ta -> ti', expert_outs, expert_weights)
+        return combined_output
+
+class MOEFeedForward(nn.Module):
+    def __init__(self, config) -> None:
+        super().__init__()
+        # Initialize layers normally
+        self.gate = nn.Linear(config.dim, config.num_experts, bias=False)
+        self.cond_ffn = ConditionalFeedForward(config)
+        
+        self.dim = config.dim
+        self.num_activated_experts = config.num_activated_experts
+        self.rank = get_rank()
+        
+    def forward(self, x: Tensor) -> Tensor:
+        # Save original shape to reshape output back
+        original_shape = x.shape  # [batch_size, seq_len, dim] or [seq_len, dim]
+        x = x.view(-1, self.dim)
+        # T = num_tokens, E = num_experts, D = hidden dim, A = activated experts
+        # x: [T, D]
+        scores = self.gate(x) # [T, E]
+        gates = F.softmax(scores, dim=-1)
+        expert_weights, expert_indices = torch.topk(gates, self.num_activated_experts, dim=-1) # [T, A], [T, A]
+        expert_weights /= expert_weights.sum(dim=-1, keepdim=True) # [T, A]
+
+        output = self.cond_ffn(x, expert_indices, expert_weights)
+        return output.view(original_shape)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-5):
+        super().__init__()
+        self.eps = eps
+        # Initialize weight normally (ones for RMSNorm)
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
+
+    def forward(self, x: Tensor) -> Tensor:
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+def precompute_freqs_cis(
+    seq_len: int, n_elem: int, base: int = 10000
+) -> Tensor:
+    freqs = 1.0 / (base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem))
+    t = torch.arange(seq_len, device=freqs.device)
+    freqs = torch.outer(t, freqs)
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+    cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
+    return cache.to(dtype=torch.bfloat16)
+
+
+def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
+    xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
+    freqs_cis = freqs_cis.reshape(1, xshaped.size(1), 1, xshaped.size(3), 2)
+    x_out2 = torch.stack(
+        [
+            xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
+            xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
+        ],
+        -1,
+    )
+
+    x_out2 = x_out2.flatten(3)
+    return x_out2.type_as(x)
\ No newline at end of file
diff --git a/test/models/mixtral.py b/test/models/mixtral.py
index 353027b..61c8963 100644
--- a/test/models/mixtral.py
+++ b/test/models/mixtral.py
@@ -116,9 +116,8 @@ def setup_caches(self, max_batch_size, max_seq_length):
         self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
 
     def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        self.first_arg = idx.shape
         with torch.fx.traceback.annotate({"stage": 0}):
-            idx += 0
-            input_pos += 0
             mask = self.causal_mask[None, None, input_pos]
             freqs_cis = self.freqs_cis[input_pos]
             x = self.tok_embeddings(idx)
@@ -137,7 +136,6 @@ def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
     def from_name(cls, name: str):
         return cls(ModelArgs.from_name(name))
 
-
 class TransformerBlock(nn.Module):
     def __init__(self, config: ModelArgs) -> None:
         super().__init__()
diff --git a/test/models/mixtral_a2a.py b/test/models/mixtral_a2a.py
new file mode 100644
index 0000000..affbffd
--- /dev/null
+++ b/test/models/mixtral_a2a.py
@@ -0,0 +1,314 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+from dataclasses import dataclass
+from typing import Optional
+import math
+import torch.cuda.nvtx as nvtx
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+from torch.nn import functional as F
+from torch.distributed import get_rank
+from torch.distributed.tensor import DeviceMesh
+
+
+def find_multiple(n: int, k: int) -> int:
+    if n % k == 0:
+        return n
+    return n + k - (n % k)
+
+
+@dataclass
+class ModelArgs:
+    block_size: int = 2048
+    vocab_size: int = 32000
+    n_layer: int = 32
+    n_head: int = 32
+    dim: int = 4096
+    intermediate_size: int = None
+    n_local_heads: int = -1
+    head_dim: int = 64
+    rope_base: float = 10000
+    norm_eps: float = 1e-5
+    num_experts: int = 8
+    num_activated_experts: int = 2
+
+    def __post_init__(self):
+        if self.n_local_heads == -1:
+            self.n_local_heads = self.n_head
+        if self.intermediate_size is None:
+            hidden_dim = 4 * self.dim
+            n_hidden = int(2 * hidden_dim / 3)
+            self.intermediate_size = find_multiple(n_hidden, 256)
+        self.head_dim = self.dim // self.n_head
+
+    @classmethod
+    def from_name(cls, name: str):
+        if name in transformer_configs:
+            return cls(**transformer_configs[name])
+        # fuzzy search
+        config = [config for config in transformer_configs if config in str(name).upper() or config in str(name)]
+        assert len(config) == 1, name
+        return cls(**transformer_configs[config[0]])
+
+
+transformer_configs = {
+    "tiny": dict(block_size=128, n_layer=2, n_head=2, n_local_heads=1, dim=256, intermediate_size=512, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "small": dict(block_size=512, n_layer=8, n_head=8, n_local_heads=2, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "medium": dict(block_size=512, n_layer=24, n_head=32, n_local_heads=8, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "Mixtral-8x7B-v0.1": dict(block_size=32768, n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+}
+
+class KVCache(nn.Module):
+    def __init__(self, max_batch_size, max_seq_length, n_heads, head_dim, dtype=torch.bfloat16):
+        super().__init__()
+        cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
+        self.register_buffer('k_cache', torch.zeros(cache_shape, dtype=dtype))
+        self.register_buffer('v_cache', torch.zeros(cache_shape, dtype=dtype))
+
+    def update(self, input_pos, k_val, v_val):
+        # input_pos: [S], k_val: [B, H, S, D]
+        assert input_pos.shape[0] == k_val.shape[2]
+
+        k_out = self.k_cache
+        v_out = self.v_cache
+        k_out[:, :, input_pos] = k_val
+        v_out[:, :, input_pos] = v_val
+
+        return k_out, v_out
+
+class Transformer(nn.Module):
+    def __init__(self, config: ModelArgs, group: Optional[DeviceMesh] = None) -> None:
+        super().__init__()
+        self.config = config
+        self.group = group
+
+        # Initialize layers normally
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
+        self.layers = nn.ModuleList(TransformerBlock(config, group) for _ in range(config.n_layer))
+        self.norm = RMSNorm(config.dim, group, eps=config.norm_eps)
+        self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
+
+        self.freqs_cis: Optional[Tensor] = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base).to(f"cuda:{get_rank()}")
+        self.causal_mask: Optional[Tensor] = torch.tril(torch.ones(self.config.block_size, self.config.block_size, dtype=torch.bool)).to(f"cuda:{get_rank()}")
+        
+        self.mask_cache: Optional[Tensor] = None
+        self.max_batch_size = -1
+        self.max_seq_length = -1
+
+    def setup_caches(self, max_batch_size, max_seq_length):
+        assert False, "Should not be called"
+        if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
+            return
+        head_dim = self.config.dim // self.config.n_head
+        max_seq_length = find_multiple(max_seq_length, 8)
+        self.max_seq_length = max_seq_length
+        self.max_batch_size = max_batch_size
+        for b in self.layers:
+            b.attention.kv_cache = KVCache(max_batch_size, max_seq_length, self.config.n_local_heads, head_dim)
+
+        self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base)
+        self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
+
+    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        assert self.freqs_cis is not None, "Caches must be initialized first"
+        mask = self.causal_mask[None, None, input_pos]
+        freqs_cis = self.freqs_cis[input_pos]
+        x = self.tok_embeddings(idx) if self.tok_embeddings else idx
+
+        for i, layer in enumerate(self.layers):
+            x = layer(x, input_pos, freqs_cis, mask)
+        x = self.norm(x) if self.norm else x
+        logits = self.output(x) if self.output else x
+        return logits
+
+    @classmethod
+    def from_name(cls, name: str, group: DeviceMesh):
+        return cls(ModelArgs.from_name(name), group)
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, config: ModelArgs, group: Optional[DeviceMesh] = None) -> None:
+        super().__init__()
+        self.attention = Attention(config, group)
+        self.block_sparse_moe = MOEFeedForward(config, group)
+        self.ffn_norm = RMSNorm(config.dim, group, config.norm_eps)
+        self.attention_norm = RMSNorm(config.dim, group, config.norm_eps)
+
+    def forward(self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor) -> Tensor:
+        h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos)
+        out = h + self.block_sparse_moe(self.ffn_norm(h))
+        return out
+
+
+class Attention(nn.Module):
+    def __init__(self, config: ModelArgs, group: Optional[DeviceMesh] = None):
+        super().__init__()
+        assert config.dim % config.n_head == 0
+
+        total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
+        # key, query, value projections for all heads, but in a batch
+        # Initialize layers normally
+        self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
+        self.wo = nn.Linear(config.dim, config.dim, bias=False)
+        
+        self.kv_cache = None
+        self.n_head = config.n_head
+        self.head_dim = config.head_dim
+        self.n_local_heads = config.n_local_heads
+        self.dim = config.dim
+        self._register_load_state_dict_pre_hook(self.load_hook)
+
+    def load_hook(self, state_dict, prefix, *args):
+        if prefix + "wq.weight" in state_dict:
+            wq = state_dict.pop(prefix + "wq.weight")
+            wk = state_dict.pop(prefix + "wk.weight")
+            wv = state_dict.pop(prefix + "wv.weight")
+            state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
+
+    def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        bsz, seqlen, _ = x.shape
+
+        kv_size = self.n_local_heads * self.head_dim
+        q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
+
+        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
+        k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+        v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+
+        q = apply_rotary_emb(q, freqs_cis)
+        k = apply_rotary_emb(k, freqs_cis)
+
+        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
+
+        if self.kv_cache is not None:
+            k, v = self.kv_cache.update(input_pos, k, v)
+
+        k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+        v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
+
+        y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
+
+        y = self.wo(y)
+        return y
+
+
+class ConditionalFeedForward(nn.Module):
+    def __init__(self, config, group: Optional[DeviceMesh] = None):
+        super().__init__()
+        self.num_experts = config.num_experts
+        self.group = group
+        self.ep_size = 2
+        self.num_local_experts = config.num_experts // self.ep_size
+        self.config = config
+
+        self.w1 = nn.Parameter(torch.empty(self.num_local_experts, config.intermediate_size, config.dim))
+        self.w2 = nn.Parameter(torch.empty(self.num_local_experts, config.dim, config.intermediate_size))
+        self.w3 = nn.Parameter(torch.empty(self.num_local_experts, config.intermediate_size, config.dim))
+        
+        self.rank = get_rank()
+
+    def forward(self, x: Tensor, dispatch_mask: Tensor, combine_weights: Tensor, capacity: int) -> Tensor:
+        """
+        x: [T, D]
+        dispatch_mask: [T, E, C]
+        combine_weights: [T, E, C]
+        capacity: int
+        """
+        expert_inputs = torch.einsum("tec,tm->ecm", dispatch_mask.type_as(x[0]), x) # [E, C, D]
+        buf = torch.empty_like(expert_inputs)
+        torch.distributed.all_to_all_single(buf, expert_inputs, group=self.group)
+        expert_inputs = buf
+
+        expert_inputs = expert_inputs.reshape(self.ep_size, self.num_local_experts, -1, self.config.dim) # [G, L, C, D]
+        x1 = F.silu(torch.einsum('glcd, lmd -> glcm', expert_inputs, self.w1))
+        x3 = torch.einsum('glcd, lmd -> glcm', expert_inputs, self.w3)
+        expert_output =  torch.einsum('glcm, ldm -> glcd', (x1 * x3), self.w2) # [G, L, C, D]
+
+        expert_output = expert_output.reshape(self.num_experts, -1, self.config.dim) # [E, C, D]
+        buf = torch.empty_like(expert_output)
+        torch.distributed.all_to_all_single(buf, expert_output, group=self.group)
+        expert_output = buf
+        
+        combined_output = torch.einsum("tec,ecd->td", combine_weights, expert_output)
+        return combined_output
+
+class MOEFeedForward(nn.Module):
+    def __init__(self, config, group: Optional[DeviceMesh] = None) -> None:
+        super().__init__()
+        # Initialize layers normally
+        self.gate = nn.Linear(config.dim, config.num_experts, bias=False)
+        self.cond_ffn = ConditionalFeedForward(config, group)
+        
+        self.dim = config.dim
+        self.num_activated_experts = config.num_activated_experts
+        self.rank = get_rank()
+        self.group = group
+        
+    def forward(self, x: Tensor) -> Tensor:
+        # Save original shape to reshape output back
+        shape = x.shape
+        x = x.view(-1, self.dim)
+        # T = num_tokens, E = num_experts, D = hidden dim, A = activated experts
+        # x: [T, D]
+        scores = self.gate(x) # [T, E]
+        gates = F.softmax(scores, dim=-1)
+        expert_weights, expert_indices = torch.topk(gates, self.num_activated_experts, dim=-1) # [T, A], [T, A]
+        expert_weights /= expert_weights.sum(dim=-1, keepdim=True) # [T, A]
+
+        num_experts = scores.shape[-1]
+        mask = F.one_hot(expert_indices, num_classes=num_experts).sum(dim=1).bool()  # [T, E]
+        capacity = x.shape[0] # // num_experts * 2
+        locations = torch.cumsum(mask, dim=0) - 1 # [T, E]
+        locations_sc = F.one_hot(locations * mask, capacity).float() # [T, E, C]
+        combine_weights = torch.einsum("se,sec->sec", gates * mask, locations_sc)
+        dispatch_mask = combine_weights.bool()
+
+        output = self.cond_ffn(x, dispatch_mask, combine_weights, capacity)
+        return output.view(shape)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, group: Optional[DeviceMesh] = None, eps: float = 1e-5):
+        super().__init__()
+        self.eps = eps
+        # Initialize weight normally (ones for RMSNorm)
+        self.weight = nn.Parameter(torch.ones(dim))
+        
+    def _norm(self, x):
+        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
+
+    def forward(self, x: Tensor) -> Tensor:
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+def precompute_freqs_cis(
+    seq_len: int, n_elem: int, base: int = 10000
+) -> Tensor:
+    freqs = 1.0 / (base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem))
+    t = torch.arange(seq_len, device=freqs.device)
+    freqs = torch.outer(t, freqs)
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+    cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
+    return cache.to(dtype=torch.bfloat16)
+
+
+def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
+    xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
+    freqs_cis = freqs_cis.reshape(1, xshaped.size(1), 1, xshaped.size(3), 2)
+    x_out2 = torch.stack(
+        [
+            xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
+            xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
+        ],
+        -1,
+    )
+
+    x_out2 = x_out2.flatten(3)
+    return x_out2.type_as(x)
\ No newline at end of file
diff --git a/test/test_llama.py b/test/test_llama.py
index a13bdb4..e171946 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -5,6 +5,8 @@
 import os
 from torch import nn, optim
 from torch.profiler import profile, record_function, ProfilerActivity
+import numpy as np
+import itertools
 
 from src.piper_exec import Task, piper_exec
 from src.piper_compile import piper_setup
@@ -22,111 +24,31 @@
     no_pp_schedule
 )
 
-def parse_args():
-    parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
-    parser.add_argument('--model', choices=['LLAMA_DEBUG', 'LLAMA_1B', 'LLAMA_3B', 'LLAMA_8B'], default='LLAMA_DEBUG',
-                        help='Model configuration: LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, or LLAMA_8B (default: LLAMA_DEBUG)')
-    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'no-pp'], default='1f1b',
-                        help='Schedule type: gpipe, 1f1b, or interleaved-1f1b (default: 1f1b)')
-    parser.add_argument('--dp_degree', type=int, default=1,
-                        help='Number of data parallel degrees (default: 1)')
-    parser.add_argument('--pp_degree', type=int, default=2,
-                        help='Number of pipeline parallel degrees (default: 2)')
-    parser.add_argument('--batch_size', type=int, default=16,
-                        help='Batch size (default: 16)')
-    parser.add_argument('--num_mbs', type=int, default=4,
-                        help='Number of microbatches (default: 4)')
-    parser.add_argument('--seq_len', type=int, default=256,
-                        help='Sequence length (default: 256)')
-    parser.add_argument('--warmup', type=int, default=5,
-                        help='Number of warmup iterations (default: 5)')
-    parser.add_argument('--iters', type=int, default=20,
-                        help='Number of timing iterations (default: 20)')
-    parser.add_argument('--tracing', action='store_true', default=False,
-                        help='Enable tracing')
-    parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
-                        help='Enable naive gradient sync')
-    return parser.parse_args()
-
-
-def print_mean_timing_data(trace_data: dict, actor_id: int) -> None:
-    """Prints mean timing and memory statistics from trace_data for a given actor.
-
-    Args:
-        trace_data (dict): Trace data dictionary from the actor containing timing and memory metrics.
-        actor_id (int): The ID of the actor.
-    """
-    import numpy as np
-
-    print(f"\nTiming and Memory statistics for Actor {actor_id}:")
-    
-    for stage_id, stage_data in trace_data.items():
-        if stage_id == 'update':
-            # Handle update data (global optimizer step)
-            print(f"  Update:")
-            for metric, values in stage_data.items():
-                if not values:
-                    mean_val = float('nan')
-                else:
-                    mean_val = float(np.mean(values))
-                
-                if 'memory' in metric:
-                    if 'delta' in metric:
-                        print(f"    {metric}: {mean_val:.3f} GB")
-                    else:
-                        print(f"    {metric}: {mean_val:.3f} GB")
-                else:
-                    print(f"    {metric}: {mean_val:.3f} ms")
-        else:
-            # Handle stage data (forward/backward passes)
-            print(f"  Stage {stage_id}:")
-            for phase, phase_data in stage_data.items():
-                print(f"    {phase.capitalize()}:")
-                for metric, values in phase_data.items():
-                    if not values:
-                        mean_val = float('nan')
-                    else:
-                        mean_val = float(np.mean(values))
-                    
-                    if 'memory' in metric:
-                        if 'delta' in metric:
-                            print(f"      {metric}: {mean_val:.3f} GB")
-                        else:
-                            print(f"      {metric}: {mean_val:.3f} GB")
-                    else:
-                        print(f"      {metric}: {mean_val:.3f} ms")
-
 
 def main(args):
     
     # Set model configuration based on argument
     match args.model:
-        case 'LLAMA_DEBUG':
+        case 'debug':
             llama_config = LLAMA_DEBUG
-        case 'LLAMA_1B':
+        case '1b':
             llama_config = LLAMA_1B
-        case 'LLAMA_3B':
+        case '3b':
             llama_config = LLAMA_3B
-        case 'LLAMA_8B':
+        case '8b':
             llama_config = LLAMA_8B
     print(args) 
 
     loss_fn = torch.nn.CrossEntropyLoss()
     
-    batch_size = args.batch_size
-    num_mbs = args.num_mbs
-    seq_len = args.seq_len
-    warmup = args.warmup
-    iters = args.iters
-    
-    x = torch.randint(0, llama_config.vocab_size, (batch_size, seq_len))
-    y = torch.randn((batch_size, seq_len, llama_config.vocab_size))
+    x = torch.randint(0, llama_config.vocab_size, (args.batch_size, args.seq_len))
+    y = torch.randn((args.batch_size, args.seq_len, llama_config.vocab_size))
 
     # Generate different input data for each data parallel rank so that model weights get updated differently
-    if args.dp_degree > 1:
+    if args.dp > 1:
         dp_rank = int(os.environ['PIPER_DP_RANK'])
         torch.manual_seed(dp_rank)
-        x = torch.randint(0, llama_config.vocab_size, (batch_size, seq_len))
+        x = torch.randint(0, llama_config.vocab_size, (args.batch_size, args.seq_len))
         torch.manual_seed(0)
 
     schedule = None
@@ -134,77 +56,98 @@ def main(args):
         case "no-pp":
             schedule = no_pp_schedule
         case "interleaved-1f1b":
-            schedule = pp2_interleaved_1f1b_grid_schedule if args.pp_degree == 2 else pp4_interleaved_1f1b_grid_schedule
+            schedule = pp2_interleaved_1f1b_grid_schedule if args.pp == 2 else pp4_interleaved_1f1b_grid_schedule
         case "1f1b":
-            schedule = build_1f1b_schedule(num_mbs, args.pp_degree)
+            schedule = build_1f1b_schedule(args.mbs, args.pp)
         case "gpipe":
-            schedule = build_gpipe_schedule(num_mbs, args.pp_degree)
+            schedule = build_gpipe_schedule(args.mbs, args.pp)
     print("Schedule:")
     print_schedule(schedule)
 
     compiled = piper_setup(
         Transformer, 
-        (llama_config, seq_len), 
+        (llama_config, args.seq_len), 
         torch.optim.Adam, 
         [x], 
-        schedule
+        schedule,
+        args.naive_gradient_sync,
     )
-    
 
     # Send data to actors ahead of time
     actors = piper_metadata.actors
     ray.get(actors[0].load_input.remote([x]))
     ray.get(actors[len(actors)-1].load_labels.remote(y))
 
-    # Definte one iteration of the schedule
-    def iter_schedule():
-        losses = piper_exec(compiled, schedule, [x], y, loss_fn)
-
     # Warmup
-    print(f"Running {warmup} warmup iterations...")
-    for _ in range(warmup):
-        iter_schedule()
+    print(f"Running {args.warmup} warmup iterations...")
+    for _ in range(args.warmup):
+        piper_exec(compiled, schedule, [x], y, loss_fn, args.dp)
 
     # Clear tracing data
     ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
 
     # Time training steps
-    start = time.perf_counter()
-    print(f"Running {iters} timed iterations...")
-    for _ in range(iters):
-        iter_schedule()
-    end = time.perf_counter()
+    print(f"Running {args.iters} timed iterations...")
+    iter_times = []
+    for _ in range(args.iters):
+        start = time.perf_counter()
+        piper_exec(compiled, schedule, [x], y, loss_fn, args.dp)
+        end = time.perf_counter()
+        iter_times.append(end - start)
     
-    print(f"Iteration time: {(end - start)*1e3/iters:.0f} ms")
+    dp_rank = int(os.environ['PIPER_DP_RANK'])
     print(
-        f"{args.schedule} throughput: {(iters * batch_size * num_mbs * seq_len)/(end - start):.0f} tokens/sec"
+        f"rank {dp_rank} iter time= {np.mean(iter_times):.2f} ± {np.std(iter_times):.2f} s ({len(iter_times)} samples)\n"
+        f"rank {dp_rank} throughput= {(args.batch_size * args.mbs * args.seq_len)/np.mean(iter_times):.2f} tokens/sec ({len(iter_times)} samples)"
     )
 
     if args.tracing:
-        # Get overall peak memory
-        peak_memory = ray.get([actor.get_peak_memory.remote() for actor in actors.values()])
-        print("Peak memory:")
-        for actor_id, peak_memory in enumerate(peak_memory):
-            print(f"\tActor {actor_id}: {peak_memory:.1f} GB")
-
-        # Get tracing data from actors
-        for actor in actors.values():
-            trace_data = ray.get(actor.get_trace_data.remote())
-            actor_id = ray.get(actor.id.remote())
-            print_mean_timing_data(trace_data, actor_id)
-    
+        trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
+        for rank, trace_data in trace_data_ret:
+            for key in trace_data:
+                all_times = trace_data[key]
+                print(f"rank {rank} {key} time= {np.mean(all_times):.2f} ± {np.std(all_times):.2f} ms ({len(all_times)} samples)")
+
     if args.naive_gradient_sync:
         suffix = "-naive-sync"
     else:
         suffix = ""
-    timeline_filename = f"out/{args.model}-pp{args.pp_degree}-dp{args.dp_degree}-{args.schedule}{suffix}.json"
+    timeline_filename = f"out/{args.model}-pp{args.pp}-dp{args.dp}-{args.schedule}{suffix}.json"
     ray.timeline(timeline_filename)
-    print(f"\nRay timeline saved to: {timeline_filename}")
+    print(f"Ray timeline saved to: {timeline_filename}")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
+    parser.add_argument('--model', choices=['debug', '1b', '3b', '8b'], default='debug',
+                        help='Model configuration: debug, 1b, 3b, or 8b (default: debug)')
+    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'no-pp'], default='1f1b',
+                        help='Schedule type: gpipe, 1f1b, or interleaved-1f1b (default: 1f1b)')
+    parser.add_argument('--dp', type=int, default=1,
+                        help='Number of data parallel degrees (default: 1)')
+    parser.add_argument('--pp', type=int, default=2,
+                        help='Number of pipeline parallel degrees (default: 2)')
+    parser.add_argument('--batch_size', type=int, default=16,
+                        help='Batch size (default: 16)')
+    parser.add_argument('--mbs', type=int, default=4,
+                        help='Number of microbatches (default: 4)')
+    parser.add_argument('--seq_len', type=int, default=256,
+                        help='Sequence length (default: 256)')
+    parser.add_argument('--warmup', type=int, default=5,
+                        help='Number of warmup iterations (default: 5)')
+    parser.add_argument('--iters', type=int, default=10,
+                        help='Number of timing iterations (default: 10)')
+    parser.add_argument('--tracing', action='store_true', default=False,
+                        help='Enable tracing')
+    parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
+                        help='Enable naive gradient sync')
+    return parser.parse_args()
+
 
 if __name__ == "__main__":
     ray.init(include_dashboard=False, log_to_driver=True, namespace="llama")
     args = parse_args()
-    piper_coordinator = PiperProgramCoordinator.remote(dp_degree=args.dp_degree, pp_degree=args.pp_degree)
+    piper_coordinator = PiperProgramCoordinator.remote(dp_degree=args.dp, pp_degree=args.pp)
     ray.get(piper_coordinator.run_program.remote(main, args))
     time.sleep(3)
-    ray.shutdown()
+    ray.shutdown()
\ No newline at end of file
diff --git a/test/test_mixtral.py b/test/test_mixtral.py
index 6e2d1d0..0151707 100644
--- a/test/test_mixtral.py
+++ b/test/test_mixtral.py
@@ -4,6 +4,8 @@
 import argparse
 import time
 import os
+import numpy as np
+import itertools
 
 from src.piper_coordinator import PiperProgramCoordinator
 from src.piper_compile import piper_setup
@@ -14,20 +16,27 @@
 from .schedule_helpers import build_1f1b_schedule, print_schedule
 from torch._dynamo.backends.debugging import eager
 
-def main(dp_degree, pp_degree):
+def main(args):
 
-    world_size = dp_degree * pp_degree
-    num_mbs = 1
-    batch_size = 8
+    world_size = args.dp * args.pp
+    mbs = args.mbs
+    batch_size = args.batch_size
 
-    config = ModelArgs.from_name("tiny")
-    dp_rank = int(os.environ['PIPER_DP_RANK'])
+    match args.model:
+        case 'tiny':
+            config = ModelArgs.from_name("tiny")
+        case 'small':
+            config = ModelArgs.from_name("small")
+        case 'medium':
+            config = ModelArgs.from_name("medium")
+        case 'large':
+            config = ModelArgs.from_name("Mixtral-8x7B-v0.1")
 
     x = torch.randint(0, config.vocab_size, (batch_size, config.block_size))
     input_pos = torch.arange(config.block_size)
     y = torch.randn(batch_size, config.block_size, config.vocab_size)
 
-    schedule = build_1f1b_schedule(num_mbs, pp_degree)
+    schedule = build_1f1b_schedule(mbs, args.pp)
     print_schedule(schedule)
     model = piper_setup(
         Transformer, 
@@ -35,6 +44,7 @@ def main(dp_degree, pp_degree):
         torch.optim.Adam, 
         [x, input_pos], 
         schedule,
+        args.naive_gradient_sync,
     )
 
     # Send data to actors ahead of time
@@ -44,17 +54,62 @@ def main(dp_degree, pp_degree):
 
     loss_fn = torch.nn.CrossEntropyLoss()
 
-    start = time.perf_counter()
-    piper_exec(model, schedule, [x, input_pos], y, loss_fn)
-    end = time.perf_counter()
-    print(f"Iter time: {end - start:.2f} s")
+    print(f"Running {args.warmup} warmup iterations...")
+    for _ in range(args.warmup):
+        piper_exec(model, schedule, [x, input_pos], y, loss_fn, args.dp)
+    
+    ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
+
+    print(f"Running {args.iters} timed iterations...")
+    iter_times = []
+    for _ in range(args.iters):
+        start = time.perf_counter()
+        piper_exec(model, schedule, [x, input_pos], y, loss_fn, args.dp)
+        end = time.perf_counter()
+        iter_times.append(end - start)
+    
+    dp_rank = int(os.environ['PIPER_DP_RANK'])
+    print(
+        f"rank {dp_rank} iter time= {np.mean(iter_times):.2f} ± {np.std(iter_times):.2f} s ({len(iter_times)} samples)\n"
+        f"rank {dp_rank} throughput= {(args.batch_size * args.mbs * config.block_size)/np.mean(iter_times):.2f} tokens/s ({len(iter_times)} samples)"
+    )
+
+    if args.tracing:
+        trace_data_dicts = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
+        for key in trace_data_dicts[0]:
+            all_times = list(itertools.chain.from_iterable(trace_data_dict[key] for trace_data_dict in trace_data_dicts))
+            print(f"rank {dp_rank} {key} time= {np.mean(all_times):.2f} ± {np.std(all_times):.2f} ms ({len(all_times)} samples)")
+
+    timeline_filename = f"out/mixtral-pp{args.pp}-dp{args.dp}"
+    ray.timeline(timeline_filename)
+    print(f"Ray timeline saved to: {timeline_filename}")
 
-    ray.timeline(f"out/mixtral.json")
+def parse_args():
+    parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
+    parser.add_argument('--model', choices=['tiny', 'small', 'medium', 'large'], default='tiny',
+                        help='Model configuration: tiny, small, medium, or large (default: tiny)')
+    parser.add_argument('--dp', type=int, default=2,
+                        help='Number of data parallel degrees (default: 2)')
+    parser.add_argument('--pp', type=int, default=2,
+                        help='Number of pipeline parallel degrees (default: 2)')
+    parser.add_argument('--batch_size', type=int, default=16,
+                        help='Batch size (default: 16)')
+    parser.add_argument('--mbs', type=int, default=4,
+                        help='Number of microbatches (default: 4)')
+    parser.add_argument('--warmup', type=int, default=5,
+                        help='Number of warmup iterations (default: 5)')
+    parser.add_argument('--iters', type=int, default=10,
+                        help='Number of timing iterations (default: 10)')
+    parser.add_argument('--tracing', action='store_true', default=False,
+                        help='Enable tracing')
+    parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
+                        help='Enable naive gradient sync')
+    return parser.parse_args()
 
 if __name__ == "__main__":
     ray.init(include_dashboard=False, log_to_driver=True, namespace="llama", _temp_dir="/m-coriander/coriander/mfris/tmp/ray")
-    dp_degree, pp_degree = 2, 2
-    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=pp_degree, dp_degree=dp_degree)
-    handles = piper_coordinator.run_program.remote(main, dp_degree, pp_degree)
+    args = parse_args()
+    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
+    handles = piper_coordinator.run_program.remote(main, args)
     ray.get(handles)
     ray.shutdown()
diff --git a/test/test_mixtral_multiple_iters.py b/test/test_mixtral_multiple_iters.py
index 3a9f41c..7533885 100644
--- a/test/test_mixtral_multiple_iters.py
+++ b/test/test_mixtral_multiple_iters.py
@@ -47,7 +47,6 @@ def main(dp_degree, pp_degree, num_warmup=2, num_iters=5):
     print(f"running {warmup} warmup iters...")
     for i in range(num_warmup):
         piper_exec(model, schedule, [x, input_pos], y, loss_fn)
-        print(f"warmup iter {i + 1}/{num_warmup} completed")
 
     print(f"running {num_iters} iters...")
     iter_times = []
@@ -57,7 +56,6 @@ def main(dp_degree, pp_degree, num_warmup=2, num_iters=5):
         end = time.perf_counter()
         iter_time = end - start
         iter_times.append(iter_time)
-        print(f"iter {i + 1}/{num_iters} took {iter_time:.2f} s")
     
     if iter_times:
         avg_time = sum(iter_times) / len(iter_times)
@@ -71,7 +69,7 @@ def main(dp_degree, pp_degree, num_warmup=2, num_iters=5):
     ray.init(include_dashboard=False, log_to_driver=True, namespace="llama", _temp_dir="/m-coriander/coriander/mfris/tmp/ray")
     dp_degree, pp_degree = 2, 2
     warmup = 2  
-    num_iterations = 10 
+    num_iterations = 2 
     piper_coordinator = PiperProgramCoordinator.remote(pp_degree=pp_degree, dp_degree=dp_degree)
     handles = piper_coordinator.run_program.remote(main, dp_degree, pp_degree, warmup, num_iterations)
     ray.get(handles)
diff --git a/test/test_mixtral_pippy.py b/test/test_mixtral_pippy.py
new file mode 100644
index 0000000..8a28ce5
--- /dev/null
+++ b/test/test_mixtral_pippy.py
@@ -0,0 +1,104 @@
+import os
+import torch
+import torch.nn as nn
+import torch.distributed as dist
+from torch.distributed.tensor import DeviceMesh
+
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.distributed import pipelining as pippy  # adjust if your version differs
+from .models.mixtral_a2a import Transformer, ModelArgs
+
+
+def main() -> None:
+    """Run a simple Mixtral tiny pipeline-parallel + data-parallel training step."""
+    dist.init_process_group(backend="nccl")
+
+    rank = dist.get_rank()
+    world_size = dist.get_world_size()
+    dp_degree = 2
+    pp_degree = 2
+    assert world_size == dp_degree * pp_degree, "This example assumes 2-way DP and 2-way PP (world_size=4)"
+
+    device = torch.device(f"cuda:{rank}")
+    torch.cuda.set_device(device)
+
+    # Create a 2x2 device mesh for 2-way data parallel (DP) and 2-way pipeline parallel (PP)
+    # Mesh layout: [dp_rank, pp_rank]
+    mesh = DeviceMesh(
+        "cuda",
+        torch.arange(world_size).reshape(dp_degree, pp_degree),
+        mesh_dim_names=("dp", "pp"),
+    )
+
+    # Label DP/PP groups and compute this process's rank within each group
+    dp_group = mesh.get_group(mesh_dim="dp")
+    pp_group = mesh.get_group(mesh_dim="pp")
+
+    dp_rank = rank // pp_degree
+    pp_rank = rank % pp_degree
+
+    dp_devices = [f"cuda:{i}" for i in range(world_size) if i % pp_degree == pp_rank]
+    print(f"dp_devices: {dp_devices}")
+
+    print(
+        f"Global rank {rank} -> dp_rank={dp_rank}, pp_rank={pp_rank}, "
+        f"dp_group={dp_group}, pp_group={pp_group}"
+    )
+
+    # Tiny config from your mixtral-a2a.py
+    config = ModelArgs.from_name("tiny")
+    config.n_layer = config.n_layer // pp_degree
+
+    model = Transformer(config, dp_group)
+
+    if pp_rank == 0:
+        model.norm = None
+        model.output = None
+    if pp_rank == 1:
+        model.tok_embeddings = None
+    
+    model.to(device)
+
+    model = DDP(model, device_ids=dp_devices)
+
+    # NOTE: The exact constructor signature may vary slightly between PyTorch versions.
+    stage = pippy.PipelineStage(
+        model,
+        pp_rank,
+        pp_degree,
+        device,
+        group=pp_group,
+    )
+
+    batch_size = 16
+    mbs = 1
+    loss_fn = nn.CrossEntropyLoss()
+    schedule = pippy.Schedule1F1B(stage, mbs, loss_fn)
+
+    x = torch.randint(0, config.vocab_size, (batch_size*mbs, config.block_size), device=device)
+    input_pos = torch.arange(config.block_size, device=device)
+    y = torch.randn((batch_size*mbs, config.block_size, config.vocab_size), device=device)
+
+    warmup = 3
+    iters = 5
+
+    for _ in range(warmup):
+        if pp_rank == 0:
+            schedule.step(x, input_pos)
+        else:
+            schedule.step()
+
+    start = time.perf_counter()
+    for _ in range(iters):
+        if pp_rank == 0:
+            schedule.step(x, input_pos)
+        else:
+            schedule.step()
+    end = time.perf_counter()
+
+    print(f"Iter time: {(end - start)/iters:.2f} s")
+
+    dist.destroy_process_group()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/test/test_mixtral_single.py b/test/test_mixtral_single.py
new file mode 100644
index 0000000..72d4f18
--- /dev/null
+++ b/test/test_mixtral_single.py
@@ -0,0 +1,51 @@
+import os
+import torch
+import torch.nn as nn
+import torch.distributed as dist
+from torch.distributed.tensor import DeviceMesh
+import time
+
+from torch.distributed import pipelining as pippy  # adjust if your version differs
+from .models.mixtral_a2a import Transformer, ModelArgs
+
+
+def main() -> None:
+    """Run a simple Mixtral tiny pipeline-parallel + data-parallel training step."""
+    dist.init_process_group(backend="nccl")
+
+    rank = dist.get_rank()
+    world_size = dist.get_world_size()
+    assert world_size == 2
+
+    device = torch.device(f"cuda:{rank}")
+    torch.cuda.set_device(device)
+
+    config = ModelArgs.from_name("tiny")
+
+    model = Transformer(config, group=None)
+    model.to(device)
+
+    batch_size = 16
+    loss_fn = nn.CrossEntropyLoss()
+
+    x = torch.randint(0, config.vocab_size, (batch_size, config.block_size), device=device)
+    input_pos = torch.arange(config.block_size, device=device)
+    y = torch.randn((batch_size, config.block_size, config.vocab_size), device=device)
+
+    warmup = 3
+    iters = 5
+
+    for _ in range(warmup):
+        model(x, input_pos)
+
+    start = time.perf_counter()
+    for _ in range(iters):
+        model(x, input_pos)
+    end = time.perf_counter()
+
+    print(f"Iter time: {1e3*(end - start)/iters:.2f} ms")
+
+    dist.destroy_process_group()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/test/test_simple.py b/test/test_simple.py
new file mode 100644
index 0000000..cf42220
--- /dev/null
+++ b/test/test_simple.py
@@ -0,0 +1,50 @@
+import torch
+import ray
+
+from src.piper_coordinator import PiperProgramCoordinator
+from src.piper_compile import piper_setup
+from src.piper_exec import piper_exec
+from src.piper_utils import piper_metadata
+
+from .schedule_helpers import build_1f1b_schedule, print_schedule
+
+class SimpleModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.layer1 = torch.nn.Linear(10, 10)
+        self.layer2 = torch.nn.Linear(10, 10)
+        self.freqs_cis = torch.randn(10, 10)
+
+    def forward(self, x):
+        with torch.fx.traceback.annotate({"stage": 0}):
+            freqs_cis = self.freqs_cis
+            x = self.layer1(x) + freqs_cis
+        with torch.fx.traceback.annotate({"stage": 1}):
+            x = self.layer2(x) + freqs_cis
+        return x
+
+def main():
+    stages = 2
+    mbs = 1
+    x = torch.randn(10)
+    y = torch.randn((10, 10))
+
+    schedule = build_1f1b_schedule(mbs, stages)
+    print_schedule(schedule)
+
+    model = piper_setup(SimpleModel, [], torch.optim.Adam, [x], schedule)
+
+    actors = piper_metadata.actors
+    ray.get(actors[0].load_input.remote([x]))
+    ray.get(actors[len(actors)-1].load_labels.remote(y))
+
+    piper_exec(model, schedule, [x], y, torch.nn.MSELoss())
+
+    ray.timeline(f"out/simple.json")
+
+if __name__ == "__main__":
+    ray.init(include_dashboard=False, log_to_driver=True, namespace="llama", _temp_dir="/m-coriander/coriander/mfris/tmp/ray")
+    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=2, dp_degree=1)
+    handles = piper_coordinator.run_program.remote(main)
+    ray.get(handles)
+    ray.shutdown()
\ No newline at end of file

From 900348fdfaa77243896cc386054f87cf20898fc0 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Fri, 13 Feb 2026 21:57:10 +0000
Subject: [PATCH 026/100] rm files

---
 src/test_compile.py                 |   7 --
 test/test_mixtral_multiple_iters.py |  76 --------------------
 test/test_mixtral_pippy.py          | 104 ----------------------------
 test/test_mixtral_single.py         |  51 --------------
 test/test_simple.py                 |  50 -------------
 5 files changed, 288 deletions(-)
 delete mode 100644 src/test_compile.py
 delete mode 100644 test/test_mixtral_multiple_iters.py
 delete mode 100644 test/test_mixtral_pippy.py
 delete mode 100644 test/test_mixtral_single.py
 delete mode 100644 test/test_simple.py

diff --git a/src/test_compile.py b/src/test_compile.py
deleted file mode 100644
index da9256c..0000000
--- a/src/test_compile.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from test.models.llama import Transformer, LLAMA_1B
-
-from torch._dynamo.backends.debugging import eager
-
-model = Transformer(LLAMA_1B, 1024)
-dummy = torch.compile(model, backend=eager, fullgraph=True)
-dummy(torch.randint(0, 1024, (1, 1024)))
\ No newline at end of file
diff --git a/test/test_mixtral_multiple_iters.py b/test/test_mixtral_multiple_iters.py
deleted file mode 100644
index 7533885..0000000
--- a/test/test_mixtral_multiple_iters.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import ray
-import torch
-import torch.nn as nn
-import argparse
-import time
-import os
-
-from src.piper_coordinator import PiperProgramCoordinator
-from src.piper_compile import piper_setup
-from src.piper_exec import piper_exec
-from src.piper_utils import piper_metadata
-
-from .models.mixtral import Transformer, ModelArgs
-from .schedule_helpers import build_1f1b_schedule, print_schedule
-from torch._dynamo.backends.debugging import eager
-
-def main(dp_degree, pp_degree, num_warmup=2, num_iters=5):
-
-    world_size = dp_degree * pp_degree
-    num_mbs = 1
-    batch_size = 8
-
-    config = ModelArgs.from_name("tiny")
-    dp_rank = int(os.environ['PIPER_DP_RANK'])
-
-    x = torch.randint(0, config.vocab_size, (batch_size, config.block_size))
-    input_pos = torch.arange(config.block_size)
-    y = torch.randn(batch_size, config.block_size, config.vocab_size)
-
-    schedule = build_1f1b_schedule(num_mbs, pp_degree)
-    print_schedule(schedule)
-    model = piper_setup(
-        Transformer, 
-        [config], 
-        torch.optim.Adam, 
-        [x, input_pos], 
-        schedule,
-    )
-
-    # Send data to actors ahead of time
-    actors = piper_metadata.actors
-    ray.get(actors[0].load_input.remote([x]))
-    ray.get(actors[len(actors)-1].load_labels.remote(y))
-
-    loss_fn = torch.nn.CrossEntropyLoss()
-
-    print(f"running {warmup} warmup iters...")
-    for i in range(num_warmup):
-        piper_exec(model, schedule, [x, input_pos], y, loss_fn)
-
-    print(f"running {num_iters} iters...")
-    iter_times = []
-    for i in range(num_iters):
-        start = time.perf_counter()
-        piper_exec(model, schedule, [x, input_pos], y, loss_fn)
-        end = time.perf_counter()
-        iter_time = end - start
-        iter_times.append(iter_time)
-    
-    if iter_times:
-        avg_time = sum(iter_times) / len(iter_times)
-        print(f"average iter time: {avg_time:.2f} s")
-        print(f"min iter time: {min(iter_times):.2f} s")
-        print(f"max iter time: {max(iter_times):.2f} s")
-
-    ray.timeline(f"out/mixtral.json")
-
-if __name__ == "__main__":
-    ray.init(include_dashboard=False, log_to_driver=True, namespace="llama", _temp_dir="/m-coriander/coriander/mfris/tmp/ray")
-    dp_degree, pp_degree = 2, 2
-    warmup = 2  
-    num_iterations = 2 
-    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=pp_degree, dp_degree=dp_degree)
-    handles = piper_coordinator.run_program.remote(main, dp_degree, pp_degree, warmup, num_iterations)
-    ray.get(handles)
-    ray.shutdown()
\ No newline at end of file
diff --git a/test/test_mixtral_pippy.py b/test/test_mixtral_pippy.py
deleted file mode 100644
index 8a28ce5..0000000
--- a/test/test_mixtral_pippy.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import os
-import torch
-import torch.nn as nn
-import torch.distributed as dist
-from torch.distributed.tensor import DeviceMesh
-
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.distributed import pipelining as pippy  # adjust if your version differs
-from .models.mixtral_a2a import Transformer, ModelArgs
-
-
-def main() -> None:
-    """Run a simple Mixtral tiny pipeline-parallel + data-parallel training step."""
-    dist.init_process_group(backend="nccl")
-
-    rank = dist.get_rank()
-    world_size = dist.get_world_size()
-    dp_degree = 2
-    pp_degree = 2
-    assert world_size == dp_degree * pp_degree, "This example assumes 2-way DP and 2-way PP (world_size=4)"
-
-    device = torch.device(f"cuda:{rank}")
-    torch.cuda.set_device(device)
-
-    # Create a 2x2 device mesh for 2-way data parallel (DP) and 2-way pipeline parallel (PP)
-    # Mesh layout: [dp_rank, pp_rank]
-    mesh = DeviceMesh(
-        "cuda",
-        torch.arange(world_size).reshape(dp_degree, pp_degree),
-        mesh_dim_names=("dp", "pp"),
-    )
-
-    # Label DP/PP groups and compute this process's rank within each group
-    dp_group = mesh.get_group(mesh_dim="dp")
-    pp_group = mesh.get_group(mesh_dim="pp")
-
-    dp_rank = rank // pp_degree
-    pp_rank = rank % pp_degree
-
-    dp_devices = [f"cuda:{i}" for i in range(world_size) if i % pp_degree == pp_rank]
-    print(f"dp_devices: {dp_devices}")
-
-    print(
-        f"Global rank {rank} -> dp_rank={dp_rank}, pp_rank={pp_rank}, "
-        f"dp_group={dp_group}, pp_group={pp_group}"
-    )
-
-    # Tiny config from your mixtral-a2a.py
-    config = ModelArgs.from_name("tiny")
-    config.n_layer = config.n_layer // pp_degree
-
-    model = Transformer(config, dp_group)
-
-    if pp_rank == 0:
-        model.norm = None
-        model.output = None
-    if pp_rank == 1:
-        model.tok_embeddings = None
-    
-    model.to(device)
-
-    model = DDP(model, device_ids=dp_devices)
-
-    # NOTE: The exact constructor signature may vary slightly between PyTorch versions.
-    stage = pippy.PipelineStage(
-        model,
-        pp_rank,
-        pp_degree,
-        device,
-        group=pp_group,
-    )
-
-    batch_size = 16
-    mbs = 1
-    loss_fn = nn.CrossEntropyLoss()
-    schedule = pippy.Schedule1F1B(stage, mbs, loss_fn)
-
-    x = torch.randint(0, config.vocab_size, (batch_size*mbs, config.block_size), device=device)
-    input_pos = torch.arange(config.block_size, device=device)
-    y = torch.randn((batch_size*mbs, config.block_size, config.vocab_size), device=device)
-
-    warmup = 3
-    iters = 5
-
-    for _ in range(warmup):
-        if pp_rank == 0:
-            schedule.step(x, input_pos)
-        else:
-            schedule.step()
-
-    start = time.perf_counter()
-    for _ in range(iters):
-        if pp_rank == 0:
-            schedule.step(x, input_pos)
-        else:
-            schedule.step()
-    end = time.perf_counter()
-
-    print(f"Iter time: {(end - start)/iters:.2f} s")
-
-    dist.destroy_process_group()
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/test/test_mixtral_single.py b/test/test_mixtral_single.py
deleted file mode 100644
index 72d4f18..0000000
--- a/test/test_mixtral_single.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import os
-import torch
-import torch.nn as nn
-import torch.distributed as dist
-from torch.distributed.tensor import DeviceMesh
-import time
-
-from torch.distributed import pipelining as pippy  # adjust if your version differs
-from .models.mixtral_a2a import Transformer, ModelArgs
-
-
-def main() -> None:
-    """Run a simple Mixtral tiny pipeline-parallel + data-parallel training step."""
-    dist.init_process_group(backend="nccl")
-
-    rank = dist.get_rank()
-    world_size = dist.get_world_size()
-    assert world_size == 2
-
-    device = torch.device(f"cuda:{rank}")
-    torch.cuda.set_device(device)
-
-    config = ModelArgs.from_name("tiny")
-
-    model = Transformer(config, group=None)
-    model.to(device)
-
-    batch_size = 16
-    loss_fn = nn.CrossEntropyLoss()
-
-    x = torch.randint(0, config.vocab_size, (batch_size, config.block_size), device=device)
-    input_pos = torch.arange(config.block_size, device=device)
-    y = torch.randn((batch_size, config.block_size, config.vocab_size), device=device)
-
-    warmup = 3
-    iters = 5
-
-    for _ in range(warmup):
-        model(x, input_pos)
-
-    start = time.perf_counter()
-    for _ in range(iters):
-        model(x, input_pos)
-    end = time.perf_counter()
-
-    print(f"Iter time: {1e3*(end - start)/iters:.2f} ms")
-
-    dist.destroy_process_group()
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/test/test_simple.py b/test/test_simple.py
deleted file mode 100644
index cf42220..0000000
--- a/test/test_simple.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-import ray
-
-from src.piper_coordinator import PiperProgramCoordinator
-from src.piper_compile import piper_setup
-from src.piper_exec import piper_exec
-from src.piper_utils import piper_metadata
-
-from .schedule_helpers import build_1f1b_schedule, print_schedule
-
-class SimpleModel(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.layer1 = torch.nn.Linear(10, 10)
-        self.layer2 = torch.nn.Linear(10, 10)
-        self.freqs_cis = torch.randn(10, 10)
-
-    def forward(self, x):
-        with torch.fx.traceback.annotate({"stage": 0}):
-            freqs_cis = self.freqs_cis
-            x = self.layer1(x) + freqs_cis
-        with torch.fx.traceback.annotate({"stage": 1}):
-            x = self.layer2(x) + freqs_cis
-        return x
-
-def main():
-    stages = 2
-    mbs = 1
-    x = torch.randn(10)
-    y = torch.randn((10, 10))
-
-    schedule = build_1f1b_schedule(mbs, stages)
-    print_schedule(schedule)
-
-    model = piper_setup(SimpleModel, [], torch.optim.Adam, [x], schedule)
-
-    actors = piper_metadata.actors
-    ray.get(actors[0].load_input.remote([x]))
-    ray.get(actors[len(actors)-1].load_labels.remote(y))
-
-    piper_exec(model, schedule, [x], y, torch.nn.MSELoss())
-
-    ray.timeline(f"out/simple.json")
-
-if __name__ == "__main__":
-    ray.init(include_dashboard=False, log_to_driver=True, namespace="llama", _temp_dir="/m-coriander/coriander/mfris/tmp/ray")
-    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=2, dp_degree=1)
-    handles = piper_coordinator.run_program.remote(main)
-    ray.get(handles)
-    ray.shutdown()
\ No newline at end of file

From d528f48b1ab717c5e6b7c1a42dd1023a882efc8d Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Fri, 13 Feb 2026 22:04:36 +0000
Subject: [PATCH 027/100] remove actor._forward requires_grad hack

---
 src/piper_actor.py   | 1 -
 src/piper_compile.py | 5 -----
 src/piper_utils.py   | 2 +-
 3 files changed, 1 insertion(+), 7 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index 33bcc8d..c14a399 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -301,7 +301,6 @@ def _forward(self, stage_id: int, mb_idx: int):
             self._stop_timing(self.p2p_stream, "fwd_p2p_recv")
 
             # save first input that requires grad as input activation
-            self.forward_args[stage_id][self.input_idxs[stage_id][-1]].requires_grad_()
             inp_with_grad = [self.forward_args[stage_id][i] for i in self.input_idxs[stage_id] if self.forward_args[stage_id][i].requires_grad]
             assert len(inp_with_grad) == 1, "Exactly one input per stage should require a gradient"
             self.logger.debug(f"Saving input activation {inp_with_grad[0].shape} for stage {stage_id} mb {mb_idx}")
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 4a28e94..c3b8c0a 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -30,11 +30,6 @@ def piper_setup(model_class, model_args, optim_fn, example_inputs, schedule, nai
 
     model = model_class(*model_args)
 
-    logger.debug("Compiling model with full graph")
-    dummy = torch.compile(model, backend=eager, fullgraph=True)
-    dummy(*example_inputs)
-    logger.debug("Model compiled with full graph")
-
     compiled = torch.compile(model, backend=piper)
 
     dp_rank = int(os.environ['PIPER_DP_RANK'])
diff --git a/src/piper_utils.py b/src/piper_utils.py
index eb9eb25..b489a56 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -12,7 +12,7 @@
 
 import src.piper_patches
 
-LOG_LEVEL = "DEBUG"
+LOG_LEVEL = "INFO"
 
 """ 
 Print the backward graph of a tensor

From 51b47b165fc914803291458a9d6e92974e40b808 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Fri, 13 Feb 2026 22:40:26 +0000
Subject: [PATCH 028/100] deadlock happens when --mbs > 1 due to p2p send/recv
 not ordered properly

---
 src/piper_actor.py       | 10 ++++-
 src/piper_compile.py     |  2 +
 src/piper_utils.py       |  2 +-
 test/models/llama.py     | 84 +++++++++++++++++++---------------------
 test/schedule_helpers.py | 29 +++++++++++++-
 test/test_llama.py       | 16 ++++++--
 6 files changed, 91 insertions(+), 52 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index c14a399..9852f28 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -295,10 +295,12 @@ def _forward(self, stage_id: int, mb_idx: int):
             pp_rank = piper_metadata.stage_to_device[stage_id - 1]
             global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
+            self.logger.debug(f"Dispatch fwd p2p recv on {self.global_rank} from {global_src_rank}")
             self._start_timing(self.p2p_stream, "fwd_p2p_recv")
             for i in self.input_idxs[stage_id]:
                 dist.recv(self.forward_args[stage_id][i], src=global_src_rank, group=self.pp_group)
             self._stop_timing(self.p2p_stream, "fwd_p2p_recv")
+            self.logger.debug(f"Completed fwd p2p recv on {self.global_rank} from {global_src_rank}")
 
             # save first input that requires grad as input activation
             inp_with_grad = [self.forward_args[stage_id][i] for i in self.input_idxs[stage_id] if self.forward_args[stage_id][i].requires_grad]
@@ -325,10 +327,12 @@ def _forward(self, stage_id: int, mb_idx: int):
             pp_rank = piper_metadata.stage_to_device[stage_id + 1]
             global_dst_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
+            self.logger.debug(f"Dispatch fwd p2p send on {self.global_rank} to {global_dst_rank}")
             self._start_timing(self.p2p_stream, "fwd_p2p_send")
             for i in range(len(output)):
                 dist.send(output[i], dst=global_dst_rank, group=self.pp_group)
             self._stop_timing(self.p2p_stream, "fwd_p2p_send")
+            self.logger.debug(f"Completed fwd p2p send on {self.global_rank} to {global_dst_rank}")
 
         if CLEANUP_MEMORY:
             gc.collect()
@@ -351,10 +355,12 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
             pp_rank = piper_metadata.stage_to_device[stage_id + 1]
             global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
+            self.logger.debug(f"Dispatch bwd p2p recv on {self.global_rank} from {global_src_rank}")
             self._start_timing(self.p2p_stream, "bwd_p2p_recv")
             with torch.cuda.stream(self.p2p_stream):
                 dist.recv(input_grad, src=global_src_rank, group=self.pp_group)
             self._stop_timing(self.p2p_stream, "bwd_p2p_recv")
+            self.logger.debug(f"Completed bwd p2p recv on {self.global_rank} from {global_src_rank}")
 
             out_activation.backward(gradient=input_grad)
         else:
@@ -375,11 +381,13 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
             pp_rank = piper_metadata.stage_to_device[stage_id - 1]
             global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
+            self.logger.debug(f"Dispatch bwd p2p send on {self.global_rank} to {global_src_rank}")
             self._start_timing(self.p2p_stream, "bwd_p2p_send")
             with torch.cuda.stream(self.p2p_stream):
                 dist.send(output_grad, dst=global_src_rank, group=self.pp_group)
             self._stop_timing(self.p2p_stream, "bwd_p2p_send")
-
+            self.logger.debug(f"Completed bwd p2p send on {self.global_rank} to {global_src_rank}")
+            
             self.inp_activation[stage_id][mb_idx] = None
 
         if CLEANUP_MEMORY:
diff --git a/src/piper_compile.py b/src/piper_compile.py
index c3b8c0a..2337a81 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -25,6 +25,8 @@ def piper_setup(model_class, model_args, optim_fn, example_inputs, schedule, nai
     num_stages = len(set([task.stage_id for row in schedule for task in row if task is not None]))
     piper_metadata.stage_to_device = {task.stage_id: task.pp_rank for row in schedule for task in row if task is not None}
 
+    logger.debug(f"mbs: {num_mbs}, stages: {num_stages}, devices: {num_devices}")
+
     _create_actors(num_devices, optim_fn, num_mbs, num_stages, naive_gradient_sync)
     ray.get([actor._join_process_groups.remote() for actor in piper_metadata.actors.values()])
 
diff --git a/src/piper_utils.py b/src/piper_utils.py
index b489a56..eb9eb25 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -12,7 +12,7 @@
 
 import src.piper_patches
 
-LOG_LEVEL = "INFO"
+LOG_LEVEL = "DEBUG"
 
 """ 
 Print the backward graph of a tensor
diff --git a/test/models/llama.py b/test/models/llama.py
index a8a6017..3883955 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -397,66 +397,60 @@ def log_size(layer, indent=0):
         mask = torch.triu(mask, diagonal=1)
         self.mask = torch.hstack([torch.zeros((self.seq_len, 0)), mask])
 
-    # """
-    # forward method for interleaved-1f1b schedule
-    # requires:
-    # - 2 devices
-    # - 4 stages
-    # - n_layers is divisible by 4
-    # """
-    # def forward(self, tokens: torch.Tensor):
-
-    #     distributed_stage(0, actor_id=0)
-
-    #     h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-    #     start_pos = 0
-        
-    #     for layer in self.layers[:self.n_layers//4]:
-    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     distributed_stage(1, actor_id=1)
-
-    #     for layer in self.layers[self.n_layers//4:self.n_layers//2]:
-    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     distributed_stage(2, actor_id=0)
-
-    #     for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
-    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     distributed_stage(3, actor_id=1)
-
-    #     for layer in self.layers[3*self.n_layers//4:]:
-    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     h = self.norm(h) if self.norm else h
-    #     output = self.output(h).float() if self.output else h
-
-    #     return output
-
     """
-    forward method for 1f1b schedule
+    forward method for interleaved-1f1b schedule
     requires:
     - 2 devices
-    - 2 stages
-    - n_layers is divisible by 2
+    - 4 stages
+    - n_layers is divisible by 4
     """
     def forward(self, tokens: torch.Tensor):
 
         with torch.fx.traceback.annotate({"stage": 0}):
-            h = self.tok_embeddings(tokens)
+            h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
             start_pos = 0
-            for layer in self.layers[:self.n_layers//2]:
+            for layer in self.layers[:self.n_layers//4]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
 
         with torch.fx.traceback.annotate({"stage": 1}):
-            for layer in self.layers[self.n_layers//2:]:
+            for layer in self.layers[self.n_layers//4:self.n_layers//2]:
+                h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+        with torch.fx.traceback.annotate({"stage": 2}):
+            for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
+                h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+        with torch.fx.traceback.annotate({"stage": 3}):
+            for layer in self.layers[3*self.n_layers//4:]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
-            h = self.norm(h)
-            output = self.output(h).float()
+            h = self.norm(h) if self.norm else h
+            output = self.output(h).float() if self.output else h
 
         return output
 
+    # """
+    # forward method for 1f1b schedule
+    # requires:
+    # - 2 devices
+    # - 2 stages
+    # - n_layers is divisible by 2
+    # """
+    # def forward(self, tokens: torch.Tensor):
+
+    #     with torch.fx.traceback.annotate({"stage": 0}):
+    #         h = self.tok_embeddings(tokens)
+    #         start_pos = 0
+    #         for layer in self.layers[:self.n_layers//2]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 1}):
+    #         for layer in self.layers[self.n_layers//2:]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+    #         h = self.norm(h)
+    #         output = self.output(h).float()
+
+    #     return output
+
     # """
     # forward method for no pp
     # requires:
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index b50c70c..54fc05c 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -69,7 +69,32 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int):
     ]
 ]
 
-pp2_interleaved_1f1b_grid_schedule = [
+pp2_interleaved_1f1b_grid_schedule_mb1 = [
+    [
+        Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False),
+        None,
+        Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=True, upd=False),
+        None,
+        None,
+        Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=False, upd=False),
+        None,
+        Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False),
+        Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=True),
+    ],
+    [
+        None,
+        Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False),
+        None,
+        Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=True, upd=False),
+        Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=False, upd=False),
+        None,
+        Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False),
+        Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=True),
+        None,
+    ],
+]
+
+pp2_interleaved_1f1b_grid_schedule_mb4 = [
     [
         Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False),
         Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False),
@@ -114,7 +139,7 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int):
     ],
 ]
 
-pp4_interleaved_1f1b_grid_schedule = [
+pp4_interleaved_1f1b_grid_schedule_mb4 = [
     [
         Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False),
         Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False),
diff --git a/test/test_llama.py b/test/test_llama.py
index e171946..071334c 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -19,8 +19,9 @@
     build_1f1b_schedule, 
     build_gpipe_schedule, 
     print_schedule,
-    pp2_interleaved_1f1b_grid_schedule, 
-    pp4_interleaved_1f1b_grid_schedule,
+    pp2_interleaved_1f1b_grid_schedule_mb1, 
+    pp2_interleaved_1f1b_grid_schedule_mb4, 
+    pp4_interleaved_1f1b_grid_schedule_mb4,
     no_pp_schedule
 )
 
@@ -56,7 +57,16 @@ def main(args):
         case "no-pp":
             schedule = no_pp_schedule
         case "interleaved-1f1b":
-            schedule = pp2_interleaved_1f1b_grid_schedule if args.pp == 2 else pp4_interleaved_1f1b_grid_schedule
+            assert args.mbs == 1 or args.mbs == 4, "--mbs must be 1 or 4 for interleaved-1f1b schedule"
+            if args.mbs == 1:
+                assert args.pp == 2, "--pp must be 2 or 4 for interleaved-1f1b schedule with --mbs 1"
+                schedule = pp2_interleaved_1f1b_grid_schedule_mb1
+            else:
+                assert args.pp == 2 or args.pp == 4, "--pp must be 2 or 4 for interleaved-1f1b schedule with --mbs 4"
+                if args.pp == 2:
+                    schedule = pp2_interleaved_1f1b_grid_schedule_mb4
+                else:
+                    schedule = pp4_interleaved_1f1b_grid_schedule_mb4
         case "1f1b":
             schedule = build_1f1b_schedule(args.mbs, args.pp)
         case "gpipe":

From 789a2f598a1e74c510085d70d099174087c740d9 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Wed, 18 Feb 2026 18:58:23 +0000
Subject: [PATCH 029/100] manually reorder p2p ops for marked schedule tasks

---
 src/piper.py                 |  24 +++++-
 src/piper_actor.py           | 156 +++++++++++++++++++++++++++--------
 src/piper_compile.py         |  13 +--
 src/piper_coordinator.py     |   2 +-
 src/piper_exec.py            |  12 +--
 src/piper_graph_transform.py |   4 +-
 test/models/llama.py         |  35 +++++++-
 test/schedule_helpers.py     |  44 ++++++++--
 test/test_llama.py           |  49 ++++++-----
 test/test_mixtral.py         |  12 +--
 10 files changed, 265 insertions(+), 86 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index 89ac526..b9de7d2 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -3,6 +3,7 @@
 import ray
 import torch
 import os
+import gc
 from torch._dynamo.backends.registry import register_backend
 
 from .piper_utils import _serialize_graphmodule, piper_metadata, create_logger, LOG_LEVEL
@@ -22,11 +23,13 @@ def piper(gm, example_inputs, **kwargs):
     pp_degree = int(os.environ['PIPER_PP_DEGREE'])
     dp_degree = int(os.environ['PIPER_DP_DEGREE'])
 
+    del top_level_gm
+
     refs = []
     actor_stages = []
     for (stage_id, stage_gm, input_idxs, graphargs, placeholders) in submodules:
         if dp_degree > 1:
-            stage_gm = _insert_a2a_ops(stage_gm)
+            # stage_gm = _insert_a2a_ops(stage_gm)
             comm_ops, tids = _get_dp_comm_ops(graphargs, placeholders)
         else:
             comm_ops, tids = [], []
@@ -44,6 +47,14 @@ def piper(gm, example_inputs, **kwargs):
             tids,
             input_idxs,
         ))
+
+        del stage_gm
+        del graphargs
+        del placeholders
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
     ray.get(refs)
 
     # TODO: build dag by analyzing stage dependencies
@@ -51,9 +62,18 @@ def piper(gm, example_inputs, **kwargs):
     for stage_id in piper_metadata.stage_to_device.keys():
         if stage_id < len(piper_metadata.stage_to_device.keys()) - 1:
             piper_metadata.dag.add((stage_id, stage_id+1))
+    
+    example_outputs = original_gm(*example_inputs)
 
     def callback(*args):
         logger.warning("Should not directly call compiled module, running non-distributed execution")
-        return original_gm(*args)
+        return example_outputs
+
+    del original_gm
+    del example_inputs
+
+    gc.collect()
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
 
     return callback
\ No newline at end of file
diff --git a/src/piper_actor.py b/src/piper_actor.py
index c14a399..045c8cc 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -13,7 +13,7 @@
 
 from .piper_utils import _deserialize_graphmodule, create_logger, LOG_LEVEL, piper_metadata
 
-CLEANUP_MEMORY = False
+CLEANUP_MEMORY = True
 
 logger = create_logger("piper_actor", LOG_LEVEL)
 
@@ -79,6 +79,8 @@ def __init__(self, pp_rank, optim_class, world_size, num_mbs, num_stages, naive_
         self.graph_modules = dict()
         # map stage id -> model parameters used by the fx.Graph with holes (None values) for input tensors
         self.forward_args = dict()
+        # map stage id -> input idx -> input tensor metadata
+        self.forward_input_meta = defaultdict(dict)
         # map stage id -> indices of the input tensors (as opposed to model parameters) used by the fx.Graph
         self.input_idxs = dict()
         # map stage id -> optimizer for the fx.Graph
@@ -87,6 +89,7 @@ def __init__(self, pp_rank, optim_class, world_size, num_mbs, num_stages, naive_
         self.inp_activation = defaultdict(dict)
         # map stage id -> mb_idx -> current activation
         self.out_activation = defaultdict(dict)
+        self.bwd_recv_buf = None
         # accumuate loss for each microbatch
         self.loss = []
         # map stage id -> data parallel communication operations
@@ -131,7 +134,7 @@ def get_peak_memory(self):
 
     def load_input(self, inputs):
         self.inputs = [inp.to(self.device) for inp in inputs]
-        self.logger.debug(f"Actor {self.global_rank} loaded inputs {len(self.inputs)}")
+        self.logger.debug(f"Actor {self.global_rank} loaded {len(self.inputs)} inputs")
     
     def load_labels(self, labels):
         self.labels = labels.to(self.device)
@@ -183,6 +186,10 @@ def _join_pp_process_group(self):
                 self.pp_group = process_group
                 self.logger.info(f"Global rank {self.global_rank} joined its pp group {pp_group_id} along with ranks {group_ranks}")
 
+    def shutdown(self):
+        dist.destroy_process_group()
+        self.logger.info(f"Actor {self.global_rank} shutdown")
+
     def _prepare_comm_ops(self, stage_id, comm_ops, graphargs, ids):
         def hook_maker(tensor_id):
             def post_backward_hook(grad):
@@ -236,14 +243,24 @@ def _wait_for_comm_ops(self):
                 while not done:
                     if comm_op.tensor_id in self.comm_op_handles[stage_id]:
                         self.logger.debug(f"Waiting for comm op to be finished dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={comm_op.tensor.shape}")
-                        self.comm_op_handles[stage_id][comm_op.tensor_id].wait()
+                        handle = self.comm_op_handles[stage_id][comm_op.tensor_id]
+                        handle.wait()
+                        del handle
                         done = True
 
-    def _wait_for_p2p_ops(self):
-        self.logger.debug(f"Actor {self.global_rank} waiting for p2p ops")
-        for stage_id, handles in self.p2p_op_handles.items():
-            for handle in handles:
-                handle.wait()
+    # def _p2p_send(self, dst, group, *tensors):
+    #     with torch.cuda.stream(self.p2p_stream):
+    #         for tensor in tensors:
+    #             dist.send(tensor, dst=dst, group=group)
+
+    # def _p2p_recv(self, src, group, *tensors_meta):
+    #     with torch.cuda.stream(self.p2p_stream):
+    #         tensors = []
+    #         for shape, dtype, requires_grad in tensors_meta:
+    #             buf = torch.empty(shape, dtype=dtype, requires_grad=requires_grad, device=self.device)
+    #             dist.recv(buf, src=src, group=group)
+    #             tensors.append(buf)
+    #         return tensors
 
     def _load_stage(self, stage_id: int, gm_data, comm_ops, forward_args, ids, input_idxs):
         self.logger.info(f"Loading stage {stage_id} graph on actor {self.global_rank}")
@@ -269,20 +286,30 @@ def move_to_device(idx, arg):
         self.comm_ops[stage_id] = list(reversed(comm_ops))
 
         # save parameters
-        self.input_idxs[stage_id] = input_idxs
         self.forward_args[stage_id] = forward_args
         self.stage_id = stage_id
 
+        # save metadata for input tensors
+        self.input_idxs[stage_id] = input_idxs
+        for i in self.input_idxs[stage_id]:
+            self.forward_input_meta[stage_id][i] = (forward_args[i].shape, forward_args[i].dtype, forward_args[i].requires_grad)
+            self.forward_args[stage_id][i] = None
+
         # add the parameters to the optimizer for this stage
-        params = [param for param in forward_args if param.requires_grad]
+        params = [param for param in forward_args if param is not None and param.requires_grad]
         if stage_id not in self.optims:
             self.optims[stage_id] = self.optim_class(params)
         else:
             self.optims[stage_id].add_param_group({'params': params})
+        
+        if CLEANUP_MEMORY:
+            gc.collect()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
 
         del gm_data
 
-    def _forward(self, stage_id: int, mb_idx: int):
+    def _forward(self, stage_id: int, mb_idx: int, recv_bwd_b4_snd_fwd: bool = False):
 
         self.logger.debug(f"Calling forward {stage_id} mb {mb_idx} on actor {self.global_rank}")
 
@@ -292,22 +319,32 @@ def _forward(self, stage_id: int, mb_idx: int):
                 self.forward_args[stage_id][i] = inp
         else:
             # For non-first stages, receive input tensors from the previous stage
+
+            # Create empty input tensors
+            for i, (shape, dtype, requires_grad) in self.forward_input_meta[stage_id].items():
+                self.forward_args[stage_id][i] = torch.empty(shape, dtype=dtype, requires_grad=requires_grad, device=self.device)
+
             pp_rank = piper_metadata.stage_to_device[stage_id - 1]
             global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
+            self.logger.debug(f"Dispatched fwd recv from {global_src_rank} on {self.global_rank}")
             self._start_timing(self.p2p_stream, "fwd_p2p_recv")
             for i in self.input_idxs[stage_id]:
                 dist.recv(self.forward_args[stage_id][i], src=global_src_rank, group=self.pp_group)
             self._stop_timing(self.p2p_stream, "fwd_p2p_recv")
+            self.logger.debug(f"Completed fwd recv from {global_src_rank} on {self.global_rank}")
 
             # save first input that requires grad as input activation
             inp_with_grad = [self.forward_args[stage_id][i] for i in self.input_idxs[stage_id] if self.forward_args[stage_id][i].requires_grad]
             assert len(inp_with_grad) == 1, "Exactly one input per stage should require a gradient"
             self.logger.debug(f"Saving input activation {inp_with_grad[0].shape} for stage {stage_id} mb {mb_idx}")
             self.inp_activation[stage_id][mb_idx] = inp_with_grad[0]
+        
 
         # Run the forward pass
+        self._start_timing(self.comp_stream, "forward_comp")
         output = self.forward_fns[stage_id](*self.forward_args[stage_id])
+        self._stop_timing(self.comp_stream, "forward_comp")
 
         # Save first output that requires grad as output activation
         # TODO: support multiple outputs
@@ -317,29 +354,46 @@ def _forward(self, stage_id: int, mb_idx: int):
         self.out_activation[stage_id][mb_idx] = out_with_grad[0]
 
         # clear the input tensors
-        # for i in self.input_idxs[stage_id]:
-        #     self.forward_args[stage_id][i] = torch.empty_like(self.forward_args[stage_id][i])
+        for i in self.input_idxs[stage_id]:
+            self.forward_args[stage_id][i] = None
 
         if stage_id < self.num_stages - 1:
             # For non-final stages, send output tensors to the next stage
             pp_rank = piper_metadata.stage_to_device[stage_id + 1]
             global_dst_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
+            # TEMP: receive backward for the prev microbatch before sending forward for the current microbatch
+            if recv_bwd_b4_snd_fwd:
+                self.bwd_recv_buf = torch.empty_like(self.out_activation[stage_id][mb_idx-1])
+                pp_rank = piper_metadata.stage_to_device[stage_id + 1]
+                global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+
+                self.logger.debug(f"Dispatched bwd recv mb {mb_idx-1} from {global_src_rank} on {self.global_rank}")
+                self._start_timing(self.p2p_stream, "bwd_p2p_recv")
+                with torch.cuda.stream(self.p2p_stream):
+                    dist.recv(self.bwd_recv_buf, src=global_src_rank, group=self.pp_group)
+                self._stop_timing(self.p2p_stream, "bwd_p2p_recv")
+                self.logger.debug(f"Completed bwd recv mb {mb_idx-1} from {global_src_rank} on {self.global_rank}")
+
+            self.logger.debug(f"Dispatched fwd send mb {mb_idx} to {global_dst_rank} on {self.global_rank}")
             self._start_timing(self.p2p_stream, "fwd_p2p_send")
             for i in range(len(output)):
                 dist.send(output[i], dst=global_dst_rank, group=self.pp_group)
             self._stop_timing(self.p2p_stream, "fwd_p2p_send")
+            self.logger.debug(f"Completed fwd send mb {mb_idx} to {global_dst_rank} on {self.global_rank}")
+            
+            del output
 
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
+        # if CLEANUP_MEMORY:
+        #     gc.collect()
+        #     if torch.cuda.is_available():
+        #         torch.cuda.empty_cache()
         
-        self.logger.debug(f"Forward {stage_id} mb {mb_idx} on actor {self.global_rank} returning {[out.shape for out in output]}")
+        self.logger.debug(f"Forward {stage_id} mb {mb_idx} on actor {self.global_rank} completed")
         torch.cuda.synchronize()
 
     # @ray.method(tensor_transport="nccl")
-    def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
+    def _backward(self, stage_id: int, mb_idx: int, loss_fn=None, recv_bwd_b4_snd_fwd: bool = False):
 
         self.logger.debug(f"Calling backward {stage_id} mb {mb_idx} on actor {self.global_rank}")
         
@@ -347,25 +401,42 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
 
         if stage_id < self.num_stages - 1:
             # For non-final stages, recieve input gradients from the subsequent backward pass
-            input_grad = torch.empty_like(out_activation)
-            pp_rank = piper_metadata.stage_to_device[stage_id + 1]
-            global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
-
-            self._start_timing(self.p2p_stream, "bwd_p2p_recv")
-            with torch.cuda.stream(self.p2p_stream):
-                dist.recv(input_grad, src=global_src_rank, group=self.pp_group)
-            self._stop_timing(self.p2p_stream, "bwd_p2p_recv")
-
+            # TEMP: recv in _forward for all but last microbatch
+            if recv_bwd_b4_snd_fwd:
+                input_grad = self.bwd_recv_buf
+            else:
+                input_grad = torch.empty_like(out_activation)
+                pp_rank = piper_metadata.stage_to_device[stage_id + 1]
+                global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+
+                self.logger.debug(f"Dispatched bwd recv mb {mb_idx} from {global_src_rank} on {self.global_rank}")
+                self._start_timing(self.p2p_stream, "bwd_p2p_recv")
+                with torch.cuda.stream(self.p2p_stream):
+                    dist.recv(input_grad, src=global_src_rank, group=self.pp_group)
+                self._stop_timing(self.p2p_stream, "bwd_p2p_recv")
+                self.logger.debug(f"Completed bwd recv mb {mb_idx} from {global_src_rank} on {self.global_rank}")
+                
+            self._start_timing(self.comp_stream, "backward_comp")
             out_activation.backward(gradient=input_grad)
+            self._stop_timing(self.comp_stream, "backward_comp")
+            
+            del input_grad
+            self.bwd_recv_buf = None
         else:
             # For the final stage, wait for the forward pass to complete
             assert loss_fn is not None
             labels = self.labels
             assert out_activation.shape == labels.shape
+
+            self._start_timing(self.comp_stream, "backward_comp")
             loss = loss_fn(out_activation, labels)
             loss.backward()
+            self._stop_timing(self.comp_stream, "backward_comp")
+
             self.loss.append(loss.item())
 
+        # Clear output activation after backward pass
+        self.out_activation[stage_id][mb_idx] = None
         del out_activation
 
         if stage_id > 0:
@@ -375,17 +446,21 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
             pp_rank = piper_metadata.stage_to_device[stage_id - 1]
             global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
+            self.logger.debug(f"Dispatched bwd send mb {mb_idx} to {global_src_rank} on {self.global_rank}")
             self._start_timing(self.p2p_stream, "bwd_p2p_send")
             with torch.cuda.stream(self.p2p_stream):
                 dist.send(output_grad, dst=global_src_rank, group=self.pp_group)
             self._stop_timing(self.p2p_stream, "bwd_p2p_send")
+            self.logger.debug(f"Completed bwd send mb {mb_idx} to {global_src_rank} on {self.global_rank}")
 
+            # Clear input activation and its gradient after sending
             self.inp_activation[stage_id][mb_idx] = None
+            del output_grad
 
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
+        # if CLEANUP_MEMORY:
+        #     gc.collect()
+        #     if torch.cuda.is_available():
+        #         torch.cuda.empty_cache()
         
         torch.cuda.synchronize()
         
@@ -402,22 +477,33 @@ def _update(self):
             
         # if dp degree > 1, make sure all gradients are synchronized before optimizer step
         # TODO: this does not allow overlapping with the optimizer step
-        if self.pp_degree > 1:
-            self._wait_for_p2p_ops()
-            
+        self._start_timing(self.comm_stream, "backward_sync")
         if self.dp_degree > 1:
             if self.naive_gradient_sync:
                 self._synchronize_gradients()
             else:
                 self._wait_for_comm_ops()
+                # Clear comm op handles after waiting
+                for stage_id in list(self.comm_op_handles.keys()):
+                    self.comm_op_handles[stage_id].clear()
+        self._stop_timing(self.comm_stream, "backward_sync")
         
         # step the optimizer for each stage
+        self._start_timing(self.comp_stream, "optim_step")
         for _, optim in self.optims.items():
             optim.step()
             optim.zero_grad()
+        self._stop_timing(self.comp_stream, "optim_step")
+
         losses = self.loss
         self.loss.clear()
 
+        # Aggressive memory cleanup after optimizer step
+        # if CLEANUP_MEMORY:
+        #     gc.collect()
+        #     if torch.cuda.is_available():
+        #         torch.cuda.empty_cache()
+
         torch.cuda.synchronize()
 
         return losses
diff --git a/src/piper_compile.py b/src/piper_compile.py
index c3b8c0a..7e4a6e3 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -14,11 +14,10 @@
 logger = create_logger("piper_compile", LOG_LEVEL)
 
 
-def piper_setup(model_class, model_args, optim_fn, example_inputs, schedule, naive_gradient_sync=False):
+def piper_setup(model, optim_fn, example_inputs, schedule, naive_gradient_sync=False):
     """
     Compile a model with the piper backend.
     """
-    piper_metadata.naive_gradient_sync = naive_gradient_sync
 
     num_devices = len(schedule)
     num_mbs = len(set([task.mb_idx for row in schedule for task in row if task is not None]))
@@ -28,8 +27,6 @@ def piper_setup(model_class, model_args, optim_fn, example_inputs, schedule, nai
     _create_actors(num_devices, optim_fn, num_mbs, num_stages, naive_gradient_sync)
     ray.get([actor._join_process_groups.remote() for actor in piper_metadata.actors.values()])
 
-    model = model_class(*model_args)
-
     compiled = torch.compile(model, backend=piper)
 
     dp_rank = int(os.environ['PIPER_DP_RANK'])
@@ -38,5 +35,9 @@ def piper_setup(model_class, model_args, optim_fn, example_inputs, schedule, nai
     output = compiled(*example_inputs)
 
     logger.info(f"DP rank {dp_rank+1} done.")
- 
-    return compiled
+
+    del compiled
+    del model
+    gc.collect()
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
diff --git a/src/piper_coordinator.py b/src/piper_coordinator.py
index ed71060..6504330 100644
--- a/src/piper_coordinator.py
+++ b/src/piper_coordinator.py
@@ -7,7 +7,7 @@
 from .piper_utils import create_logger, LOG_LEVEL
 
 
-@ray.remote(num_gpus=0.1)
+@ray.remote(num_gpus=0.6)
 def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, master_addr, master_port, training_func: Callable, *args, **kwargs):
     logger = create_logger("piper_coordinator", LOG_LEVEL)
     logger.debug(f"Running DP rank {dp_rank+1} of {dp_degree}")
diff --git a/src/piper_exec.py b/src/piper_exec.py
index b76a7ac..4336b22 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -16,6 +16,7 @@ class Task(NamedTuple):
     mb_idx: int
     is_fwd: bool
     upd: bool
+    recv_bwd_b4_snd_fwd: bool = False
 
 class DAGEdge(NamedTuple):
     from_stage: int
@@ -142,12 +143,11 @@ def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdg
                         f"before backward stage {from_stage} (time {bwd_times[from_stage]})"
                     )
 
-def piper_exec(model, schedule, inputs, truth, loss_fn, dp_degree=1):
+def piper_exec(schedule, inputs, truth, loss_fn, dp_degree=1, naive_gradient_sync=False):
     """
     Execute one step of the pipeline schedule on the distributed model.
 
     Args:
-        model: A model that has been compiled with the piper backend.
         schedule: A 2D list (device x time_step) of Tasks specifying execution order.
         inputs: Inputs to the model.
         truth: Ground-truth labels or targets.
@@ -160,7 +160,7 @@ def piper_exec(model, schedule, inputs, truth, loss_fn, dp_degree=1):
     num_steps, num_devices = len(schedule[0]), len(schedule)
 
     actors = piper_metadata.actors
-    if dp_degree > 1:
+    if dp_degree > 1 and not naive_gradient_sync:
         [actor._comm_loop.remote() for actor in actors.values()]
     
     num_mbs = len(set([task.mb_idx for row in schedule for task in row if task is not None]))
@@ -174,13 +174,13 @@ def piper_exec(model, schedule, inputs, truth, loss_fn, dp_degree=1):
         for j in range(num_devices-1, -1, -1):
             task = schedule[j][i]
             if task:
-                _, stage_id, mb_idx, is_fwd, upd = task
+                _, stage_id, mb_idx, is_fwd, upd, recv_bwd_b4_snd_fwd = task
                 actor_id = j
                 actor = actors[actor_id]
                 if upd:
                     ret.append(actor._update.remote())
                 elif is_fwd:
-                    actor._forward.remote(stage_id, mb_idx)
+                    actor._forward.remote(stage_id, mb_idx, recv_bwd_b4_snd_fwd=recv_bwd_b4_snd_fwd)
                 else:
-                    actor._backward.remote(stage_id, mb_idx, loss_fn=loss_fn)
+                    actor._backward.remote(stage_id, mb_idx, loss_fn=loss_fn, recv_bwd_b4_snd_fwd=recv_bwd_b4_snd_fwd)
     return ray.get(ret)
\ No newline at end of file
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index 3f04509..49216ae 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -459,7 +459,7 @@ def collect_dependencies(node):
                 graphargs.append(placeholder.meta["grapharg"]._example())
             else:
                 assert (stage_annotation_id == 0 and 'self' not in placeholder.name) or (stage_annotation_id > 0 and placeholder_to_original.get(placeholder) in prev_stage_outputs)
-                graphargs.append(torch.zeros(placeholder.meta["example_value"].shape, dtype=placeholder.meta["example_value"].dtype, requires_grad=placeholder.meta["example_value"].requires_grad))
+                graphargs.append(torch.zeros(placeholder.meta["example_value"].shape, dtype=placeholder.meta["example_value"].dtype, requires_grad=placeholder.meta["example_value"].requires_grad, device=placeholder.meta["example_value"].device))
             # For the first stage, the input indices are everything that's not an attribute
             if stage_annotation_id == 0:
                 if 'self' not in placeholder.name:
@@ -671,7 +671,7 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> fx.GraphModule:
                     annotated_nodes.append((idx, node, annotation_key, reshape))
     
     if not annotated_nodes:
-        logger.info("No communication annotations found in graph")
+        logger.debug("No communication annotations found in graph")
         return gm
     
     # Group annotated nodes into contiguous blocks
diff --git a/test/models/llama.py b/test/models/llama.py
index a8a6017..8aadfa1 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -391,11 +391,42 @@ def log_size(layer, indent=0):
             params.dim // params.n_heads,
             self.seq_len,
             params.rope_theta,
-        )
+        ).to('cuda')
 
         mask = torch.full((self.seq_len, self.seq_len), float("-inf"))
         mask = torch.triu(mask, diagonal=1)
-        self.mask = torch.hstack([torch.zeros((self.seq_len, 0)), mask])
+        self.mask = torch.hstack([torch.zeros((self.seq_len, 0)), mask]).to('cuda')
+
+    # """
+    # forward method for 1f1b schedule
+    # requires:
+    # - 4 devices
+    # - 4 stages
+    # - n_layers is divisible by 4
+    # """
+    # def forward(self, tokens: torch.Tensor):
+
+    #     with torch.fx.traceback.annotate({"stage": 0}):
+    #         h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
+    #         start_pos = 0
+    #         for layer in self.layers[:self.n_layers//4]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 1}):
+    #         for layer in self.layers[self.n_layers//4:self.n_layers//2]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 2}):
+    #         for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 3}):
+    #         for layer in self.layers[3*self.n_layers//4:]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+    #         h = self.norm(h) if self.norm else h
+    #         output = self.output(h).float() if self.output else h
+
+    #     return output
 
     # """
     # forward method for interleaved-1f1b schedule
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index b50c70c..6c393f2 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -5,9 +5,9 @@ def print_schedule(schedule):
     for stage in schedule:
         for step in stage:
             if step:
-                string = f"{step.stage_id}:{step.mb_idx}:{'u' if step.upd else 'f' if step.is_fwd else 'b'}"
+                string = f"{step.stage_id}:{step.mb_idx}:{'u' if step.upd else 'f' if step.is_fwd else 'b'}:{'r' if step.recv_bwd_b4_snd_fwd else '_'}"
             else:
-                string = " -- "
+                string = " ----- "
             print(string, end="\t")
         print()
 
@@ -39,8 +39,12 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int):
                 mb_idx = stage_mb[stage_id][0]
                 if mb_idx >= 0 and mb_idx < n_mbs:
                     schedule[stage_id][step] = Task(
-                        pp_rank=stage_id, stage_id=stage_id, 
-                        mb_idx=mb_idx, is_fwd=True, upd=False
+                        pp_rank=stage_id, 
+                        stage_id=stage_id,
+                        mb_idx=mb_idx, 
+                        is_fwd=True, 
+                        upd=False,
+                        recv_bwd_b4_snd_fwd=False
                     )
                     stage_mb[stage_id][0] += 1
     for step in range(n_stages, 2 * steps):
@@ -53,12 +57,38 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int):
                 mb_idx = stage_mb[stage_id][fwd_or_bwd]
                 if mb_idx >= 0 and mb_idx < n_mbs:
                     schedule[stage_id][step] = Task(
-                        pp_rank=stage_id, stage_id=stage_id,
-                        mb_idx=mb_idx, is_fwd=task_type, upd=False
+                        pp_rank=stage_id, 
+                        stage_id=stage_id,
+                        mb_idx=mb_idx, 
+                        is_fwd=task_type, 
+                        upd=False,
+                        recv_bwd_b4_snd_fwd=False
                     )
                     stage_mb[stage_id][fwd_or_bwd] += 1
     for i, stage in enumerate(range(n_stages)):
-        schedule[stage][-i-1] = Task(stage_id=stage, pp_rank=stage, mb_idx=n_mbs-1, is_fwd=False, upd=True)
+        schedule[stage][-i-1] = Task(
+            stage_id=stage, 
+            pp_rank=stage, 
+            mb_idx=n_mbs - 1,
+            is_fwd=False, 
+            upd=True, 
+            recv_bwd_b4_snd_fwd=False
+        )
+
+    for row in schedule:
+        non_none = [(col_idx, task) for col_idx, task in enumerate(row) if task is not None]
+        for k in range(len(non_none) - 1):
+            prev_idx, prev_task = non_none[k]
+            curr_idx, curr_task = non_none[k + 1]
+            if (
+                prev_task.is_fwd
+                and not curr_task.is_fwd
+                and not curr_task.upd
+                and curr_task.mb_idx < prev_task.mb_idx
+            ):
+                row[curr_idx] = curr_task._replace(recv_bwd_b4_snd_fwd=True)
+                row[prev_idx] = prev_task._replace(recv_bwd_b4_snd_fwd=True)
+
     return schedule
 
 no_pp_schedule = [
diff --git a/test/test_llama.py b/test/test_llama.py
index e171946..ce108d8 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -7,6 +7,7 @@
 from torch.profiler import profile, record_function, ProfilerActivity
 import numpy as np
 import itertools
+import gc
 
 from src.piper_exec import Task, piper_exec
 from src.piper_compile import piper_setup
@@ -41,15 +42,11 @@ def main(args):
 
     loss_fn = torch.nn.CrossEntropyLoss()
     
-    x = torch.randint(0, llama_config.vocab_size, (args.batch_size, args.seq_len))
-    y = torch.randn((args.batch_size, args.seq_len, llama_config.vocab_size))
+    model = Transformer(llama_config, args.seq_len)
+    model.to('cuda')
 
-    # Generate different input data for each data parallel rank so that model weights get updated differently
-    if args.dp > 1:
-        dp_rank = int(os.environ['PIPER_DP_RANK'])
-        torch.manual_seed(dp_rank)
-        x = torch.randint(0, llama_config.vocab_size, (args.batch_size, args.seq_len))
-        torch.manual_seed(0)
+    x = torch.randint(0, llama_config.vocab_size, (args.batch_size, args.seq_len)).to('cuda')
+    y = torch.randn((args.batch_size, args.seq_len, llama_config.vocab_size)).to('cuda')
 
     schedule = None
     match args.schedule:
@@ -64,9 +61,8 @@ def main(args):
     print("Schedule:")
     print_schedule(schedule)
 
-    compiled = piper_setup(
-        Transformer, 
-        (llama_config, args.seq_len), 
+    piper_setup(
+        model,
         torch.optim.Adam, 
         [x], 
         schedule,
@@ -78,35 +74,46 @@ def main(args):
     ray.get(actors[0].load_input.remote([x]))
     ray.get(actors[len(actors)-1].load_labels.remote(y))
 
+    del model
+    del x
+    del y 
+    gc.collect()
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
     # Warmup
     print(f"Running {args.warmup} warmup iterations...")
-    for _ in range(args.warmup):
-        piper_exec(compiled, schedule, [x], y, loss_fn, args.dp)
-
-    # Clear tracing data
-    ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
+    for i in range(args.warmup):
+        piper_exec(schedule, [None], None, loss_fn, args.dp, args.naive_gradient_sync)
+        print(f"Warmup {i} completed")
 
     # Time training steps
     print(f"Running {args.iters} timed iterations...")
     iter_times = []
     for _ in range(args.iters):
         start = time.perf_counter()
-        piper_exec(compiled, schedule, [x], y, loss_fn, args.dp)
+        piper_exec(schedule, [None], None, loss_fn, args.dp, args.naive_gradient_sync)
         end = time.perf_counter()
         iter_times.append(end - start)
     
     dp_rank = int(os.environ['PIPER_DP_RANK'])
     print(
-        f"rank {dp_rank} iter time= {np.mean(iter_times):.2f} ± {np.std(iter_times):.2f} s ({len(iter_times)} samples)\n"
-        f"rank {dp_rank} throughput= {(args.batch_size * args.mbs * args.seq_len)/np.mean(iter_times):.2f} tokens/sec ({len(iter_times)} samples)"
+        f"rank {dp_rank} iter time= {np.mean(iter_times):.3f} ± {np.std(iter_times):.3f} s ({len(iter_times)} samples)\n"
+        f"rank {dp_rank} throughput= {(args.batch_size * args.mbs * args.seq_len)/np.mean(iter_times):.3f} tokens/sec ({len(iter_times)} samples)"
     )
 
     if args.tracing:
+        ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
+
+        print(f"Running {args.warmup} tracing iterations...")
+        for _ in range(args.warmup):
+            piper_exec(schedule, [None], None, loss_fn, args.dp, args.naive_gradient_sync)
+
         trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
         for rank, trace_data in trace_data_ret:
             for key in trace_data:
                 all_times = trace_data[key]
-                print(f"rank {rank} {key} time= {np.mean(all_times):.2f} ± {np.std(all_times):.2f} ms ({len(all_times)} samples)")
+                print(f"rank {rank} {key} time= {np.mean(all_times):.3f} ± {np.std(all_times):.3f} ms ({len(all_times)} samples)")
 
     if args.naive_gradient_sync:
         suffix = "-naive-sync"
@@ -116,6 +123,8 @@ def main(args):
     ray.timeline(timeline_filename)
     print(f"Ray timeline saved to: {timeline_filename}")
 
+    ray.get([actor.shutdown.remote() for actor in actors.values()])
+
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
diff --git a/test/test_mixtral.py b/test/test_mixtral.py
index 0151707..a39ebf7 100644
--- a/test/test_mixtral.py
+++ b/test/test_mixtral.py
@@ -32,15 +32,17 @@ def main(args):
         case 'large':
             config = ModelArgs.from_name("Mixtral-8x7B-v0.1")
 
-    x = torch.randint(0, config.vocab_size, (batch_size, config.block_size))
-    input_pos = torch.arange(config.block_size)
-    y = torch.randn(batch_size, config.block_size, config.vocab_size)
+    model = Transformer(config)
+    model.to('cuda')
+
+    x = torch.randint(0, config.vocab_size, (batch_size, config.block_size)).to('cuda')
+    input_pos = torch.arange(config.block_size).to('cuda')
+    y = torch.randn(batch_size, config.block_size, config.vocab_size).to('cuda')
 
     schedule = build_1f1b_schedule(mbs, args.pp)
     print_schedule(schedule)
     model = piper_setup(
-        Transformer, 
-        [config], 
+        model, 
         torch.optim.Adam, 
         [x, input_pos], 
         schedule,

From e4b337265193f3e46fcac47e65847f67ea21e925 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Wed, 18 Feb 2026 19:05:38 +0000
Subject: [PATCH 030/100] Revert "merge"

This reverts commit f25e515983f5c27327a97bad468b2f6bce0b6914, reversing
changes made to 789a2f598a1e74c510085d70d099174087c740d9.
---
 src/piper_actor.py       | 53 ++++++++++++++++---------
 src/piper_compile.py     |  2 -
 src/piper_utils.py       |  2 +-
 test/models/llama.py     | 84 +++++++++++++++++++++-------------------
 test/schedule_helpers.py | 29 +-------------
 test/test_llama.py       | 16 ++------
 6 files changed, 86 insertions(+), 100 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index 6689936..045c8cc 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -327,12 +327,12 @@ def _forward(self, stage_id: int, mb_idx: int, recv_bwd_b4_snd_fwd: bool = False
             pp_rank = piper_metadata.stage_to_device[stage_id - 1]
             global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
-            self.logger.debug(f"Dispatch fwd p2p recv on {self.global_rank} from {global_src_rank}")
+            self.logger.debug(f"Dispatched fwd recv from {global_src_rank} on {self.global_rank}")
             self._start_timing(self.p2p_stream, "fwd_p2p_recv")
             for i in self.input_idxs[stage_id]:
                 dist.recv(self.forward_args[stage_id][i], src=global_src_rank, group=self.pp_group)
             self._stop_timing(self.p2p_stream, "fwd_p2p_recv")
-            self.logger.debug(f"Completed fwd p2p recv on {self.global_rank} from {global_src_rank}")
+            self.logger.debug(f"Completed fwd recv from {global_src_rank} on {self.global_rank}")
 
             # save first input that requires grad as input activation
             inp_with_grad = [self.forward_args[stage_id][i] for i in self.input_idxs[stage_id] if self.forward_args[stage_id][i].requires_grad]
@@ -362,17 +362,32 @@ def _forward(self, stage_id: int, mb_idx: int, recv_bwd_b4_snd_fwd: bool = False
             pp_rank = piper_metadata.stage_to_device[stage_id + 1]
             global_dst_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
-            self.logger.debug(f"Dispatch fwd p2p send on {self.global_rank} to {global_dst_rank}")
+            # TEMP: receive backward for the prev microbatch before sending forward for the current microbatch
+            if recv_bwd_b4_snd_fwd:
+                self.bwd_recv_buf = torch.empty_like(self.out_activation[stage_id][mb_idx-1])
+                pp_rank = piper_metadata.stage_to_device[stage_id + 1]
+                global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+
+                self.logger.debug(f"Dispatched bwd recv mb {mb_idx-1} from {global_src_rank} on {self.global_rank}")
+                self._start_timing(self.p2p_stream, "bwd_p2p_recv")
+                with torch.cuda.stream(self.p2p_stream):
+                    dist.recv(self.bwd_recv_buf, src=global_src_rank, group=self.pp_group)
+                self._stop_timing(self.p2p_stream, "bwd_p2p_recv")
+                self.logger.debug(f"Completed bwd recv mb {mb_idx-1} from {global_src_rank} on {self.global_rank}")
+
+            self.logger.debug(f"Dispatched fwd send mb {mb_idx} to {global_dst_rank} on {self.global_rank}")
             self._start_timing(self.p2p_stream, "fwd_p2p_send")
             for i in range(len(output)):
                 dist.send(output[i], dst=global_dst_rank, group=self.pp_group)
             self._stop_timing(self.p2p_stream, "fwd_p2p_send")
-            self.logger.debug(f"Completed fwd p2p send on {self.global_rank} to {global_dst_rank}")
+            self.logger.debug(f"Completed fwd send mb {mb_idx} to {global_dst_rank} on {self.global_rank}")
+            
+            del output
 
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
+        # if CLEANUP_MEMORY:
+        #     gc.collect()
+        #     if torch.cuda.is_available():
+        #         torch.cuda.empty_cache()
         
         self.logger.debug(f"Forward {stage_id} mb {mb_idx} on actor {self.global_rank} completed")
         torch.cuda.synchronize()
@@ -394,13 +409,14 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None, recv_bwd_b4_snd_fw
                 pp_rank = piper_metadata.stage_to_device[stage_id + 1]
                 global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
-            self.logger.debug(f"Dispatch bwd p2p recv on {self.global_rank} from {global_src_rank}")
-            self._start_timing(self.p2p_stream, "bwd_p2p_recv")
-            with torch.cuda.stream(self.p2p_stream):
-                dist.recv(input_grad, src=global_src_rank, group=self.pp_group)
-            self._stop_timing(self.p2p_stream, "bwd_p2p_recv")
-            self.logger.debug(f"Completed bwd p2p recv on {self.global_rank} from {global_src_rank}")
-
+                self.logger.debug(f"Dispatched bwd recv mb {mb_idx} from {global_src_rank} on {self.global_rank}")
+                self._start_timing(self.p2p_stream, "bwd_p2p_recv")
+                with torch.cuda.stream(self.p2p_stream):
+                    dist.recv(input_grad, src=global_src_rank, group=self.pp_group)
+                self._stop_timing(self.p2p_stream, "bwd_p2p_recv")
+                self.logger.debug(f"Completed bwd recv mb {mb_idx} from {global_src_rank} on {self.global_rank}")
+                
+            self._start_timing(self.comp_stream, "backward_comp")
             out_activation.backward(gradient=input_grad)
             self._stop_timing(self.comp_stream, "backward_comp")
             
@@ -430,13 +446,14 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None, recv_bwd_b4_snd_fw
             pp_rank = piper_metadata.stage_to_device[stage_id - 1]
             global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
-            self.logger.debug(f"Dispatch bwd p2p send on {self.global_rank} to {global_src_rank}")
+            self.logger.debug(f"Dispatched bwd send mb {mb_idx} to {global_src_rank} on {self.global_rank}")
             self._start_timing(self.p2p_stream, "bwd_p2p_send")
             with torch.cuda.stream(self.p2p_stream):
                 dist.send(output_grad, dst=global_src_rank, group=self.pp_group)
             self._stop_timing(self.p2p_stream, "bwd_p2p_send")
-            self.logger.debug(f"Completed bwd p2p send on {self.global_rank} to {global_src_rank}")
-            
+            self.logger.debug(f"Completed bwd send mb {mb_idx} to {global_src_rank} on {self.global_rank}")
+
+            # Clear input activation and its gradient after sending
             self.inp_activation[stage_id][mb_idx] = None
             del output_grad
 
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 87178fe..7e4a6e3 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -24,8 +24,6 @@ def piper_setup(model, optim_fn, example_inputs, schedule, naive_gradient_sync=F
     num_stages = len(set([task.stage_id for row in schedule for task in row if task is not None]))
     piper_metadata.stage_to_device = {task.stage_id: task.pp_rank for row in schedule for task in row if task is not None}
 
-    logger.debug(f"mbs: {num_mbs}, stages: {num_stages}, devices: {num_devices}")
-
     _create_actors(num_devices, optim_fn, num_mbs, num_stages, naive_gradient_sync)
     ray.get([actor._join_process_groups.remote() for actor in piper_metadata.actors.values()])
 
diff --git a/src/piper_utils.py b/src/piper_utils.py
index eb9eb25..b489a56 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -12,7 +12,7 @@
 
 import src.piper_patches
 
-LOG_LEVEL = "DEBUG"
+LOG_LEVEL = "INFO"
 
 """ 
 Print the backward graph of a tensor
diff --git a/test/models/llama.py b/test/models/llama.py
index 18aa8cd..8aadfa1 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -428,60 +428,66 @@ def log_size(layer, indent=0):
 
     #     return output
 
+    # """
+    # forward method for interleaved-1f1b schedule
+    # requires:
+    # - 2 devices
+    # - 4 stages
+    # - n_layers is divisible by 4
+    # """
+    # def forward(self, tokens: torch.Tensor):
+
+    #     distributed_stage(0, actor_id=0)
+
+    #     h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
+    #     start_pos = 0
+        
+    #     for layer in self.layers[:self.n_layers//4]:
+    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     distributed_stage(1, actor_id=1)
+
+    #     for layer in self.layers[self.n_layers//4:self.n_layers//2]:
+    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     distributed_stage(2, actor_id=0)
+
+    #     for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
+    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     distributed_stage(3, actor_id=1)
+
+    #     for layer in self.layers[3*self.n_layers//4:]:
+    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     h = self.norm(h) if self.norm else h
+    #     output = self.output(h).float() if self.output else h
+
+    #     return output
+
     """
-    forward method for interleaved-1f1b schedule
+    forward method for 1f1b schedule
     requires:
     - 2 devices
-    - 4 stages
-    - n_layers is divisible by 4
+    - 2 stages
+    - n_layers is divisible by 2
     """
     def forward(self, tokens: torch.Tensor):
 
         with torch.fx.traceback.annotate({"stage": 0}):
-            h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
+            h = self.tok_embeddings(tokens)
             start_pos = 0
-            for layer in self.layers[:self.n_layers//4]:
+            for layer in self.layers[:self.n_layers//2]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
 
         with torch.fx.traceback.annotate({"stage": 1}):
-            for layer in self.layers[self.n_layers//4:self.n_layers//2]:
-                h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-        with torch.fx.traceback.annotate({"stage": 2}):
-            for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
+            for layer in self.layers[self.n_layers//2:]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-        with torch.fx.traceback.annotate({"stage": 3}):
-            for layer in self.layers[3*self.n_layers//4:]:
-                h = layer(h, start_pos, self.freqs_cis, self.mask)
-            h = self.norm(h) if self.norm else h
-            output = self.output(h).float() if self.output else h
+            h = self.norm(h)
+            output = self.output(h).float()
 
         return output
 
-    # """
-    # forward method for 1f1b schedule
-    # requires:
-    # - 2 devices
-    # - 2 stages
-    # - n_layers is divisible by 2
-    # """
-    # def forward(self, tokens: torch.Tensor):
-
-    #     with torch.fx.traceback.annotate({"stage": 0}):
-    #         h = self.tok_embeddings(tokens)
-    #         start_pos = 0
-    #         for layer in self.layers[:self.n_layers//2]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[self.n_layers//2:]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-    #         h = self.norm(h)
-    #         output = self.output(h).float()
-
-    #     return output
-
     # """
     # forward method for no pp
     # requires:
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index a7e83df..6c393f2 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -99,32 +99,7 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int):
     ]
 ]
 
-pp2_interleaved_1f1b_grid_schedule_mb1 = [
-    [
-        Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False),
-        None,
-        Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=True, upd=False),
-        None,
-        None,
-        Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=False, upd=False),
-        None,
-        Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False),
-        Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=True),
-    ],
-    [
-        None,
-        Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False),
-        None,
-        Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=True, upd=False),
-        Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=False, upd=False),
-        None,
-        Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False),
-        Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=True),
-        None,
-    ],
-]
-
-pp2_interleaved_1f1b_grid_schedule_mb4 = [
+pp2_interleaved_1f1b_grid_schedule = [
     [
         Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False),
         Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False),
@@ -169,7 +144,7 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int):
     ],
 ]
 
-pp4_interleaved_1f1b_grid_schedule_mb4 = [
+pp4_interleaved_1f1b_grid_schedule = [
     [
         Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False),
         Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False),
diff --git a/test/test_llama.py b/test/test_llama.py
index cfee77a..ce108d8 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -20,9 +20,8 @@
     build_1f1b_schedule, 
     build_gpipe_schedule, 
     print_schedule,
-    pp2_interleaved_1f1b_grid_schedule_mb1, 
-    pp2_interleaved_1f1b_grid_schedule_mb4, 
-    pp4_interleaved_1f1b_grid_schedule_mb4,
+    pp2_interleaved_1f1b_grid_schedule, 
+    pp4_interleaved_1f1b_grid_schedule,
     no_pp_schedule
 )
 
@@ -54,16 +53,7 @@ def main(args):
         case "no-pp":
             schedule = no_pp_schedule
         case "interleaved-1f1b":
-            assert args.mbs == 1 or args.mbs == 4, "--mbs must be 1 or 4 for interleaved-1f1b schedule"
-            if args.mbs == 1:
-                assert args.pp == 2, "--pp must be 2 or 4 for interleaved-1f1b schedule with --mbs 1"
-                schedule = pp2_interleaved_1f1b_grid_schedule_mb1
-            else:
-                assert args.pp == 2 or args.pp == 4, "--pp must be 2 or 4 for interleaved-1f1b schedule with --mbs 4"
-                if args.pp == 2:
-                    schedule = pp2_interleaved_1f1b_grid_schedule_mb4
-                else:
-                    schedule = pp4_interleaved_1f1b_grid_schedule_mb4
+            schedule = pp2_interleaved_1f1b_grid_schedule if args.pp == 2 else pp4_interleaved_1f1b_grid_schedule
         case "1f1b":
             schedule = build_1f1b_schedule(args.mbs, args.pp)
         case "gpipe":

From 4db0979df824863a8ede376ca93f3b7eb7d00165 Mon Sep 17 00:00:00 2001
From: Yuhan Ruan <andyubryh@gmail.com>
Date: Wed, 18 Feb 2026 12:42:23 -0800
Subject: [PATCH 031/100] Order P2P ops

---
 src/piper_actor.py   | 456 ++++++++++++++++++++++++++++++++-----------
 src/piper_compile.py | 107 +++++++++-
 2 files changed, 441 insertions(+), 122 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index 9852f28..a44a954 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -11,45 +11,89 @@
 import threading
 from typing import Callable
 
-from .piper_utils import _deserialize_graphmodule, create_logger, LOG_LEVEL, piper_metadata
+from .piper_utils import (
+    _deserialize_graphmodule,
+    create_logger,
+    LOG_LEVEL,
+    piper_metadata,
+)
 
 CLEANUP_MEMORY = False
 
 logger = create_logger("piper_actor", LOG_LEVEL)
 
+
 def _get_rank(pp_rank, dp_rank, pp_degree):
     return pp_rank + dp_rank * pp_degree
 
-def _create_actors(num_actors, optim_class, num_mbs, num_stages, naive_gradient_sync=False):
-    dp_rank = int(os.environ['PIPER_DP_RANK'])
-    world_size = int(os.environ['PIPER_WORLD_SIZE'])
-    dp_degree = int(os.environ['PIPER_DP_DEGREE'])
-    pp_degree = int(os.environ['PIPER_PP_DEGREE'])
+
+def _create_actors(
+    num_actors,
+    optim_class,
+    num_mbs,
+    num_stages,
+    p2p_schedules,
+    naive_gradient_sync=False,
+):
+    dp_rank = int(os.environ["PIPER_DP_RANK"])
+    world_size = int(os.environ["PIPER_WORLD_SIZE"])
+    dp_degree = int(os.environ["PIPER_DP_DEGREE"])
+    pp_degree = int(os.environ["PIPER_PP_DEGREE"])
 
     from .piper_utils import piper_metadata
+
     for pp_rank in range(num_actors):
         global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
         if dp_degree > 1:
             max_concurrency = 2
         else:
             max_concurrency = 1
-        actor = PiperActor.options(num_gpus=1, max_concurrency=max_concurrency).remote(pp_rank, optim_class, world_size, num_mbs, num_stages, naive_gradient_sync, dp_rank=dp_rank, dp_degree=dp_degree, pp_degree=pp_degree)
+        p2p_schedule = p2p_schedules[pp_rank]
+        actor = PiperActor.options(num_gpus=1, max_concurrency=max_concurrency).remote(
+            pp_rank,
+            optim_class,
+            world_size,
+            num_mbs,
+            num_stages,
+            p2p_schedule,
+            naive_gradient_sync,
+            dp_rank=dp_rank,
+            dp_degree=dp_degree,
+            pp_degree=pp_degree,
+        )
         piper_metadata.actors[pp_rank] = actor
-        logger.debug(f"DP rank {dp_rank} created actor {actor} global rank {global_rank}")
+        logger.debug(
+            f"DP rank {dp_rank} created actor {actor} global rank {global_rank}"
+        )
+
 
 def _get_actor(pp_rank):
     from .piper_utils import piper_metadata
+
     return piper_metadata.actors[pp_rank]
 
+
 @ray.remote
 class PiperActor:
-    def __init__(self, pp_rank, optim_class, world_size, num_mbs, num_stages, naive_gradient_sync=False, dp_rank=0, dp_degree=1, pp_degree=1):
+    def __init__(
+        self,
+        pp_rank,
+        optim_class,
+        world_size,
+        num_mbs,
+        num_stages,
+        p2p_schedule,
+        naive_gradient_sync=False,
+        dp_rank=0,
+        dp_degree=1,
+        pp_degree=1,
+    ):
         self.logger = create_logger("piper_actor", LOG_LEVEL)
 
         self.pp_rank = pp_rank
         self.optim_class = optim_class
         self.naive_gradient_sync = naive_gradient_sync
-        
+
         self.dp_rank = dp_rank
         self.dp_degree = dp_degree
         self.pp_degree = pp_degree
@@ -57,13 +101,20 @@ def __init__(self, pp_rank, optim_class, world_size, num_mbs, num_stages, naive_
 
         self.num_mbs = num_mbs
         self.num_stages = num_stages
+        # list of (src_stage, dst_stage, mb_idx, is_sender)
+        self.p2p_schedule = p2p_schedule
+        self.next_p2p_idx = 0
+        # set of (src_stage, dst_stage, mb_idx, is_sender) for completed p2p ops
+        self.executed_p2ps = set()
         self.dp_group = None
         self.pp_group = None
-        self.device = 'cuda'
+        self.device = "cuda"
 
         self.global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
 
-        self.logger.info(f"Initializing Ray actor {pp_rank} global rank {self.global_rank} GPU {os.environ['CUDA_VISIBLE_DEVICES']}")
+        self.logger.info(
+            f"Initializing Ray actor {pp_rank} global rank {self.global_rank} GPU {os.environ['CUDA_VISIBLE_DEVICES']}"
+        )
 
         self.input = None
         self.labels = None
@@ -97,30 +148,37 @@ def __init__(self, pp_rank, optim_class, world_size, num_mbs, num_stages, naive_
         self.comm_op_handles = defaultdict(dict)
         # map stage id -> p2p op handle
         self.p2p_op_handles = defaultdict(list)
-        
+
         self.tracing = False
         self.trace_events = dict()
         self.trace_data = defaultdict(list)
 
         from .piper_utils import piper_metadata
+
         piper_metadata.actor_self = self
 
     def get_trace_data(self) -> dict:
         return self.global_rank, self.trace_data
-    
+
     def clear_trace_data(self) -> None:
         self.trace_data.clear()
 
     def set_tracing(self, enabled: bool) -> None:
         self.tracing = enabled
-        self.logger.info(f"Actor {self.global_rank}: Tracing {'enabled' if enabled else 'disabled'}")
+        self.logger.info(
+            f"Actor {self.global_rank}: Tracing {'enabled' if enabled else 'disabled'}"
+        )
 
     def start_mem_tracing(self) -> None:
         torch.cuda.memory._record_memory_history()
-    
+
     def stop_mem_tracing(self) -> None:
-        torch.cuda.memory._dump_snapshot(f"actor{self.global_rank}_memory_snapshot_mb4_gpipe.pickle")
-        self.logger.info(f"Saved memory snapshot to actor{self.global_rank}_memory_snapshot_mb4_gpipe.pickle")
+        torch.cuda.memory._dump_snapshot(
+            f"actor{self.global_rank}_memory_snapshot_mb4_gpipe.pickle"
+        )
+        self.logger.info(
+            f"Saved memory snapshot to actor{self.global_rank}_memory_snapshot_mb4_gpipe.pickle"
+        )
         torch.cuda.memory._record_memory_history(enabled=None)
 
     def reset_peak_memory(self):
@@ -132,7 +190,7 @@ def get_peak_memory(self):
     def load_input(self, inputs):
         self.inputs = [inp.to(self.device) for inp in inputs]
         self.logger.debug(f"Actor {self.global_rank} loaded inputs {len(self.inputs)}")
-    
+
     def load_labels(self, labels):
         self.labels = labels.to(self.device)
         self.logger.debug(f"Actor {self.global_rank} loaded labels {self.labels.shape}")
@@ -140,7 +198,10 @@ def load_labels(self, labels):
     def _start_timing(self, stream, label):
         if self.tracing:
             if label not in self.trace_events:
-                self.trace_events[label] = (torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True))
+                self.trace_events[label] = (
+                    torch.cuda.Event(enable_timing=True),
+                    torch.cuda.Event(enable_timing=True),
+                )
             start, _ = self.trace_events[label]
             start.record(stream)
 
@@ -153,89 +214,131 @@ def _stop_timing(self, stream, label):
                 self.trace_data[label].append(start.elapsed_time(stop))
 
     def _join_process_groups(self):
-        master_addr = os.environ.get('PIPER_MASTER_ADDR', "127.0.0.1")
-        master_port = os.environ.get('PIPER_MASTER_PORT', "10000")
+        master_addr = os.environ.get("PIPER_MASTER_ADDR", "127.0.0.1")
+        master_port = os.environ.get("PIPER_MASTER_PORT", "10000")
         init_method = f"tcp://{master_addr}:{master_port}"
 
-        dist.init_process_group("nccl", init_method=init_method, rank=self.global_rank, world_size=self.world_size)
-        self.logger.debug(f"Actor {self.global_rank} has GPU {os.environ['CUDA_VISIBLE_DEVICES']}, joined the global process group")
+        dist.init_process_group(
+            "nccl",
+            init_method=init_method,
+            rank=self.global_rank,
+            world_size=self.world_size,
+        )
+        self.logger.debug(
+            f"Actor {self.global_rank} has GPU {os.environ['CUDA_VISIBLE_DEVICES']}, joined the global process group"
+        )
 
         if self.dp_degree > 1:
             self._join_dp_process_group()
         if self.pp_degree > 1:
             self._join_pp_process_group()
-    
+
     def _join_dp_process_group(self):
         num_dp_groups = self.world_size // self.dp_degree
         for dp_group_id in range(num_dp_groups):
-            group_ranks = [(dp_group_id + num_dp_groups * i) for i in range(self.dp_degree)]
-            process_group = dist.new_group(ranks=group_ranks, backend='nccl')
+            group_ranks = [
+                (dp_group_id + num_dp_groups * i) for i in range(self.dp_degree)
+            ]
+            process_group = dist.new_group(ranks=group_ranks, backend="nccl")
             if self.global_rank % num_dp_groups == dp_group_id:
                 self.dp_group = process_group
-                self.logger.info(f"Global rank {self.global_rank} joined its dp group {dp_group_id} along with ranks {group_ranks}")
-    
+                self.logger.info(
+                    f"Global rank {self.global_rank} joined its dp group {dp_group_id} along with ranks {group_ranks}"
+                )
+
     def _join_pp_process_group(self):
         num_pp_groups = self.world_size // self.pp_degree
         for pp_group_id in range(num_pp_groups):
-            group_ranks = [(pp_group_id * self.pp_degree + i) for i in range(self.pp_degree)]
-            process_group = dist.new_group(ranks=group_ranks, backend='nccl')
+            group_ranks = [
+                (pp_group_id * self.pp_degree + i) for i in range(self.pp_degree)
+            ]
+            process_group = dist.new_group(ranks=group_ranks, backend="nccl")
             if self.global_rank // self.pp_degree == pp_group_id:
                 self.pp_group = process_group
-                self.logger.info(f"Global rank {self.global_rank} joined its pp group {pp_group_id} along with ranks {group_ranks}")
+                self.logger.info(
+                    f"Global rank {self.global_rank} joined its pp group {pp_group_id} along with ranks {group_ranks}"
+                )
 
     def _prepare_comm_ops(self, stage_id, comm_ops, graphargs, ids):
         def hook_maker(tensor_id):
             def post_backward_hook(grad):
                 self.comm_op_status[stage_id][tensor_id] += 1
-                self.logger.debug(f"Updating tensor status dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={grad.shape}, status={self.comm_op_status[stage_id][tensor_id]}")
+                self.logger.debug(
+                    f"Updating tensor status dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={grad.shape}, status={self.comm_op_status[stage_id][tensor_id]}"
+                )
                 return grad
+
             return post_backward_hook
-        
+
         comm_op_ids = [op.tensor_id for op in comm_ops]
-        for (t, i) in zip(graphargs, ids):
+        for t, i in zip(graphargs, ids):
             if i in comm_op_ids:
-               comm_op = comm_ops[comm_op_ids.index(i)]
-               comm_op.tensor = t
-               match comm_op.op:
-                   case "allreduce":
+                comm_op = comm_ops[comm_op_ids.index(i)]
+                comm_op.tensor = t
+                match comm_op.op:
+                    case "allreduce":
                         if comm_op.pass_type == "backward" and comm_op.dep == "post":
                             tensor_id = i
                             tensor_shape = t.shape
                             t.register_post_accumulate_grad_hook(hook_maker(tensor_id))
                         else:
-                            raise ValueError(f"Unknown comm op type or dependency: {comm_op.op} {comm_op.dep}")
-                   case _:
-                       raise ValueError(f"Unknown communication operation: {comm_op.op}")
+                            raise ValueError(
+                                f"Unknown comm op type or dependency: {comm_op.op} {comm_op.dep}"
+                            )
+                    case _:
+                        raise ValueError(
+                            f"Unknown communication operation: {comm_op.op}"
+                        )
 
     def _comm_loop(self):
         stage_id = self.stage_id
         # look into: nccl priority for sync vs async ops, stream priority
-        self.logger.info(f"Running comm loop for stage {stage_id} on actor {self.global_rank}")
+        self.logger.info(
+            f"Running comm loop for stage {stage_id} on actor {self.global_rank}"
+        )
         for comm_op in self.comm_ops[stage_id]:
             done = False
             while not done:
-                if comm_op.op == "allreduce" and comm_op.pass_type == "backward" and comm_op.dep == "post" and comm_op.group == "dp":
+                if (
+                    comm_op.op == "allreduce"
+                    and comm_op.pass_type == "backward"
+                    and comm_op.dep == "post"
+                    and comm_op.group == "dp"
+                ):
                     # perform the all reduce when all gradients have been accumulated
                     if self.comm_op_status[stage_id][comm_op.tensor_id] == self.num_mbs:
                         with torch.cuda.stream(self.comm_stream):
-                            handle = dist.all_reduce(comm_op.tensor, op=dist.ReduceOp.AVG, group=self.dp_group, async_op=True)
-                        self.logger.debug(f"Allreduce on dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={comm_op.tensor.shape}")
+                            handle = dist.all_reduce(
+                                comm_op.tensor,
+                                op=dist.ReduceOp.AVG,
+                                group=self.dp_group,
+                                async_op=True,
+                            )
+                        self.logger.debug(
+                            f"Allreduce on dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={comm_op.tensor.shape}"
+                        )
                         self.comm_op_status[stage_id][comm_op.tensor_id] = 0
                         self.comm_op_handles[stage_id][comm_op.tensor_id] = handle
                         done = True
                 else:
                     raise ValueError(f"Unknown comm op: {comm_op}")
-        self.logger.debug(f"Completed comm loop for stage {stage_id} on actor {self.global_rank}")
+        self.logger.debug(
+            f"Completed comm loop for stage {stage_id} on actor {self.global_rank}"
+        )
 
     def _wait_for_comm_ops(self):
         self.logger.debug(f"Actor {self.global_rank} waiting for comm ops")
         for stage_id, comm_ops in self.comm_ops.items():
             for comm_op in comm_ops:
-                self.logger.debug(f"Waiting for comm op to be launched dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={comm_op.tensor.shape}")
+                self.logger.debug(
+                    f"Waiting for comm op to be launched dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={comm_op.tensor.shape}"
+                )
                 done = False
                 while not done:
                     if comm_op.tensor_id in self.comm_op_handles[stage_id]:
-                        self.logger.debug(f"Waiting for comm op to be finished dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={comm_op.tensor.shape}")
+                        self.logger.debug(
+                            f"Waiting for comm op to be finished dp_rank: {self.dp_rank}, tensor={comm_op.name}, shape={comm_op.tensor.shape}"
+                        )
                         self.comm_op_handles[stage_id][comm_op.tensor_id].wait()
                         done = True
 
@@ -245,7 +348,9 @@ def _wait_for_p2p_ops(self):
             for handle in handles:
                 handle.wait()
 
-    def _load_stage(self, stage_id: int, gm_data, comm_ops, forward_args, ids, input_idxs):
+    def _load_stage(
+        self, stage_id: int, gm_data, comm_ops, forward_args, ids, input_idxs
+    ):
         self.logger.info(f"Loading stage {stage_id} graph on actor {self.global_rank}")
 
         # compile the graph with the given graphargs
@@ -261,6 +366,7 @@ def move_to_device(idx, arg):
                 return arg.to(self.device).detach().requires_grad_(True)
             else:
                 return arg.to(self.device)
+
         forward_args = list(map(move_to_device, range(len(forward_args)), forward_args))
 
         # prepare tensors with comm ops
@@ -278,34 +384,180 @@ def move_to_device(idx, arg):
         if stage_id not in self.optims:
             self.optims[stage_id] = self.optim_class(params)
         else:
-            self.optims[stage_id].add_param_group({'params': params})
+            self.optims[stage_id].add_param_group({"params": params})
 
         del gm_data
 
+    def _exec_p2p_op(
+        self, src_stage: int, dst_stage: int, mb_idx: int, is_sender: bool, **kwargs
+    ):
+        p2p_op = (src_stage, dst_stage, mb_idx, is_sender)
+        if p2p_op in self.executed_p2ps:
+            return
+
+        op_idx = self.next_p2p_idx
+        while op_idx < len(self.p2p_schedule):
+            op = self.p2p_schedule[op_idx]
+            if op == p2p_op:
+                break
+            op_idx += 1
+        assert op_idx < len(
+            self.p2p_schedule
+        ), f"P2P op {p2p_op} not found in schedule for actor {self.global_rank}"
+
+        # Execute everything before and including the given p2p op
+        for idx in range(self.next_p2p_idx, op_idx + 1):
+            op = self.p2p_schedule[idx]
+            src_stage, dst_stage, mb_idx, is_sender = op
+            is_fwd = src_stage == dst_stage - 1
+            is_bwd = src_stage == dst_stage + 1
+            assert is_fwd or is_bwd
+            is_recver = not is_sender
+
+            op_name = "P2P(unknown)"
+            if is_fwd and is_sender:
+                self._exec_fwd_send(src_stage, mb_idx, **kwargs)
+                op_name = (
+                    f"P2P(fwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
+                )
+            elif is_fwd and is_recver:
+                self._exec_fwd_recv(dst_stage, mb_idx)
+                op_name = (
+                    f"P2P(fwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
+                )
+            elif is_bwd and is_sender:
+                self._exec_bwd_send(src_stage, mb_idx)
+                op_name = (
+                    f"P2P(bwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
+                )
+            elif is_bwd and is_recver:
+                self._exec_bwd_recv(dst_stage, mb_idx)
+                op_name = (
+                    f"P2P(bwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
+                )
+            else:
+                raise ValueError(f"Invalid p2p op: {op}")
+
+            self.executed_p2ps.add(op)
+            logger.debug(f"Executed {op_name} on actor {self.global_rank}")
+
+        self.next_p2p_idx = op_idx + 1
+
+    def _exec_fwd_recv(self, stage_id: int, mb_idx: int):
+        if stage_id == 0:
+            return
+
+        # For non-first stages, receive input tensors from the previous stage
+        pp_rank = piper_metadata.stage_to_device[stage_id - 1]
+        global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+
+        self.logger.debug(
+            f"Dispatch fwd p2p recv on {self.global_rank} from {global_src_rank}"
+        )
+        self._start_timing(self.p2p_stream, "fwd_p2p_recv")
+        with torch.cuda.stream(self.p2p_stream):
+            for i in self.input_idxs[stage_id]:
+                dist.recv(
+                    self.forward_args[stage_id][i],
+                    src=global_src_rank,
+                    group=self.pp_group,
+                )
+        self._stop_timing(self.p2p_stream, "fwd_p2p_recv")
+        self.logger.debug(
+            f"Completed fwd p2p recv on {self.global_rank} from {global_src_rank}"
+        )
+
+    def _exec_fwd_send(self, stage_id: int, mb_idx: int, output):
+        if stage_id == self.num_stages - 1:
+            return
+
+        # For non-final stages, send output tensors to the next stage
+        pp_rank = piper_metadata.stage_to_device[stage_id + 1]
+        global_dst_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+
+        self.logger.debug(
+            f"Dispatch fwd p2p send on {self.global_rank} to {global_dst_rank}"
+        )
+        self._start_timing(self.p2p_stream, "fwd_p2p_send")
+        with torch.cuda.stream(self.p2p_stream):
+            for i in range(len(output)):
+                dist.send(output[i], dst=global_dst_rank, group=self.pp_group)
+        self._stop_timing(self.p2p_stream, "fwd_p2p_send")
+        self.logger.debug(
+            f"Completed fwd p2p send on {self.global_rank} to {global_dst_rank}"
+        )
+
+    def _exec_bwd_recv(self, stage_id: int, mb_idx: int):
+        if stage_id >= self.num_stages - 1:
+            return
+
+        out_activation = self.out_activation[stage_id][mb_idx]
+        # For non-final stages, recieve input gradients from the subsequent backward pass
+        input_grad = torch.empty_like(out_activation)
+        pp_rank = piper_metadata.stage_to_device[stage_id + 1]
+        global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+
+        self.logger.debug(
+            f"Dispatch bwd p2p recv on {self.global_rank} from {global_src_rank}"
+        )
+        self._start_timing(self.p2p_stream, "bwd_p2p_recv")
+        with torch.cuda.stream(self.p2p_stream):
+            dist.recv(input_grad, src=global_src_rank, group=self.pp_group)
+        self._stop_timing(self.p2p_stream, "bwd_p2p_recv")
+        self.logger.debug(
+            f"Completed bwd p2p recv on {self.global_rank} from {global_src_rank}"
+        )
+
+        out_activation.backward(gradient=input_grad)
+
+    def _exec_bwd_send(self, stage_id: int, mb_idx: int):
+        if stage_id <= 0:
+            return
+
+        # For non-first stages, send output gradients to the previous backward stage
+        output_grad = self.inp_activation[stage_id][mb_idx].grad
+        assert output_grad is not None
+        pp_rank = piper_metadata.stage_to_device[stage_id - 1]
+        global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+
+        self.logger.debug(
+            f"Dispatch bwd p2p send on {self.global_rank} to {global_src_rank}"
+        )
+        self._start_timing(self.p2p_stream, "bwd_p2p_send")
+        with torch.cuda.stream(self.p2p_stream):
+            dist.send(output_grad, dst=global_src_rank, group=self.pp_group)
+        self._stop_timing(self.p2p_stream, "bwd_p2p_send")
+        self.logger.debug(
+            f"Completed bwd p2p send on {self.global_rank} to {global_src_rank}"
+        )
+
+        self.inp_activation[stage_id][mb_idx] = None
+
     def _forward(self, stage_id: int, mb_idx: int):
 
-        self.logger.debug(f"Calling forward {stage_id} mb {mb_idx} on actor {self.global_rank}")
+        self.logger.debug(
+            f"Calling forward {stage_id} mb {mb_idx} on actor {self.global_rank}"
+        )
 
         if stage_id == 0:
             # For the first stage, load input tensors from self.inputs
             for i, inp in zip(self.input_idxs[stage_id], self.inputs):
                 self.forward_args[stage_id][i] = inp
         else:
-            # For non-first stages, receive input tensors from the previous stage
-            pp_rank = piper_metadata.stage_to_device[stage_id - 1]
-            global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
-
-            self.logger.debug(f"Dispatch fwd p2p recv on {self.global_rank} from {global_src_rank}")
-            self._start_timing(self.p2p_stream, "fwd_p2p_recv")
-            for i in self.input_idxs[stage_id]:
-                dist.recv(self.forward_args[stage_id][i], src=global_src_rank, group=self.pp_group)
-            self._stop_timing(self.p2p_stream, "fwd_p2p_recv")
-            self.logger.debug(f"Completed fwd p2p recv on {self.global_rank} from {global_src_rank}")
+            self._exec_p2p_op(stage_id - 1, stage_id, mb_idx, False)
 
             # save first input that requires grad as input activation
-            inp_with_grad = [self.forward_args[stage_id][i] for i in self.input_idxs[stage_id] if self.forward_args[stage_id][i].requires_grad]
-            assert len(inp_with_grad) == 1, "Exactly one input per stage should require a gradient"
-            self.logger.debug(f"Saving input activation {inp_with_grad[0].shape} for stage {stage_id} mb {mb_idx}")
+            inp_with_grad = [
+                self.forward_args[stage_id][i]
+                for i in self.input_idxs[stage_id]
+                if self.forward_args[stage_id][i].requires_grad
+            ]
+            assert (
+                len(inp_with_grad) == 1
+            ), "Exactly one input per stage should require a gradient"
+            self.logger.debug(
+                f"Saving input activation {inp_with_grad[0].shape} for stage {stage_id} mb {mb_idx}"
+            )
             self.inp_activation[stage_id][mb_idx] = inp_with_grad[0]
 
         # Run the forward pass
@@ -314,8 +566,12 @@ def _forward(self, stage_id: int, mb_idx: int):
         # Save first output that requires grad as output activation
         # TODO: support multiple outputs
         out_with_grad = [out for out in output if out.requires_grad]
-        assert len(out_with_grad) == 1, "Piper only supports one output per subgraph with requires_grad"
-        self.logger.debug(f"Saving output activation {out_with_grad[0].shape} for stage {stage_id} mb {mb_idx}")
+        assert (
+            len(out_with_grad) == 1
+        ), "Piper only supports one output per subgraph with requires_grad"
+        self.logger.debug(
+            f"Saving output activation {out_with_grad[0].shape} for stage {stage_id} mb {mb_idx}"
+        )
         self.out_activation[stage_id][mb_idx] = out_with_grad[0]
 
         # clear the input tensors
@@ -323,46 +579,29 @@ def _forward(self, stage_id: int, mb_idx: int):
         #     self.forward_args[stage_id][i] = torch.empty_like(self.forward_args[stage_id][i])
 
         if stage_id < self.num_stages - 1:
-            # For non-final stages, send output tensors to the next stage
-            pp_rank = piper_metadata.stage_to_device[stage_id + 1]
-            global_dst_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
-
-            self.logger.debug(f"Dispatch fwd p2p send on {self.global_rank} to {global_dst_rank}")
-            self._start_timing(self.p2p_stream, "fwd_p2p_send")
-            for i in range(len(output)):
-                dist.send(output[i], dst=global_dst_rank, group=self.pp_group)
-            self._stop_timing(self.p2p_stream, "fwd_p2p_send")
-            self.logger.debug(f"Completed fwd p2p send on {self.global_rank} to {global_dst_rank}")
+            self._exec_p2p_op(stage_id, stage_id + 1, mb_idx, True, output=output)
 
         if CLEANUP_MEMORY:
             gc.collect()
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
-        
-        self.logger.debug(f"Forward {stage_id} mb {mb_idx} on actor {self.global_rank} returning {[out.shape for out in output]}")
+
+        self.logger.debug(
+            f"Forward {stage_id} mb {mb_idx} on actor {self.global_rank} returning {[out.shape for out in output]}"
+        )
         torch.cuda.synchronize()
 
     # @ray.method(tensor_transport="nccl")
     def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
 
-        self.logger.debug(f"Calling backward {stage_id} mb {mb_idx} on actor {self.global_rank}")
-        
+        self.logger.debug(
+            f"Calling backward {stage_id} mb {mb_idx} on actor {self.global_rank}"
+        )
+
         out_activation = self.out_activation[stage_id][mb_idx]
 
         if stage_id < self.num_stages - 1:
-            # For non-final stages, recieve input gradients from the subsequent backward pass
-            input_grad = torch.empty_like(out_activation)
-            pp_rank = piper_metadata.stage_to_device[stage_id + 1]
-            global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
-
-            self.logger.debug(f"Dispatch bwd p2p recv on {self.global_rank} from {global_src_rank}")
-            self._start_timing(self.p2p_stream, "bwd_p2p_recv")
-            with torch.cuda.stream(self.p2p_stream):
-                dist.recv(input_grad, src=global_src_rank, group=self.pp_group)
-            self._stop_timing(self.p2p_stream, "bwd_p2p_recv")
-            self.logger.debug(f"Completed bwd p2p recv on {self.global_rank} from {global_src_rank}")
-
-            out_activation.backward(gradient=input_grad)
+            self._exec_p2p_op(stage_id + 1, stage_id, mb_idx, False)
         else:
             # For the final stage, wait for the forward pass to complete
             assert loss_fn is not None
@@ -375,50 +614,39 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
         del out_activation
 
         if stage_id > 0:
-            # For non-first stages, send output gradients to the previous backward stage
-            output_grad = self.inp_activation[stage_id][mb_idx].grad
-            assert output_grad is not None
-            pp_rank = piper_metadata.stage_to_device[stage_id - 1]
-            global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
-
-            self.logger.debug(f"Dispatch bwd p2p send on {self.global_rank} to {global_src_rank}")
-            self._start_timing(self.p2p_stream, "bwd_p2p_send")
-            with torch.cuda.stream(self.p2p_stream):
-                dist.send(output_grad, dst=global_src_rank, group=self.pp_group)
-            self._stop_timing(self.p2p_stream, "bwd_p2p_send")
-            self.logger.debug(f"Completed bwd p2p send on {self.global_rank} to {global_src_rank}")
-            
-            self.inp_activation[stage_id][mb_idx] = None
+            self._exec_p2p_op(stage_id, stage_id - 1, mb_idx, True)
 
         if CLEANUP_MEMORY:
             gc.collect()
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
-        
+
         torch.cuda.synchronize()
-        
+
     def _synchronize_gradients(self):
         self.logger.debug(f"Actor {self.global_rank} synchronizing gradients")
         # Iterate over all stages on this actor and synchronize their parameters
         for stage_id, parameters in self.forward_args.items():
             for param in parameters:
                 if param is not None and param.grad is not None:
-                    dist.all_reduce(param.grad, op=dist.ReduceOp.AVG, group=self.dp_group)
+                    dist.all_reduce(
+                        param.grad, op=dist.ReduceOp.AVG, group=self.dp_group
+                    )
 
     def _update(self):
         self.logger.debug(f"Actor {self.global_rank} waiting for backward sync events")
-            
+
         # if dp degree > 1, make sure all gradients are synchronized before optimizer step
         # TODO: this does not allow overlapping with the optimizer step
         if self.pp_degree > 1:
             self._wait_for_p2p_ops()
-            
+
         if self.dp_degree > 1:
             if self.naive_gradient_sync:
                 self._synchronize_gradients()
             else:
                 self._wait_for_comm_ops()
-        
+
         # step the optimizer for each stage
         for _, optim in self.optims.items():
             optim.step()
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 2337a81..7ef8388 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -14,31 +14,122 @@
 logger = create_logger("piper_compile", LOG_LEVEL)
 
 
-def piper_setup(model_class, model_args, optim_fn, example_inputs, schedule, naive_gradient_sync=False):
+def order_p2p_comms(schedule, num_devices, num_stages, stage_to_device):
+    is_fwd_bwd = lambda task: task is not None and not task.upd
+    schedule = [
+        [task for task in rank_tasks if is_fwd_bwd(task)] for rank_tasks in schedule
+    ]
+    logger.debug(f"Fwd/bwd schedule: {schedule}")
+    assert num_devices == len(schedule), "Some rank has no fwd/bwd tasks"
+    # Tasks are guaranteed to be fwd or bwd
+    num_fwd_bwd_tasks = sum(len(rank_tasks) for rank_tasks in schedule)
+
+    num_executed_tasks = 0
+    # (src_stage, dst_stage, mb_idx) for each p2p comm in execution order
+    # is_fwd if src_stage < dst_stage else bwd
+    p2p_comms = []
+    rank_to_next_task = {rank: 0 for rank in range(len(schedule))}
+    # P2P comm is enqueued when sender is ready
+    # Pick the next executable task in a round-robin fashion
+    while True:
+        executable_tasks = []
+        for rank, time_step in rank_to_next_task.items():
+            if time_step < len(schedule[rank]):
+                task = schedule[rank][time_step]
+                # Check the task's dependency is satisfied
+                # i.e., the sender has enqueued the comm
+                dependee = None
+                if task.is_fwd and task.stage_id > 0:
+                    dependee = (task.stage_id - 1, task.stage_id, task.mb_idx)
+                elif not task.is_fwd and task.stage_id < num_stages - 1:
+                    dependee = (task.stage_id + 1, task.stage_id, task.mb_idx)
+
+                can_exec = dependee is None or dependee in p2p_comms
+                if can_exec:
+                    executable_tasks.append((rank, task))
+
+        if not executable_tasks:
+            break
+
+        for rank, task in executable_tasks:
+            if task.is_fwd and task.stage_id < num_stages - 1:
+                p2p_comms.append((task.stage_id, task.stage_id + 1, task.mb_idx))
+            elif not task.is_fwd and task.stage_id > 0:
+                p2p_comms.append((task.stage_id, task.stage_id - 1, task.mb_idx))
+            rank_to_next_task[rank] += 1
+
+        num_executed_tasks += len(executable_tasks)
+
+    if num_executed_tasks != num_fwd_bwd_tasks:
+        raise ValueError(
+            f"Only {num_executed_tasks} out of {num_fwd_bwd_tasks} "
+            f"fwd/bwd tasks can be executed"
+        )
+
+    logger.debug(f"P2P comms: {p2p_comms}")
+    # (src_stage, dst_stage, mb_idx, is_sender)
+    p2p_schedules = {rank: [] for rank in range(num_devices)}
+    for src_stage, dst_stage, mb_idx in p2p_comms:
+        src_rank = stage_to_device[src_stage]
+        dst_rank = stage_to_device[dst_stage]
+        p2p_schedules[src_rank].append((src_stage, dst_stage, mb_idx, True))
+        p2p_schedules[dst_rank].append((src_stage, dst_stage, mb_idx, False))
+    logger.debug(f"P2P schedules: {p2p_schedules}")
+
+    return p2p_schedules
+
+
+def piper_setup(
+    model_class,
+    model_args,
+    optim_fn,
+    example_inputs,
+    schedule,
+    naive_gradient_sync=False,
+):
     """
     Compile a model with the piper backend.
     """
     piper_metadata.naive_gradient_sync = naive_gradient_sync
 
     num_devices = len(schedule)
-    num_mbs = len(set([task.mb_idx for row in schedule for task in row if task is not None]))
-    num_stages = len(set([task.stage_id for row in schedule for task in row if task is not None]))
-    piper_metadata.stage_to_device = {task.stage_id: task.pp_rank for row in schedule for task in row if task is not None}
+    num_mbs = len(
+        set([task.mb_idx for row in schedule for task in row if task is not None])
+    )
+    num_stages = len(
+        set([task.stage_id for row in schedule for task in row if task is not None])
+    )
+    piper_metadata.stage_to_device = {
+        task.stage_id: task.pp_rank
+        for row in schedule
+        for task in row
+        if task is not None
+    }
 
     logger.debug(f"mbs: {num_mbs}, stages: {num_stages}, devices: {num_devices}")
 
-    _create_actors(num_devices, optim_fn, num_mbs, num_stages, naive_gradient_sync)
-    ray.get([actor._join_process_groups.remote() for actor in piper_metadata.actors.values()])
+    p2p_schedules = order_p2p_comms(
+        schedule, num_devices, num_stages, piper_metadata.stage_to_device
+    )
+    _create_actors(
+        num_devices, optim_fn, num_mbs, num_stages, p2p_schedules, naive_gradient_sync
+    )
+    ray.get(
+        [
+            actor._join_process_groups.remote()
+            for actor in piper_metadata.actors.values()
+        ]
+    )
 
     model = model_class(*model_args)
 
     compiled = torch.compile(model, backend=piper)
 
-    dp_rank = int(os.environ['PIPER_DP_RANK'])
+    dp_rank = int(os.environ["PIPER_DP_RANK"])
     logger.info(f"DP rank {dp_rank+1} compiling...")
 
     output = compiled(*example_inputs)
 
     logger.info(f"DP rank {dp_rank+1} done.")
- 
+
     return compiled

From 92ace460a138456a2654142f73e6871c090f632c Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Wed, 18 Feb 2026 22:32:13 +0000
Subject: [PATCH 032/100] use comp stream for fwd/bwd computation

---
 src/piper.py         |  22 ++++++++-
 src/piper_actor.py   |  43 +++++++---------
 src/piper_utils.py   |   2 +-
 test/models/llama.py | 114 +++++++++++++++----------------------------
 4 files changed, 77 insertions(+), 104 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index b9de7d2..2440fa5 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -16,6 +16,16 @@
 @register_backend
 def piper(gm, example_inputs, **kwargs):
 
+    # gm(*example_inputs)
+    # start = torch.cuda.Event(enable_timing=True)
+    # start.record()
+    # for i in range(10):
+    #     gm(*example_inputs)
+    # end = torch.cuda.Event(enable_timing=True)
+    # end.record()
+    # torch.cuda.synchronize()
+    # print(f"Entire model time measured on controller: {start.elapsed_time(end) / 10:.2f} ms")
+
     original_gm = gm
     top_level_gm, submodules = _split_gm_by_stages(gm)
     num_stages = len(piper_metadata.stage_to_device.keys())
@@ -33,7 +43,17 @@ def piper(gm, example_inputs, **kwargs):
             comm_ops, tids = _get_dp_comm_ops(graphargs, placeholders)
         else:
             comm_ops, tids = [], []
-            
+
+        # stage_gm(*graphargs)
+        # start = torch.cuda.Event(enable_timing=True)
+        # start.record()
+        # for i in range(10):
+        #     stage_gm(*graphargs)
+        # end = torch.cuda.Event(enable_timing=True)
+        # end.record()
+        # torch.cuda.synchronize()
+        # print(f"Stage {stage_id} time measured on controller: {start.elapsed_time(end) / 10:.2f} ms")
+
         actor_id = piper_metadata.stage_to_device[stage_id]
         actor = _get_actor(actor_id)
         actor_stages.append((actor, stage_id))
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 045c8cc..3cee795 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -10,6 +10,7 @@
 import torch.distributed as dist
 import threading
 from typing import Callable
+import asyncio
 
 from .piper_utils import _deserialize_graphmodule, create_logger, LOG_LEVEL, piper_metadata
 
@@ -29,7 +30,7 @@ def _create_actors(num_actors, optim_class, num_mbs, num_stages, naive_gradient_
     from .piper_utils import piper_metadata
     for pp_rank in range(num_actors):
         global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
-        if dp_degree > 1:
+        if dp_degree > 1 and not naive_gradient_sync:
             max_concurrency = 2
         else:
             max_concurrency = 1
@@ -152,7 +153,7 @@ def _stop_timing(self, stream, label):
             if label in self.trace_events:
                 start, stop = self.trace_events[label]
                 stop.record(stream)
-                stream.synchronize()
+                torch.cuda.synchronize()
                 self.trace_data[label].append(start.elapsed_time(stop))
 
     def _join_process_groups(self):
@@ -214,10 +215,10 @@ def post_backward_hook(grad):
                    case _:
                        raise ValueError(f"Unknown communication operation: {comm_op.op}")
 
-    def _comm_loop(self):
+    async def _comm_loop(self):
         stage_id = self.stage_id
         # look into: nccl priority for sync vs async ops, stream priority
-        self.logger.info(f"Running comm loop for stage {stage_id} on actor {self.global_rank}")
+        self.logger.debug(f"Running comm loop for stage {stage_id} on actor {self.global_rank}")
         for comm_op in self.comm_ops[stage_id]:
             done = False
             while not done:
@@ -248,20 +249,6 @@ def _wait_for_comm_ops(self):
                         del handle
                         done = True
 
-    # def _p2p_send(self, dst, group, *tensors):
-    #     with torch.cuda.stream(self.p2p_stream):
-    #         for tensor in tensors:
-    #             dist.send(tensor, dst=dst, group=group)
-
-    # def _p2p_recv(self, src, group, *tensors_meta):
-    #     with torch.cuda.stream(self.p2p_stream):
-    #         tensors = []
-    #         for shape, dtype, requires_grad in tensors_meta:
-    #             buf = torch.empty(shape, dtype=dtype, requires_grad=requires_grad, device=self.device)
-    #             dist.recv(buf, src=src, group=group)
-    #             tensors.append(buf)
-    #         return tensors
-
     def _load_stage(self, stage_id: int, gm_data, comm_ops, forward_args, ids, input_idxs):
         self.logger.info(f"Loading stage {stage_id} graph on actor {self.global_rank}")
 
@@ -343,7 +330,8 @@ def _forward(self, stage_id: int, mb_idx: int, recv_bwd_b4_snd_fwd: bool = False
 
         # Run the forward pass
         self._start_timing(self.comp_stream, "forward_comp")
-        output = self.forward_fns[stage_id](*self.forward_args[stage_id])
+        with torch.cuda.stream(self.comp_stream):
+            output = self.forward_fns[stage_id](*self.forward_args[stage_id])
         self._stop_timing(self.comp_stream, "forward_comp")
 
         # Save first output that requires grad as output activation
@@ -417,7 +405,8 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None, recv_bwd_b4_snd_fw
                 self.logger.debug(f"Completed bwd recv mb {mb_idx} from {global_src_rank} on {self.global_rank}")
                 
             self._start_timing(self.comp_stream, "backward_comp")
-            out_activation.backward(gradient=input_grad)
+            with torch.cuda.stream(self.comp_stream):
+                out_activation.backward(gradient=input_grad)
             self._stop_timing(self.comp_stream, "backward_comp")
             
             del input_grad
@@ -429,8 +418,9 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None, recv_bwd_b4_snd_fw
             assert out_activation.shape == labels.shape
 
             self._start_timing(self.comp_stream, "backward_comp")
-            loss = loss_fn(out_activation, labels)
-            loss.backward()
+            with torch.cuda.stream(self.comp_stream):
+                loss = loss_fn(out_activation, labels)
+                loss.backward()
             self._stop_timing(self.comp_stream, "backward_comp")
 
             self.loss.append(loss.item())
@@ -465,20 +455,21 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None, recv_bwd_b4_snd_fw
         torch.cuda.synchronize()
         
     def _synchronize_gradients(self):
-        self.logger.debug(f"Actor {self.global_rank} synchronizing gradients")
+        self.logger.info(f"Actor {self.global_rank} synchronizing gradients")
         # Iterate over all stages on this actor and synchronize their parameters
         for stage_id, parameters in self.forward_args.items():
             for param in parameters:
                 if param is not None and param.grad is not None:
-                    dist.all_reduce(param.grad, op=dist.ReduceOp.AVG, group=self.dp_group)
+                    with torch.cuda.stream(self.comm_stream):
+                        dist.all_reduce(param.grad, op=dist.ReduceOp.AVG, group=self.dp_group)
 
     def _update(self):
         self.logger.debug(f"Actor {self.global_rank} waiting for backward sync events")
             
         # if dp degree > 1, make sure all gradients are synchronized before optimizer step
         # TODO: this does not allow overlapping with the optimizer step
-        self._start_timing(self.comm_stream, "backward_sync")
         if self.dp_degree > 1:
+            self._start_timing(self.comm_stream, "backward_sync")
             if self.naive_gradient_sync:
                 self._synchronize_gradients()
             else:
@@ -486,7 +477,7 @@ def _update(self):
                 # Clear comm op handles after waiting
                 for stage_id in list(self.comm_op_handles.keys()):
                     self.comm_op_handles[stage_id].clear()
-        self._stop_timing(self.comm_stream, "backward_sync")
+            self._stop_timing(self.comm_stream, "backward_sync")
         
         # step the optimizer for each stage
         self._start_timing(self.comp_stream, "optim_step")
diff --git a/src/piper_utils.py b/src/piper_utils.py
index b489a56..eb9eb25 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -12,7 +12,7 @@
 
 import src.piper_patches
 
-LOG_LEVEL = "INFO"
+LOG_LEVEL = "DEBUG"
 
 """ 
 Print the backward graph of a tensor
diff --git a/test/models/llama.py b/test/models/llama.py
index 8aadfa1..c820834 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -397,97 +397,59 @@ def log_size(layer, indent=0):
         mask = torch.triu(mask, diagonal=1)
         self.mask = torch.hstack([torch.zeros((self.seq_len, 0)), mask]).to('cuda')
 
-    # """
-    # forward method for 1f1b schedule
-    # requires:
-    # - 4 devices
-    # - 4 stages
-    # - n_layers is divisible by 4
-    # """
-    # def forward(self, tokens: torch.Tensor):
-
-    #     with torch.fx.traceback.annotate({"stage": 0}):
-    #         h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-    #         start_pos = 0
-    #         for layer in self.layers[:self.n_layers//4]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[self.n_layers//4:self.n_layers//2]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 2}):
-    #         for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 3}):
-    #         for layer in self.layers[3*self.n_layers//4:]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-    #         h = self.norm(h) if self.norm else h
-    #         output = self.output(h).float() if self.output else h
-
-    #     return output
-
-    # """
-    # forward method for interleaved-1f1b schedule
-    # requires:
-    # - 2 devices
-    # - 4 stages
-    # - n_layers is divisible by 4
-    # """
-    # def forward(self, tokens: torch.Tensor):
-
-    #     distributed_stage(0, actor_id=0)
-
-    #     h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-    #     start_pos = 0
-        
-    #     for layer in self.layers[:self.n_layers//4]:
-    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     distributed_stage(1, actor_id=1)
-
-    #     for layer in self.layers[self.n_layers//4:self.n_layers//2]:
-    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     distributed_stage(2, actor_id=0)
-
-    #     for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
-    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     distributed_stage(3, actor_id=1)
-
-    #     for layer in self.layers[3*self.n_layers//4:]:
-    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     h = self.norm(h) if self.norm else h
-    #     output = self.output(h).float() if self.output else h
-
-    #     return output
-
     """
     forward method for 1f1b schedule
     requires:
-    - 2 devices
-    - 2 stages
-    - n_layers is divisible by 2
+    - 4 devices
+    - 4 stages
+    - n_layers is divisible by 4
     """
     def forward(self, tokens: torch.Tensor):
 
         with torch.fx.traceback.annotate({"stage": 0}):
-            h = self.tok_embeddings(tokens)
+            h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
             start_pos = 0
-            for layer in self.layers[:self.n_layers//2]:
+            for layer in self.layers[:self.n_layers//4]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
 
         with torch.fx.traceback.annotate({"stage": 1}):
-            for layer in self.layers[self.n_layers//2:]:
+            for layer in self.layers[self.n_layers//4:self.n_layers//2]:
+                h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+        with torch.fx.traceback.annotate({"stage": 2}):
+            for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
+                h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+        with torch.fx.traceback.annotate({"stage": 3}):
+            for layer in self.layers[3*self.n_layers//4:]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
-            h = self.norm(h)
-            output = self.output(h).float()
+            h = self.norm(h) if self.norm else h
+            output = self.output(h).float() if self.output else h
 
         return output
 
+    # """
+    # forward method for 1f1b schedule
+    # requires:
+    # - 2 stages
+    # - n_layers is divisible by 2
+    # """
+    # def forward(self, tokens: torch.Tensor):
+
+    #     with torch.fx.traceback.annotate({"stage": 0}):
+    #         h = self.tok_embeddings(tokens)
+    #         start_pos = 0
+    #         for layer in self.layers[:self.n_layers//2]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 1}):
+    #         for layer in self.layers[self.n_layers//2:]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+    #         h = self.norm(h)
+    #         output = self.output(h).float()
+
+    #     return output
+
     # """
     # forward method for no pp
     # requires:

From 1fd1aef843dfaa65690b049998614591ce4ddd80 Mon Sep 17 00:00:00 2001
From: Andy Ruan <andyubryh@gmail.com>
Date: Thu, 19 Feb 2026 00:15:55 +0000
Subject: [PATCH 033/100] fix: reset to start of p2p schedule before each
 iteration

Signed-off-by: Andy Ruan <andyubryh@gmail.com>
---
 src/piper_actor.py | 4 ++++
 src/piper_exec.py  | 1 +
 2 files changed, 5 insertions(+)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index a44a954..7165388 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -157,6 +157,10 @@ def __init__(
 
         piper_metadata.actor_self = self
 
+    def reset_p2p_states(self):
+        self.next_p2p_idx = 0
+        self.executed_p2ps = set()
+
     def get_trace_data(self) -> dict:
         return self.global_rank, self.trace_data
 
diff --git a/src/piper_exec.py b/src/piper_exec.py
index b76a7ac..b663ad5 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -169,6 +169,7 @@ def piper_exec(model, schedule, inputs, truth, loss_fn, dp_degree=1):
     
     _validate_schedule(schedule, dag_edges, num_mbs)
 
+    ray.get([actor.reset_p2p_states.remote() for actor in actors.values()])
     ret = []
     for i in range(num_steps):
         for j in range(num_devices-1, -1, -1):

From 02bf621db86ed28f2f1c9f8ccdfcbca5393036be Mon Sep 17 00:00:00 2001
From: Andy Ruan <andyubryh@gmail.com>
Date: Thu, 19 Feb 2026 00:16:43 +0000
Subject: [PATCH 034/100] scripts and results

Signed-off-by: Andy Ruan <andyubryh@gmail.com>
---
 buggy-logs/interleaved_debug-exception.log    |  108 +
 buggy-logs/interleaved_debug.log              | 1388 +++++++
 buggy-logs/interleaved_debug.trace            | 1388 +++++++
 buggy-out/debug-pp2-dp1-interleaved-1f1b.json |    1 +
 logs/interleaved_3b.trace                     | 3432 +++++++++++++++++
 logs/interleaved_debug.log                    | 3404 ++++++++++++++++
 logs/interleaved_debug.trace                  | 3412 ++++++++++++++++
 .../3b-pp2-dp1-interleaved-1f1b.json          |    1 +
 ...LAMA_1B-pp2-dp1-interleaved-1f1b copy.json |    1 +
 .../debug-pp2-dp1-interleaved-1f1b.json       |    1 +
 run_dp.sh                                     |   21 +
 run_interleaved-3b.sh                         |   21 +
 run_interleaved-debug.sh                      |   21 +
 13 files changed, 13199 insertions(+)
 create mode 100644 buggy-logs/interleaved_debug-exception.log
 create mode 100644 buggy-logs/interleaved_debug.log
 create mode 100644 buggy-logs/interleaved_debug.trace
 create mode 100644 buggy-out/debug-pp2-dp1-interleaved-1f1b.json
 create mode 100644 logs/interleaved_3b.trace
 create mode 100644 logs/interleaved_debug.log
 create mode 100644 logs/interleaved_debug.trace
 create mode 100644 out-p2p_reorder/3b-pp2-dp1-interleaved-1f1b.json
 create mode 100644 out-p2p_reorder/LLAMA_1B-pp2-dp1-interleaved-1f1b copy.json
 create mode 100644 out-p2p_reorder/debug-pp2-dp1-interleaved-1f1b.json
 create mode 100644 run_dp.sh
 create mode 100644 run_interleaved-3b.sh
 create mode 100644 run_interleaved-debug.sh

diff --git a/buggy-logs/interleaved_debug-exception.log b/buggy-logs/interleaved_debug-exception.log
new file mode 100644
index 0000000..cdd96f5
--- /dev/null
+++ b/buggy-logs/interleaved_debug-exception.log
@@ -0,0 +1,108 @@
+2026-02-18 20:58:35,869	INFO worker.py:2007 -- Started a local Ray instance.
+/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+  warnings.warn(
+(run_dp_rank pid=139993) Namespace(model='debug', schedule='interleaved-1f1b', dp=1, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False)
+(run_dp_rank pid=139993) Schedule:
+(run_dp_rank pid=139993) 0:0:f	0:1:f	2:0:f	2:1:f	0:2:f	0:3:f	2:0:b	 -- 	2:1:b	2:2:f	0:0:b	2:3:f	0:1:b	 -- 	2:2:b	2:3:b	0:2:b	0:3:b	0:0:u	
+(run_dp_rank pid=139993)  -- 	1:0:f	1:1:f	3:0:f	3:0:b	3:1:f	3:1:b	1:2:f	1:0:b	1:3:f	1:1:b	3:2:f	3:2:b	3:3:f	3:3:b	1:2:b	1:3:b	1:0:u	 -- 	
+(run_dp_rank pid=139993) 2026-02-18 20:58:49,890 - piper_coordinator - DEBUG - Running DP rank 1 of 1
+(run_dp_rank pid=139993) 2026-02-18 20:58:49,913 - piper_compile - DEBUG - mbs: 4, stages: 4, devices: 2
+(run_dp_rank pid=139993) 2026-02-18 20:58:49,913 - piper_compile - DEBUG - Fwd/bwd schedule: [[Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=False, upd=False)], [Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=False, upd=False)]]
+(run_dp_rank pid=139993) 2026-02-18 20:58:49,913 - piper_compile - DEBUG - P2P comms: [(0, 1, 0), (0, 1, 1), (1, 2, 0), (2, 3, 0), (1, 2, 1), (2, 3, 1), (0, 1, 2), (3, 2, 0), (0, 1, 3), (2, 1, 0), (3, 2, 1), (2, 1, 1), (1, 2, 2), (2, 3, 2), (1, 0, 0), (1, 2, 3), (2, 3, 3), (1, 0, 1), (3, 2, 2), (2, 1, 2), (3, 2, 3), (2, 1, 3), (1, 0, 2), (1, 0, 3)]
+(run_dp_rank pid=139993) 2026-02-18 20:58:49,913 - piper_compile - DEBUG - P2P schedules: {0: [(0, 1, 0, True), (0, 1, 1, True), (1, 2, 0, False), (2, 3, 0, True), (1, 2, 1, False), (2, 3, 1, True), (0, 1, 2, True), (3, 2, 0, False), (0, 1, 3, True), (2, 1, 0, True), (3, 2, 1, False), (2, 1, 1, True), (1, 2, 2, False), (2, 3, 2, True), (1, 0, 0, False), (1, 2, 3, False), (2, 3, 3, True), (1, 0, 1, False), (3, 2, 2, False), (2, 1, 2, True), (3, 2, 3, False), (2, 1, 3, True), (1, 0, 2, False), (1, 0, 3, False)], 1: [(0, 1, 0, False), (0, 1, 1, False), (1, 2, 0, True), (2, 3, 0, False), (1, 2, 1, True), (2, 3, 1, False), (0, 1, 2, False), (3, 2, 0, True), (0, 1, 3, False), (2, 1, 0, False), (3, 2, 1, True), (2, 1, 1, False), (1, 2, 2, True), (2, 3, 2, False), (1, 0, 0, True), (1, 2, 3, True), (2, 3, 3, False), (1, 0, 1, True), (3, 2, 2, True), (2, 1, 2, False), (3, 2, 3, True), (2, 1, 3, False), (1, 0, 2, True), (1, 0, 3, True)]}
+(run_dp_rank pid=139993) 2026-02-18 20:58:49,977 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, 47c7c968b042444eb31bf62301000000) global rank 0
+(run_dp_rank pid=139993) 2026-02-18 20:58:49,978 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, 20b0ecd7cec7c281aba2212e01000000) global rank 1
+(PiperActor pid=139938) 2026-02-18 20:58:52,963 - piper_actor - INFO - Initializing Ray actor 0 global rank 0 GPU 1
+(PiperActor pid=139938) 2026-02-18 20:58:55,394 - piper_actor - DEBUG - Actor 0 has GPU 1, joined the global process group
+(PiperActor pid=139938) 2026-02-18 20:58:55,394 - piper_actor - INFO - Global rank 0 joined its pp group 0 along with ranks [0, 1]
+(run_dp_rank pid=139993) 2026-02-18 20:58:55,612 - piper_compile - INFO - DP rank 1 compiling...
+(PiperActor pid=139938) 2026-02-18 20:58:59,482 - piper_actor - INFO - Loading stage 0 graph on actor 0
+(PiperActor pid=139941) 2026-02-18 20:58:53,089 - piper_actor - INFO - Initializing Ray actor 1 global rank 1 GPU 2
+(PiperActor pid=139941) 2026-02-18 20:58:55,390 - piper_actor - DEBUG - Actor 1 has GPU 2, joined the global process group
+(PiperActor pid=139941) 2026-02-18 20:58:55,391 - piper_actor - INFO - Global rank 1 joined its pp group 0 along with ranks [0, 1]
+(run_dp_rank pid=139993) 2026-02-18 20:59:01,436 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=139993) Running 5 warmup iterations...
+(run_dp_rank pid=139993) 2026-02-18 20:59:07,894 - piper_compile - INFO - DP rank 1 done.
+(PiperActor pid=139941) 2026-02-18 20:59:00,937 - piper_actor - INFO - Loading stage 3 graph on actor 1 [repeated 3x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.)
+(PiperActor pid=139938) 2026-02-18 20:59:07,920 - piper_actor - DEBUG - Actor 0 loaded inputs 1
+(PiperActor pid=139938) 2026-02-18 20:59:07,935 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=139941) 2026-02-18 20:59:07,932 - piper_actor - DEBUG - Actor 1 loaded labels torch.Size([16, 256, 512])
+(PiperActor pid=139941) 2026-02-18 20:59:07,935 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=139941) [rank1]:[W218 20:59:07.417812711 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=139938) 2026-02-18 20:59:08,160 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=139938) 2026-02-18 20:59:08,160 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=139938) 2026-02-18 20:59:10,370 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=139938) 2026-02-18 20:59:10,370 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=139938) 2026-02-18 20:59:10,370 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=139938) 2026-02-18 20:59:10,381 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=139938) 2026-02-18 20:59:10,381 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=139938) 2026-02-18 20:59:10,382 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=139938) 2026-02-18 20:59:10,382 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=139938) 2026-02-18 20:59:10,382 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=139941) 2026-02-18 20:59:10,379 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=139941) 2026-02-18 20:59:10,380 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=139941) 2026-02-18 20:59:11,139 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=139938) 2026-02-18 20:59:11,352 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=139938) 2026-02-18 20:59:11,353 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=139938) 2026-02-18 20:59:11,362 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=139938) 2026-02-18 20:59:11,362 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=139938) 2026-02-18 20:59:11,371 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=139938) 2026-02-18 20:59:11,371 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=139941) 2026-02-18 20:59:11,370 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=139938) 2026-02-18 20:59:11,394 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=139938) 2026-02-18 20:59:11,394 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=139941) 2026-02-18 20:59:11,447 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=139941) 2026-02-18 20:59:11,447 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=139941) 2026-02-18 20:59:11,453 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=139938) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
+(PiperActor pid=139938)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+(PiperActor pid=139938) 2026-02-18 20:59:11,837 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=139938) 2026-02-18 20:59:11,837 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(run_dp_rank pid=139993) Running 10 timed iterations...
+(PiperActor pid=139938) 2026-02-18 20:59:12,400 - piper_actor - INFO - Actor 0: Tracing disabled
+(PiperActor pid=139938) 2026-02-18 20:59:12,966 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0 [repeated 224x across cluster]
+(PiperActor pid=139938) 2026-02-18 20:59:11,693 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1 [repeated 11x across cluster]
+(PiperActor pid=139941) [rank1]:[W218 20:59:11.620865474 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator()) [repeated 3x across cluster]
+(run_dp_rank pid=139993) rank 0 iter time= 0.06 ± 0.00 s (10 samples)
+(run_dp_rank pid=139993) rank 0 throughput= 265017.74 tokens/sec (10 samples)
+(run_dp_rank pid=139993) Ray timeline saved to: out/debug-pp2-dp1-interleaved-1f1b.json
+(PiperActor pid=139938) [rank0]:[W218 20:59:14.105155325 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+(PiperActor pid=139941) 2026-02-18 20:59:13,008 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3 [repeated 238x across cluster]
+(PiperActor pid=139938) 2026-02-18 20:59:11,696 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1 [repeated 10x across cluster]
+(PiperActor pid=139941) [rank1]:[W218 20:59:15.878600225 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=76, addr=[localhost]:59112, remote=[localhost]:12388): Failed to recv, got 0 bytes. Connection was likely closed. Did the remote server shutdown or crash?
+(PiperActor pid=139941) Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:682 (most recent call first):
+(PiperActor pid=139941) frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x9d (0x7fb5104cdfdd in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libc10.so)
+(PiperActor pid=139941) frame #1: <unknown function> + 0x6a3325d (0x7f84a28fa25d in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
+(PiperActor pid=139941) frame #2: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x273 (0x7f84a28f81f3 in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
+(PiperActor pid=139941) frame #3: c10d::ProcessGroupNCCL::HeartbeatMonitor::runLoop() + 0x44c (0x7f845f9daeec in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
+(PiperActor pid=139941) frame #4: <unknown function> + 0xdf0e6 (0x7fb5350e40e6 in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/bin/../lib/libstdc++.so.6)
+(PiperActor pid=139941) frame #5: <unknown function> + 0x8b2ea (0x7fb54488b2ea in /lib64/libc.so.6)
+(PiperActor pid=139941) frame #6: <unknown function> + 0x1103c0 (0x7fb5449103c0 in /lib64/libc.so.6)
+(PiperActor pid=139941) 
+(PiperActor pid=139941) [rank1]:[W218 20:59:15.881369993 ProcessGroupNCCL.cpp:1802] [PG ID 0 PG GUID 0 Rank 1] Failed to check the "should dump" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: Failed to recv, got 0 bytes. Connection was likely closed. Did the remote server shutdown or crash?
+(PiperActor pid=139938) 2026-02-18 20:59:11,697 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1 [repeated 7x across cluster]
+(PiperActor pid=139941) 2026-02-18 20:59:11,830 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1 [repeated 46x across cluster]
+(PiperActor pid=139941) 2026-02-18 20:59:13,009 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])] [repeated 235x across cluster]
+(PiperActor pid=139938) 2026-02-18 20:59:11,693 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1 [repeated 10x across cluster]
+(PiperActor pid=139941) 2026-02-18 20:59:13,006 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3 [repeated 179x across cluster]
+(PiperActor pid=139941) [rank1]:[W218 20:59:16.881594253 TCPStore.cpp:106] [c10d] sendBytes failed on SocketImpl(fd=76, addr=[localhost]:59112, remote=[localhost]:12388): Broken pipe
+(PiperActor pid=139941) Exception raised from sendBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:653 (most recent call first):
+(PiperActor pid=139941) frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x9d (0x7fb5104cdfdd in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libc10.so)
+(PiperActor pid=139941) frame #1: <unknown function> + 0x6a326d1 (0x7f84a28f96d1 in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
+(PiperActor pid=139941) frame #2: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x24d (0x7f84a28f81cd in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
+(PiperActor pid=139941) frame #3: c10d::ProcessGroupNCCL::HeartbeatMonitor::runLoop() + 0x44c (0x7f845f9daeec in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
+(PiperActor pid=139941) frame #4: <unknown function> + 0xdf0e6 (0x7fb5350e40e6 in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/bin/../lib/libstdc++.so.6)
+(PiperActor pid=139941) frame #5: <unknown function> + 0x8b2ea (0x7fb54488b2ea in /lib64/libc.so.6)
+(PiperActor pid=139941) frame #6: <unknown function> + 0x1103c0 (0x7fb5449103c0 in /lib64/libc.so.6)
+(PiperActor pid=139941) 
+(PiperActor pid=139941) [rank1]:[W218 20:59:16.884401436 ProcessGroupNCCL.cpp:1802] [PG ID 0 PG GUID 0 Rank 1] Failed to check the "should dump" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: Broken pipe
+(PiperActor pid=139941) 2026-02-18 20:59:13,017 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1 [repeated 238x across cluster]
+(PiperActor pid=139941) 2026-02-18 20:59:11,824 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0 [repeated 11x across cluster]
+(PiperActor pid=139941) 2026-02-18 20:59:11,824 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0 [repeated 11x across cluster]
+(PiperActor pid=139941) 2026-02-18 20:59:11,830 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0 [repeated 11x across cluster]
+(PiperActor pid=139941) 2026-02-18 20:59:11,830 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0 [repeated 11x across cluster]
+(PiperActor pid=139941) 2026-02-18 20:59:13,017 - piper_actor - DEBUG - Actor 1 waiting for backward sync events [repeated 29x across cluster]
+(PiperActor pid=139941) 2026-02-18 20:59:13,017 - piper_actor - DEBUG - Actor 1 waiting for p2p ops [repeated 29x across cluster]
+(PiperActor pid=139941) 2026-02-18 20:59:12,400 - piper_actor - INFO - Actor 1: Tracing disabled
+(PiperActor pid=139941) 2026-02-18 20:59:13,006 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1 [repeated 15x across cluster]
+(PiperActor pid=139941) [rank1]:[W218 20:59:15.513592754 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
diff --git a/buggy-logs/interleaved_debug.log b/buggy-logs/interleaved_debug.log
new file mode 100644
index 0000000..0e2c40e
--- /dev/null
+++ b/buggy-logs/interleaved_debug.log
@@ -0,0 +1,1388 @@
+2026-02-18 21:01:17,682	INFO worker.py:2007 -- Started a local Ray instance.
+/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+  warnings.warn(
+(run_dp_rank pid=159418) Namespace(model='debug', schedule='interleaved-1f1b', dp=1, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False)
+(run_dp_rank pid=159418) Schedule:
+(run_dp_rank pid=159418) 0:0:f	0:1:f	2:0:f	2:1:f	0:2:f	0:3:f	2:0:b	 -- 	2:1:b	2:2:f	0:0:b	2:3:f	0:1:b	 -- 	2:2:b	2:3:b	0:2:b	0:3:b	0:0:u	
+(run_dp_rank pid=159418)  -- 	1:0:f	1:1:f	3:0:f	3:0:b	3:1:f	3:1:b	1:2:f	1:0:b	1:3:f	1:1:b	3:2:f	3:2:b	3:3:f	3:3:b	1:2:b	1:3:b	1:0:u	 -- 	
+(run_dp_rank pid=159418) 2026-02-18 21:01:31,233 - piper_coordinator - DEBUG - Running DP rank 1 of 1
+(run_dp_rank pid=159418) 2026-02-18 21:01:31,256 - piper_compile - DEBUG - mbs: 4, stages: 4, devices: 2
+(run_dp_rank pid=159418) 2026-02-18 21:01:31,256 - piper_compile - DEBUG - Fwd/bwd schedule: [[Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=False, upd=False)], [Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=False, upd=False)]]
+(run_dp_rank pid=159418) 2026-02-18 21:01:31,256 - piper_compile - DEBUG - P2P comms: [(0, 1, 0), (0, 1, 1), (1, 2, 0), (2, 3, 0), (1, 2, 1), (2, 3, 1), (0, 1, 2), (3, 2, 0), (0, 1, 3), (2, 1, 0), (3, 2, 1), (2, 1, 1), (1, 2, 2), (2, 3, 2), (1, 0, 0), (1, 2, 3), (2, 3, 3), (1, 0, 1), (3, 2, 2), (2, 1, 2), (3, 2, 3), (2, 1, 3), (1, 0, 2), (1, 0, 3)]
+(run_dp_rank pid=159418) 2026-02-18 21:01:31,256 - piper_compile - DEBUG - P2P schedules: {0: [(0, 1, 0, True), (0, 1, 1, True), (1, 2, 0, False), (2, 3, 0, True), (1, 2, 1, False), (2, 3, 1, True), (0, 1, 2, True), (3, 2, 0, False), (0, 1, 3, True), (2, 1, 0, True), (3, 2, 1, False), (2, 1, 1, True), (1, 2, 2, False), (2, 3, 2, True), (1, 0, 0, False), (1, 2, 3, False), (2, 3, 3, True), (1, 0, 1, False), (3, 2, 2, False), (2, 1, 2, True), (3, 2, 3, False), (2, 1, 3, True), (1, 0, 2, False), (1, 0, 3, False)], 1: [(0, 1, 0, False), (0, 1, 1, False), (1, 2, 0, True), (2, 3, 0, False), (1, 2, 1, True), (2, 3, 1, False), (0, 1, 2, False), (3, 2, 0, True), (0, 1, 3, False), (2, 1, 0, False), (3, 2, 1, True), (2, 1, 1, False), (1, 2, 2, True), (2, 3, 2, False), (1, 0, 0, True), (1, 2, 3, True), (2, 3, 3, False), (1, 0, 1, True), (3, 2, 2, True), (2, 1, 2, False), (3, 2, 3, True), (2, 1, 3, False), (1, 0, 2, True), (1, 0, 3, True)]}
+(run_dp_rank pid=159418) 2026-02-18 21:01:31,321 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, 042ea13152142cbaf192fd3c01000000) global rank 0
+(run_dp_rank pid=159418) 2026-02-18 21:01:31,322 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, 29eb62f8af01f33300603da701000000) global rank 1
+(PiperActor pid=159417) 2026-02-18 21:01:34,404 - piper_actor - INFO - Initializing Ray actor 1 global rank 1 GPU 2
+(PiperActor pid=159486) 2026-02-18 21:01:34,449 - piper_actor - INFO - Initializing Ray actor 0 global rank 0 GPU 1
+(PiperActor pid=159417) 2026-02-18 21:01:36,596 - piper_actor - DEBUG - Actor 1 has GPU 2, joined the global process group
+(PiperActor pid=159417) 2026-02-18 21:01:36,596 - piper_actor - INFO - Global rank 1 joined its pp group 0 along with ranks [0, 1]
+(PiperActor pid=159486) 2026-02-18 21:01:36,596 - piper_actor - DEBUG - Actor 0 has GPU 1, joined the global process group
+(PiperActor pid=159486) 2026-02-18 21:01:36,596 - piper_actor - INFO - Global rank 0 joined its pp group 0 along with ranks [0, 1]
+(run_dp_rank pid=159418) 2026-02-18 21:01:36,811 - piper_compile - INFO - DP rank 1 compiling...
+(PiperActor pid=159486) 2026-02-18 21:01:39,962 - piper_actor - INFO - Loading stage 0 graph on actor 0
+(PiperActor pid=159417) 2026-02-18 21:01:40,068 - piper_actor - INFO - Loading stage 1 graph on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:41,379 - piper_actor - INFO - Loading stage 3 graph on actor 1
+(PiperActor pid=159486) 2026-02-18 21:01:41,408 - piper_actor - INFO - Loading stage 2 graph on actor 0
+(run_dp_rank pid=159418) 2026-02-18 21:01:42,059 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=159418) Running 5 warmup iterations...
+(run_dp_rank pid=159418) 2026-02-18 21:01:48,650 - piper_compile - INFO - DP rank 1 done.
+(PiperActor pid=159417) 2026-02-18 21:01:48,702 - piper_actor - DEBUG - Actor 1 loaded labels torch.Size([16, 256, 512])
+(PiperActor pid=159417) 2026-02-18 21:01:48,707 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:48,708 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=159417) [rank1]:[W218 21:01:48.189986789 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=159486) 2026-02-18 21:01:48,688 - piper_actor - DEBUG - Actor 0 loaded inputs 1
+(PiperActor pid=159486) 2026-02-18 21:01:48,705 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:49,455 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:49,455 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=159486) [rank0]:[W218 21:01:49.937937824 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=159417) 2026-02-18 21:01:51,781 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:51,782 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:51,782 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:51,774 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:51,774 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:51,774 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:51,776 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:51,786 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:51,786 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:51,787 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:51,787 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:51,787 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:51,788 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:51,788 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=159486) [rank0]:[W218 21:01:51.269954479 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=159417) 2026-02-18 21:01:52,548 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,548 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,549 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,549 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,549 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=159417) [rank1]:[W218 21:01:52.031116421 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=159417) 2026-02-18 21:01:52,784 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,784 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,784 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:52,786 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,786 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,794 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,794 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,794 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,795 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,795 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,795 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,795 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,795 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:52,796 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,796 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,804 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,804 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:52,805 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,784 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,784 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,784 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,793 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,793 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,793 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,794 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,794 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:52,794 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,795 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,795 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,795 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,795 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,803 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,803 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,803 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,803 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,803 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:52,804 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,837 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:52,837 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,837 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,838 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,838 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:52,838 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,882 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,883 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,883 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,883 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,883 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,883 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,883 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,883 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,883 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,884 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,884 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,887 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,887 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:52,888 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,897 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,898 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,898 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,898 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,898 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,912 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,912 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,912 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,912 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,879 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:52,880 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,880 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,950 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,950 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:52,953 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:52,953 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=159486) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
+(PiperActor pid=159486)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+(PiperActor pid=159486) 2026-02-18 21:01:52,946 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,947 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,947 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,947 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,947 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:52,949 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,949 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,949 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,949 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,950 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,950 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,950 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,953 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,958 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,958 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,958 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,958 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,958 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:52,959 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,959 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,960 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,960 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,960 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,960 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,960 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,961 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,961 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:52,963 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:52,963 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,963 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,963 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,963 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:52,964 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,964 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,965 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,965 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:52,965 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,965 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:52,965 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,954 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,954 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,955 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,955 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,956 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,956 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,956 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,956 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,956 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:52,958 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:52,959 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,959 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,959 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,959 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:52,960 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:52,960 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:52,960 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,409 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,409 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,412 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,412 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,413 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,403 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,404 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,404 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,405 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,405 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,405 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,407 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,407 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,408 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,408 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,408 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,409 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,409 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,409 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,414 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,415 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,415 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,415 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,420 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,420 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,420 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,420 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,515 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,515 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,516 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,516 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,517 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,519 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,520 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,521 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,527 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,527 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,531 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,532 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,532 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,532 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,533 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,533 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,533 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,537 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,537 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,538 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,538 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,539 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,539 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,539 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,539 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,540 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=159417) 2026-02-18 21:01:53,540 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:53,602 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,603 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,605 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,605 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,606 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,607 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,609 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,609 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,610 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,610 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,612 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,613 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,614 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,621 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,621 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,624 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,624 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,625 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,631 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,632 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,634 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,634 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,517 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,517 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,517 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,522 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,522 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,523 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,523 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,533 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,533 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,533 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,538 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,540 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,540 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,541 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,545 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,546 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=159486) 2026-02-18 21:01:53,546 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=159486) 2026-02-18 21:01:53,602 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,605 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,605 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,606 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,608 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,609 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,610 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,610 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,612 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,612 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,614 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,614 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,616 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,616 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,617 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,619 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,620 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,621 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,623 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,623 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,625 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,625 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,626 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,626 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,630 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,630 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,631 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,631 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,631 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,635 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,636 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,636 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,639 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,639 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,640 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,640 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,641 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,646 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,646 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,647 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,656 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,656 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,658 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,658 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,660 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,666 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,666 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,667 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=159417) 2026-02-18 21:01:53,667 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:53,670 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,670 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,672 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,673 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,674 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,674 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,637 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,638 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,639 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,639 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,640 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,640 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,640 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,641 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=159486) 2026-02-18 21:01:53,641 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=159486) 2026-02-18 21:01:53,670 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,676 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,676 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,677 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,677 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,680 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,680 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,681 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,687 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,688 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,690 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,690 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,691 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,698 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,698 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,700 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,700 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,702 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,702 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,702 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,704 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,704 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,706 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,706 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,706 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,708 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,709 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,710 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,696 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,696 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,697 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,700 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,700 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,701 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,701 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,703 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,703 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,704 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,704 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,706 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,706 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,708 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,710 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,710 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,711 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,713 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,714 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,715 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,715 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,716 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,716 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,718 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,719 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,720 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,726 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,727 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,727 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=159417) 2026-02-18 21:01:53,727 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:53,730 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,731 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,733 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,733 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,734 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,734 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,736 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,736 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,738 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,738 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,740 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,740 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,741 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,748 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,748 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,750 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,750 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,752 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,716 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,716 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,718 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,718 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,719 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,720 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,720 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,722 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,722 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,723 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,724 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,724 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,725 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,725 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,726 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=159486) 2026-02-18 21:01:53,726 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=159486) 2026-02-18 21:01:53,730 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,732 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,733 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,734 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,736 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,736 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,737 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,737 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,739 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,740 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,741 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,741 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,743 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,743 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,745 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,747 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,747 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,748 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,750 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,750 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,752 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,752 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,753 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,753 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,758 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,758 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,760 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,760 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,761 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,762 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,762 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,764 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,764 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,766 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,766 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,766 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,768 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,769 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,770 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,776 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,776 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,779 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,779 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,780 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,787 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,787 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,787 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=159417) 2026-02-18 21:01:53,788 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:53,791 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,791 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,755 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,755 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,756 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,757 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,757 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,759 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,759 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,760 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,761 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,761 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,762 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,762 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,762 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=159486) 2026-02-18 21:01:53,763 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=159486) 2026-02-18 21:01:53,791 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,793 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,794 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,794 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,794 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,797 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,797 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,798 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,798 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,800 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,800 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,802 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,808 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,808 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,810 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,811 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,812 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,818 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,818 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,820 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,821 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,822 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,822 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,822 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,824 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,825 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,826 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,826 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,826 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,829 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,829 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,830 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,793 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,793 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,794 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,796 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,797 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,798 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,798 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,800 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,800 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,801 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,802 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,804 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,804 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,805 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,807 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,807 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,809 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,811 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,811 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,812 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,813 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,813 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,813 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,815 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,816 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,817 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,817 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,817 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,819 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,819 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,821 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,821 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,822 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,822 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,823 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,823 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=159486) 2026-02-18 21:01:53,823 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(run_dp_rank pid=159418) Running 10 timed iterations...
+(PiperActor pid=159417) 2026-02-18 21:01:53,837 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,837 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,839 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,839 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,840 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,847 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,847 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,847 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=159417) 2026-02-18 21:01:53,848 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:53,851 - piper_actor - INFO - Actor 1: Tracing disabled
+(PiperActor pid=159417) 2026-02-18 21:01:53,852 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,853 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,855 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,855 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,856 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,856 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,858 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,858 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,860 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,860 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,862 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,862 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,863 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,870 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,870 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,851 - piper_actor - INFO - Actor 0: Tracing disabled
+(PiperActor pid=159486) 2026-02-18 21:01:53,852 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,855 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,855 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,856 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,858 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,858 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,860 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,860 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,864 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,864 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,865 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,865 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,867 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,868 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,869 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,871 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,872 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,872 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,872 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,874 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,880 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,880 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,882 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,882 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,884 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,884 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,884 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,886 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,886 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,888 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,888 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,888 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,890 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,890 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,892 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,898 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,898 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,900 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,901 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,902 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,908 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,909 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,909 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=159486) 2026-02-18 21:01:53,873 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,875 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,875 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,876 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,877 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,877 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,877 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,880 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,880 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,881 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,882 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,882 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,884 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,884 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,885 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,886 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,886 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,887 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,887 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,888 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=159486) 2026-02-18 21:01:53,888 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:53,909 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:53,913 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,913 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,915 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,915 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,916 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,916 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,918 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,919 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,920 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,920 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,922 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,922 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,924 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,930 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,930 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,932 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,933 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,934 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,940 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,940 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,942 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,943 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,944 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,944 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,944 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,946 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,947 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,913 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,915 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,916 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,917 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,919 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,919 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,920 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,920 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,922 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,923 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,924 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,924 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,926 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,926 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,927 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,930 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,930 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,931 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,933 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,934 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,935 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,935 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,936 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,936 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,938 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,938 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,939 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,940 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,940 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,942 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,942 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,943 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,944 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,945 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,945 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,945 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,946 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=159486) 2026-02-18 21:01:53,946 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:53,948 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,948 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,948 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,950 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:53,951 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,952 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,958 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,958 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,961 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:53,961 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,962 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,969 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,969 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,969 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=159417) 2026-02-18 21:01:53,969 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:53,973 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,973 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,975 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,975 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,976 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,976 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,978 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,979 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,980 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,980 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,982 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,982 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,984 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,973 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,975 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,976 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,977 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,979 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,979 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,980 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,980 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,983 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,983 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,984 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,984 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,986 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,986 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,988 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=159417) 2026-02-18 21:01:53,990 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,990 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,992 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:53,993 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:53,994 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,000 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,000 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,002 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,003 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,004 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,004 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,004 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,006 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,007 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,008 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,008 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,008 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,010 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,011 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,012 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,018 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,018 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,021 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,021 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,022 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=159486) 2026-02-18 21:01:53,990 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,990 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,991 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,993 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:53,994 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:53,995 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,995 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,996 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:53,996 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,998 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:53,998 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,000 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,000 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,000 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,002 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,002 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,004 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,004 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,005 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,005 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,006 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,006 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=159486) 2026-02-18 21:01:54,006 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:54,029 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,029 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,029 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=159417) 2026-02-18 21:01:54,029 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:54,033 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,033 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,035 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,035 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,036 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,036 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,038 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,039 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,040 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,040 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,042 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,042 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,044 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,050 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,050 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,053 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,053 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,054 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,060 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,060 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,062 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,063 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,033 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,035 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,036 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,037 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,039 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,039 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,040 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,041 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,043 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,043 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,044 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,044 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,046 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,047 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,048 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,050 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,050 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,051 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,054 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,054 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,055 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,056 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,056 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,056 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,058 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,059 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,060 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,060 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,060 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,062 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,063 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,064 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,064 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,064 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,064 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,064 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,066 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,067 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,068 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,068 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,069 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,071 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,071 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,072 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,079 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,079 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,081 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,081 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,083 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,089 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,089 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,090 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=159417) 2026-02-18 21:01:54,090 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:54,093 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,093 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,095 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,096 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,097 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,097 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,099 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,099 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,100 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,101 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,103 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,103 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,065 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,065 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,066 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,066 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=159486) 2026-02-18 21:01:54,066 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=159486) 2026-02-18 21:01:54,093 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,096 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,096 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,097 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,099 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,100 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,101 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,101 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,103 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,103 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,104 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,111 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,111 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,113 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,113 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,114 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,121 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,121 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,123 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,123 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,124 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,125 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,125 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,127 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,127 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,128 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,129 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,129 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,131 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,131 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,133 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,139 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,139 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,141 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,142 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,143 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,104 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,105 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,107 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,107 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,108 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,110 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,111 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,112 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,114 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,114 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,115 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,116 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,116 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,116 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,119 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,119 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,120 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,121 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,121 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,123 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,123 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,124 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,125 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,125 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,126 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,126 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,126 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=159486) 2026-02-18 21:01:54,127 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:54,149 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,150 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,150 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=159417) 2026-02-18 21:01:54,150 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:54,153 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,154 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,156 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,156 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,157 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,157 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,159 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,160 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,161 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,161 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,163 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,163 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,164 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,171 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,171 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,173 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,173 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,174 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,181 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,181 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,154 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,157 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,157 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,158 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,160 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,160 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,161 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,161 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,163 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,164 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,165 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,165 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,167 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,167 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,168 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,171 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,171 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,172 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,174 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,175 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,176 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,176 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,177 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,177 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,179 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,179 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,180 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,181 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,181 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,183 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,183 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,184 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,185 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,185 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,187 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,187 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,189 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,189 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,189 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,191 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,191 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,193 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,199 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,199 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,201 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,202 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,203 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,209 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,210 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,210 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=159417) 2026-02-18 21:01:54,210 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:54,213 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,214 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,216 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,216 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,217 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,217 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,183 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,183 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,184 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,185 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,185 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,186 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,186 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,187 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=159486) 2026-02-18 21:01:54,187 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=159486) 2026-02-18 21:01:54,213 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,216 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,216 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,217 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,219 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,219 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,219 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,220 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,221 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,221 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,223 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,223 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,224 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,231 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,231 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,233 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,233 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,235 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,241 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,241 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,243 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,243 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,245 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,245 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,245 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,247 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,248 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,249 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,250 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,250 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,252 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,253 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,254 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,221 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,221 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,223 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,223 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,224 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,224 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,226 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,227 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,228 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,230 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,230 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,231 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,234 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,234 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,235 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,236 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,236 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,236 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,238 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,238 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,240 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,240 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,240 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,242 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,243 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,244 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,244 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,245 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,245 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,246 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,246 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=159486) 2026-02-18 21:01:54,246 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:54,260 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,260 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,263 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,263 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,264 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,270 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,271 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,271 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=159417) 2026-02-18 21:01:54,271 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:54,278 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,278 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,281 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,281 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,282 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,282 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,284 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,285 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,286 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,286 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,288 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,288 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,289 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,296 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,296 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,279 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,281 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,281 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,283 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,285 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,285 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,286 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,286 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,288 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,289 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,290 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,290 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,292 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,292 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,293 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,295 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,296 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,297 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,299 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,299 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,300 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,306 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,306 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,308 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,309 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,310 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,310 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,310 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,312 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,313 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,314 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,314 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,314 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,316 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,317 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,318 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,324 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,325 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,327 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,327 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,328 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,335 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,335 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,299 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,299 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,300 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,301 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,301 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,301 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,303 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,304 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,305 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,305 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,305 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,308 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,308 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,309 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,310 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,310 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,311 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,311 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,311 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=159486) 2026-02-18 21:01:54,311 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:54,335 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=159417) 2026-02-18 21:01:54,336 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:54,339 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,339 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,341 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,341 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,342 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,342 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,344 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,345 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,346 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,346 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,348 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,349 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,350 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,356 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,356 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,358 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,359 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,360 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,366 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,366 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,368 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,369 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,370 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,370 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,370 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,372 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,373 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,339 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,341 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,341 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,342 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,344 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,345 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,346 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,346 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,348 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,348 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,350 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,350 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,352 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,352 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,353 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,355 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,355 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,357 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,359 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,359 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,360 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,361 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,361 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,361 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,363 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,363 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,365 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,365 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,365 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,367 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,368 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,369 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,369 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,370 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,370 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,371 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,371 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=159486) 2026-02-18 21:01:54,371 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:54,374 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,374 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,374 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,376 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,377 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,378 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,384 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,384 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,387 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,387 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,388 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,394 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,395 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,395 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=159417) 2026-02-18 21:01:54,395 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=159417) 2026-02-18 21:01:54,398 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,399 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,401 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,401 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,402 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,402 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,404 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,405 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,406 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,406 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,408 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,408 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,409 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,416 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,416 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,399 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,401 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,401 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,402 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,405 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,405 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,406 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,406 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,408 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,408 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,410 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,410 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,412 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=159486) 2026-02-18 21:01:54,412 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,413 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,415 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,415 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,417 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=159417) 2026-02-18 21:01:54,418 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,418 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,420 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,426 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,426 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,428 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,428 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,429 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,430 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,430 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,432 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,432 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,433 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,434 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,434 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,436 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=159417) 2026-02-18 21:01:54,436 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,438 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,444 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,444 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,446 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=159417) 2026-02-18 21:01:54,447 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159417) 2026-02-18 21:01:54,448 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,454 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,455 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=159417) 2026-02-18 21:01:54,455 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=159417) 2026-02-18 21:01:54,455 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=159486) 2026-02-18 21:01:54,419 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,419 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,420 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,421 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,421 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,421 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,423 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=159486) 2026-02-18 21:01:54,424 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,425 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,425 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,425 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,427 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=159486) 2026-02-18 21:01:54,428 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=159486) 2026-02-18 21:01:54,429 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,429 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,430 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,430 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,431 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=159486) 2026-02-18 21:01:54,431 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=159486) 2026-02-18 21:01:54,431 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(run_dp_rank pid=159418) rank 0 iter time= 0.06 ± 0.00 s (10 samples)
+(run_dp_rank pid=159418) rank 0 throughput= 270680.89 tokens/sec (10 samples)
+(run_dp_rank pid=159418) Ray timeline saved to: out/debug-pp2-dp1-interleaved-1f1b.json
+(PiperActor pid=159417) [rank1]:[W218 21:01:56.671824083 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+(PiperActor pid=159486) [rank0]:[W218 21:01:56.707232376 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
diff --git a/buggy-logs/interleaved_debug.trace b/buggy-logs/interleaved_debug.trace
new file mode 100644
index 0000000..70e2adc
--- /dev/null
+++ b/buggy-logs/interleaved_debug.trace
@@ -0,0 +1,1388 @@
+2026-02-18 22:46:13,994	INFO worker.py:2007 -- Started a local Ray instance.
+/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+  warnings.warn(
+(run_dp_rank pid=955564) Namespace(model='debug', schedule='interleaved-1f1b', dp=1, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=True, naive_gradient_sync=False)
+(run_dp_rank pid=955564) Schedule:
+(run_dp_rank pid=955564) 0:0:f	0:1:f	2:0:f	2:1:f	0:2:f	0:3:f	2:0:b	 -- 	2:1:b	2:2:f	0:0:b	2:3:f	0:1:b	 -- 	2:2:b	2:3:b	0:2:b	0:3:b	0:0:u	
+(run_dp_rank pid=955564)  -- 	1:0:f	1:1:f	3:0:f	3:0:b	3:1:f	3:1:b	1:2:f	1:0:b	1:3:f	1:1:b	3:2:f	3:2:b	3:3:f	3:3:b	1:2:b	1:3:b	1:0:u	 -- 	
+(run_dp_rank pid=955564) 2026-02-18 22:46:28,513 - piper_coordinator - DEBUG - Running DP rank 1 of 1
+(run_dp_rank pid=955564) 2026-02-18 22:46:28,536 - piper_compile - DEBUG - mbs: 4, stages: 4, devices: 2
+(run_dp_rank pid=955564) 2026-02-18 22:46:28,536 - piper_compile - DEBUG - Fwd/bwd schedule: [[Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=False, upd=False)], [Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=False, upd=False)]]
+(run_dp_rank pid=955564) 2026-02-18 22:46:28,536 - piper_compile - DEBUG - P2P comms: [(0, 1, 0), (0, 1, 1), (1, 2, 0), (2, 3, 0), (1, 2, 1), (2, 3, 1), (0, 1, 2), (3, 2, 0), (0, 1, 3), (2, 1, 0), (3, 2, 1), (2, 1, 1), (1, 2, 2), (2, 3, 2), (1, 0, 0), (1, 2, 3), (2, 3, 3), (1, 0, 1), (3, 2, 2), (2, 1, 2), (3, 2, 3), (2, 1, 3), (1, 0, 2), (1, 0, 3)]
+(run_dp_rank pid=955564) 2026-02-18 22:46:28,536 - piper_compile - DEBUG - P2P schedules: {0: [(0, 1, 0, True), (0, 1, 1, True), (1, 2, 0, False), (2, 3, 0, True), (1, 2, 1, False), (2, 3, 1, True), (0, 1, 2, True), (3, 2, 0, False), (0, 1, 3, True), (2, 1, 0, True), (3, 2, 1, False), (2, 1, 1, True), (1, 2, 2, False), (2, 3, 2, True), (1, 0, 0, False), (1, 2, 3, False), (2, 3, 3, True), (1, 0, 1, False), (3, 2, 2, False), (2, 1, 2, True), (3, 2, 3, False), (2, 1, 3, True), (1, 0, 2, False), (1, 0, 3, False)], 1: [(0, 1, 0, False), (0, 1, 1, False), (1, 2, 0, True), (2, 3, 0, False), (1, 2, 1, True), (2, 3, 1, False), (0, 1, 2, False), (3, 2, 0, True), (0, 1, 3, False), (2, 1, 0, False), (3, 2, 1, True), (2, 1, 1, False), (1, 2, 2, True), (2, 3, 2, False), (1, 0, 0, True), (1, 2, 3, True), (2, 3, 3, False), (1, 0, 1, True), (3, 2, 2, True), (2, 1, 2, False), (3, 2, 3, True), (2, 1, 3, False), (1, 0, 2, True), (1, 0, 3, True)]}
+(run_dp_rank pid=955564) 2026-02-18 22:46:28,601 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, d70769c002e896b98808756401000000) global rank 0
+(run_dp_rank pid=955564) 2026-02-18 22:46:28,602 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, d340db486ad9cc6bfeb980b401000000) global rank 1
+(PiperActor pid=955512) 2026-02-18 22:46:31,728 - piper_actor - INFO - Initializing Ray actor 1 global rank 1 GPU 2
+(PiperActor pid=955511) 2026-02-18 22:46:31,725 - piper_actor - INFO - Initializing Ray actor 0 global rank 0 GPU 1
+(PiperActor pid=955512) 2026-02-18 22:46:33,577 - piper_actor - DEBUG - Actor 1 has GPU 2, joined the global process group
+(PiperActor pid=955512) 2026-02-18 22:46:33,577 - piper_actor - INFO - Global rank 1 joined its pp group 0 along with ranks [0, 1]
+(PiperActor pid=955511) 2026-02-18 22:46:33,585 - piper_actor - DEBUG - Actor 0 has GPU 1, joined the global process group
+(PiperActor pid=955511) 2026-02-18 22:46:33,586 - piper_actor - INFO - Global rank 0 joined its pp group 0 along with ranks [0, 1]
+(run_dp_rank pid=955564) 2026-02-18 22:46:33,801 - piper_compile - INFO - DP rank 1 compiling...
+(PiperActor pid=955511) 2026-02-18 22:46:37,303 - piper_actor - INFO - Loading stage 0 graph on actor 0
+(PiperActor pid=955512) 2026-02-18 22:46:37,397 - piper_actor - INFO - Loading stage 1 graph on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:38,644 - piper_actor - INFO - Loading stage 3 graph on actor 1
+(PiperActor pid=955511) 2026-02-18 22:46:38,627 - piper_actor - INFO - Loading stage 2 graph on actor 0
+(run_dp_rank pid=955564) 2026-02-18 22:46:39,322 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=955564) Running 5 warmup iterations...
+(run_dp_rank pid=955564) 2026-02-18 22:46:45,770 - piper_compile - INFO - DP rank 1 done.
+(PiperActor pid=955512) 2026-02-18 22:46:45,808 - piper_actor - DEBUG - Actor 1 loaded labels torch.Size([16, 256, 512])
+(PiperActor pid=955512) 2026-02-18 22:46:45,811 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:45,811 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=955512) [rank1]:[W218 22:46:45.293417309 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=955511) 2026-02-18 22:46:45,794 - piper_actor - DEBUG - Actor 0 loaded inputs 1
+(PiperActor pid=955511) 2026-02-18 22:46:45,811 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:46,070 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:46,070 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=955511) [rank0]:[W218 22:46:46.552214999 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=955512) 2026-02-18 22:46:48,232 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:48,233 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:48,233 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:48,223 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:48,223 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:48,223 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:48,225 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:48,246 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:48,246 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:48,246 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:48,246 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:48,247 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:48,247 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:48,247 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=955511) [rank0]:[W218 22:46:48.729440883 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=955512) 2026-02-18 22:46:48,978 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:48,979 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:48,980 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:48,980 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:48,980 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=955512) [rank1]:[W218 22:46:48.462066620 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=955512) 2026-02-18 22:46:49,181 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,181 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,181 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:49,183 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,183 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,194 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,194 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,194 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,194 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,194 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,195 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,195 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,195 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:49,195 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,195 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,203 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,204 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:49,204 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,181 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,181 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,181 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,191 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,191 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,191 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,191 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,191 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:49,192 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,192 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,193 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,193 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,193 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,201 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,201 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,201 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,201 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,202 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:49,202 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,210 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:49,210 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,210 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,210 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,211 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:49,211 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,240 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:49,241 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,242 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,285 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,285 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,285 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,285 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,286 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,286 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,286 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,286 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,286 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,287 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,287 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,290 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,290 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:49,291 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,302 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,302 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,302 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,302 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,303 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,317 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,317 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,318 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,318 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,340 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,340 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:49,342 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:49,342 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,343 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,347 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,348 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,349 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,349 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,349 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:49,350 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,350 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,351 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,351 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,351 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,351 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,351 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,351 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,352 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:49,354 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:49,354 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,354 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,354 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,354 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:49,355 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,355 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,356 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,356 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,356 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=955511) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
+(PiperActor pid=955511)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+(PiperActor pid=955511) 2026-02-18 22:46:49,337 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,337 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,337 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,337 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,338 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:49,339 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,339 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,339 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,339 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,340 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,340 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,340 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,344 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,344 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,344 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,344 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,346 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,346 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,346 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,346 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,346 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:49,349 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:49,349 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,349 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,349 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,349 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:49,350 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,351 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,351 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,356 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,356 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,394 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,394 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:49,397 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:49,397 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:49,389 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,390 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,390 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,390 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,390 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,390 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:49,393 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:49,393 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,393 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,394 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,394 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:49,394 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,394 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,395 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,398 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,399 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,400 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,401 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,401 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,405 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,405 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,405 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,405 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,508 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,509 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,509 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,510 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,510 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:49,512 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:49,513 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:49,514 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,520 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,520 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,525 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,525 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,525 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,525 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,527 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,527 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,527 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,531 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,531 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,531 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,531 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,532 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,533 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,533 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=955512) 2026-02-18 22:46:49,533 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:49,533 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=955512) 2026-02-18 22:46:49,533 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=955511) 2026-02-18 22:46:49,510 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,510 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,511 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,516 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,516 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,516 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,516 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,526 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,526 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,526 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,530 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,534 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,534 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,534 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=955511) 2026-02-18 22:46:49,538 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:49,539 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=955511) 2026-02-18 22:46:49,540 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,008 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,008 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,011 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,011 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,012 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,012 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,014 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,015 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,016 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,016 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,018 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,018 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,020 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,028 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,028 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,030 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,031 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,032 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,038 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,038 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,040 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,040 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,042 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,042 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,042 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,051 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,051 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,052 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,052 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,052 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,060 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,060 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,060 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,072 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,072 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,008 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,012 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,012 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,013 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,015 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,015 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,016 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,016 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,018 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,019 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,020 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,020 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,022 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,022 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,024 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,026 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,026 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,027 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,029 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,029 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,031 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,031 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,032 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,032 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,041 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,041 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,042 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,043 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,043 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,057 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,057 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,058 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,059 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,059 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,060 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,060 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,060 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=955511) 2026-02-18 22:46:50,061 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,074 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,074 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,075 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,082 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,082 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,083 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=955512) 2026-02-18 22:46:50,083 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,086 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,086 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,088 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,089 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,090 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,090 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,092 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,092 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,093 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,094 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,096 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,096 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,097 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,103 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,103 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,106 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,106 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,107 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,113 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,113 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,086 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,115 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,116 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,117 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,117 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,118 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,120 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,120 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,121 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,122 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,122 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,124 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,124 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,125 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,132 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,132 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,134 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,134 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,135 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,141 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,142 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,142 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=955512) 2026-02-18 22:46:50,142 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=955511) 2026-02-18 22:46:50,139 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,139 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,140 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,148 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,148 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,149 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,149 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,151 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,151 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,152 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,152 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,177 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,178 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,180 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,180 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,181 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,182 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,184 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,184 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,185 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,185 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,187 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,187 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,189 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,155 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,155 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,156 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,158 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,158 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,160 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,162 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,162 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,163 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,164 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,164 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,164 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,166 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,167 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,168 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,168 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,168 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,170 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,171 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,172 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,172 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,173 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,173 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,174 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,174 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=955511) 2026-02-18 22:46:50,174 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=955511) 2026-02-18 22:46:50,177 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,179 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,180 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,181 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,183 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,183 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,184 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,184 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,187 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,187 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,188 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,188 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,190 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,190 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,192 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,195 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,195 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,197 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,197 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,199 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,205 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,205 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,207 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,207 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,209 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,209 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,209 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,211 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,211 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,213 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,213 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,213 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,215 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,216 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,217 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,223 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,223 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,225 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,226 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,227 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,194 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,194 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,195 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,197 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,197 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,199 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,199 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,200 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,200 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,202 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,202 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,203 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,204 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,204 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,206 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,206 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,208 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,208 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,208 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,209 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,209 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,210 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=955511) 2026-02-18 22:46:50,210 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,233 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,234 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,234 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=955512) 2026-02-18 22:46:50,234 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,238 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,238 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,240 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,240 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,241 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,241 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,243 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,244 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,245 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,245 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,247 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,247 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,249 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,255 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,255 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,257 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,257 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,258 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,265 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,265 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,267 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,267 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,268 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,269 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,269 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,271 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,271 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,272 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,273 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,273 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,238 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,240 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,240 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,241 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,244 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,244 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,245 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,245 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,247 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,248 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,249 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,249 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,251 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,251 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,252 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,254 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,255 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,256 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,258 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,258 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,259 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,260 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,260 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,260 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,262 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,263 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,264 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,264 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,265 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,266 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,267 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,268 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,269 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,269 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,270 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,270 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,270 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=955511) 2026-02-18 22:46:50,270 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(run_dp_rank pid=955564) Running 10 timed iterations...
+(PiperActor pid=955512) 2026-02-18 22:46:50,275 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,275 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,277 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,283 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,283 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,285 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,285 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,286 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,293 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,293 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,294 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=955512) 2026-02-18 22:46:50,294 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,297 - piper_actor - INFO - Actor 1: Tracing enabled
+(PiperActor pid=955512) 2026-02-18 22:46:50,299 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,299 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,301 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,301 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,302 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,302 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,304 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,305 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,306 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,306 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,308 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,308 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,310 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,297 - piper_actor - INFO - Actor 0: Tracing enabled
+(PiperActor pid=955511) 2026-02-18 22:46:50,298 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,301 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,301 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,302 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,304 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,305 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,306 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,306 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,308 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,308 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,310 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,310 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,312 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,312 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,313 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,316 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,316 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,318 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,318 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,319 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,326 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,326 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,328 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,328 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,329 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,330 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,330 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,332 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,332 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,333 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,334 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,334 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,336 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,336 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,338 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,344 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,344 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,346 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,346 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,348 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,315 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,315 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,317 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,319 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,319 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,320 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,321 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,321 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,321 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,323 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,324 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,325 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,325 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,325 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,327 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,328 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,329 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,330 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,330 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,330 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,331 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,331 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=955511) 2026-02-18 22:46:50,331 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,354 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,354 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,355 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=955512) 2026-02-18 22:46:50,355 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,358 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,358 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,360 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,360 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,362 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,362 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,364 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,364 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,365 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,365 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,367 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,368 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,369 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,375 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,375 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,377 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,377 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,379 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,385 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,385 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,387 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,387 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,388 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,389 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,389 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,391 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,358 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,360 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,360 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,362 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,364 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,364 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,365 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,365 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,367 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,368 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,369 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,369 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,371 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,371 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,372 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,374 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,375 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,376 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,378 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,378 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,379 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,380 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,380 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,381 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,383 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,383 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,384 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,385 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,385 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,387 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,387 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,388 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,389 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,389 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,390 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,390 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,391 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=955511) 2026-02-18 22:46:50,391 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,392 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,393 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,393 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,393 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,396 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,396 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,397 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,403 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,403 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,405 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,406 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,407 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,413 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,414 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,414 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=955512) 2026-02-18 22:46:50,414 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,419 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,419 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,421 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,422 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,423 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,423 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,425 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,425 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,426 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,426 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,428 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,429 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,419 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,422 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,422 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,423 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,425 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,426 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,427 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,427 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,429 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,429 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,430 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,430 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,430 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,436 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,436 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,438 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,438 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,440 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,446 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,446 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,448 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,448 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,449 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,450 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,450 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,452 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,452 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,454 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,454 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,454 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,456 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,456 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,458 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,464 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,464 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,466 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,466 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,432 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,433 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,434 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,436 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,436 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,437 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,439 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,440 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,441 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,441 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,442 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,442 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,444 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,444 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,445 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,446 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,446 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,448 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,448 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,449 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,450 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,450 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,451 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,451 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,452 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=955511) 2026-02-18 22:46:50,452 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,468 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,474 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,474 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,475 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=955512) 2026-02-18 22:46:50,475 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,478 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,478 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,480 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,480 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,481 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,482 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,484 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,484 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,485 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,485 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,487 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,487 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,489 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,495 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,495 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,497 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,497 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,499 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,505 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,505 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,507 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,507 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,508 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,509 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,509 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,511 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,511 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,478 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,480 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,480 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,481 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,483 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,484 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,485 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,485 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,487 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,487 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,489 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,489 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,491 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,491 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,492 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,494 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,494 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,496 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,498 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,498 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,499 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,500 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,500 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,500 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,502 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,503 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,504 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,504 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,504 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,506 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,507 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,508 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,508 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,509 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,509 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,510 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,510 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=955511) 2026-02-18 22:46:50,510 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,512 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,513 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,513 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,515 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,515 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,517 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,523 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,523 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,525 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,525 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,527 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,533 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,533 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,534 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=955512) 2026-02-18 22:46:50,534 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,537 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,537 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,539 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,539 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,540 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,541 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,543 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,543 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,544 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,544 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,546 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,546 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,548 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,537 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,539 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,540 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,541 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,543 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,543 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,544 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,544 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,546 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,546 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,548 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,548 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,550 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,550 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,551 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,554 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,554 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,556 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,556 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,558 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,564 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,564 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,566 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,566 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,567 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,568 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,568 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,570 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,570 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,571 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,572 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,572 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,574 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,574 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,575 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,582 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,582 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,584 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,584 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,585 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,591 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,592 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,592 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=955512) 2026-02-18 22:46:50,592 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=955511) 2026-02-18 22:46:50,553 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,554 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,555 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,557 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,557 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,558 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,559 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,559 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,559 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,561 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,561 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,563 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,563 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,563 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,565 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,566 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,567 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,567 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,568 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,568 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,569 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,569 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=955511) 2026-02-18 22:46:50,569 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,595 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,596 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,598 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,598 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,599 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,599 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,601 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,601 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,603 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,603 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,605 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,605 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,606 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,612 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,612 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,615 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,615 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,616 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,622 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,622 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,624 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,625 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,626 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,626 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,626 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,628 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,629 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,630 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,630 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,630 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,595 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,598 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,598 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,599 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,601 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,601 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,603 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,603 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,605 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,605 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,606 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,606 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,608 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,609 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,610 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,612 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,612 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,613 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,615 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,616 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,617 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,617 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,618 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,618 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,620 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,620 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,621 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,622 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,622 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,624 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,624 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,626 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,626 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,627 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,627 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,627 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,628 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=955511) 2026-02-18 22:46:50,628 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,632 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,633 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,634 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,640 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,640 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,642 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,642 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,644 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,650 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,650 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,651 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=955512) 2026-02-18 22:46:50,651 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,654 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,654 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,656 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,656 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,658 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,658 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,660 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,660 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,661 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,661 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,663 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,664 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,665 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,671 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,671 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,654 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,656 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,657 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,658 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,660 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,660 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,661 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,661 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,663 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,664 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,665 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,665 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,667 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,667 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,669 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,671 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,671 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,672 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,674 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,674 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,675 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,681 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,681 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,684 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,684 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,685 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,686 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,686 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,688 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,688 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,689 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,690 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,690 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,692 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,692 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,693 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,699 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,700 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,702 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,702 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,703 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,709 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,710 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,710 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=955512) 2026-02-18 22:46:50,710 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=955511) 2026-02-18 22:46:50,674 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,675 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,676 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,676 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,677 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,677 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,679 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,679 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,680 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,681 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,681 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,683 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,683 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,684 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,685 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,686 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,686 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,686 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,687 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=955511) 2026-02-18 22:46:50,687 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,714 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,714 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,716 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,716 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,717 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,717 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,719 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,720 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,721 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,721 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,723 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,723 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,724 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,731 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,731 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,733 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,733 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,734 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,740 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,741 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,743 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,743 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,744 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,745 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,745 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,747 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,747 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,748 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,749 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,749 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,714 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,716 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,716 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,718 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,720 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,720 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,721 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,721 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,723 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,723 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,725 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,725 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,727 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,727 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,728 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,730 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,730 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,732 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,734 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,734 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,735 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,736 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,736 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,736 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,738 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,738 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,740 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,740 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,740 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,742 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,742 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,744 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,744 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,745 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,745 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,746 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,746 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=955511) 2026-02-18 22:46:50,746 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,751 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,751 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,752 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,759 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,759 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,761 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,761 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,762 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,769 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,769 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,769 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=955512) 2026-02-18 22:46:50,769 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,773 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,773 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,775 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,775 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,776 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,777 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,779 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,779 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,780 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,780 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,782 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,782 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,784 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,773 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,775 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,775 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,777 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,779 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,779 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,780 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,780 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,782 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,782 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,784 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,784 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,786 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,786 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,787 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,789 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,790 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,790 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,792 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,792 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,794 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,800 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,800 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,802 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,802 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,803 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,804 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,804 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,806 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,806 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,807 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,808 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,808 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,810 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,810 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,811 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,818 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,818 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,820 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,820 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,821 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,828 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,828 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,829 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=955512) 2026-02-18 22:46:50,829 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=955511) 2026-02-18 22:46:50,790 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,791 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,793 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,793 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,794 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,795 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,795 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,795 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,797 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,798 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,799 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,799 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,800 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,802 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,802 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,803 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,804 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,804 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,805 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,805 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,805 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=955511) 2026-02-18 22:46:50,805 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=955512) 2026-02-18 22:46:50,832 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,832 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,834 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,834 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,835 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,836 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,838 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,838 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,839 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,839 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,841 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=955512) 2026-02-18 22:46:50,841 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,843 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,849 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,849 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,851 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,851 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,853 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,859 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,859 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,861 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,861 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,862 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,863 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,863 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,865 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,865 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,867 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,867 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,867 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,832 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,834 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,835 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,836 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,838 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,838 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,839 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,839 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,841 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,842 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,843 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,843 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,845 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=955511) 2026-02-18 22:46:50,845 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,846 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,848 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,849 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,850 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,852 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,852 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,854 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,854 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,855 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,855 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,857 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=955511) 2026-02-18 22:46:50,857 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,858 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,859 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,859 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,861 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=955511) 2026-02-18 22:46:50,861 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955511) 2026-02-18 22:46:50,862 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,863 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,863 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,864 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,864 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=955511) 2026-02-18 22:46:50,865 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=955511) 2026-02-18 22:46:50,865 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(run_dp_rank pid=955564) rank 0 iter time= 0.06 ± 0.00 s (10 samples)
+(run_dp_rank pid=955564) rank 0 throughput= 276802.15 tokens/sec (10 samples)
+(PiperActor pid=955512) 2026-02-18 22:46:50,869 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=955512) 2026-02-18 22:46:50,869 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,871 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,877 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,877 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,879 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=955512) 2026-02-18 22:46:50,879 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=955512) 2026-02-18 22:46:50,881 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,887 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,887 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=955512) 2026-02-18 22:46:50,888 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=955512) 2026-02-18 22:46:50,888 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(run_dp_rank pid=955564) Ray timeline saved to: out/debug-pp2-dp1-interleaved-1f1b.json
+(PiperActor pid=955512) [rank1]:[W218 22:46:52.083216106 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+(PiperActor pid=955511) [rank0]:[W218 22:46:52.307387103 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
diff --git a/buggy-out/debug-pp2-dp1-interleaved-1f1b.json b/buggy-out/debug-pp2-dp1-interleaved-1f1b.json
new file mode 100644
index 0000000..9dc9217
--- /dev/null
+++ b/buggy-out/debug-pp2-dp1-interleaved-1f1b.json
@@ -0,0 +1 @@
+[{"cat": "task:deserialize_arguments", "name": "task:deserialize_arguments", "pid": "10.158.48.71", "tid": "worker:f9802518598bc66b813e49d2738c5d6b03334b6935cd6807f19ffc93", "ts": 1771454810721389.5, "dur": 126.828, "ph": "X", "cname": "rail_load", "args": {}}, {"cat": "task:execute", "name": "task:execute", "pid": "10.158.48.71", "tid": "worker:f9802518598bc66b813e49d2738c5d6b03334b6935cd6807f19ffc93", "ts": 1771454810721533.8, "dur": 3191.825, "ph": "X", "cname": "rail_animation", "args": {}}, {"cat": "task:store_outputs", "name": "task:store_outputs", "pid": "10.158.48.71", "tid": "worker:f9802518598bc66b813e49d2738c5d6b03334b6935cd6807f19ffc93", "ts": 1771454810724730.8, "dur": 36.365, "ph": "X", "cname": "rail_idle", "args": {}}, {"cat": "task::PiperActor._forward", "name": "_forward", "pid": "10.158.48.71", "tid": "worker:f9802518598bc66b813e49d2738c5d6b03334b6935cd6807f19ffc93", "ts": 1771454810721379.8, "dur": 3398.992, "ph": "X", "cname": "generic_work", "args": {"name": 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\ No newline at end of file
diff --git a/logs/interleaved_3b.trace b/logs/interleaved_3b.trace
new file mode 100644
index 0000000..e02b24b
--- /dev/null
+++ b/logs/interleaved_3b.trace
@@ -0,0 +1,3432 @@
+2026-02-18 22:59:33,807	INFO worker.py:2007 -- Started a local Ray instance.
+/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+  warnings.warn(
+(run_dp_rank pid=1005832) 2026-02-18 22:59:48,059 - piper_coordinator - DEBUG - Running DP rank 1 of 1
+(run_dp_rank pid=1005832) Namespace(model='3b', schedule='interleaved-1f1b', dp=1, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=True, naive_gradient_sync=False)
+(run_dp_rank pid=1005832) 2026-02-18 22:59:51,185 - piper_compile - DEBUG - mbs: 4, stages: 4, devices: 2
+(run_dp_rank pid=1005832) 2026-02-18 22:59:51,185 - piper_compile - DEBUG - Fwd/bwd schedule: [[Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=False, upd=False)], [Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=False, upd=False)]]
+(run_dp_rank pid=1005832) 2026-02-18 22:59:51,186 - piper_compile - DEBUG - P2P comms: [(0, 1, 0), (0, 1, 1), (1, 2, 0), (2, 3, 0), (1, 2, 1), (2, 3, 1), (0, 1, 2), (3, 2, 0), (0, 1, 3), (2, 1, 0), (3, 2, 1), (2, 1, 1), (1, 2, 2), (2, 3, 2), (1, 0, 0), (1, 2, 3), (2, 3, 3), (1, 0, 1), (3, 2, 2), (2, 1, 2), (3, 2, 3), (2, 1, 3), (1, 0, 2), (1, 0, 3)]
+(run_dp_rank pid=1005832) 2026-02-18 22:59:51,186 - piper_compile - DEBUG - P2P schedules: {0: [(0, 1, 0, True), (0, 1, 1, True), (1, 2, 0, False), (2, 3, 0, True), (1, 2, 1, False), (2, 3, 1, True), (0, 1, 2, True), (3, 2, 0, False), (0, 1, 3, True), (2, 1, 0, True), (3, 2, 1, False), (2, 1, 1, True), (1, 2, 2, False), (2, 3, 2, True), (1, 0, 0, False), (1, 2, 3, False), (2, 3, 3, True), (1, 0, 1, False), (3, 2, 2, False), (2, 1, 2, True), (3, 2, 3, False), (2, 1, 3, True), (1, 0, 2, False), (1, 0, 3, False)], 1: [(0, 1, 0, False), (0, 1, 1, False), (1, 2, 0, True), (2, 3, 0, False), (1, 2, 1, True), (2, 3, 1, False), (0, 1, 2, False), (3, 2, 0, True), (0, 1, 3, False), (2, 1, 0, False), (3, 2, 1, True), (2, 1, 1, False), (1, 2, 2, True), (2, 3, 2, False), (1, 0, 0, True), (1, 2, 3, True), (2, 3, 3, False), (1, 0, 1, True), (3, 2, 2, True), (2, 1, 2, False), (3, 2, 3, True), (2, 1, 3, False), (1, 0, 2, True), (1, 0, 3, True)]}
+(run_dp_rank pid=1005832) Schedule:
+(run_dp_rank pid=1005832) 0:0:f	0:1:f	2:0:f	2:1:f	0:2:f	0:3:f	2:0:b	 -- 	2:1:b	2:2:f	0:0:b	2:3:f	0:1:b	 -- 	2:2:b	2:3:b	0:2:b	0:3:b	0:0:u	
+(run_dp_rank pid=1005832)  -- 	1:0:f	1:1:f	3:0:f	3:0:b	3:1:f	3:1:b	1:2:f	1:0:b	1:3:f	1:1:b	3:2:f	3:2:b	3:3:f	3:3:b	1:2:b	1:3:b	1:0:u	 -- 	
+(run_dp_rank pid=1005832) 2026-02-18 22:59:51,251 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, affdbe8bbc93827bddf60b5b01000000) global rank 0
+(run_dp_rank pid=1005832) 2026-02-18 22:59:51,252 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, 10e1a4aa93117a7b13c0803b01000000) global rank 1
+(PiperActor pid=1005951) 2026-02-18 22:59:54,183 - piper_actor - INFO - Initializing Ray actor 1 global rank 1 GPU 2
+(PiperActor pid=1005902) 2026-02-18 22:59:54,267 - piper_actor - INFO - Initializing Ray actor 0 global rank 0 GPU 1
+(PiperActor pid=1005902) 2026-02-18 22:59:57,203 - piper_actor - DEBUG - Actor 0 has GPU 1, joined the global process group
+(PiperActor pid=1005902) 2026-02-18 22:59:57,203 - piper_actor - INFO - Global rank 0 joined its pp group 0 along with ranks [0, 1]
+(PiperActor pid=1005951) 2026-02-18 22:59:57,197 - piper_actor - DEBUG - Actor 1 has GPU 2, joined the global process group
+(PiperActor pid=1005951) 2026-02-18 22:59:57,198 - piper_actor - INFO - Global rank 1 joined its pp group 0 along with ranks [0, 1]
+(run_dp_rank pid=1005832) 2026-02-18 23:00:16,622 - piper_compile - INFO - DP rank 1 compiling...
+(PiperActor pid=1005902) 2026-02-18 23:00:32,203 - piper_actor - INFO - Loading stage 0 graph on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:00:36,596 - piper_actor - INFO - Loading stage 1 graph on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:00:37,912 - piper_actor - INFO - Loading stage 2 graph on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:00:46,498 - piper_actor - INFO - Loading stage 3 graph on actor 1
+(run_dp_rank pid=1005832) 2026-02-18 23:00:47,229 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=1005832) 2026-02-18 23:08:53,104 - piper_compile - INFO - DP rank 1 done.
+(PiperActor pid=1005902) 2026-02-18 23:08:53,373 - piper_actor - DEBUG - Actor 0 loaded inputs 1
+(run_dp_rank pid=1005832) Running 5 warmup iterations...
+(PiperActor pid=1005902) 2026-02-18 23:08:55,982 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:08:55,967 - piper_actor - DEBUG - Actor 1 loaded labels torch.Size([16, 256, 128256])
+(PiperActor pid=1005951) 2026-02-18 23:08:55,982 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:08:55,983 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) [rank1]:[W218 23:08:55.465516511 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=1005902) 2026-02-18 23:08:56,819 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:08:56,819 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) [rank0]:[W218 23:08:56.301850145 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=1005902) 2026-02-18 23:08:59,163 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:08:59,164 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:08:59,164 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:08:59,165 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:08:59,173 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:08:59,173 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:08:59,173 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:08:59,900 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:08:59,900 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:08:59,901 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:08:59,901 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:08:59,901 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:08:59,902 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:08:59,902 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) [rank0]:[W218 23:08:59.384122351 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=1005951) 2026-02-18 23:09:00,001 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:00,001 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:00,002 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:00,002 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:00,002 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) [rank1]:[W218 23:09:00.484384625 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=1005902) 2026-02-18 23:09:00,232 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:00,232 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:00,232 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:00,232 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:00,232 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:00,232 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:00,234 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:00,234 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:00,996 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:00,996 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:00,997 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:00,997 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:00,997 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:01,011 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:01,011 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:01,011 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:01,011 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:01,011 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:01,067 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:01,067 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:01,068 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:01,068 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:01,068 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:00,995 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:00,996 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:00,997 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:00,997 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:00,997 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:00,997 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:00,997 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:00,997 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:01,009 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:01,009 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:01,069 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:01,069 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:01,137 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:01,205 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:01,205 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:01,206 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:01,206 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:01,206 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:01,198 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:01,612 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:01,609 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:01,610 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:01,611 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:01,611 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:01,611 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:01,611 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:01,611 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:01,611 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:01,611 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:01,613 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:01,613 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:01,776 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:01,776 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:01,838 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,047 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:02,047 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,048 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,135 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:02,135 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,136 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,136 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:02,136 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:02,341 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:02,341 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,342 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,342 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:02,342 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:02,343 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,343 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,358 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:02,358 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,358 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,358 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,318 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,319 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,319 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:02,319 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,320 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,334 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:02,335 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,335 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,335 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,589 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:02,589 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,590 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,590 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:02,590 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:02,598 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:02,599 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,599 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,599 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:02,599 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:02,568 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:02,568 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:02,576 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:02,576 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,576 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,591 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:02,591 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,591 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,591 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:02,592 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:02,715 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:02,716 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:02,716 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:02,936 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:02,936 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,937 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,937 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:02,937 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,938 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,938 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:02,938 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:02,938 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:02,947 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:02,947 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,948 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:02,948 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:02,948 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:03,047 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:03,048 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:03,048 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:03,048 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:03,049 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:03,049 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:03,056 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:03,056 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:03,056 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:03,056 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:03,057 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:03,064 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:03,064 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:03,065 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:03,065 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:03,065 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:03,065 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:03,065 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:03,066 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:03,066 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:03,075 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:03,075 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:03,173 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:03,174 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:03,174 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:03,191 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:03,253 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:03,423 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:03,423 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:03,423 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:03,438 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:03,438 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:03,438 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:03,438 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:03,707 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:03,707 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:03,708 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:03,722 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:03,722 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:03,723 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:03,723 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:03,638 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:03,639 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:03,639 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:03,640 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:03,640 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:03,649 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:03,649 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:03,825 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:04,325 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:04,325 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:04,326 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:04,341 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,305 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,306 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,321 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:04,321 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,322 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,322 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:04,553 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:04,553 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,554 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,569 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:04,570 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,570 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,570 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:04,616 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:04,617 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:04,617 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:04,632 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,800 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:04,801 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,801 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,801 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:04,802 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1005951) 2026-02-18 23:09:04,802 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:04,896 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1005902) 2026-02-18 23:09:04,897 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:04,986 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:04,994 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:04,995 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:04,995 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:04,995 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:04,995 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:04,986 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:04,986 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,987 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,987 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:04,987 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,995 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,996 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,996 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,996 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:04,996 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,997 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:04,997 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:04,997 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:05,110 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:05,117 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:05,118 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:05,118 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:05,118 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:05,118 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:05,138 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:05,138 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:05,146 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:05,147 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:05,148 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:05,148 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:05,148 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:05,148 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:05,148 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:05,148 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:05,233 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:05,234 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:05,234 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:05,234 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:05,234 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:05,241 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:05,242 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:05,242 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:05,242 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:05,242 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:05,282 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:05,282 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:05,358 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:05,358 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:05,359 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:05,359 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:05,359 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:05,368 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:05,368 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:05,369 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:05,369 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:05,369 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:05,482 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:05,870 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:05,871 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:05,872 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:05,872 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:05,872 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:05,872 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:05,873 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:05,945 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:06,325 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:06,322 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:06,324 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:06,324 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:06,324 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:06,324 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:06,324 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:06,324 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:06,325 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:06,325 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:06,326 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:06,326 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:06,534 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:06,535 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:06,595 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:06,878 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:06,879 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:06,880 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:06,896 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:06,896 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:06,897 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:06,897 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:06,897 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:07,201 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:07,201 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,201 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,202 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:07,202 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:07,202 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,203 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,217 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:07,218 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,218 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,218 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,196 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,198 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,198 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:07,198 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,198 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,222 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:07,222 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,223 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,223 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,452 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:07,452 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,453 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,453 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:07,453 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:07,459 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:07,460 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,460 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,461 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:07,461 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:07,443 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:07,443 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:07,450 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:07,451 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,451 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,465 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:07,465 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,466 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,466 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:07,466 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:07,579 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:07,579 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,580 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,763 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,811 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:07,811 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,812 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,812 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:07,812 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,812 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,812 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:07,813 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:07,813 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:07,820 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:07,821 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,821 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,821 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:07,821 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:07,864 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:07,865 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,865 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,865 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:07,865 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:07,876 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:07,877 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,877 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,877 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:07,878 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:07,936 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:07,937 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,937 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,937 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:07,937 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,937 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:07,938 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:07,938 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:07,938 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:07,945 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:07,946 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:07,989 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:07,989 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:07,990 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:08,032 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:08,123 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:08,248 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:08,248 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:08,249 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:08,264 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:08,264 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:08,264 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:08,264 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:08,681 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:08,682 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:08,682 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:08,697 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:08,697 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:08,697 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:08,697 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:08,678 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:08,680 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:08,680 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:08,681 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:08,681 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:08,690 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:08,690 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:08,866 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:09,266 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:09,267 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:09,267 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:09,282 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,247 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,248 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,264 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:09,264 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,265 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,265 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:09,558 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:09,558 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:09,558 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:09,574 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,495 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:09,495 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,496 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,511 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:09,511 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,512 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,512 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:09,742 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:09,743 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,743 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,743 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:09,744 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1005951) 2026-02-18 23:09:09,744 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:09,838 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1005902) 2026-02-18 23:09:09,838 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:09,881 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:09,888 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:09,889 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:09,889 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:09,889 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:09,889 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:09,881 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:09,881 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,882 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,882 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:09,882 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,889 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,890 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,890 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,891 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:09,891 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,891 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:09,891 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:09,891 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:10,004 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:10,012 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,012 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,013 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,013 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:10,013 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:10,032 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:10,033 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:10,040 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:10,040 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:10,041 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:10,041 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:10,041 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:10,041 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:10,041 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:10,041 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:10,128 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:10,128 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,129 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,129 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:10,129 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:10,136 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:10,136 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,137 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,137 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:10,137 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:10,176 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:10,176 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:10,183 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:10,184 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:10,252 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:10,252 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,253 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,253 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:10,253 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,260 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,260 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,261 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,261 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:10,261 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:10,377 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:10,384 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:10,384 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,385 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,385 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:10,385 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:10,362 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,742 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:10,750 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:10,750 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,751 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,765 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:10,765 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,766 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:10,766 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:10,766 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:10,740 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:10,741 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:10,741 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:10,741 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:10,742 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:10,742 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:10,742 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:10,742 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:10,742 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:10,743 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:10,744 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:10,752 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:10,752 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:10,928 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,108 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:11,108 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,108 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,108 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:11,313 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:11,313 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,313 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:11,309 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:11,312 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:11,312 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:11,312 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:11,312 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:11,328 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:11,328 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,328 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,328 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:11,328 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:11,328 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:11,329 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:11,329 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,562 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:11,563 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,563 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,563 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:11,563 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:11,570 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:11,571 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,571 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,571 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:11,571 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:11,557 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:11,557 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:11,564 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:11,565 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:11,565 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:11,580 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:11,580 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:11,580 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:11,580 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:11,580 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:11,690 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:11,690 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,691 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,705 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:11,977 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:11,977 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,977 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,978 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:11,978 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:11,984 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:11,985 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,985 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:11,985 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:11,985 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:11,925 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:11,925 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:11,926 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:11,926 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:11,926 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:11,926 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:11,926 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:11,927 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:11,927 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:11,934 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:11,935 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:11,935 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:11,935 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:11,935 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:12,050 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:12,051 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:12,051 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:12,051 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:12,052 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:12,052 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:12,052 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:12,052 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:12,052 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:12,060 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:12,060 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:12,101 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:12,101 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:12,102 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:12,278 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:12,237 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:12,349 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:12,350 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:12,350 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:12,365 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:12,365 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:12,365 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:12,366 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:12,642 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:12,643 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:12,643 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:12,658 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:12,658 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:12,658 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:12,659 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:12,617 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:12,619 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:12,619 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:12,620 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:12,620 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:12,627 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:12,627 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:12,804 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:13,205 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:13,205 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:13,206 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:13,220 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,185 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,187 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,202 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:13,202 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,202 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,202 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:13,496 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:13,497 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:13,497 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:13,512 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,435 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:13,435 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,436 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,451 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:13,451 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,451 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,451 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:13,682 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:13,682 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,683 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,683 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:13,683 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1005951) 2026-02-18 23:09:13,684 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:13,777 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1005902) 2026-02-18 23:09:13,777 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:13,820 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:13,827 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:13,827 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:13,828 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:13,828 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:13,828 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:13,820 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:13,820 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,821 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,821 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:13,821 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,828 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,829 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,829 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,829 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:13,829 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,830 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,830 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:13,830 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:13,943 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:13,950 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:13,950 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:13,951 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:13,951 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:13,951 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:13,971 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:13,971 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:13,978 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:13,979 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,979 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,979 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:13,980 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,980 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:13,980 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:13,980 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:14,067 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:14,067 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,067 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,067 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:14,067 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:14,074 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:14,075 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,075 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,075 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:14,075 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:14,114 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:14,115 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:14,122 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:14,122 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:14,191 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:14,191 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,191 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,191 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:14,191 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,198 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,199 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,199 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,199 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:14,199 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:14,315 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:14,299 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,322 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:14,322 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,323 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,323 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:14,323 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:14,679 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:14,687 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:14,687 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,688 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,702 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:14,702 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,703 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:14,703 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:14,703 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:14,676 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:14,678 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:14,678 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:14,678 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:14,679 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:14,679 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:14,679 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:14,679 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:14,679 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:14,680 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:14,680 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:14,688 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:14,688 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:14,864 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,045 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:15,045 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,046 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,046 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:15,248 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:15,248 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,249 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,264 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:15,264 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,264 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,264 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,244 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,247 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,247 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:15,247 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,248 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,263 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:15,263 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,263 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,263 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,497 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:15,497 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,498 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,498 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:15,498 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:15,507 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:15,507 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,507 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,508 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:15,508 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:15,492 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:15,493 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:15,500 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:15,501 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,501 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,515 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:15,515 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,516 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,516 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:15,516 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:15,625 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:15,626 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,626 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,641 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,861 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:15,861 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,862 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,862 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:15,862 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,863 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,863 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:15,863 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:15,863 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:15,871 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:15,872 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,872 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,872 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:15,872 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:15,913 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:15,913 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,914 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,914 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:15,914 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:15,921 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:15,921 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,922 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:15,922 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:15,922 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:16,037 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:16,037 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:16,038 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:16,053 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,987 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:15,987 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,988 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,988 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:15,988 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,988 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:15,988 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:15,989 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:15,989 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:15,997 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:15,997 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:16,286 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:16,287 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:16,287 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:16,173 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:16,305 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:16,306 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:16,306 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:16,306 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:16,582 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:16,582 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:16,583 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:16,598 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:16,599 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:16,599 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:16,599 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:16,554 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:16,555 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:16,555 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:16,556 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:16,557 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:16,564 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:16,564 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:16,741 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:17,144 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:17,144 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:17,145 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:17,122 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,123 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,138 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:17,138 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,138 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,139 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:17,164 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:17,437 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:17,438 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:17,438 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:17,455 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,370 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:17,370 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,371 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,387 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:17,387 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,387 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,387 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:17,618 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:17,619 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,620 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,620 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:17,620 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1005951) 2026-02-18 23:09:17,620 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:17,717 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1005902) 2026-02-18 23:09:17,717 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:17,762 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,762 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:17,762 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,763 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,763 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:17,763 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:17,770 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:17,771 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:17,772 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:17,772 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:17,772 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:17,771 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,772 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,772 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,772 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:17,773 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,773 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,773 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:17,773 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:17,885 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:17,892 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:17,893 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:17,894 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:17,894 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:17,894 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:17,913 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:17,913 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:17,920 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:17,921 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,921 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,922 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:17,922 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,922 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:17,922 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:17,922 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:18,009 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:18,009 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,010 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,010 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:18,010 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:18,017 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:18,017 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,017 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,017 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:18,018 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:18,133 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:18,133 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,134 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,134 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:18,134 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,141 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,141 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,142 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,142 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:18,142 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:18,057 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:18,058 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:18,065 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:18,066 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:18,257 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:18,264 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:18,265 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,265 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,265 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:18,265 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:18,243 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,623 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:18,631 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:18,632 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,633 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,648 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:18,648 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,648 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,649 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:18,649 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:18,620 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:18,621 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:18,622 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:18,622 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:18,622 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:18,622 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:18,622 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:18,622 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:18,622 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:18,623 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:18,623 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:18,630 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:18,631 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:18,808 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,991 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:18,991 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,992 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:18,992 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:19,192 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:19,193 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,193 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,211 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:19,212 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,212 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,212 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,188 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,190 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,190 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:19,190 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,190 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,205 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:19,205 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,206 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,206 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,442 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:19,442 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,443 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,443 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:19,443 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:19,451 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:19,452 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,452 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,452 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:19,452 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:19,434 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:19,435 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:19,442 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:19,443 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,443 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,457 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:19,457 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,458 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,458 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:19,458 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:19,570 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:19,570 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,571 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,588 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:19,857 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:19,857 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,858 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,858 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:19,858 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:19,867 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:19,867 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,868 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,868 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:19,868 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:19,803 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:19,803 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,804 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,804 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:19,804 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,804 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,804 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:19,805 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:19,805 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:19,813 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:19,814 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,815 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,815 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:19,815 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:19,929 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:19,930 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,930 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,931 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:19,931 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,931 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:19,931 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:19,932 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:19,932 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:19,939 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:19,940 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:19,981 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:19,982 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,982 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:19,998 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:20,116 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:20,231 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:20,231 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:20,232 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:20,248 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:20,248 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:20,248 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:20,249 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:20,524 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:20,525 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:20,525 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:20,540 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:20,540 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:20,541 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:20,541 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:20,497 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:20,499 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:20,499 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:20,500 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:20,500 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:20,507 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:20,507 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:20,684 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:21,085 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:21,086 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:21,087 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:21,105 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,065 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,066 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,081 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:21,081 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,081 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,081 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:21,378 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:21,379 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:21,379 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:21,396 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,312 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:21,313 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,313 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,327 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:21,327 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,328 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,328 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:21,559 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:21,560 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,560 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,560 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:21,561 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1005951) 2026-02-18 23:09:21,561 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(run_dp_rank pid=1005832) Running 10 timed iterations...
+(PiperActor pid=1005902) 2026-02-18 23:09:21,658 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1005902) 2026-02-18 23:09:21,658 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:21,700 - piper_actor - INFO - Actor 0: Tracing enabled
+(PiperActor pid=1005902) 2026-02-18 23:09:21,704 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:21,712 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:21,712 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:21,714 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:21,714 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:21,714 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:21,701 - piper_actor - INFO - Actor 1: Tracing enabled
+(PiperActor pid=1005951) 2026-02-18 23:09:21,704 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:21,704 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,714 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,714 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:21,714 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,721 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,722 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:21,828 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:21,834 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:21,835 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:21,837 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:21,837 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:21,837 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:21,838 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,838 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:21,838 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,841 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,841 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:21,841 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:21,860 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:21,861 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:21,868 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:21,868 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:21,952 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:21,952 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:21,953 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:21,953 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:21,953 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:21,960 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:21,960 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:21,963 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:21,963 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:21,963 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:21,963 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,963 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:21,963 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,967 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:21,967 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:21,967 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:22,002 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:22,002 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:22,010 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:22,010 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:22,076 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:22,076 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:22,077 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:22,077 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:22,077 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:22,084 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:22,084 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:22,087 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:22,087 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:22,087 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:22,200 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:22,208 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:22,208 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005951) 2026-02-18 23:09:22,187 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:22,566 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:22,567 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:22,567 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:22,568 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:22,576 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:22,577 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:22,578 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:22,593 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:22,593 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005951) 2026-02-18 23:09:22,565 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:22,566 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:22,566 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:22,566 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:22,566 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:22,567 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:22,567 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:22,567 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:22,567 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:22,568 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:22,568 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:22,575 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:22,576 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:22,595 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:22,596 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:22,596 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:22,753 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:22,934 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:22,935 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:23,135 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:23,135 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:23,136 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:23,136 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:23,151 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:23,168 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:23,169 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:23,170 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:23,170 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,133 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,135 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,135 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:23,135 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,135 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,149 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:23,149 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,151 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,151 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:23,379 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:23,379 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:23,387 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:23,387 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,389 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:23,399 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:23,399 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:23,406 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:23,407 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:23,407 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:23,415 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:23,415 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:23,416 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:23,416 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:23,416 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:23,403 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:23,403 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,406 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,407 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:23,407 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:23,530 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:23,531 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:23,750 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:23,766 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,748 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:23,748 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,749 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,749 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:23,749 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,750 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,750 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:23,750 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:23,750 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:23,758 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:23,758 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,760 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:23,760 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:23,760 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:23,874 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:23,874 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:23,995 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:23,996 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:23,996 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:23,996 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:23,996 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:24,003 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:24,004 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:24,006 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:24,006 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:24,006 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:24,006 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:24,006 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:24,006 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:24,007 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:24,007 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:24,008 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:24,008 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:24,015 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:24,016 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:24,120 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:24,120 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:24,121 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:24,136 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:24,192 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:24,368 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:24,369 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:24,576 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:24,574 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:24,575 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:24,576 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:24,577 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:24,577 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:24,585 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:24,586 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:24,592 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:24,593 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:24,594 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:24,594 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:24,762 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:24,822 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:24,823 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:25,160 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:25,142 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:25,144 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:25,158 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:25,158 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:25,160 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:25,160 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:25,178 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:25,178 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:25,179 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:25,179 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:25,408 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:25,409 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:25,410 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:25,426 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:25,390 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:25,390 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:25,391 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:25,405 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:25,406 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:25,407 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:25,408 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:25,658 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:25,658 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:25,659 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:25,674 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:25,637 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:25,638 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:25,638 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:25,638 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:25,639 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1005951) 2026-02-18 23:09:25,639 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:25,906 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1005902) 2026-02-18 23:09:25,906 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:25,950 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:25,957 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:25,958 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:25,960 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:25,960 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:25,960 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:25,950 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:25,950 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:25,960 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:25,960 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:25,960 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:25,967 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:25,968 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:26,073 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:26,080 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,081 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,083 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,083 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:26,083 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:26,197 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:26,197 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,198 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,198 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:26,198 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:26,204 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:26,205 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,207 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,207 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:26,207 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:26,083 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:26,084 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:26,084 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:26,087 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:26,087 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:26,087 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:26,106 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:26,106 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:26,113 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:26,114 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:26,207 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:26,208 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:26,208 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:26,208 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:26,208 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:26,209 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:26,248 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:26,248 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:26,255 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:26,256 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:26,321 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:26,321 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,322 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,322 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:26,322 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,328 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,329 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,331 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,331 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:26,331 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:26,445 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:26,452 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:26,452 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005951) 2026-02-18 23:09:26,433 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,812 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,813 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:26,813 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:26,814 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:26,821 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:26,821 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,823 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,837 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:26,837 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,841 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:26,841 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:26,841 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:26,810 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:26,812 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:26,812 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:26,812 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:26,812 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:26,812 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:26,812 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:26,813 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:26,813 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:26,814 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:26,814 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:26,821 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:26,822 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:26,998 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:27,179 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:27,180 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:27,381 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:27,381 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:27,382 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:27,382 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:27,378 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:27,381 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:27,381 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:27,381 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:27,381 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:27,396 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:27,396 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005902) 2026-02-18 23:09:27,399 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:27,413 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:27,413 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:27,416 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:27,416 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:27,399 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:27,399 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:27,645 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:27,645 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:27,654 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:27,654 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:27,654 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:27,662 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:27,662 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:27,663 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:27,664 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:27,664 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:27,626 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:27,627 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:27,634 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:27,635 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:27,636 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:27,651 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:27,651 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:27,654 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:27,654 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:27,654 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:27,777 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:27,778 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:27,996 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:28,012 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:27,995 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:27,995 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:27,996 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:27,996 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:27,996 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:27,996 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:27,996 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:27,997 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:27,997 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:28,004 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:28,005 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:28,006 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:28,006 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:28,006 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:28,120 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:28,121 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:28,241 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:28,242 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:28,242 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:28,242 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:28,242 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:28,251 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:28,252 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:28,252 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:28,252 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:28,253 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:28,253 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:28,253 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:28,253 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:28,254 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:28,254 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:28,254 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:28,255 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:28,261 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:28,262 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:28,366 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:28,366 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:28,367 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:28,382 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:28,439 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:28,614 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:28,615 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:28,822 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:28,838 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:28,839 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:28,841 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:28,841 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:28,820 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:28,822 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:28,822 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:28,823 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:28,823 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:28,831 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:28,831 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:29,069 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:29,070 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:29,008 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:29,406 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:29,422 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:29,422 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:29,424 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:29,424 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:29,388 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:29,390 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:29,404 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:29,405 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:29,406 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:29,406 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:29,653 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:29,653 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:29,654 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:29,669 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:29,636 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:29,637 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:29,637 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:29,652 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:29,653 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:29,654 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:29,654 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:29,902 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:29,902 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:29,903 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:29,917 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:29,884 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:29,884 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:29,885 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:29,885 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:29,885 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1005951) 2026-02-18 23:09:29,886 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:30,150 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1005902) 2026-02-18 23:09:30,150 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:30,193 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:30,201 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:30,201 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:30,203 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:30,203 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:30,203 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:30,194 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:30,194 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:30,203 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:30,203 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:30,203 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:30,211 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:30,211 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:30,317 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:30,324 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:30,325 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:30,327 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:30,327 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:30,327 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:30,440 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:30,441 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:30,441 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:30,441 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:30,441 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:30,327 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:30,327 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:30,327 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:30,330 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:30,331 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:30,331 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:30,350 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:30,350 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:30,357 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:30,357 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:30,448 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:30,448 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:30,451 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:30,451 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:30,451 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:30,451 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:30,451 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:30,451 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:30,452 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:30,452 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:30,452 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:30,492 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:30,492 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:30,499 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:30,499 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:30,565 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:30,565 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:30,565 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:30,566 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:30,566 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:30,572 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:30,573 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:30,575 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:30,575 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:30,575 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:30,689 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:30,696 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:30,697 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005951) 2026-02-18 23:09:30,677 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,056 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,057 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:31,057 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:31,058 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:31,064 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:31,065 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,067 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,082 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:31,082 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,085 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,085 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:31,085 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:31,054 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:31,056 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:31,056 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:31,056 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:31,056 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:31,056 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:31,057 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:31,057 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:31,057 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:31,058 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:31,058 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:31,065 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:31,066 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:31,243 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,423 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:31,423 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,625 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,625 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:31,627 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:31,627 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,642 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,657 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:31,657 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,659 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,659 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:31,623 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:31,625 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:31,625 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:31,625 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:31,625 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:31,640 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:31,640 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:31,642 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:31,642 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,888 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:31,888 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:31,869 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:31,870 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:31,877 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:31,877 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:31,879 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:31,897 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,897 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:31,897 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:31,904 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:31,905 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,906 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:31,906 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:31,906 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:31,894 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:31,895 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:31,897 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:31,897 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:31,897 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:32,020 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:32,020 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:32,239 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:32,254 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:32,237 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:32,238 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:32,238 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:32,238 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:32,238 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:32,239 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:32,239 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:32,239 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:32,240 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:32,246 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:32,247 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:32,249 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:32,249 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:32,249 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:32,363 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:32,363 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:32,483 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:32,484 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:32,484 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:32,484 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:32,484 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:32,491 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:32,492 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:32,494 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:32,494 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:32,494 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:32,494 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:32,494 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:32,494 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:32,495 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:32,495 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:32,496 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:32,496 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:32,503 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:32,503 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:32,608 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:32,608 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:32,608 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:32,623 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:32,680 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:32,856 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:32,856 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:33,062 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:33,077 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:33,077 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:33,080 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:33,080 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:33,061 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:33,062 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:33,062 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:33,063 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:33,063 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:33,070 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:33,071 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:33,308 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:33,309 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:33,247 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:33,647 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:33,664 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:33,665 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:33,666 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:33,666 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:33,629 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:33,630 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:33,645 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:33,646 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:33,647 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:33,647 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:33,894 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:33,895 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:33,895 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:33,911 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:33,876 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:33,876 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:33,877 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:33,892 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:33,893 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:33,895 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:33,895 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,143 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,144 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,145 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,161 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:34,124 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:34,124 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:34,125 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:34,125 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:34,125 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1005951) 2026-02-18 23:09:34,125 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:34,393 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1005902) 2026-02-18 23:09:34,393 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:34,437 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,444 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,445 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,447 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,447 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,447 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:34,437 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:34,437 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:34,447 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:34,447 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:34,447 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:34,454 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:34,454 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,560 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,568 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,568 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,570 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,570 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,570 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:34,684 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,684 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,685 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,685 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,685 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,692 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,692 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005951) 2026-02-18 23:09:34,570 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:34,571 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:34,571 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:34,574 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:34,574 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:34,574 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:34,593 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:34,593 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:34,600 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:34,600 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:34,694 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:34,695 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:34,695 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,694 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,694 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,694 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:34,699 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:34,699 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:34,699 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:34,734 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:34,735 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:34,742 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:34,742 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:34,808 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,808 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,809 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,809 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,809 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,816 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,816 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,819 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:34,819 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,819 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:34,932 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:34,939 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:34,940 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005951) 2026-02-18 23:09:34,919 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:35,298 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:35,298 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:35,298 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:35,299 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:35,306 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:35,307 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:35,309 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:35,323 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:35,323 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:35,326 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:35,326 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:35,326 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:35,297 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:35,298 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:35,298 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:35,298 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:35,298 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:35,298 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:35,298 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:35,299 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:35,299 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:35,300 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:35,300 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:35,307 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:35,307 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:35,484 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:35,665 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:35,665 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:35,866 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:35,866 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:35,867 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:35,868 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:35,883 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:35,897 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:35,898 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:35,900 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:35,900 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:35,865 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:35,866 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:35,866 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:35,866 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:35,867 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:35,881 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:35,881 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:35,883 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:35,883 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:36,110 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:36,111 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:36,118 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:36,118 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:36,120 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:36,129 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:36,129 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:36,138 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:36,138 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:36,138 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:36,145 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:36,145 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:36,147 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:36,147 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:36,147 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:36,135 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:36,136 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:36,138 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:36,138 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:36,138 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:36,261 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:36,262 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:36,480 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:36,495 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:36,479 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:36,479 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:36,480 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:36,480 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:36,480 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:36,480 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:36,480 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:36,481 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:36,481 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:36,488 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:36,488 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:36,490 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:36,490 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:36,490 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:36,604 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:36,604 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:36,725 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:36,725 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:36,726 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:36,726 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:36,726 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:36,733 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:36,733 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:36,735 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:36,735 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:36,735 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:36,735 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:36,736 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:36,736 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:36,737 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:36,737 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:36,737 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:36,738 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:36,745 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:36,745 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:36,849 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:36,849 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:36,850 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:36,865 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:36,922 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:37,098 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:37,098 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:37,304 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:37,303 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:37,304 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:37,304 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:37,305 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:37,305 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:37,312 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:37,313 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:37,319 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:37,319 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:37,321 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:37,321 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:37,489 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:37,550 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:37,551 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:37,889 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:37,870 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:37,872 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:37,887 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:37,888 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:37,889 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:37,889 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:37,904 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:37,904 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:37,906 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:37,906 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:38,135 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:38,135 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:38,137 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:38,152 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,119 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:38,119 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,120 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,135 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:38,135 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,137 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,137 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:38,385 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:38,385 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:38,386 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:38,400 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,366 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:38,366 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,367 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,367 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:38,368 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1005951) 2026-02-18 23:09:38,368 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:38,633 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1005902) 2026-02-18 23:09:38,633 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:38,676 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:38,683 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:38,683 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:38,686 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:38,686 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:38,686 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:38,676 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:38,676 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,686 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,686 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:38,686 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,694 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,694 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:38,799 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:38,806 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:38,807 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:38,809 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:38,809 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:38,809 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:38,923 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:38,923 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:38,923 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:38,924 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:38,924 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:38,930 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:38,931 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005951) 2026-02-18 23:09:38,809 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,810 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:38,810 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,814 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,814 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:38,814 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:38,832 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:38,832 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:38,839 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:38,840 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:38,933 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:38,933 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:38,933 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:38,933 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,933 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:38,934 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,937 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,937 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:38,937 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:38,974 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:38,974 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,981 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:38,981 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:39,047 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:39,047 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:39,048 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:39,048 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:39,048 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:39,055 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:39,055 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:39,057 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:39,057 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:39,057 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:39,171 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:39,178 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:39,179 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005951) 2026-02-18 23:09:39,158 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:39,539 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:39,539 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:39,539 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:39,540 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:39,547 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:39,547 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:39,549 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:39,563 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:39,564 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:39,567 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:39,567 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:39,567 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:39,536 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:39,538 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:39,538 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:39,539 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:39,539 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:39,539 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:39,539 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:39,539 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:39,539 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:39,540 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:39,541 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:39,548 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:39,549 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:39,725 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:39,905 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:39,906 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:40,107 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:40,107 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:40,108 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:40,108 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:40,125 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:40,105 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,107 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,107 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:40,107 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,107 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,122 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:40,122 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,125 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,125 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:40,139 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:40,140 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:40,141 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:40,141 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,352 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:40,352 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:40,371 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:40,371 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:40,379 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:40,379 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:40,379 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:40,386 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:40,387 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:40,389 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:40,389 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:40,389 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:40,359 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:40,360 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,362 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,376 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:40,377 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,379 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,379 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:40,379 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:40,503 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:40,503 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:40,722 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:40,736 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,720 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:40,721 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,721 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,721 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:40,721 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,722 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,722 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:40,722 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:40,723 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:40,730 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:40,730 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,732 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,732 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:40,732 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:40,846 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:40,846 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:40,966 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:40,967 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:40,967 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:40,967 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:40,967 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:40,974 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:40,975 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:40,977 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:40,977 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:40,977 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:40,977 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,977 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:40,977 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,978 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:40,978 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:40,979 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:40,979 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:40,986 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:40,987 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:41,091 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:41,091 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:41,091 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:41,106 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:41,163 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:41,339 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:41,339 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:41,546 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:41,544 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:41,546 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:41,546 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:41,548 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:41,548 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:41,561 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:41,562 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:41,564 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:41,564 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:41,555 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:41,556 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:41,732 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:41,793 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:41,793 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:42,113 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:42,115 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:42,131 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:42,146 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:42,146 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:42,149 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:42,149 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:42,129 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:42,130 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:42,131 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:42,131 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:42,377 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:42,378 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:42,380 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:42,394 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:42,361 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:42,361 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:42,362 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:42,377 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:42,377 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:42,380 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:42,380 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:42,628 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:42,628 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:42,628 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:42,643 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:42,609 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:42,609 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:42,610 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:42,610 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:42,610 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1005951) 2026-02-18 23:09:42,610 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:42,876 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1005902) 2026-02-18 23:09:42,876 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:42,919 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:42,926 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:42,926 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:42,928 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:42,929 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:42,929 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:42,919 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:42,919 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:42,928 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:42,929 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:42,929 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:42,936 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:42,937 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:43,042 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:43,049 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,050 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,052 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,052 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:43,052 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:43,052 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:43,052 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:43,053 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:43,056 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:43,056 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:43,056 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:43,075 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:43,075 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:43,082 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:43,083 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:43,166 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:43,166 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,166 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,166 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:43,167 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:43,173 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:43,174 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,176 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,176 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:43,176 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:43,176 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:43,176 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:43,176 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:43,180 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:43,180 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:43,180 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:43,217 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:43,217 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:43,224 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:43,224 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:43,290 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:43,290 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,291 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,291 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:43,291 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,297 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,298 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,300 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,300 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:43,300 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:43,414 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:43,401 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,421 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:43,422 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,781 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,781 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:43,781 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:43,782 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:43,789 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:43,790 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,791 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,805 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:43,806 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,809 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:43,809 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:43,809 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:43,778 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:43,781 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:43,781 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:43,781 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:43,781 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:43,781 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:43,781 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:43,782 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:43,782 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:43,783 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:43,783 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:43,790 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:43,791 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:43,967 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:44,148 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:44,148 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:44,350 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:44,350 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:44,351 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:44,351 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:44,367 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:44,381 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:44,381 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:44,384 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:44,384 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,348 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,349 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,349 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:44,350 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,350 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,364 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:44,364 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,367 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,367 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:44,613 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:44,613 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:44,622 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:44,622 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:44,622 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:44,629 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:44,630 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:44,632 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:44,632 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:44,632 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:44,595 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:44,595 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:44,603 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:44,603 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,605 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,618 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:44,619 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,622 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,622 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:44,622 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:44,746 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:44,746 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:44,965 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:44,980 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,964 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:44,964 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,965 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,965 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:44,965 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,965 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,965 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:44,966 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:44,966 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:44,973 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:44,974 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,975 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:44,975 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:44,975 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:45,089 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:45,089 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:45,210 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:45,210 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:45,211 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:45,211 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:45,211 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:45,218 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:45,218 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:45,220 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:45,220 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:45,221 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:45,221 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:45,221 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:45,221 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:45,222 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:45,222 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:45,223 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:45,223 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:45,230 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:45,230 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:45,334 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:45,334 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:45,335 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:45,350 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:45,407 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:45,583 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:45,583 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:45,789 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:45,788 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:45,789 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:45,789 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:45,790 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:45,790 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:45,797 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:45,798 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:45,804 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:45,804 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:45,806 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:45,806 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:45,975 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:46,035 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:46,036 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:46,373 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:46,355 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:46,356 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:46,370 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:46,371 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:46,373 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:46,373 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:46,387 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:46,388 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:46,390 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:46,391 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:46,603 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:46,603 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:46,619 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:46,619 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:46,621 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:46,636 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:46,604 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:46,618 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:46,618 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:46,621 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:46,621 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:46,869 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:46,869 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:46,869 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:46,884 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:46,850 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:46,850 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:46,851 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:46,851 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:46,851 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1005951) 2026-02-18 23:09:46,851 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:47,117 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1005902) 2026-02-18 23:09:47,117 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:47,160 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:47,166 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:47,167 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:47,169 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:47,169 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:47,170 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:47,160 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:47,160 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:47,169 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:47,170 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:47,170 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:47,177 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:47,177 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:47,283 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:47,290 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:47,291 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:47,293 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:47,293 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:47,293 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:47,293 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:47,293 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:47,293 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:47,297 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:47,297 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:47,297 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:47,316 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:47,316 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:47,323 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:47,324 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:47,407 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:47,407 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:47,407 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:47,408 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:47,408 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:47,414 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:47,415 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:47,417 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:47,417 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:47,417 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:47,531 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:47,531 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:47,532 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:47,532 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:47,532 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:47,539 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:47,417 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:47,418 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:47,418 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:47,421 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:47,421 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:47,421 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:47,458 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:47,458 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:47,465 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:47,465 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:47,539 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:47,541 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:47,541 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:47,541 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:47,655 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:47,662 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:47,662 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005951) 2026-02-18 23:09:47,642 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,021 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,021 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:48,022 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:48,022 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:48,029 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:48,030 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,032 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,046 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:48,046 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,049 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,049 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:48,050 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:48,020 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,021 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,021 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:48,021 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,021 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,021 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:48,022 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,022 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,022 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:48,023 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:48,023 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:48,030 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:48,030 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:48,207 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,388 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:48,388 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,589 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,589 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:48,590 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:48,590 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,606 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:48,587 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,588 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,588 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:48,588 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,589 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,602 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:48,603 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,606 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,606 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,621 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:48,621 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,624 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,624 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,833 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:48,833 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:48,852 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:48,852 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,860 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,860 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:48,860 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:48,867 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:48,867 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,869 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:48,869 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:48,869 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:48,840 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:48,841 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,842 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,856 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:48,856 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,860 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:48,860 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:48,860 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:48,983 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:48,984 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:49,202 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:49,217 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:49,201 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:49,201 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:49,202 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:49,202 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:49,202 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:49,202 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:49,202 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:49,203 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:49,203 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:49,210 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:49,211 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:49,212 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:49,212 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:49,213 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:49,326 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:49,327 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:49,447 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:49,447 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:49,448 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:49,448 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:49,448 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:49,455 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:49,455 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:49,457 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:49,457 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:49,457 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:49,457 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:49,458 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:49,458 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:49,458 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:49,458 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:49,459 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:49,459 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:49,467 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:49,467 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:49,571 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:49,571 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:49,572 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:49,587 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:49,644 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:49,820 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:49,820 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:50,025 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005902) 2026-02-18 23:09:50,026 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:50,041 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:50,041 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:50,044 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:50,044 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:50,026 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:50,026 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:50,027 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:50,027 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:50,034 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:50,035 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:50,212 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:50,272 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:50,273 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:50,593 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:50,594 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:50,612 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:50,628 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:50,628 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:50,629 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:50,629 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:50,608 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:50,608 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:50,612 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:50,612 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:50,859 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:50,859 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:50,859 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:50,874 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:50,840 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:50,841 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:50,841 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:50,855 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:50,855 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:50,859 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:50,859 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,107 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,107 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,108 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,122 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:51,087 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:51,088 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:51,089 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:51,089 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:51,089 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1005951) 2026-02-18 23:09:51,089 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:51,355 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1005902) 2026-02-18 23:09:51,355 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:51,398 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,407 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,409 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,412 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,412 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,412 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:51,399 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:51,399 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:51,412 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:51,412 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:51,412 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:51,420 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:51,420 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,522 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,529 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,530 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,532 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,532 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,532 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:51,645 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,646 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,646 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,646 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,646 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,653 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,654 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005951) 2026-02-18 23:09:51,532 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:51,532 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:51,532 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:51,535 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:51,536 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:51,536 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:51,558 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:51,558 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:51,565 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:51,566 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:51,656 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:51,656 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:51,656 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,656 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,656 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,656 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:51,659 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:51,660 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:51,660 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:51,700 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:51,700 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:51,707 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:51,708 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:51,770 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,770 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,770 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,770 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,770 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,777 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,778 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,780 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:51,780 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,780 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:51,894 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:51,901 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:51,901 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005951) 2026-02-18 23:09:51,884 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:52,263 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:52,264 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:52,264 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:52,265 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:52,273 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:52,274 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:52,275 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:52,290 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:52,290 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:52,292 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:52,292 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:52,292 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:52,261 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:52,263 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:52,263 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:52,263 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:52,263 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:52,263 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:52,263 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:52,264 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:52,264 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:52,265 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:52,265 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:52,272 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:52,272 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:52,449 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:52,631 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:52,631 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:52,830 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:52,830 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:52,831 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:52,832 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:52,847 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:52,829 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:52,830 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:52,830 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:52,830 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:52,830 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:52,844 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:52,844 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:52,846 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:52,847 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:52,861 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:52,861 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:52,864 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:52,864 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:53,093 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:53,093 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:53,102 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:53,102 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:53,102 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:53,109 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:53,109 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:53,111 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:53,111 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:53,111 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:53,074 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:53,075 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:53,082 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:53,082 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:53,084 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:53,098 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:53,098 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:53,102 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:53,102 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:53,102 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:53,225 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:53,226 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:53,444 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:53,459 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:53,442 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:53,443 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:53,443 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:53,443 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:53,443 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:53,444 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:53,444 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:53,444 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:53,445 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:53,451 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:53,452 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:53,454 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:53,454 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:53,454 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:53,568 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:53,568 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:53,689 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:53,689 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:53,689 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:53,690 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:53,690 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:53,697 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:53,697 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:53,699 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:53,699 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:53,699 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:53,699 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:53,700 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:53,700 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:53,701 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:53,701 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:53,702 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:53,702 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:53,710 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:53,711 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:53,813 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:53,813 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:53,814 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:53,829 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:53,887 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:54,061 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:54,062 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:54,269 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:54,267 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:54,269 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:54,269 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:54,270 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:54,270 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:54,284 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:54,284 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:54,287 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:54,287 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:54,277 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:54,278 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:54,454 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:54,515 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:54,515 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:54,836 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:54,837 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:54,854 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:54,870 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:54,870 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:54,872 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:54,872 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:54,852 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:54,853 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:54,854 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:54,854 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:55,101 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:55,101 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:55,102 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:55,117 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,084 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:55,084 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,085 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,099 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:55,099 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,101 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,101 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:55,349 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:55,350 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:55,350 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:55,365 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,331 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:55,331 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,332 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,332 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:55,333 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1005951) 2026-02-18 23:09:55,333 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:55,598 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1005902) 2026-02-18 23:09:55,598 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:55,642 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:55,650 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:55,651 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:55,652 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:55,652 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:55,652 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:55,642 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:55,642 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,652 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,652 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:55,652 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,659 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,660 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:55,765 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:55,772 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:55,773 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:55,775 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:55,775 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:55,775 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:55,889 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:55,889 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:55,775 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,776 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:55,776 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,780 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,780 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:55,780 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:55,798 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:55,798 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:55,805 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:55,806 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:55,889 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:55,890 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:55,890 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:55,896 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:55,897 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:55,899 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:55,899 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:55,899 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:55,899 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,899 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:55,900 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,903 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,903 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:55,903 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:55,940 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:55,940 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,947 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:55,947 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:56,013 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:56,013 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:56,014 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:56,014 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:56,014 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:56,020 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:09:56,021 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:56,023 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:56,023 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:56,023 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:56,137 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:56,144 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:56,144 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005951) 2026-02-18 23:09:56,125 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:56,504 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:56,504 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:56,505 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:56,505 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:56,512 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:56,513 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:56,515 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:56,529 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:56,529 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:56,532 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:56,532 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:56,533 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:56,503 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:56,504 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:56,504 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:56,504 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:56,504 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:56,505 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:56,505 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:56,505 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:56,505 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:56,506 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:56,506 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:56,513 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:09:56,514 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:56,690 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:56,871 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:56,871 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:57,072 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:57,072 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:57,073 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:57,074 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:57,089 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:57,103 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:57,103 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:57,106 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:57,107 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,070 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,072 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,072 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:57,072 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,073 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,086 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:57,087 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,089 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,089 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:57,317 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:57,317 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:57,325 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:57,325 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,327 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:57,335 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:57,335 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:57,345 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:57,345 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:57,345 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:57,351 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:09:57,352 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:57,354 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:57,354 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:57,354 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:57,341 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:57,341 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,344 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,345 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:57,345 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:09:57,468 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:57,468 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:57,687 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:57,701 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,685 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:57,686 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,686 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,686 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:57,686 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,687 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,687 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:57,687 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:57,687 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:57,694 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:57,695 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,697 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,697 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:57,697 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:57,811 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:57,811 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:09:57,931 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:57,932 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:57,932 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:57,932 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:57,932 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:57,939 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:09:57,939 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:57,942 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:57,942 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:57,942 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:09:57,942 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,942 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:57,942 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,943 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:57,943 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:57,944 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:57,944 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:57,951 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:09:57,951 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:58,055 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:58,056 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:58,056 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:58,071 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:58,128 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:58,304 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:58,304 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:58,511 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:58,509 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:58,510 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:58,510 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:58,511 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:58,511 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:58,518 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:09:58,519 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:09:58,527 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:58,527 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:58,529 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:58,529 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:58,696 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:58,758 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:58,758 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:09:59,077 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:59,078 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:59,092 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:59,093 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005902) 2026-02-18 23:09:59,095 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:59,109 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:59,110 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:59,111 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:59,111 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:59,094 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:59,095 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:59,341 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:59,342 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:59,343 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:59,357 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:59,325 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:59,325 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:59,326 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:59,341 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:59,341 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:59,343 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:59,343 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:09:59,590 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:59,591 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:59,591 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:09:59,606 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:09:59,572 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:59,573 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:59,574 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:09:59,574 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:59,574 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1005951) 2026-02-18 23:09:59,574 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:59,838 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1005902) 2026-02-18 23:09:59,839 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1005902) 2026-02-18 23:09:59,882 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:59,888 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:09:59,889 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:59,891 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:09:59,891 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:09:59,891 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:09:59,882 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:59,882 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:59,891 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:09:59,891 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:09:59,891 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:59,898 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:09:59,899 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:10:00,005 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:00,012 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,012 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,015 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,015 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:00,015 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:10:00,128 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:00,129 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,129 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,129 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:00,129 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:10:00,015 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:00,015 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:00,015 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:00,020 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:00,020 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:00,020 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:10:00,038 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:00,038 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:10:00,045 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:10:00,046 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:10:00,136 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 0
+(PiperActor pid=1005902) 2026-02-18 23:10:00,136 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,139 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,139 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:00,139 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:10:00,139 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:00,139 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:00,139 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:00,140 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:00,140 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:00,140 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:10:00,180 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:00,180 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:10:00,187 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 0
+(PiperActor pid=1005951) 2026-02-18 23:10:00,187 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:10:00,252 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:00,253 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,253 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,253 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:00,253 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,260 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,260 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,263 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,263 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:00,263 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:10:00,376 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:00,383 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:10:00,384 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005951) 2026-02-18 23:10:00,364 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,743 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,744 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:00,744 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005902) 2026-02-18 23:10:00,745 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:00,752 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 0 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:10:00,752 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,754 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,769 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:00,769 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,772 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:00,772 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:00,772 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 3072]), torch.Size([256, 64]), torch.Size([256, 256])]
+(PiperActor pid=1005951) 2026-02-18 23:10:00,741 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:00,743 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:00,743 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:00,743 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:00,744 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:00,744 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:00,744 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:00,744 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:00,744 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:00,745 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:00,745 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:10:00,753 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 1
+(PiperActor pid=1005951) 2026-02-18 23:10:00,754 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005951) 2026-02-18 23:10:00,930 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:10:01,110 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:01,110 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:01,312 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:01,312 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:01,313 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:01,313 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:01,328 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:01,343 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:01,343 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:01,345 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:01,345 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,310 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,312 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,312 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:01,312 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,312 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,327 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:01,327 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,328 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,329 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:10:01,574 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:01,575 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:10:01,556 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:01,557 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:10:01,564 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:10:01,564 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,566 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:10:01,584 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:01,584 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:01,584 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:10:01,590 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 2
+(PiperActor pid=1005902) 2026-02-18 23:10:01,591 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:01,593 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:01,593 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:01,593 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:10:01,579 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:01,580 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,584 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,584 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:01,584 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005902) 2026-02-18 23:10:01,707 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:01,707 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:01,926 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:01,940 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,924 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:01,925 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,925 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,925 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:01,925 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,926 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,926 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:01,926 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:01,926 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:10:01,933 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 1 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:10:01,934 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,936 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:01,936 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:01,936 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:10:02,050 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:02,051 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1005902) 2026-02-18 23:10:02,170 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:02,171 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:02,171 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:02,171 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:02,171 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:10:02,178 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 3072]) for stage 2 mb 3
+(PiperActor pid=1005902) 2026-02-18 23:10:02,178 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:02,181 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:02,181 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:02,181 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 3072])]
+(PiperActor pid=1005951) 2026-02-18 23:10:02,181 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:02,181 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:02,181 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:02,182 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:02,182 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:02,183 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:02,183 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:10:02,192 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 2
+(PiperActor pid=1005951) 2026-02-18 23:10:02,193 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:10:02,295 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:02,295 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:02,295 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:02,310 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:10:02,368 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:10:02,543 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:02,543 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:02,750 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:02,765 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:02,766 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:02,768 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:02,768 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:10:02,749 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:02,750 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:02,750 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:02,751 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:02,751 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 3072]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:10:02,758 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 128256]) for stage 3 mb 3
+(PiperActor pid=1005951) 2026-02-18 23:10:02,759 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 128256])]
+(PiperActor pid=1005902) 2026-02-18 23:10:02,996 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:02,997 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:10:02,935 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:10:03,336 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:03,351 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:03,351 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:03,354 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1005902) 2026-02-18 23:10:03,354 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:10:03,317 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:03,318 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:03,333 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:03,333 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:03,336 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:03,336 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:10:03,582 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:03,583 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:03,583 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005951) 2026-02-18 23:10:03,565 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:03,566 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:03,566 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1005951) 2026-02-18 23:10:03,580 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:03,581 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:03,583 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:03,583 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1005902) 2026-02-18 23:10:03,598 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:10:03,812 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:03,813 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1005902) 2026-02-18 23:10:03,831 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1005902) 2026-02-18 23:10:03,831 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:03,832 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1005902) 2026-02-18 23:10:03,846 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1005951) 2026-02-18 23:10:03,813 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1005951) 2026-02-18 23:10:03,814 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1005951) 2026-02-18 23:10:03,814 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1005951) 2026-02-18 23:10:03,814 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(run_dp_rank pid=1005832) rank 0 iter time= 4.24 ± 0.00 s (10 samples)
+(run_dp_rank pid=1005832) rank 0 throughput= 3862.65 tokens/sec (10 samples)
+(run_dp_rank pid=1005832) rank 0 fwd_p2p_send time= 46.67 ± 118.11 ms (80 samples)
+(run_dp_rank pid=1005832) rank 0 fwd_p2p_recv time= 2.43 ± 3.40 ms (40 samples)
+(run_dp_rank pid=1005832) rank 0 bwd_p2p_recv time= 97.66 ± 126.63 ms (80 samples)
+(run_dp_rank pid=1005832) rank 0 bwd_p2p_send time= 51.78 ± 86.01 ms (40 samples)
+(run_dp_rank pid=1005832) rank 1 fwd_p2p_recv time= 43.81 ± 54.43 ms (80 samples)
+(run_dp_rank pid=1005832) rank 1 fwd_p2p_send time= 2.64 ± 1.05 ms (40 samples)
+(run_dp_rank pid=1005832) rank 1 bwd_p2p_send time= 1.09 ± 0.86 ms (80 samples)
+(run_dp_rank pid=1005832) rank 1 bwd_p2p_recv time= 0.68 ± 0.46 ms (40 samples)
+(PiperActor pid=1005902) 2026-02-18 23:10:04,079 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1005902) 2026-02-18 23:10:04,079 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(run_dp_rank pid=1005832) Ray timeline saved to: out/3b-pp2-dp1-interleaved-1f1b.json
+(PiperActor pid=1005902) [rank0]:[W218 23:10:06.734336263 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+(PiperActor pid=1005951) [rank1]:[W218 23:10:06.963454515 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+(PiperActor pid=1005951) [rank1]:[W218 23:10:06.324138171 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=76, addr=[localhost]:43062, remote=[localhost]:12388): Failed to recv, got 0 bytes. Connection was likely closed. Did the remote server shutdown or crash?
+(PiperActor pid=1005951) Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:682 (most recent call first):
+(PiperActor pid=1005951) frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x9d (0x7f86444cdfdd in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libc10.so)
+(PiperActor pid=1005951) frame #1: <unknown function> + 0x6a3325d (0x7f55d68fa25d in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
+(PiperActor pid=1005951) frame #2: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x273 (0x7f55d68f81f3 in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
+(PiperActor pid=1005951) frame #3: c10d::ProcessGroupNCCL::HeartbeatMonitor::runLoop() + 0x44c (0x7f55939daeec in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
+(PiperActor pid=1005951) frame #4: <unknown function> + 0xdf0e6 (0x7f86698e40e6 in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/bin/../lib/libstdc++.so.6)
+(PiperActor pid=1005951) frame #5: <unknown function> + 0x8b2ea (0x7f867908b2ea in /lib64/libc.so.6)
+(PiperActor pid=1005951) frame #6: <unknown function> + 0x1103c0 (0x7f86791103c0 in /lib64/libc.so.6)
+(PiperActor pid=1005951) 
+(PiperActor pid=1005951) [rank1]:[W218 23:10:06.326963972 ProcessGroupNCCL.cpp:1802] [PG ID 0 PG GUID 0 Rank 1] Failed to check the "should dump" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: Failed to recv, got 0 bytes. Connection was likely closed. Did the remote server shutdown or crash?
+(PiperActor pid=1005951) [rank1]:[W218 23:10:07.327150032 TCPStore.cpp:106] [c10d] sendBytes failed on SocketImpl(fd=76, addr=[localhost]:43062, remote=[localhost]:12388): Broken pipe
+(PiperActor pid=1005951) Exception raised from sendBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:653 (most recent call first):
+(PiperActor pid=1005951) frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x9d (0x7f86444cdfdd in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libc10.so)
+(PiperActor pid=1005951) frame #1: <unknown function> + 0x6a326d1 (0x7f55d68f96d1 in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
+(PiperActor pid=1005951) frame #2: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x24d (0x7f55d68f81cd in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
+(PiperActor pid=1005951) frame #3: c10d::ProcessGroupNCCL::HeartbeatMonitor::runLoop() + 0x44c (0x7f55939daeec in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
+(PiperActor pid=1005951) frame #4: <unknown function> + 0xdf0e6 (0x7f86698e40e6 in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/bin/../lib/libstdc++.so.6)
+(PiperActor pid=1005951) frame #5: <unknown function> + 0x8b2ea (0x7f867908b2ea in /lib64/libc.so.6)
+(PiperActor pid=1005951) frame #6: <unknown function> + 0x1103c0 (0x7f86791103c0 in /lib64/libc.so.6)
+(PiperActor pid=1005951) 
+(PiperActor pid=1005951) [rank1]:[W218 23:10:07.329947390 ProcessGroupNCCL.cpp:1802] [PG ID 0 PG GUID 0 Rank 1] Failed to check the "should dump" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: Broken pipe
diff --git a/logs/interleaved_debug.log b/logs/interleaved_debug.log
new file mode 100644
index 0000000..88df6ee
--- /dev/null
+++ b/logs/interleaved_debug.log
@@ -0,0 +1,3404 @@
+2026-02-18 23:03:36,994	INFO worker.py:2007 -- Started a local Ray instance.
+/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+  warnings.warn(
+(run_dp_rank pid=1051295) 2026-02-18 23:03:51,344 - piper_coordinator - DEBUG - Running DP rank 1 of 1
+(run_dp_rank pid=1051295) 2026-02-18 23:03:51,367 - piper_compile - DEBUG - mbs: 4, stages: 4, devices: 2
+(run_dp_rank pid=1051295) 2026-02-18 23:03:51,368 - piper_compile - DEBUG - Fwd/bwd schedule: [[Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=False, upd=False)], [Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=False, upd=False)]]
+(run_dp_rank pid=1051295) 2026-02-18 23:03:51,368 - piper_compile - DEBUG - P2P comms: [(0, 1, 0), (0, 1, 1), (1, 2, 0), (2, 3, 0), (1, 2, 1), (2, 3, 1), (0, 1, 2), (3, 2, 0), (0, 1, 3), (2, 1, 0), (3, 2, 1), (2, 1, 1), (1, 2, 2), (2, 3, 2), (1, 0, 0), (1, 2, 3), (2, 3, 3), (1, 0, 1), (3, 2, 2), (2, 1, 2), (3, 2, 3), (2, 1, 3), (1, 0, 2), (1, 0, 3)]
+(run_dp_rank pid=1051295) 2026-02-18 23:03:51,368 - piper_compile - DEBUG - P2P schedules: {0: [(0, 1, 0, True), (0, 1, 1, True), (1, 2, 0, False), (2, 3, 0, True), (1, 2, 1, False), (2, 3, 1, True), (0, 1, 2, True), (3, 2, 0, False), (0, 1, 3, True), (2, 1, 0, True), (3, 2, 1, False), (2, 1, 1, True), (1, 2, 2, False), (2, 3, 2, True), (1, 0, 0, False), (1, 2, 3, False), (2, 3, 3, True), (1, 0, 1, False), (3, 2, 2, False), (2, 1, 2, True), (3, 2, 3, False), (2, 1, 3, True), (1, 0, 2, False), (1, 0, 3, False)], 1: [(0, 1, 0, False), (0, 1, 1, False), (1, 2, 0, True), (2, 3, 0, False), (1, 2, 1, True), (2, 3, 1, False), (0, 1, 2, False), (3, 2, 0, True), (0, 1, 3, False), (2, 1, 0, False), (3, 2, 1, True), (2, 1, 1, False), (1, 2, 2, True), (2, 3, 2, False), (1, 0, 0, True), (1, 2, 3, True), (2, 3, 3, False), (1, 0, 1, True), (3, 2, 2, True), (2, 1, 2, False), (3, 2, 3, True), (2, 1, 3, False), (1, 0, 2, True), (1, 0, 3, True)]}
+(run_dp_rank pid=1051295) Namespace(model='debug', schedule='interleaved-1f1b', dp=1, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False)
+(run_dp_rank pid=1051295) Schedule:
+(run_dp_rank pid=1051295) 0:0:f	0:1:f	2:0:f	2:1:f	0:2:f	0:3:f	2:0:b	 -- 	2:1:b	2:2:f	0:0:b	2:3:f	0:1:b	 -- 	2:2:b	2:3:b	0:2:b	0:3:b	0:0:u	
+(run_dp_rank pid=1051295)  -- 	1:0:f	1:1:f	3:0:f	3:0:b	3:1:f	3:1:b	1:2:f	1:0:b	1:3:f	1:1:b	3:2:f	3:2:b	3:3:f	3:3:b	1:2:b	1:3:b	1:0:u	 -- 	
+(run_dp_rank pid=1051295) 2026-02-18 23:03:51,434 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, 35b7629b787175335082b6d901000000) global rank 0
+(run_dp_rank pid=1051295) 2026-02-18 23:03:51,435 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, 22db841fd471dd96fed9c10e01000000) global rank 1
+(PiperActor pid=1051303) 2026-02-18 23:03:54,502 - piper_actor - INFO - Initializing Ray actor 1 global rank 1 GPU 5
+(PiperActor pid=1051350) 2026-02-18 23:03:54,542 - piper_actor - INFO - Initializing Ray actor 0 global rank 0 GPU 4
+(PiperActor pid=1051303) 2026-02-18 23:03:57,056 - piper_actor - DEBUG - Actor 1 has GPU 5, joined the global process group
+(PiperActor pid=1051303) 2026-02-18 23:03:57,056 - piper_actor - INFO - Global rank 1 joined its pp group 0 along with ranks [0, 1]
+(PiperActor pid=1051350) 2026-02-18 23:03:57,061 - piper_actor - DEBUG - Actor 0 has GPU 4, joined the global process group
+(PiperActor pid=1051350) 2026-02-18 23:03:57,061 - piper_actor - INFO - Global rank 0 joined its pp group 0 along with ranks [0, 1]
+(run_dp_rank pid=1051295) 2026-02-18 23:03:57,281 - piper_compile - INFO - DP rank 1 compiling...
+(PiperActor pid=1051303) 2026-02-18 23:04:00,872 - piper_actor - INFO - Loading stage 1 graph on actor 1
+(PiperActor pid=1051350) 2026-02-18 23:04:00,764 - piper_actor - INFO - Loading stage 0 graph on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:02,600 - piper_actor - INFO - Loading stage 2 graph on actor 0
+(PiperActor pid=1051303) 2026-02-18 23:04:02,633 - piper_actor - INFO - Loading stage 3 graph on actor 1
+(run_dp_rank pid=1051295) 2026-02-18 23:04:02,770 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=1051295) 2026-02-18 23:04:09,209 - piper_compile - INFO - DP rank 1 done.
+(run_dp_rank pid=1051295) Running 5 warmup iterations...
+(PiperActor pid=1051303) 2026-02-18 23:04:09,249 - piper_actor - DEBUG - Actor 1 loaded labels torch.Size([16, 256, 512])
+(PiperActor pid=1051303) 2026-02-18 23:04:09,254 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:09,254 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) [rank1]:[W218 23:04:09.736660570 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=1051350) 2026-02-18 23:04:09,236 - piper_actor - DEBUG - Actor 0 loaded inputs 1
+(PiperActor pid=1051350) 2026-02-18 23:04:09,254 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:09,896 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:09,896 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) [rank0]:[W218 23:04:09.378183418 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=1051303) 2026-02-18 23:04:19,399 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:19,400 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:19,400 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:19,389 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:19,390 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:19,390 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:19,391 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:19,399 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:19,399 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:19,400 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:19,400 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:19,400 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:19,401 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:19,401 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) [rank0]:[W218 23:04:19.883031967 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=1051303) 2026-02-18 23:04:19,647 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:19,647 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:19,648 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:19,648 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:19,648 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) [rank1]:[W218 23:04:19.130470431 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=1051303) 2026-02-18 23:04:27,801 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,802 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,802 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:27,806 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,806 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,821 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,821 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,822 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,822 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,822 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,822 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,822 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,822 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:27,823 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,823 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,840 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,840 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:27,842 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,800 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,801 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,801 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,811 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,812 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,812 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,812 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,812 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:27,814 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,814 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,814 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,814 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,814 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,822 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,823 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,823 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,823 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,823 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:27,824 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,835 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:27,835 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,836 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,836 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,836 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:27,836 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,845 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:27,846 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,846 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,921 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,922 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,922 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,922 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,923 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,923 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,923 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,923 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,923 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,924 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,924 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,927 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,927 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:27,928 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,934 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,935 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,935 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,935 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,935 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,942 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,942 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,942 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,942 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,962 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,962 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:27,964 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:27,964 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,965 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,969 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,969 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,970 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,970 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,970 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:27,971 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,972 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,972 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,972 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,972 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,972 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,972 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,973 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,973 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:27,975 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:27,975 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,975 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,975 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,975 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:27,976 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,977 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,977 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,977 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:27,977 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,977 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:27,977 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,024 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,024 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:28,028 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:28,028 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,029 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,039 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,039 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,039 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,040 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,040 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:28,044 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:28,044 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,045 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,050 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,051 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,055 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,055 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,056 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,056 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,057 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,057 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,057 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051350) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
+(PiperActor pid=1051350)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+(PiperActor pid=1051350) 2026-02-18 23:04:27,959 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,959 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,959 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,959 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,960 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:27,961 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,961 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,961 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,961 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,962 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,962 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,962 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,966 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,966 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,967 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,967 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,968 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,968 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,968 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,968 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,968 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:27,971 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:27,971 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,971 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,971 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,971 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:27,972 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:27,972 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:27,973 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,018 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,019 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,020 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,020 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,020 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,020 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:28,023 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:28,023 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,023 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,023 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,023 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,024 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,024 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,024 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,029 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,030 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,030 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,030 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,035 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,035 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,035 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,035 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,040 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,040 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,040 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,044 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,044 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,045 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,045 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,056 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,056 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,057 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,061 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,062 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,062 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,062 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,063 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,063 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,063 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,063 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,064 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1051303) 2026-02-18 23:04:28,064 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:28,061 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,064 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,064 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,065 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,069 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,070 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1051350) 2026-02-18 23:04:28,070 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1051303) 2026-02-18 23:04:28,661 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,661 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,662 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,662 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,662 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,666 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,666 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,667 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,667 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,667 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,668 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,668 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,668 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,669 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,669 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,672 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,672 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,672 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,672 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,672 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,673 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,673 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,673 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,673 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,673 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,676 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,676 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,677 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,684 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,684 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,684 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,684 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,685 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,685 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,685 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,685 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,685 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,686 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,686 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,688 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,688 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,690 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,695 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,696 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,696 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,696 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,696 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,661 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,664 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,664 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,665 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,665 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,665 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,665 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,668 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,668 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,668 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,668 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,668 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,669 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,669 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,669 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,669 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,670 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,672 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,672 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,672 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,672 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,672 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,673 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,673 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,673 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,673 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,674 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,676 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,676 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,676 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,676 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,676 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,677 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,679 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:28,680 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,680 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,680 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,680 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,681 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,683 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:28,683 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,683 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,689 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,689 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,690 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,690 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,690 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,690 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,691 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,691 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,691 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,691 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,692 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,692 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,696 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,696 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,697 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,697 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,700 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,700 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,701 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,701 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,702 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,702 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:28,705 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:28,705 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,706 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,710 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,710 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,710 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,710 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,710 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,711 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,711 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,712 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,712 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,712 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,712 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,712 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,712 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,713 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:28,715 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:28,715 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,715 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,715 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,715 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,717 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,717 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,718 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,718 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,718 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,718 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,718 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,721 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,721 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:28,724 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:28,725 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,726 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,732 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,732 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,732 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,733 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,733 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:28,736 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:28,736 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,737 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,701 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,701 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,702 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,702 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,702 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:28,704 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:28,704 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,705 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,705 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,705 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,711 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,711 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,711 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,716 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,717 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,717 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,717 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,718 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,718 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:28,720 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:28,720 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,720 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,721 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,721 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,721 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,721 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,722 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,726 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,727 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,727 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,728 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,732 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,732 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,732 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,732 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,735 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,735 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,735 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,739 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,739 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,740 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,740 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,743 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,743 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,748 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,748 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,748 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,749 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,749 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,749 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,750 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,754 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,754 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,754 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,754 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,755 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,755 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,755 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,756 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,756 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1051303) 2026-02-18 23:04:28,756 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1051303) 2026-02-18 23:04:28,777 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,777 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,777 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,777 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,777 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,780 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,780 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,781 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,781 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,781 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,749 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,749 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,749 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,754 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,757 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,757 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,757 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,761 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,763 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1051350) 2026-02-18 23:04:28,763 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:28,776 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,779 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,779 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,780 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,780 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,780 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,780 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,781 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,781 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,781 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,784 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,784 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,786 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,787 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,787 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,787 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,787 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,787 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,787 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,787 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,788 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,788 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,790 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,791 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,792 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,798 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,798 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,798 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,799 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,799 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,799 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,799 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,799 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,799 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,800 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,800 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,802 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,803 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,804 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,809 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,810 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,810 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,810 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,810 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,815 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,815 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,815 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,815 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,816 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,816 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:28,818 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:28,783 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,783 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,783 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,784 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,784 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,784 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,784 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,784 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,785 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,785 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,787 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,787 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,787 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,787 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,787 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,788 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,788 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,788 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,789 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,789 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,791 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,791 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,791 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,791 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,791 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,792 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,794 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:28,795 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,795 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,795 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,795 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,796 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,798 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:28,798 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,799 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,803 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,803 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,804 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,804 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,804 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,804 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,804 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,805 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,805 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,805 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,805 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,806 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,810 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,810 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,810 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,810 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,815 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,816 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,816 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,816 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,816 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:28,818 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:28,818 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,819 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,819 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,819 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,819 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,819 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,823 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,824 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,824 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,824 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,824 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,825 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,825 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,825 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,825 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,825 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,825 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,825 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,826 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,826 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:28,829 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:28,829 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,829 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,829 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,829 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,830 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,830 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,830 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,830 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,830 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,831 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,831 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,834 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,834 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:28,837 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:28,837 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,838 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,844 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,844 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,845 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,845 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,845 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:28,847 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:28,848 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,849 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,855 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,855 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,824 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,825 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,825 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,829 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,830 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,830 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,831 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,831 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,831 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:28,833 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:28,833 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,834 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,834 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,834 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,835 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,835 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,835 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,839 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,841 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,841 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,841 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,845 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,845 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,846 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,846 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,848 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,848 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,848 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,852 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,852 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,852 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,852 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,860 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,860 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,860 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,860 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,861 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,861 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,861 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,866 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,866 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,866 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,866 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,867 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,867 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,867 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,867 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,868 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1051303) 2026-02-18 23:04:28,868 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1051303) 2026-02-18 23:04:28,880 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,880 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,880 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,880 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,880 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,883 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,884 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,884 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,884 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,884 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,884 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,885 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,885 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,887 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,887 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,890 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,890 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,891 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,891 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,891 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,891 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,891 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,891 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,892 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,892 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,894 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,894 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,895 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,861 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,861 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,861 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,866 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,868 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,869 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,869 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,873 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,874 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1051350) 2026-02-18 23:04:28,874 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:28,879 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,882 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,882 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,883 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,883 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,883 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,884 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,886 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,886 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,886 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,887 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,887 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,887 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,887 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,887 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,888 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,888 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,890 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,890 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,890 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,891 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,891 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,891 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,891 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,891 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,892 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,892 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,894 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,894 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,894 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,894 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,894 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,895 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,897 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:28,902 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,902 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,902 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,902 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,902 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,902 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,903 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,903 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,903 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,903 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,903 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,906 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,906 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,907 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,913 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,914 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,914 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,914 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,914 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,918 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,919 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,919 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,919 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,919 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,920 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:28,922 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:28,922 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,923 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,927 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,927 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,928 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,928 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,928 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,928 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,929 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,929 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,929 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,929 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,929 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,929 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,930 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,930 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:28,932 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:28,932 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,933 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,933 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,933 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,933 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,933 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,934 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,934 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,934 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,934 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,934 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,898 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,898 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,898 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,898 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,899 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,901 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:28,901 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,901 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,906 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,906 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,906 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,907 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,907 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,908 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,908 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,908 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,908 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,908 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,909 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,909 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,913 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,913 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,913 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,913 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,919 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,919 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,920 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,920 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,920 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:28,922 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:28,922 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,923 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,923 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,923 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,928 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,928 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,929 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,933 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,934 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,934 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,935 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,935 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,935 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:28,937 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:28,937 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,937 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,938 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,938 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,938 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:28,940 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:28,941 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,942 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,948 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,948 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,948 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,949 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,949 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:28,951 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:28,952 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,953 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,958 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,959 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,964 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,964 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,964 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,964 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,965 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,965 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,965 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,970 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,970 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,970 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,970 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,971 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,971 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,971 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,971 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,971 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1051303) 2026-02-18 23:04:28,971 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:28,938 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,938 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,939 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,939 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,943 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,944 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,945 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,945 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,949 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,949 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,949 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,949 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,951 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,952 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,952 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,956 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,956 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,956 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,956 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,965 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,965 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,965 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,969 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,972 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,973 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,973 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,984 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,984 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,984 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,984 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,984 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,987 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,987 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,987 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,988 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,988 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,988 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,988 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,988 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,991 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,991 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,993 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,994 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,994 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,994 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,994 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,994 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,994 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,994 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,995 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:28,995 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,997 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:28,998 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:28,999 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,005 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,005 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,005 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,005 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,006 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,006 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,006 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,006 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,006 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,006 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,007 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,009 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,009 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,010 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,977 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,978 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1051350) 2026-02-18 23:04:28,978 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:28,983 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,986 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,986 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,987 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,987 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,987 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,987 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,990 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,990 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,991 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,991 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,991 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,991 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,991 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,992 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,992 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,992 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,994 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,994 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,994 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,994 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,995 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,995 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,995 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,996 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,996 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,996 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,998 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,998 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,998 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:28,998 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:28,998 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:28,999 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,001 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,002 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,002 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,002 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,002 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,003 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,005 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,005 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,006 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,010 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,010 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,010 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,010 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,010 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,011 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,011 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,012 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,012 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,012 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,012 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,013 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,016 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,017 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,017 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,017 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,017 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,022 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,022 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,022 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,022 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,023 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,023 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,025 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,025 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,026 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,030 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,030 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,031 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,031 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,031 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,031 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,031 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,032 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,032 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,032 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,032 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,032 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,032 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,033 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,035 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,035 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,035 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,036 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,036 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,036 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,036 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,037 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,037 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,037 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,037 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,037 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,041 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,041 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,043 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,043 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,045 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,051 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,051 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,051 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,052 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,052 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,017 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,017 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,017 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,017 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,022 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,022 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,023 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,023 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,023 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,025 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,025 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,026 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,026 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,026 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,031 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,031 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,032 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,036 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,037 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,037 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,037 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,038 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,038 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,040 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,040 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,040 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,040 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,041 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,041 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,041 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,042 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,046 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,047 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,047 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,048 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,052 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,052 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,052 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,052 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,054 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,054 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,055 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(run_dp_rank pid=1051295) Running 10 timed iterations...
+(PiperActor pid=1051303) 2026-02-18 23:04:29,054 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,054 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,056 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,061 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,062 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,066 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,066 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,066 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,067 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,067 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,067 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,068 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,072 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,072 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,072 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,073 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,073 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,073 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,074 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,074 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,074 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1051303) 2026-02-18 23:04:29,074 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1051303) 2026-02-18 23:04:29,085 - piper_actor - INFO - Actor 1: Tracing disabled
+(PiperActor pid=1051303) 2026-02-18 23:04:29,087 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,087 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,088 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,088 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,088 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,090 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,091 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,091 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,091 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,091 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,092 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,092 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,092 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,059 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,059 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,059 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,059 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,067 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,067 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,067 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,072 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,075 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,075 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,075 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,079 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,081 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1051350) 2026-02-18 23:04:29,081 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:29,085 - piper_actor - INFO - Actor 0: Tracing disabled
+(PiperActor pid=1051350) 2026-02-18 23:04:29,087 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,090 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,090 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,091 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,091 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,091 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,091 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,093 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,095 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,095 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,097 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,097 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,098 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,098 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,098 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,098 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,098 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,098 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,099 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,099 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,101 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,102 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,102 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,109 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,109 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,109 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,109 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,109 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,110 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,110 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,110 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,110 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,110 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,110 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,113 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,113 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,114 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,120 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,121 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,121 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,121 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,121 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,125 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,125 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,126 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,126 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,126 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,127 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,129 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,129 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,130 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,094 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,094 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,094 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,094 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,095 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,095 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,095 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,095 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,095 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,097 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,098 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,098 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,098 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,098 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,099 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,099 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,099 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,099 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,099 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,101 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,102 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,102 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,102 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,102 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,103 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,105 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,105 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,105 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,106 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,106 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,106 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,109 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,109 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,109 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,113 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,113 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,114 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,114 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,114 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,115 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,115 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,115 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,115 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,116 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,116 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,116 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,120 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,120 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,121 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,121 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,126 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,126 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,126 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,127 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,127 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,129 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,129 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,129 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,130 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,130 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,134 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,134 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,135 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,135 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,135 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,135 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,135 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,136 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,136 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,136 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,136 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,136 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,136 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,137 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,139 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,139 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,139 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,140 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,140 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,140 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,140 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,141 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,141 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,141 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,141 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,141 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,145 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,145 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,147 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,148 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,149 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,155 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,155 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,155 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,156 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,156 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,158 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,159 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,160 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,165 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,166 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,135 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,135 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,136 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,140 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,141 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,141 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,141 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,142 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,142 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,144 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,144 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,144 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,145 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,145 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,145 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,145 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,146 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,150 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,151 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,151 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,152 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,156 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,156 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,156 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,156 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,158 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,158 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,159 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,162 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,163 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,163 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,163 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,170 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,170 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,171 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,171 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,171 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,172 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,172 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,176 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,177 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,177 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,177 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,177 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,178 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,178 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,178 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,178 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1051303) 2026-02-18 23:04:29,178 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1051303) 2026-02-18 23:04:29,190 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,190 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,191 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,191 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,191 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,193 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,194 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,194 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,194 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,194 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,194 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,194 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,195 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,198 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,198 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,200 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,200 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,200 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,201 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,201 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,201 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,201 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,201 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,201 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,202 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,204 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,204 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,205 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,171 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,171 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,172 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,176 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,179 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,179 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,180 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,184 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,185 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1051350) 2026-02-18 23:04:29,185 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:29,190 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,193 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,193 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,193 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,194 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,194 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,194 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,196 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,197 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,197 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,197 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,197 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,198 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,198 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,198 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,198 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,198 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,200 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,201 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,201 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,201 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,201 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,202 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,202 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,202 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,202 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,202 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,204 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,205 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,205 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,205 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,205 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,206 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,208 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,208 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,208 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,208 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,209 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,209 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,211 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,212 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,212 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,212 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,212 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,212 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,212 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,213 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,213 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,213 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,213 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,216 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,216 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,217 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,223 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,224 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,224 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,224 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,224 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,228 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,228 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,229 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,229 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,229 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,230 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,232 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,232 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,233 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,237 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,237 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,238 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,238 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,238 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,238 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,238 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,239 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,239 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,239 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,239 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,239 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,239 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,240 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,242 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,242 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,243 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,243 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,243 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,243 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,243 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,244 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,244 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,244 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,244 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,244 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,248 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,248 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,211 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,212 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,212 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,216 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,216 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,217 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,217 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,217 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,218 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,218 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,218 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,218 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,219 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,219 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,219 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,223 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,224 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,224 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,224 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,229 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,229 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,229 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,230 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,230 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,232 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,232 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,233 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,233 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,233 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,238 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,238 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,239 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,243 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,244 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,244 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,244 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,245 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,245 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,247 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,247 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,247 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,248 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,248 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,248 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,248 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,249 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,250 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,251 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,252 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,258 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,258 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,258 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,259 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,259 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,261 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,262 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,263 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,269 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,269 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,274 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,274 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,274 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,274 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,275 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,275 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,275 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,280 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,280 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,280 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,280 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,281 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,281 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,281 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,281 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,282 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1051303) 2026-02-18 23:04:29,282 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:29,253 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,254 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,254 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,255 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,259 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,259 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,259 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,259 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,261 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,262 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,262 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,266 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,266 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,266 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,266 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,275 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,275 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,275 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,280 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,283 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,283 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,283 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,287 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,294 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,294 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,295 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,295 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,295 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,297 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,298 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,298 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,298 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,298 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,298 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,298 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,298 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,302 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,302 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,304 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,304 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,304 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,305 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,305 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,305 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,305 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,305 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,306 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,306 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,308 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,308 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,309 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,315 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,316 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,316 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,316 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,316 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,316 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,317 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,317 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,317 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,317 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,317 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,320 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,320 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,321 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,288 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1051350) 2026-02-18 23:04:29,288 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:29,294 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,297 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,297 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,297 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,298 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,298 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,298 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,300 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,301 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,301 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,301 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,301 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,302 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,302 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,302 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,302 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,302 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,304 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,304 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,305 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,305 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,305 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,306 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,306 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,306 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,306 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,306 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,308 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,308 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,309 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,309 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,309 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,310 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,312 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,312 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,312 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,312 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,312 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,313 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,315 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,315 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,316 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,320 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,320 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,321 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,321 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,321 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,322 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,322 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,322 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,322 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,323 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,323 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,323 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,327 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,327 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,327 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,328 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,327 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,327 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,328 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,328 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,328 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,332 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,332 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,333 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,333 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,333 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,333 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,336 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,336 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,337 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,341 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,341 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,341 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,341 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,342 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,342 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,342 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,342 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,343 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,343 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,343 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,343 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,343 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,343 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,346 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,346 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,346 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,346 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,346 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,347 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,347 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,347 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,347 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,347 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,348 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,348 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,352 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,352 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,354 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,354 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,356 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,362 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,362 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,362 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,363 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,363 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,365 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,365 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,366 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,333 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,333 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,333 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,334 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,334 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,336 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,336 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,336 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,336 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,337 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,342 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,342 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,342 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,347 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,348 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,348 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,348 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,348 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,348 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,351 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,351 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,351 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,351 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,351 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,352 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,352 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,352 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,357 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,358 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,358 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,358 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,363 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,363 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,363 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,363 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,365 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,365 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,365 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,372 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,373 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,377 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,377 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,377 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,378 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,378 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,379 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,379 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,383 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,383 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,384 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,384 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,384 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,385 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,385 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,385 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,385 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1051303) 2026-02-18 23:04:29,385 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1051303) 2026-02-18 23:04:29,397 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,397 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,398 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,398 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,398 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,400 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,401 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,401 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,401 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,401 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,401 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,402 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,402 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,404 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,404 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,369 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,370 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,370 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,370 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,378 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,378 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,378 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,383 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,386 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,386 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,386 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,391 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,392 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1051350) 2026-02-18 23:04:29,392 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:29,397 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,400 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,400 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,400 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,400 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,400 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,401 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,403 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,403 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,404 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,404 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,404 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,405 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,405 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,405 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,405 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,405 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,407 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,407 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,407 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,407 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,407 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,408 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,408 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,408 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,408 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,408 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,411 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,411 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,412 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,418 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,419 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,419 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,419 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,419 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,419 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,419 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,419 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,419 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,420 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,420 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,423 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,423 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,424 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,430 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,431 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,431 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,431 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,431 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,436 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,436 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,436 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,436 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,437 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,437 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,439 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,439 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,440 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,407 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,407 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,408 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,408 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,408 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,408 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,409 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,409 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,409 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,409 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,411 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,411 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,411 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,412 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,412 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,412 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,415 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,415 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,415 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,415 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,415 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,416 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,418 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,418 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,419 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,423 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,423 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,423 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,423 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,424 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,425 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,425 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,425 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,425 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,425 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,426 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,426 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,430 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,430 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,430 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,430 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,436 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,437 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,437 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,437 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,437 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,439 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,440 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,440 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,440 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,440 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,444 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,445 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,445 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,445 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,445 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,446 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,446 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,446 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,446 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,446 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,446 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,446 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,447 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,447 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,449 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,449 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,450 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,450 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,450 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,450 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,451 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,451 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,451 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,451 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,451 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,451 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,455 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,455 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,457 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,458 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,459 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,465 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,465 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,465 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,466 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,466 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,469 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,469 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,470 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,476 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,476 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,480 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,481 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,481 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,481 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,482 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,482 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,482 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,445 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,445 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,446 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,450 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,451 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,451 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,452 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,452 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,452 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,454 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,454 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,455 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,455 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,455 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,455 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,456 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,456 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,460 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,461 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,462 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,462 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,466 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,466 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,466 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,466 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,468 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,469 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,469 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,473 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,473 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,473 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,473 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,481 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,481 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,482 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,487 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,487 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,487 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,487 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,488 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,488 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,488 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,488 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,488 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1051303) 2026-02-18 23:04:29,488 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1051303) 2026-02-18 23:04:29,500 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,500 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,501 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,501 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,501 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,503 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,504 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,504 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,504 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,504 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,504 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,505 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,505 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,507 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,508 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,510 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,510 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,510 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,510 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,510 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,511 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,511 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,511 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,511 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,511 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,514 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,514 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,515 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,521 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,522 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,522 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,522 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,522 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,522 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,522 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,522 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,522 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,523 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,523 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,486 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,489 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,489 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,490 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,494 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,495 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1051350) 2026-02-18 23:04:29,495 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:29,500 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,503 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,503 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,503 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,503 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,504 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,504 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,506 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,506 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,507 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,507 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,507 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,508 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,508 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,508 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,508 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,508 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,510 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,510 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,511 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,511 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,511 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,512 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,512 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,512 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,512 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,512 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,514 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,514 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,515 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,515 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,515 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,515 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,518 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,518 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,518 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,518 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,518 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,519 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,521 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,521 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,522 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,525 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,526 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,527 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,533 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,533 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,533 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,533 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,533 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,538 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,538 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,538 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,538 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,539 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,539 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,541 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,542 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,542 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,547 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,547 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,547 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,547 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,547 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,548 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,548 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,548 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,548 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,548 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,548 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,548 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,549 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,549 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,551 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,551 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,552 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,552 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,552 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,553 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,553 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,553 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,553 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,553 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,553 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,553 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,557 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,557 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,560 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,560 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,561 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,526 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,526 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,526 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,526 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,526 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,528 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,528 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,528 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,528 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,528 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,529 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,529 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,533 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,533 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,533 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,533 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,539 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,539 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,539 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,539 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,539 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,541 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,541 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,542 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,542 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,542 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,547 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,548 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,548 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,552 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,553 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,553 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,554 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,554 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,554 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,556 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,556 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,557 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,557 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,557 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,558 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,558 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,558 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,563 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,563 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,568 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,568 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,569 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,569 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,569 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,572 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,572 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,573 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,579 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,579 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,584 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,584 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,584 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,584 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,585 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,585 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,585 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,590 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,590 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,590 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,590 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,591 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,591 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,591 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,591 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,592 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1051303) 2026-02-18 23:04:29,592 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:29,564 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,564 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,568 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,568 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,568 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,568 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,571 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,571 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,571 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,575 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,575 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,575 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,576 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,585 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,585 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,585 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,590 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,593 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,593 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,593 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,597 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,598 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1051350) 2026-02-18 23:04:29,598 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1051303) 2026-02-18 23:04:29,604 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,604 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,605 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,605 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,605 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,607 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,607 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,608 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,608 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,608 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,608 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,608 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,608 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,611 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,611 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,613 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,614 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,614 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,614 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,614 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,614 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,614 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,614 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,615 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,615 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,617 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,618 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,619 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,625 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,625 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,626 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,626 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,626 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,626 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,626 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,626 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,626 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,627 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,627 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,629 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,630 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,631 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,637 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,637 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,637 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,637 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,637 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,642 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,642 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,642 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,642 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,604 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,606 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,607 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,607 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,607 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,607 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,608 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,610 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,610 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,611 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,611 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,611 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,611 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,611 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,612 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,612 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,612 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,614 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,614 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,614 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,615 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,615 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,615 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,615 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,616 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,616 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,616 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,618 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,618 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,618 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,618 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,618 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,619 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,621 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,622 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,622 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,622 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,622 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,623 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,625 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,625 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,625 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,630 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,630 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,630 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,630 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,630 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,631 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,631 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,632 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,632 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,632 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,632 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,633 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,638 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,638 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,638 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,638 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,643 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,643 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,643 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,643 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,643 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,643 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,643 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,645 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,646 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,646 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,651 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,652 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,652 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,652 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,652 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,653 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,653 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,653 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,653 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,653 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,653 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,653 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,654 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,654 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,656 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,656 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,657 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,657 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,657 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,658 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,658 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,658 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,658 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,658 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,658 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,658 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,663 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,663 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,666 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,666 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,667 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,673 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,673 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,673 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,674 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,674 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,677 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,677 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,678 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,645 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,646 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,646 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,646 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,646 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,653 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,653 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,653 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,659 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,659 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,659 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,659 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,660 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,660 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,662 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,662 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,663 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,663 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,663 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,663 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,663 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,664 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,669 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,670 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,670 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,670 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,675 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,675 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,675 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,675 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,677 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,677 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,677 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,684 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,684 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,689 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,689 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,689 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,689 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,690 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,690 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,690 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,695 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,695 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,695 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,695 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,696 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,696 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,696 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,696 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,697 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1051303) 2026-02-18 23:04:29,697 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1051303) 2026-02-18 23:04:29,711 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,712 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,712 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,712 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,712 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,715 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,715 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,715 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,715 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,716 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,716 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,716 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,716 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,719 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,719 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,682 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,682 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,682 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,682 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,690 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,690 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,690 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,696 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,698 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,698 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,698 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,703 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,704 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1051350) 2026-02-18 23:04:29,704 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:29,711 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,714 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,715 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,715 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,715 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,715 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,716 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,718 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,718 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,719 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,719 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,719 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,719 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,719 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,720 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,720 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,720 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,722 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,722 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,722 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,722 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,722 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,723 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,723 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,723 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,723 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,723 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,725 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,726 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,727 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,733 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,734 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,734 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,734 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,734 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,734 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,734 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,734 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,734 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,735 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,735 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,737 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,738 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,739 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,745 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,745 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,745 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,745 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,745 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,750 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,750 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,750 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,751 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,751 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,751 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,754 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,754 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,754 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,759 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,759 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,759 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,759 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,760 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,722 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,722 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,722 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,723 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,723 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,723 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,723 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,724 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,724 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,724 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,726 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,726 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,726 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,726 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,726 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,727 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,729 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,730 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,730 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,730 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,730 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,731 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,733 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,733 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,733 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,739 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,739 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,739 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,739 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,739 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,740 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,740 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,740 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,740 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,741 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,741 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,741 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,746 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,746 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,746 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,746 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,751 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,751 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,751 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,751 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,751 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,754 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,754 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,754 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,755 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,755 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,760 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,760 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,760 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,760 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,760 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,760 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,761 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,761 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,761 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,761 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,761 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,761 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,764 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,764 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,764 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,765 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,765 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,765 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,765 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,765 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,766 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,766 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,766 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,766 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,770 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,770 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,773 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,773 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,774 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,780 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,781 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,781 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,781 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,781 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,784 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,784 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,785 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,791 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,792 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,796 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,797 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,797 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,797 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,798 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,798 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,798 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,765 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,766 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,766 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,766 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,766 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,766 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,769 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,769 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,769 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,770 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,770 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,770 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,770 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,771 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,775 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,776 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,776 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,777 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,781 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,781 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,781 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,782 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,783 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,784 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,784 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,788 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,788 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,789 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,789 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,797 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,798 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,798 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,802 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,803 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,803 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,803 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,804 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,804 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,804 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,804 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,804 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1051303) 2026-02-18 23:04:29,804 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1051303) 2026-02-18 23:04:29,817 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,817 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,817 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,817 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,817 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,820 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,820 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,820 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,821 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,821 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,821 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,821 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,821 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,824 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,824 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,826 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,827 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,827 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,827 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,827 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,827 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,827 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,827 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,828 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,828 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,830 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,831 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,832 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,803 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,805 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,805 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,806 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,810 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,811 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1051350) 2026-02-18 23:04:29,811 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:29,817 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,819 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,820 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,820 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,820 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,820 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,821 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,823 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,823 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,824 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,824 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,824 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,824 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,824 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,825 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,825 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,825 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,827 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,827 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,828 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,828 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,828 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,828 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,828 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,829 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,829 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,829 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,831 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,831 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,831 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,831 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,832 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,832 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,834 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,835 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,835 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,835 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,835 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,836 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,838 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,838 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,838 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,838 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,839 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,839 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,839 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,839 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,839 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,839 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,839 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,839 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,840 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,840 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,842 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,843 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,844 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,849 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,850 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,850 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,850 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,850 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,855 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,855 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,855 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,855 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,856 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,856 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,858 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,859 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,859 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,863 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,864 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,864 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,864 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,864 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,865 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,865 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,865 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,865 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,865 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,865 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,865 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,866 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,866 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,868 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,869 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,869 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,869 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,869 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,870 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,870 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,870 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,870 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,870 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,870 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,870 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,874 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,875 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,843 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,844 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,844 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,844 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,844 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,845 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,845 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,845 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,845 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,846 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,846 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,846 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,850 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,851 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,851 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,851 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,855 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,856 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,856 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,856 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,856 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,858 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,859 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,859 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,859 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,859 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,865 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,865 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,865 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,870 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,870 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,871 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,871 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,871 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,871 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,873 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,874 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,874 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,874 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,874 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,875 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,875 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,875 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,877 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,877 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,878 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,884 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,885 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,885 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,885 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,885 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,889 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,889 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,890 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,896 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,896 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,901 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,901 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,901 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,901 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,902 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,902 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,902 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,907 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,907 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,907 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,907 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,908 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,908 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,908 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,908 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,909 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1051303) 2026-02-18 23:04:29,909 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:29,880 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,881 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,881 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,881 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,885 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,886 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,886 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,886 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,888 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,888 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,888 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,892 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,892 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,893 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,893 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,902 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,902 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,902 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,907 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,910 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,910 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,910 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,915 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,916 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1051350) 2026-02-18 23:04:29,916 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1051303) 2026-02-18 23:04:29,921 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,921 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,922 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,922 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,922 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,924 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,925 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,925 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,925 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,925 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,925 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,925 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,925 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,929 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,929 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,931 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,931 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,932 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,932 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,932 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,932 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,932 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,932 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,933 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,933 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,935 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,935 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,936 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,943 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,944 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,944 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,944 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,944 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,944 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,944 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,944 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,944 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,945 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,945 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,947 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,948 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,949 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,955 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,955 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,955 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,955 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,955 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,921 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,924 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,924 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,925 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,925 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,925 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,925 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,928 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,928 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,928 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,928 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,928 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,929 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,929 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,929 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,929 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,929 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,931 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,932 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,932 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,932 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,932 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,933 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,933 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,933 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,933 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,933 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,935 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,936 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,936 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,936 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,936 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,937 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,939 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,939 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,940 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,940 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,940 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,940 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,942 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,943 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,943 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,948 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,949 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,949 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,949 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,949 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,950 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,950 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,950 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,950 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,951 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,951 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,951 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,955 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,956 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,956 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,956 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,960 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,960 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,960 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,960 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,961 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,961 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,963 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,964 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,964 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,969 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,969 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,969 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,969 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,970 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,970 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,970 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,970 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,970 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,971 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,971 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,971 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,971 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,971 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,974 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,974 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,974 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,974 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,974 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,975 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,975 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,975 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,975 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,975 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,976 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,976 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,980 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,981 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,983 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:29,983 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,984 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,990 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,991 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:29,991 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,991 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:29,992 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,994 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:29,994 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:29,995 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,961 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,961 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,961 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,961 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,961 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,964 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:29,964 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,964 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,964 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,964 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,970 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,970 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,970 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,976 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,976 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,977 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,977 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,977 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,977 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,979 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:29,980 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,980 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,980 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,980 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:29,981 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,981 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,981 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,986 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,987 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,987 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,987 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,992 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,992 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,992 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,992 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,994 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,994 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,994 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,001 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,002 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,006 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,006 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,007 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,007 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,007 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,008 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,008 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,012 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,012 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,013 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,013 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,013 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,013 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,014 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,014 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,014 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1051303) 2026-02-18 23:04:30,014 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1051303) 2026-02-18 23:04:30,026 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,027 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,027 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,027 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,027 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,029 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,030 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,030 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,030 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,030 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,031 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,031 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,031 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:30,034 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,034 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,036 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,036 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,999 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:29,999 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,999 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:29,999 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,007 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,007 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,008 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,013 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,015 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,015 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,015 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,020 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,021 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1051350) 2026-02-18 23:04:30,021 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:30,026 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,029 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,029 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,030 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,030 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,030 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:30,030 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,033 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,033 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,033 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,033 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,034 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:30,034 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,034 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,034 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,034 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,035 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,037 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,037 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,037 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,037 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,037 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:30,037 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,037 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,037 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,037 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,037 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,037 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:30,038 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,038 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,040 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,040 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:30,042 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,048 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,048 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,048 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,048 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,049 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,049 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,049 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,049 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,049 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,049 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,049 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,052 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,052 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:30,053 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,059 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,060 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,060 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,060 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,060 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,064 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,065 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,065 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,065 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,065 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,066 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:30,068 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:30,068 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,069 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,073 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,073 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,074 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,074 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,074 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:30,074 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,074 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,075 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,075 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,075 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,075 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,075 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,076 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,076 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:30,038 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,038 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,038 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,038 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,038 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,041 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,041 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,041 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,041 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,041 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:30,042 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,044 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:30,044 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,045 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,045 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,045 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:30,045 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,048 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:30,048 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,048 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,053 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,053 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,054 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,054 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,054 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=1051350) 2026-02-18 23:04:30,054 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,054 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,055 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,055 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,055 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,055 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,056 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,060 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,060 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,061 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,061 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,065 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,065 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,066 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,066 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,066 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:30,068 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=1051350) 2026-02-18 23:04:30,069 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,069 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,069 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,069 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:30,074 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,074 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,075 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,078 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:30,078 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,079 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,079 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,079 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:30,079 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,079 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,080 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,080 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,080 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,080 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,080 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,084 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,084 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:30,087 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=1051303) 2026-02-18 23:04:30,087 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:30,088 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,094 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,095 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,095 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,095 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,095 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:30,098 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=1051303) 2026-02-18 23:04:30,098 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051303) 2026-02-18 23:04:30,100 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,107 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,108 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,112 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,112 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,113 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,113 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,113 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,114 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,114 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,080 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,080 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,080 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,081 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,081 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,081 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:30,083 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=1051350) 2026-02-18 23:04:30,084 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,084 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,084 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,084 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=1051350) 2026-02-18 23:04:30,085 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,085 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,085 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,090 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,091 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,091 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,091 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,096 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,096 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,096 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,096 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,098 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,098 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,098 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,103 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,103 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,103 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,103 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,113 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,113 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,114 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(run_dp_rank pid=1051295) rank 0 iter time= 0.10 ± 0.00 s (10 samples)
+(run_dp_rank pid=1051295) rank 0 throughput= 156858.18 tokens/sec (10 samples)
+(PiperActor pid=1051303) 2026-02-18 23:04:30,118 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,118 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,119 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,119 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,119 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,120 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,120 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=1051303) 2026-02-18 23:04:30,120 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=1051303) 2026-02-18 23:04:30,120 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=1051303) 2026-02-18 23:04:30,120 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=1051350) 2026-02-18 23:04:30,119 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,121 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,121 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,121 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=1051350) 2026-02-18 23:04:30,126 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=1051350) 2026-02-18 23:04:30,127 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=1051350) 2026-02-18 23:04:30,127 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(run_dp_rank pid=1051295) Ray timeline saved to: out/debug-pp2-dp1-interleaved-1f1b.json
+(PiperActor pid=1051303) [rank1]:[W218 23:04:31.366773054 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+(PiperActor pid=1051350) [rank0]:[W218 23:04:32.601739028 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
diff --git a/logs/interleaved_debug.trace b/logs/interleaved_debug.trace
new file mode 100644
index 0000000..950374f
--- /dev/null
+++ b/logs/interleaved_debug.trace
@@ -0,0 +1,3412 @@
+2026-02-18 22:57:22,282	INFO worker.py:2007 -- Started a local Ray instance.
+/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+  warnings.warn(
+(run_dp_rank pid=985582) Namespace(model='debug', schedule='interleaved-1f1b', dp=1, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=True, naive_gradient_sync=False)
+(run_dp_rank pid=985582) 2026-02-18 22:57:36,845 - piper_coordinator - DEBUG - Running DP rank 1 of 1
+(run_dp_rank pid=985582) Schedule:
+(run_dp_rank pid=985582) 0:0:f	0:1:f	2:0:f	2:1:f	0:2:f	0:3:f	2:0:b	 -- 	2:1:b	2:2:f	0:0:b	2:3:f	0:1:b	 -- 	2:2:b	2:3:b	0:2:b	0:3:b	0:0:u	
+(run_dp_rank pid=985582)  -- 	1:0:f	1:1:f	3:0:f	3:0:b	3:1:f	3:1:b	1:2:f	1:0:b	1:3:f	1:1:b	3:2:f	3:2:b	3:3:f	3:3:b	1:2:b	1:3:b	1:0:u	 -- 	
+(run_dp_rank pid=985582) 2026-02-18 22:57:36,868 - piper_compile - DEBUG - mbs: 4, stages: 4, devices: 2
+(run_dp_rank pid=985582) 2026-02-18 22:57:36,868 - piper_compile - DEBUG - Fwd/bwd schedule: [[Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=False, upd=False)], [Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=False, upd=False)]]
+(run_dp_rank pid=985582) 2026-02-18 22:57:36,868 - piper_compile - DEBUG - P2P comms: [(0, 1, 0), (0, 1, 1), (1, 2, 0), (2, 3, 0), (1, 2, 1), (2, 3, 1), (0, 1, 2), (3, 2, 0), (0, 1, 3), (2, 1, 0), (3, 2, 1), (2, 1, 1), (1, 2, 2), (2, 3, 2), (1, 0, 0), (1, 2, 3), (2, 3, 3), (1, 0, 1), (3, 2, 2), (2, 1, 2), (3, 2, 3), (2, 1, 3), (1, 0, 2), (1, 0, 3)]
+(run_dp_rank pid=985582) 2026-02-18 22:57:36,868 - piper_compile - DEBUG - P2P schedules: {0: [(0, 1, 0, True), (0, 1, 1, True), (1, 2, 0, False), (2, 3, 0, True), (1, 2, 1, False), (2, 3, 1, True), (0, 1, 2, True), (3, 2, 0, False), (0, 1, 3, True), (2, 1, 0, True), (3, 2, 1, False), (2, 1, 1, True), (1, 2, 2, False), (2, 3, 2, True), (1, 0, 0, False), (1, 2, 3, False), (2, 3, 3, True), (1, 0, 1, False), (3, 2, 2, False), (2, 1, 2, True), (3, 2, 3, False), (2, 1, 3, True), (1, 0, 2, False), (1, 0, 3, False)], 1: [(0, 1, 0, False), (0, 1, 1, False), (1, 2, 0, True), (2, 3, 0, False), (1, 2, 1, True), (2, 3, 1, False), (0, 1, 2, False), (3, 2, 0, True), (0, 1, 3, False), (2, 1, 0, False), (3, 2, 1, True), (2, 1, 1, False), (1, 2, 2, True), (2, 3, 2, False), (1, 0, 0, True), (1, 2, 3, True), (2, 3, 3, False), (1, 0, 1, True), (3, 2, 2, True), (2, 1, 2, False), (3, 2, 3, True), (2, 1, 3, False), (1, 0, 2, True), (1, 0, 3, True)]}
+(run_dp_rank pid=985582) 2026-02-18 22:57:36,934 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, 684731f6d9371809297f9d1901000000) global rank 0
+(run_dp_rank pid=985582) 2026-02-18 22:57:36,935 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, 0a439890f9b401c87eee707e01000000) global rank 1
+(PiperActor pid=985635) 2026-02-18 22:57:39,966 - piper_actor - INFO - Initializing Ray actor 1 global rank 1 GPU 2
+(PiperActor pid=985628) 2026-02-18 22:57:40,132 - piper_actor - INFO - Initializing Ray actor 0 global rank 0 GPU 1
+(PiperActor pid=985635) 2026-02-18 22:57:43,104 - piper_actor - DEBUG - Actor 1 has GPU 2, joined the global process group
+(PiperActor pid=985635) 2026-02-18 22:57:43,105 - piper_actor - INFO - Global rank 1 joined its pp group 0 along with ranks [0, 1]
+(PiperActor pid=985628) 2026-02-18 22:57:43,104 - piper_actor - DEBUG - Actor 0 has GPU 1, joined the global process group
+(PiperActor pid=985628) 2026-02-18 22:57:43,105 - piper_actor - INFO - Global rank 0 joined its pp group 0 along with ranks [0, 1]
+(run_dp_rank pid=985582) 2026-02-18 22:57:43,319 - piper_compile - INFO - DP rank 1 compiling...
+(PiperActor pid=985628) 2026-02-18 22:57:46,742 - piper_actor - INFO - Loading stage 0 graph on actor 0
+(PiperActor pid=985635) 2026-02-18 22:57:46,842 - piper_actor - INFO - Loading stage 1 graph on actor 1
+(PiperActor pid=985628) 2026-02-18 22:57:48,067 - piper_actor - INFO - Loading stage 2 graph on actor 0
+(PiperActor pid=985635) 2026-02-18 22:57:48,703 - piper_actor - INFO - Loading stage 3 graph on actor 1
+(run_dp_rank pid=985582) 2026-02-18 22:57:48,830 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=985582) 2026-02-18 22:57:55,300 - piper_compile - INFO - DP rank 1 done.
+(run_dp_rank pid=985582) Running 5 warmup iterations...
+(PiperActor pid=985635) 2026-02-18 22:57:55,343 - piper_actor - DEBUG - Actor 1 loaded labels torch.Size([16, 256, 512])
+(PiperActor pid=985635) 2026-02-18 22:57:55,349 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:55,349 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) [rank1]:[W218 22:57:55.831571789 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=985628) 2026-02-18 22:57:55,329 - piper_actor - DEBUG - Actor 0 loaded inputs 1
+(PiperActor pid=985628) 2026-02-18 22:57:55,349 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:56,101 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=985628) 2026-02-18 22:57:56,101 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) [rank0]:[W218 22:57:56.583910970 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=985635) 2026-02-18 22:57:58,389 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:58,390 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:58,390 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985628) 2026-02-18 22:57:58,380 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:58,381 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:58,381 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:57:58,382 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:58,391 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=985628) 2026-02-18 22:57:58,391 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:58,391 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:58,391 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:58,391 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:57:58,392 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:58,392 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) [rank0]:[W218 22:57:58.874209274 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=985635) 2026-02-18 22:57:59,210 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,211 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,212 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,212 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,212 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) [rank1]:[W218 22:57:59.694541566 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=985635) 2026-02-18 22:57:59,430 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,430 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,430 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:57:59,432 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,432 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,441 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,441 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,442 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,442 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,442 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,442 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,442 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,442 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:57:59,443 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,443 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,452 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,452 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:57:59,453 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,430 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,430 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,430 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,440 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,440 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,441 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,441 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,441 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:57:59,442 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,442 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,442 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,442 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,442 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,450 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,450 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,450 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,450 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,451 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:57:59,451 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,484 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=985628) 2026-02-18 22:57:59,484 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,484 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,484 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,485 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:57:59,485 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,544 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,545 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,545 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,545 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,545 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,545 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,545 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,546 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,546 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,547 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,547 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,549 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,550 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:57:59,551 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,523 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=985628) 2026-02-18 22:57:59,523 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,523 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,565 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,565 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,565 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,566 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,566 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,578 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,578 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,578 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,578 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,601 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,601 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:57:59,604 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:57:59,604 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,604 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,609 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,609 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,609 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,609 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,609 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:57:59,611 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,611 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,611 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,611 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,611 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,611 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,611 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,612 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,612 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:57:59,615 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:57:59,616 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,616 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,616 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,616 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:57:59,617 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,618 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,618 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,618 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:57:59,618 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,618 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:57:59,618 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=985628) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
+(PiperActor pid=985628)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+(PiperActor pid=985628) 2026-02-18 22:57:59,598 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,598 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,599 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,599 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,599 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:57:59,600 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,600 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,600 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,600 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,601 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,601 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,601 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,605 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,606 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,606 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,606 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,607 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,607 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,607 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,607 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,607 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:57:59,610 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:57:59,610 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,610 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,610 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,610 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:57:59,612 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:57:59,612 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:57:59,612 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,060 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,061 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,064 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,064 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,065 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,053 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,055 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,055 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,055 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,055 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,056 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,058 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,058 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,059 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,059 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,059 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,060 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,060 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,060 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,065 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,066 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,066 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,066 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,071 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,071 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,071 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,071 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,168 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,169 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,169 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,170 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,170 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,173 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,173 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,174 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,180 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,181 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,185 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,185 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,185 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,185 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,186 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,187 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,187 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,191 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,191 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,191 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,191 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,192 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,192 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,193 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,193 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,193 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=985635) 2026-02-18 22:58:00,193 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=985635) 2026-02-18 22:58:00,260 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,260 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,260 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,261 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,261 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,263 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,264 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,264 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,264 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,264 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,265 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,265 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,265 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,268 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,268 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,270 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,270 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,270 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,270 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,270 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,271 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,271 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,271 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,272 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,272 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,170 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,171 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,171 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,176 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,176 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,176 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,176 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,186 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,186 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,186 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,191 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,194 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,194 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,194 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,198 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,199 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=985628) 2026-02-18 22:58:00,199 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=985628) 2026-02-18 22:58:00,259 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,263 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,263 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,264 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,264 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,264 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,264 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,266 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,267 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,267 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,267 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,267 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,268 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,268 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,268 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,268 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,268 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,270 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,271 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,271 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,271 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,271 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,272 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,272 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,272 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,272 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,272 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,274 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,274 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,275 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,282 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,282 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,282 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,282 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,282 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,283 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,283 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,283 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,283 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,283 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,283 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,286 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,286 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,287 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,293 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,293 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,293 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,293 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,294 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,297 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,297 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,298 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,298 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,299 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,299 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,301 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,301 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,302 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,306 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,306 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,306 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,306 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,306 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,308 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,308 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,308 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,308 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,308 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,308 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,308 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,309 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,309 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,311 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,311 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,311 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,311 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,311 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,312 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,312 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,313 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,313 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,313 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,274 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,275 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,275 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,275 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,275 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,276 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,278 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,278 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,279 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,279 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,279 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,279 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,282 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,282 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,282 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,288 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,288 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,288 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,288 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,288 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,289 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,289 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,289 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,289 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,290 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,290 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,290 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,294 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,294 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,295 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,295 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,298 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,298 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,298 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,299 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,299 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,301 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,301 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,301 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,301 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,301 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,307 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,307 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,307 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,313 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,313 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,321 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,321 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,323 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,324 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,325 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,331 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,331 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,331 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,332 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,332 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,334 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,334 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,336 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,341 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,342 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,345 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,346 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,346 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,346 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,347 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,347 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,348 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,351 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,352 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,352 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,352 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,317 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,317 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,317 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,318 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,318 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,318 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,320 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,320 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,321 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,321 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,321 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,322 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,322 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,322 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,326 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,327 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,328 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,328 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,332 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,332 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,332 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,332 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,334 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,335 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,335 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,339 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,339 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,339 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,339 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,346 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,347 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,347 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,351 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,353 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,353 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,353 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,353 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,354 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=985635) 2026-02-18 22:58:00,354 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=985635) 2026-02-18 22:58:00,366 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,366 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,367 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,367 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,367 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,369 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,369 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,370 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,370 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,370 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,370 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,370 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,370 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,373 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,374 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,376 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,376 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,376 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,376 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,376 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,376 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,376 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,377 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,377 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,377 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,380 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,380 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,381 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,387 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,387 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,387 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,387 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,388 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,388 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,388 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,388 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,388 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,389 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,389 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,391 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,391 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,354 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,354 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,355 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,359 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,360 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=985628) 2026-02-18 22:58:00,360 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=985628) 2026-02-18 22:58:00,366 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,369 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,369 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,369 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,370 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,370 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,370 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,372 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,372 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,373 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,373 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,373 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,374 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,374 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,374 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,374 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,374 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,376 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,376 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,377 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,377 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,377 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,378 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,378 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,378 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,378 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,378 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,380 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,380 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,381 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,381 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,381 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,382 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,384 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,384 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,384 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,384 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,384 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,385 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,387 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,387 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,388 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,392 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,392 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,392 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,392 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,393 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,392 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,398 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,399 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,399 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,399 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,399 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,403 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,403 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,403 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,403 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,404 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,404 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,407 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,407 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,407 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,411 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,411 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,411 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,412 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,412 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,413 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,413 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,413 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,413 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,413 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,413 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,413 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,414 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,414 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,416 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,416 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,417 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,417 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,417 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,418 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,418 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,418 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,418 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,418 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,418 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,418 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,422 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,422 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,424 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,424 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,426 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,394 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,394 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,394 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,394 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,394 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,395 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,395 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,399 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,399 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,399 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,399 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,404 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,404 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,404 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,404 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,404 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,406 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,407 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,407 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,407 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,407 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,412 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,412 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,412 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,417 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,418 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,418 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,419 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,419 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,419 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,421 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,421 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,422 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,422 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,422 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,422 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,423 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,423 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,427 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,428 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,428 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,429 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,431 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,432 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,432 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,432 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,433 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,435 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,435 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,436 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,442 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,442 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,446 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,446 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,447 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,447 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,448 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,448 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,448 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,452 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,452 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,453 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,453 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,454 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,454 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,454 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,454 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,454 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=985635) 2026-02-18 22:58:00,455 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=985635) 2026-02-18 22:58:00,466 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,466 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,467 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,467 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,467 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,469 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,469 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,469 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,469 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,470 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,470 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,470 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,470 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,433 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,433 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,433 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,433 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,435 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,435 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,436 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,439 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,440 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,440 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,440 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,447 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,447 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,448 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,452 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,455 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,455 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,455 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,459 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,461 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=985628) 2026-02-18 22:58:00,461 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=985628) 2026-02-18 22:58:00,466 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,468 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,469 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,469 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,469 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,469 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,470 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,472 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,472 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,472 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,473 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,473 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,473 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,473 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,475 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,475 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,475 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,476 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,476 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,476 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,476 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,476 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,477 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,477 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,479 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,479 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,481 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,486 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,487 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,487 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,487 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,487 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,487 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,487 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,488 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,488 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,488 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,488 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,490 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,491 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,492 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,498 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,498 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,498 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,498 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,499 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,502 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,502 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,503 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,503 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,504 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,504 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,506 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,506 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,507 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,473 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,473 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,474 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,474 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,474 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,476 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,476 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,476 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,476 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,476 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,477 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,477 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,478 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,478 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,478 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,480 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,480 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,480 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,480 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,480 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,481 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,483 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,483 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,484 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,484 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,484 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,485 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,487 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,487 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,487 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,492 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,492 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,492 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,492 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,492 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,493 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,493 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,493 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,494 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,494 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,494 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,494 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,498 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,499 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,499 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,499 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,503 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,503 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,504 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,504 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,504 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,506 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,506 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,506 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,507 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,507 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,511 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,511 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,511 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,511 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,511 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,512 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,513 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,513 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,513 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,513 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,513 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,513 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,514 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,514 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,516 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,516 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,516 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,516 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,516 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,517 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,517 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,518 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,518 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,518 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,518 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,518 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,522 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,522 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,524 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,524 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,526 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,531 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,532 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,532 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,532 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,532 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,535 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,535 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,536 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,542 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,542 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,546 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,547 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,547 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,547 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,548 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,548 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,548 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,512 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,512 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,512 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,517 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,518 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,518 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,518 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,518 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,518 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,521 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,521 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,521 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,521 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,521 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,522 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,522 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,523 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,527 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,528 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,528 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,528 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,532 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,533 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,533 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,533 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,535 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,535 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,535 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,540 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,540 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,540 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,540 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,547 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,548 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,548 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,552 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,552 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,553 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,553 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,554 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,554 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,554 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,554 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,555 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=985635) 2026-02-18 22:58:00,555 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=985635) 2026-02-18 22:58:00,566 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,566 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,567 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,567 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,567 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,569 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,569 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,570 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,570 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,570 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,570 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,570 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,570 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,573 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,574 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,576 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,576 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,576 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,576 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,576 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,577 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,577 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,577 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,577 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,577 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,579 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,580 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,581 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,587 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,587 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,587 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,587 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,588 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,588 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,588 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,588 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,588 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,589 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,589 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,552 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,555 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,555 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,555 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,560 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,561 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=985628) 2026-02-18 22:58:00,561 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=985628) 2026-02-18 22:58:00,566 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,569 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,569 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,570 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,570 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,570 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,570 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,572 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,573 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,573 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,573 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,573 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,574 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,574 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,574 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,574 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,574 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,576 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,577 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,577 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,577 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,577 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,578 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,578 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,578 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,578 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,578 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,580 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,580 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,581 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,581 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,581 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,582 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,584 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,584 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,584 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,584 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,585 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,585 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,587 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,588 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,588 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,591 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,591 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,592 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,598 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,599 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,599 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,599 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,599 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,603 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,603 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,604 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,604 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,604 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,605 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,607 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,607 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,608 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,612 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,612 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,612 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,612 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,612 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,613 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,613 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,613 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,614 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,614 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,614 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,614 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,614 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,614 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,617 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,617 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,617 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,617 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,617 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,618 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,618 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,618 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,618 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,618 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,619 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,619 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,622 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,623 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,625 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,625 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,626 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,592 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,593 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,593 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,593 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,593 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,594 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,594 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,594 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,594 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,595 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,595 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,595 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,599 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,599 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,600 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,600 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,604 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,604 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,604 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,605 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,605 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,607 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,607 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,607 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,608 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,608 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,613 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,613 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,613 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,618 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,619 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,619 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,619 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,619 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,619 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,622 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,622 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,622 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,622 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,622 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,623 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,623 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,623 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,628 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,629 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,629 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,629 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(run_dp_rank pid=985582) Running 10 timed iterations...
+(PiperActor pid=985635) 2026-02-18 22:58:00,632 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,632 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,633 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,633 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,633 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,635 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,636 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,637 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,643 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,643 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,647 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,647 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,648 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,648 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,649 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,649 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,649 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,653 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,653 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,653 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,654 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,655 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,655 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,655 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,655 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,655 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=985635) 2026-02-18 22:58:00,655 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=985635) 2026-02-18 22:58:00,667 - piper_actor - INFO - Actor 1: Tracing enabled
+(PiperActor pid=985635) 2026-02-18 22:58:00,669 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,669 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,633 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,633 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,634 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,634 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,636 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,636 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,636 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,640 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,640 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,641 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,641 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,648 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,648 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,649 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,653 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,656 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,656 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,656 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,660 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,662 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=985628) 2026-02-18 22:58:00,662 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=985628) 2026-02-18 22:58:00,666 - piper_actor - INFO - Actor 0: Tracing enabled
+(PiperActor pid=985628) 2026-02-18 22:58:00,669 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,673 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,673 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,673 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,675 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,675 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,676 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,676 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,676 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,676 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,677 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,677 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,677 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,677 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,679 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,679 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,680 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,680 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,680 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,680 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,680 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,681 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,681 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,681 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,683 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,683 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,685 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,691 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,691 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,691 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,691 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,691 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,692 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,692 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,692 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,692 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,692 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,692 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,695 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,695 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,696 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,702 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,702 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,703 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,703 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,703 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,707 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,707 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,707 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,708 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,672 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,672 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,673 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,673 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,673 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,673 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,675 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,676 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,676 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,676 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,676 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,677 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,677 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,677 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,677 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,677 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,679 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,680 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,680 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,680 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,680 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,681 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,681 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,681 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,681 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,681 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,683 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,683 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,684 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,684 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,684 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,685 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,687 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,687 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,688 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,688 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,688 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,688 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,690 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,691 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,692 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,696 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,696 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,697 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,697 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,697 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,698 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,698 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,698 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,698 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,698 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,699 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,707 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,708 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,708 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,710 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,711 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,712 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,716 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,716 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,716 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,716 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,716 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,718 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,718 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,719 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,719 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,719 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,720 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,720 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,720 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,720 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,722 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,723 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,723 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,723 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,723 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,724 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,724 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,729 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,729 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,730 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,730 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,730 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,731 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,731 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,735 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,736 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,736 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,742 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,743 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,743 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,744 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,744 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,747 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,748 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,748 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,712 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,712 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,712 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,712 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,713 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,713 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,716 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,716 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,716 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,719 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,719 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,719 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,719 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,719 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,720 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,720 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,721 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,725 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,726 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,726 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,726 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,726 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,727 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,729 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,729 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,729 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,729 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,729 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,730 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,730 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,730 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,735 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,736 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,736 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,743 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,747 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,747 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,747 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,748 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,748 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,749 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,754 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,755 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,759 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,759 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,759 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,759 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,760 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,760 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,764 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,767 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,768 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,768 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,768 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,769 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,769 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,770 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,770 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,770 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=985635) 2026-02-18 22:58:00,770 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=985635) 2026-02-18 22:58:00,785 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,785 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,759 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,763 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,763 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,764 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,764 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,765 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,765 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,768 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,772 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,774 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,774 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,774 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,778 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,779 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=985628) 2026-02-18 22:58:00,779 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=985628) 2026-02-18 22:58:00,785 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,787 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,787 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,788 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,788 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,788 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,788 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,788 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,790 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,791 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,792 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,792 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,792 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,792 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,792 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,793 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,793 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,793 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,795 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,795 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,796 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,796 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,796 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,796 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,796 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,796 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,797 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,797 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,799 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,799 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,801 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,806 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,807 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,807 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,807 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,807 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,807 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,807 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,808 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,808 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,808 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,808 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,811 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,812 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,812 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,818 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,819 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,819 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,819 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,819 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,823 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,823 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,823 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,823 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,825 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,825 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,788 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,789 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,791 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,791 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,792 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,792 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,792 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,793 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,793 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,793 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,793 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,793 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,795 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,795 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,796 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,796 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,796 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,797 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,797 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,797 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,797 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,797 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,799 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,799 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,800 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,800 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,800 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,801 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,803 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,803 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,803 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,804 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,804 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,804 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,806 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,806 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,808 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,812 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,812 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,813 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,813 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,813 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,813 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,813 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,814 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,814 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,814 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,814 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,823 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,828 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,828 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,828 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,832 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,832 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,833 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,833 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,833 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,834 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,834 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,836 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,836 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,836 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,836 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,836 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,837 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,837 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,840 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,840 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,841 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,841 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,841 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,841 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,841 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,846 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,846 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,846 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,846 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,847 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,847 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,847 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,850 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,851 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,851 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,857 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,858 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,858 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,858 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,858 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,861 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,861 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,862 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,827 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,828 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,828 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,828 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,829 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,829 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,833 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,833 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,833 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,835 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,836 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,836 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,836 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,836 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,837 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,837 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,837 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,841 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,843 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,843 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,843 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,843 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,843 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,845 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,845 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,846 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,846 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,846 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,847 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,847 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,847 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,851 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,853 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,853 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,858 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,862 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,862 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,862 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,862 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,863 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,863 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,868 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,868 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,872 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,872 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,872 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,872 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,874 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,874 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,878 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,882 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,882 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,882 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,882 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,883 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,883 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,884 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,884 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,884 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=985635) 2026-02-18 22:58:00,884 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=985635) 2026-02-18 22:58:00,900 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,900 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,903 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,903 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,903 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,905 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,905 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,872 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,877 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,877 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,878 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,878 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,878 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,879 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,882 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,886 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,888 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,888 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,888 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,892 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,893 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=985628) 2026-02-18 22:58:00,894 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=985628) 2026-02-18 22:58:00,899 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,902 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,902 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,903 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,903 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,903 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,904 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,906 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,906 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,907 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,907 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,907 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,907 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,907 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,907 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,907 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,907 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,907 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,907 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,907 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,908 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,908 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,910 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,910 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,910 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,910 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,911 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,911 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,911 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,911 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,911 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,911 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,913 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,914 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,915 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,921 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,921 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,921 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,921 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,922 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,922 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,922 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,922 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,922 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,922 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,923 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,925 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,925 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,926 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,932 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,932 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,932 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,932 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,933 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,936 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,936 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,937 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,937 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,938 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,938 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,940 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,940 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,941 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,908 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,908 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,908 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,910 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,910 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,910 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,910 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,911 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,911 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,911 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,912 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,912 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,912 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,914 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,914 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,914 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,914 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,915 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,915 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,917 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,918 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,918 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,918 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,918 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,919 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,921 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,921 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,922 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,926 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,926 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,927 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,927 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,927 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,928 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,928 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,928 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,928 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,928 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,929 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,937 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,941 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,941 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,941 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,941 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,942 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,942 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,945 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,946 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,946 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,945 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,945 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,945 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,946 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,946 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,947 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,947 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,948 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,949 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,949 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,949 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,949 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,949 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,950 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,952 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,952 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,952 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,952 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,952 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,953 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,953 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,959 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,959 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,959 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,959 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,959 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,960 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,960 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,962 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:00,962 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,963 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,969 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,969 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,969 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,970 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,970 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,972 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:00,972 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:00,974 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,979 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,980 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,984 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,984 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,948 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:00,948 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,948 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,949 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,949 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,949 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,949 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,950 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,954 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,955 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,955 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,956 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,956 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,956 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,958 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:00,958 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,959 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,959 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,959 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:00,959 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,960 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,960 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,964 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,965 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,966 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,969 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,974 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,974 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,974 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,974 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,975 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,975 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,984 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,984 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,984 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,985 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,985 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,989 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,992 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,992 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,993 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,993 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,994 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,994 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,995 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:00,995 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:00,995 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=985635) 2026-02-18 22:58:00,995 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=985635) 2026-02-18 22:58:01,010 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,010 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,013 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,014 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,014 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,016 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,016 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,017 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,017 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,017 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,017 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,018 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,018 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,018 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,018 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,020 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,020 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,021 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,021 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,021 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,021 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,022 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,022 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,022 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,022 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,988 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,988 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,989 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,989 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,990 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,990 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,993 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,997 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,998 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:00,998 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:00,999 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,003 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,004 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=985628) 2026-02-18 22:58:01,004 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=985628) 2026-02-18 22:58:01,010 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,013 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,013 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,013 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,014 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,014 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,014 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,016 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,016 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,017 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,017 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,017 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,018 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,018 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,018 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,018 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,018 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,020 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,021 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,021 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,021 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,021 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,022 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,022 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,022 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,022 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,022 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,024 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,024 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,024 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,024 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,026 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,031 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,032 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,032 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,032 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,032 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,032 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,032 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,033 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,033 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,033 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,033 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,035 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,036 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,037 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,043 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,043 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,043 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,043 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,043 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,047 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,047 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,047 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,047 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,049 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,049 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,051 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,051 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,052 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,056 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,056 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,056 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,056 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,056 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,057 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,058 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,059 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,059 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,059 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,059 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,059 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,060 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,060 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,062 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,062 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,062 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,063 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,063 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,064 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,064 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,025 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,025 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,025 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,026 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,028 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,028 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,029 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,029 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,029 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,029 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,031 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,032 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,033 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,037 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,037 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,037 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,038 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,038 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,038 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,038 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,039 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,039 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,039 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,039 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,047 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,051 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,052 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,052 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,052 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,053 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,053 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,056 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,056 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,056 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,058 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,059 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,059 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,059 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,059 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,060 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,060 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,060 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,065 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,066 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,066 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,069 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,069 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,069 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,069 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,070 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,070 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,070 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,072 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,072 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,074 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,080 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,080 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,080 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,081 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,081 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,083 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,083 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,084 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,090 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,090 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,094 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,094 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,095 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,095 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,096 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,096 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,099 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,103 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,103 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,066 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,066 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,066 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,068 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,069 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,069 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,069 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,069 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,070 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,070 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,070 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,075 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,076 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,076 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,080 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,084 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,084 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,085 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,085 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,086 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,086 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,095 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,099 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,099 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,099 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,100 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,100 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,101 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,104 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,104 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,104 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,105 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,105 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,105 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,105 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,106 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=985635) 2026-02-18 22:58:01,106 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=985635) 2026-02-18 22:58:01,121 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,121 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,124 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,124 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,124 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,126 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,127 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,128 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,128 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,128 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,128 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,128 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,128 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,129 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,129 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,131 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,131 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,132 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,132 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,132 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,132 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,132 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,132 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,133 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,133 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,135 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,135 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,136 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,142 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,142 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,143 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,143 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,143 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,143 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,143 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,108 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,109 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,109 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,110 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,114 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,115 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=985628) 2026-02-18 22:58:01,115 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=985628) 2026-02-18 22:58:01,120 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,123 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,123 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,124 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,124 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,124 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,125 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,127 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,127 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,128 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,128 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,128 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,128 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,128 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,129 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,129 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,129 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,131 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,131 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,132 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,132 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,132 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,132 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,132 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,133 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,133 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,133 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,135 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,135 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,135 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,136 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,136 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,136 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,138 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,139 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,139 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,139 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,139 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,140 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,142 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,142 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,143 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,143 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,143 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,144 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,144 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,146 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,146 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,148 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,153 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,154 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,154 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,154 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,154 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,158 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,158 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,158 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,158 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,159 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,160 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,161 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,162 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,163 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,166 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,166 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,167 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,167 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,167 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,168 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,168 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,170 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,170 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,170 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,170 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,170 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,171 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,171 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,173 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,173 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,173 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,173 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,173 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,174 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,174 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,180 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,180 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,180 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,180 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,180 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,181 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,181 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,183 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,183 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,185 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,148 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,148 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,148 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,148 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,148 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,149 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,149 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,149 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,149 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,150 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,150 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,158 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,162 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,162 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,163 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,163 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,164 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,164 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,167 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,167 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,167 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,169 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,169 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,170 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,170 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,170 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,171 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,171 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,171 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,175 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,176 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,177 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,177 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,177 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,177 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,179 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,179 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,180 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,180 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,180 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,181 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,181 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,181 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,185 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,187 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,187 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,190 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,191 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,191 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,191 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,191 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,193 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,194 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,195 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,201 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,201 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,205 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,205 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,205 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,205 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,207 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,207 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,210 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,213 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,213 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,214 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,214 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,215 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,215 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,215 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,215 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,216 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=985635) 2026-02-18 22:58:01,216 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=985628) 2026-02-18 22:58:01,191 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,195 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,195 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,195 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,195 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,196 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,196 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,205 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,209 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,209 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,209 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,210 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,211 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,211 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,214 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,218 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,219 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,219 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,220 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,224 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,225 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=985628) 2026-02-18 22:58:01,225 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=985635) 2026-02-18 22:58:01,230 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,231 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,234 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,234 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,234 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,236 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,236 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,237 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,237 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,237 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,238 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,238 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,238 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,238 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,238 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,240 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,241 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,241 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,241 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,241 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,242 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,242 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,242 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,242 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,242 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,244 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,245 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,246 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,252 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,252 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,252 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,252 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,253 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,253 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,253 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,253 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,253 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,254 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,254 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,256 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,256 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,257 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,263 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,263 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,264 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,264 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,264 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,230 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,233 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,233 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,234 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,234 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,234 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,234 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,237 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,237 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,237 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,237 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,237 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,238 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,238 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,238 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,238 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,239 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,241 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,241 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,241 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,241 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,241 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,242 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,242 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,242 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,242 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,242 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,245 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,245 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,245 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,245 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,245 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,246 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,248 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,248 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,249 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,249 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,249 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,250 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,252 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,252 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,253 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,257 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,257 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,258 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,258 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,258 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,259 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,259 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,259 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,259 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,260 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,260 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,267 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,267 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,268 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,268 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,269 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,269 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,271 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,272 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,272 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,276 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,276 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,277 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,277 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,277 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,278 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,278 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,280 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,280 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,280 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,280 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,280 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,281 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,281 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,283 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,283 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,283 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,283 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,284 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,284 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,285 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,290 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,290 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,290 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,290 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,290 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,291 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,291 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,293 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,293 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,294 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,300 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,300 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,301 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,301 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,301 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,268 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,272 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,272 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,272 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,273 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,273 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,273 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,277 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,277 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,277 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,279 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,279 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,280 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,280 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,280 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,281 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,281 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,281 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,285 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,286 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,287 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,287 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,287 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,287 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,289 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,289 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,290 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,290 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,290 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,290 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,291 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,291 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,295 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,296 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,297 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,300 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,303 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,303 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,305 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,310 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,311 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,315 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,315 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,315 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,315 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,316 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,317 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,320 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,324 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,324 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,324 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,324 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,325 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,326 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,326 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,326 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,326 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=985635) 2026-02-18 22:58:01,326 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=985635) 2026-02-18 22:58:01,341 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,341 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,304 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,305 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,305 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,305 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,306 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,306 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,315 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,319 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,320 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,320 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,320 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,321 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,321 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,324 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,328 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,330 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,330 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,330 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,334 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,335 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=985628) 2026-02-18 22:58:01,335 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=985628) 2026-02-18 22:58:01,341 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,344 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,344 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,345 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,345 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,345 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,347 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,347 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,348 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,348 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,348 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,349 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,349 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,349 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,349 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,349 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,351 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,351 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,352 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,352 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,352 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,353 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,353 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,353 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,353 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,353 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,355 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,355 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,357 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,362 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,363 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,363 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,363 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,365 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,365 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,365 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,365 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,366 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,366 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,366 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,368 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,368 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,370 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,375 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,376 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,376 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,376 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,376 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,380 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,380 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,380 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,380 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,382 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,382 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,345 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,345 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,345 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,345 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,347 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,348 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,348 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,348 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,348 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,349 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,349 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,349 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,349 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,349 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,351 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,352 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,352 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,352 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,352 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,353 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,353 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,355 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,356 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,356 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,359 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,359 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,360 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,360 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,360 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,362 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,364 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,365 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,365 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,365 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,365 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,366 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,368 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,368 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,369 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,373 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,373 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,374 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,374 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,374 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,375 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,375 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,375 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,375 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,376 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,376 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,380 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,384 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,384 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,385 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,389 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,389 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,389 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,389 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,389 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,390 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,390 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,392 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,392 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,392 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,393 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,393 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,393 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,393 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,395 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,395 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,396 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,396 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,396 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,397 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,397 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,402 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,402 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,402 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,402 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,402 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,403 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,403 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,405 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,405 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,407 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,412 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,413 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,413 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,413 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,413 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,415 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,416 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,417 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,384 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,385 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,385 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,385 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,386 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,386 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,389 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,389 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,389 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,391 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,392 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,392 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,392 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,392 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,393 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,393 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,393 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,397 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,399 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,399 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,399 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,399 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,399 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,401 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,402 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,402 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,402 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,402 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,403 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,403 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,403 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,407 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,409 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,409 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,413 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,417 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,417 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,417 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,417 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,418 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,418 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,423 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,423 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,427 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,427 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,427 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,427 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,429 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,429 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,432 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,436 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,436 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,436 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,436 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,438 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,438 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,438 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,438 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,438 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=985635) 2026-02-18 22:58:01,438 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=985635) 2026-02-18 22:58:01,454 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,454 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,457 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,457 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,457 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,459 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,459 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,461 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,461 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,461 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,461 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,461 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,461 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,427 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,432 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,432 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,432 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,432 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,433 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,433 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,436 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,440 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,442 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,442 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,442 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,446 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,447 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=985628) 2026-02-18 22:58:01,448 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=985628) 2026-02-18 22:58:01,454 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,456 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,456 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,457 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,457 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,457 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,458 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,460 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,460 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,461 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,461 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,461 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,461 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,461 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,462 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,462 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,462 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,462 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,462 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,464 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,464 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,464 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,465 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,465 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,465 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,465 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,465 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,466 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,466 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,468 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,468 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,469 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,475 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,475 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,476 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,476 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,476 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,476 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,476 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,476 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,476 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,477 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,477 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,479 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,479 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,481 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,486 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,487 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,487 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,487 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,487 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,491 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,491 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,491 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,491 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,492 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,492 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,494 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,495 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,496 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,499 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,500 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,500 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,500 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,500 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,501 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,501 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,464 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,464 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,464 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,465 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,465 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,465 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,465 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,466 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,466 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,466 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,468 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,468 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,468 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,469 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,469 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,469 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,471 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,472 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,472 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,472 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,472 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,473 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,475 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,475 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,476 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,481 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,481 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,481 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,481 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,481 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,482 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,482 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,482 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,482 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,483 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,483 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,491 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,495 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,495 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,496 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,496 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,497 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,497 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,500 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,500 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,500 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,502 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,502 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,503 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,503 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,503 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,503 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,503 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,504 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,504 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,506 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,506 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,507 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,507 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,507 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,508 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,508 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,513 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,513 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,513 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,513 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,513 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,514 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,514 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,516 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,516 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,517 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,525 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,525 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,526 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,526 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,526 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,528 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,529 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,530 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,536 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,536 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,540 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,540 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,540 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,540 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,503 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,503 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,503 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,504 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,504 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,504 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,508 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,510 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,510 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,510 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,510 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,510 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,512 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,512 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,513 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,513 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,513 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,514 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,514 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,514 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,518 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,520 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,520 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,525 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,530 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,530 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,530 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,530 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,531 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,531 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,540 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,542 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,542 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,545 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,549 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,549 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,549 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,549 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,550 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,551 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,551 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,551 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,551 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=985635) 2026-02-18 22:58:01,551 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=985635) 2026-02-18 22:58:01,566 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,566 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,570 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,570 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,570 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,572 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,572 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,573 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,573 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,573 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,573 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,574 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,574 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,574 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,574 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,576 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,576 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,577 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,577 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,577 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,577 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,577 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,578 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,578 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,578 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,544 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,545 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,545 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,545 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,546 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,546 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,549 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,553 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,555 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,555 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,555 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,559 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,561 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=985628) 2026-02-18 22:58:01,561 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=985628) 2026-02-18 22:58:01,566 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,569 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,569 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,570 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,570 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,570 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,570 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,572 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,573 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,573 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,573 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,573 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,574 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,574 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,574 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,574 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,574 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,576 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,576 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,577 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,577 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,577 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,578 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,578 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,578 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,578 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,578 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,580 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,580 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,582 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,587 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,588 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,588 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,588 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,588 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,588 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,588 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,588 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,589 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,589 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,589 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,591 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,591 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,593 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,598 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,599 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,599 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,599 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,599 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,603 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,603 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,604 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,604 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,605 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,605 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,607 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,607 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,608 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,612 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,612 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,613 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,613 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,613 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,614 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,614 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,616 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,616 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,616 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,616 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,616 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,617 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,617 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,619 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,619 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,619 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,619 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,619 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,580 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,580 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,581 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,581 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,581 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,581 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,584 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,584 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,584 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,584 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,584 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,585 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,587 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,587 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,588 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,593 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,594 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,594 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,594 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,594 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,595 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,595 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,595 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,595 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,596 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,596 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,604 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,608 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,608 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,608 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,608 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,609 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,609 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,613 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,613 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,613 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,615 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,615 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,616 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,616 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,616 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,617 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,617 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,617 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,620 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,620 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,626 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,626 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,626 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,626 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,626 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,627 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,627 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,629 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,629 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,631 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,636 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,637 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,637 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,637 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,637 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,639 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,640 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,641 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,647 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,647 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,651 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,651 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,652 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,652 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,653 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,653 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,656 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,621 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,622 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,623 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,623 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,623 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,623 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,625 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,625 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,626 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,626 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,626 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,627 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,627 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,627 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,631 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,633 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,633 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,637 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,641 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,641 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,641 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,641 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,642 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,642 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,652 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,656 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,656 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,656 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,656 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,657 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,657 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,660 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,660 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,661 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,661 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,662 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,662 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,662 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,662 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,662 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=985635) 2026-02-18 22:58:01,663 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=985635) 2026-02-18 22:58:01,679 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,679 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,682 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,682 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,682 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,684 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,684 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,685 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,686 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,686 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,686 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,686 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,686 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,686 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,687 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,688 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,689 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,689 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,689 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,689 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,690 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,690 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,690 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,690 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,690 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,693 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,693 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,694 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,661 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,665 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,666 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,666 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,667 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,671 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,672 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=985628) 2026-02-18 22:58:01,672 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(PiperActor pid=985628) 2026-02-18 22:58:01,679 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,681 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,681 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,682 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,682 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,682 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,682 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,685 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,685 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,685 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,686 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,686 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,686 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,686 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,686 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,687 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,687 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,689 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,689 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,689 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,689 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,689 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,690 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,690 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,690 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,691 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,691 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,693 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,693 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,693 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,693 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,693 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,694 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,696 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,700 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,700 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,700 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,700 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,701 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,701 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,701 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,701 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,701 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,702 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,702 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,704 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,704 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,705 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,711 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,712 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,712 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,712 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,712 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,716 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,716 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,716 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,716 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,717 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,718 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,720 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,720 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,721 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,725 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,725 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,725 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,725 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,725 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,726 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,726 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,728 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,728 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,729 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,729 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,729 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,729 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,729 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,731 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,732 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,732 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,732 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,732 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,733 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,733 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,696 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,697 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,697 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,697 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,698 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,700 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,700 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,701 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,706 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,706 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,706 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,706 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,706 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,707 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,707 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,707 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,707 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,708 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,708 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,716 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,720 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,721 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,721 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,721 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,722 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,722 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,725 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,725 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,725 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,728 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=985628) 2026-02-18 22:58:01,728 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,728 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,728 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,728 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,729 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,729 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,730 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,734 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,739 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,739 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,739 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,739 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,739 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,740 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,740 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,742 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=985635) 2026-02-18 22:58:01,742 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,743 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,749 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,750 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,750 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,750 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,750 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,752 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=985635) 2026-02-18 22:58:01,753 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985635) 2026-02-18 22:58:01,754 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,760 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,760 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,764 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,764 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,764 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,764 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,765 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,766 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,769 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,773 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,773 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,773 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,773 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,735 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,735 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,735 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,736 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,736 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,738 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=985628) 2026-02-18 22:58:01,738 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,739 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,739 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,739 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=985628) 2026-02-18 22:58:01,739 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,739 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,740 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,744 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,745 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,745 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,749 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,754 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,754 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,754 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,754 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,755 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,755 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,764 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,768 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,769 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,769 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,769 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,770 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,770 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,773 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(run_dp_rank pid=985582) rank 0 iter time= 0.11 ± 0.00 s (10 samples)
+(run_dp_rank pid=985582) rank 0 throughput= 146292.06 tokens/sec (10 samples)
+(run_dp_rank pid=985582) rank 0 fwd_p2p_send time= 0.32 ± 0.09 ms (80 samples)
+(run_dp_rank pid=985582) rank 0 fwd_p2p_recv time= 0.97 ± 1.31 ms (40 samples)
+(run_dp_rank pid=985582) rank 0 bwd_p2p_recv time= 3.19 ± 3.38 ms (80 samples)
+(run_dp_rank pid=985582) rank 0 bwd_p2p_send time= 0.22 ± 0.05 ms (40 samples)
+(run_dp_rank pid=985582) rank 1 fwd_p2p_recv time= 1.48 ± 1.61 ms (80 samples)
+(run_dp_rank pid=985582) rank 1 fwd_p2p_send time= 0.22 ± 0.06 ms (40 samples)
+(run_dp_rank pid=985582) rank 1 bwd_p2p_send time= 0.18 ± 0.03 ms (80 samples)
+(run_dp_rank pid=985582) rank 1 bwd_p2p_recv time= 1.11 ± 1.27 ms (40 samples)
+(PiperActor pid=985635) 2026-02-18 22:58:01,774 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,775 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,775 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0
+(PiperActor pid=985635) 2026-02-18 22:58:01,775 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=985635) 2026-02-18 22:58:01,775 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=985635) 2026-02-18 22:58:01,775 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=985628) 2026-02-18 22:58:01,777 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,779 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,779 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,779 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1
+(PiperActor pid=985628) 2026-02-18 22:58:01,783 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=985628) 2026-02-18 22:58:01,784 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=985628) 2026-02-18 22:58:01,785 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(run_dp_rank pid=985582) Ray timeline saved to: out/debug-pp2-dp1-interleaved-1f1b.json
+(PiperActor pid=985635) [rank1]:[W218 22:58:03.064745650 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+(PiperActor pid=985628) [rank0]:[W218 22:58:03.293190310 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
diff --git a/out-p2p_reorder/3b-pp2-dp1-interleaved-1f1b.json b/out-p2p_reorder/3b-pp2-dp1-interleaved-1f1b.json
new file mode 100644
index 0000000..c4ead88
--- /dev/null
+++ b/out-p2p_reorder/3b-pp2-dp1-interleaved-1f1b.json
@@ -0,0 +1 @@
+[{"cat": "task:deserialize_arguments", "name": "task:deserialize_arguments", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922530897.0, "dur": 175.331, "ph": "X", "cname": "rail_load", "args": {}}, {"cat": "task:execute", "name": "task:execute", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922531077.8, "dur": 149.602, "ph": "X", "cname": "rail_animation", "args": {}}, {"cat": "task:store_outputs", "name": "task:store_outputs", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922531230.8, "dur": 26.66, "ph": "X", "cname": "rail_idle", "args": {}}, {"cat": "task::PiperActor._backward", "name": "_backward", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922530887.5, "dur": 378.823, "ph": "X", "cname": "generic_work", "args": {"name": "_backward", "task_id": "ceec0f0dc49ffb667372a9e516aef2991244e5d801000000"}}, {"cat": "task:deserialize_arguments", "name": "task:deserialize_arguments", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922531375.5, "dur": 1.181, "ph": "X", "cname": "rail_load", "args": {}}, {"cat": "task:execute", "name": "task:execute", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922531380.2, "dur": 23622.109, "ph": "X", "cname": "rail_animation", "args": {}}, {"cat": "task:store_outputs", "name": "task:store_outputs", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922555012.2, "dur": 98.427, "ph": "X", "cname": "rail_idle", "args": {}}, {"cat": "task::PiperActor._update", "name": "_update", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922531366.5, "dur": 23762.809, "ph": "X", "cname": "generic_work", "args": {"name": "_update", "task_id": "d6f65f88f8d4305a7372a9e516aef2991244e5d801000000"}}, {"cat": "task:deserialize_arguments", "name": "task:deserialize_arguments", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922530016.2, "dur": 360.807, "ph": "X", "cname": "rail_load", "args": {}}, {"cat": "task:execute", "name": "task:execute", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922530385.0, "dur": 344.893, "ph": "X", "cname": "rail_animation", "args": {}}, {"cat": "task:store_outputs", "name": "task:store_outputs", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922530734.2, "dur": 28.352, "ph": "X", "cname": "rail_idle", "args": {}}, {"cat": "task::PiperActor._backward", "name": "_backward", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922530003.0, "dur": 770.356, "ph": "X", "cname": "generic_work", "args": {"name": "_backward", "task_id": "9a4c0325a8f388d17372a9e516aef2991244e5d801000000"}}, {"cat": "task:deserialize_arguments", "name": "task:deserialize_arguments", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922147353.5, "dur": 394.346, "ph": "X", "cname": "rail_load", "args": {}}, {"cat": "task:execute", "name": "task:execute", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922147756.5, "dur": 381424.299, "ph": "X", "cname": "rail_animation", "args": {}}, {"cat": "task:store_outputs", "name": "task:store_outputs", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922529204.0, "dur": 498.741, "ph": "X", "cname": "rail_idle", "args": {}}, {"cat": "task::PiperActor._backward", "name": "_backward", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922147340.8, "dur": 382391.386, "ph": "X", "cname": "generic_work", "args": {"name": "_backward", "task_id": "757b0baf3edfb31e7372a9e516aef2991244e5d801000000"}}, {"cat": "task:deserialize_arguments", "name": "task:deserialize_arguments", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385921578331.5, "dur": 359.084, "ph": "X", "cname": "rail_load", "args": {}}, {"cat": "task:execute", "name": "task:execute", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385921578700.0, "dur": 382889.667, "ph": "X", "cname": "rail_animation", "args": {}}, {"cat": "task:store_outputs", "name": "task:store_outputs", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385921961608.8, "dur": 423.058, "ph": "X", "cname": "rail_idle", "args": {}}, {"cat": "task::PiperActor._backward", "name": "_backward", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385921578319.0, "dur": 383740.491, "ph": "X", "cname": "generic_work", "args": {"name": "_backward", "task_id": "9a4eaf56827e44ab7372a9e516aef2991244e5d801000000"}}, {"cat": "task:deserialize_arguments", "name": "task:deserialize_arguments", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385921962326.5, "dur": 236.682, "ph": "X", "cname": "rail_load", "args": {}}, {"cat": "task:execute", "name": "task:execute", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385921962570.5, "dur": 184321.135, "ph": "X", "cname": "rail_animation", "args": {}}, {"cat": "task:store_outputs", "name": "task:store_outputs", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385922146914.0, "dur": 116.002, "ph": "X", "cname": "rail_idle", "args": {}}, {"cat": "task::PiperActor._forward", "name": "_forward", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385921962313.8, "dur": 184751.415, "ph": "X", "cname": "generic_work", "args": {"name": "_forward", "task_id": "e4ed0fe352c403417372a9e516aef2991244e5d801000000"}}, {"cat": "task:deserialize_arguments", "name": "task:deserialize_arguments", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385921394205.0, "dur": 129.262, "ph": "X", "cname": "rail_load", "args": {}}, {"cat": "task:execute", "name": "task:execute", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385921394341.0, "dur": 183572.863, "ph": "X", "cname": "rail_animation", "args": {}}, {"cat": "task:store_outputs", "name": "task:store_outputs", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385921577935.5, "dur": 94.921, "ph": "X", "cname": "rail_idle", "args": {}}, {"cat": "task::PiperActor._forward", "name": "_forward", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385921394192.5, "dur": 183865.648, "ph": "X", "cname": "generic_work", "args": {"name": "_forward", "task_id": "70a52e38436ab3877372a9e516aef2991244e5d801000000"}}, {"cat": "task:deserialize_arguments", "name": "task:deserialize_arguments", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385921393253.0, "dur": 376.62, "ph": "X", "cname": "rail_load", "args": {}}, {"cat": "task:execute", "name": "task:execute", "pid": "10.158.48.71", "tid": "worker:49e6add8d10984338729449430f9fafea3b1e4dd4dee95bbf06ec4ef", "ts": 1771385921393639.0, 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\ No newline at end of file
diff --git a/out-p2p_reorder/LLAMA_1B-pp2-dp1-interleaved-1f1b copy.json b/out-p2p_reorder/LLAMA_1B-pp2-dp1-interleaved-1f1b copy.json
new file mode 100644
index 0000000..bc00841
--- /dev/null
+++ b/out-p2p_reorder/LLAMA_1B-pp2-dp1-interleaved-1f1b copy.json	
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/out-p2p_reorder/debug-pp2-dp1-interleaved-1f1b.json b/out-p2p_reorder/debug-pp2-dp1-interleaved-1f1b.json
new file mode 100644
index 0000000..28fded4
--- /dev/null
+++ b/out-p2p_reorder/debug-pp2-dp1-interleaved-1f1b.json
@@ -0,0 +1 @@
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"worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514342568.2, "dur": 131.456, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514342749.2, "dur": 126.949, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514342928.5, "dur": 131.215, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514343108.5, "dur": 68.321, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514343218.5, "dur": 138.145, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514343404.5, "dur": 56.534, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514397898.8, "dur": 150.884, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514398117.2, "dur": 235.971, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514398412.5, "dur": 67.311, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514398549.0, "dur": 62.563, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514398667.0, "dur": 53.21, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514398764.2, "dur": 79.949, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514398895.0, "dur": 49.023, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514398994.5, "dur": 209.531, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514399264.5, "dur": 55.312, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514399366.0, "dur": 49.503, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514399458.8, "dur": 45.808, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514399572.2, "dur": 154.118, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514399783.8, "dur": 144.395, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514399983.8, "dur": 53.009, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514400089.2, "dur": 140.329, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514400301.5, "dur": 138.375, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514400495.0, "dur": 66.82, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514400616.8, "dur": 57.315, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514400725.5, "dur": 139.086, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514400916.8, "dur": 137.575, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514401103.5, "dur": 49.524, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514401195.0, "dur": 47.341, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514401289.0, "dur": 132.317, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514401471.0, "dur": 165.516, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514401695.2, "dur": 54.12, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514401795.5, "dur": 130.154, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514401977.5, "dur": 127.89, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514402156.8, "dur": 129.172, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514402341.2, "dur": 131.295, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514402541.2, "dur": 143.263, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514402740.5, "dur": 132.146, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514402925.0, "dur": 72.688, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514403041.2, "dur": 143.183, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "worker:2f0550639c2c6886a86a504718eb7349bed32bdeb7fcdb3315483527", "ts": 1771448514403234.5, "dur": 54.812, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "driver:01000000ffffffffffffffffffffffffffffffffffffffffffffffff", "ts": 1771448482802144.2, "dur": 30791.941, "ph": "X", "cname": "background_memory_dump", "args": {}}, {"cat": "submit_task", "name": "submit_task", "pid": "10.158.48.71", "tid": "driver:01000000ffffffffffffffffffffffffffffffffffffffffffffffff", "ts": 1771448482833237.2, "dur": 6270.701, "ph": "X", "cname": "background_memory_dump", "args": {}}]
\ No newline at end of file
diff --git a/run_dp.sh b/run_dp.sh
new file mode 100644
index 0000000..0a92758
--- /dev/null
+++ b/run_dp.sh
@@ -0,0 +1,21 @@
+#!/bin/bash
+LOG_DIR=logs
+mkdir -p $LOG_DIR
+# RAY_TMP=$HOME/ray_tmp
+RAY_TMP=/m-coriander/coriander/tmp/ray
+mkdir -p $RAY_TMP
+export RAY_TMPDIR="$RAY_TMP"
+export PYTHONUNBUFFERED=1
+
+export CUDA_VISIBLE_DEVICES=0,1,2
+export RAY_DEDUP_LOGS=0
+export PIPER_MASTER_PORT=12388
+
+# python3 -m test.test_llama --iters 5 --schedule interleaved-1f1b --mbs 4 > $LOG_DIR/interleaved.log 2>&1
+python3 -m test.test_llama \
+    --dp 2 \
+    --warmup 5 \
+    --iters 10 \
+    --schedule interleaved-1f1b \
+    --mbs 4 \
+    --model debug > $LOG_DIR/interleaved_debug.log 2>&1
\ No newline at end of file
diff --git a/run_interleaved-3b.sh b/run_interleaved-3b.sh
new file mode 100644
index 0000000..3caa9ed
--- /dev/null
+++ b/run_interleaved-3b.sh
@@ -0,0 +1,21 @@
+#!/bin/bash
+LOG_DIR=logs
+mkdir -p $LOG_DIR
+# RAY_TMP=$HOME/ray_tmp
+RAY_TMP=/m-coriander/coriander/tmp/ray
+mkdir -p $RAY_TMP
+export RAY_TMPDIR="$RAY_TMP"
+export PYTHONUNBUFFERED=1
+
+export CUDA_VISIBLE_DEVICES=3,4,5
+export RAY_DEDUP_LOGS=0
+export PIPER_MASTER_PORT=12388
+
+# python3 -m test.test_llama --iters 5 --schedule interleaved-1f1b --mbs 4 > $LOG_DIR/interleaved.log 2>&1
+python3 -m test.test_llama \
+    --warmup 5 \
+    --iters 10 \
+    --schedule interleaved-1f1b \
+    --mbs 4 \
+    --model 3b > $LOG_DIR/interleaved_3b.trace 2>&1
+    # --tracing \
\ No newline at end of file
diff --git a/run_interleaved-debug.sh b/run_interleaved-debug.sh
new file mode 100644
index 0000000..6618908
--- /dev/null
+++ b/run_interleaved-debug.sh
@@ -0,0 +1,21 @@
+#!/bin/bash
+LOG_DIR=logs
+mkdir -p $LOG_DIR
+# RAY_TMP=$HOME/ray_tmp
+RAY_TMP=/m-coriander/coriander/tmp/ray
+mkdir -p $RAY_TMP
+export RAY_TMPDIR="$RAY_TMP"
+export PYTHONUNBUFFERED=1
+
+export CUDA_VISIBLE_DEVICES=3,4,5
+export RAY_DEDUP_LOGS=0
+export PIPER_MASTER_PORT=12389
+
+# python3 -m test.test_llama --iters 5 --schedule interleaved-1f1b --mbs 4 > $LOG_DIR/interleaved.log 2>&1
+python3 -m test.test_llama \
+    --warmup 5 \
+    --iters 10 \
+    --schedule interleaved-1f1b \
+    --mbs 4 \
+    --model debug > $LOG_DIR/interleaved_debug.log 2>&1
+    # --tracing \
\ No newline at end of file

From 40548720a37c2f327caa267543d1026b26819bf2 Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Thu, 19 Feb 2026 18:16:35 +0000
Subject: [PATCH 035/100] Pushing latest profiling code before merging

---
 piper/actor.py                                |  69 +++-
 run.sh                                        |   1 -
 test/pippy_profiling/1f1b_event_profile.py    | 300 +++++++++++++++++
 .../1f1b_expected_schedule.dot                | 118 +++++++
 .../1f1b_expected_schedule.png                | Bin 0 -> 60730 bytes
 .../zb1p_expected_schedule.dot                | 122 +++++++
 .../zb1p_expected_schedule.png                | Bin 0 -> 100276 bytes
 .../zb_1f1b_memory_profiling.py               | 206 ++++++++++++
 .../zerobubble_event_profile.py               | 318 ++++++++++++++++++
 test/pippy_profiling/zerobubble_example.py    | 270 +++++++++++++++
 test/test_llama_events.py                     | 170 ++++++++++
 tools/strip_ray_call.py                       |  64 ++++
 12 files changed, 1630 insertions(+), 8 deletions(-)
 create mode 100644 test/pippy_profiling/1f1b_event_profile.py
 create mode 100644 test/pippy_profiling/1f1b_expected_schedule.dot
 create mode 100644 test/pippy_profiling/1f1b_expected_schedule.png
 create mode 100644 test/pippy_profiling/zb1p_expected_schedule.dot
 create mode 100644 test/pippy_profiling/zb1p_expected_schedule.png
 create mode 100644 test/pippy_profiling/zb_1f1b_memory_profiling.py
 create mode 100644 test/pippy_profiling/zerobubble_event_profile.py
 create mode 100644 test/pippy_profiling/zerobubble_example.py
 create mode 100644 test/test_llama_events.py
 create mode 100644 tools/strip_ray_call.py

diff --git a/piper/actor.py b/piper/actor.py
index f3764a7..91fb72c 100644
--- a/piper/actor.py
+++ b/piper/actor.py
@@ -137,6 +137,13 @@ def compile_graph(self, stage_id, gm_data, compiler_fn, graphargs, input_idxs):
                 'peak_memory_delta': [],
                 'peak_memory': []
             },
+            'backward_input': {
+                'time': [],
+                'graph_pruning': [],
+            },
+            'backward_weight': {
+                'time': [],
+            },
         }
 
         # compile the graph with the given graphargs
@@ -169,7 +176,7 @@ def compile_graph(self, stage_id, gm_data, compiler_fn, graphargs, input_idxs):
         self.logger.debug(f"compile_graph took {(end-start)*1000:.2f}ms")
         return "Finished compiling"
 
-    # @ray.method(tensor_transport="nccl")
+    @ray.method(tensor_transport="nccl")
     def forward(self, stage_id: int, mb_idx: int, *args):
         self.logger.debug(f"Calling forward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
 
@@ -230,10 +237,7 @@ def unwrap(x):
         if self.tracing:
             forward_end_event.record()
             torch.cuda.synchronize()
-            
-            # Calculate total forward time
             forward_time = forward_start_event.elapsed_time(forward_end_event)
-            
             forward_peak_memory_delta_gb = (torch.cuda.max_memory_allocated() - forward_start_memory) / (1024**3)
             forward_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
 
@@ -319,7 +323,7 @@ def unwrap(x):
         
         return out
 
-    # @ray.method(tensor_transport="nccl")
+    @ray.method(tensor_transport="nccl")
     def backward(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
         self.logger.debug(f"Calling backward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
 
@@ -475,6 +479,13 @@ def clear_trace_data(self) -> None:
                     'peak_memory_delta': [],
                     'peak_memory': []
                 },
+                'backward_input': {
+                    'time': [],
+                    'graph_pruning': [],
+                },
+                'backward_weight': {
+                    'time': [],
+                },
             }
         self.trace_data['update'] = {
             'total': [],
@@ -523,6 +534,11 @@ def time_object_retrieval(self, obj) -> dict:
     
     @ray.method(tensor_transport="nccl")
     def backward_input(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
+        if self.tracing:
+            start_event = torch.cuda.Event(enable_timing=True)
+            end_event = torch.cuda.Event(enable_timing=True)
+            start_event.record()
+
         is_last_stage = loss_fn is not None
         activation = self.activation[stage_id][mb_idx]
 
@@ -542,12 +558,23 @@ def backward_input(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
         # In first stage, we treat the backward_input call like a NOOP
         if stage_id == 0:
             gx = torch.zeros_like(activation) # May get slight performance boost if we don't send the full ones tensor
+            if self.tracing:
+                end_event.record()
+                torch.cuda.synchronize()
+                total_time = start_event.elapsed_time(end_event)
+                self.trace_data[stage_id]['backward_input']['time'].append(total_time)
             return [gx, upstream_grads]
 
         stage_input = self.prev_activation[stage_id][mb_idx]
         stage_params = [p for p in self.parameters[stage_id] if p is not None and p.requires_grad]
 
         # Lists of autograd nodes for the layer outputs, inputs, and parameters
+        # Tracing for graph pruning computation time
+        if self.tracing:
+            graph_pruning_start = torch.cuda.Event(enable_timing=True)
+            graph_pruning_end = torch.cuda.Event(enable_timing=True)
+            graph_pruning_start.record()
+        
         output_nodes = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [activation_or_loss]) if n is not None]
         input_nodes  = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [stage_input]) if n is not None]
         param_nodes   = [n for n in (_get_grad_fn_or_grad_acc(p) for p in stage_params) if n is not None]
@@ -576,6 +603,13 @@ def hook(grad_inputs):
                     return hook
                 handles.append(intermediate_node.register_prehook(make_hook(pg, i)))
 
+        if self.tracing:
+            graph_pruning_end.record()
+            torch.cuda.synchronize()
+            graph_pruning_time = graph_pruning_start.elapsed_time(graph_pruning_end)
+            if 'graph_pruning' not in self.trace_data[stage_id]['backward_input']:
+                self.trace_data[stage_id]['backward_input']['graph_pruning'] = []
+            self.trace_data[stage_id]['backward_input']['graph_pruning'].append(graph_pruning_time)
         gx = torch.autograd.grad(
             outputs=activation_or_loss,
             inputs=stage_input,
@@ -604,10 +638,21 @@ def hook(grad_inputs):
         # Save parameter groups for use in backward_weight
         self._bw_param_groups[stage_id][mb_idx] = param_groups
 
+        if self.tracing:
+            end_event.record()
+            torch.cuda.synchronize()
+            total_time = start_event.elapsed_time(end_event)
+            self.trace_data[stage_id]['backward_input']['time'].append(total_time)
+
         return [gx, upstream_grads]
 
     @ray.method(tensor_transport="nccl")
     def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, loss_fn=None):
+        if self.tracing:
+            start_event = torch.cuda.Event(enable_timing=True)
+            end_event = torch.cuda.Event(enable_timing=True)
+            start_event.record()
+
         stage_params = [p for p in self.parameters[stage_id] if p is not None and p.requires_grad]
 
         # We only pass to enforce temporal dependency.
@@ -651,6 +696,12 @@ def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, loss_fn=None
 
             del self.activation[stage_id][mb_idx]
 
+            if self.tracing:
+                end_event.record()
+                torch.cuda.synchronize()
+                total_time = start_event.elapsed_time(end_event)
+                self.trace_data[stage_id]['backward_weight']['time'].append(total_time)
+
             return [None] + ["done"]
 
         # Create mapping from autograd nodes -> parameters
@@ -730,6 +781,10 @@ def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, loss_fn=None
                 else:
                     weight.grad.add_(dw)
 
-        return [None] + ["done"]
-
+        if self.tracing:
+            end_event.record()
+            torch.cuda.synchronize()
+            total_time = start_event.elapsed_time(end_event)
+            self.trace_data[stage_id]['backward_weight']['time'].append(total_time)
 
+        return [None] + ["done"]
diff --git a/run.sh b/run.sh
index 2d25823..9786146 100755
--- a/run.sh
+++ b/run.sh
@@ -7,7 +7,6 @@ GPU_DEVICES="${GPU_DEVICES:-0}"
 docker run --rm -it \
   --gpus "\"device=${GPU_DEVICES}\"" \
   -e CUDA_VISIBLE_DEVICES="${GPU_DEVICES}" \
-  --network host \
   --shm-size=32g \
   -v "$PWD:/workspace/piper" \
   -v "$PWD/.venv/lib/python3.10/site-packages:/opt/venv/lib/python3.10/site-packages" \
diff --git a/test/pippy_profiling/1f1b_event_profile.py b/test/pippy_profiling/1f1b_event_profile.py
new file mode 100644
index 0000000..5904935
--- /dev/null
+++ b/test/pippy_profiling/1f1b_event_profile.py
@@ -0,0 +1,300 @@
+"""
+CUDA-event timing for pipeline parallel Schedule1F1B.
+Measures per-rank forward and backward timings (backward not split) without using torch.profiler.
+"""
+
+import argparse
+from collections import defaultdict
+
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+import torch.nn as nn
+from torch.cuda import Event
+from torch.distributed.pipelining import (
+    SplitPoint,
+    Schedule1F1B,
+    pipeline,
+)
+
+from model import Transformer, LLAMA_3B
+
+
+def op_key(op) -> str:
+    """Normalize schedule op to key 'F' | 'B'."""
+    try:
+        s = str(op)
+    except Exception:
+        s = f"{op}"
+    s = s.upper()
+    if "FORWARD" in s or s == "F":
+        return "F"
+    if "BACKWARD" in s or s == "B":
+        return "B"
+    return s  # fallback
+
+
+class TimingTracker:
+    """Collect CUDA event pairs for forward/backward timings per microbatch."""
+
+    def __init__(self, device: torch.device):
+        self.device = device
+        self.reset_iteration()
+
+    def reset_iteration(self):
+        self.fwd_pairs = []  # list of (mb, start, end)
+        self.bwd_pairs = []
+        self._fwd_ctr = 0
+        self._bwd_ctr = 0
+
+    def next_fwd_mb(self):
+        mb = self._fwd_ctr
+        self._fwd_ctr += 1
+        return mb
+
+    def next_bwd_input_mb(self):
+        mb = self._bwd_ctr
+        self._bwd_ctr += 1
+        return mb
+
+    # alias for clarity
+    next_bwd_weight_mb = next_bwd_input_mb
+
+    def add_fwd(self, mb: int):
+        torch.cuda.set_device(self.device)
+        start, end = Event(enable_timing=True), Event(enable_timing=True)
+        self.fwd_pairs.append((mb, start, end))
+        return start, end
+
+    def add_bwd(self, mb: int):
+        torch.cuda.set_device(self.device)
+        start, end = Event(enable_timing=True), Event(enable_timing=True)
+        self.bwd_pairs.append((mb, start, end))
+        return start, end
+
+    def summarize_iteration(self):
+        """Return per-op ordered lists of seconds: {'F': [...], 'B': [...]}."""
+        if not torch.cuda.is_available():
+            return {"F": [], "B": []}
+        torch.cuda.set_device(self.device)
+        torch.cuda.synchronize(self.device)
+
+        def to_list(pairs):
+            return [s.elapsed_time(e) / 1000.0 for _, s, e in pairs]
+
+        return {
+            "F": to_list(self.fwd_pairs),
+            "B": to_list(self.bwd_pairs),
+        }
+
+
+def wrap_stage_forward(stage, tracker: TimingTracker):
+    """Wrap forward to record CUDA event timing."""
+    orig_forward = stage.submod.forward
+
+    def wrapped_forward(self, *args, **kwargs):
+        mb = int(tracker.next_fwd_mb())
+        f_start, f_end = tracker.add_fwd(mb)
+        f_start.record()
+        out = orig_forward(self, *args, **kwargs)
+        f_end.record()
+        return out
+
+    stage.submod.forward = wrapped_forward
+
+
+def wrap_stage_backward(stage, tracker: TimingTracker):
+    """Wrap backward (activation/weight together in 1f1b) to time gradients."""
+    orig_bwd_one = stage.__class__.backward_one_chunk
+
+    def _normalize_mb(mb_val):
+        try:
+            return int(mb_val)
+        except Exception:
+            try:
+                return int(mb_val.item())
+            except Exception:
+                return int(tracker.next_bwd_input_mb())
+
+    def wrapped_bwd_one(self, *args, **kwargs):
+        raw_mb = kwargs.get("bwd_chunk_id", args[0] if args else tracker.next_bwd_input_mb())
+        mb = _normalize_mb(raw_mb)
+        b_start, b_end = tracker.add_bwd(mb)
+        b_start.record()
+        result = orig_bwd_one(self, *args, **kwargs)
+        b_end.record()
+        return result
+
+    stage.backward_one_chunk = wrapped_bwd_one.__get__(stage, stage.__class__)
+
+
+def setup(rank, world_size):
+    import os
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "29500"
+    dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
+
+
+def cleanup():
+    dist.destroy_process_group()
+
+
+def run_pipeline(rank, world_size, mb_size, num_mbs, seq_len, model_cfg):
+    device = torch.device(f"cuda:{rank}")
+    torch.cuda.set_device(device)
+
+    setup(rank, world_size)
+    dist.barrier()
+    print(f"[Rank {rank}] using torch device {device}")
+
+    model = Transformer(model_cfg).to(device)
+
+    def tokenwise_loss_fn(outputs, targets):
+        loss_fn = nn.CrossEntropyLoss()
+        outputs = outputs.reshape(-1, model_cfg.vocab_size)
+        targets = targets.reshape(-1)
+        return loss_fn(outputs, targets)
+
+    layers_per_rank = model_cfg.n_layers // world_size
+    split_spec = {
+        f"layers.{i * layers_per_rank}": SplitPoint.BEGINNING for i in range(1, world_size)
+    }
+
+    example_input = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size, seq_len),
+        dtype=torch.long,
+        device=device,
+    )
+
+    pipe = pipeline(module=model, mb_args=(example_input,), split_spec=split_spec)
+    stage = pipe.build_stage(rank, device=device)
+
+    tracker = TimingTracker(device)
+    wrap_stage_forward(stage, tracker)
+    wrap_stage_backward(stage, tracker)
+
+    optimizer = torch.optim.Adam(stage.submod.parameters(), lr=1e-3)
+    pipe_schedule = Schedule1F1B(stage, n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn)
+
+    x = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size * num_mbs, seq_len),
+        dtype=torch.long,
+        device=device,
+    )
+    y = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size * num_mbs, seq_len),
+        dtype=torch.long,
+        device=device,
+    )
+
+    warmup_steps = 2
+    measure_steps = 1
+    total_tokens = mb_size * num_mbs * seq_len
+
+    # Warmup
+    for _ in range(warmup_steps):
+        optimizer.zero_grad(set_to_none=True)
+        if rank != world_size - 1:
+            pipe_schedule.step(x)
+        else:
+            _ = pipe_schedule.step(target=y, losses=[])
+        optimizer.step()
+        torch.cuda.synchronize()
+        dist.barrier()
+
+    iter_times = []
+    avg_accum = {"F": defaultdict(list), "B": defaultdict(list)}
+
+    for _ in range(measure_steps):
+        dist.barrier()
+        torch.cuda.synchronize()
+
+        tracker.reset_iteration()
+
+        iter_start = Event(enable_timing=True)
+        iter_end = Event(enable_timing=True)
+        iter_start.record()
+
+        optimizer.zero_grad(set_to_none=True)
+
+        if rank != world_size - 1:
+            pipe_schedule.step(x)
+        else:
+            losses = []
+            _ = pipe_schedule.step(target=y, losses=losses)
+
+        optimizer.step()
+        torch.cuda.synchronize()
+        iter_end.record()
+        torch.cuda.synchronize()
+        dist.barrier()
+
+        per_op_lists = tracker.summarize_iteration()
+        for key_in, times in per_op_lists.items():
+            norm_key = op_key(key_in)
+            for idx, t in enumerate(times):
+                avg_accum[norm_key][idx].append(t)
+
+        if rank == world_size - 1:
+            iter_times.append(iter_start.elapsed_time(iter_end) / 1000.0)
+
+    # Compute per-op per-occurrence averages across measured steps
+    op_idx_avg = {
+        op: {idx: sum(times) / len(times) for idx, times in idx_dict.items()}
+        for op, idx_dict in avg_accum.items()
+    }
+
+    if rank == world_size - 1 and iter_times:
+        avg_iter = sum(iter_times) / len(iter_times)
+        tput = total_tokens / avg_iter
+
+        print(f"[Rank {rank}] Total iter time: {sum(iter_times):.6f}s")
+        print(f"[Rank {rank}] Avg iter time:   {avg_iter:.6f}s")
+        print(f"[Rank {rank}] Throughput:      {tput:.2f} tokens/s")
+        if 'losses' in locals():
+            print(f"[Rank {rank}] final losses: {losses}")
+
+    # Print pipeline order with average op timings for this rank
+    print(f"\n=== Pipeline order for rank {rank} (avg over {measure_steps} steps) ===")
+    op_counters = defaultdict(int)
+    for step, action in enumerate(pipe_schedule.pipeline_order[rank]):
+        if action is None:
+            continue
+        stage_idx, op, mb = action
+        key = op_key(op)
+        idx = op_counters[key]
+        op_counters[key] += 1
+        t_val = op_idx_avg.get(key, {}).get(idx)
+        t_str = f"{t_val:.6f}s" if t_val is not None else "n/a"
+        print(f"t={step:03d} | stage={stage_idx} | op={op} | microbatch={mb} | time={t_str}")
+
+    dist.barrier()
+    cleanup()
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--microbatch_size", type=int, default=16)
+    parser.add_argument("--num_microbatches", type=int, default=4)
+    parser.add_argument("--seq_len", type=int, default=256)
+    args = parser.parse_args()
+
+    world_size = 2
+    mp.set_start_method("spawn", force=True)
+
+    mp.spawn(
+        run_pipeline,
+        args=(world_size, args.microbatch_size, args.num_microbatches, args.seq_len, LLAMA_3B),
+        nprocs=world_size,
+        join=True,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/test/pippy_profiling/1f1b_expected_schedule.dot b/test/pippy_profiling/1f1b_expected_schedule.dot
new file mode 100644
index 0000000..cab5522
--- /dev/null
+++ b/test/pippy_profiling/1f1b_expected_schedule.dot
@@ -0,0 +1,118 @@
+digraph PipelineDAG_1F1B {
+  rankdir=LR;
+  node [shape=box, fontname="Helvetica", fontsize=10];
+
+  // -----------------------
+  // Nodes (durations used in calculations)
+  // -----------------------
+  subgraph cluster_s0 {
+    label="Stage 0 (rank 0)";
+    style=rounded;
+
+    S0_F0 [label="S0 F mb0\nt=000 274.75ms"];
+    S0_F1 [label="S0 F mb1\nt=003 274.75ms"];
+    S0_B0 [label="S0 B mb0\nt=004 551ms"];
+    S0_F2 [label="S0 F mb2\nt=005 274.75ms"];
+    S0_B1 [label="S0 B mb1\nt=006 551ms"];
+    S0_F3 [label="S0 F mb3\nt=007 274.75ms"];
+    S0_B2 [label="S0 B mb2\nt=008 551ms"];
+    S0_B3 [label="S0 B mb3\nt=010 551ms"];
+
+    Update0 [shape=box, style="rounded", label="Update (rank 0)\n40ms"];
+  }
+
+  subgraph cluster_s1 {
+    label="Stage 1 (rank 1)";
+    style=rounded;
+
+    S1_F1 [label="S1 F mb1\nt=001 274.75ms"];
+    S1_B0 [label="S1 B mb0\nt=002 551ms"];
+    S1_F2 [label="S1 F mb2\nt=003 274.75ms"];
+    S1_B1 [label="S1 B mb1\nt=004 551ms"];
+    S1_F3 [label="S1 F mb3\nt=005 274.75ms"];
+    S1_B2 [label="S1 B mb2\nt=006 551ms"];
+    S1_F4 [label="S1 F mb4\nt=007 274.75ms"];
+    S1_B3 [label="S1 B mb3\nt=008 551ms"];
+
+    Update1 [shape=box, style="rounded", label="Update (rank 1)\n40ms"];
+  }
+
+  Done [shape=oval, label="Done\n0ms"];
+
+  // -----------------------
+  // Temporal edges (serialization on each stage)
+  // -----------------------
+  edge [color=black, penwidth=1.2];
+
+  // Stage 0 order
+  S0_F0 -> S0_F1 -> S0_B0 -> S0_F2 -> S0_B1 -> S0_F3 -> S0_B2 -> S0_B3 -> Update0;
+
+  // Stage 1 order
+  S1_F1 -> S1_B0 -> S1_F2 -> S1_B1 -> S1_F3 -> S1_B2 -> S1_F4 -> S1_B3 -> Update1;
+
+  // -----------------------
+  // Intra-microbatch logical deps (within a stage)
+  // -----------------------
+  edge [color=gray50, style=dashed, penwidth=1.0];
+
+  // Stage 0: Fm -> Bm
+  S0_F0 -> S0_B0;
+  S0_F1 -> S0_B1;
+  S0_F2 -> S0_B2;
+  S0_F3 -> S0_B3;
+
+  // Stage 1: F(m+1) -> Bm (offseted microbatch labeling in your trace)
+  S1_F1 -> S1_B0;
+  S1_F2 -> S1_B1;
+  S1_F3 -> S1_B2;
+  S1_F4 -> S1_B3;
+
+  // -----------------------
+  // Cross-stage data deps
+  // -----------------------
+  // Forward activations (offset mapping): S0_Fm -> S1_F(m+1)
+  edge [color=blue, penwidth=2.0];
+  S0_F0 -> S1_F1;
+  S0_F1 -> S1_F2;
+  S0_F2 -> S1_F3;
+  S0_F3 -> S1_F4;
+
+  // Backward grads: S1_Bm -> S0_Bm
+  edge [color=red, penwidth=2.0];
+  S1_B0 -> S0_B0;
+  S1_B1 -> S0_B1;
+  S1_B2 -> S0_B2;
+  S1_B3 -> S0_B3;
+
+  // -----------------------
+  // Done joins both updates
+  // -----------------------
+  edge [color=purple, style=dashed, penwidth=1.5];
+  Update0 -> Done;
+  Update1 -> Done;
+
+  // ============================================================
+  // Highlight critical path edges (longest path to Done)
+  // Chain sums to 4168.75ms with F=274.75, B=551, U=40
+  // ============================================================
+  edge [color=green, penwidth=4.0];
+
+  // activation + stage1 compute + grad back
+  S0_F0 -> S1_F1;
+  S1_F1 -> S1_B0;
+  S1_B0 -> S0_B0;
+
+  // stage0 tail (forced by temporal order + future grad dependency)
+  S0_B0 -> S0_F2;
+  S0_F2 -> S0_B1;
+  S0_B1 -> S0_F3;
+
+  // activation to stage1 last forward, then last backward, then final stage0 backward
+  S0_F3 -> S1_F4;
+  S1_F4 -> S1_B3;
+  S1_B3 -> S0_B3;
+
+  // update + done
+  S0_B3 -> Update0;
+  Update0 -> Done;
+}
diff --git a/test/pippy_profiling/1f1b_expected_schedule.png b/test/pippy_profiling/1f1b_expected_schedule.png
new file mode 100644
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diff --git a/test/pippy_profiling/zb1p_expected_schedule.dot b/test/pippy_profiling/zb1p_expected_schedule.dot
new file mode 100644
index 0000000..a119c49
--- /dev/null
+++ b/test/pippy_profiling/zb1p_expected_schedule.dot
@@ -0,0 +1,122 @@
+digraph PipelineDAG {
+  rankdir=LR;
+  node [shape=box, fontname="Helvetica", fontsize=10];
+
+  // -----------------------
+  // Nodes (durations used in calculations)
+  // -----------------------
+  subgraph cluster_s0 {
+    label="Stage 0 (rank 0)";
+    style=rounded;
+
+    S0_F0 [label="S0 F mb0\nt=000 274.75ms"];
+    S0_F1 [label="S0 F mb1\nt=002 274.75ms"];
+    S0_I0 [label="S0 I mb0\nt=003 0ms"];
+    S0_W0 [label="S0 W mb0\nt=004 551ms"];
+    S0_F2 [label="S0 F mb2\nt=005 274.75ms"];
+    S0_I1 [label="S0 I mb1\nt=006 0ms"];
+    S0_W1 [label="S0 W mb1\nt=007 551ms"];
+    S0_F3 [label="S0 F mb3\nt=008 274.75ms"];
+    S0_I2 [label="S0 I mb2\nt=009 0ms"];
+    S0_W2 [label="S0 W mb2\nt=010 551ms"];
+    S0_I3 [label="S0 I mb3\nt=011 0ms"];
+    S0_W3 [label="S0 W mb3\nt=012 551ms"];
+  }
+
+  subgraph cluster_s1 {
+    label="Stage 1 (rank 1)";
+    style=rounded;
+
+    S1_F0 [label="S1 F mb0\nt=001 274.75ms"];
+    S1_I0 [label="S1 I mb0\nt=002 275.5ms"];
+    S1_F1 [label="S1 F mb1\nt=003 274.75ms"];
+    S1_I1 [label="S1 I mb1\nt=004 275.5ms"];
+    S1_W0 [label="S1 W mb0\nt=005 275.5ms"];
+    S1_F2 [label="S1 F mb2\nt=006 274.75ms"];
+    S1_I2 [label="S1 I mb2\nt=007 275.5ms"];
+    S1_W1 [label="S1 W mb1\nt=008 275.5ms"];
+    S1_F3 [label="S1 F mb3\nt=009 274.75ms"];
+    S1_I3 [label="S1 I mb3\nt=010 275.5ms"];
+    S1_W2 [label="S1 W mb2\nt=011 275.5ms"];
+    S1_W3 [label="S1 W mb3\nt=012 275.5ms"];
+  }
+
+  // Iteration end when both ranks complete their last op
+  Done [shape=oval, label="Done\n0ms"];
+
+  // -----------------------
+  // Temporal edges (serialization on each stage)
+  // -----------------------
+  edge [color=black, penwidth=1.2];
+
+  // Stage 0 order
+  S0_F0 -> S0_F1 -> S0_I0 -> S0_W0 -> S0_F2 -> S0_I1 -> S0_W1 -> S0_F3 -> S0_I2 -> S0_W2 -> S0_I3 -> S0_W3;
+
+  // Stage 1 order
+  S1_F0 -> S1_I0 -> S1_F1 -> S1_I1 -> S1_W0 -> S1_F2 -> S1_I2 -> S1_W1 -> S1_F3 -> S1_I3 -> S1_W2 -> S1_W3;
+
+  // -----------------------
+  // Intra-microbatch logical deps (within a stage)
+  // -----------------------
+  edge [color=gray50, style=dashed, penwidth=1.0];
+
+  // Stage 0: F -> I -> W
+  S0_F0 -> S0_I0 -> S0_W0;
+  S0_F1 -> S0_I1 -> S0_W1;
+  S0_F2 -> S0_I2 -> S0_W2;
+  S0_F3 -> S0_I3 -> S0_W3;
+
+  // Stage 1: F -> I -> W
+  S1_F0 -> S1_I0 -> S1_W0;
+  S1_F1 -> S1_I1 -> S1_W1;
+  S1_F2 -> S1_I2 -> S1_W2;
+  S1_F3 -> S1_I3 -> S1_W3;
+
+  // -----------------------
+  // Cross-stage data deps
+  // -----------------------
+  edge [color=blue, penwidth=2.0];
+  S0_F0 -> S1_F0;
+  S0_F1 -> S1_F1;
+  S0_F2 -> S1_F2;
+  S0_F3 -> S1_F3;
+
+  edge [color=red, penwidth=2.0];
+  S1_I0 -> S0_I0;
+  S1_I1 -> S0_I1;
+  S1_I2 -> S0_I2;
+  S1_I3 -> S0_I3;
+
+  // -----------------------
+  // Join (Done)
+  // -----------------------
+  edge [color=purple, style=dashed, penwidth=1.5];
+  S0_W3 -> Done;
+  S1_W3 -> Done;
+
+  // ============================================================
+  // Highlight critical path edges (longest path to Done)
+  // ============================================================
+  edge [color=green, penwidth=4.0];
+
+  // activation dep mb0
+  S0_F0 -> S1_F0;
+
+  // stage1 temporal + grad dep back to stage0
+  S1_F0 -> S1_I0;
+  S1_I0 -> S0_I0;
+
+  // stage0 temporal tail to end (critical)
+  S0_I0 -> S0_W0;
+  S0_W0 -> S0_F2;
+  S0_F2 -> S0_I1;
+  S0_I1 -> S0_W1;
+  S0_W1 -> S0_F3;
+  S0_F3 -> S0_I2;
+  S0_I2 -> S0_W2;
+  S0_W2 -> S0_I3;
+  S0_I3 -> S0_W3;
+
+  // done
+  S0_W3 -> Done;
+}
diff --git a/test/pippy_profiling/zb1p_expected_schedule.png b/test/pippy_profiling/zb1p_expected_schedule.png
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diff --git a/test/pippy_profiling/zb_1f1b_memory_profiling.py b/test/pippy_profiling/zb_1f1b_memory_profiling.py
new file mode 100644
index 0000000..6da4fe5
--- /dev/null
+++ b/test/pippy_profiling/zb_1f1b_memory_profiling.py
@@ -0,0 +1,206 @@
+import os
+import torch
+import torch.multiprocessing as mp
+import argparse
+import torch.distributed as dist
+import torch.nn as nn
+import subprocess
+from torch.distributed.pipelining import pipeline, SplitPoint, ScheduleInterleavedZeroBubble, Schedule1F1B
+from torch.profiler import profile, ProfilerActivity, record_function, schedule
+from model import Transformer, LLAMA_DEBUG, LLAMA_3B, LLAMA_1B
+
+def setup(rank, world_size):
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "29500"
+
+    backend = "nccl" if torch.cuda.is_available() else "gloo"
+    dist.init_process_group(backend=backend, rank=rank, world_size=world_size)
+    if backend == "nccl":
+        torch.cuda.set_device(rank)
+
+def cleanup():
+    dist.destroy_process_group()
+
+def wrap_stage_forward(stage, name):
+    orig_forward = stage.submod.forward
+    mb_counter = {"i": 0}
+
+    def wrapped_forward(self, *args, **kwargs):
+        i = mb_counter["i"]
+        mb_counter["i"] += 1
+
+        with record_function(f"{name}_F_mb{i}"):
+            return orig_forward(self, *args, **kwargs)
+
+    stage.submod.forward = wrapped_forward
+
+def wrap_stage_backward_ZB(stage, name):
+    orig_bwd_one = stage.__class__.backward_one_chunk
+    def wrapped_bwd_one(self, *args, **kwargs):
+        mb = kwargs.get("bwd_chunk_id", args[0] if args else "unk")
+        with record_function(f"{name}_B_mb{mb}"):
+            return orig_bwd_one(self, *args, **kwargs)
+    stage.backward_one_chunk = wrapped_bwd_one.__get__(stage, stage.__class__)
+
+    orig_bwd_w = stage.__class__.backward_weight_one_chunk
+    def wrapped_bwd_w(self, *args, **kwargs):
+       mb = kwargs.get("bwd_chunk_id", args[0] if args else "unk")
+       with record_function(f"{name}_W_mb{mb}"):
+            return orig_bwd_w(self, *args, **kwargs)
+    stage.backward_weight_one_chunk = wrapped_bwd_w.__get__(stage, stage.__class__)
+
+def wrap_stage_backward_1f1b(stage, name):
+    orig_bwd_one = stage.__class__.backward_one_chunk
+    def wrapped_bwd_one(self, *args, **kwargs):
+        mb = kwargs.get("bwd_chunk_id", args[0] if args else "unk")
+        with record_function(f"{name}_B_mb{mb}"):
+            return orig_bwd_one(self, *args, **kwargs)
+    stage.backward_one_chunk = wrapped_bwd_one.__get__(stage, stage.__class__)
+
+
+def run_pipeline(rank, world_size, schedule_name, mb_size, num_mbs, result_queue, model_cfg=LLAMA_DEBUG):
+    setup(rank, world_size)
+
+    device = torch.device(f"cuda:{rank}") if torch.cuda.is_available() else torch.device("cpu")
+
+    model = Transformer(model_cfg)
+    model.to(device)
+
+    # Helper loss function that automatically reshapes output and targets tensors
+    def tokenwise_loss_fn(outputs, targets):
+        loss_fn = nn.CrossEntropyLoss()
+        outputs = outputs.reshape(-1, model_cfg.vocab_size)
+        targets = targets.reshape(-1)
+        return loss_fn(outputs, targets)
+
+    # Partition the model into world_size stages and create the stage for this rank
+    layers_per_rank = model_cfg.n_layers // world_size
+
+    split_spec = {
+        f"layers.{i * layers_per_rank}": SplitPoint.BEGINNING
+        for i in range(1, world_size)
+    }
+
+    if rank == 0:
+        print(model_cfg.n_layers)
+        print(world_size)
+        print(layers_per_rank)
+        print(split_spec)
+
+    example_input = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size, model_cfg.max_seq_len),
+        dtype=torch.long,
+        device=device
+    )
+    
+    pipe = pipeline(
+        module=model,
+        mb_args=(example_input,),
+        split_spec=split_spec,
+    )
+    
+    stage = pipe.build_stage(rank, device=device) # Currently assumes one stage per rank  
+    schedule = None      
+    wrap_stage_forward(stage, f"stage{rank}")
+    if schedule_name == "1f1b":
+        wrap_stage_backward_1f1b(stage, f"stage{rank}")
+        schedule = Schedule1F1B(stage, n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn)
+    else:
+        wrap_stage_backward_ZB(stage, f"stage{rank}")
+        schedule = ScheduleInterleavedZeroBubble([stage], n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn)
+
+    # Print the pipeline schedule
+    print(f"\n=== Pipeline order for rank {rank} ===")
+    for step, action in enumerate(schedule.pipeline_order[rank]):
+        if action is None:
+            continue
+        stage_idx, op, mb = action
+        print(f"t={step:03d} | stage={stage_idx} | op={op} | microbatch={mb}")
+
+    prof_schedule = torch.profiler.schedule(wait=0, warmup=0, active=1, repeat=1)
+
+    # Generate random training tensors
+    x = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size * num_mbs, model_cfg.max_seq_len),
+        dtype=torch.long,
+        device=device                 
+    )
+
+    y = torch.randint(
+        low=0, 
+        high=model_cfg.vocab_size, 
+        size=(mb_size * num_mbs, model_cfg.max_seq_len), 
+        dtype=torch.long,
+        device=device
+    )
+
+    # Initialize profiler activity based on device availability
+    activities = [ProfilerActivity.CPU]
+    if torch.cuda.is_available():
+        activities += [ProfilerActivity.CUDA]
+
+    with profile(
+        activities=activities, 
+        schedule=prof_schedule,
+        profile_memory=True,
+        with_stack=True,
+        record_shapes=True,
+        experimental_config=torch._C._profiler._ExperimentalConfig(
+            verbose=True,
+            profile_all_threads=True,
+            capture_overload_names=True,
+        )) as prof:
+        if rank == 0:
+            schedule.step(x)
+        elif rank == 1:
+            losses = []
+            output = schedule.step(target=y, losses=losses)
+            print(f"final losses: {losses}")
+        prof.step()
+
+    trace_dir = os.path.join(os.path.dirname(__file__), "traces")
+    out_path = os.path.join(trace_dir, f"trace_{rank}.json")
+    prof.export_chrome_trace(out_path)
+    
+    dist.barrier()
+    if rank == 1:
+        # Create traces folder if it doesn't exist
+        os.makedirs(trace_dir, exist_ok=True)
+
+        merged_out = os.path.join(trace_dir, "merged_trace.json")
+        cmd = ["python", "filter_trace.py", os.path.join(trace_dir,"trace_0.json"), os.path.join(trace_dir,"user_only_trace_0.json")]
+        subprocess.run(cmd, check=True)
+
+        cmd = ["python", "filter_trace.py", os.path.join(trace_dir, "trace_1.json"),  os.path.join(trace_dir,"user_only_trace_1.json")]
+        subprocess.run(cmd, check=True)
+
+        cmd = ["python", "merge_traces.py", os.path.join(trace_dir,"user_only_trace_0.json"), os.path.join(trace_dir,"user_only_trace_1.json"), merged_out]
+        subprocess.run(cmd, check=True)
+        print(f"Merged trace available at {merged_out}")
+    
+    cleanup()
+   
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--microbatch_size', type=int, default=64)
+    parser.add_argument('--num_microbatches', type=int, default=256)
+    parser.add_argument("--schedule", type=str, default="1f1b", choices=["1f1b", "zb"])
+    args = parser.parse_args()
+    
+    world_size = 4
+
+    mp.set_start_method("spawn", force=True)
+    manager = mp.Manager()
+    result_queue = manager.Queue()
+
+    mp.spawn(
+        run_pipeline,
+        args=(world_size, args.schedule, args.microbatch_size, args.num_microbatches, result_queue),
+        nprocs=world_size,
+        join=True,
+    )
diff --git a/test/pippy_profiling/zerobubble_event_profile.py b/test/pippy_profiling/zerobubble_event_profile.py
new file mode 100644
index 0000000..804eecc
--- /dev/null
+++ b/test/pippy_profiling/zerobubble_event_profile.py
@@ -0,0 +1,318 @@
+import os
+import argparse
+from collections import defaultdict
+
+import torch
+import torch.multiprocessing as mp
+import torch.distributed as dist
+import torch.nn as nn
+from torch.cuda import Event
+from torch.distributed.pipelining import pipeline, SplitPoint, ScheduleInterleavedZeroBubble
+
+from model import Transformer, LLAMA_3B
+
+
+def op_key(op) -> str:
+    """Normalize schedule op to string key 'F' | 'I' | 'W'."""
+    try:
+        s = str(op)
+    except Exception:
+        s = f"{op}"
+    s = s.upper()
+    if "FORWARD" in s:
+        return "F"
+    if "BACKWARD_INPUT" in s or s == "I":
+        return "I"
+    if "BACKWARD_WEIGHT" in s or s == "W":
+        return "W"
+    return s  # fall back (will not match counters)
+
+
+class TimingTracker:
+    """Collect CUDA event pairs for forward/backward timings per microbatch."""
+
+    def __init__(self, device: torch.device):
+        self.device = device
+        self.reset_iteration()
+
+    def reset_iteration(self):
+        self.fwd_pairs = []  # list of (mb, start, end)
+        self.bwd_input_pairs = []
+        self.bwd_weight_pairs = []
+        self._fwd_ctr = 0
+        self._bwd_input_ctr = 0
+        self._bwd_weight_ctr = 0
+
+    def next_fwd_mb(self):
+        mb = self._fwd_ctr
+        self._fwd_ctr += 1
+        return mb
+
+    def next_bwd_input_mb(self):
+        mb = self._bwd_input_ctr
+        self._bwd_input_ctr += 1
+        return mb
+
+    def next_bwd_weight_mb(self):
+        mb = self._bwd_weight_ctr
+        self._bwd_weight_ctr += 1
+        return mb
+
+    def add_fwd(self, mb: int):
+        torch.cuda.set_device(self.device)
+        start, end = Event(enable_timing=True), Event(enable_timing=True)
+        self.fwd_pairs.append((mb, start, end))
+        return start, end
+
+    def add_bwd_input(self, mb: int):
+        torch.cuda.set_device(self.device)
+        start, end = Event(enable_timing=True), Event(enable_timing=True)
+        self.bwd_input_pairs.append((mb, start, end))
+        return start, end
+
+    def add_bwd_weight(self, mb: int):
+        torch.cuda.set_device(self.device)
+        start, end = Event(enable_timing=True), Event(enable_timing=True)
+        self.bwd_weight_pairs.append((mb, start, end))
+        return start, end
+
+    def summarize_iteration(self):
+        """Return per-op ordered lists of seconds: {'F': [...], 'I': [...], 'W': [...]}."""
+        if not torch.cuda.is_available():
+            return {"F": [], "I": [], "W": []}
+        torch.cuda.set_device(self.device)
+        torch.cuda.synchronize(self.device)
+
+        def to_list(pairs):
+            return [s.elapsed_time(e) / 1000.0 for _, s, e in pairs]
+
+        return {
+            "F": to_list(self.fwd_pairs),
+            "I": to_list(self.bwd_input_pairs),
+            "W": to_list(self.bwd_weight_pairs),
+        }
+
+
+def wrap_stage_forward(stage, tracker: TimingTracker):
+    """Wrap forward to record CUDA event timing."""
+    orig_forward = stage.submod.forward
+
+    def wrapped_forward(self, *args, **kwargs):
+        mb = int(tracker.next_fwd_mb())
+        f_start, f_end = tracker.add_fwd(mb)
+        f_start.record()
+        out = orig_forward(self, *args, **kwargs)
+        f_end.record()
+        return out
+
+    stage.submod.forward = wrapped_forward
+
+def wrap_stage_backward(stage, tracker: TimingTracker):
+    """Wrap backward passes to time activation and weight gradients separately."""
+    orig_bwd_one = stage.__class__.backward_one_chunk
+    def _normalize_mb(mb_val):
+        try:
+            return int(mb_val)
+        except Exception:
+            try:
+                return int(mb_val.item())
+            except Exception:
+                return int(tracker.next_bwd_input_mb())
+
+    def wrapped_bwd_one(self, *args, **kwargs):
+        raw_mb = kwargs.get("bwd_chunk_id", args[0] if args else tracker.next_bwd_input_mb())
+        mb = _normalize_mb(raw_mb)
+        b_start, b_end = tracker.add_bwd_input(mb)
+        b_start.record()
+        result = orig_bwd_one(self, *args, **kwargs)
+        b_end.record()
+        return result
+    
+    stage.backward_one_chunk = wrapped_bwd_one.__get__(stage, stage.__class__)
+
+    orig_bwd_w = stage.__class__.backward_weight_one_chunk
+
+    def wrapped_bwd_w(self, *args, **kwargs):
+        raw_mb = kwargs.get("bwd_chunk_id", args[0] if args else tracker.next_bwd_weight_mb())
+        mb = _normalize_mb(raw_mb)
+        w_start, w_end = tracker.add_bwd_weight(mb)
+        w_start.record()
+        result = orig_bwd_w(self, *args, **kwargs)
+        w_end.record()
+        return result
+
+    stage.backward_weight_one_chunk = wrapped_bwd_w.__get__(stage, stage.__class__)
+
+
+def setup(rank, world_size):
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "29500"
+    dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
+
+
+def cleanup():
+    dist.destroy_process_group()
+
+
+def run_pipeline(rank, world_size, mb_size, num_mbs, seq_len, model_cfg):
+    setup(rank, world_size)
+    dist.barrier()
+    device = torch.device(f"cuda:{rank}")
+    torch.cuda.set_device(device)
+    print(f"[Rank {rank}] using torch device {device}")
+
+    model = Transformer(model_cfg).to(device)
+
+    def tokenwise_loss_fn(outputs, targets):
+        loss_fn = nn.CrossEntropyLoss()
+        outputs = outputs.reshape(-1, model_cfg.vocab_size)
+        targets = targets.reshape(-1)
+        return loss_fn(outputs, targets)
+
+    layers_per_rank = model_cfg.n_layers // world_size
+    split_spec = {
+        f"layers.{i * layers_per_rank}": SplitPoint.BEGINNING for i in range(1, world_size)
+    }
+
+    example_input = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size, seq_len),
+        dtype=torch.long,
+        device=device,
+    )
+
+    pipe = pipeline(module=model, mb_args=(example_input,), split_spec=split_spec)
+    stage = pipe.build_stage(rank, device=device)
+
+    tracker = TimingTracker(device)
+    wrap_stage_forward(stage, tracker)
+    wrap_stage_backward(stage, tracker)
+
+    optimizer = torch.optim.Adam(stage.submod.parameters(), lr=1e-3)
+    pipe_schedule = ScheduleInterleavedZeroBubble(
+        [stage], n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn
+    )
+
+    x = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size * num_mbs, seq_len),
+        dtype=torch.long,
+        device=device,
+    )
+    y = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size * num_mbs, seq_len),
+        dtype=torch.long,
+        device=device,
+    )
+
+    warmup_steps = 2
+    measure_steps = 1
+    total_tokens = mb_size * num_mbs * seq_len
+
+    # Warmup
+    for _ in range(warmup_steps):
+        optimizer.zero_grad(set_to_none=True)
+        if rank != world_size - 1:
+            pipe_schedule.step(x)
+        else:
+            _ = pipe_schedule.step(target=y, losses=[])
+        optimizer.step()
+        torch.cuda.synchronize()
+        dist.barrier()
+
+    iter_times = []
+    avg_accum = {"F": defaultdict(list), "I": defaultdict(list), "W": defaultdict(list)}
+
+    for _ in range(measure_steps):
+        dist.barrier()
+        torch.cuda.synchronize()
+
+        tracker.reset_iteration()
+
+        iter_start = Event(enable_timing=True)
+        iter_end = Event(enable_timing=True)
+        iter_start.record()
+
+        optimizer.zero_grad(set_to_none=True)
+
+        if rank != world_size - 1:
+            pipe_schedule.step(x)
+        else:
+            losses = []
+            _ = pipe_schedule.step(target=y, losses=losses)
+
+        optimizer.step()
+
+        torch.cuda.synchronize()
+        iter_end.record()
+        torch.cuda.synchronize()
+        dist.barrier()
+
+        per_op_lists = tracker.summarize_iteration()
+        # accumulate by occurrence index (per op) to align with pipeline order
+        for key_in, times in per_op_lists.items():
+            norm_key = op_key(key_in)
+            for idx, t in enumerate(times):
+                avg_accum[norm_key][idx].append(t)
+
+        if rank == world_size - 1:
+            iter_times.append(iter_start.elapsed_time(iter_end) / 1000.0)
+
+    # Compute per-op per-occurrence averages across measured steps
+    op_idx_avg = {
+        op: {idx: sum(times) / len(times) for idx, times in idx_dict.items()}
+        for op, idx_dict in avg_accum.items()
+    }
+
+    if rank == world_size - 1 and iter_times:
+        avg_iter = sum(iter_times) / len(iter_times)
+        tput = total_tokens / avg_iter
+
+        print(f"[Rank {rank}] Total iter time: {sum(iter_times):.6f}s")
+        print(f"[Rank {rank}] Avg iter time:   {avg_iter:.6f}s")
+        print(f"[Rank {rank}] Throughput:      {tput:.2f} tokens/s")
+        if rank == world_size - 1 and 'losses' in locals():
+            print(f"[Rank {rank}] final losses: {losses}")
+
+    # Print pipeline order with average op timings for this rank
+    print(f"\n=== Pipeline order for rank {rank} (avg over {measure_steps} steps) ===")
+    op_counters = defaultdict(int)
+    for step, action in enumerate(pipe_schedule.pipeline_order[rank]):
+        if action is None:
+            continue
+        stage_idx, op, mb = action
+        key = op_key(op)
+        idx = op_counters[key]
+        op_counters[key] += 1
+        t_val = op_idx_avg.get(key, {}).get(idx)
+        t_str = f"{t_val:.6f}s" if t_val is not None else "n/a"
+        print(f"t={step:03d} | stage={stage_idx} | op={op} | microbatch={mb} | time={t_str}")
+
+    dist.barrier()
+    cleanup()
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--microbatch_size", type=int, default=16)
+    parser.add_argument("--num_microbatches", type=int, default=4)
+    parser.add_argument("--seq_len", type=int, default=256)
+    args = parser.parse_args()
+
+    world_size = 2
+    mp.set_start_method("spawn", force=True)
+
+    mp.spawn(
+        run_pipeline,
+        args=(world_size, args.microbatch_size, args.num_microbatches, args.seq_len, LLAMA_3B),
+        nprocs=world_size,
+        join=True,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/test/pippy_profiling/zerobubble_example.py b/test/pippy_profiling/zerobubble_example.py
new file mode 100644
index 0000000..0d3a189
--- /dev/null
+++ b/test/pippy_profiling/zerobubble_example.py
@@ -0,0 +1,270 @@
+import os
+import torch
+import torch.multiprocessing as mp
+import argparse
+import torch.distributed as dist
+import torch.nn as nn
+import time
+import subprocess
+from torch.cuda import Event
+from torch.distributed.pipelining import pipeline, SplitPoint, ScheduleInterleavedZeroBubble, Schedule1F1B, ScheduleInterleaved1F1B
+from torch.profiler import profile, ProfilerActivity, record_function, schedule as profiler_schedule
+from model import Transformer, LLAMA_DEBUG, LLAMA_3B, LLAMA_1B
+
+
+def wrap_stage_forward(stage, name):
+    """Tag each microbatch forward pass for profiling."""
+    orig_forward = stage.submod.forward
+    mb_counter = {"i": 0}
+
+    def wrapped_forward(self, *args, **kwargs):
+        i = mb_counter["i"]
+        mb_counter["i"] += 1
+
+        with record_function(f"{name}_F_mb{i}"):
+            return orig_forward(self, *args, **kwargs)
+
+    stage.submod.forward = wrapped_forward
+
+
+def wrap_stage_backward(stage, name):
+    """Tag activation and weight backward passes for profiling."""
+    orig_bwd_one = stage.__class__.backward_one_chunk
+
+    def wrapped_bwd_one(self, *args, **kwargs):
+        mb = kwargs.get("bwd_chunk_id", args[0] if args else "unk")
+        with record_function(f"{name}_B_mb{mb}"):
+            return orig_bwd_one(self, *args, **kwargs)
+
+    stage.backward_one_chunk = wrapped_bwd_one.__get__(stage, stage.__class__)
+
+    orig_bwd_w = stage.__class__.backward_weight_one_chunk
+
+    def wrapped_bwd_w(self, *args, **kwargs):
+        mb = kwargs.get("bwd_chunk_id", args[0] if args else "unk")
+        with record_function(f"{name}_W_mb{mb}"):
+            return orig_bwd_w(self, *args, **kwargs)
+
+    stage.backward_weight_one_chunk = wrapped_bwd_w.__get__(stage, stage.__class__)
+
+def setup(rank, world_size):
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "29500"
+
+    backend = "nccl"
+    dist.init_process_group(backend=backend, rank=rank, world_size=world_size)
+
+
+def cleanup():
+    dist.destroy_process_group()
+
+
+def run_pipeline(rank, world_size, mb_size, num_mbs, seq_len, model_cfg):
+    setup(rank, world_size)
+    dist.barrier()
+    device = torch.device(f"cuda:{rank}")
+    torch.cuda.set_device(device)
+    print(f"[Rank {rank}] using torch device {device}")
+
+    model = Transformer(model_cfg)
+    model.to(device)
+
+    # Helper loss function that automatically reshapes output and targets tensors
+    def tokenwise_loss_fn(outputs, targets):
+        loss_fn = nn.CrossEntropyLoss()
+        outputs = outputs.reshape(-1, model_cfg.vocab_size)
+        targets = targets.reshape(-1)
+        return loss_fn(outputs, targets)
+
+    # Partition the model into world_size stages and create the stage for this rank
+    layers_per_rank = model_cfg.n_layers // world_size
+
+    split_spec = {
+        f"layers.{i * layers_per_rank}": SplitPoint.BEGINNING
+        for i in range(1, world_size)
+    }
+
+    example_input = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size, seq_len),
+        dtype=torch.long,
+        device=device
+    )
+    
+    pipe = pipeline(
+        module=model,
+        mb_args=(example_input,),
+        split_spec=split_spec,
+    )
+
+    stage = pipe.build_stage(rank, device=device)  # Currently assumes one stage per rank
+
+    # Add profiling markers to forward / backward passes (names align with zerobubble.py)
+    wrap_stage_forward(stage, f"stage{rank}")
+    wrap_stage_backward(stage, f"stage{rank}")
+
+    # Optimizer for this stage's parameters; profiled per-iteration
+    optimizer = torch.optim.Adam(stage.submod.parameters(), lr=1e-3)
+
+    # pipe_schedule = ScheduleInterleavedZeroBubble([stage], n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn)
+    pipe_schedule = Schedule1F1B(stage, n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn)
+
+    # Print the pipeline schedule
+    print(f"\n=== Pipeline order for rank {rank} ===")
+    for step, action in enumerate(pipe_schedule.pipeline_order[rank]):
+        if action is None:
+            continue
+        stage_idx, op, mb = action
+        print(f"t={step:03d} | stage={stage_idx} | op={op} | microbatch={mb}")
+
+    # Generate random training tensors
+    x = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size * num_mbs, seq_len),
+        dtype=torch.long,
+        device=device                 
+    )
+
+    y = torch.randint(
+        low=0, 
+        high=model_cfg.vocab_size, 
+        size=(mb_size * num_mbs, seq_len), 
+        dtype=torch.long,
+        device=device
+    )
+
+    total_tokens = mb_size * num_mbs * seq_len
+
+    warmup_steps = 2
+    measure_steps = 4
+
+    for _ in range(warmup_steps):
+        optimizer.zero_grad(set_to_none=True)
+        if rank != world_size - 1:
+            pipe_schedule.step(x)
+        else:
+            _losses = []
+            _ = pipe_schedule.step(target=y, losses=_losses)
+        optimizer.step()
+        torch.cuda.synchronize()
+        dist.barrier()
+
+    elapsed_times = []
+
+    # ---------------------------
+    # Profiling + timing section
+    # ---------------------------
+
+    activities = [ProfilerActivity.CPU]
+    if torch.cuda.is_available():
+        activities.append(ProfilerActivity.CUDA)
+
+    # Profile the full measure_steps window (after warmup) and export chrome trace.
+    prof_schedule = profiler_schedule(wait=0, warmup=0, active=measure_steps, repeat=1)
+
+    trace_dir = os.path.join(os.path.dirname(__file__), "traces")
+    os.makedirs(trace_dir, exist_ok=True)
+    trace_path = os.path.join(trace_dir, f"trace_{rank}.json")
+
+    def _remove_if_exists(path: str):
+        try:
+            os.remove(path)
+        except FileNotFoundError:
+            pass
+
+    with profile(
+        activities=activities,
+        schedule=prof_schedule,
+        with_stack=False,
+        record_shapes=False,
+    ) as prof:
+        for _ in range(measure_steps):
+            dist.barrier()
+            torch.cuda.synchronize()
+
+            start = torch.cuda.Event(enable_timing=True)
+            end = torch.cuda.Event(enable_timing=True)
+
+            start.record()
+
+            optimizer.zero_grad(set_to_none=True)
+
+            if rank != world_size - 1:
+                pipe_schedule.step(x)
+            else:
+                losses = []
+                _ = pipe_schedule.step(target=y, losses=losses)
+
+            with record_function(f"optimizer_step_rank{rank}"):
+                optimizer.step()
+            
+            torch.cuda.synchronize()
+            end.record()
+            torch.cuda.synchronize()
+            dist.barrier()
+
+            if rank == world_size - 1:
+                elapsed_ms = start.elapsed_time(end)
+                elapsed_times.append(elapsed_ms / 1000.0)
+
+            prof.step()
+
+    _remove_if_exists(trace_path)
+    prof.export_chrome_trace(trace_path)
+    dist.barrier()  # ensure both traces are written before merging
+
+    if rank == world_size - 1:
+        print(f"[Rank {rank}] chrome trace saved to {trace_path}")
+
+        # Create user-only traces and merge (mirrors zerobubble.py workflow)
+        trace0 = os.path.join(trace_dir, "trace_0.json")
+        trace1 = os.path.join(trace_dir, "trace_1.json")
+        user0 = os.path.join(trace_dir, "user_only_trace_0.json")
+        user1 = os.path.join(trace_dir, "user_only_trace_1.json")
+        merged = os.path.join(trace_dir, "merged_trace.json")
+        merged_raw = os.path.join(trace_dir, "merged_trace_raw.json")
+
+        for p in (user0, user1, merged, merged_raw):
+            _remove_if_exists(p)
+
+        subprocess.run(["python", "filter_trace.py", trace0, user0], check=True)
+        subprocess.run(["python", "filter_trace.py", trace1, user1], check=True)
+        subprocess.run(["python", "merge_traces.py", user0, user1, merged], check=True)
+        subprocess.run(["python", "merge_traces.py", trace0, trace1, merged_raw], check=True)
+        print(f"[Rank {rank}] merged trace available at {merged}")
+        print(f"[Rank {rank}] raw merged trace available at {merged_raw}")
+
+    if rank == world_size - 1:
+        avg_elapsed = sum(elapsed_times) / len(elapsed_times)
+        tokens_per_sec = total_tokens / avg_elapsed
+        print(f"[Rank {rank}] Total elapsed: {sum(elapsed_times):.6f}s (CUDA events)")
+        print(f"[Rank {rank}] Avg elapsed: {avg_elapsed:.6f}s (CUDA events)")
+        print(f"[Rank {rank}] Throughput: {tokens_per_sec:.2f} tokens/s")
+        print(f"[Rank {rank}] final losses: {losses}")
+
+    dist.barrier()
+    cleanup()
+
+   
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--microbatch_size', type=int, default=16)
+    parser.add_argument('--num_microbatches', type=int, default=4)
+    parser.add_argument('--seq_len', type=int, default=256)
+    # parser.add_argument("--schedule", type=str, default="1f1b", choices=["1f1b", "zb"])
+    args = parser.parse_args()
+    
+    world_size = 2
+
+    mp.set_start_method("spawn", force=True)
+    manager = mp.Manager()
+
+    mp.spawn(
+        run_pipeline,
+        args=(world_size, args.microbatch_size, args.num_microbatches, args.seq_len, LLAMA_3B),
+        nprocs=world_size,
+        join=True,
+    )
diff --git a/test/test_llama_events.py b/test/test_llama_events.py
new file mode 100644
index 0000000..3b9e450
--- /dev/null
+++ b/test/test_llama_events.py
@@ -0,0 +1,170 @@
+import argparse
+import os
+import time
+import numpy as np
+import ray
+import torch
+
+from piper.exec import piper_exec
+from piper.compile import piper_setup
+from piper.piper import piper
+from piper.utils import piper_metadata
+
+from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B
+from .schedule_helpers import (
+    build_1f1b_schedule,
+    build_zb1p_schedule,
+    print_schedule,
+    no_pp_schedule,
+)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="CUDA-event timing for LLaMA pipeline schedules")
+    parser.add_argument(
+        "--model",
+        choices=["LLAMA_DEBUG", "LLAMA_1B", "LLAMA_3B", "LLAMA_8B"],
+        default="LLAMA_DEBUG",
+    )
+    parser.add_argument("--schedule", choices=["1f1b", "zb1p", "no-pp"], default="1f1b")
+    parser.add_argument("--num_stages", type=int, default=2, help="Number of stages (default: 2)")
+    parser.add_argument("--dp_degree", type=int, default=1, help="Data parallel degree (default: 1)")
+    parser.add_argument("--pp_degree", type=int, default=2, help="Pipeline parallel degree (default: 2)")
+    parser.add_argument("--batch_size", type=int, default=16)
+    parser.add_argument("--num_mbs", type=int, default=4, help="Number of microbatches")
+    parser.add_argument("--seq_len", type=int, default=256)
+    parser.add_argument("--warmup", type=int, default=5)
+    parser.add_argument("--iters", type=int, default=20)
+    parser.add_argument(
+        "--tracing",
+        action="store_true",
+        default=False,
+        help="Enable CUDA-event tracing inside actors (required for timings)",
+    )
+    return parser.parse_args()
+
+
+def select_model(name: str):
+    if name == "LLAMA_DEBUG":
+        return LLAMA_DEBUG
+    if name == "LLAMA_1B":
+        return LLAMA_1B
+    if name == "LLAMA_3B":
+        return LLAMA_3B
+    if name == "LLAMA_8B":
+        return LLAMA_8B
+    raise ValueError(f"Unknown model {name}")
+
+
+def build_schedule(schedule_name: str, num_mbs: int, num_stages: int):
+    if schedule_name == "1f1b":
+        sched = build_1f1b_schedule(num_mbs, num_stages)
+        sched[0][2] = sched[0][4]
+        sched[0][4] = sched[0][6]
+        sched[0][6] = None
+        return sched
+    if schedule_name == "zb1p":
+        return build_zb1p_schedule(num_mbs, num_stages)
+    if schedule_name == "no-pp":
+        return no_pp_schedule
+    raise NotImplementedError(f"Schedule {schedule_name} not supported for event timing.")
+
+
+def print_mean_timing_data(trace_data: dict, actor_id: int) -> None:
+    """Pretty-print mean timings collected via CUDA events."""
+    print(f"\nTiming statistics for Actor {actor_id}:")
+    for stage_id, stage_data in trace_data.items():
+        if stage_id == "update":
+            print("  Update:")
+            for metric, values in stage_data.items():
+                mean_val = float("nan") if not values else float(np.mean(values))
+                unit = "GB" if "memory" in metric else "ms"
+                print(f"    {metric}: {mean_val:.3f} {unit}")
+            continue
+
+        print(f"  Stage {stage_id}:")
+        for phase_name, phase_data in stage_data.items():
+            if not phase_data:
+                continue
+            print(f"    {phase_name}:")
+            for metric, values in sorted(phase_data.items()):
+                mean_val = float("nan") if not values else float(np.mean(values))
+                unit = "GB" if "memory" in metric else "ms"
+                if metric == "graph_pruning":
+                    print(f"      {metric} (graph analysis/pruning overhead): {mean_val:.3f} {unit}")
+                else:
+                    print(f"      {metric}: {mean_val:.3f} {unit}")
+
+
+def main(args):
+    llama_config = select_model(args.model)
+    loss_fn = torch.nn.CrossEntropyLoss()
+    device = "cuda"
+
+    batch_size = args.batch_size
+    num_mbs = args.num_mbs
+    seq_len = args.seq_len
+    warmup = args.warmup
+    iters = args.iters
+
+    x = torch.randint(0, llama_config.vocab_size, (batch_size, seq_len)).to(device)
+    y = torch.zeros((batch_size, llama_config.vocab_size), dtype=torch.long).to(device)
+
+    if args.dp_degree > 1:
+        dp_rank = int(os.environ["PIPER_DP_RANK"])
+        torch.manual_seed(dp_rank)
+        x = torch.randint(0, llama_config.vocab_size, (batch_size, seq_len)).to(device)
+        torch.manual_seed(0)
+
+    model = Transformer(llama_config, seq_len, device).to(device)
+    compiled = piper_setup(model, [x], backend=piper, num_stages=args.num_stages, pp_size=args.pp_degree)
+    assert args.num_stages == len(piper_metadata.dag) + 1
+
+    schedule = build_schedule(args.schedule, num_mbs, args.num_stages)
+    print("SCHEDULE:")
+    print_schedule(schedule)
+
+    actors = piper_metadata.actors
+
+    ray.get([actor.set_tracing.remote(True if args.tracing is None else args.tracing) for actor in actors.values()])
+
+    def iter_schedule():
+        out = piper_exec(compiled, schedule, [x], y, loss_fn, num_mbs, args.num_stages)
+        ray.get(out)
+
+    print(f"Running {warmup} warmup iterations...")
+    for _ in range(warmup):
+        iter_schedule()
+
+    ray.get([actor.clear_trace_data.remote() for actor in actors.values()])
+    ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
+
+    print(f"Running {iters} timed iterations...")
+    torch.cuda.synchronize()
+    iter_start = torch.cuda.Event(enable_timing=True)
+    iter_end = torch.cuda.Event(enable_timing=True)
+    iter_start.record()
+    for _ in range(iters):
+        iter_schedule()
+    torch.cuda.synchronize()
+    iter_end.record()
+
+    elapsed_ms = iter_start.elapsed_time(iter_end)
+    total_s = elapsed_ms / 1000.0
+
+    print(f"Iteration time: {elapsed_ms / iters:.0f} ms")
+    print(f"{args.schedule} throughput: {(iters * batch_size * num_mbs * seq_len) / total_s:.0f} tokens/sec")
+
+    for actor in actors.values():
+        trace = ray.get(actor.get_trace_data.remote())
+        actor_id = ray.get(actor.id.remote())
+        print_mean_timing_data(trace, actor_id)
+
+    ray.timeline(f"out/{args.model}-pp{args.pp_degree}-dp{args.dp_degree}-{args.schedule}-events.json")
+
+
+if __name__ == "__main__":
+    ray.init(include_dashboard=False, log_to_driver=True, namespace="llama-events")
+    args = parse_args()
+    main(args)
+    ray.shutdown()
diff --git a/tools/strip_ray_call.py b/tools/strip_ray_call.py
new file mode 100644
index 0000000..3ed94e4
--- /dev/null
+++ b/tools/strip_ray_call.py
@@ -0,0 +1,64 @@
+#!/usr/bin/env python3
+"""
+Remove __ray_call__ entries from Ray timeline JSON traces.
+
+Usage:
+  python tools/strip_ray_call.py path/to/trace.json [more.json] [--output OUTPUT]
+
+If --output is omitted, files are rewritten in place. When multiple input files
+are provided, --output is ignored and each file is cleaned in place.
+"""
+import argparse
+import json
+import pathlib
+import sys
+from typing import Iterable, Tuple
+
+
+def clean_trace(path: pathlib.Path) -> Tuple[int, int]:
+    """Return (removed, remaining) after filtering a single file."""
+    with path.open("r") as f:
+        data = json.load(f)
+
+    if not isinstance(data, list):
+        raise ValueError(f"{path} is not a Ray timeline list")
+
+    before = len(data)
+    filtered = [event for event in data
+                if not (isinstance(event, dict) and event.get("name") == "__ray_call__")]
+    removed = before - len(filtered)
+
+    with path.open("w") as f:
+        json.dump(filtered, f, indent=2)
+
+    return removed, len(filtered)
+
+
+def main(argv: Iterable[str]) -> int:
+    parser = argparse.ArgumentParser(description="Strip __ray_call__ events from Ray timeline JSON traces.")
+    parser.add_argument("traces", nargs="+", type=pathlib.Path, help="Trace file(s) to clean")
+    parser.add_argument("--output", "-o", type=pathlib.Path,
+                        help="Optional output file (only valid when cleaning a single input)")
+    args = parser.parse_args(list(argv))
+
+    if args.output and len(args.traces) != 1:
+        parser.error("--output can only be used with a single input file")
+
+    total_removed = 0
+
+    for trace_path in args.traces:
+        target = args.output if args.output else trace_path
+        # If writing elsewhere, copy first to avoid overwriting input before read.
+        if args.output and target != trace_path:
+            target.write_text(trace_path.read_text())
+            trace_path = target
+
+        removed, remaining = clean_trace(trace_path)
+        total_removed += removed
+        print(f"{trace_path}: removed {removed} __ray_call__ events, kept {remaining}")
+
+    return 0 if total_removed >= 0 else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main(sys.argv[1:]))

From e6e32dd39dac8e5c39a815f67705f1bae9e7bf42 Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Fri, 20 Feb 2026 00:15:08 +0000
Subject: [PATCH 036/100] Refactoring from /src to /piper

---
 piper/actor.py                                | 926 +++++++++++-------
 .../{piper_coordinator.py => coordinator.py}  |   2 +-
 piper/exec.py                                 |  35 +-
 ..._graph_transform.py => graph_transform.py} |   0
 piper/{piper_patches.py => patches.py}        |   2 +-
 piper/piper.py                                |   8 +-
 test/test_graph_break.py                      |  10 +-
 test/test_llama.py                            |   2 +-
 test/test_llama_single.py                     |   8 +-
 test/test_moe.py                              |  10 +-
 10 files changed, 589 insertions(+), 414 deletions(-)
 rename piper/{piper_coordinator.py => coordinator.py} (97%)
 rename piper/{piper_graph_transform.py => graph_transform.py} (100%)
 rename piper/{piper_patches.py => patches.py} (99%)

diff --git a/piper/actor.py b/piper/actor.py
index 80e391f..2f2bd2e 100644
--- a/piper/actor.py
+++ b/piper/actor.py
@@ -16,145 +16,46 @@
 
 torch.set_float32_matmul_precision('high')
 torch._dynamo.config.compiled_autograd = False
+everse_graph, _get_grad_fn_or_grad_acc
 
-CLEANUP_MEMORY = False
-torch.autograd.set_detect_anomaly(True)
-
-logger = create_logger("piper_actor", LOG_LEVEL)
-
-def create_actors(num_actors, optim_class):
-    dp_rank = int(os.environ['PIPER_DP_RANK'])
-    world_size = int(os.environ['PIPER_WORLD_SIZE'])
-    dp_degree = int(os.environ['PIPER_DP_DEGREE'])
-    pp_degree = int(os.environ['PIPER_PP_DEGREE'])
-
-    from .piper_utils import piper_metadata
-    for actor_id in range(num_actors):
-        global_rank = dp_rank * dp_degree + actor_id
-        actor = PiperActor.options(num_gpus=0.9, max_concurrency=2).remote(actor_id, optim_class, world_size, dp_rank=dp_rank, dp_degree=dp_degree, pp_degree=pp_degree)
-        piper_metadata.actors[actor_id] = actor
-
-    ray.get([actor.load_actor_handles.remote(piper_metadata.actors) for actor in piper_metadata.actors.values()])
-
-def get_actor(actor_id):
-    from .piper_utils import piper_metadata
-    return piper_metadata.actors[actor_id]
-
-class ExpertRayFunction(torch.autograd.Function):
-    """
-    Custom autograd Function that dispatches expert calls to Ray actors.
-    """
-    
-    @staticmethod
-    def forward(ctx, global_expert_id: int, local_expert_id: int, batch_idx: int, pp_degree: int, *args):
-        """
-        Dispatch a forward call to the expert `global_expert_id` 
-        on the actor `local_expert_id % pp_degree`. 
-        Future work will allow custom actor placement. 
-        """
-        # Get the actor for this expert
-        actor_id = local_expert_id % pp_degree
-        actor = get_actor(actor_id)
-        current_actor = ray.get_runtime_context().current_actor
-        
-        # Store metadata for backward pass
-        ctx.global_expert_id = global_expert_id
-        ctx.local_expert_id = local_expert_id
-        ctx.batch_idx = batch_idx
-        ctx.pp_degree = pp_degree
-        ctx.actor_id = actor_id
-
-        from .piper_utils import piper_metadata
-        actor_self = piper_metadata.actor_self
-        
-        # Store which inputs require gradients
-        ctx.input_requires_grad = [arg.requires_grad if isinstance(arg, torch.Tensor) else False for arg in args]
-        ctx.num_inputs = len(args)
-        
-        # Dispatch a remote call if actor is not current actor
-        if actor == current_actor:
-            output = actor_self.run_expert(global_expert_id, batch_idx, *args)
-        else:
-            ref = actor.run_expert.remote(global_expert_id, batch_idx, *args)
-            output = ray.get(ref)
-
-        # Detach the output so the graph is not carried over when 
-        # the expert runs on the current actor. 
-        if isinstance(output, (list, tuple)):
-            output = [t.detach() for t in output]
-        else:
-            output = output.detach()
-        
-        return output
-    
-    @staticmethod
-    def backward(ctx, grad_output):
-        """
-        Dispatch a backward expert call corresponding to the 
-        forward expert call from the ctx. 
-        """
-        # Get the actor for this expert
-        actor = get_actor(ctx.actor_id)
-        current_actor = ray.get_runtime_context().current_actor
-        logger.debug(f"Expert {ctx.global_expert_id} {ctx.batch_idx} backward on actor: {current_actor} calling actor: {actor}")
-        num_inputs = ctx.num_inputs
-
-        from .piper_utils import piper_metadata
-        actor_self = piper_metadata.actor_self
-        
-        # Dispatch a remote call if actor is not current actor
-        if actor == current_actor:
-            grad_inputs = actor_self.backward_expert(ctx.global_expert_id, ctx.batch_idx, grad_output)
-        else:
-            refs = actor.backward_expert.options(num_returns=num_inputs).remote(ctx.global_expert_id, ctx.batch_idx, grad_output)
-            grad_inputs = ray.get(refs)
-        
-        # Return gradients for inputs (None for metadata arguments)
-        result = [None, None, None, None] + list(grad_inputs)
-        
-        return tuple(result)
-
-def dispatch_expert_ray(global_expert_id: int, local_expert_id: int, batch_idx: int, pp_degree: int, *args):
-    """
-    Dispatch a Ray remote call to run an expert GraphModule
-    using a custom autograd Function. This function is called
-    from the FX graph, so it needs to be allowed in the graph.
-    """
-    return ExpertRayFunction.apply(global_expert_id, local_expert_id, batch_idx, pp_degree, *args)
+torch.set_float32_matmul_precision('high')
+torch._dynamo.config.compiled_autograd = False
 
-# Allow the dispatch function in the graph
-torch.compiler.allow_in_graph(dispatch_expert_ray)
+CLEANUP_MEMORY = False
 
 @ray.remote
 class PiperActor:
-    def __init__(self, actor_id, optim_class, world_size, dp_rank=0, dp_degree=1, pp_degree=1):
-        self.logger = create_logger("piper_actor", LOG_LEVEL)
-
-        self.actor_id = actor_id
-        self.optim_class = optim_class
+    def __init__(self, actor_id, world_size, dp_rank=0, dp_degree=1, pp_degree=1, optim_fn=None):
+        self.logger = create_logger("piper_actor", "DEBUG")
         
+        st    from piper.utils import piper_metadata
+r_id = actor_id
+        self.optim_fn = optim_fn
+        
+        # Data parallel attributes
         self.dp_rank = dp_rank
         self.dp_degree = dp_degree
-        self.pp_degree = pp_degree
         self.world_size = world_size
-
         self.dp_group = None
-        self.device = 'cuda'
+        self.pp_degree = pp_degree
 
-        if dp_degree == 1:
-            self.global_rank = actor_id
-        elif pp_degree == 1:
+        if pp_degree == 1:
             self.global_rank = dp_rank
+        elif dp_degree == 1:
+            self.global_rank = actor_id
         else:
-            self.global_rank = dp_rank * dp_degree + actor_id
-
-        self.logger.info(f"Initializing Ray actor {actor_id} global rank {self.global_rank} GPU {os.environ['CUDA_VISIBLE_DEVICES']}")
+            self.global_rank = dp_rank *    from piper.utils import piper_metadata
+nfo(f"Initializing Ray actor {actor_id} global rank {self.global_rank} with PID: {os.getpid()}")
 
         self.input = None
         self.truth = None
+        self.fwd_objs = {}
+        self.bwd_objs = {}
 
+        # ordered list of frame ids for ordering the fx.Graphs on this actor
+        self.frame_ids = []
         # map stage id -> compiled fx.Graph function
-        self.forward_fns = dict()
+        self.compiled_fns = dict()
         # map stage id -> original GraphModule (for hook registration)
         self.graph_modules = dict()
         # map stage id -> model parameters used by the fx.Graph with holes (None values) for input tensors
@@ -163,24 +64,12 @@ def __init__(self, actor_id, optim_class, world_size, dp_rank=0, dp_degree=1, pp
         self.input_idxs = dict()
         # map stage id -> optimizer for the fx.Graph
         self.optims = dict()
-        # map stage id -> mb_idx -> previous activation (if this stage is not first)
-        self.inp_activation = defaultdict(dict)
+        # map stage -> mb_idx -> previous activation (if this stage is not first)
+       piper.rev_activation = defaultdict(dict)
         # map stage id -> mb_idx -> current activation
-        self.out_activation = defaultdict(dict)
+        self.activation = defaultdict(dict)
         # accumuate loss for each microbatch
         self.loss = []
-        # map expert id -> expert
-        self.experts = dict()
-        # map expert id -> expert parameters
-        self.expert_parameters = dict()
-        # map expert id -> expert input indices
-        self.expert_input_idxs = dict()
-        # map expert id -> expert module
-        self.expert_modules = dict()
-        # map expert id -> expert input activations
-        self.expert_input_activations = defaultdict(dict)
-        # map (expert_id, batch_idx) -> output activation for backward
-        self.expert_output_activations = defaultdict(dict)
         # stage_id -> mb_idx -> param_groups
         self._bw_param_groups = defaultdict(dict)
 
@@ -188,22 +77,24 @@ def __init__(self, actor_id, optim_class, world_size, dp_rank=0, dp_degree=1, pp
         self.tracing = False  # Toggle for timing and memory tracing
         self.trace_data = {'update': {'total': [], 'peak_memory_delta': [], 'peak_memory': []}}
 
-        from .piper_utils import piper_metadata
-        piper_metadata.actor_self = self
+        end = time.perf_counter()
+        self.logger.debug(f"__init__ took {(end-start)*1000:.2f}ms")
+
+        self.logger.debug(f"Initialized actor {self.actor_id} for global rank {self.global_rank}")
 
-    def load_actor_handles(self, actor_handles):
-        from .piper_utils import piper_metadata
-        piper_metadata.actors = actor_handles
-    
     def id(self):
         return self.actor_id
 
     def send_input(self, tensor):
-        self.input = tensor.to(self.device)
+        self.input = tensor.to('cuda')
         return "done"
     
     def send_truth(self, tensor):
-        self.truth = tensor.to(self.device)
+        self.truth = tensor.to('cuda')
+        return "done"
+
+    def reset_peak_memory(self):
+        torch.cuda.reset_peak_memory_stats()
         return "done"
 
     def get_peak_memory(self):
@@ -215,7 +106,7 @@ def join_process_groups(self):
         master_port = os.environ.get('PIPER_MASTER_PORT', "10000")
         init_method = f"tcp://{master_addr}:{master_port}"
 
-        dist.init_process_group("nccl", init_method=init_method, rank=self.global_rank, world_size=self.world_size)
+        dpiper.t_process_group("nccl", init_method=init_method, rank=self.global_rank, world_size=self.world_size)
         self.logger.debug(f"Actor {self.actor_id} global rank {self.global_rank} has GPU {os.environ['CUDA_VISIBLE_DEVICES']}, joined the global process group")
 
         if self.dp_degree > 1:
@@ -231,15 +122,11 @@ def join_dp_process_group(self):
                 self.dp_group = process_group
                 self.logger.info(f"Global rank {self.global_rank} joined its dp group {dp_group_id} along with ranks {group_ranks}")
 
-    def load_graph(self, stage_id: int, gm_data, compiler_fn, graphargs, input_idxs):
-        self.logger.debug(f"Compiling graph on actor {self.actor_id} for stage id: {stage_id} with inputs: {len(graphargs)} and input indices: {input_idxs}")
-
-        # set up tracing data structure
-        self.trace_data[stage_id] = {
-            'forward': {
-                'forward': [],
-                'total': [],
-                'peak_memory_delta': [],
+    def compile_graph(self, stage_id, gm_data, compiler_fn, graphargs, input_idxs):
+        self.logger.debug(f"Compiling graph on actor {self.actor_id} for stage id: {stage_id} with inputs: {len(graphargs)}")
+      def __init__(self, actor_id, optim_class, world_size, dp_rank=0, dp_degree=1, pp_degree=1):
+        self.logger = create_logger("piper_actor", LOG_LEVEL)
+            'peak_memory_delta': [],
                 'peak_memory': []
             },
             'backward': {
@@ -248,6 +135,7 @@ def load_graph(self, stage_id: int, gm_data, compiler_fn, graphargs, input_idxs)
                 'peak_memory_delta': [],
                 'peak_memory': []
             },
+            # Split-backward variants (zero-bubble). These stay empty when not used.
             'backward_input': {
                 'time': [],
                 'graph_pruning': [],
@@ -259,38 +147,144 @@ def load_graph(self, stage_id: int, gm_data, compiler_fn, graphargs, input_idxs)
 
         # compile the graph with the given graphargs
         gm = deserialize_graphmodule(gm_data)
-
+        
         # Store GraphModule reference
         self.graph_modules[stage_id] = gm
-        self.forward_fns[stage_id] = gm.forward
         
-        # save the parameters and initialize the optimizer
-        self.add_param_group(stage_id, graphargs, input_idxs)
+        compiled_fn = compiler_fn(gm, graphargs)
+        assert callable(compiled_fn), "compiler_fn did not return callable"
+        self.compiled_fns[stage_id] = compiled_fn
+
+        # discard the graphargs that correspond to input tensors
+        for i in input_idxs:
+            graphargs[i] = None
+        self.input_idxs[stage_id] = input_idxs
+
+        # save the graphargs that correspond to model parameters
+        self.parameters[stage_id] = graphargs
+
+        # initialize the optimizer for this stage
+        ## Added this manual_seed for debugging
+        torch.manual_seed(0)
+        if [param for param in self.parameters[stage_id] if param is not None]:
+            self.optims[stage_id] = self.optim_fn([param for param in self.parameters[stage_id] if param is not None])
+
+        de        # map expert id -> expert
+        self.experts = dict()
+        # map expert id -> expert parameters
+        self.expert_parameters = dict()
+        # map expert id -> expert input indices
+        self.expert_input_idxs = dict()
+        # map expert id -> expert module
+        self.expert_modules = dict()
+        # map expert id -> expert input activations
+        self.expert_input_activations = defaultdict(dict)
+        # map (expert_id, batch_idx) -> output activation for backward
+        self.expert_output_activations = defaultdict(dict)
+        # stage_id -> mb_idx -> param_groups
+        self._bw_param_groups = defaultdict(dict)
+l gm_data
 
-        del gm_data
+        end = time.perf_counter()
+        self.logger.debug(f"compile_graph took {(end-start)*1000:.2f}ms")
+        return "Finished compiling"
 
-    def add_param_group(self, stage_id: int, params, input_idxs):
-        self.logger.debug(f"Adding param group for stage {stage_id}")
+    @ray.method(tensor_transport="nccl")
+    def forwardpiper.stage_id: int, mb_idx: int, *args):
+        self.logger.debug(f"Calling forward {stage_id} mb {mb_idx} on actor {self.actor_id} globpiper. {self.global_rank}")
 
-        if stage_id in self.parameters:
-            self.logger.debug(f"Param group already exists for stage {stage_id}")
-            return
+        if self.tracing:
+            beginning_event = torch.cuda.Event(enable_timing=True)
+            forward_start_event = torch.cuda.Event(enable_timing=True)
+            forward_end_event = torch.cuda.Event(enable_timing=True)
+            end_event = torch.cuda.Event(enable_timing=True)
+            beginning_event.record()
         
-        # place parameters on the device
-        def move_to_device(idx, arg):
-            if idx not in input_idxs:
-                return arg.to(self.device).detach().requires_grad_(True)
+        def pre_loaded_input(param):
+            if param is Non    # @ray.method
+e:
+                return self.input
             else:
-                return arg.to(self.device)
-        params = list(map(move_to_device, range(len(params)), params))
+                return param
+        args = list(map(pre_loaded_input, args))
 
-        # discard the graphargs that correspond to input tensors
-        for i in input_idxs:
-            params[i] = None
+        def unwrap_arg(arg):
+            if isinstance(arg, list) or isinstance(arg, tuple):
+                assert len(arg) == 1
+                return arg[0]
+            else:
+                return arg
+        args = list(map(unwrap_arg, args))
 
-        # save the parameters
-        self.input_idxs[stage_id] = input_idxs
-        self.parameters[stage_id] = params
+        def detach_arg(arg):
+            return arg.detach().clone()
+        args = list(map(detach_arg, args))
+
+        # Ray object refs resolve to a single element list
+        def unwrap(x):
+            if isinstance(x, list) or isinstance(x, tuple):
+                assert len(x) == 1
+            return x[0] if isinstance(x, list) or isinstance(x, tuple) else x
+        args = list(map(unwrap, args))
+
+        # place input tensors in the correct indices
+        for i, arg in zip(self.input_idxs[stage_id], args):
+            self.parameters[stage_id][i] = arg
+
+        # save first input as previous activation
+        if stage_id != 0:
+            if not args[0].requires_grad:
+                args[0].requires_grad_()
+            self.prev_activation[stage_id][mb_idx] = args[0]
+
+
+        # Record start event for forward timing
+        if self.tracing:
+            # torch.cuda.reset_peak_memory_stats()
+            # forward_start_memory = torch.cuda.memory_allocated()
+            forward_start_event.record()
+
+        # Call compiled function
+        out = self.compiled_fns[stage_id](*self.parameters[stage_id])
+
+        # Record end event and calculate forward timing
+        if self.tracing:
+            forward_end_event.record()
+            torch.cuda.synchronize()
+            
+            # Calculate total forward time
+            forward_time = forward_start_event.elapsed_time(forward_end_event)
+            
+            # forward_peak_memory_delta_gb = (torch.cuda.max_memory_allocated(            'backward_input': {
+                'time': [],
+                'graph_pruning': [],
+            },
+            'backward_weight': {
+                'time': [],
+            },
+) - forward_start_memory) / (1024**3)
+            # forward_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
+
+        # save first output as activation BEFORE clearing input tensors
+        # This ensures the activation doesn't share memory with parameters
+        # that might be modified when processing other stages on the same actor
+        activation_tensor = out[0] if isinstance(out, (list, tuple)) else out
+        self.activation[stage_id][mb_idx] = activation_tensor
+
+        # clear the input tensors
+        for i in self.input_idxs[stage_id]:
+            self.parameters[stage_id][i] = None
+        del args
+
+        if self.tracing:
+            end_event.record()
+            torch.cuda.synchronize()
+            total_time = forward_start_event.elapsed_time(end_event)
+            # Store in trace_data (all microbatches stored sequentially)
+            self.trace_data[stage_id]['forward']['forward'].append(forward_time)
+            self.trace_data[stage_id]['forward']['total'].append(forward_time)
+            # self.trace_data[stage_id]['forward']['peak_memory_delta'].append(forward_peak_memory_delta_gb)
+            # self.trace_data[stage_id]['forward']['peak_memor        self.parameters[stage_id] = params
 
         # add the parameters to the optimizer for this stage
         if stage_id not in self.optims:
@@ -393,17 +387,8 @@ def backward_expert(self, expert_id, batch_idx, grad_output):
         
         return grad_inputs
     
-    # @ray.method(tensor_transport="nccl")
-    def forward(self, stage_id: int, mb_idx: int, *args):
-        self.logger.debug(f"Calling forward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
+ng c#  forward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
 
-        if self.tracing:
-            beginning_event = torch.cuda.Event(enable_timing=True)
-            forward_start_event = torch.cuda.Event(enable_timing=True)
-            forward_end_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-            beginning_event.record()
-        
         def pre_loaded_input(param):
             if param is None:
                 return self.input
@@ -415,212 +400,127 @@ def pre_loaded_input(param):
         def unwrap(x):
             if isinstance(x, list) or isinstance(x, tuple):
                 assert len(x) == 1
-            return x[0] if isinstance(x, list) or isinstance(x, tuple) else x
-        args = list(map(unwrap, args))
-
-        def place(arg):
-            if arg.device == torch.device('cpu'):
-                return arg.to(self.device)
+                return x[0]
             else:
-                return arg
-        args = list(map(place, args))
+                return x
+        args = list(map(unwrap, args))
 
         # place input tensors in the correct indices
         for i, arg in zip(self.input_idxs[stage_id], args):
             self.parameters[stage_id][i] = arg
 
         # save first input as previous activation
+        if stage_id != 0:
+            # args[0].requires_grad_()
+            assert args[0].requires_grad 
+            self.prev_activation[stage_id][mb_idx] = args[0]
+
+        out = self.compiled_fns[stage_id](*self.parameters[stage_id])
+        
+        # save first output as activation BEFORE clearing input tensors
+        # This ensures the activation doesn't share memory with parameters
+        # that might be modified when processing other stages on the same actor
+        activation_tensor = out[0] if isinstance(out, (list, tuple)) else out
+        self.activation[stage_id][mb_idx] = activation_tensor
+        
+           # save first input as previous activation
         if stage_id != 0:
             if not args[0].requires_grad:
                 args[0].requires_grad_()
-            self.prev_activation[stage_id][mb_idx] = args[0]
+            self.inp_activation[stage_id][mb_idx] = args[0]
+ct()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+        
+        return out
 
-        # Record start event for forward timing
-        if self.tracing:
-            torch.cuda.reset_peak_memory_stats()
-            forward_start_memory = torch.cuda.memory_allocated()
-            forward_start_event.record()
+    @ray.method(tensor_transport="nccl")
+    def backward(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
+        self.logger.debug(f"Calling backward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
 
-        # Call compiled function
-        output = self.forward_fns[stage_id](*self.parameters[stage_id])
-        assert isinstance(output, (list, tuple)) and len(output) == 1, "Piper only supports one output per subgraph"
-        output = output[0]
+        if self.tracing:
+            beginning_event = torch.cuda.Event(enable_timing=True)
+            backward_start_event = torch.cuda.Event(enable_timing=True)
+            backward_end_event = torch.cuda.Event(enable_timing=True)
+            end_event = torch.cuda.Event(enable    # get the activation for the current stage
+        assert mb_idx in self.activation[stage_id], f"mb_idx {mb_idx} not in activation[stage_id {stage_id}]"
+        activation = self.activation[stage_id][mb_idx]
+        
+        # Record start event for backward timing
+        if self.tracing:
+            # torch.cuda.reset_peak_memory_stats()
+            # backward_start_memory = torch.cuda.memory_allocated()
+            backward_start_event.record()
+        
+        # compute loss in the last stage. use the saved activation rather
+        # than inp because the saved activation remembers the computation graph
+        if loss_fn is not None:
+            if self.truth is not None:
+                labels = self.truth
+            else:
+                labels = inp[0]
+            loss = loss_fn(activation, labels)
+            loss.backward()
+            self.loss.append(loss.item())
+            # Clean up loss tensor after extracting item
+            del loss
+        # if not the last stage, backprop on the stored activation given 
+        # the input gradient from the subsequent stage
+        else:
+            assert inp is not None
+            assert activation.shape == inp.shape
+            activation.backward(gradient=inp)
 
-        # Record end event and calculate forward timing
+        # Record end event and calculate backward timing
         if self.tracing:
-            forward_end_event.record()
+            backward_end_event.record()
             torch.cuda.synchronize()
-            forward_time = forward_start_event.elapsed_time(forward_end_event)
-            forward_peak_memory_delta_gb = (torch.cuda.max_memory_allocated() - forward_start_memory) / (1024**3)
-            forward_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
-
-        # save output as output activation
-        self.logger.debug(f"Saving output activation {output.shape} for stage {stage_id} mb {mb_idx}")
-        self.out_activation[stage_id][mb_idx] = output
+            
+            # Calculate total backward time
+            backward_time = backward_start_event.elapsed_time(backward_end_event)
+            
+            # backward_peak_memory_delta_gb = (torch.cuda.max_memory_allocated() - backward_start_memory) / (1024**3)
+            # backward_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
 
-        # clear the input tensors
-        for i in self.input_idxs[stage_id]:
-            self.parameters[stage_id][i] = None
-        del args
+        del self.activation[stage_id][mb_idx]
+        del activation
 
+        # propagate the gradient backwards if not the first stage
+        if stage_id != 0:
+            ret = [self.prev_activation[stage_id][mb_idx]]
+        else:
+            ret = ["done"]
+        
         if self.tracing:
             end_event.record()
             torch.cuda.synchronize()
-            total_time = forward_start_event.elapsed_time(end_event)
-            self.trace_data[stage_id]['forward']['forward'].append(forward_time)
-            self.trace_data[stage_id]['forward']['total'].append(forward_time)
-            self.trace_data[stage_id]['forward']['peak_memory_delta'].append(forward_peak_memory_delta_gb)
-            self.trace_data[stage_id]['forward']['peak_memory'].append(forward_peak_memory_gb)
-
+            total_time = beginning_event.elapsed_time(end_event)
+            # Store in trace_data (all microbatches stored sequentially)
+            self.trace_data[stage_id]['backward']['backward'].append(backward_time)
+            self        # save first input as input activation
+        if args[0].dtype == torch.float:
+            args[0].requires_grad_().retain_grad()
+        self.inp_activation[stage_id][mb_idx] = args[0]
+       # self.trace_data[stage_id]['backward']['peak_memory'].append(backward_peak_memory_gb)
+            if stage_id != 0:
+                self.bwd_objs[stage_id] = torch.ones_like(ret[0])
+        
         if CLEANUP_MEMORY:
             gc.collect()
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
         
-        return output
-
-    def forward_cpu(self, stage_id: int, mb_idx: int, *args):
-        self.logger.debug(f"Calling cpu forward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
-
-        def pre_loaded_input(param):
-            if param is None:
-                return self.input
-            else:
-                return param
-        args = list(map(pre_loaded_input, args))
+        return ret + ret
 
-        # Ray object refs resolve to a single element list
-        def unwrap(x):
-            if isinstance(x, list) or isinstance(x, tuple):
-                assert len(x) == 1
-                return x[0]
-            else:
-                return x
-        args = list(map(unwrap, args))
+    def synchronize_gradients(self):
+        """Synchronize gradients across all DP ranks for this stage using all-reduce."""     
+        self.logger.debug(f"Actor {self.actor_id} global rank {self.global_rank} synchr# izing gradients")
         
-        def place(arg):
-            if arg.device == torch.device('cpu'):
-                return arg.to(self.device)
-            else:
-                return arg
-        args = list(map(place, args))
-
-        # place input tensors in the correct indices
-        for i, arg in zip(self.input_idxs[stage_id], args):
-            self.parameters[stage_id][i] = arg
-
-        # save first input as input activation
-        if args[0].dtype == torch.float:
-            args[0].requires_grad_().retain_grad()
-        self.inp_activation[stage_id][mb_idx] = args[0]
-
-        out = self.forward_fns[stage_id](*self.parameters[stage_id])
-
-        # save output as output activation
-        activation_tensor = out[0] if isinstance(out, (list, tuple)) else out
-        self.out_activation[stage_id][mb_idx] = activation_tensor
-        
-        # clear the input tensors
-        for i in self.input_idxs[stage_id]:
-            self.parameters[stage_id][i] = None
-        del args
-        
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-        
-        return out
-
-    # @ray.method(tensor_transport="nccl")
-    def backward(self, stage_id: int, mb_idx: int, inp, truth=None, loss_fn=None):
-        self.logger.debug(f"Calling backward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
-
-        if self.tracing:
-            beginning_event = torch.cuda.Event(enable_timing=True)
-            backward_start_event = torch.cuda.Event(enable_timing=True)
-            backward_end_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-            beginning_event.record()
-            torch.cuda.reset_peak_memory_stats()
-            backward_start_memory = torch.cuda.memory_allocated()
-            backward_start_event.record()
-
-        if isinstance(inp, list) or isinstance(inp, tuple):
-            assert len(inp) == 1
-            inp = inp[0]
-
-        # Get the activations for this stage and microbatch
-        out_activation = self.out_activation[stage_id][mb_idx]
-        inp_activation = self.inp_activation[stage_id][mb_idx]
-        self.logger.debug(f"Stage {stage_id} backward graph:")
-        print_backward_graph(self.logger.debug, out_activation)
-
-        # compute loss with the final activation of the final stage. 
-        # use the saved activation rather than inp because the saved activation stores the computation graph
-        if loss_fn is not None:
-            assert out_activation.shape == inp.shape
-            if self.truth is not None:
-                labels = self.truth
-            else:
-                labels = truth.to(self.device)
-            assert out_activation.shape == labels.shape
-            loss = loss_fn(out_activation, labels)
-            loss.backward()
-            self.loss.append(loss.item())
-        # if not the last stage, backprop on the stored activation given 
-        # the input gradient from the subsequent stage
-        else:
-            assert inp is not None
-            assert out_activation.shape == inp.shape
-            out_activation.backward(gradient=inp)
-
-        del out_activation
-
-        # propagate the gradient backwards if not the first stage
-        if stage_id == 0:
-            ret = "done"
-        else:
-            ret = inp_activation.grad
-        
-        del inp_activation
-
-        # Record end event and calculate backward timing
-        if self.tracing:
-            backward_end_event.record()
-            torch.cuda.synchronize()
-            
-            # Calculate total backward time
-            backward_time = backward_start_event.elapsed_time(backward_end_event)
-            
-            backward_peak_memory_delta_gb = (torch.cuda.max_memory_allocated() - backward_start_memory) / (1024**3)
-            backward_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
-
-            end_event.record()
-            torch.cuda.synchronize()
-            total_time = beginning_event.elapsed_time(end_event)
-            self.trace_data[stage_id]['backward']['backward'].append(backward_time)
-            self.trace_data[stage_id]['backward']['total'].append(total_time)
-            self.trace_data[stage_id]['backward']['peak_memory_delta'].append(backward_peak_memory_delta_gb)
-            self.trace_data[stage_id]['backward']['peak_memory'].append(backward_peak_memory_gb)
-        
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-        
-        return [ret] * 2
-
-    def synchronize_gradients(self):
-        """Synchronize gradients across all DP ranks for this stage using all-reduce."""     
-        self.logger.debug(f"Actor {self.actor_id} global rank {self.global_rank} synchronizing gradients")
-        
-        # Iterate over all stages on this actor and synchronize their parameters
+        #     def backward(self, stage_id: int, mb_idx: int, inp, truth=None, loss_fn=None):
         for stage_id, parameters in self.parameters.items():
             for param in parameters:
                 if param is not None and param.grad is not None:
                     dist.all_reduce(param.grad, op=dist.ReduceOp.AVG, group=self.dp_group)
-        self.logger.info(f"Actor {self.actor_id} global rank {self.global_rank} finished synchronizing gradients")
 
     def update(self, *done_mbs):
         self.logger.debug(f"Calling update on actor {self.actor_id} global rank {self.global_rank}")
@@ -629,8 +529,8 @@ def update(self, *done_mbs):
             start_event = torch.cuda.Event(enable_timing=True)
             end_event = torch.cuda.Event(enable_timing=True)
 
-            torch.cuda.reset_peak_memory_stats()
-            update_start_memory = torch.cuda.memory_allocated()
+            # torch.cuda.reset_peak_memory_stats()
+            # update_start_memory = torch.cuda.memory_allocated()
 
             start_event.record()
         
@@ -639,6 +539,7 @@ def update(self, *done_mbs):
             self.synchronize_gradients()
         
         # step the optimizer for each stage
+        assert self.optim_fn
         for _, optim in self.optims.items():
             optim.step()
             optim.zero_grad()
@@ -649,15 +550,27 @@ def update(self, *done_mbs):
             end_event.record()
             torch.cuda.synchronize()
             total_time = start_event.elapsed_time(end_event)
-            update_peak_memory_delta_gb = (torch.cuda.max_memory_allocated() - update_start_memory) / (1024**3)
-            update_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
+            # update_peak_memory_delta_gb = (torch.cuda.max_memory_allocated() - update_start_memory) / (1024**3)
+            # update_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
 
             self.trace_data['update']['total'].append(total_time)
-            self.trace_data['update']['peak_memory_delta'].append(update_peak_memory_delta_gb)
-            self.trace_data['update']['peak_memory'].append(update_peak_memory_gb)
+            # self.trace_data['update']['peak_memory_delta'].append(update_peak_memory_delta_gb)
+            # self.trace_data['update']['peak_memory'].append(update_peak_memory_gb)
 
         return losses
 
+    def verify_weights(self, msg="first parameter"):
+        for stage_id, parameters in self.parameters.items():
+            for param in parameters:
+                if param is not None and param.grad is not None:
+                    if len(param.shape) == 2:
+                        self.logger.info(f"Actor {self.actor_id} global rank {self.global_rank} stage {stage_id} {msg}: {param.shape, param[0][0], param.grad[0][0]}")
+                    elif len(param.shape) == 1:
+                        self.logger.info(f"Actor {self.actor_id} global rank {self.global_rank} stage {stage_id} {msg}: {param.shape, param[0], param.grad[0]}")
+                    else:
+                        assert False, f"Unsupported parameter shape: {param.shape}"
+                    return "done"
+
     def clear_trace_data(self) -> None:
         """
         Clear all collected timing data.
@@ -712,7 +625,73 @@ def stop_mem_tracing(self) -> None:
         self.logger.info(f"Saved memory snapshot to actor{self.actor_id}_memory_snapshot_mb4_gpipe.pickle")
         torch.cuda.memory._record_memory_history(enabled=None)
         return "done"
+    
+    @ray.method(tensor_transport="nccl")
+    def get_object(self, is_fwd, stage_id):
+        if is_fwd:
+            return self.fwd_objs[stage_id]
+        else:
+            return self.bwd_objs[stage_id]
+
+    def time_object_retrieval(self, obj) -> dict:
+        objs = []
+        if isinstance(obj, tuple) or isinstance(obj, list):
+            objs = [t + 1 for t in obj]
+        else:
+            objs = [obj + 1]
+        self.objs = objs
+        return "done"
+    
+    @ray.method(tensor_transport="nccl")
+    def backward_input(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
+        if self.tracing:
+            start_event = torch.cuda.Event(enable_timing=True)
+            end_event = torch.cuda.Event(enable_timing=True)
+            start_event.record()
+
+        is_last_stage = loss_fn is not None
+        activation = self.activation[stage_id][mb_idx]
+
+        # Use loss instead of activation if this is the backward_input call for the last stage
+        if is_last_stage:
+            labels = self.truth if self.truth is not None else inp[0]
+            loss = loss_fn(activation, labels)
+            self.loss.append(loss)
+
+            activation_or_loss = loss
+            upstream_grads = torch.ones_like(loss)
+
+        else:
+            activation_or_loss = activation
+            upstream_grads = inp
+
+        # In first stage, we treat the backward_input call like a NOOP
+        if stage_id == 0:
+            gx = torch.zeros_like(activation) # May get slight performance boost if we don't send the full ones tensor
+            if self.tracing:
+                end_event.record()
+                torch.cuda.synchronize()
+                total_time = start_event.elapsed_time(end_event)
+                self.trace_data[stage_id]['backward_input']['time'].append(total_time)
+            return [gx, upstream_grads]
 
+        stage_input = self.prev_activation[stage_id][mb_idx]
+        stage_params = [p for p in self.parameters[stage_id] if p is not None and p.requires_grad]
+
+        # Lists of autograd nodes for the layer outputs, inputs, and parameters
+        ## Start graph pruning computation timing here ##
+        if self.tracing:
+            graph_pruning_start = torch.cuda.Event(enable_timing=True)
+            graph_pruning_end = torch.cuda.Event(enable_timing=True)
+            graph_pruning_start.record()
+        
+        output_nodes = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [activation_or_loss]) if n is not None]
+        input_nodes  = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [stage_input]) if n is not None]
+        param_nodes   = [n for n in (_get_grad_fn_or_grad_acc(p) for p in stage_params) if n is not None]
+
+        # Use the autograd graph with edges reversed to compute parameter groups, which are groups 
+        # of parameters that share the same intermediate nodes. Intermediate nodes are the nodes that
+        # lie on both (1) a backward path from the output node(s) to the
     def reset_peak_memory(self):
         torch.cuda.reset_peak_memory_stats()
         return "done"
@@ -720,7 +699,7 @@ def reset_peak_memory(self):
     def get_peak_memory(self):
         return torch.cuda.max_memory_allocated() / (1024**3)
     
-    @ray.method(tensor_transport="nccl")
+    # @ray.method(tensor_transport="nccl")
     def get_object(self, is_fwd, stage_id):
         if is_fwd:
             return self.fwd_objs[stage_id]
@@ -736,7 +715,7 @@ def time_object_retrieval(self, obj) -> dict:
         self.objs = objs
         return "done"
     
-    @ray.method(tensor_transport="nccl")
+    # @ray.method(tensor_transport="nccl")
     def backward_input(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
         if self.tracing:
             start_event = torch.cuda.Event(enable_timing=True)
@@ -769,7 +748,7 @@ def backward_input(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
                 self.trace_data[stage_id]['backward_input']['time'].append(total_time)
             return [gx, upstream_grads]
 
-        stage_input = self.prev_activation[stage_id][mb_idx]
+        stage_input = self.inp_activation[stage_id][mb_idx]
         stage_params = [p for p in self.parameters[stage_id] if p is not None and p.requires_grad]
 
         # Lists of autograd nodes for the layer outputs, inputs, and parameters
@@ -850,6 +829,213 @@ def hook(grad_inputs):
 
         return [gx, upstream_grads]
 
+    # @ray.method(tensor_transport="nccl")
+    def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, loss_fn=None):
+        if self.tracing:
+            start_event = torch.cuda.Event(enable_timing=True)
+            end_event = torch.cuda.Event(enable_timing=True)
+            start_event.record()
+
+        stage_params = [p for p in self.parameters[stage_id] if p is not None and p.requires_grad]
+
+        # We only pass to enforce temporal dependency.
+        # upstream is only required for the stage 0 case
+        gx = gx
+        upstream = upstream
+
+        # Special case to handle stage 0 since backward_input is a NOOP, 
+        # meaning no parameter groups are created
+        if stage_id == 0:
+            is_last_stage = loss_fn is not None
+            activation = self.activation[stage_id][mb_idx]
+
+            if is_last_stage:
+                labels = self.truth if self.truth is not None else None
+                loss = loss_fn(activation, labels)
+
+                gparams = torch.autograd.grad(
+                    outputs=loss,
+                    inputs=stage_params,
+                    retain_graph=False,
+                )
+
+            else:
+                gparams = torch.autograd.grad(
+                    outputs=activation,
+                    inputs=stage_params,
+                    grad_outputs=upstream,
+                    retain_graph=False,
+                )
+
+            assert len(gparams) == len(stage_params), (
+                f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(stage_params)}"
+            )
+            
+            for p, pg in zip(stage_params, gparams):
+                if p.grad is None:
+                    p.grad = pg.clone()
+                else:
+                    p.grad.add_(pg)
+
+            del self.activation[stage_id][mb_idx]
+
+            if self.tracing:
+                end_event.record()
+                torch.cuda.synchronize()
+                total_time = start_event.elapsed_time(end_event)
+                self.trace_data[stage_id]['backward_weight']['time'].append(total_time)
+
+            return [None] + ["done"]
+
+        # Create mapping from autograd nodes -> parameters
+        grad_acc_to_weight: Dict[Node, Tuple[Parameter, int]] = {}
+        for param in stage_params:
+            node = _get_grad_fn_or_grad_acc(param)
+            grad_acc_to_weight[node] = param
+
+        param_groups = self._bw_param_groups[stage_id][mb_idx]
+
+        # Perform the weight updates separately for each param_group, beginning
+        # backprop from each the intermediate node(s) of each group
+        for pg in param_groups:
+            intermediates: List[Node] = pg.get("intermediates", [])
+            intermediate_grads = pg.get("grads", None) # List of intermediate node gradients, captured by the hooks
+
+            # Skip groups without intermediate nodes (could happen in weird cases
+            # where one node is disconnected from the rest of the autograd graph for some reason)
+            if not intermediates or intermediate_grads is None:
+                continue
+
+            intermediate_edges: List[GradientEdge] = []
+            intermediate_edge_grads: List[torch.Tensor] = []
+
+            for intermediate_node, grad_inputs in zip(intermediates, intermediate_grads):
+                if grad_inputs is None:
+                    continue
+
+                gs = [x for x in grad_inputs if x is not None]
+                if not gs:
+                    continue
+                
+                # Sum all gradients arriving at the current intermediate node
+                # in case the node has multiple source of gradients
+                summed = sum(gs)
+
+                # Create a GradientEdge for each intermediate node (we can backprop with respect to these)
+                # and store the summed gradient for that node
+                intermediate_edges.append(GradientEdge(intermediate_node, 0))
+                intermediate_edge_grads.append(summed)
+
+            del pg["intermediates"]
+
+            if not intermediate_edges:
+                continue
+
+            # Grab params for the param_nodes in this param group using our grad_acc_to_weight map from earlier
+            mapped_param_nodes = [p for p in pg["params"] if p in grad_acc_to_weight]
+            if not mapped_param_nodes:
+                continue
+
+            # Use these parameters to create a GradientEdge that we'll use as our input to autograd.grad
+            weight_edges = tuple(GradientEdge(p, 0) for p in mapped_param_nodes)
+
+            gparams = torch.autograd.grad(
+                outputs=intermediate_edges,
+                inputs=weight_edges,
+                grad_outputs=intermediate_edge_grads,
+                retain_graph=False,
+            )
+
+            del pg["grads"]
+
+            assert len(gparams) == len(mapped_param_nodes), (
+                f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(mapped_param_nodes)}"
+            )
+            
+            # Finally, update gradients for the params in this param_group
+            for param_node, dw in zip(pg["params"], gparams):
+                if dw is None:
+                    continue
+
+                weight = grad_acc_to_weight[param_node]
+
+                if weight.grad is None:
+                    weight.grad = dw
+                else:
+                    weight.grad.add_(dw)
+
+        if self.tracing:
+            end_event.record()
+            torch.cuda.synchronize()
+            total_time = start_event.elapsed_time(end_event)
+            self.trace_data[stage_id]['backward_weight']['time'].append(total_time)
+
+        return [None] + ["done"]
+ stage input nodes and 
+        # (2) in a path from the output node(s) a parameter node/gradient accumulator
+        reverse_edges = construct_reverse_graph(output_nodes)
+        param_groups = get_param_groups(input_nodes, param_nodes, reverse_edges)
+
+        # Hooks to capture grads at intermediate nodes. In backward_weight,
+        # we'll backprop from these intermediate values
+        handles = []
+        for pg in param_groups:
+            intermediates = pg["intermediates"]
+            if not intermediates:
+                continue
+
+            pg["grads"] = [None] * len(intermediates)
+
+            for i, intermediate_node in enumerate(intermediates):
+                def make_hook(group: Dict[str, Any], idx: int):
+                    def hook(grad_inputs):
+                        group["grads"][idx] = grad_inputs
+                    return hook
+                handles.append(intermediate_node.register_prehook(make_hook(pg, i)))
+        ## End graph pruning computation timing here ##
+        if self.tracing:
+            graph_pruning_end.record()
+            torch.cuda.synchronize()
+            graph_pruning_time = graph_pruning_start.elapsed_time(graph_pruning_end)
+            if 'graph_pruning' not in self.trace_data[stage_id]['backward_input']:
+                self.trace_data[stage_id]['backward_input']['graph_pruning'] = []
+            self.trace_data[stage_id]['backward_input']['graph_pruning'].append(graph_pruning_time)
+        gx = torch.autograd.grad(
+            outputs=activation_or_loss,
+            inputs=stage_input,
+            grad_outputs=upstream_grads,
+            retain_graph=True,
+            allow_unused=True,
+        )
+
+        gx = gx[0] # Take the gx gradient out of the tuple returned by autograd.grad
+        if gx is not None and stage_input.requires_grad:
+            if stage_input.grad is None:
+                stage_input.grad = gx
+            else:
+                stage_input.grad.add_(gx)
+
+        # Free output tensors between the output nodes and the intermediate nodes
+        if not isinstance(activation_or_loss, list):
+            activation_or_loss = [activation_or_loss]
+
+        for t in activation_or_loss:
+            t.detach_()
+
+        for h in handles:
+            h.remove()
+
+        # Save parameter groups for use in backward_weight
+        self._bw_param_groups[stage_id][mb_idx] = param_groups
+
+        if self.tracing:
+            end_event.record()
+            torch.cuda.synchronize()
+            total_time = start_event.elapsed_time(end_event)
+            self.trace_data[stage_id]['backward_input']['time'].append(total_time)
+
+        return [gx, upstream_grads]
+
     @ray.method(tensor_transport="nccl")
     def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, loss_fn=None):
         if self.tracing:
diff --git a/piper/piper_coordinator.py b/piper/coordinator.py
similarity index 97%
rename from piper/piper_coordinator.py
rename to piper/coordinator.py
index 17c0f91..18b2b71 100644
--- a/piper/piper_coordinator.py
+++ b/piper/coordinator.py
@@ -4,7 +4,7 @@
 import os
 import socket
 
-from .piper_utils import create_logger, LOG_LEVEL
+from piper.utils import create_logger, LOG_LEVEL
 
 
 @ray.remote(num_gpus=0.1)
diff --git a/piper/exec.py b/piper/exec.py
index 52459f3..8f6692b 100644
--- a/piper/exec.py
+++ b/piper/exec.py
@@ -1,10 +1,15 @@
 import os
 import torch
+import threading
+import time
+import ray
 import torch.distributed as dist
 from typing import NamedTuple
 from strenum import StrEnum
 
-from piper.utils import piper_metadata, create_logger
+from piper.utils import piper_metadata, create_logger, LOG_LEVEL
+
+logger = create_logger("piper_exec", LOG_LEVEL)
 
 class TaskType(StrEnum):
     FORWARD = "f"
@@ -12,10 +17,6 @@ class TaskType(StrEnum):
     UPDATE = "u"
     BACKWARD_INPUT = "i"
     BACKWARD_WEIGHT = "w"
-import threading
-import time
-
-logger = create_logger("piper_exec", "INFO")
 
 class Task(NamedTuple):
     device_id: int
@@ -253,6 +254,7 @@ def piper_exec(model, schedule, inputs, truth, loss_fn, num_mbs, num_stages):
     
     def run_model(inputs, mb_idx, events, actor_mutexes):
         from piper.utils import events_tls
+        logger.debug(f"Controller launching thread {mb_idx}")
         events_tls.events = events
         events_tls.mb_idx = mb_idx
         events_tls.actor_mutexes = actor_mutexes
@@ -266,11 +268,11 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
             if task:
                 device_id, stage_id, mb_idx, task_type = task
                 actor_id = j
+                num_bwd_targets = len(get_backward_targets(stage_id, dag_edges))
+                if num_bwd_targets == 0:
+                    num_bwd_targets = 1
                 if task_type == TaskType.UPDATE:
                     logger.debug(f"Controller updating stage {stage_id} mb {mb_idx}")
-                    num_bwd_targets = len(get_backward_targets(stage_id, dag_edges))
-                    if num_bwd_targets == 0:
-                        num_bwd_targets = 1
 
                     done_refs = set()
                     for _, bwd_ref_dict in bwd_ref_dicts.items():
@@ -298,9 +300,6 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
                 elif task_type == TaskType.BACKWARD:
                     # log order of task dispatch by printing
                     # also see output_graph.py:1785 where we log forward dispatch
-                    num_bwd_targets = len(get_backward_targets(stage_id, dag_edges))
-                    # Hack to make single stage work
-                    if num_bwd_targets == 0: num_bwd_targets = 1
                     if mb_idx not in bwd_ref_dicts:
                         # if this is the first backward task for a microbatch, dispatch the
                         # backward task and cache the resulting ref(s)S
@@ -316,7 +315,7 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
                                 actors[actor_id]
                                 .backward.options(num_returns=num_bwd_targets*2)
                                 .remote(
-                                    stage_id, mb_idx, fwd_ref.get_ref(), loss_fn=loss_fn
+                                    stage_id, mb_idx, fwd_ref.get_ref(), truth=truth, loss_fn=loss_fn
                                 )
                             )
                     else:
@@ -355,8 +354,6 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
                         bwd_refs = bwd_ref_dicts[mb_idx][to_stage]
                         bwd_ref = bwd_refs[idx]
                         # dispatch the current stage's backward and cache the resulting ref(s)
-                        if num_bwd_targets == 0:
-                            num_bwd_targets = 1
                         # print(f"[PIPER] Backward stage {stage_id} mb {mb_idx}")
                         logger.debug(f"Controller waiting for actor {actor_id} mutex")
                         with actor_mutexes[actor_id]:
@@ -367,10 +364,6 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
                                 .remote(stage_id, mb_idx, bwd_ref)
                             )
                 elif task_type == TaskType.BACKWARD_INPUT:
-                    num_bwd_targets = len(get_backward_targets(stage_id, dag_edges))
-                    if num_bwd_targets == 0:
-                        num_bwd_targets = 1
-
                     if mb_idx not in bwd_ref_dicts:
                         logger.debug(f"Controller waiting for thread {mb_idx} to join")
                         threads[mb_idx].join()
@@ -423,10 +416,6 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
                             )
 
                 elif task_type == TaskType.BACKWARD_WEIGHT:
-                    num_bwd_targets = len(get_backward_targets(stage_id, dag_edges))
-                    if num_bwd_targets == 0:
-                        num_bwd_targets = 1
-
                     is_last_stage = len(
                         [e for e in dag_edges if e.from_stage == stage_id]
                     ) == 0
@@ -453,4 +442,4 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
                 else:
                     raise ValueError(f"Unknown task type: {task_type}")
 
-    return ret
+    return ray.get(ret)
diff --git a/piper/piper_graph_transform.py b/piper/graph_transform.py
similarity index 100%
rename from piper/piper_graph_transform.py
rename to piper/graph_transform.py
diff --git a/piper/piper_patches.py b/piper/patches.py
similarity index 99%
rename from piper/piper_patches.py
rename to piper/patches.py
index 18e0958..dd5a074 100644
--- a/piper/piper_patches.py
+++ b/piper/patches.py
@@ -5,7 +5,7 @@
 and Ray's tensor transport backends.
 
 Import this module early in your application to apply patches:
-    import src.piper_patches
+    import piper.patches
 """
 
 import functools
diff --git a/piper/piper.py b/piper/piper.py
index 6ee1cc7..39ef68c 100644
--- a/piper/piper.py
+++ b/piper/piper.py
@@ -1,4 +1,4 @@
-from . import piper_patches
+from . import patches
 
 import ray
 import torch
@@ -6,9 +6,9 @@
 from torch._dynamo.backends.registry import register_backend
 from torch._dynamo.decorators import _disallow_in_graph_helper
 
-from .piper_utils import RemoteTensor, serialize_graphmodule, piper_metadata, create_logger, print_backward_graph, LOG_LEVEL
-from .piper_graph_transform import split_gm_by_experts
-from .piper_actor import get_actor
+from piper.utils import RemoteTensor, serialize_graphmodule, piper_metadata, create_logger, print_backward_graph, LOG_LEVEL
+from piper.graph_transform import split_gm_by_experts
+from piper.actor import get_actor
 
 logger = create_logger("piper_backend", LOG_LEVEL)
 
diff --git a/test/test_graph_break.py b/test/test_graph_break.py
index 2807769..4f0e639 100644
--- a/test/test_graph_break.py
+++ b/test/test_graph_break.py
@@ -3,10 +3,10 @@
 import ray
 import time
 
-from src.piper_compile import piper_setup
-from src.piper_exec import piper_exec
-from src.piper import distributed_stage
-from src.piper_coordinator import PiperProgramCoordinator
+from piper.compile import piper_setup
+from piper.exec import piper_exec
+from piper.piper import distributed_stage
+from piper.coordinator import PiperProgramCoordinator
 
 from .schedule_helpers import build_1f1b_schedule, print_schedule
 
@@ -41,7 +41,7 @@ def forward(self, x):
     print_schedule(schedule)
     loss_fn = torch.nn.CrossEntropyLoss()
 
-    from src.piper_utils import piper_metadata
+    from piper.utils import piper_metadata
     stage1_weights = ray.get(piper_metadata.actors[0].get_weights.remote(0))
     stage2_weights = ray.get(piper_metadata.actors[1].get_weights.remote(1))
     print("stage 1 weights:", stage1_weights)
diff --git a/test/test_llama.py b/test/test_llama.py
index c392c16..e41b6f5 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -13,7 +13,7 @@
 
 from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B
 from .schedule_helpers import build_1f1b_schedule, build_gpipe_schedule, build_zb1p_schedule, print_schedule
-from piper.piper_coordinator import PiperProgramCoordinator
+from piper.coordinator import PiperProgramCoordinator
 
 from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B
 from .schedule_helpers import (
diff --git a/test/test_llama_single.py b/test/test_llama_single.py
index 6f36db6..e5b3c99 100644
--- a/test/test_llama_single.py
+++ b/test/test_llama_single.py
@@ -5,10 +5,10 @@
 from torch import nn, optim
 from torch.profiler import profile, record_function, ProfilerActivity
 
-from src.piper_exec import Task, piper_exec
-from src.piper_compile import piper_setup
-from src.piper import piper
-from src.piper_utils import piper_metadata
+from piper.exec import Task, piper_exec
+from piper.compile import piper_setup
+from piper.piper import piper
+from piper.utils import piper_metadata
 
 from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B
 from .schedule_helpers import print_schedule
diff --git a/test/test_moe.py b/test/test_moe.py
index 2647b8f..6e3218f 100644
--- a/test/test_moe.py
+++ b/test/test_moe.py
@@ -4,11 +4,11 @@
 import argparse
 import time
 
-from src.piper_coordinator import PiperProgramCoordinator
-from src.piper_compile import piper_setup
-from src.piper_exec import piper_exec
-from src.piper import distributed_stage, piper
-from src.piper_actor import get_actor
+from piper.coordinator import PiperProgramCoordinator
+from piper.compile import piper_setup
+from piper.exec import piper_exec
+from piper.piper import distributed_stage, piper
+from piper.actor import get_actor
 
 from .schedule_helpers import no_pp_schedule, print_schedule, build_1f1b_schedule
 

From 21ec38886c3762fee54a86823f99df4d08bcb725 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Fri, 20 Feb 2026 02:06:29 +0000
Subject: [PATCH 037/100] dag schedules instead of 2D array with graph
 visualization

---
 src/piper_compile.py     |  94 ++++--------
 src/piper_exec.py        | 319 ++++++++++++++++++++++-----------------
 test/print_schedules.py  |  21 +++
 test/schedule_helpers.py | 286 ++++++++++++++++++++++++++++-------
 4 files changed, 463 insertions(+), 257 deletions(-)
 create mode 100644 test/print_schedules.py

diff --git a/src/piper_compile.py b/src/piper_compile.py
index b257af2..c86c6a8 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -9,74 +9,35 @@
 
 from .piper_actor import _create_actors
 from .piper_utils import piper_metadata, create_logger, LOG_LEVEL
-from .piper_exec import CompType
+from .piper_exec import CompType, Schedule, _topological_order
 from .piper import piper
 
 logger = create_logger("piper_compile", LOG_LEVEL)
 
 
-def order_p2p_comms(schedule, num_devices, num_stages, stage_to_device):
-    is_fwd_bwd = lambda task: task is not None and task.type != CompType.UPD
-    schedule = [
-        [task for task in rank_tasks if is_fwd_bwd(task)] for rank_tasks in schedule
-    ]
-    logger.debug(f"Fwd/bwd schedule: {schedule}")
-    assert num_devices == len(schedule), "Some rank has no fwd/bwd tasks"
-    # Tasks are guaranteed to be fwd or bwd
-    num_fwd_bwd_tasks = sum(len(rank_tasks) for rank_tasks in schedule)
-
-    num_executed_tasks = 0
-    # (src_stage, dst_stage, mb_idx) for each p2p comm in execution order
-    # is_fwd if src_stage < dst_stage else bwd
-    p2p_comms = []
-    rank_to_next_task = {rank: 0 for rank in range(len(schedule))}
-    # P2P comm is enqueued when sender is ready
-    # Pick the next executable task in a round-robin fashion
-    while True:
-        executable_tasks = []
-        for rank, time_step in rank_to_next_task.items():
-            if time_step < len(schedule[rank]):
-                task = schedule[rank][time_step]
-                # Check the task's dependency is satisfied
-                # i.e., the sender has enqueued the comm
-                dependee = None
-                if task.type == CompType.FWD and task.stage_id > 0:
-                    dependee = (task.stage_id - 1, task.stage_id, task.mb_idx)
-                elif task.type == CompType.BWD and task.stage_id < num_stages - 1:
-                    dependee = (task.stage_id + 1, task.stage_id, task.mb_idx)
-
-                can_exec = dependee is None or dependee in p2p_comms
-                if can_exec:
-                    executable_tasks.append((rank, task))
-
-        if not executable_tasks:
-            break
-
-        for rank, task in executable_tasks:
-            if task.type == CompType.FWD and task.stage_id < num_stages - 1:
-                p2p_comms.append((task.stage_id, task.stage_id + 1, task.mb_idx))
-            elif task.type == CompType.BWD and task.stage_id > 0:
-                p2p_comms.append((task.stage_id, task.stage_id - 1, task.mb_idx))
-            rank_to_next_task[rank] += 1
-
-        num_executed_tasks += len(executable_tasks)
-
-    if num_executed_tasks != num_fwd_bwd_tasks:
-        raise ValueError(
-            f"Only {num_executed_tasks} out of {num_fwd_bwd_tasks} "
-            f"fwd/bwd tasks can be executed"
-        )
-
+def order_p2p_comms_from_dag(
+    schedule: Schedule,
+    num_devices: int,
+    num_stages: int,
+    stage_to_device: dict[int, int],
+) -> dict[int, list[tuple[int, int, int, bool]]]:
+    """Build P2P communication schedule from DAG in topological order."""
+    order = _topological_order(schedule)
+    p2p_comms: list[tuple[int, int, int]] = []
+    for ti in order:
+        task = schedule.tasks[ti]
+        for mb in task.microbatches:
+            if mb.comp_type == CompType.FWD and mb.stage_id < num_stages - 1:
+                p2p_comms.append((mb.stage_id, mb.stage_id + 1, mb.mb_idx))
+            elif mb.comp_type == CompType.BWD and mb.stage_id > 0:
+                p2p_comms.append((mb.stage_id, mb.stage_id - 1, mb.mb_idx))
     logger.debug(f"P2P comms: {p2p_comms}")
-    # (src_stage, dst_stage, mb_idx, is_sender)
     p2p_schedules = {rank: [] for rank in range(num_devices)}
     for src_stage, dst_stage, mb_idx in p2p_comms:
         src_rank = stage_to_device[src_stage]
         dst_rank = stage_to_device[dst_stage]
         p2p_schedules[src_rank].append((src_stage, dst_stage, mb_idx, True))
         p2p_schedules[dst_rank].append((src_stage, dst_stage, mb_idx, False))
-    logger.debug(f"P2P schedules: {p2p_schedules}")
-
     return p2p_schedules
 
 
@@ -84,20 +45,23 @@ def piper_setup(
     model,
     optim_fn,
     example_inputs,
-    schedule,
+    schedule: Schedule,
     naive_gradient_sync=False,
 ):
     """
     Compile a model with the piper backend.
+    schedule: Schedule DAG (tasks and edges).
     """
-
-    num_devices = len(schedule)
-    num_mbs = len(set([task.mb_idx for row in schedule for task in row if task is not None]))
-    num_stages = len(set([task.stage_id for row in schedule for task in row if task is not None]))
-    piper_metadata.stage_to_device = {task.stage_id: task.pp_rank for row in schedule for task in row if task is not None}
-
-    p2p_schedules = order_p2p_comms(
-        schedule, num_devices, num_stages, piper_metadata.stage_to_device
+    num_mbs = schedule.num_mbs()
+    num_stages = schedule.num_stages()
+    stage_to_device = {}
+    for task in schedule.tasks:
+        for mb in task.microbatches:
+            stage_to_device[mb.stage_id] = task.pp_rank
+    num_devices = max(stage_to_device.values()) + 1 if stage_to_device else 0
+    piper_metadata.stage_to_device = stage_to_device
+    p2p_schedules = order_p2p_comms_from_dag(
+        schedule, num_devices, num_stages, stage_to_device
     )
     _create_actors(
         num_devices, optim_fn, num_mbs, num_stages, p2p_schedules, naive_gradient_sync
diff --git a/src/piper_exec.py b/src/piper_exec.py
index e081d37..14fa990 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -4,8 +4,10 @@
 import torch.distributed as dist
 from typing import NamedTuple
 from enum import Enum
+from dataclasses import dataclass, field
 import threading
 import time
+from collections import defaultdict
 
 from .piper_utils import piper_metadata, create_logger, LOG_LEVEL
 
@@ -17,13 +19,62 @@ class CompType(Enum):
     UPD = "update"
     FWD_BWD = "forward_backward"
 
+
+class MicrobatchMetadata(NamedTuple):
+    """Metadata for one microbatch executed as part of a task."""
+    stage_id: int
+    mb_idx: int
+    comp_type: CompType
+
+
+class ScheduleTask(NamedTuple):
+    """A node in the schedule DAG: runs on pp_rank with one or more microbatches."""
+    pp_rank: int
+    microbatches: tuple[MicrobatchMetadata, ...]
+
+
+class ScheduleEdge(NamedTuple):
+    """Data dependency: to_task consumes output of from_task (source and destination only)."""
+    from_task_idx: int
+    to_task_idx: int
+
+
+class TemporalEdge(NamedTuple):
+    """Temporal dependency: to_task must run after from_task (ordering only)."""
+    from_task_idx: int
+    to_task_idx: int
+
+
+@dataclass
+class Schedule:
+    """DAG schedule: tasks (nodes), data edges, and temporal edges."""
+    tasks: list[ScheduleTask]
+    edges: list[ScheduleEdge]
+    temporal_edges: list[TemporalEdge] = field(default_factory=list)
+
+    def num_stages(self) -> int:
+        stages = set()
+        for task in self.tasks:
+            for mb in task.microbatches:
+                stages.add(mb.stage_id)
+        return max(stages) + 1 if stages else 0
+
+    def num_mbs(self) -> int:
+        mbs = set()
+        for task in self.tasks:
+            for mb in task.microbatches:
+                mbs.add(mb.mb_idx)
+        return max(mbs) + 1 if mbs else 0
+
+
+# Legacy single-microbatch task (for backward compatibility / conversion).
 class Task(NamedTuple):
     pp_rank: int
     stage_id: int
     mb_idx: int
     type: CompType
-    second_stage_id: int = None
-    second_mb_idx: int = None
+    second_stage_id: int | None = None
+    second_mb_idx: int | None = None
 
 class DAGEdge(NamedTuple):
     from_stage: int
@@ -34,158 +85,156 @@ def _get_backward_targets(stage_id: int, dag_edges: list[DAGEdge]):
     return [edge for edge in dag_edges if edge.to_stage == stage_id]
 
 
-def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdge], num_mbs: int) -> None:
+def _validate_schedule_dag(
+    schedule: Schedule,
+    dag_edges: list[DAGEdge],
+    num_stages: int,
+    num_mbs: int,
+) -> None:
     """
-    Validate that the schedule respects well-formedness rules and DAG dependencies.
-    
-    Args:
-        schedule: 2D array with one row per device and one column per time step
-        dag_edges: List of DAG edges defining stage dependencies
-        num_mbs: Number of microbatches in the schedule
-        
-    Raises:
-        ValueError: If the schedule violates any validation rules
+    Validate that the schedule DAG has valid data dependencies for all tasks.
+    For each microbatch: FWD/BWD have correct input/output edges; UPD has none.
     """
-    num_devices, num_steps = len(schedule), len(schedule[0]) if schedule else 0
-    
-    # Check well-formedness: no duplicates, pp_rank matches row, and all stages present
-    all_tasks = set()
-    microbatch_tasks = {}  # mb_idx -> set of (stage_id, type)
-    
-    for stage_id in range(num_devices):
-        for time_step in range(num_steps):
-            task = schedule[stage_id][time_step]
-            if task is not None:
-                # Check pp_rank matches row
-                if task.pp_rank != stage_id:
-                    raise ValueError(
-                        f"Task pp_rank {task.pp_rank} does not match row {stage_id} "
-                        f"at time step {time_step}"
-                    )
-                
-                # Check for duplicates
-                task_key = (task.stage_id, task.mb_idx, task.type)
-                if task_key in all_tasks:
-                    raise ValueError(
-                        f"Duplicate task found: stage_id={task.stage_id}, "
-                        f"mb_idx={task.mb_idx}, type={task.type}"
-                    )
-                all_tasks.add(task_key)
-                
-                # Track tasks by microbatch
-                if task.mb_idx not in microbatch_tasks:
-                    microbatch_tasks[task.mb_idx] = set()
-                microbatch_tasks[task.mb_idx].add((task.stage_id, task.type))
-    
-    # Get all required stages from DAG edges
+    tasks = schedule.tasks
+    edges = schedule.edges
+    edges_into: dict[int, list[int]] = defaultdict(list)
+    edges_out_of: dict[int, list[int]] = defaultdict(list)
+    for e in edges:
+        edges_into[e.to_task_idx].append(e.from_task_idx)
+        edges_out_of[e.from_task_idx].append(e.to_task_idx)
+
     all_required_stages = set()
     for edge in dag_edges:
         all_required_stages.add(edge.from_stage)
         all_required_stages.add(edge.to_stage)
-    
-    # Check that each microbatch has all required forward and backward stages
-    for mb_idx, tasks in microbatch_tasks.items():
-        # Find all stages that have forward/backward tasks for this microbatch
-        fwd_stages = {stage_id for stage_id, task_type in tasks if task_type == CompType.FWD}
-        bwd_stages = {stage_id for stage_id, task_type in tasks if task_type == CompType.BWD}
-        
-        # Check that all required stages have forward tasks
-        missing_fwd = all_required_stages - fwd_stages
+
+    task_produces: dict[int, set[tuple[int, int, CompType]]] = defaultdict(set)
+    for ti, task in enumerate(tasks):
+        for mb in task.microbatches:
+            task_produces[ti].add((mb.stage_id, mb.mb_idx, mb.comp_type))
+
+    for ti, task in enumerate(tasks):
+        predecessors = edges_into[ti]
+        successors = edges_out_of[ti]
+        for mb in task.microbatches:
+            s, m, c = mb.stage_id, mb.mb_idx, mb.comp_type
+            if c == CompType.UPD:
+                continue
+            if c == CompType.FWD:
+                if s > 0:
+                    required = (s - 1, m, c)
+                    if not any(required in task_produces[u] for u in predecessors):
+                        raise ValueError(
+                            f"Task {ti} microbatch (stage_id={s}, mb_idx={m}, FWD) missing input: "
+                            f"no predecessor produces {required}"
+                        )
+                if s < num_stages - 1:
+                    required = (s + 1, m, c)
+                    if not any(required in task_produces[v] for v in successors):
+                        raise ValueError(
+                            f"Task {ti} microbatch (stage_id={s}, mb_idx={m}, FWD) missing output: "
+                            f"no successor consumes {required}"
+                        )
+            else:  # BWD
+                if s == num_stages - 1:
+                    required = (s, m, CompType.FWD)
+                    if not any(required in task_produces[u] for u in predecessors):
+                        raise ValueError(
+                            f"Task {ti} microbatch (stage_id={s}, mb_idx={m}, BWD) missing FWD_BWD input: "
+                            f"no predecessor produces {required}"
+                        )
+                if s < num_stages - 1:
+                    required = (s + 1, m, c)
+                    if not any(required in task_produces[u] for u in predecessors):
+                        raise ValueError(
+                            f"Task {ti} microbatch (stage_id={s}, mb_idx={m}, BWD) missing BWD input: "
+                            f"no predecessor produces {required}"
+                        )
+                if s > 0:
+                    required = (s - 1, m, c)
+                    if not any(required in task_produces[v] for v in successors):
+                        raise ValueError(
+                            f"Task {ti} microbatch (stage_id={s}, mb_idx={m}, BWD) missing output: "
+                            f"no successor consumes {required}"
+                        )
+
+    mb_fwd_stages: dict[int, set[int]] = defaultdict(set)
+    mb_bwd_stages: dict[int, set[int]] = defaultdict(set)
+    for task in tasks:
+        for mb in task.microbatches:
+            if mb.comp_type == CompType.FWD:
+                mb_fwd_stages[mb.mb_idx].add(mb.stage_id)
+            elif mb.comp_type == CompType.BWD:
+                mb_bwd_stages[mb.mb_idx].add(mb.stage_id)
+    for mb_idx in range(num_mbs):
+        missing_fwd = all_required_stages - mb_fwd_stages.get(mb_idx, set())
         if missing_fwd:
-            raise ValueError(
-                f"Microbatch {mb_idx} missing forward stages: {missing_fwd}"
-            )
-        
-        # Check that all required stages have backward tasks
-        missing_bwd = all_required_stages - bwd_stages
+            raise ValueError(f"Microbatch {mb_idx} missing forward stages: {missing_fwd}")
+        missing_bwd = all_required_stages - mb_bwd_stages.get(mb_idx, set())
         if missing_bwd:
-            raise ValueError(
-                f"Microbatch {mb_idx} missing backward stages: {missing_bwd}"
-            )
-    
-    # Check pipeline stage dependencies
-    for mb_idx in range(num_mbs):
-        # Find all tasks for this microbatch
-        fwd_times = {}  # stage_id -> time_step
-        bwd_times = {}  # stage_id -> time_step
-        
-        for pp_rank in range(num_devices):
-            for time_step in range(num_steps):
-                task = schedule[pp_rank][time_step]
-                if task is not None and task.mb_idx == mb_idx:
-                    if task.type == CompType.FWD:
-                        fwd_times[task.stage_id] = time_step
-                    elif task.type == CompType.BWD:
-                        bwd_times[task.stage_id] = time_step
-        
-        # Check forward stage ordering: if A -> B, then fwd(A) < fwd(B)
-        for edge in dag_edges:
-            from_stage, to_stage = edge.from_stage, edge.to_stage
-            if from_stage in fwd_times and to_stage in fwd_times:
-                if fwd_times[from_stage] >= fwd_times[to_stage]:
-                    raise ValueError(
-                        f"Forward stage ordering violation for microbatch {mb_idx}: "
-                        f"forward stage {from_stage} (time {fwd_times[from_stage]}) must come "
-                        f"before forward stage {to_stage} (time {fwd_times[to_stage]})"
-                    )
-        
-        # Check forward-backward ordering: fwd(A) < bwd(A)
-        for stage_id in fwd_times:
-            if stage_id in bwd_times:
-                if fwd_times[stage_id] >= bwd_times[stage_id]:
-                    raise ValueError(
-                        f"Forward-backward ordering violation for microbatch {mb_idx}, "
-                        f"stage {stage_id}: forward (time {fwd_times[stage_id]}) must come "
-                        f"before backward (time {bwd_times[stage_id]})"
-                    )
-        
-        # Check backward stage ordering: if A -> B, then bwd(B) < bwd(A)
-        for edge in dag_edges:
-            from_stage, to_stage = edge.from_stage, edge.to_stage
-            if from_stage in bwd_times and to_stage in bwd_times:
-                if bwd_times[to_stage] >= bwd_times[from_stage]:
-                    raise ValueError(
-                        f"Backward stage ordering violation for microbatch {mb_idx}: "
-                        f"backward stage {to_stage} (time {bwd_times[to_stage]}) must come "
-                        f"before backward stage {from_stage} (time {bwd_times[from_stage]})"
-                    )
-
-def piper_exec(schedule, inputs, truth, loss_fn, dp_degree=1, naive_gradient_sync=False):
+            raise ValueError(f"Microbatch {mb_idx} missing backward stages: {missing_bwd}")
+
+
+def _topological_order(schedule: Schedule) -> list[int]:
+    """Return task indices in topological order (data + temporal predecessors)."""
+    tasks = schedule.tasks
+    edges = schedule.edges
+    temporal = schedule.temporal_edges or []
+    n = len(tasks)
+    in_degree = [0] * n
+    for e in edges:
+        in_degree[e.to_task_idx] += 1
+    for e in temporal:
+        in_degree[e.to_task_idx] += 1
+    queue = [i for i in range(n) if in_degree[i] == 0]
+    order = []
+    while queue:
+        u = queue.pop(0)
+        order.append(u)
+        for e in edges:
+            if e.from_task_idx == u:
+                in_degree[e.to_task_idx] -= 1
+                if in_degree[e.to_task_idx] == 0:
+                    queue.append(e.to_task_idx)
+        for e in temporal:
+            if e.from_task_idx == u:
+                in_degree[e.to_task_idx] -= 1
+                if in_degree[e.to_task_idx] == 0:
+                    queue.append(e.to_task_idx)
+    if len(order) != n:
+        raise ValueError("Schedule DAG has a cycle")
+    return order
+
+
+def piper_exec(schedule: Schedule, inputs, truth, loss_fn, dp_degree=1, naive_gradient_sync=False):
     """
     Execute one step of the pipeline schedule on the distributed model.
 
     Args:
-        schedule: A 2D list (device x time_step) of Tasks specifying execution order.
+        schedule: Schedule DAG (tasks and edges).
         inputs: Inputs to the model.
         truth: Ground-truth labels or targets.
         loss_fn: Loss function to be used for training.
-        num_mbs: Number of microbatches in the schedule.
 
     Returns:
-        List of losses per microbatch.)
+        List of losses per microbatch (from UPD tasks).
     """
-    num_steps, num_devices = len(schedule[0]), len(schedule)
+    dag_edges = [DAGEdge(e[0], e[1]) for e in piper_metadata.dag]
+    num_stages = schedule.num_stages()
+    num_mbs = schedule.num_mbs()
+    _validate_schedule_dag(schedule, dag_edges, num_stages, num_mbs)
 
+    order = _topological_order(schedule)
     actors = piper_metadata.actors
-
-    num_mbs = len(set([task.mb_idx for row in schedule for task in row if task is not None]))
-    dag_edges = piper_metadata.dag
-    dag_edges = list(map(lambda e: (DAGEdge(e[0], e[1])), list(piper_metadata.dag)))
-    
-    _validate_schedule(schedule, dag_edges, num_mbs)
-
     ret = []
-    for i in range(num_steps):
-        for j in range(num_devices-1, -1, -1):
-            task = schedule[j][i]
-            if task:
-                _, stage_id, mb_idx, task_type, _, _ = task
-                actor_id = j
-                actor = actors[actor_id]
-                if task_type == CompType.UPD:
-                    ret.append(actor._update.remote())
-                elif task_type == CompType.FWD:
-                    actor._forward.remote(stage_id, mb_idx)
-                else:  # CompType.BWD
-                    actor._backward.remote(stage_id, mb_idx, loss_fn=loss_fn)
+    for ti in order:
+        task = schedule.tasks[ti]
+        actor = actors[task.pp_rank]
+        for mb in task.microbatches:
+            if mb.comp_type == CompType.UPD:
+                ret.append(actor._update.remote())
+            elif mb.comp_type == CompType.FWD:
+                actor._forward.remote(mb.stage_id, mb.mb_idx)
+            elif mb.comp_type == CompType.BWD:
+                actor._backward.remote(mb.stage_id, mb.mb_idx, loss_fn=loss_fn)
     return ray.get(ret)
\ No newline at end of file
diff --git a/test/print_schedules.py b/test/print_schedules.py
new file mode 100644
index 0000000..f5159b1
--- /dev/null
+++ b/test/print_schedules.py
@@ -0,0 +1,21 @@
+from .schedule_helpers import print_schedule, build_1f1b_schedule, build_gpipe_schedule
+
+num_mbs = 4
+num_stages = 2
+schedule = build_1f1b_schedule(num_mbs, num_stages)
+print("1F1B Schedule -> 1f1b_schedule.pdf")
+print_schedule(schedule, path="1f1b_schedule_pp2")
+
+schedule = build_gpipe_schedule(num_mbs, num_stages)
+print("GPipe Schedule -> gpipe_schedule.pdf")
+print_schedule(schedule, path="gpipe_schedule_pp2")
+
+num_mbs = 8
+num_stages = 4
+schedule = build_1f1b_schedule(num_mbs, num_stages)
+print("1F1B Schedule -> 1f1b_schedule.pdf")
+print_schedule(schedule, path="1f1b_schedule_pp4")
+
+schedule = build_gpipe_schedule(num_mbs, num_stages)
+print("GPipe Schedule -> gpipe_schedule.pdf")
+print_schedule(schedule, path="gpipe_schedule_pp4")
\ No newline at end of file
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index cee5ace..68ad038 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -1,51 +1,139 @@
-from src.piper_exec import Task, CompType
+from graphviz import Digraph
 
+from src.piper_exec import (
+    Task,
+    CompType,
+    Schedule,
+    ScheduleTask,
+    MicrobatchMetadata,
+    ScheduleEdge,
+    TemporalEdge,
+    DAGEdge,
+    _topological_order,
+)
 
-def print_schedule(schedule):
-    for stage in schedule:
-        for step in stage:
-            if step:
-                type_char = 'u' if step.type == CompType.UPD else 'f' if step.type == CompType.FWD else 'b'
-                string = f"{step.stage_id}:{step.mb_idx}:{type_char}"
-            else:
-                string = " --- "
-            print(string, end="\t")
-        print()
 
-def build_gpipe_schedule(n_mbs: int, n_stages: int):
-    steps = n_mbs + n_stages - 1
-    schedule = [[None] * (steps * 2 + 1) for _ in range(n_stages)]
-    for step in range(steps):
+def print_schedule(schedule: Schedule, path: str = "schedule") -> None:
+    """Render Schedule DAG to a visual (graphviz) file. Default output: schedule.pdf."""
+    print_schedule_dag(schedule, path=path)
+
+
+def print_schedule_dag(schedule: Schedule, path: str = "schedule") -> None:
+    """
+    Use graphviz to produce a visual of the schedule DAG.
+    Tasks for the same pp_rank are drawn on the same row (one row per rank).
+    Creates path.pdf in the current directory.
+    """
+    dot = Digraph(comment="Schedule DAG")
+    dot.attr(rankdir="TB")
+    dot.attr("node", shape="box", style="rounded,filled", fontname="sans")
+
+    rank_to_tasks: dict[int, list[int]] = {}
+    task_labels: dict[int, str] = {}
+    for ti, task in enumerate(schedule.tasks):
+        rank_to_tasks.setdefault(task.pp_rank, []).append(ti)
+        mb_parts = []
+        for mb in task.microbatches:
+            c = "u" if mb.comp_type == CompType.UPD else "f" if mb.comp_type == CompType.FWD else "b"
+            mb_parts.append(f"{mb.stage_id}:{mb.mb_idx}:{c}")
+        task_labels[ti] = f"rank{task.pp_rank}\\n" + "\\n".join(mb_parts)
+
+    for rank in sorted(rank_to_tasks.keys()):
+        with dot.subgraph() as sub:
+            sub.attr(rank="same")
+            for ti in rank_to_tasks[rank]:
+                sub.node(str(ti), label=task_labels[ti])
+
+    for e in schedule.edges:
+        dot.edge(str(e.from_task_idx), str(e.to_task_idx), color="red")
+
+    for e in schedule.temporal_edges or []:
+        dot.edge(str(e.from_task_idx), str(e.to_task_idx), style="dotted", color="blue")
+
+    dot.render(path, format="pdf", cleanup=True)
+
+
+def build_gpipe_schedule(n_mbs: int, n_stages: int) -> Schedule:
+    """
+    Build a GPipe schedule as a DAG: all FWD, then all BWD, then UPD.
+    One task per (stage_id, mb_idx, comp_type); edges follow pipeline data dependencies.
+    Temporal: forwards ordered by microbatch per stage (s:m:f → s:m+1:f); all forwards before
+    backwards (last fwd → first bwd on last stage); backwards in ascending microbatch per stage
+    (s:m:b → s:m+1:b); update after final backward (s:M-1:b → s:UPD).
+    """
+    tasks: list[ScheduleTask] = []
+    edges: list[ScheduleEdge] = []
+    temporal_edges: list[TemporalEdge] = []
+    producer: dict[tuple[int, int, CompType], int] = {}
+
+    for mb_idx in range(n_mbs):
         for stage_id in range(n_stages):
-            mb_idx = step - stage_id
-            if mb_idx >= 0 and mb_idx < n_mbs:
-                schedule[stage_id][step] = Task(stage_id, stage_id, mb_idx, CompType.FWD)
-
-    for step in range(steps, steps * 2):
-        for stage_id in reversed(range(n_stages)):
-            mb_idx = (step - steps) - (n_stages - stage_id - 1)
-            if mb_idx >= 0 and mb_idx < n_mbs:
-                schedule[stage_id][step] = Task(stage_id, stage_id, mb_idx, CompType.BWD)
-    for i, stage in enumerate(range(n_stages)):
-        schedule[stage][-i-1] = Task(stage_id=stage, pp_rank=stage, mb_idx=0, type=CompType.UPD)
-    return schedule
+            mb = MicrobatchMetadata(stage_id=stage_id, mb_idx=mb_idx, comp_type=CompType.FWD)
+            ti = len(tasks)
+            tasks.append(ScheduleTask(pp_rank=stage_id, microbatches=(mb,)))
+            if stage_id > 0:
+                edges.append(ScheduleEdge(producer[(stage_id - 1, mb_idx, CompType.FWD)], ti))
+            producer[(stage_id, mb_idx, CompType.FWD)] = ti
+
+    for mb_idx in range(n_mbs):
+        for stage_id in range(n_stages - 1, -1, -1):
+            mb = MicrobatchMetadata(stage_id=stage_id, mb_idx=mb_idx, comp_type=CompType.BWD)
+            ti = len(tasks)
+            tasks.append(ScheduleTask(pp_rank=stage_id, microbatches=(mb,)))
+            # Same-stage fwd→bwd only on last stage (loss and backward there)
+            if stage_id == n_stages - 1:
+                edges.append(ScheduleEdge(producer[(stage_id, mb_idx, CompType.FWD)], ti))
+            if stage_id < n_stages - 1:
+                edges.append(ScheduleEdge(producer[(stage_id + 1, mb_idx, CompType.BWD)], ti))
+            producer[(stage_id, mb_idx, CompType.BWD)] = ti
+
+    for stage_id in range(n_stages):
+        for m in range(n_mbs - 1):
+            temporal_edges.append(
+                TemporalEdge(producer[(stage_id, m, CompType.FWD)], producer[(stage_id, m + 1, CompType.FWD)])
+            )
+    last_stage = n_stages - 1
+    temporal_edges.append(
+        TemporalEdge(producer[(last_stage, n_mbs - 1, CompType.FWD)], producer[(last_stage, 0, CompType.BWD)])
+    )
+
+    for stage_id in range(n_stages):
+        for m in range(n_mbs - 1):
+            temporal_edges.append(
+                TemporalEdge(producer[(stage_id, m, CompType.BWD)], producer[(stage_id, m + 1, CompType.BWD)])
+            )
+
+    for stage_id in range(n_stages):
+        ti = len(tasks)
+        mb = MicrobatchMetadata(stage_id=stage_id, mb_idx=0, comp_type=CompType.UPD)
+        tasks.append(ScheduleTask(pp_rank=stage_id, microbatches=(mb,)))
+        temporal_edges.append(TemporalEdge(producer[(stage_id, n_mbs - 1, CompType.BWD)], ti))
+
+    return Schedule(tasks=tasks, edges=edges, temporal_edges=temporal_edges)
 
-def build_1f1b_schedule(n_mbs: int, n_stages: int):
+
+def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule:
+    """
+    Build a 1F1B (one forward one backward) schedule as a DAG.
+    FWD and BWD are interleaved per stage; one UPD per stage at the end.
+    Temporal (per rank): backwards in ascending microbatch order (s:m:b → s:m+1:b);
+    update after final backward (s:M-1:b → s:UPD).
+    """
     steps = n_mbs + n_stages - 1
-    schedule = [[None] * (steps * 2 + 1) for _ in range(n_stages)]
+    num_steps = steps * 2 + 1
+    schedule_2d: list[list[Task | None]] = [[None] * num_steps for _ in range(n_stages)]
     stage_mb = [[0, 0] for _ in range(n_stages)]
+
     for step in range(n_stages):
         for stage_id in range(n_stages):
             if step >= stage_id:
                 mb_idx = stage_mb[stage_id][0]
-                if mb_idx >= 0 and mb_idx < n_mbs:
-                    schedule[stage_id][step] = Task(
-                        pp_rank=stage_id, 
-                        stage_id=stage_id,
-                        mb_idx=mb_idx, 
-                        type=CompType.FWD
+                if 0 <= mb_idx < n_mbs:
+                    schedule_2d[stage_id][step] = Task(
+                        pp_rank=stage_id, stage_id=stage_id, mb_idx=mb_idx, type=CompType.FWD
                     )
                     stage_mb[stage_id][0] += 1
+
     for step in range(n_stages, 2 * steps):
         relative_step = step - n_stages
         for stage_id in range(n_stages):
@@ -54,33 +142,115 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int):
                 fwd_or_bwd = 1 - (relative_step + inv_stage) % 2
                 task_type = CompType.FWD if fwd_or_bwd == 0 else CompType.BWD
                 mb_idx = stage_mb[stage_id][fwd_or_bwd]
-                if mb_idx >= 0 and mb_idx < n_mbs:
-                    schedule[stage_id][step] = Task(
-                        pp_rank=stage_id, 
-                        stage_id=stage_id,
-                        mb_idx=mb_idx, 
-                        type=task_type
+                if 0 <= mb_idx < n_mbs:
+                    schedule_2d[stage_id][step] = Task(
+                        pp_rank=stage_id, stage_id=stage_id, mb_idx=mb_idx, type=task_type
                     )
                     stage_mb[stage_id][fwd_or_bwd] += 1
+
     for i, stage in enumerate(range(n_stages)):
-        schedule[stage][-i-1] = Task(
-            stage_id=stage, 
-            pp_rank=stage, 
-            mb_idx=n_mbs - 1,
-            type=CompType.UPD
+        schedule_2d[stage][-i - 1] = Task(
+            stage_id=stage, pp_rank=stage, mb_idx=n_mbs - 1, type=CompType.UPD
         )
 
-    return schedule
+    tasks = []
+    edges = []
+    temporal_edges: list[TemporalEdge] = []
+    producer: dict[tuple[int, int, CompType], int] = {}
+    for time_step in range(num_steps):
+        for rank in range(n_stages - 1, -1, -1):
+            cell = schedule_2d[rank][time_step]
+            if cell is None:
+                continue
+            mb = MicrobatchMetadata(
+                stage_id=cell.stage_id, mb_idx=cell.mb_idx, comp_type=cell.type
+            )
+            ti = len(tasks)
+            tasks.append(ScheduleTask(pp_rank=cell.pp_rank, microbatches=(mb,)))
+            c, s, m = cell.type, cell.stage_id, cell.mb_idx
+            if c == CompType.FWD:
+                if s > 0:
+                    edges.append(ScheduleEdge(producer[(s - 1, m, c)], ti))
+                producer[(s, m, c)] = ti
+            elif c == CompType.BWD:
+                # Same-stage fwd→bwd only on last stage (loss and backward there)
+                if s == n_stages - 1:
+                    edges.append(ScheduleEdge(producer[(s, m, CompType.FWD)], ti))
+                if s < n_stages - 1:
+                    edges.append(ScheduleEdge(producer[(s + 1, m, c)], ti))
+                producer[(s, m, c)] = ti
+            elif c == CompType.UPD:
+                temporal_edges.append(TemporalEdge(producer[(s, n_mbs - 1, CompType.BWD)], ti))
 
-no_pp_schedule = [
-    [
-        Task(pp_rank=0, stage_id=0, mb_idx=0, type=CompType.FWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=0, type=CompType.BWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=0, type=CompType.UPD),
-    ]
-]
+    for s in range(n_stages):
+        for m in range(n_mbs - 1):
+            temporal_edges.append(
+                TemporalEdge(producer[(s, m, CompType.BWD)], producer[(s, m + 1, CompType.BWD)])
+            )
+
+    return Schedule(tasks=tasks, edges=edges, temporal_edges=temporal_edges)
+
+
+def _grid_to_dag(
+    schedule_2d: list[list[Task | None]],
+    n_stages: int,
+) -> Schedule:
+    """Convert a 2D grid (device x time_step) of Tasks into a Schedule DAG. Sequential pipeline."""
+    dag_edges = [DAGEdge(i, i + 1) for i in range(n_stages - 1)]
+    num_devices = len(schedule_2d)
+    num_steps = len(schedule_2d[0]) if schedule_2d else 0
+    tasks_list: list[ScheduleTask] = []
+    cell_to_task: dict[tuple[int, int], int] = {}
+    for j in range(num_devices):
+        for i in range(num_steps):
+            task = schedule_2d[j][i] if j < len(schedule_2d) and i < len(schedule_2d[j]) else None
+            if task is not None:
+                mb = MicrobatchMetadata(
+                    stage_id=task.stage_id, mb_idx=task.mb_idx, comp_type=task.type
+                )
+                tasks_list.append(ScheduleTask(pp_rank=task.pp_rank, microbatches=(mb,)))
+                cell_to_task[(j, i)] = len(tasks_list) - 1
+    order = []
+    for i in range(num_steps):
+        for j in range(num_devices - 1, -1, -1):
+            if (j, i) in cell_to_task:
+                order.append((j, i))
+    edges_list: list[ScheduleEdge] = []
+    producer: dict[tuple[int, int, CompType], int] = {}
+    for (j, i) in order:
+        ti = cell_to_task[(j, i)]
+        st = tasks_list[ti]
+        for mb in st.microbatches:
+            s, m, c = mb.stage_id, mb.mb_idx, mb.comp_type
+            if c == CompType.UPD:
+                continue
+            if c == CompType.FWD and s > 0:
+                pred_key = (s - 1, m, c)
+                if pred_key in producer:
+                    edges_list.append(ScheduleEdge(producer[pred_key], ti))
+            if c == CompType.BWD:
+                if s == n_stages - 1 and (s, m, CompType.FWD) in producer:
+                    edges_list.append(ScheduleEdge(producer[(s, m, CompType.FWD)], ti))
+                if s < n_stages - 1:
+                    pred_key = (s + 1, m, c)
+                    if pred_key in producer:
+                        edges_list.append(ScheduleEdge(producer[pred_key], ti))
+            producer[(s, m, c)] = ti
+    return Schedule(tasks=tasks_list, edges=edges_list, temporal_edges=[])
 
-pp2_interleaved_1f1b_grid_schedule = [
+
+no_pp_schedule = Schedule(
+    tasks=[
+        ScheduleTask(0, (MicrobatchMetadata(0, 0, CompType.FWD),)),
+        ScheduleTask(0, (MicrobatchMetadata(0, 0, CompType.BWD),)),
+        ScheduleTask(0, (MicrobatchMetadata(0, 0, CompType.UPD),)),
+    ],
+    edges=[],
+    temporal_edges=[],
+)
+
+# 2D grid literals for interleaved 1F1B (used only to build DAGs below)
+_PP2_GRID = [
     [
         Task(pp_rank=0, stage_id=0, mb_idx=0, type=CompType.FWD),
         Task(pp_rank=0, stage_id=0, mb_idx=1, type=CompType.FWD),
@@ -124,8 +294,9 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int):
         None,
     ],
 ]
+pp2_interleaved_1f1b_grid_schedule = _grid_to_dag(_PP2_GRID, 4)
 
-pp4_interleaved_1f1b_grid_schedule = [
+_PP4_GRID = [
     [
         Task(pp_rank=0, stage_id=0, mb_idx=0, type=CompType.FWD),
         Task(pp_rank=0, stage_id=0, mb_idx=1, type=CompType.FWD),
@@ -226,4 +397,5 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int):
         None,
         None,
     ],
-]
\ No newline at end of file
+]
+pp4_interleaved_1f1b_grid_schedule = _grid_to_dag(_PP4_GRID, 8)
\ No newline at end of file

From ce765e91b68e41ea10cfe0af36bf32f278de7d31 Mon Sep 17 00:00:00 2001
From: Andy Ruan <andyubryh@gmail.com>
Date: Fri, 20 Feb 2026 02:10:19 +0000
Subject: [PATCH 038/100] verify p2p correctness: execution matches schedule

Signed-off-by: Andy Ruan <andyubryh@gmail.com>
---
 ...nterleaved_debug-1warm_1it.actor0.grep.log |  48 ++
 ...nterleaved_debug-1warm_1it.actor1.grep.log |  48 ++
 logs/interleaved_debug-1warm_1it.grep.log     |  96 ++++
 logs/interleaved_debug-1warm_1it.log          | 496 ++++++++++++++++++
 run_interleaved-debug.sh                      |   8 +-
 src/piper_actor.py                            |  16 +-
 6 files changed, 700 insertions(+), 12 deletions(-)
 create mode 100644 logs/interleaved_debug-1warm_1it.actor0.grep.log
 create mode 100644 logs/interleaved_debug-1warm_1it.actor1.grep.log
 create mode 100644 logs/interleaved_debug-1warm_1it.grep.log
 create mode 100644 logs/interleaved_debug-1warm_1it.log

diff --git a/logs/interleaved_debug-1warm_1it.actor0.grep.log b/logs/interleaved_debug-1warm_1it.actor0.grep.log
new file mode 100644
index 0000000..4d30888
--- /dev/null
+++ b/logs/interleaved_debug-1warm_1it.actor0.grep.log
@@ -0,0 +1,48 @@
+Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
diff --git a/logs/interleaved_debug-1warm_1it.actor1.grep.log b/logs/interleaved_debug-1warm_1it.actor1.grep.log
new file mode 100644
index 0000000..9dd8034
--- /dev/null
+++ b/logs/interleaved_debug-1warm_1it.actor1.grep.log
@@ -0,0 +1,48 @@
+Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
diff --git a/logs/interleaved_debug-1warm_1it.grep.log b/logs/interleaved_debug-1warm_1it.grep.log
new file mode 100644
index 0000000..2b23bed
--- /dev/null
+++ b/logs/interleaved_debug-1warm_1it.grep.log
@@ -0,0 +1,96 @@
+Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
\ No newline at end of file
diff --git a/logs/interleaved_debug-1warm_1it.log b/logs/interleaved_debug-1warm_1it.log
new file mode 100644
index 0000000..394c942
--- /dev/null
+++ b/logs/interleaved_debug-1warm_1it.log
@@ -0,0 +1,496 @@
+2026-02-20 01:25:55,222	INFO worker.py:2007 -- Started a local Ray instance.
+/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+  warnings.warn(
+(run_dp_rank pid=841337) Namespace(model='debug', schedule='interleaved-1f1b', dp=1, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=1, iters=1, tracing=False, naive_gradient_sync=False)
+(run_dp_rank pid=841337) Schedule:
+(run_dp_rank pid=841337) 0:0:f	0:1:f	2:0:f	2:1:f	0:2:f	0:3:f	2:0:b	 -- 	2:1:b	2:2:f	0:0:b	2:3:f	0:1:b	 -- 	2:2:b	2:3:b	0:2:b	0:3:b	0:0:u	
+(run_dp_rank pid=841337)  -- 	1:0:f	1:1:f	3:0:f	3:0:b	3:1:f	3:1:b	1:2:f	1:0:b	1:3:f	1:1:b	3:2:f	3:2:b	3:3:f	3:3:b	1:2:b	1:3:b	1:0:u	 -- 	
+(run_dp_rank pid=841337) 2026-02-20 01:26:09,526 - piper_coordinator - DEBUG - Running DP rank 1 of 1
+(run_dp_rank pid=841337) 2026-02-20 01:26:09,549 - piper_compile - DEBUG - mbs: 4, stages: 4, devices: 2
+(run_dp_rank pid=841337) 2026-02-20 01:26:09,549 - piper_compile - DEBUG - Fwd/bwd schedule: [[Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=False, upd=False)], [Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=False, upd=False)]]
+(run_dp_rank pid=841337) 2026-02-20 01:26:09,549 - piper_compile - DEBUG - P2P comms: 
+[(0, 1, 0), (0, 1, 1), (1, 2, 0), (2, 3, 0), (1, 2, 1), (2, 3, 1), (0, 1, 2), (3, 2, 0), (0, 1, 3), (2, 1, 0), (3, 2, 1), (2, 1, 1), (1, 2, 2), (2, 3, 2), (1, 0, 0), (1, 2, 3), (2, 3, 3), (1, 0, 1), (3, 2, 2), (2, 1, 2), (3, 2, 3), (2, 1, 3), (1, 0, 2), (1, 0, 3)]
+(run_dp_rank pid=841337) 2026-02-20 01:26:09,549 - piper_compile - DEBUG - P2P schedules: {
+0: [(0, 1, 0, True), (0, 1, 1, True), (1, 2, 0, False), (2, 3, 0, True), (1, 2, 1, False), (2, 3, 1, True), (0, 1, 2, True), (3, 2, 0, False), (0, 1, 3, True), (2, 1, 0, True), (3, 2, 1, False), (2, 1, 1, True), (1, 2, 2, False), (2, 3, 2, True), (1, 0, 0, False), (1, 2, 3, False), (2, 3, 3, True), (1, 0, 1, False), (3, 2, 2, False), (2, 1, 2, True), (3, 2, 3, False), (2, 1, 3, True), (1, 0, 2, False), (1, 0, 3, False)],
+1: [(0, 1, 0, False), (0, 1, 1, False), (1, 2, 0, True), (2, 3, 0, False), (1, 2, 1, True), (2, 3, 1, False), (0, 1, 2, False), (3, 2, 0, True), (0, 1, 3, False), (2, 1, 0, False), (3, 2, 1, True), (2, 1, 1, False), (1, 2, 2, True), (2, 3, 2, False), (1, 0, 0, True), (1, 2, 3, True), (2, 3, 3, False), (1, 0, 1, True), (3, 2, 2, True), (2, 1, 2, False), (3, 2, 3, True), (2, 1, 3, False), (1, 0, 2, True), (1, 0, 3, True)]
+}
+(run_dp_rank pid=841337) 2026-02-20 01:26:09,615 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, 7e3c38e5f4c64be09e368f2901000000) global rank 0
+(run_dp_rank pid=841337) 2026-02-20 01:26:09,616 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, dc7ca51c30ec954ff3b0615001000000) global rank 1
+(PiperActor pid=841341) 2026-02-20 01:26:12,718 - piper_actor - INFO - Initializing Ray actor 1 global rank 1 GPU 2
+(PiperActor pid=841331) 2026-02-20 01:26:12,685 - piper_actor - INFO - Initializing Ray actor 0 global rank 0 GPU 1
+(PiperActor pid=841341) 2026-02-20 01:26:15,873 - piper_actor - DEBUG - Actor 1 has GPU 2, joined the global process group
+(PiperActor pid=841341) 2026-02-20 01:26:15,873 - piper_actor - INFO - Global rank 1 joined its pp group 0 along with ranks [0, 1]
+(PiperActor pid=841331) 2026-02-20 01:26:15,883 - piper_actor - DEBUG - Actor 0 has GPU 1, joined the global process group
+(PiperActor pid=841331) 2026-02-20 01:26:15,883 - piper_actor - INFO - Global rank 0 joined its pp group 0 along with ranks [0, 1]
+(run_dp_rank pid=841337) 2026-02-20 01:26:16,094 - piper_compile - INFO - DP rank 1 compiling...
+(PiperActor pid=841331) 2026-02-20 01:26:20,599 - piper_actor - INFO - Loading stage 0 graph on actor 0
+(PiperActor pid=841341) 2026-02-20 01:26:20,770 - piper_actor - INFO - Loading stage 1 graph on actor 1
+(PiperActor pid=841331) 2026-02-20 01:26:22,068 - piper_actor - INFO - Loading stage 2 graph on actor 0
+(PiperActor pid=841341) 2026-02-20 01:26:22,648 - piper_actor - INFO - Loading stage 3 graph on actor 1
+(run_dp_rank pid=841337) 2026-02-20 01:26:23,248 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=841337) 2026-02-20 01:26:32,888 - piper_compile - INFO - DP rank 1 done.
+(run_dp_rank pid=841337) Running 1 warmup iterations...
+(PiperActor pid=841341) 2026-02-20 01:26:33,159 - piper_actor - DEBUG - Actor 1 loaded labels torch.Size([16, 256, 512])
+(PiperActor pid=841341) 2026-02-20 01:26:33,164 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:33,164 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 0)
+(PiperActor pid=841341) [rank1]:[W220 01:26:33.646901436 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=841331) 2026-02-20 01:26:33,148 - piper_actor - DEBUG - Actor 0 loaded inputs 1
+(PiperActor pid=841331) 2026-02-20 01:26:33,163 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:33,942 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=841331) 2026-02-20 01:26:33,942 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 0)
+(PiperActor pid=841331) [rank0]:[W220 01:26:33.424491825 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=841341) 2026-02-20 01:26:36,999 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:36,999 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:36,999 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=841331) 2026-02-20 01:26:36,991 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:36,992 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:36,992 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=841331) 2026-02-20 01:26:36,994 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:37,002 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=841331) 2026-02-20 01:26:37,002 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:37,002 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:37,002 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:37,003 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=841331) 2026-02-20 01:26:37,003 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:37,003 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 0)
+(PiperActor pid=841331) [rank0]:[W220 01:26:37.485378643 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=841341) 2026-02-20 01:26:37,787 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=841341) 2026-02-20 01:26:37,788 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:37,788 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:37,788 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:37,788 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 0)
+(PiperActor pid=841341) [rank1]:[W220 01:26:37.270506387 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=841341) 2026-02-20 01:26:38,112 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:38,112 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,112 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841331) 2026-02-20 01:26:38,112 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:38,112 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,112 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=841341) 2026-02-20 01:26:38,114 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,114 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,125 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,125 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:38,125 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:38,125 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,126 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:38,126 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:38,126 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,126 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841341) 2026-02-20 01:26:38,126 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,127 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,123 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,124 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:38,124 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:38,124 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,124 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841331) 2026-02-20 01:26:38,125 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,125 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:38,125 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:38,125 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,125 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,198 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=841341) 2026-02-20 01:26:38,198 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841341) 2026-02-20 01:26:38,199 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,331 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:38,331 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:38,331 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,331 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:38,332 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:38,332 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,332 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:38,332 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:38,332 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,333 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,333 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=841331) 2026-02-20 01:26:38,193 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=841331) 2026-02-20 01:26:38,194 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:38,194 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:38,194 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,194 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841331) 2026-02-20 01:26:38,195 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,257 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=841331) 2026-02-20 01:26:38,257 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:38,258 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:38,258 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,258 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=841331) 2026-02-20 01:26:38,259 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,328 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=841331) 2026-02-20 01:26:38,329 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:38,329 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:38,336 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,336 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841341) 2026-02-20 01:26:38,337 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,352 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:38,352 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:38,352 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,352 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:38,352 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:38,365 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,365 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:38,365 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:38,365 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,414 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,415 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=841341) 2026-02-20 01:26:38,417 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=841341) 2026-02-20 01:26:38,417 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:38,418 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:38,422 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,422 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:38,422 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:38,422 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,423 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841341) 2026-02-20 01:26:38,424 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,424 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:38,424 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:38,424 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,424 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:38,424 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:38,425 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,425 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,425 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=841341) 2026-02-20 01:26:38,427 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=841341) 2026-02-20 01:26:38,427 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:38,428 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:38,428 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,428 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841341) 2026-02-20 01:26:38,429 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,429 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:38,429 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:38,429 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,429 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:38,429 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:38,429 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,504 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,504 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=841341) 2026-02-20 01:26:38,507 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=841341) 2026-02-20 01:26:38,507 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841341) 2026-02-20 01:26:38,509 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=841331) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
+(PiperActor pid=841331)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+(PiperActor pid=841331) 2026-02-20 01:26:38,411 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,411 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:38,412 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:38,412 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,412 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=841331) 2026-02-20 01:26:38,413 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,413 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:38,414 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:38,414 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,414 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,414 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:38,415 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:38,418 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,419 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:38,419 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:38,419 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,420 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,420 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:38,420 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:38,420 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,420 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=841331) 2026-02-20 01:26:38,423 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=841331) 2026-02-20 01:26:38,423 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:38,423 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:38,423 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,423 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841331) 2026-02-20 01:26:38,425 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,425 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:38,425 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:38,499 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,500 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,500 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:38,500 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:38,500 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,500 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=841331) 2026-02-20 01:26:38,503 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=841331) 2026-02-20 01:26:38,503 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:38,503 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:38,503 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,503 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841331) 2026-02-20 01:26:38,505 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,505 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:38,505 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:38,509 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,511 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,511 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:38,511 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:38,515 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,515 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:38,515 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:38,515 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=841341) 2026-02-20 01:26:38,700 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:38,700 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:38,700 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,701 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,701 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=841341) 2026-02-20 01:26:38,704 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=841341) 2026-02-20 01:26:38,704 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841341) 2026-02-20 01:26:38,705 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,711 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:38,712 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:38,716 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,716 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:38,716 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:38,716 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,717 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,717 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:38,718 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:38,722 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,722 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:38,722 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:38,722 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,723 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,723 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:38,723 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:38,724 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:38,724 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=841341) 2026-02-20 01:26:38,724 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=841331) 2026-02-20 01:26:38,701 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,702 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:38,702 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:38,707 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,707 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:38,707 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:38,707 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,717 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,717 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:38,717 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:38,721 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,724 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,724 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:38,725 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:38,729 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:38,730 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=841331) 2026-02-20 01:26:38,730 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(run_dp_rank pid=841337) Running 1 timed iterations...
+(PiperActor pid=841341) 2026-02-20 01:26:39,492 - piper_actor - INFO - Actor 1: Tracing disabled
+(PiperActor pid=841341) 2026-02-20 01:26:39,495 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,495 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:39,495 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:39,495 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,496 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=841341) 2026-02-20 01:26:39,498 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=841341) 2026-02-20 01:26:39,499 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:39,499 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:39,499 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,499 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:39,499 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:39,499 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,500 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841341) 2026-02-20 01:26:39,503 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,503 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,505 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,505 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:39,505 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:39,505 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,505 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:39,506 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:39,506 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,506 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841341) 2026-02-20 01:26:39,507 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,507 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=841341) 2026-02-20 01:26:39,509 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=841341) 2026-02-20 01:26:39,509 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841341) 2026-02-20 01:26:39,510 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,517 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:39,517 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:39,517 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,517 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:39,517 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:39,518 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,518 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:39,518 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:39,518 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,518 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,518 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,521 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,521 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841341) 2026-02-20 01:26:39,522 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,528 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:39,528 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:39,528 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,529 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:39,529 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:39,533 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,533 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:39,533 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:39,533 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,534 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,534 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=841341) 2026-02-20 01:26:39,536 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=841341) 2026-02-20 01:26:39,537 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:39,537 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:39,492 - piper_actor - INFO - Actor 0: Tracing disabled
+(PiperActor pid=841331) 2026-02-20 01:26:39,495 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,498 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,498 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:39,499 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:39,499 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,499 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=841331) 2026-02-20 01:26:39,499 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,501 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=841331) 2026-02-20 01:26:39,502 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:39,502 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:39,502 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,502 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=841331) 2026-02-20 01:26:39,503 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,503 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:39,503 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:39,503 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,503 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,505 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,506 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:39,506 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:39,506 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,506 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841331) 2026-02-20 01:26:39,507 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,507 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:39,507 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:39,507 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,507 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=841331) 2026-02-20 01:26:39,509 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=841331) 2026-02-20 01:26:39,510 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:39,510 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:39,510 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,510 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841331) 2026-02-20 01:26:39,511 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,513 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=841331) 2026-02-20 01:26:39,513 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:39,513 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:39,514 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,514 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=841331) 2026-02-20 01:26:39,514 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,516 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=841331) 2026-02-20 01:26:39,517 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:39,517 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:39,522 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,522 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:39,523 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:39,523 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,523 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=841331) 2026-02-20 01:26:39,524 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,524 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:39,524 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:39,524 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,524 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,525 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:39,525 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:39,529 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,529 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:39,529 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:39,529 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,534 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,534 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:39,534 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:39,534 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,534 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=841331) 2026-02-20 01:26:39,536 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=841331) 2026-02-20 01:26:39,536 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:39,537 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:39,537 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,537 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841341) 2026-02-20 01:26:39,541 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,541 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:39,541 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:39,541 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,541 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841341) 2026-02-20 01:26:39,543 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,543 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:39,543 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:39,543 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,543 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:39,543 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 0)
+(PiperActor pid=841341) 2026-02-20 01:26:39,543 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,544 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,544 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=841341) 2026-02-20 01:26:39,546 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=841341) 2026-02-20 01:26:39,546 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:39,547 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:39,547 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,547 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841341) 2026-02-20 01:26:39,547 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,548 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:39,548 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:39,548 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,548 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:39,548 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 1)
+(PiperActor pid=841341) 2026-02-20 01:26:39,548 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,552 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,552 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=841341) 2026-02-20 01:26:39,554 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=841341) 2026-02-20 01:26:39,554 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841341) 2026-02-20 01:26:39,555 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,561 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:39,562 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:39,562 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,562 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,562 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=841341) 2026-02-20 01:26:39,565 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=841341) 2026-02-20 01:26:39,565 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841341) 2026-02-20 01:26:39,566 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,572 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:39,572 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:39,576 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,576 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:39,577 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:39,577 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,578 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,578 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:39,578 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:39,542 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,542 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:39,542 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 0)
+(PiperActor pid=841331) 2026-02-20 01:26:39,547 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,548 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,548 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:39,548 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:39,548 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,549 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=841331) 2026-02-20 01:26:39,551 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=841331) 2026-02-20 01:26:39,551 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:39,551 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:39,551 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,551 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=841331) 2026-02-20 01:26:39,552 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,552 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:39,553 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 1)
+(PiperActor pid=841331) 2026-02-20 01:26:39,557 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,558 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,558 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:39,558 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:39,562 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,562 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:39,563 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:39,563 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,565 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,565 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:39,565 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:39,569 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,569 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:39,569 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:39,570 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,577 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,577 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:39,578 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 2)
+(PiperActor pid=841331) 2026-02-20 01:26:39,582 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(run_dp_rank pid=841337) rank 0 iter time= 0.11 ± 0.00 s (1 samples)
+(run_dp_rank pid=841337) rank 0 throughput= 143871.38 tokens/sec (1 samples)
+(PiperActor pid=841341) 2026-02-20 01:26:39,582 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,582 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:39,582 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 2)
+(PiperActor pid=841341) 2026-02-20 01:26:39,582 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,584 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,584 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:39,584 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 3)
+(PiperActor pid=841341) 2026-02-20 01:26:39,584 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=841341) 2026-02-20 01:26:39,584 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=841341) 2026-02-20 01:26:39,584 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=841331) 2026-02-20 01:26:39,585 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,585 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:39,585 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 3)
+(PiperActor pid=841331) 2026-02-20 01:26:39,589 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=841331) 2026-02-20 01:26:39,591 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=841331) 2026-02-20 01:26:39,591 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(run_dp_rank pid=841337) Ray timeline saved to: out/debug-pp2-dp1-interleaved-1f1b.json
+(PiperActor pid=841341) [rank1]:[W220 01:26:41.664394923 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+(PiperActor pid=841331) [rank0]:[W220 01:26:41.685941314 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
diff --git a/run_interleaved-debug.sh b/run_interleaved-debug.sh
index 6618908..3b8d694 100644
--- a/run_interleaved-debug.sh
+++ b/run_interleaved-debug.sh
@@ -7,15 +7,15 @@ mkdir -p $RAY_TMP
 export RAY_TMPDIR="$RAY_TMP"
 export PYTHONUNBUFFERED=1
 
-export CUDA_VISIBLE_DEVICES=3,4,5
+export CUDA_VISIBLE_DEVICES=0,1,2
 export RAY_DEDUP_LOGS=0
 export PIPER_MASTER_PORT=12389
 
 # python3 -m test.test_llama --iters 5 --schedule interleaved-1f1b --mbs 4 > $LOG_DIR/interleaved.log 2>&1
 python3 -m test.test_llama \
-    --warmup 5 \
-    --iters 10 \
+    --warmup 1 \
+    --iters 1 \
     --schedule interleaved-1f1b \
     --mbs 4 \
-    --model debug > $LOG_DIR/interleaved_debug.log 2>&1
+    --model debug > $LOG_DIR/interleaved_debug-1warm_1it.log 2>&1
     # --tracing \
\ No newline at end of file
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 7165388..369422d 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -456,7 +456,7 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int):
         global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
         self.logger.debug(
-            f"Dispatch fwd p2p recv on {self.global_rank} from {global_src_rank}"
+            f"Dispatch fwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
         )
         self._start_timing(self.p2p_stream, "fwd_p2p_recv")
         with torch.cuda.stream(self.p2p_stream):
@@ -468,7 +468,7 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int):
                 )
         self._stop_timing(self.p2p_stream, "fwd_p2p_recv")
         self.logger.debug(
-            f"Completed fwd p2p recv on {self.global_rank} from {global_src_rank}"
+            f"Completed fwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
         )
 
     def _exec_fwd_send(self, stage_id: int, mb_idx: int, output):
@@ -480,7 +480,7 @@ def _exec_fwd_send(self, stage_id: int, mb_idx: int, output):
         global_dst_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
         self.logger.debug(
-            f"Dispatch fwd p2p send on {self.global_rank} to {global_dst_rank}"
+            f"Dispatch fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
         )
         self._start_timing(self.p2p_stream, "fwd_p2p_send")
         with torch.cuda.stream(self.p2p_stream):
@@ -488,7 +488,7 @@ def _exec_fwd_send(self, stage_id: int, mb_idx: int, output):
                 dist.send(output[i], dst=global_dst_rank, group=self.pp_group)
         self._stop_timing(self.p2p_stream, "fwd_p2p_send")
         self.logger.debug(
-            f"Completed fwd p2p send on {self.global_rank} to {global_dst_rank}"
+            f"Completed fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
         )
 
     def _exec_bwd_recv(self, stage_id: int, mb_idx: int):
@@ -502,14 +502,14 @@ def _exec_bwd_recv(self, stage_id: int, mb_idx: int):
         global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
         self.logger.debug(
-            f"Dispatch bwd p2p recv on {self.global_rank} from {global_src_rank}"
+            f"Dispatch bwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id+1} -> {stage_id}, mb {mb_idx})"
         )
         self._start_timing(self.p2p_stream, "bwd_p2p_recv")
         with torch.cuda.stream(self.p2p_stream):
             dist.recv(input_grad, src=global_src_rank, group=self.pp_group)
         self._stop_timing(self.p2p_stream, "bwd_p2p_recv")
         self.logger.debug(
-            f"Completed bwd p2p recv on {self.global_rank} from {global_src_rank}"
+            f"Completed bwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id+1} -> {stage_id}, mb {mb_idx})"
         )
 
         out_activation.backward(gradient=input_grad)
@@ -525,14 +525,14 @@ def _exec_bwd_send(self, stage_id: int, mb_idx: int):
         global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
         self.logger.debug(
-            f"Dispatch bwd p2p send on {self.global_rank} to {global_src_rank}"
+            f"Dispatch bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
         )
         self._start_timing(self.p2p_stream, "bwd_p2p_send")
         with torch.cuda.stream(self.p2p_stream):
             dist.send(output_grad, dst=global_src_rank, group=self.pp_group)
         self._stop_timing(self.p2p_stream, "bwd_p2p_send")
         self.logger.debug(
-            f"Completed bwd p2p send on {self.global_rank} to {global_src_rank}"
+            f"Completed bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
         )
 
         self.inp_activation[stage_id][mb_idx] = None

From 42fc7c7845ebc46fa7051e5478a2dd79f076df11 Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Fri, 20 Feb 2026 03:20:54 +0000
Subject: [PATCH 039/100] Debugging Torch autograd error

---
 piper/actor.py     | 925 +++++++++++++++++++--------------------------
 piper/compile.py   |   2 +-
 piper/exec.py      |   3 +-
 test/test_llama.py |   4 +-
 4 files changed, 403 insertions(+), 531 deletions(-)

diff --git a/piper/actor.py b/piper/actor.py
index 2f2bd2e..64102da 100644
--- a/piper/actor.py
+++ b/piper/actor.py
@@ -14,48 +14,147 @@
 from piper.utils import deserialize_graphmodule, piper_metadata, create_logger, RemoteTensor, print_backward_graph, LOG_LEVEL
 from piper.backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
 
-torch.set_float32_matmul_precision('high')
-torch._dynamo.config.compiled_autograd = False
-everse_graph, _get_grad_fn_or_grad_acc
-
 torch.set_float32_matmul_precision('high')
 torch._dynamo.config.compiled_autograd = False
 
 CLEANUP_MEMORY = False
+torch.autograd.set_detect_anomaly(True)
 
-@ray.remote
-class PiperActor:
-    def __init__(self, actor_id, world_size, dp_rank=0, dp_degree=1, pp_degree=1, optim_fn=None):
-        self.logger = create_logger("piper_actor", "DEBUG")
+logger = create_logger("piper_actor", LOG_LEVEL)
+
+def create_actors(num_actors, optim_class):
+    dp_rank = int(os.environ['PIPER_DP_RANK'])
+    world_size = int(os.environ['PIPER_WORLD_SIZE'])
+    dp_degree = int(os.environ['PIPER_DP_DEGREE'])
+    pp_degree = int(os.environ['PIPER_PP_DEGREE'])
+
+    from piper.utils import piper_metadata
+    for actor_id in range(num_actors):
+        global_rank = dp_rank * dp_degree + actor_id
+        actor = PiperActor.options(num_gpus=0.9, max_concurrency=2).remote(actor_id, optim_class, world_size, dp_rank=dp_rank, dp_degree=dp_degree, pp_degree=pp_degree)
+        piper_metadata.actors[actor_id] = actor
+
+    ray.get([actor.load_actor_handles.remote(piper_metadata.actors) for actor in piper_metadata.actors.values()])
+
+def get_actor(actor_id):
+    from piper.utils import piper_metadata
+    return piper_metadata.actors[actor_id]
+
+class ExpertRayFunction(torch.autograd.Function):
+    """
+    Custom autograd Function that dispatches expert calls to Ray actors.
+    """
+    
+    @staticmethod
+    def forward(ctx, global_expert_id: int, local_expert_id: int, batch_idx: int, pp_degree: int, *args):
+        """
+        Dispatch a forward call to the expert `global_expert_id` 
+        on the actor `local_expert_id % pp_degree`. 
+        Future work will allow custom actor placement. 
+        """
+        # Get the actor for this expert
+        actor_id = local_expert_id % pp_degree
+        actor = get_actor(actor_id)
+        current_actor = ray.get_runtime_context().current_actor
+        
+        # Store metadata for backward pass
+        ctx.global_expert_id = global_expert_id
+        ctx.local_expert_id = local_expert_id
+        ctx.batch_idx = batch_idx
+        ctx.pp_degree = pp_degree
+        ctx.actor_id = actor_id
+
+        from piper.utils import piper_metadata
+        actor_self = piper_metadata.actor_self
+        
+        # Store which inputs require gradients
+        ctx.input_requires_grad = [arg.requires_grad if isinstance(arg, torch.Tensor) else False for arg in args]
+        ctx.num_inputs = len(args)
+        
+        # Dispatch a remote call if actor is not current actor
+        if actor == current_actor:
+            output = actor_self.run_expert(global_expert_id, batch_idx, *args)
+        else:
+            ref = actor.run_expert.remote(global_expert_id, batch_idx, *args)
+            output = ray.get(ref)
+
+        # Detach the output so the graph is not carried over when 
+        # the expert runs on the current actor. 
+        if isinstance(output, (list, tuple)):
+            output = [t.detach() for t in output]
+        else:
+            output = output.detach()
+        
+        return output
+    
+    @staticmethod
+    def backward(ctx, grad_output):
+        """
+        Dispatch a backward expert call corresponding to the 
+        forward expert call from the ctx. 
+        """
+        # Get the actor for this expert
+        actor = get_actor(ctx.actor_id)
+        current_actor = ray.get_runtime_context().current_actor
+        logger.debug(f"Expert {ctx.global_expert_id} {ctx.batch_idx} backward on actor: {current_actor} calling actor: {actor}")
+        num_inputs = ctx.num_inputs
+
+        from piper.utils import piper_metadata
+        actor_self = piper_metadata.actor_self
+        
+        # Dispatch a remote call if actor is not current actor
+        if actor == current_actor:
+            grad_inputs = actor_self.backward_expert(ctx.global_expert_id, ctx.batch_idx, grad_output)
+        else:
+            refs = actor.backward_expert.options(num_returns=num_inputs).remote(ctx.global_expert_id, ctx.batch_idx, grad_output)
+            grad_inputs = ray.get(refs)
         
-        st    from piper.utils import piper_metadata
-r_id = actor_id
-        self.optim_fn = optim_fn
+        # Return gradients for inputs (None for metadata arguments)
+        result = [None, None, None, None] + list(grad_inputs)
+        
+        return tuple(result)
+
+def dispatch_expert_ray(global_expert_id: int, local_expert_id: int, batch_idx: int, pp_degree: int, *args):
+    """
+    Dispatch a Ray remote call to run an expert GraphModule
+    using a custom autograd Function. This function is called
+    from the FX graph, so it needs to be allowed in the graph.
+    """
+    return ExpertRayFunction.apply(global_expert_id, local_expert_id, batch_idx, pp_degree, *args)
+
+# Allow the dispatch function in the graph
+torch.compiler.allow_in_graph(dispatch_expert_ray)
+
+@ray.remote(enable_tensor_transport=True)
+class PiperActor:
+    def __init__(self, actor_id, optim_class, world_size, dp_rank=0, dp_degree=1, pp_degree=1):
+        self.logger = create_logger("piper_actor", LOG_LEVEL)
+
+        self.actor_id = actor_id
+        self.optim_class = optim_class
         
-        # Data parallel attributes
         self.dp_rank = dp_rank
         self.dp_degree = dp_degree
+        self.pp_degree = pp_degree
         self.world_size = world_size
+
         self.dp_group = None
-        self.pp_degree = pp_degree
+        self.device = 'cuda'
 
-        if pp_degree == 1:
-            self.global_rank = dp_rank
-        elif dp_degree == 1:
+        if dp_degree == 1:
             self.global_rank = actor_id
+        elif pp_degree == 1:
+            self.global_rank = dp_rank
         else:
-            self.global_rank = dp_rank *    from piper.utils import piper_metadata
-nfo(f"Initializing Ray actor {actor_id} global rank {self.global_rank} with PID: {os.getpid()}")
+            self.global_rank = dp_rank * dp_degree + actor_id
+
+        self.logger.info(f"Initializing Ray actor {actor_id} global rank {self.global_rank} GPU {os.environ['CUDA_VISIBLE_DEVICES']}")
 
         self.input = None
         self.truth = None
-        self.fwd_objs = {}
-        self.bwd_objs = {}
 
-        # ordered list of frame ids for ordering the fx.Graphs on this actor
-        self.frame_ids = []
         # map stage id -> compiled fx.Graph function
-        self.compiled_fns = dict()
+        self.forward_fns = dict()
         # map stage id -> original GraphModule (for hook registration)
         self.graph_modules = dict()
         # map stage id -> model parameters used by the fx.Graph with holes (None values) for input tensors
@@ -64,12 +163,24 @@ def __init__(self, actor_id, world_size, dp_rank=0, dp_degree=1, pp_degree=1, op
         self.input_idxs = dict()
         # map stage id -> optimizer for the fx.Graph
         self.optims = dict()
-        # map stage -> mb_idx -> previous activation (if this stage is not first)
-       piper.rev_activation = defaultdict(dict)
+        # map stage id -> mb_idx -> previous activation (if this stage is not first)
+        self.inp_activation = defaultdict(dict)
         # map stage id -> mb_idx -> current activation
-        self.activation = defaultdict(dict)
+        self.out_activation = defaultdict(dict)
         # accumuate loss for each microbatch
         self.loss = []
+        # map expert id -> expert
+        self.experts = dict()
+        # map expert id -> expert parameters
+        self.expert_parameters = dict()
+        # map expert id -> expert input indices
+        self.expert_input_idxs = dict()
+        # map expert id -> expert module
+        self.expert_modules = dict()
+        # map expert id -> expert input activations
+        self.expert_input_activations = defaultdict(dict)
+        # map (expert_id, batch_idx) -> output activation for backward
+        self.expert_output_activations = defaultdict(dict)
         # stage_id -> mb_idx -> param_groups
         self._bw_param_groups = defaultdict(dict)
 
@@ -77,24 +188,22 @@ def __init__(self, actor_id, world_size, dp_rank=0, dp_degree=1, pp_degree=1, op
         self.tracing = False  # Toggle for timing and memory tracing
         self.trace_data = {'update': {'total': [], 'peak_memory_delta': [], 'peak_memory': []}}
 
-        end = time.perf_counter()
-        self.logger.debug(f"__init__ took {(end-start)*1000:.2f}ms")
-
-        self.logger.debug(f"Initialized actor {self.actor_id} for global rank {self.global_rank}")
+        from piper.utils import piper_metadata
+        piper_metadata.actor_self = self
 
+    def load_actor_handles(self, actor_handles):
+        from piper.utils import piper_metadata
+        piper_metadata.actors = actor_handles
+    
     def id(self):
         return self.actor_id
 
     def send_input(self, tensor):
-        self.input = tensor.to('cuda')
+        self.input = tensor.to(self.device)
         return "done"
     
     def send_truth(self, tensor):
-        self.truth = tensor.to('cuda')
-        return "done"
-
-    def reset_peak_memory(self):
-        torch.cuda.reset_peak_memory_stats()
+        self.truth = tensor.to(self.device)
         return "done"
 
     def get_peak_memory(self):
@@ -106,7 +215,7 @@ def join_process_groups(self):
         master_port = os.environ.get('PIPER_MASTER_PORT', "10000")
         init_method = f"tcp://{master_addr}:{master_port}"
 
-        dpiper.t_process_group("nccl", init_method=init_method, rank=self.global_rank, world_size=self.world_size)
+        dist.init_process_group("nccl", init_method=init_method, rank=self.global_rank, world_size=self.world_size)
         self.logger.debug(f"Actor {self.actor_id} global rank {self.global_rank} has GPU {os.environ['CUDA_VISIBLE_DEVICES']}, joined the global process group")
 
         if self.dp_degree > 1:
@@ -122,11 +231,15 @@ def join_dp_process_group(self):
                 self.dp_group = process_group
                 self.logger.info(f"Global rank {self.global_rank} joined its dp group {dp_group_id} along with ranks {group_ranks}")
 
-    def compile_graph(self, stage_id, gm_data, compiler_fn, graphargs, input_idxs):
-        self.logger.debug(f"Compiling graph on actor {self.actor_id} for stage id: {stage_id} with inputs: {len(graphargs)}")
-      def __init__(self, actor_id, optim_class, world_size, dp_rank=0, dp_degree=1, pp_degree=1):
-        self.logger = create_logger("piper_actor", LOG_LEVEL)
-            'peak_memory_delta': [],
+    def load_graph(self, stage_id: int, gm_data, compiler_fn, graphargs, input_idxs):
+        self.logger.debug(f"Compiling graph on actor {self.actor_id} for stage id: {stage_id} with inputs: {len(graphargs)} and input indices: {input_idxs}")
+
+        # set up tracing data structure
+        self.trace_data[stage_id] = {
+            'forward': {
+                'forward': [],
+                'total': [],
+                'peak_memory_delta': [],
                 'peak_memory': []
             },
             'backward': {
@@ -135,7 +248,6 @@ def __init__(self, actor_id, optim_class, world_size, dp_rank=0, dp_degree=1, pp
                 'peak_memory_delta': [],
                 'peak_memory': []
             },
-            # Split-backward variants (zero-bubble). These stay empty when not used.
             'backward_input': {
                 'time': [],
                 'graph_pruning': [],
@@ -147,144 +259,38 @@ def __init__(self, actor_id, optim_class, world_size, dp_rank=0, dp_degree=1, pp
 
         # compile the graph with the given graphargs
         gm = deserialize_graphmodule(gm_data)
-        
+
         # Store GraphModule reference
         self.graph_modules[stage_id] = gm
+        self.forward_fns[stage_id] = gm.forward
         
-        compiled_fn = compiler_fn(gm, graphargs)
-        assert callable(compiled_fn), "compiler_fn did not return callable"
-        self.compiled_fns[stage_id] = compiled_fn
-
-        # discard the graphargs that correspond to input tensors
-        for i in input_idxs:
-            graphargs[i] = None
-        self.input_idxs[stage_id] = input_idxs
-
-        # save the graphargs that correspond to model parameters
-        self.parameters[stage_id] = graphargs
-
-        # initialize the optimizer for this stage
-        ## Added this manual_seed for debugging
-        torch.manual_seed(0)
-        if [param for param in self.parameters[stage_id] if param is not None]:
-            self.optims[stage_id] = self.optim_fn([param for param in self.parameters[stage_id] if param is not None])
-
-        de        # map expert id -> expert
-        self.experts = dict()
-        # map expert id -> expert parameters
-        self.expert_parameters = dict()
-        # map expert id -> expert input indices
-        self.expert_input_idxs = dict()
-        # map expert id -> expert module
-        self.expert_modules = dict()
-        # map expert id -> expert input activations
-        self.expert_input_activations = defaultdict(dict)
-        # map (expert_id, batch_idx) -> output activation for backward
-        self.expert_output_activations = defaultdict(dict)
-        # stage_id -> mb_idx -> param_groups
-        self._bw_param_groups = defaultdict(dict)
-l gm_data
+        # save the parameters and initialize the optimizer
+        self.add_param_group(stage_id, graphargs, input_idxs)
 
-        end = time.perf_counter()
-        self.logger.debug(f"compile_graph took {(end-start)*1000:.2f}ms")
-        return "Finished compiling"
+        del gm_data
 
-    @ray.method(tensor_transport="nccl")
-    def forwardpiper.stage_id: int, mb_idx: int, *args):
-        self.logger.debug(f"Calling forward {stage_id} mb {mb_idx} on actor {self.actor_id} globpiper. {self.global_rank}")
+    def add_param_group(self, stage_id: int, params, input_idxs):
+        self.logger.debug(f"Adding param group for stage {stage_id}")
 
-        if self.tracing:
-            beginning_event = torch.cuda.Event(enable_timing=True)
-            forward_start_event = torch.cuda.Event(enable_timing=True)
-            forward_end_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-            beginning_event.record()
+        if stage_id in self.parameters:
+            self.logger.debug(f"Param group already exists for stage {stage_id}")
+            return
         
-        def pre_loaded_input(param):
-            if param is Non    # @ray.method
-e:
-                return self.input
-            else:
-                return param
-        args = list(map(pre_loaded_input, args))
-
-        def unwrap_arg(arg):
-            if isinstance(arg, list) or isinstance(arg, tuple):
-                assert len(arg) == 1
-                return arg[0]
+        # place parameters on the device
+        def move_to_device(idx, arg):
+            if idx not in input_idxs:
+                return arg.to(self.device).detach().requires_grad_(True)
             else:
-                return arg
-        args = list(map(unwrap_arg, args))
-
-        def detach_arg(arg):
-            return arg.detach().clone()
-        args = list(map(detach_arg, args))
-
-        # Ray object refs resolve to a single element list
-        def unwrap(x):
-            if isinstance(x, list) or isinstance(x, tuple):
-                assert len(x) == 1
-            return x[0] if isinstance(x, list) or isinstance(x, tuple) else x
-        args = list(map(unwrap, args))
-
-        # place input tensors in the correct indices
-        for i, arg in zip(self.input_idxs[stage_id], args):
-            self.parameters[stage_id][i] = arg
-
-        # save first input as previous activation
-        if stage_id != 0:
-            if not args[0].requires_grad:
-                args[0].requires_grad_()
-            self.prev_activation[stage_id][mb_idx] = args[0]
-
-
-        # Record start event for forward timing
-        if self.tracing:
-            # torch.cuda.reset_peak_memory_stats()
-            # forward_start_memory = torch.cuda.memory_allocated()
-            forward_start_event.record()
-
-        # Call compiled function
-        out = self.compiled_fns[stage_id](*self.parameters[stage_id])
-
-        # Record end event and calculate forward timing
-        if self.tracing:
-            forward_end_event.record()
-            torch.cuda.synchronize()
-            
-            # Calculate total forward time
-            forward_time = forward_start_event.elapsed_time(forward_end_event)
-            
-            # forward_peak_memory_delta_gb = (torch.cuda.max_memory_allocated(            'backward_input': {
-                'time': [],
-                'graph_pruning': [],
-            },
-            'backward_weight': {
-                'time': [],
-            },
-) - forward_start_memory) / (1024**3)
-            # forward_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
-
-        # save first output as activation BEFORE clearing input tensors
-        # This ensures the activation doesn't share memory with parameters
-        # that might be modified when processing other stages on the same actor
-        activation_tensor = out[0] if isinstance(out, (list, tuple)) else out
-        self.activation[stage_id][mb_idx] = activation_tensor
+                return arg.to(self.device)
+        params = list(map(move_to_device, range(len(params)), params))
 
-        # clear the input tensors
-        for i in self.input_idxs[stage_id]:
-            self.parameters[stage_id][i] = None
-        del args
+        # discard the graphargs that correspond to input tensors
+        for i in input_idxs:
+            params[i] = None
 
-        if self.tracing:
-            end_event.record()
-            torch.cuda.synchronize()
-            total_time = forward_start_event.elapsed_time(end_event)
-            # Store in trace_data (all microbatches stored sequentially)
-            self.trace_data[stage_id]['forward']['forward'].append(forward_time)
-            self.trace_data[stage_id]['forward']['total'].append(forward_time)
-            # self.trace_data[stage_id]['forward']['peak_memory_delta'].append(forward_peak_memory_delta_gb)
-            # self.trace_data[stage_id]['forward']['peak_memor        self.parameters[stage_id] = params
+        # save the parameters
+        self.input_idxs[stage_id] = input_idxs
+        self.parameters[stage_id] = params
 
         # add the parameters to the optimizer for this stage
         if stage_id not in self.optims:
@@ -387,8 +393,17 @@ def backward_expert(self, expert_id, batch_idx, grad_output):
         
         return grad_inputs
     
-ng c#  forward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
+    # @ray.method(tensor_transport="nccl")
+    def forward(self, stage_id: int, mb_idx: int, *args):
+        self.logger.debug(f"Calling forward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
 
+        if self.tracing:
+            beginning_event = torch.cuda.Event(enable_timing=True)
+            forward_start_event = torch.cuda.Event(enable_timing=True)
+            forward_end_event = torch.cuda.Event(enable_timing=True)
+            end_event = torch.cuda.Event(enable_timing=True)
+            beginning_event.record()
+        
         def pre_loaded_input(param):
             if param is None:
                 return self.input
@@ -400,127 +415,219 @@ def pre_loaded_input(param):
         def unwrap(x):
             if isinstance(x, list) or isinstance(x, tuple):
                 assert len(x) == 1
-                return x[0]
-            else:
-                return x
+            return x[0] if isinstance(x, list) or isinstance(x, tuple) else x
         args = list(map(unwrap, args))
 
+        def place(arg):
+            if arg.device == torch.device('cpu'):
+                return arg.to(self.device)
+            else:
+                return arg
+        args = list(map(place, args))
+
         # place input tensors in the correct indices
         for i, arg in zip(self.input_idxs[stage_id], args):
             self.parameters[stage_id][i] = arg
 
         # save first input as previous activation
+        # if args[0].dtype == torch.float:
+        #     args[0].requires_grad_().retain_grad()
+        # self.inp_activation[stage_id][mb_idx] = args[0]
         if stage_id != 0:
-            # args[0].requires_grad_()
-            assert args[0].requires_grad 
-            self.prev_activation[stage_id][mb_idx] = args[0]
+            if not args[0].requires_grad:
+                args[0].requires_grad_()
+            self.inp_activation[stage_id][mb_idx] = args[0]
+
+        # Record start event for forward timing
+        if self.tracing:
+            torch.cuda.reset_peak_memory_stats()
+            forward_start_memory = torch.cuda.memory_allocated()
+            forward_start_event.record()
+
+        # Call compiled function
+        output = self.forward_fns[stage_id](*self.parameters[stage_id])
+        assert isinstance(output, (list, tuple)) and len(output) == 1, "Piper only supports one output per subgraph"
+        output = output[0]
+
+        # Record end event and calculate forward timing
+        if self.tracing:
+            forward_end_event.record()
+            torch.cuda.synchronize()
+            forward_time = forward_start_event.elapsed_time(forward_end_event)
+            forward_peak_memory_delta_gb = (torch.cuda.max_memory_allocated() - forward_start_memory) / (1024**3)
+            forward_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
+
+        # save output as output activation
+        self.logger.debug(f"Saving output activation {output.shape} for stage {stage_id} mb {mb_idx}")
+        self.out_activation[stage_id][mb_idx] = output
+
+        # clear the input tensors
+        for i in self.input_idxs[stage_id]:
+            self.parameters[stage_id][i] = None
+        del args
 
-        out = self.compiled_fns[stage_id](*self.parameters[stage_id])
+        if self.tracing:
+            end_event.record()
+            torch.cuda.synchronize()
+            total_time = forward_start_event.elapsed_time(end_event)
+            self.trace_data[stage_id]['forward']['forward'].append(forward_time)
+            self.trace_data[stage_id]['forward']['total'].append(forward_time)
+            self.trace_data[stage_id]['forward']['peak_memory_delta'].append(forward_peak_memory_delta_gb)
+            self.trace_data[stage_id]['forward']['peak_memory'].append(forward_peak_memory_gb)
+
+        if CLEANUP_MEMORY:
+            gc.collect()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
         
-        # save first output as activation BEFORE clearing input tensors
-        # This ensures the activation doesn't share memory with parameters
-        # that might be modified when processing other stages on the same actor
-        activation_tensor = out[0] if isinstance(out, (list, tuple)) else out
-        self.activation[stage_id][mb_idx] = activation_tensor
+        return output
+
+    def forward_cpu(self, stage_id: int, mb_idx: int, *args):
+        self.logger.debug(f"Calling cpu forward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
+
+        def pre_loaded_input(param):
+            if param is None:
+                return self.input
+            else:
+                return param
+        args = list(map(pre_loaded_input, args))
+
+        # Ray object refs resolve to a single element list
+        def unwrap(x):
+            if isinstance(x, list) or isinstance(x, tuple):
+                assert len(x) == 1
+                return x[0]
+            else:
+                return x
+        args = list(map(unwrap, args))
         
-           # save first input as previous activation
+        def place(arg):
+            if arg.device == torch.device('cpu'):
+                return arg.to(self.device)
+            else:
+                return arg
+        args = list(map(place, args))
+
+        # place input tensors in the correct indices
+        for i, arg in zip(self.input_idxs[stage_id], args):
+            self.parameters[stage_id][i] = arg
+
+        # save first input as input activation
+        # if args[0].dtype == torch.float:
+        #     args[0].requires_grad_().retain_grad()
+        # self.inp_activation[stage_id][mb_idx] = args[0]
         if stage_id != 0:
             if not args[0].requires_grad:
                 args[0].requires_grad_()
             self.inp_activation[stage_id][mb_idx] = args[0]
-ct()
+
+        out = self.forward_fns[stage_id](*self.parameters[stage_id])
+
+        # save output as output activation
+        activation_tensor = out[0] if isinstance(out, (list, tuple)) else out
+        self.out_activation[stage_id][mb_idx] = activation_tensor
+        
+        # clear the input tensors
+        for i in self.input_idxs[stage_id]:
+            self.parameters[stage_id][i] = None
+        del args
+        
+        if CLEANUP_MEMORY:
+            gc.collect()
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
         
         return out
 
-    @ray.method(tensor_transport="nccl")
-    def backward(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
+    # @ray.method(tensor_transport="nccl")
+    def backward(self, stage_id: int, mb_idx: int, inp, truth=None, loss_fn=None):
         self.logger.debug(f"Calling backward {stage_id} mb {mb_idx} on actor {self.actor_id} global rank {self.global_rank}")
 
         if self.tracing:
             beginning_event = torch.cuda.Event(enable_timing=True)
             backward_start_event = torch.cuda.Event(enable_timing=True)
             backward_end_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable    # get the activation for the current stage
-        assert mb_idx in self.activation[stage_id], f"mb_idx {mb_idx} not in activation[stage_id {stage_id}]"
-        activation = self.activation[stage_id][mb_idx]
-        
-        # Record start event for backward timing
-        if self.tracing:
-            # torch.cuda.reset_peak_memory_stats()
-            # backward_start_memory = torch.cuda.memory_allocated()
+            end_event = torch.cuda.Event(enable_timing=True)
+            beginning_event.record()
+            torch.cuda.reset_peak_memory_stats()
+            backward_start_memory = torch.cuda.memory_allocated()
             backward_start_event.record()
-        
-        # compute loss in the last stage. use the saved activation rather
-        # than inp because the saved activation remembers the computation graph
+
+        if isinstance(inp, list) or isinstance(inp, tuple):
+            assert len(inp) == 1
+            inp = inp[0]
+
+        # Get the activations for this stage and microbatch
+        out_activation = self.out_activation[stage_id][mb_idx]
+        inp_activation = self.inp_activation[stage_id][mb_idx]
+        self.logger.debug(f"Stage {stage_id} backward graph:")
+        print_backward_graph(self.logger.debug, out_activation)
+
+        # compute loss with the final activation of the final stage. 
+        # use the saved activation rather than inp because the saved activation stores the computation graph
         if loss_fn is not None:
+            assert out_activation.shape == inp.shape
             if self.truth is not None:
                 labels = self.truth
             else:
-                labels = inp[0]
-            loss = loss_fn(activation, labels)
+                labels = truth.to(self.device)
+            assert out_activation.shape == labels.shape
+            loss = loss_fn(out_activation, labels)
             loss.backward()
             self.loss.append(loss.item())
-            # Clean up loss tensor after extracting item
-            del loss
         # if not the last stage, backprop on the stored activation given 
         # the input gradient from the subsequent stage
         else:
             assert inp is not None
-            assert activation.shape == inp.shape
-            activation.backward(gradient=inp)
+            assert out_activation.shape == inp.shape
+            out_activation.backward(gradient=inp)
 
-        # Record end event and calculate backward timing
-        if self.tracing:
-            backward_end_event.record()
-            torch.cuda.synchronize()
-            
-            # Calculate total backward time
-            backward_time = backward_start_event.elapsed_time(backward_end_event)
-            
-            # backward_peak_memory_delta_gb = (torch.cuda.max_memory_allocated() - backward_start_memory) / (1024**3)
-            # backward_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
-
-        del self.activation[stage_id][mb_idx]
-        del activation
+        del out_activation
 
         # propagate the gradient backwards if not the first stage
-        if stage_id != 0:
-            ret = [self.prev_activation[stage_id][mb_idx]]
+        if stage_id == 0:
+            ret = "done"
         else:
-            ret = ["done"]
+            ret = inp_activation.grad
         
+        del inp_activation
+
+        # Record end event and calculate backward timing
         if self.tracing:
+            backward_end_event.record()
+            torch.cuda.synchronize()
+            
+            # Calculate total backward time
+            backward_time = backward_start_event.elapsed_time(backward_end_event)
+            
+            backward_peak_memory_delta_gb = (torch.cuda.max_memory_allocated() - backward_start_memory) / (1024**3)
+            backward_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
+
             end_event.record()
             torch.cuda.synchronize()
             total_time = beginning_event.elapsed_time(end_event)
-            # Store in trace_data (all microbatches stored sequentially)
             self.trace_data[stage_id]['backward']['backward'].append(backward_time)
-            self        # save first input as input activation
-        if args[0].dtype == torch.float:
-            args[0].requires_grad_().retain_grad()
-        self.inp_activation[stage_id][mb_idx] = args[0]
-       # self.trace_data[stage_id]['backward']['peak_memory'].append(backward_peak_memory_gb)
-            if stage_id != 0:
-                self.bwd_objs[stage_id] = torch.ones_like(ret[0])
+            self.trace_data[stage_id]['backward']['total'].append(total_time)
+            self.trace_data[stage_id]['backward']['peak_memory_delta'].append(backward_peak_memory_delta_gb)
+            self.trace_data[stage_id]['backward']['peak_memory'].append(backward_peak_memory_gb)
         
         if CLEANUP_MEMORY:
             gc.collect()
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
         
-        return ret + ret
+        return [ret] * 2
 
     def synchronize_gradients(self):
         """Synchronize gradients across all DP ranks for this stage using all-reduce."""     
-        self.logger.debug(f"Actor {self.actor_id} global rank {self.global_rank} synchr# izing gradients")
+        self.logger.debug(f"Actor {self.actor_id} global rank {self.global_rank} synchronizing gradients")
         
-        #     def backward(self, stage_id: int, mb_idx: int, inp, truth=None, loss_fn=None):
+        # Iterate over all stages on this actor and synchronize their parameters
         for stage_id, parameters in self.parameters.items():
             for param in parameters:
                 if param is not None and param.grad is not None:
                     dist.all_reduce(param.grad, op=dist.ReduceOp.AVG, group=self.dp_group)
+        self.logger.info(f"Actor {self.actor_id} global rank {self.global_rank} finished synchronizing gradients")
 
     def update(self, *done_mbs):
         self.logger.debug(f"Calling update on actor {self.actor_id} global rank {self.global_rank}")
@@ -529,8 +636,8 @@ def update(self, *done_mbs):
             start_event = torch.cuda.Event(enable_timing=True)
             end_event = torch.cuda.Event(enable_timing=True)
 
-            # torch.cuda.reset_peak_memory_stats()
-            # update_start_memory = torch.cuda.memory_allocated()
+            torch.cuda.reset_peak_memory_stats()
+            update_start_memory = torch.cuda.memory_allocated()
 
             start_event.record()
         
@@ -539,7 +646,6 @@ def update(self, *done_mbs):
             self.synchronize_gradients()
         
         # step the optimizer for each stage
-        assert self.optim_fn
         for _, optim in self.optims.items():
             optim.step()
             optim.zero_grad()
@@ -550,27 +656,15 @@ def update(self, *done_mbs):
             end_event.record()
             torch.cuda.synchronize()
             total_time = start_event.elapsed_time(end_event)
-            # update_peak_memory_delta_gb = (torch.cuda.max_memory_allocated() - update_start_memory) / (1024**3)
-            # update_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
+            update_peak_memory_delta_gb = (torch.cuda.max_memory_allocated() - update_start_memory) / (1024**3)
+            update_peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
 
             self.trace_data['update']['total'].append(total_time)
-            # self.trace_data['update']['peak_memory_delta'].append(update_peak_memory_delta_gb)
-            # self.trace_data['update']['peak_memory'].append(update_peak_memory_gb)
+            self.trace_data['update']['peak_memory_delta'].append(update_peak_memory_delta_gb)
+            self.trace_data['update']['peak_memory'].append(update_peak_memory_gb)
 
         return losses
 
-    def verify_weights(self, msg="first parameter"):
-        for stage_id, parameters in self.parameters.items():
-            for param in parameters:
-                if param is not None and param.grad is not None:
-                    if len(param.shape) == 2:
-                        self.logger.info(f"Actor {self.actor_id} global rank {self.global_rank} stage {stage_id} {msg}: {param.shape, param[0][0], param.grad[0][0]}")
-                    elif len(param.shape) == 1:
-                        self.logger.info(f"Actor {self.actor_id} global rank {self.global_rank} stage {stage_id} {msg}: {param.shape, param[0], param.grad[0]}")
-                    else:
-                        assert False, f"Unsupported parameter shape: {param.shape}"
-                    return "done"
-
     def clear_trace_data(self) -> None:
         """
         Clear all collected timing data.
@@ -625,73 +719,7 @@ def stop_mem_tracing(self) -> None:
         self.logger.info(f"Saved memory snapshot to actor{self.actor_id}_memory_snapshot_mb4_gpipe.pickle")
         torch.cuda.memory._record_memory_history(enabled=None)
         return "done"
-    
-    @ray.method(tensor_transport="nccl")
-    def get_object(self, is_fwd, stage_id):
-        if is_fwd:
-            return self.fwd_objs[stage_id]
-        else:
-            return self.bwd_objs[stage_id]
-
-    def time_object_retrieval(self, obj) -> dict:
-        objs = []
-        if isinstance(obj, tuple) or isinstance(obj, list):
-            objs = [t + 1 for t in obj]
-        else:
-            objs = [obj + 1]
-        self.objs = objs
-        return "done"
-    
-    @ray.method(tensor_transport="nccl")
-    def backward_input(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
-        if self.tracing:
-            start_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-            start_event.record()
-
-        is_last_stage = loss_fn is not None
-        activation = self.activation[stage_id][mb_idx]
-
-        # Use loss instead of activation if this is the backward_input call for the last stage
-        if is_last_stage:
-            labels = self.truth if self.truth is not None else inp[0]
-            loss = loss_fn(activation, labels)
-            self.loss.append(loss)
-
-            activation_or_loss = loss
-            upstream_grads = torch.ones_like(loss)
-
-        else:
-            activation_or_loss = activation
-            upstream_grads = inp
-
-        # In first stage, we treat the backward_input call like a NOOP
-        if stage_id == 0:
-            gx = torch.zeros_like(activation) # May get slight performance boost if we don't send the full ones tensor
-            if self.tracing:
-                end_event.record()
-                torch.cuda.synchronize()
-                total_time = start_event.elapsed_time(end_event)
-                self.trace_data[stage_id]['backward_input']['time'].append(total_time)
-            return [gx, upstream_grads]
-
-        stage_input = self.prev_activation[stage_id][mb_idx]
-        stage_params = [p for p in self.parameters[stage_id] if p is not None and p.requires_grad]
-
-        # Lists of autograd nodes for the layer outputs, inputs, and parameters
-        ## Start graph pruning computation timing here ##
-        if self.tracing:
-            graph_pruning_start = torch.cuda.Event(enable_timing=True)
-            graph_pruning_end = torch.cuda.Event(enable_timing=True)
-            graph_pruning_start.record()
-        
-        output_nodes = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [activation_or_loss]) if n is not None]
-        input_nodes  = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [stage_input]) if n is not None]
-        param_nodes   = [n for n in (_get_grad_fn_or_grad_acc(p) for p in stage_params) if n is not None]
 
-        # Use the autograd graph with edges reversed to compute parameter groups, which are groups 
-        # of parameters that share the same intermediate nodes. Intermediate nodes are the nodes that
-        # lie on both (1) a backward path from the output node(s) to the
     def reset_peak_memory(self):
         torch.cuda.reset_peak_memory_stats()
         return "done"
@@ -716,18 +744,18 @@ def time_object_retrieval(self, obj) -> dict:
         return "done"
     
     # @ray.method(tensor_transport="nccl")
-    def backward_input(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
+    def backward_input(self, stage_id: int, mb_idx: int, inp, truth=None, loss_fn=None):
         if self.tracing:
             start_event = torch.cuda.Event(enable_timing=True)
             end_event = torch.cuda.Event(enable_timing=True)
             start_event.record()
 
         is_last_stage = loss_fn is not None
-        activation = self.activation[stage_id][mb_idx]
+        activation = self.out_activation[stage_id][mb_idx]
 
         # Use loss instead of activation if this is the backward_input call for the last stage
         if is_last_stage:
-            labels = self.truth if self.truth is not None else inp[0]
+            labels = self.truth if self.truth is not None else truth.to(self.device)
             loss = loss_fn(activation, labels)
             self.loss.append(loss)
 
@@ -769,212 +797,38 @@ def backward_input(self, stage_id: int, mb_idx: int, inp, loss_fn=None):
         reverse_edges = construct_reverse_graph(output_nodes)
         param_groups = get_param_groups(input_nodes, param_nodes, reverse_edges)
 
-        # Hooks to capture grads at intermediate nodes. In backward_weight,
-        # we'll backprop from these intermediate values
-        handles = []
-        for pg in param_groups:
-            intermediates = pg["intermediates"]
-            if not intermediates:
-                continue
-
-            pg["grads"] = [None] * len(intermediates)
-
-            for i, intermediate_node in enumerate(intermediates):
-                def make_hook(group: Dict[str, Any], idx: int):
-                    def hook(grad_inputs):
-                        group["grads"][idx] = grad_inputs
-                    return hook
-                handles.append(intermediate_node.register_prehook(make_hook(pg, i)))
-
-        if self.tracing:
-            graph_pruning_end.record()
-            torch.cuda.synchronize()
-            graph_pruning_time = graph_pruning_start.elapsed_time(graph_pruning_end)
-            if 'graph_pruning' not in self.trace_data[stage_id]['backward_input']:
-                self.trace_data[stage_id]['backward_input']['graph_pruning'] = []
-            self.trace_data[stage_id]['backward_input']['graph_pruning'].append(graph_pruning_time)
-        gx = torch.autograd.grad(
-            outputs=activation_or_loss,
-            inputs=stage_input,
-            grad_outputs=upstream_grads,
-            retain_graph=True,
-            allow_unused=True,
+        pgs = param_groups
+        with_int = [pg for pg in pgs if pg.get("intermediates")]
+        self.logger.debug(
+            f"stage {stage_id} mb {mb_idx}: groups={len(pgs)} with_intermediates={len(with_int)} "
+            f"intermediates_sizes={[len(pg.get('intermediates', [])) for pg in with_int]}"
         )
 
-        gx = gx[0] # Take the gx gradient out of the tuple returned by autograd.grad
-        if gx is not None and stage_input.requires_grad:
-            if stage_input.grad is None:
-                stage_input.grad = gx
-            else:
-                stage_input.grad.add_(gx)
-
-        # Free output tensors between the output nodes and the intermediate nodes
-        if not isinstance(activation_or_loss, list):
-            activation_or_loss = [activation_or_loss]
-
-        for t in activation_or_loss:
-            t.detach_()
-
-        for h in handles:
-            h.remove()
-
-        # Save parameter groups for use in backward_weight
-        self._bw_param_groups[stage_id][mb_idx] = param_groups
-
-        if self.tracing:
-            end_event.record()
-            torch.cuda.synchronize()
-            total_time = start_event.elapsed_time(end_event)
-            self.trace_data[stage_id]['backward_input']['time'].append(total_time)
-
-        return [gx, upstream_grads]
-
-    # @ray.method(tensor_transport="nccl")
-    def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, loss_fn=None):
-        if self.tracing:
-            start_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-            start_event.record()
-
-        stage_params = [p for p in self.parameters[stage_id] if p is not None and p.requires_grad]
-
-        # We only pass to enforce temporal dependency.
-        # upstream is only required for the stage 0 case
-        gx = gx
-        upstream = upstream
-
-        # Special case to handle stage 0 since backward_input is a NOOP, 
-        # meaning no parameter groups are created
-        if stage_id == 0:
-            is_last_stage = loss_fn is not None
-            activation = self.activation[stage_id][mb_idx]
-
-            if is_last_stage:
-                labels = self.truth if self.truth is not None else None
-                loss = loss_fn(activation, labels)
-
-                gparams = torch.autograd.grad(
-                    outputs=loss,
-                    inputs=stage_params,
-                    retain_graph=False,
-                )
-
-            else:
-                gparams = torch.autograd.grad(
-                    outputs=activation,
-                    inputs=stage_params,
-                    grad_outputs=upstream,
-                    retain_graph=False,
-                )
-
-            assert len(gparams) == len(stage_params), (
-                f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(stage_params)}"
-            )
-            
-            for p, pg in zip(stage_params, gparams):
-                if p.grad is None:
-                    p.grad = pg.clone()
-                else:
-                    p.grad.add_(pg)
-
-            del self.activation[stage_id][mb_idx]
-
-            if self.tracing:
-                end_event.record()
-                torch.cuda.synchronize()
-                total_time = start_event.elapsed_time(end_event)
-                self.trace_data[stage_id]['backward_weight']['time'].append(total_time)
-
-            return [None] + ["done"]
-
-        # Create mapping from autograd nodes -> parameters
-        grad_acc_to_weight: Dict[Node, Tuple[Parameter, int]] = {}
-        for param in stage_params:
-            node = _get_grad_fn_or_grad_acc(param)
-            grad_acc_to_weight[node] = param
-
-        param_groups = self._bw_param_groups[stage_id][mb_idx]
-
-        # Perform the weight updates separately for each param_group, beginning
-        # backprop from each the intermediate node(s) of each group
-        for pg in param_groups:
-            intermediates: List[Node] = pg.get("intermediates", [])
-            intermediate_grads = pg.get("grads", None) # List of intermediate node gradients, captured by the hooks
-
-            # Skip groups without intermediate nodes (could happen in weird cases
-            # where one node is disconnected from the rest of the autograd graph for some reason)
-            if not intermediates or intermediate_grads is None:
-                continue
-
-            intermediate_edges: List[GradientEdge] = []
-            intermediate_edge_grads: List[torch.Tensor] = []
-
-            for intermediate_node, grad_inputs in zip(intermediates, intermediate_grads):
-                if grad_inputs is None:
+        def ancestors(start_nodes, reverse_edges):
+            # reverse_edges: dict[node] -> list[node] giving "incoming" edges when traversing backward
+            seen = set()
+            stack = list(start_nodes)
+            while stack:
+                n = stack.pop()
+                if n in seen:
                     continue
+                seen.add(n)
+                for p in reverse_edges.get(n, []):
+                    stack.append(p)
+            return seen
 
-                gs = [x for x in grad_inputs if x is not None]
-                if not gs:
-                    continue
-                
-                # Sum all gradients arriving at the current intermediate node
-                # in case the node has multiple source of gradients
-                summed = sum(gs)
-
-                # Create a GradientEdge for each intermediate node (we can backprop with respect to these)
-                # and store the summed gradient for that node
-                intermediate_edges.append(GradientEdge(intermediate_node, 0))
-                intermediate_edge_grads.append(summed)
+        # build ancestor sets for a handful of groups
+        anc_sets = []
+        for pg in param_groups[:10]:  # sample first 10 to keep it cheap
+            ints = pg.get("intermediates", [])
+            anc_sets.append(ancestors(ints, reverse_edges))
 
-            del pg["intermediates"]
+        shared = 0
+        for i in range(len(anc_sets)):
+            for j in range(i+1, len(anc_sets)):
+                shared += len(anc_sets[i].intersection(anc_sets[j])) > 0
 
-            if not intermediate_edges:
-                continue
-
-            # Grab params for the param_nodes in this param group using our grad_acc_to_weight map from earlier
-            mapped_param_nodes = [p for p in pg["params"] if p in grad_acc_to_weight]
-            if not mapped_param_nodes:
-                continue
-
-            # Use these parameters to create a GradientEdge that we'll use as our input to autograd.grad
-            weight_edges = tuple(GradientEdge(p, 0) for p in mapped_param_nodes)
-
-            gparams = torch.autograd.grad(
-                outputs=intermediate_edges,
-                inputs=weight_edges,
-                grad_outputs=intermediate_edge_grads,
-                retain_graph=False,
-            )
-
-            del pg["grads"]
-
-            assert len(gparams) == len(mapped_param_nodes), (
-                f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(mapped_param_nodes)}"
-            )
-            
-            # Finally, update gradients for the params in this param_group
-            for param_node, dw in zip(pg["params"], gparams):
-                if dw is None:
-                    continue
-
-                weight = grad_acc_to_weight[param_node]
-
-                if weight.grad is None:
-                    weight.grad = dw
-                else:
-                    weight.grad.add_(dw)
-
-        if self.tracing:
-            end_event.record()
-            torch.cuda.synchronize()
-            total_time = start_event.elapsed_time(end_event)
-            self.trace_data[stage_id]['backward_weight']['time'].append(total_time)
-
-        return [None] + ["done"]
- stage input nodes and 
-        # (2) in a path from the output node(s) a parameter node/gradient accumulator
-        reverse_edges = construct_reverse_graph(output_nodes)
-        param_groups = get_param_groups(input_nodes, param_nodes, reverse_edges)
+        self.logger.info(f"stage {stage_id} mb {mb_idx}: sampled_pairs_with_shared_ancestors={shared}")
 
         # Hooks to capture grads at intermediate nodes. In backward_weight,
         # we'll backprop from these intermediate values
@@ -992,7 +846,7 @@ def hook(grad_inputs):
                         group["grads"][idx] = grad_inputs
                     return hook
                 handles.append(intermediate_node.register_prehook(make_hook(pg, i)))
-        ## End graph pruning computation timing here ##
+
         if self.tracing:
             graph_pruning_end.record()
             torch.cuda.synchronize()
@@ -1000,6 +854,7 @@ def hook(grad_inputs):
             if 'graph_pruning' not in self.trace_data[stage_id]['backward_input']:
                 self.trace_data[stage_id]['backward_input']['graph_pruning'] = []
             self.trace_data[stage_id]['backward_input']['graph_pruning'].append(graph_pruning_time)
+
         gx = torch.autograd.grad(
             outputs=activation_or_loss,
             inputs=stage_input,
@@ -1036,8 +891,8 @@ def hook(grad_inputs):
 
         return [gx, upstream_grads]
 
-    @ray.method(tensor_transport="nccl")
-    def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, loss_fn=None):
+    # @ray.method(tensor_transport="nccl")
+    def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, truth=None, loss_fn=None):
         if self.tracing:
             start_event = torch.cuda.Event(enable_timing=True)
             end_event = torch.cuda.Event(enable_timing=True)
@@ -1054,10 +909,10 @@ def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, loss_fn=None
         # meaning no parameter groups are created
         if stage_id == 0:
             is_last_stage = loss_fn is not None
-            activation = self.activation[stage_id][mb_idx]
+            activation = self.out_activation[stage_id][mb_idx]
 
             if is_last_stage:
-                labels = self.truth if self.truth is not None else None
+                labels = self.truth if self.truth is not None else truth.to(self.device)
                 loss = loss_fn(activation, labels)
 
                 gparams = torch.autograd.grad(
@@ -1084,7 +939,7 @@ def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, loss_fn=None
                 else:
                     p.grad.add_(pg)
 
-            del self.activation[stage_id][mb_idx]
+            del self.out_activation[stage_id][mb_idx]
 
             if self.tracing:
                 end_event.record()
@@ -1102,6 +957,22 @@ def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, loss_fn=None
 
         param_groups = self._bw_param_groups[stage_id][mb_idx]
 
+        pgs = param_groups
+        with_int = [pg for pg in pgs if pg.get("intermediates")]
+        self.logger.info(
+            f"stage {stage_id} mb {mb_idx}: groups={len(pgs)} with_intermediates={len(with_int)} "
+            f"intermediates_sizes={[len(pg.get('intermediates', [])) for pg in with_int]}"
+        )
+
+        seen = set()
+        overlap = 0
+        for pg in param_groups:
+            for n in pg.get("intermediates", []) or []:
+                k = id(n)
+                overlap += (k in seen)
+                seen.add(k)
+        self.logger.info(f"stage {stage_id} mb {mb_idx}: intermediate_overlaps={overlap}")
+
         # Perform the weight updates separately for each param_group, beginning
         # backprop from each the intermediate node(s) of each group
         for pg in param_groups:
@@ -1150,7 +1021,7 @@ def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, loss_fn=None
                 outputs=intermediate_edges,
                 inputs=weight_edges,
                 grad_outputs=intermediate_edge_grads,
-                retain_graph=False,
+                retain_graph=True,
             )
 
             del pg["grads"]
diff --git a/piper/compile.py b/piper/compile.py
index 300e7d3..f048017 100644
--- a/piper/compile.py
+++ b/piper/compile.py
@@ -39,7 +39,7 @@ def piper_setup(model_class, model_args, optim_fn, example_inputs, num_stages, p
     compiled = torch.compile(model, dynamic=dynamic, backend=piper)
 
     from piper.utils import events_tls
-    events_tls.actor_mutexes = dict([(actor_id, threading.Lock()) for actor_id in range(pp_size)])
+    events_tls.actor_mutexes = dict([(actor_id, threading.Lock()) for actor_id in range(pp_degree)])
     events_tls.events = [threading.Event() for _ in range(num_stages)]
     for event in events_tls.events:
         event.set()
diff --git a/piper/exec.py b/piper/exec.py
index 8f6692b..d46cfa2 100644
--- a/piper/exec.py
+++ b/piper/exec.py
@@ -380,6 +380,7 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
                                     stage_id,
                                     mb_idx,
                                     fwd_ref.get_ref(),
+                                    truth=truth,
                                     loss_fn=loss_fn,
                                 )
                             )
@@ -411,7 +412,6 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
                                     stage_id,
                                     mb_idx,
                                     upstream_ref,
-                                    loss_fn=None,
                                 )
                             )
 
@@ -436,6 +436,7 @@ def run_model(inputs, mb_idx, events, actor_mutexes):
                                 mb_idx,
                                 gx,
                                 upstream_grad,
+                                truth=truth if is_last_stage else None,
                                 loss_fn=loss_fn if is_last_stage else None,
                             )
                         )
diff --git a/test/test_llama.py b/test/test_llama.py
index e41b6f5..baf29ce 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -124,8 +124,8 @@ def main(args):
     iters = args.iters
     
     # creating the training data; might make this a new method to get the dataloader to work properly
-    x = torch.randint(0, llama_config.vocab_size, (batch_size, seq_len)).to(device)
-    y = torch.zeros((batch_size, llama_config.vocab_size), dtype=torch.long).to(device)
+    x = torch.randint(0, llama_config.vocab_size, (batch_size, seq_len))
+    y = torch.randn((batch_size, seq_len, llama_config.vocab_size))
 
     # Generate different input data for each data parallel rank so that model weights get updated differently
     if args.dp_degree > 1:

From 274c0aaa11af886be43dfde217d71f9447ca284b Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Fri, 20 Feb 2026 22:24:35 +0000
Subject: [PATCH 040/100] add_param_group patch to fix split backward autograd
 issue

---
 piper/actor.py | 98 ++++++++++++++++++--------------------------------
 1 file changed, 34 insertions(+), 64 deletions(-)

diff --git a/piper/actor.py b/piper/actor.py
index 64102da..e675d55 100644
--- a/piper/actor.py
+++ b/piper/actor.py
@@ -275,28 +275,45 @@ def add_param_group(self, stage_id: int, params, input_idxs):
         if stage_id in self.parameters:
             self.logger.debug(f"Param group already exists for stage {stage_id}")
             return
-        
-        # place parameters on the device
+
+        input_set = set(input_idxs)
+
         def move_to_device(idx, arg):
-            if idx not in input_idxs:
-                return arg.to(self.device).detach().requires_grad_(True)
-            else:
-                return arg.to(self.device)
-        params = list(map(move_to_device, range(len(params)), params))
+            if arg is None:
+                return None
+
+            if not isinstance(arg, torch.Tensor):
+                return arg
+
+            t = arg.to(self.device)
+
+            # ignore inputs
+            if idx in input_set:
+                return t
+
+            # keep model weights as nn.Parameter
+            if isinstance(arg, Parameter):
+                return Parameter(t.detach(), requires_grad=True)
+
+            # everything else is not a parameter, so 
+            # keep them as a tensor with requires_grad=False
+            t = t.detach()
+            t.requires_grad_(False)
+            return t
+
+        params = [move_to_device(i, a) for i, a in enumerate(params)]
 
-        # discard the graphargs that correspond to input tensors
         for i in input_idxs:
             params[i] = None
 
-        # save the parameters
         self.input_idxs[stage_id] = input_idxs
         self.parameters[stage_id] = params
 
-        # add the parameters to the optimizer for this stage
+        stage_params = [p for p in params if isinstance(p, Parameter)]
         if stage_id not in self.optims:
-            self.optims[stage_id] = self.optim_class([param for param in params if param is not None])
+            self.optims[stage_id] = self.optim_class(stage_params)
         else:
-            self.optims[stage_id].add_param_group({'params': [param for param in params if param is not None]})
+            self.optims[stage_id].add_param_group({'params': stage_params})
 
     def load_expert(self, stage_id, expert_id, local_expert_id, batch_idx, expert_module, input_idxs, param_idxs, params):
         self.logger.debug(f"Loading expert {expert_id} on actor {self.actor_id} global rank {self.global_rank}")
@@ -777,7 +794,8 @@ def backward_input(self, stage_id: int, mb_idx: int, inp, truth=None, loss_fn=No
             return [gx, upstream_grads]
 
         stage_input = self.inp_activation[stage_id][mb_idx]
-        stage_params = [p for p in self.parameters[stage_id] if p is not None and p.requires_grad]
+        # stage_params = [p for p in self.parameters[stage_id] if p is not None and p.requires_grad]
+        stage_params = [p for p in self.parameters[stage_id] if isinstance(p, torch.nn.Parameter)]
 
         # Lists of autograd nodes for the layer outputs, inputs, and parameters
         # Tracing for graph pruning computation time
@@ -797,39 +815,6 @@ def backward_input(self, stage_id: int, mb_idx: int, inp, truth=None, loss_fn=No
         reverse_edges = construct_reverse_graph(output_nodes)
         param_groups = get_param_groups(input_nodes, param_nodes, reverse_edges)
 
-        pgs = param_groups
-        with_int = [pg for pg in pgs if pg.get("intermediates")]
-        self.logger.debug(
-            f"stage {stage_id} mb {mb_idx}: groups={len(pgs)} with_intermediates={len(with_int)} "
-            f"intermediates_sizes={[len(pg.get('intermediates', [])) for pg in with_int]}"
-        )
-
-        def ancestors(start_nodes, reverse_edges):
-            # reverse_edges: dict[node] -> list[node] giving "incoming" edges when traversing backward
-            seen = set()
-            stack = list(start_nodes)
-            while stack:
-                n = stack.pop()
-                if n in seen:
-                    continue
-                seen.add(n)
-                for p in reverse_edges.get(n, []):
-                    stack.append(p)
-            return seen
-
-        # build ancestor sets for a handful of groups
-        anc_sets = []
-        for pg in param_groups[:10]:  # sample first 10 to keep it cheap
-            ints = pg.get("intermediates", [])
-            anc_sets.append(ancestors(ints, reverse_edges))
-
-        shared = 0
-        for i in range(len(anc_sets)):
-            for j in range(i+1, len(anc_sets)):
-                shared += len(anc_sets[i].intersection(anc_sets[j])) > 0
-
-        self.logger.info(f"stage {stage_id} mb {mb_idx}: sampled_pairs_with_shared_ancestors={shared}")
-
         # Hooks to capture grads at intermediate nodes. In backward_weight,
         # we'll backprop from these intermediate values
         handles = []
@@ -898,7 +883,8 @@ def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, truth=None,
             end_event = torch.cuda.Event(enable_timing=True)
             start_event.record()
 
-        stage_params = [p for p in self.parameters[stage_id] if p is not None and p.requires_grad]
+        # stage_params = [p for p in self.parameters[stage_id] if p is not None and p.requires_grad]
+        stage_params = [p for p in self.parameters[stage_id] if isinstance(p, torch.nn.Parameter)]
 
         # We only pass to enforce temporal dependency.
         # upstream is only required for the stage 0 case
@@ -957,22 +943,6 @@ def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, truth=None,
 
         param_groups = self._bw_param_groups[stage_id][mb_idx]
 
-        pgs = param_groups
-        with_int = [pg for pg in pgs if pg.get("intermediates")]
-        self.logger.info(
-            f"stage {stage_id} mb {mb_idx}: groups={len(pgs)} with_intermediates={len(with_int)} "
-            f"intermediates_sizes={[len(pg.get('intermediates', [])) for pg in with_int]}"
-        )
-
-        seen = set()
-        overlap = 0
-        for pg in param_groups:
-            for n in pg.get("intermediates", []) or []:
-                k = id(n)
-                overlap += (k in seen)
-                seen.add(k)
-        self.logger.info(f"stage {stage_id} mb {mb_idx}: intermediate_overlaps={overlap}")
-
         # Perform the weight updates separately for each param_group, beginning
         # backprop from each the intermediate node(s) of each group
         for pg in param_groups:
@@ -1021,7 +991,7 @@ def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, truth=None,
                 outputs=intermediate_edges,
                 inputs=weight_edges,
                 grad_outputs=intermediate_edge_grads,
-                retain_graph=True,
+                retain_graph=False,
             )
 
             del pg["grads"]

From dfab5228ed24b96e0a1599ee98fb7c0c59c1e0a6 Mon Sep 17 00:00:00 2001
From: Andy Ruan <andyubryh@gmail.com>
Date: Fri, 20 Feb 2026 22:56:21 +0000
Subject: [PATCH 041/100] fix: explicit send and recv tensors for fwd P2Ps

Signed-off-by: Andy Ruan <andyubryh@gmail.com>
---
 logs/interleaved_debug-memopt-bug.log | 385 ++++++++++++++++
 logs/interleaved_debug-memopt.log     | 612 ++++++++++++++++++++++++++
 run_interleaved-debug.sh              |   4 +-
 src/piper_actor.py                    |  58 ++-
 4 files changed, 1052 insertions(+), 7 deletions(-)
 create mode 100644 logs/interleaved_debug-memopt-bug.log
 create mode 100644 logs/interleaved_debug-memopt.log

diff --git a/logs/interleaved_debug-memopt-bug.log b/logs/interleaved_debug-memopt-bug.log
new file mode 100644
index 0000000..8c70ce7
--- /dev/null
+++ b/logs/interleaved_debug-memopt-bug.log
@@ -0,0 +1,385 @@
+2026-02-20 09:09:40,965	INFO worker.py:2007 -- Started a local Ray instance.
+/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+  warnings.warn(
+(run_dp_rank pid=2467960) Namespace(model='debug', schedule='interleaved-1f1b', dp=1, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=1, iters=1, tracing=False, naive_gradient_sync=False)
+(run_dp_rank pid=2467960) Schedule:
+(run_dp_rank pid=2467960) 0:0:f	0:1:f	2:0:f	2:1:f	0:2:f	0:3:f	2:0:b	 -- 	2:1:b	2:2:f	0:0:b	2:3:f	0:1:b	 -- 	2:2:b	2:3:b	0:2:b	0:3:b	0:0:u	
+(run_dp_rank pid=2467960)  -- 	1:0:f	1:1:f	3:0:f	3:0:b	3:1:f	3:1:b	1:2:f	1:0:b	1:3:f	1:1:b	3:2:f	3:2:b	3:3:f	3:3:b	1:2:b	1:3:b	1:0:u	 -- 	
+(run_dp_rank pid=2467960) 2026-02-20 09:09:56,380 - piper_coordinator - DEBUG - Running DP rank 1 of 1
+(run_dp_rank pid=2467960) 2026-02-20 09:09:56,403 - piper_compile - DEBUG - mbs: 4, stages: 4, devices: 2
+(run_dp_rank pid=2467960) 2026-02-20 09:09:56,403 - piper_compile - DEBUG - Fwd/bwd schedule: [[Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=False, upd=False)], [Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=False, upd=False)]]
+(run_dp_rank pid=2467960) 2026-02-20 09:09:56,403 - piper_compile - DEBUG - P2P comms: [(0, 1, 0), (0, 1, 1), (1, 2, 0), (2, 3, 0), (1, 2, 1), (2, 3, 1), (0, 1, 2), (3, 2, 0), (0, 1, 3), (2, 1, 0), (3, 2, 1), (2, 1, 1), (1, 2, 2), (2, 3, 2), (1, 0, 0), (1, 2, 3), (2, 3, 3), (1, 0, 1), (3, 2, 2), (2, 1, 2), (3, 2, 3), (2, 1, 3), (1, 0, 2), (1, 0, 3)]
+(run_dp_rank pid=2467960) 2026-02-20 09:09:56,403 - piper_compile - DEBUG - P2P schedules: {0: [(0, 1, 0, True), (0, 1, 1, True), (1, 2, 0, False), (2, 3, 0, True), (1, 2, 1, False), (2, 3, 1, True), (0, 1, 2, True), (3, 2, 0, False), (0, 1, 3, True), (2, 1, 0, True), (3, 2, 1, False), (2, 1, 1, True), (1, 2, 2, False), (2, 3, 2, True), (1, 0, 0, False), (1, 2, 3, False), (2, 3, 3, True), (1, 0, 1, False), (3, 2, 2, False), (2, 1, 2, True), (3, 2, 3, False), (2, 1, 3, True), (1, 0, 2, False), (1, 0, 3, False)], 1: [(0, 1, 0, False), (0, 1, 1, False), (1, 2, 0, True), (2, 3, 0, False), (1, 2, 1, True), (2, 3, 1, False), (0, 1, 2, False), (3, 2, 0, True), (0, 1, 3, False), (2, 1, 0, False), (3, 2, 1, True), (2, 1, 1, False), (1, 2, 2, True), (2, 3, 2, False), (1, 0, 0, True), (1, 2, 3, True), (2, 3, 3, False), (1, 0, 1, True), (3, 2, 2, True), (2, 1, 2, False), (3, 2, 3, True), (2, 1, 3, False), (1, 0, 2, True), (1, 0, 3, True)]}
+(run_dp_rank pid=2467960) 2026-02-20 09:09:56,469 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, 4d48938144c53713213caa6701000000) global rank 0
+(run_dp_rank pid=2467960) 2026-02-20 09:09:56,470 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, fe22d6a87fc51068dbea78fd01000000) global rank 1
+(PiperActor pid=2467963) 2026-02-20 09:09:59,615 - piper_actor - INFO - Initializing Ray actor 1 global rank 1 GPU 2
+(PiperActor pid=2467971) 2026-02-20 09:09:59,644 - piper_actor - INFO - Initializing Ray actor 0 global rank 0 GPU 1
+(PiperActor pid=2467963) 2026-02-20 09:10:01,897 - piper_actor - DEBUG - Actor 1 has GPU 2, joined the global process group
+(PiperActor pid=2467963) 2026-02-20 09:10:01,898 - piper_actor - INFO - Global rank 1 joined its pp group 0 along with ranks [0, 1]
+(PiperActor pid=2467971) 2026-02-20 09:10:01,901 - piper_actor - DEBUG - Actor 0 has GPU 1, joined the global process group
+(PiperActor pid=2467971) 2026-02-20 09:10:01,902 - piper_actor - INFO - Global rank 0 joined its pp group 0 along with ranks [0, 1]
+(run_dp_rank pid=2467960) 2026-02-20 09:10:02,116 - piper_compile - INFO - DP rank 1 compiling...
+(PiperActor pid=2467971) 2026-02-20 09:10:04,964 - piper_actor - INFO - Loading stage 0 graph on actor 0
+(PiperActor pid=2467963) 2026-02-20 09:10:05,085 - piper_actor - INFO - Loading stage 1 graph on actor 1
+(PiperActor pid=2467971) 2026-02-20 09:10:06,233 - piper_actor - INFO - Loading stage 2 graph on actor 0
+(PiperActor pid=2467963) 2026-02-20 09:10:06,413 - piper_actor - INFO - Loading stage 3 graph on actor 1
+(run_dp_rank pid=2467960) 2026-02-20 09:10:06,537 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=2467960) Running 1 warmup iterations...
+(run_dp_rank pid=2467960) 2026-02-20 09:10:12,957 - piper_compile - INFO - DP rank 1 done.
+(PiperActor pid=2467963) 2026-02-20 09:10:12,992 - piper_actor - DEBUG - Actor 1 loaded labels torch.Size([16, 256, 512])
+(PiperActor pid=2467963) 2026-02-20 09:10:12,997 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:12,997 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 0 -> 1, mb 0, is_sender=False
+(PiperActor pid=2467963) 2026-02-20 09:10:12,997 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 0)
+(PiperActor pid=2467963) [rank1]:[W220 09:10:13.484752123 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2467971) 2026-02-20 09:10:12,981 - piper_actor - DEBUG - Actor 0 loaded inputs 1
+(PiperActor pid=2467971) 2026-02-20 09:10:12,997 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:13,541 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=2467971) 2026-02-20 09:10:13,541 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 0 -> 1, mb 0, is_sender=True
+(PiperActor pid=2467971) 2026-02-20 09:10:13,542 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 0)
+(PiperActor pid=2467971) [rank0]:[W220 09:10:13.024049932 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2467963) 2026-02-20 09:10:14,889 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 0)
+(PiperActor pid=2467963) 2026-02-20 09:10:14,889 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:14,890 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=2467971) 2026-02-20 09:10:14,878 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 0)
+(PiperActor pid=2467971) 2026-02-20 09:10:14,879 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:14,879 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=2467971) 2026-02-20 09:10:14,881 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:14,941 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=2467971) 2026-02-20 09:10:14,941 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 0 -> 1, mb 1, is_sender=True
+(PiperActor pid=2467971) 2026-02-20 09:10:14,941 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 1)
+(PiperActor pid=2467971) 2026-02-20 09:10:14,942 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 1)
+(PiperActor pid=2467971) 2026-02-20 09:10:14,942 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:14,942 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=2467971) 2026-02-20 09:10:14,943 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:14,943 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 2, mb 0, is_sender=False
+(PiperActor pid=2467971) 2026-02-20 09:10:14,943 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 0)
+(PiperActor pid=2467971) [rank0]:[W220 09:10:14.425450680 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2467963) 2026-02-20 09:10:15,187 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=2467963) 2026-02-20 09:10:15,187 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 2, mb 0, is_sender=True
+(PiperActor pid=2467963) 2026-02-20 09:10:15,187 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 1)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,188 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 1)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,188 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,188 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 0)
+(PiperActor pid=2467963) [rank1]:[W220 09:10:15.670307636 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2467963) 2026-02-20 09:10:15,514 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 0)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,515 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,515 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=2467963) 2026-02-20 09:10:15,516 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,516 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 0 -> 1, mb 1, is_sender=False
+(PiperActor pid=2467963) 2026-02-20 09:10:15,516 - piper_actor - DEBUG - P2P op (0, 1, 1, False) already executed on actor 1, skipping
+(PiperActor pid=2467963) 2026-02-20 09:10:15,516 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,524 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,524 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 2, mb 1, is_sender=True
+(PiperActor pid=2467963) 2026-02-20 09:10:15,524 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 0)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,524 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 0)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,524 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,524 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 1)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,525 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 1)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,525 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,525 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=2467963) 2026-02-20 09:10:15,525 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,525 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 3, mb 0, is_sender=False
+(PiperActor pid=2467963) 2026-02-20 09:10:15,525 - piper_actor - DEBUG - P2P op (2, 3, 0, False) already executed on actor 1, skipping
+(PiperActor pid=2467963) 2026-02-20 09:10:15,525 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=2467963) 2026-02-20 09:10:15,533 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=2467963) 2026-02-20 09:10:15,533 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=2467963) 2026-02-20 09:10:15,534 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=2467971) 2026-02-20 09:10:15,514 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 0)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,514 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,515 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,522 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,523 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 3, mb 0, is_sender=True
+(PiperActor pid=2467971) 2026-02-20 09:10:15,523 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 0)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,523 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 0)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,523 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,523 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=2467971) 2026-02-20 09:10:15,524 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,524 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 2, mb 1, is_sender=False
+(PiperActor pid=2467971) 2026-02-20 09:10:15,524 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 1)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,525 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 1)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,525 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,525 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=2467971) 2026-02-20 09:10:15,533 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=2467971) 2026-02-20 09:10:15,533 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 3, mb 1, is_sender=True
+(PiperActor pid=2467971) 2026-02-20 09:10:15,533 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 1)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,533 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 1)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,533 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,534 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=2467971) 2026-02-20 09:10:15,534 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,541 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=2467971) 2026-02-20 09:10:15,541 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 0 -> 1, mb 2, is_sender=True
+(PiperActor pid=2467971) 2026-02-20 09:10:15,541 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 2)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,541 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 2)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,541 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,541 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=2467971) 2026-02-20 09:10:15,542 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,548 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=2467971) 2026-02-20 09:10:15,548 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 0 -> 1, mb 3, is_sender=True
+(PiperActor pid=2467971) 2026-02-20 09:10:15,548 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 0)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,549 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 0)
+(run_dp_rank pid=2467960) Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::PiperActor._forward() (pid=2467963, ip=10.158.48.71, actor_id=fe22d6a87fc51068dbea78fd01000000, repr=<src.piper_actor.PiperActor object at 0x7fdc00baf760>)
+(run_dp_rank pid=2467960)   File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/src/piper_actor.py", line 579, in _forward
+(run_dp_rank pid=2467960)     inp_with_grad = [
+(run_dp_rank pid=2467960)   File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/src/piper_actor.py", line 582, in <listcomp>
+(run_dp_rank pid=2467960)     if self.forward_args[stage_id][i].requires_grad
+(run_dp_rank pid=2467960) AttributeError: 'NoneType' object has no attribute 'requires_grad'
+(run_dp_rank pid=2467960) Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::PiperActor._backward() (pid=2467963, ip=10.158.48.71, actor_id=fe22d6a87fc51068dbea78fd01000000, repr=<src.piper_actor.PiperActor object at 0x7fdc00baf760>)
+(run_dp_rank pid=2467960)   File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/src/piper_actor.py", line 649, in _backward
+(run_dp_rank pid=2467960)     self._exec_p2p_op(stage_id, stage_id - 1, mb_idx, True)
+(run_dp_rank pid=2467960)   File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/src/piper_actor.py", line 441, in _exec_p2p_op
+(run_dp_rank pid=2467960)     self._exec_fwd_send(src_stage, mb_idx, **kwargs)
+(run_dp_rank pid=2467960) TypeError: PiperActor._exec_fwd_send() missing 1 required positional argument: 'output'
+(run_dp_rank pid=2467960) Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::PiperActor._backward() (pid=2467963, ip=10.158.48.71, actor_id=fe22d6a87fc51068dbea78fd01000000, repr=<src.piper_actor.PiperActor object at 0x7fdc00baf760>)
+(run_dp_rank pid=2467960)   File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/src/piper_actor.py", line 649, in _backward
+(run_dp_rank pid=2467960)     self._exec_p2p_op(stage_id, stage_id - 1, mb_idx, True)
+(run_dp_rank pid=2467960)   File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/src/piper_actor.py", line 441, in _exec_p2p_op
+(run_dp_rank pid=2467960)     self._exec_fwd_send(src_stage, mb_idx, **kwargs)
+(run_dp_rank pid=2467960) TypeError: PiperActor._exec_fwd_send() missing 1 required positional argument: 'output'
+(run_dp_rank pid=2467960) Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::PiperActor._forward() (pid=2467963, ip=10.158.48.71, actor_id=fe22d6a87fc51068dbea78fd01000000, repr=<src.piper_actor.PiperActor object at 0x7fdc00baf760>)
+(run_dp_rank pid=2467960)   File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/src/piper_actor.py", line 576, in _forward
+(run_dp_rank pid=2467960)     self._exec_p2p_op(stage_id - 1, stage_id, mb_idx, False)
+(run_dp_rank pid=2467960)   File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/src/piper_actor.py", line 441, in _exec_p2p_op
+(run_dp_rank pid=2467960)     self._exec_fwd_send(src_stage, mb_idx, **kwargs)
+(run_dp_rank pid=2467960) TypeError: PiperActor._exec_fwd_send() missing 1 required positional argument: 'output'
+(run_dp_rank pid=2467960) Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::PiperActor._backward() (pid=2467963, ip=10.158.48.71, actor_id=fe22d6a87fc51068dbea78fd01000000, repr=<src.piper_actor.PiperActor object at 0x7fdc00baf760>)
+(run_dp_rank pid=2467960)   File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/src/piper_actor.py", line 633, in _backward
+(run_dp_rank pid=2467960)     out_activation = self.out_activation[stage_id][mb_idx]
+(run_dp_rank pid=2467960) KeyError: 3
+(run_dp_rank pid=2467960) Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::PiperActor._backward() (pid=2467963, ip=10.158.48.71, actor_id=fe22d6a87fc51068dbea78fd01000000, repr=<src.piper_actor.PiperActor object at 0x7fdc00baf760>)
+(run_dp_rank pid=2467960)   File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/src/piper_actor.py", line 636, in _backward
+(run_dp_rank pid=2467960)     self._exec_p2p_op(stage_id + 1, stage_id, mb_idx, False)
+(run_dp_rank pid=2467960)   File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/src/piper_actor.py", line 441, in _exec_p2p_op
+(run_dp_rank pid=2467960)     self._exec_fwd_send(src_stage, mb_idx, **kwargs)
+(run_dp_rank pid=2467960) TypeError: PiperActor._exec_fwd_send() missing 1 required positional argument: 'output'
+(run_dp_rank pid=2467960) Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::PiperActor._backward() (pid=2467963, ip=10.158.48.71, actor_id=fe22d6a87fc51068dbea78fd01000000, repr=<src.piper_actor.PiperActor object at 0x7fdc00baf760>)
+(run_dp_rank pid=2467960)   File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/src/piper_actor.py", line 633, in _backward
+(run_dp_rank pid=2467960)     out_activation = self.out_activation[stage_id][mb_idx]
+(run_dp_rank pid=2467960) KeyError: 3
+(PiperActor pid=2467963) 2026-02-20 09:10:15,703 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 3 -> 2, mb 0, is_sender=True
+(PiperActor pid=2467963) 2026-02-20 09:10:15,704 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 1)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,705 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 1)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,705 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,705 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 2)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,705 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 2)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,705 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,705 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 0)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,706 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 0)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,706 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,708 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,708 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 3, mb 1, is_sender=False
+(PiperActor pid=2467963) 2026-02-20 09:10:15,708 - piper_actor - DEBUG - P2P op (2, 3, 1, False) already executed on actor 1, skipping
+(PiperActor pid=2467963) 2026-02-20 09:10:15,708 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,711 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,711 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=2467963) 2026-02-20 09:10:15,713 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,722 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 3 -> 2, mb 1, is_sender=True
+(PiperActor pid=2467963) 2026-02-20 09:10:15,723 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 3)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,723 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 3)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,723 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,723 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 0)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,723 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 0)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,737 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,737 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 1)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,737 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 1)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,738 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,771 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,772 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 0 -> 1, mb 2, is_sender=False
+(PiperActor pid=2467963) 2026-02-20 09:10:15,772 - piper_actor - DEBUG - P2P op (0, 1, 2, False) already executed on actor 1, skipping
+(PiperActor pid=2467963) 2026-02-20 09:10:15,772 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=2467963) 2026-02-20 09:10:15,774 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=2467963) 2026-02-20 09:10:15,774 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 2, mb 2, is_sender=True
+(PiperActor pid=2467963) 2026-02-20 09:10:15,774 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 1)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,775 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 1)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,779 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,779 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 2)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,779 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 2)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,780 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,780 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=2467963) 2026-02-20 09:10:15,781 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,781 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 1, mb 0, is_sender=False
+(PiperActor pid=2467963) 2026-02-20 09:10:15,781 - piper_actor - DEBUG - P2P op (2, 1, 0, False) already executed on actor 1, skipping
+(PiperActor pid=2467963) 2026-02-20 09:10:15,781 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 0, mb 0, is_sender=True
+(PiperActor pid=2467963) 2026-02-20 09:10:15,781 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 2)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,781 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 2)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,782 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,782 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 0)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,782 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 0)
+(PiperActor pid=2467963) 2026-02-20 09:10:15,782 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,782 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,782 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 0 -> 1, mb 3, is_sender=False
+(PiperActor pid=2467963) 2026-02-20 09:10:15,782 - piper_actor - DEBUG - P2P op (0, 1, 3, False) already executed on actor 1, skipping
+(PiperActor pid=2467963) 2026-02-20 09:10:15,785 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,785 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 1, mb 1, is_sender=False
+(PiperActor pid=2467963) 2026-02-20 09:10:15,785 - piper_actor - DEBUG - P2P op (2, 1, 1, False) already executed on actor 1, skipping
+(PiperActor pid=2467963) 2026-02-20 09:10:15,785 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 0, mb 1, is_sender=True
+(PiperActor pid=2467963) 2026-02-20 09:10:15,786 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,786 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 3, mb 2, is_sender=False
+(PiperActor pid=2467963) 2026-02-20 09:10:15,787 - piper_actor - DEBUG - P2P op (2, 3, 2, False) already executed on actor 1, skipping
+(PiperActor pid=2467963) 2026-02-20 09:10:15,787 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=2467963) 2026-02-20 09:10:15,789 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=2467963) 2026-02-20 09:10:15,789 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=2467963) 2026-02-20 09:10:15,790 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,796 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 3 -> 2, mb 2, is_sender=True
+(PiperActor pid=2467963) 2026-02-20 09:10:15,800 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,800 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 3, mb 3, is_sender=False
+(PiperActor pid=2467963) 2026-02-20 09:10:15,801 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,802 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,803 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 1, mb 2, is_sender=False
+(PiperActor pid=2467963) 2026-02-20 09:10:15,804 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=2467963) 2026-02-20 09:10:15,805 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=2467963) 2026-02-20 09:10:15,805 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=2467971) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
+(PiperActor pid=2467971)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+(PiperActor pid=2467971) 2026-02-20 09:10:15,768 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,768 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 3)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,769 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 3)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,769 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,769 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([256, 8]), torch.Size([16, 256, 512]), torch.Size([256, 256])]
+(PiperActor pid=2467971) 2026-02-20 09:10:15,770 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,770 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 3 -> 2, mb 0, is_sender=False
+(PiperActor pid=2467971) 2026-02-20 09:10:15,770 - piper_actor - DEBUG - P2P op (3, 2, 0, False) already executed on actor 0, skipping
+(PiperActor pid=2467971) 2026-02-20 09:10:15,770 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 1, mb 0, is_sender=True
+(PiperActor pid=2467971) 2026-02-20 09:10:15,770 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 0)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,771 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 0)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,771 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,771 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,771 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 3 -> 2, mb 1, is_sender=False
+(PiperActor pid=2467971) 2026-02-20 09:10:15,771 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 1)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,772 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 1)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,776 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,776 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 1, mb 1, is_sender=True
+(PiperActor pid=2467971) 2026-02-20 09:10:15,776 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 1)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,776 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 1)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,776 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,777 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,777 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 2, mb 2, is_sender=False
+(PiperActor pid=2467971) 2026-02-20 09:10:15,777 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 2)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,778 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 2)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,778 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,778 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=2467971) 2026-02-20 09:10:15,780 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=2467971) 2026-02-20 09:10:15,780 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 3, mb 2, is_sender=True
+(PiperActor pid=2467971) 2026-02-20 09:10:15,780 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 2)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,780 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 2)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,780 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,781 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=2467971) 2026-02-20 09:10:15,782 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,782 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 0, mb 0, is_sender=False
+(PiperActor pid=2467971) 2026-02-20 09:10:15,782 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 0)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,782 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 0)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,904 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,905 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,905 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 2, mb 3, is_sender=False
+(PiperActor pid=2467971) 2026-02-20 09:10:15,905 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 3)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,906 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 3)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,906 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,906 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=2467971) 2026-02-20 09:10:15,909 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=2467971) 2026-02-20 09:10:15,909 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 3, mb 3, is_sender=True
+(PiperActor pid=2467971) 2026-02-20 09:10:15,909 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 3)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,909 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 3)
+(PiperActor pid=2467971) 2026-02-20 09:10:15,909 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=2467971) 2026-02-20 09:10:15,909 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=2467971) [rank0]:[E220 09:20:15.416359497 ProcessGroupNCCL.cpp:688] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=24, OpType=RECV, NumelIn=2097152, NumelOut=2097152, Timeout(ms)=600000) ran for 600028 milliseconds before timing out.
+(PiperActor pid=2467971) [rank0]:[E220 09:20:15.421218835 ProcessGroupNCCL.cpp:2277] [PG ID 1 PG GUID 1 Rank 0]  failure detected by watchdog at work sequence id: 24 PG status: last enqueued work: 25, last completed work: 23
+(PiperActor pid=2467971) [rank0]:[E220 09:20:15.421254898 ProcessGroupNCCL.cpp:735] Stack trace of the failed collective not found, potentially because FlightRecorder is disabled. You can enable it by setting TORCH_NCCL_TRACE_BUFFER_SIZE to a non-zero value.
+(PiperActor pid=2467971) [rank0]:[E220 09:20:15.421360735 ProcessGroupNCCL.cpp:2610] [PG ID 1 PG GUID 1 Rank 0] First PG on this rank to signal dumping.
+(PiperActor pid=2467963) [rank1]:[E220 09:20:16.508893157 ProcessGroupNCCL.cpp:1825] [PG ID 0 PG GUID 0 Rank 1] Observed flight recorder dump signal from another rank via TCPStore.
+(PiperActor pid=2467963) [rank1]:[E220 09:20:16.509178231 ProcessGroupNCCL.cpp:1890] [PG ID 0 PG GUID 0 Rank 1] Received a dump signal due to a collective timeout from  rank 0 and we will try our best to dump the debug info. Last enqueued NCCL work: -1, last completed NCCL work: -1.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+(PiperActor pid=2467963) [rank1]:[E220 09:20:16.510351766 ProcessGroupNCCL.cpp:1606] [PG ID 0 PG GUID 0 Rank 1] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1, only active collectives: 0
+(PiperActor pid=2467971) [rank0]:[E220 09:20:16.508834710 ProcessGroupNCCL.cpp:1890] [PG ID 0 PG GUID 0 Rank 0] Received a dump signal due to a collective timeout from this local rank and we will try our best to dump the debug info. Last enqueued NCCL work: -1, last completed NCCL work: -1.This is most likely caused by incorrect usages of collectives, e.g., wrong sizes used across ranks, the order of collectives is not same for all ranks or the scheduled collective, for some reason, didn't run. Additionally, this can be caused by GIL deadlock or other reasons such as network errors or bugs in the communications library (e.g. NCCL), etc. 
+(PiperActor pid=2467971) [rank0]:[E220 09:20:16.510330855 ProcessGroupNCCL.cpp:1606] [PG ID 0 PG GUID 0 Rank 0] ProcessGroupNCCL preparing to dump debug info. Include stack trace: 1, only active collectives: 0
+(PiperActor pid=2467971) [rank0]:[E220 09:21:15.421529580 ProcessGroupNCCL.cpp:749] [Rank 0] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
+(PiperActor pid=2467971) [rank0]:[E220 09:21:15.421675297 ProcessGroupNCCL.cpp:763] [Rank 0] To avoid data inconsistency, we are taking the entire process down.
+(PiperActor pid=2467971) [rank0]:[E220 09:21:15.426341366 ProcessGroupNCCL.cpp:2093] [PG ID 1 PG GUID 1 Rank 0] Process group watchdog thread terminated with exception: [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=24, OpType=RECV, NumelIn=2097152, NumelOut=2097152, Timeout(ms)=600000) ran for 600028 milliseconds before timing out.
+(PiperActor pid=2467971) Exception raised from checkTimeout at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:691 (most recent call first):
+(PiperActor pid=2467971) frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x9d (0x7f8bd43d9fdd in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libc10.so)
+(PiperActor pid=2467971) frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x26a (0x7f5b239ddd7a in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
+(PiperActor pid=2467971) frame #2: c10d::ProcessGroupNCCL::Watchdog::runLoop() + 0x16a1 (0x7f5b239e2261 in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
+(PiperActor pid=2467971) frame #3: c10d::ProcessGroupNCCL::Watchdog::run() + 0x105 (0x7f5b239e35a5 in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
+(PiperActor pid=2467971) frame #4: <unknown function> + 0xdf0e6 (0x7f8bf6fe40e6 in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/bin/../lib/libstdc++.so.6)
+(PiperActor pid=2467971) frame #5: <unknown function> + 0x8b2ea (0x7f8c0688b2ea in /lib64/libc.so.6)
+(PiperActor pid=2467971) frame #6: <unknown function> + 0x1103c0 (0x7f8c069103c0 in /lib64/libc.so.6)
+(PiperActor pid=2467971) 
+(PiperActor pid=2467971) [2026-02-20 09:21:16,076 E 2467971 2485050] logging.cc:118: Unhandled exception: N3c1016DistBackendErrorE. what(): [PG ID 1 PG GUID 1 Rank 0] Process group watchdog thread terminated with exception: [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=24, OpType=RECV, NumelIn=2097152, NumelOut=2097152, Timeout(ms)=600000) ran for 600028 milliseconds before timing out.
+(PiperActor pid=2467971) Exception raised from checkTimeout at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:691 (most recent call first):
+(PiperActor pid=2467971) frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x9d (0x7f8bd43d9fdd in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libc10.so)
+(PiperActor pid=2467971) frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x26a (0x7f5b239ddd7a in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
+(PiperActor pid=2467971) frame #2: c10d::ProcessGroupNCCL::Watchdog::runLoop() + 0x16a1 (0x7f5b239e2261 in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
+(PiperActor pid=2467971) frame #3: c10d::ProcessGroupNCCL::Watchdog::run() + 0x105 (0x7f5b239e35a5 in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
+(PiperActor pid=2467971) frame #4: <unknown function> + 0xdf0e6 (0x7f8bf6fe40e6 in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/bin/../lib/libstdc++.so.6)
+(PiperActor pid=2467971) frame #5: <unknown function> + 0x8b2ea (0x7f8c0688b2ea in /lib64/libc.so.6)
+(PiperActor pid=2467971) frame #6: <unknown function> + 0x1103c0 (0x7f8c069103c0 in /lib64/libc.so.6)
+(PiperActor pid=2467971) 
+(PiperActor pid=2467971) Exception raised from run at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:2099 (most recent call first):
+(PiperActor pid=2467971) frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x9d (0x7f8bd43d9fdd in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libc10.so)
+(PiperActor pid=2467971) frame #1: <unknown function> + 0x9bbbc8 (0x7f5b23249bc8 in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
+(PiperActor pid=2467971) frame #2: <unknown function> + 0xdf0e6 (0x7f8bf6fe40e6 in /m-coriander/coriander/wxdeng/conda-envs/piper_ref/bin/../lib/libstdc++.so.6)
+(PiperActor pid=2467971) frame #3: <unknown function> + 0x8b2ea (0x7f8c0688b2ea in /lib64/libc.so.6)
+(PiperActor pid=2467971) frame #4: <unknown function> + 0x1103c0 (0x7f8c069103c0 in /lib64/libc.so.6)
+(PiperActor pid=2467971)  /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_raylet.so(+0x16ad67a) [0x7f8bf87a767a] ray::operator<<()
+(PiperActor pid=2467971) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_raylet.so(+0x16ae12c) [0x7f8bf87a812c] ray::RayLog::operator<< <>()
+(PiperActor pid=2467971) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_raylet.so(+0x85eda1) [0x7f8bf7958da1] ray::TerminateHandler()
+(PiperActor pid=2467971) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/bin/../lib/libstdc++.so.6(+0xc2519) [0x7f8bf6fc7519] __cxxabiv1::__terminate()
+(PiperActor pid=2467971) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/bin/../lib/libstdc++.so.6(_ZSt10unexpectedv+0) [0x7f8bf6fc1063] std::unexpected()
+(PiperActor pid=2467971) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/bin/../lib/libstdc++.so.6(+0xc2512) [0x7f8bf6fc7512] __cxxabiv1::__terminate()
+(PiperActor pid=2467971) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so(+0x9bbc82) [0x7f5b23249c82] c10d::ProcessGroupNCCL::Watchdog::run()
+(PiperActor pid=2467971) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/bin/../lib/libstdc++.so.6(+0xdf0e6) [0x7f8bf6fe40e6] execute_native_thread_routine
+(PiperActor pid=2467971) /lib64/libc.so.6(+0x8b2ea) [0x7f8c0688b2ea] start_thread
+(PiperActor pid=2467971) /lib64/libc.so.6(+0x1103c0) [0x7f8c069103c0] __GI___clone3
+(PiperActor pid=2467971) 
+(PiperActor pid=2467971) [2026-02-20 09:21:16,093 E 2467971 2485050] logging.cc:125: Stack trace: 
+(PiperActor pid=2467971)  /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_raylet.so(+0x16ad67a) [0x7f8bf87a767a] ray::operator<<()
+(PiperActor pid=2467971) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_raylet.so(+0x16ae12c) [0x7f8bf87a812c] ray::RayLog::operator<< <>()
+(PiperActor pid=2467971) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_raylet.so(+0x16b06e8) [0x7f8bf87aa6e8] ray::TerminateHandler()
+(PiperActor pid=2467971) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/bin/../lib/libstdc++.so.6(+0xc2519) [0x7f8bf6fc7519] __cxxabiv1::__terminate()
+(PiperActor pid=2467971) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/bin/../lib/libstdc++.so.6(_ZSt10unexpectedv+0) [0x7f8bf6fc1063] std::unexpected()
+(PiperActor pid=2467971) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/bin/../lib/libstdc++.so.6(+0xc2512) [0x7f8bf6fc7512] __cxxabiv1::__terminate()
+(PiperActor pid=2467971) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so(+0x9bbc82) [0x7f5b23249c82] c10d::ProcessGroupNCCL::Watchdog::run()
+(PiperActor pid=2467971) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/bin/../lib/libstdc++.so.6(+0xdf0e6) [0x7f8bf6fe40e6] execute_native_thread_routine
+(PiperActor pid=2467971) /lib64/libc.so.6(+0x8b2ea) [0x7f8c0688b2ea] start_thread
+(PiperActor pid=2467971) /lib64/libc.so.6(+0x1103c0) [0x7f8c069103c0] __GI___clone3
+(PiperActor pid=2467971) 
+(PiperActor pid=2467971) *** SIGABRT received at time=1771579276 on cpu 173 ***
+(PiperActor pid=2467971) PC: @     0x7f8c0688d02c  (unknown)  __pthread_kill_implementation
+(PiperActor pid=2467971)     @     0x7f8c0683fc30  (unknown)  (unknown)
+(PiperActor pid=2467971) [2026-02-20 09:21:16,095 E 2467971 2485050] logging.cc:474: *** SIGABRT received at time=1771579276 on cpu 173 ***
+(PiperActor pid=2467971) [2026-02-20 09:21:16,095 E 2467971 2485050] logging.cc:474: PC: @     0x7f8c0688d02c  (unknown)  __pthread_kill_implementation
+(PiperActor pid=2467971) [2026-02-20 09:21:16,095 E 2467971 2485050] logging.cc:474:     @     0x7f8c0683fc30  (unknown)  (unknown)
+(PiperActor pid=2467971) Fatal Python error: Aborted
+(PiperActor pid=2467971) 
+(PiperActor pid=2467971) 
+(PiperActor pid=2467971) Extension modules: msgpack._cmsgpack, psutil._psutil_linux, google._upb._message, charset_normalizer.md, requests.packages.charset_normalizer.md, requests.packages.chardet.md, yaml._yaml, ray._raylet, numpy._core._multiarray_umath, numpy.linalg._umath_linalg, torch._C, torch._C._dynamo.autograd_compiler, torch._C._dynamo.eval_frame, torch._C._dynamo.guards, torch._C._dynamo.utils, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator (total: 30)
+(raylet) A worker died or was killed while executing a task by an unexpected system error. To troubleshoot the problem, check the logs for the dead worker. Lease ID: 010000009299451b288663495fbc378cce73468cad54968441361628549d6948 Worker ID: eb0cd9a472c4adbbb7946238f80410ebced80b0b9d70391be5c13dd4 Node ID: 05f9f2052e3e02c4b4cd769ea37a36de9737c05c9ec5d4cca1b3a5da Worker IP address: 10.158.48.71 Worker port: 38937 Worker PID: 2467971 Worker exit type: SYSTEM_ERROR Worker exit detail: Worker unexpectedly exits with a connection error code 2. End of file. Some common causes include: (1) the process was killed by the OOM killer due to high memory usage, (2) ray stop --force was called, or (3) the worker crashed unexpectedly due to SIGSEGV or another unexpected error.
+Traceback (most recent call last):
+  File "/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/runpy.py", line 196, in _run_module_as_main
+    return _run_code(code, main_globals, None,
+  File "/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/runpy.py", line 86, in _run_code
+    exec(code, run_globals)
+  File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/test/test_llama.py", line 161, in <module>
+    ray.get(piper_coordinator.run_program.remote(main, args))
+  File "/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/auto_init_hook.py", line 22, in auto_init_wrapper
+    return fn(*args, **kwargs)
+  File "/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/client_mode_hook.py", line 104, in wrapper
+    return func(*args, **kwargs)
+  File "/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py", line 2967, in get
+    values, debugger_breakpoint = worker.get_objects(
+  File "/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py", line 1015, in get_objects
+    raise value.as_instanceof_cause()
+ray.exceptions.RayTaskError(ActorDiedError): ray::PiperProgramCoordinator.run_program() (pid=2467957, ip=10.158.48.71, actor_id=3ebe3fe0b12c3ba9c2c691b901000000, repr=<src.piper_coordinator.PiperProgramCoordinator object at 0x7f65bd2b4730>)
+  File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/src/piper_coordinator.py", line 41, in run_program
+    return ray.get([run_dp_rank.remote(
+ray.exceptions.RayTaskError(ActorDiedError): ray::run_dp_rank() (pid=2467960, ip=10.158.48.71)
+  File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/src/piper_coordinator.py", line 22, in run_dp_rank
+    return training_func(*args, **kwargs)
+  File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/test/test_llama.py", line 94, in main
+    piper_exec(compiled, schedule, [x], y, loss_fn, args.dp)
+  File "/m-coriander/coriander/wxdeng/mlsys/piper_ref/src/piper_exec.py", line 187, in piper_exec
+    return ray.get(ret)
+ray.exceptions.ActorDiedError: The actor died unexpectedly before finishing this task.
+	class_name: PiperActor
+	actor_id: 4d48938144c53713213caa6701000000
+	pid: 2467971
+	namespace: llama
+	ip: 10.158.48.71
+The actor is dead because its worker process has died. Worker exit type: SYSTEM_ERROR Worker exit detail: Worker unexpectedly exits with a connection error code 2. End of file. Some common causes include: (1) the process was killed by the OOM killer due to high memory usage, (2) ray stop --force was called, or (3) the worker crashed unexpectedly due to SIGSEGV or another unexpected error.
diff --git a/logs/interleaved_debug-memopt.log b/logs/interleaved_debug-memopt.log
new file mode 100644
index 0000000..8d9e938
--- /dev/null
+++ b/logs/interleaved_debug-memopt.log
@@ -0,0 +1,612 @@
+2026-02-20 22:51:21,262	INFO worker.py:2007 -- Started a local Ray instance.
+/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+  warnings.warn(
+(run_dp_rank pid=3571047) Namespace(model='debug', schedule='interleaved-1f1b', dp=1, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=1, iters=1, tracing=False, naive_gradient_sync=False)
+(run_dp_rank pid=3571047) Schedule:
+(run_dp_rank pid=3571047) 0:0:f	0:1:f	2:0:f	2:1:f	0:2:f	0:3:f	2:0:b	 -- 	2:1:b	2:2:f	0:0:b	2:3:f	0:1:b	 -- 	2:2:b	2:3:b	0:2:b	0:3:b	0:0:u	
+(run_dp_rank pid=3571047)  -- 	1:0:f	1:1:f	3:0:f	3:0:b	3:1:f	3:1:b	1:2:f	1:0:b	1:3:f	1:1:b	3:2:f	3:2:b	3:3:f	3:3:b	1:2:b	1:3:b	1:0:u	 -- 	
+(run_dp_rank pid=3571047) 2026-02-20 22:51:36,090 - piper_coordinator - DEBUG - Running DP rank 1 of 1
+(run_dp_rank pid=3571047) 2026-02-20 22:51:36,131 - piper_compile - DEBUG - mbs: 4, stages: 4, devices: 2
+(run_dp_rank pid=3571047) 2026-02-20 22:51:36,131 - piper_compile - DEBUG - Fwd/bwd schedule: [[Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=False, upd=False)], [Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=False, upd=False)]]
+(run_dp_rank pid=3571047) 2026-02-20 22:51:36,131 - piper_compile - DEBUG - P2P comms: [(0, 1, 0), (0, 1, 1), (1, 2, 0), (2, 3, 0), (1, 2, 1), (2, 3, 1), (0, 1, 2), (3, 2, 0), (0, 1, 3), (2, 1, 0), (3, 2, 1), (2, 1, 1), (1, 2, 2), (2, 3, 2), (1, 0, 0), (1, 2, 3), (2, 3, 3), (1, 0, 1), (3, 2, 2), (2, 1, 2), (3, 2, 3), (2, 1, 3), (1, 0, 2), (1, 0, 3)]
+(run_dp_rank pid=3571047) 2026-02-20 22:51:36,132 - piper_compile - DEBUG - P2P schedules: {0: [(0, 1, 0, True), (0, 1, 1, True), (1, 2, 0, False), (2, 3, 0, True), (1, 2, 1, False), (2, 3, 1, True), (0, 1, 2, True), (3, 2, 0, False), (0, 1, 3, True), (2, 1, 0, True), (3, 2, 1, False), (2, 1, 1, True), (1, 2, 2, False), (2, 3, 2, True), (1, 0, 0, False), (1, 2, 3, False), (2, 3, 3, True), (1, 0, 1, False), (3, 2, 2, False), (2, 1, 2, True), (3, 2, 3, False), (2, 1, 3, True), (1, 0, 2, False), (1, 0, 3, False)], 1: [(0, 1, 0, False), (0, 1, 1, False), (1, 2, 0, True), (2, 3, 0, False), (1, 2, 1, True), (2, 3, 1, False), (0, 1, 2, False), (3, 2, 0, True), (0, 1, 3, False), (2, 1, 0, False), (3, 2, 1, True), (2, 1, 1, False), (1, 2, 2, True), (2, 3, 2, False), (1, 0, 0, True), (1, 2, 3, True), (2, 3, 3, False), (1, 0, 1, True), (3, 2, 2, True), (2, 1, 2, False), (3, 2, 3, True), (2, 1, 3, False), (1, 0, 2, True), (1, 0, 3, True)]}
+(run_dp_rank pid=3571047) 2026-02-20 22:51:36,201 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, 1aeede357bc2f5ee36c253aa01000000) global rank 0
+(run_dp_rank pid=3571047) 2026-02-20 22:51:36,202 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, 7d7c6685f075fa1ac9f1942d01000000) global rank 1
+(PiperActor pid=3571085) 2026-02-20 22:51:39,181 - piper_actor - INFO - Initializing Ray actor 1 global rank 1 GPU 4
+(PiperActor pid=3571121) 2026-02-20 22:51:39,337 - piper_actor - INFO - Initializing Ray actor 0 global rank 0 GPU 1
+(PiperActor pid=3571121) 2026-02-20 22:51:41,903 - piper_actor - DEBUG - Actor 0 has GPU 1, joined the global process group
+(PiperActor pid=3571085) 2026-02-20 22:51:41,903 - piper_actor - DEBUG - Actor 1 has GPU 4, joined the global process group
+(PiperActor pid=3571085) 2026-02-20 22:51:41,904 - piper_actor - INFO - Global rank 1 joined its pp group 0 along with ranks [0, 1]
+(PiperActor pid=3571121) 2026-02-20 22:51:41,904 - piper_actor - INFO - Global rank 0 joined its pp group 0 along with ranks [0, 1]
+(run_dp_rank pid=3571047) 2026-02-20 22:51:42,207 - piper_compile - INFO - DP rank 1 compiling...
+(PiperActor pid=3571121) 2026-02-20 22:51:46,517 - piper_actor - INFO - Loading stage 0 graph on actor 0
+(PiperActor pid=3571085) 2026-02-20 22:51:46,619 - piper_actor - INFO - Loading stage 1 graph on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:51:48,190 - piper_actor - INFO - Loading stage 3 graph on actor 1
+(PiperActor pid=3571121) 2026-02-20 22:51:48,209 - piper_actor - INFO - Loading stage 2 graph on actor 0
+(run_dp_rank pid=3571047) 2026-02-20 22:51:48,373 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=3571047) Running 1 warmup iterations...
+(run_dp_rank pid=3571047) 2026-02-20 22:51:54,936 - piper_compile - INFO - DP rank 1 done.
+(PiperActor pid=3571085) 2026-02-20 22:51:54,974 - piper_actor - DEBUG - Actor 1 loaded labels torch.Size([16, 256, 512])
+(PiperActor pid=3571085) 2026-02-20 22:51:54,979 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:51:54,979 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 0 -> 1, mb 0, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:51:54,979 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 0)
+(PiperActor pid=3571085) [rank1]:[W220 22:51:54.465338199 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=3571121) 2026-02-20 22:51:54,964 - piper_actor - DEBUG - Actor 0 loaded inputs 1
+(PiperActor pid=3571121) 2026-02-20 22:51:54,979 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:51:56,571 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=3571121) 2026-02-20 22:51:56,571 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 0 -> 1, mb 0, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:51:56,571 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 0)
+(PiperActor pid=3571121) [rank0]:[W220 22:51:56.054094514 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=3571085) 2026-02-20 22:52:05,181 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:05,181 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:05,181 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=3571121) 2026-02-20 22:52:05,171 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:05,171 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:05,171 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 512]), torch.Size([256, 256]), torch.Size([256, 8])]
+(PiperActor pid=3571121) 2026-02-20 22:52:05,173 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:05,181 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=3571121) 2026-02-20 22:52:05,181 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 0 -> 1, mb 1, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:05,181 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:05,182 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:05,182 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:05,182 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 512]), torch.Size([256, 256]), torch.Size([256, 8])]
+(PiperActor pid=3571121) 2026-02-20 22:52:05,182 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:05,183 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 2, mb 0, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:05,183 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 0)
+(PiperActor pid=3571121) [rank0]:[W220 22:52:05.664822857 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=3571085) 2026-02-20 22:52:05,417 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=3571085) 2026-02-20 22:52:05,417 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 2, mb 0, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:05,418 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:05,418 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:05,418 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:05,418 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 0)
+(PiperActor pid=3571085) [rank1]:[W220 22:52:05.900559939 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=3571085) 2026-02-20 22:52:12,785 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:12,785 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:12,786 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571085) 2026-02-20 22:52:12,788 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:12,788 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 0 -> 1, mb 1, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:12,788 - piper_actor - DEBUG - P2P op (0, 1, 1, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:12,788 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=3571121) 2026-02-20 22:52:12,785 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:12,785 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:12,786 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=3571085) 2026-02-20 22:52:12,797 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=3571085) 2026-02-20 22:52:12,797 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 2, mb 1, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:12,797 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:12,797 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:12,797 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:12,798 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:12,798 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:12,798 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:12,798 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571085) 2026-02-20 22:52:12,804 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:12,804 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 3, mb 0, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:12,804 - piper_actor - DEBUG - P2P op (2, 3, 0, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:12,804 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=3571121) 2026-02-20 22:52:12,802 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=3571121) 2026-02-20 22:52:12,802 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 3, mb 0, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:12,802 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:12,803 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:12,803 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:12,803 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571121) 2026-02-20 22:52:12,806 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:12,806 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 2, mb 1, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:12,806 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:12,806 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:12,806 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:12,806 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=3571085) 2026-02-20 22:52:12,839 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=3571085) 2026-02-20 22:52:12,839 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571085) 2026-02-20 22:52:12,840 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=3571121) 2026-02-20 22:52:12,855 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=3571121) 2026-02-20 22:52:12,855 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 3, mb 1, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:12,855 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:12,856 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:12,856 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:12,856 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571121) 2026-02-20 22:52:12,857 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=3571085) 2026-02-20 22:52:13,011 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 3 -> 2, mb 0, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:13,012 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,012 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,012 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,012 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,013 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,013 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,013 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,013 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,013 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,014 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,014 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 3, mb 1, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:13,014 - piper_actor - DEBUG - P2P op (2, 3, 1, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:13,014 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,017 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,017 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571085) 2026-02-20 22:52:13,018 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=3571121) 2026-02-20 22:52:12,918 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=3571121) 2026-02-20 22:52:12,919 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 0 -> 1, mb 2, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:12,919 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:12,919 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:12,919 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:12,919 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 512]), torch.Size([256, 256]), torch.Size([256, 8])]
+(PiperActor pid=3571121) 2026-02-20 22:52:12,920 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:12,963 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=3571121) 2026-02-20 22:52:12,963 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 0 -> 1, mb 3, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:12,963 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:12,964 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,224 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 3 -> 2, mb 1, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:13,224 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,225 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,225 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,225 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,226 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,493 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,494 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,494 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,494 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,601 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,601 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 0 -> 1, mb 2, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:13,601 - piper_actor - DEBUG - P2P op (0, 1, 2, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:13,602 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=3571085) 2026-02-20 22:52:13,604 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=3571085) 2026-02-20 22:52:13,604 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 2, mb 2, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:13,605 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,605 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,610 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,610 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,610 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,610 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,610 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571085) 2026-02-20 22:52:13,611 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,611 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 1, mb 0, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:13,611 - piper_actor - DEBUG - P2P op (2, 1, 0, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:13,611 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 0, mb 0, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:13,611 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,612 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,612 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,612 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,612 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,612 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,612 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,613 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 0 -> 1, mb 3, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:13,613 - piper_actor - DEBUG - P2P op (0, 1, 3, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:13,613 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=3571085) 2026-02-20 22:52:13,615 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=3571085) 2026-02-20 22:52:13,615 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 2, mb 3, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:13,615 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,615 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,615 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,616 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571085) 2026-02-20 22:52:13,616 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,616 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 1, mb 1, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:13,616 - piper_actor - DEBUG - P2P op (2, 1, 1, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:13,617 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 0, mb 1, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:13,617 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,617 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,617 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,617 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,617 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,617 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,725 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,725 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 3, mb 2, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:13,725 - piper_actor - DEBUG - P2P op (2, 3, 2, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:13,725 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=3571085) 2026-02-20 22:52:13,728 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=3571085) 2026-02-20 22:52:13,728 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571085) 2026-02-20 22:52:13,730 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,736 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 3 -> 2, mb 2, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:13,736 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,736 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,736 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=3571121) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
+(PiperActor pid=3571121)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+(PiperActor pid=3571121) 2026-02-20 22:52:13,597 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,598 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,598 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,598 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,598 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 512]), torch.Size([256, 256]), torch.Size([256, 8])]
+(PiperActor pid=3571121) 2026-02-20 22:52:13,600 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,600 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 3 -> 2, mb 0, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:13,600 - piper_actor - DEBUG - P2P op (3, 2, 0, False) already executed on actor 0, skipping
+(PiperActor pid=3571121) 2026-02-20 22:52:13,600 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 1, mb 0, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:13,600 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,600 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,600 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,601 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,601 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 3 -> 2, mb 1, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:13,601 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,601 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,605 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,606 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 1, mb 1, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:13,606 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,606 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,606 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,607 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,607 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 2, mb 2, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:13,607 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,607 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,607 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,607 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=3571121) 2026-02-20 22:52:13,610 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=3571121) 2026-02-20 22:52:13,610 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 3, mb 2, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:13,610 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,611 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,611 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,611 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571121) 2026-02-20 22:52:13,612 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,612 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 0, mb 0, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:13,612 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,612 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,719 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,720 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,720 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 2, mb 3, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:13,720 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,721 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,721 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,721 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=3571121) 2026-02-20 22:52:13,724 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=3571121) 2026-02-20 22:52:13,724 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 3, mb 3, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:13,724 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,724 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,724 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,724 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571121) 2026-02-20 22:52:13,725 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,725 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 0, mb 1, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:13,725 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,725 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,730 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,731 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,731 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 3 -> 2, mb 2, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:13,731 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,732 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,736 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,736 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 1, mb 2, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:13,736 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,736 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,736 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=3571085) 2026-02-20 22:52:13,737 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,737 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 3, mb 3, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:13,737 - piper_actor - DEBUG - P2P op (2, 3, 3, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:13,737 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=3571085) 2026-02-20 22:52:13,740 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=3571085) 2026-02-20 22:52:13,740 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571085) 2026-02-20 22:52:13,741 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,747 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 3 -> 2, mb 3, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:13,747 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,747 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,751 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,752 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,752 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,752 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,753 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,753 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 1, mb 2, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:13,753 - piper_actor - DEBUG - P2P op (2, 1, 2, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:13,753 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 0, mb 2, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:13,753 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,753 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,757 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,758 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,758 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,758 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,759 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,759 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 1, mb 3, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:13,759 - piper_actor - DEBUG - P2P op (2, 1, 3, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:13,759 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 0, mb 3, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:13,759 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,759 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:13,759 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:13,760 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=3571085) 2026-02-20 22:52:13,760 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=3571121) 2026-02-20 22:52:13,739 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,739 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 3 -> 2, mb 3, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:13,739 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,739 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,743 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,743 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 1, mb 3, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:13,744 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,744 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,744 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,752 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,753 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 0, mb 2, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:13,753 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,753 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,757 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,760 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,760 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 0, mb 3, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:13,760 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,761 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:13,765 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:13,766 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=3571121) 2026-02-20 22:52:13,766 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(run_dp_rank pid=3571047) Running 1 timed iterations...
+(PiperActor pid=3571085) 2026-02-20 22:52:14,434 - piper_actor - INFO - Actor 1: Tracing disabled
+(PiperActor pid=3571085) 2026-02-20 22:52:14,437 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,437 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 0 -> 1, mb 0, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:14,438 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,438 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,438 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,438 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=3571085) 2026-02-20 22:52:14,441 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=3571085) 2026-02-20 22:52:14,441 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 2, mb 0, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:14,441 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,442 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,442 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 1) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,442 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,442 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,442 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 0) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,442 - piper_actor - DEBUG - Forward 1 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571085) 2026-02-20 22:52:14,445 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,446 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 0 -> 1, mb 1, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:14,446 - piper_actor - DEBUG - P2P op (0, 1, 1, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:14,446 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,448 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,448 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 2, mb 1, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:14,448 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,448 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,448 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 0) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,448 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,449 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,449 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 1) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,449 - piper_actor - DEBUG - Forward 1 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571085) 2026-02-20 22:52:14,449 - piper_actor - DEBUG - Calling forward 3 mb 0 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,449 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 3, mb 0, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:14,450 - piper_actor - DEBUG - P2P op (2, 3, 0, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:14,450 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=3571085) 2026-02-20 22:52:14,452 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 0
+(PiperActor pid=3571085) 2026-02-20 22:52:14,452 - piper_actor - DEBUG - Forward 3 mb 0 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571085) 2026-02-20 22:52:14,453 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,464 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 3 -> 2, mb 0, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:14,464 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,465 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,465 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 1) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,465 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,465 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,465 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 2) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,465 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,465 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,466 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 0) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,466 - piper_actor - DEBUG - Calling forward 3 mb 1 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,466 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 3, mb 1, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:14,466 - piper_actor - DEBUG - P2P op (2, 3, 1, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:14,466 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,469 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,469 - piper_actor - DEBUG - Forward 3 mb 1 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571085) 2026-02-20 22:52:14,470 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,476 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 3 -> 2, mb 1, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:14,476 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,476 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,476 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 3) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,477 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,477 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,481 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 0) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,481 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,481 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,481 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 1) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,482 - piper_actor - DEBUG - Calling forward 1 mb 2 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,482 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 0 -> 1, mb 2, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:14,482 - piper_actor - DEBUG - P2P op (0, 1, 2, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:14,482 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=3571085) 2026-02-20 22:52:14,485 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 2
+(PiperActor pid=3571085) 2026-02-20 22:52:14,485 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 2, mb 2, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:14,485 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,485 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,490 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 1) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,490 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,490 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,490 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 2) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,490 - piper_actor - DEBUG - Forward 1 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571085) 2026-02-20 22:52:14,491 - piper_actor - DEBUG - Calling backward 1 mb 0 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,491 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 1, mb 0, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:14,491 - piper_actor - DEBUG - P2P op (2, 1, 0, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:14,491 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 0, mb 0, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:14,491 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,491 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,491 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 2) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,492 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,492 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 0)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,492 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 0) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,492 - piper_actor - DEBUG - Calling forward 1 mb 3 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,492 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 0 -> 1, mb 3, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:14,492 - piper_actor - DEBUG - P2P op (0, 1, 3, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:14,492 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=3571085) 2026-02-20 22:52:14,495 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 1 mb 3
+(PiperActor pid=3571085) 2026-02-20 22:52:14,495 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 2, mb 3, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:14,495 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,495 - piper_actor - DEBUG - Completed fwd p2p send on 1 to 0, op: (1 -> 2, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,495 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 1 -> 2, mb 3) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,495 - piper_actor - DEBUG - Forward 1 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571085) 2026-02-20 22:52:14,496 - piper_actor - DEBUG - Calling backward 1 mb 1 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,496 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 1, mb 1, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:14,496 - piper_actor - DEBUG - P2P op (2, 1, 1, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:14,496 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 0, mb 1, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:14,496 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,497 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (2 -> 3, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,497 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 2 -> 3, mb 3) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,497 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,497 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 1)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,497 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 1) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,501 - piper_actor - DEBUG - Calling forward 3 mb 2 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,501 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 3, mb 2, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:14,501 - piper_actor - DEBUG - P2P op (2, 3, 2, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:14,501 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=3571085) 2026-02-20 22:52:14,503 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 2
+(PiperActor pid=3571085) 2026-02-20 22:52:14,503 - piper_actor - DEBUG - Forward 3 mb 2 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571085) 2026-02-20 22:52:14,505 - piper_actor - DEBUG - Calling backward 3 mb 2 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,510 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 3 -> 2, mb 2, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:14,511 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,511 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,511 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 2) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,511 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,512 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 3, mb 3, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:14,512 - piper_actor - DEBUG - P2P op (2, 3, 3, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:14,512 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=3571085) 2026-02-20 22:52:14,514 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3
+(PiperActor pid=3571085) 2026-02-20 22:52:14,514 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571085) 2026-02-20 22:52:14,515 - piper_actor - DEBUG - Calling backward 3 mb 3 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,521 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 3 -> 2, mb 3, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:14,521 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,522 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,434 - piper_actor - INFO - Actor 0: Tracing disabled
+(PiperActor pid=3571121) 2026-02-20 22:52:14,437 - piper_actor - DEBUG - Calling forward 0 mb 0 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,440 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,441 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 0 -> 1, mb 0, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:14,441 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,441 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,441 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 0) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,441 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 512]), torch.Size([256, 256]), torch.Size([256, 8])]
+(PiperActor pid=3571121) 2026-02-20 22:52:14,442 - piper_actor - DEBUG - Calling forward 0 mb 1 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,444 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=3571121) 2026-02-20 22:52:14,444 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 0 -> 1, mb 1, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:14,445 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,445 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,445 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 1) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,445 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 512]), torch.Size([256, 256]), torch.Size([256, 8])]
+(PiperActor pid=3571121) 2026-02-20 22:52:14,446 - piper_actor - DEBUG - Calling forward 2 mb 0 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,446 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 2, mb 0, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:14,446 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,446 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,446 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 0) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,446 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,448 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,449 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 3, mb 0, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:14,449 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,449 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,449 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 0) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,449 - piper_actor - DEBUG - Forward 2 mb 0 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571121) 2026-02-20 22:52:14,450 - piper_actor - DEBUG - Calling forward 2 mb 1 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,450 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 2, mb 1, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:14,450 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,450 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,450 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 1) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,450 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=3571121) 2026-02-20 22:52:14,452 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 1
+(PiperActor pid=3571121) 2026-02-20 22:52:14,453 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 3, mb 1, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:14,453 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,453 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,453 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 1) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,453 - piper_actor - DEBUG - Forward 2 mb 1 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571121) 2026-02-20 22:52:14,454 - piper_actor - DEBUG - Calling forward 0 mb 2 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,456 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 2
+(PiperActor pid=3571121) 2026-02-20 22:52:14,456 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 0 -> 1, mb 2, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:14,456 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,457 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,457 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 2) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,457 - piper_actor - DEBUG - Forward 0 mb 2 on actor 0 returning [torch.Size([16, 256, 512]), torch.Size([256, 256]), torch.Size([256, 8])]
+(PiperActor pid=3571121) 2026-02-20 22:52:14,457 - piper_actor - DEBUG - Calling forward 0 mb 3 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,459 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 3
+(PiperActor pid=3571121) 2026-02-20 22:52:14,460 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 0 -> 1, mb 3, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:14,460 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,460 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,470 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 0) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,470 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,470 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,470 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 0 -> 1, mb 3) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,470 - piper_actor - DEBUG - Forward 0 mb 3 on actor 0 returning [torch.Size([16, 256, 512]), torch.Size([256, 256]), torch.Size([256, 8])]
+(PiperActor pid=3571121) 2026-02-20 22:52:14,471 - piper_actor - DEBUG - Calling backward 2 mb 0 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,471 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 3 -> 2, mb 0, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:14,471 - piper_actor - DEBUG - P2P op (3, 2, 0, False) already executed on actor 0, skipping
+(PiperActor pid=3571121) 2026-02-20 22:52:14,471 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 1, mb 0, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:14,471 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,472 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,472 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 0) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,472 - piper_actor - DEBUG - Calling backward 2 mb 1 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,472 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 3 -> 2, mb 1, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:14,472 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,472 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,476 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 1) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,476 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 1, mb 1, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:14,477 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,477 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,477 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 1) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,481 - piper_actor - DEBUG - Calling forward 2 mb 2 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,482 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 2, mb 2, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:14,482 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,482 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,482 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 2) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,482 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=3571121) 2026-02-20 22:52:14,484 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 2
+(PiperActor pid=3571121) 2026-02-20 22:52:14,484 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 3, mb 2, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:14,485 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,485 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,485 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 2) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,485 - piper_actor - DEBUG - Forward 2 mb 2 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571121) 2026-02-20 22:52:14,491 - piper_actor - DEBUG - Calling backward 0 mb 0 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,491 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 0, mb 0, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:14,491 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,491 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 0)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,496 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 0) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,497 - piper_actor - DEBUG - Calling forward 2 mb 3 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,497 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 2, mb 3, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:14,497 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,497 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,497 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 1 -> 2, mb 3) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,498 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=3571121) 2026-02-20 22:52:14,500 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 2 mb 3
+(PiperActor pid=3571121) 2026-02-20 22:52:14,500 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 3, mb 3, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:14,500 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,500 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,500 - piper_actor - DEBUG - Executed P2P(fwd_send, stage 2 -> 3, mb 3) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,500 - piper_actor - DEBUG - Forward 2 mb 3 on actor 0 returning [torch.Size([16, 256, 512])]
+(PiperActor pid=3571121) 2026-02-20 22:52:14,501 - piper_actor - DEBUG - Calling backward 0 mb 1 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,501 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 0, mb 1, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:14,501 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,502 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 1)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,506 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 1) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,507 - piper_actor - DEBUG - Calling backward 2 mb 2 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,507 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 3 -> 2, mb 2, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:14,507 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,508 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,512 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 2) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,512 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 1, mb 2, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:14,512 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,512 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,512 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 2) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,514 - piper_actor - DEBUG - Calling backward 2 mb 3 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,514 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 3 -> 2, mb 3, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:14,514 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (3 -> 2, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,515 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (3 -> 2, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,519 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 3 -> 2, mb 3) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,519 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 1, mb 3, is_sender=True
+(PiperActor pid=3571121) 2026-02-20 22:52:14,519 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,519 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,519 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 2 -> 1, mb 3) on actor 0
+(run_dp_rank pid=3571047) rank 0 iter time= 0.11 ± 0.00 s (1 samples)
+(run_dp_rank pid=3571047) rank 0 throughput= 147299.70 tokens/sec (1 samples)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,526 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 2) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,526 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,526 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,526 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 3 -> 2, mb 3) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,527 - piper_actor - DEBUG - Calling backward 1 mb 2 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,527 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 1, mb 2, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:14,527 - piper_actor - DEBUG - P2P op (2, 1, 2, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:14,527 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 0, mb 2, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:14,527 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,528 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,531 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 2 -> 1, mb 3) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,532 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,532 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 2)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,532 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 2) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,533 - piper_actor - DEBUG - Calling backward 1 mb 3 on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,533 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 2 -> 1, mb 3, is_sender=False
+(PiperActor pid=3571085) 2026-02-20 22:52:14,533 - piper_actor - DEBUG - P2P op (2, 1, 3, False) already executed on actor 1, skipping
+(PiperActor pid=3571085) 2026-02-20 22:52:14,533 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 0, mb 3, is_sender=True
+(PiperActor pid=3571085) 2026-02-20 22:52:14,533 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (1 -> 0, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,533 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (1 -> 0, mb 3)
+(PiperActor pid=3571085) 2026-02-20 22:52:14,534 - piper_actor - DEBUG - Executed P2P(bwd_send, stage 1 -> 0, mb 3) on actor 1
+(PiperActor pid=3571085) 2026-02-20 22:52:14,534 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=3571085) 2026-02-20 22:52:14,534 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=3571121) 2026-02-20 22:52:14,527 - piper_actor - DEBUG - Calling backward 0 mb 2 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,527 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 0, mb 2, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:14,527 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,527 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 2)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,531 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 2) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,534 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,534 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 0, mb 3, is_sender=False
+(PiperActor pid=3571121) 2026-02-20 22:52:14,535 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,535 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 3)
+(PiperActor pid=3571121) 2026-02-20 22:52:14,539 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0
+(PiperActor pid=3571121) 2026-02-20 22:52:14,540 - piper_actor - DEBUG - Actor 0 waiting for backward sync events
+(PiperActor pid=3571121) 2026-02-20 22:52:14,540 - piper_actor - DEBUG - Actor 0 waiting for p2p ops
+(run_dp_rank pid=3571047) Ray timeline saved to: out/debug-pp2-dp1-interleaved-1f1b.json
+(PiperActor pid=3571085) [rank1]:[W220 22:52:16.660518430 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+(PiperActor pid=3571121) [rank0]:[W220 22:52:16.959062396 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
diff --git a/run_interleaved-debug.sh b/run_interleaved-debug.sh
index 3b8d694..2628616 100644
--- a/run_interleaved-debug.sh
+++ b/run_interleaved-debug.sh
@@ -7,7 +7,7 @@ mkdir -p $RAY_TMP
 export RAY_TMPDIR="$RAY_TMP"
 export PYTHONUNBUFFERED=1
 
-export CUDA_VISIBLE_DEVICES=0,1,2
+export CUDA_VISIBLE_DEVICES=0,1,4
 export RAY_DEDUP_LOGS=0
 export PIPER_MASTER_PORT=12389
 
@@ -17,5 +17,5 @@ python3 -m test.test_llama \
     --iters 1 \
     --schedule interleaved-1f1b \
     --mbs 4 \
-    --model debug > $LOG_DIR/interleaved_debug-1warm_1it.log 2>&1
+    --model debug > $LOG_DIR/interleaved_debug-memopt.log 2>&1
     # --tracing \
\ No newline at end of file
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 369422d..aa363ce 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -130,6 +130,8 @@ def __init__(
         self.graph_modules = dict()
         # map stage id -> model parameters used by the fx.Graph with holes (None values) for input tensors
         self.forward_args = dict()
+        # map stage id -> input idx -> input tensor metadata
+        self.forward_input_meta = defaultdict(dict)
         # map stage id -> indices of the input tensors (as opposed to model parameters) used by the fx.Graph
         self.input_idxs = dict()
         # map stage id -> optimizer for the fx.Graph
@@ -138,6 +140,8 @@ def __init__(
         self.inp_activation = defaultdict(dict)
         # map stage id -> mb_idx -> current activation
         self.out_activation = defaultdict(dict)
+        # map (src_stage, dst_stage, mb_idx, is_sender) -> tensor for p2p communication
+        self.p2p_cache = dict()
         # accumuate loss for each microbatch
         self.loss = []
         # map stage id -> data parallel communication operations
@@ -160,6 +164,7 @@ def __init__(
     def reset_p2p_states(self):
         self.next_p2p_idx = 0
         self.executed_p2ps = set()
+        self.p2p_cache = dict()
 
     def get_trace_data(self) -> dict:
         return self.global_rank, self.trace_data
@@ -383,6 +388,13 @@ def move_to_device(idx, arg):
         self.forward_args[stage_id] = forward_args
         self.stage_id = stage_id
 
+        for i in self.input_idxs[stage_id]:
+            self.forward_input_meta[stage_id][i] = (
+                forward_args[i].shape,
+                forward_args[i].dtype,
+                forward_args[i].requires_grad,
+            )
+
         # add the parameters to the optimizer for this stage
         params = [param for param in forward_args if param.requires_grad]
         if stage_id not in self.optims:
@@ -390,13 +402,22 @@ def move_to_device(idx, arg):
         else:
             self.optims[stage_id].add_param_group({"params": params})
 
+        for i in self.input_idxs[stage_id]:
+            self.forward_args[stage_id][i] = None
+
         del gm_data
 
     def _exec_p2p_op(
-        self, src_stage: int, dst_stage: int, mb_idx: int, is_sender: bool, **kwargs
+        self, src_stage: int, dst_stage: int, mb_idx: int, is_sender: bool
     ):
+        logger.debug(
+            f"[DEBUG] Actor {self.global_rank} executing p2p op: {src_stage} -> {dst_stage}, mb {mb_idx}, is_sender={is_sender}"
+        )
         p2p_op = (src_stage, dst_stage, mb_idx, is_sender)
         if p2p_op in self.executed_p2ps:
+            logger.debug(
+                f"P2P op {p2p_op} already executed on actor {self.global_rank}, skipping"
+            )
             return
 
         op_idx = self.next_p2p_idx
@@ -420,7 +441,7 @@ def _exec_p2p_op(
 
             op_name = "P2P(unknown)"
             if is_fwd and is_sender:
-                self._exec_fwd_send(src_stage, mb_idx, **kwargs)
+                self._exec_fwd_send(src_stage, mb_idx)
                 op_name = (
                     f"P2P(fwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
                 )
@@ -451,6 +472,18 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int):
         if stage_id == 0:
             return
 
+        p2p_op = (stage_id - 1, stage_id, mb_idx, False)
+        assert p2p_op not in self.p2p_cache
+
+        inputs_to_recv = []
+        for i in self.input_idxs[stage_id]:
+            shape, dtype, requires_grad = self.forward_input_meta[stage_id][i]
+            inputs_to_recv.append(
+                torch.empty(
+                    shape, dtype=dtype, requires_grad=requires_grad, device=self.device
+                )
+            )
+
         # For non-first stages, receive input tensors from the previous stage
         pp_rank = piper_metadata.stage_to_device[stage_id - 1]
         global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
@@ -462,7 +495,7 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int):
         with torch.cuda.stream(self.p2p_stream):
             for i in self.input_idxs[stage_id]:
                 dist.recv(
-                    self.forward_args[stage_id][i],
+                    inputs_to_recv[i],
                     src=global_src_rank,
                     group=self.pp_group,
                 )
@@ -471,10 +504,14 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int):
             f"Completed fwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
         )
 
-    def _exec_fwd_send(self, stage_id: int, mb_idx: int, output):
+        self.p2p_cache[p2p_op] = inputs_to_recv
+
+    def _exec_fwd_send(self, stage_id: int, mb_idx: int):
         if stage_id == self.num_stages - 1:
             return
 
+        p2p_op = (stage_id, stage_id + 1, mb_idx, True)
+        output = self.p2p_cache.pop(p2p_op)
         # For non-final stages, send output tensors to the next stage
         pp_rank = piper_metadata.stage_to_device[stage_id + 1]
         global_dst_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
@@ -549,6 +586,11 @@ def _forward(self, stage_id: int, mb_idx: int):
                 self.forward_args[stage_id][i] = inp
         else:
             self._exec_p2p_op(stage_id - 1, stage_id, mb_idx, False)
+            inputs_from_prev_stage = self.p2p_cache.pop(
+                (stage_id - 1, stage_id, mb_idx, False)
+            )
+            for i, tensor in zip(self.input_idxs[stage_id], inputs_from_prev_stage):
+                self.forward_args[stage_id][i] = tensor
 
             # save first input that requires grad as input activation
             inp_with_grad = [
@@ -582,8 +624,14 @@ def _forward(self, stage_id: int, mb_idx: int):
         # for i in self.input_idxs[stage_id]:
         #     self.forward_args[stage_id][i] = torch.empty_like(self.forward_args[stage_id][i])
 
+        for i in self.input_idxs[stage_id]:
+            self.forward_args[stage_id][i] = None
+
         if stage_id < self.num_stages - 1:
-            self._exec_p2p_op(stage_id, stage_id + 1, mb_idx, True, output=output)
+            send_p2p_op = (stage_id, stage_id + 1, mb_idx, True)
+            assert send_p2p_op not in self.p2p_cache
+            self.p2p_cache[send_p2p_op] = output
+            self._exec_p2p_op(stage_id, stage_id + 1, mb_idx, True)
 
         if CLEANUP_MEMORY:
             gc.collect()

From 13bd51dc8d4eae8492772ef5c1a6ae9f958cc922 Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Fri, 20 Feb 2026 23:19:29 +0000
Subject: [PATCH 042/100] Reverted the stage_id != 0 check in forward to match
 main

---
 piper/actor.py | 27 +++++++++++----------------
 1 file changed, 11 insertions(+), 16 deletions(-)

diff --git a/piper/actor.py b/piper/actor.py
index e675d55..0ac15cf 100644
--- a/piper/actor.py
+++ b/piper/actor.py
@@ -447,13 +447,9 @@ def place(arg):
             self.parameters[stage_id][i] = arg
 
         # save first input as previous activation
-        # if args[0].dtype == torch.float:
-        #     args[0].requires_grad_().retain_grad()
-        # self.inp_activation[stage_id][mb_idx] = args[0]
-        if stage_id != 0:
-            if not args[0].requires_grad:
-                args[0].requires_grad_()
-            self.inp_activation[stage_id][mb_idx] = args[0]
+        if args[0].dtype == torch.float:
+            args[0].requires_grad_().retain_grad()
+        self.inp_activation[stage_id][mb_idx] = args[0]
 
         # Record start event for forward timing
         if self.tracing:
@@ -530,13 +526,9 @@ def place(arg):
             self.parameters[stage_id][i] = arg
 
         # save first input as input activation
-        # if args[0].dtype == torch.float:
-        #     args[0].requires_grad_().retain_grad()
-        # self.inp_activation[stage_id][mb_idx] = args[0]
-        if stage_id != 0:
-            if not args[0].requires_grad:
-                args[0].requires_grad_()
-            self.inp_activation[stage_id][mb_idx] = args[0]
+        if args[0].dtype == torch.float:
+            args[0].requires_grad_().retain_grad()
+        self.inp_activation[stage_id][mb_idx] = args[0]
 
         out = self.forward_fns[stage_id](*self.parameters[stage_id])
 
@@ -794,7 +786,6 @@ def backward_input(self, stage_id: int, mb_idx: int, inp, truth=None, loss_fn=No
             return [gx, upstream_grads]
 
         stage_input = self.inp_activation[stage_id][mb_idx]
-        # stage_params = [p for p in self.parameters[stage_id] if p is not None and p.requires_grad]
         stage_params = [p for p in self.parameters[stage_id] if isinstance(p, torch.nn.Parameter)]
 
         # Lists of autograd nodes for the layer outputs, inputs, and parameters
@@ -865,6 +856,9 @@ def hook(grad_inputs):
         for h in handles:
             h.remove()
 
+        del activation_or_loss
+        del stage_input
+
         # Save parameter groups for use in backward_weight
         self._bw_param_groups[stage_id][mb_idx] = param_groups
 
@@ -925,7 +919,8 @@ def backward_weight(self, stage_id: int, mb_idx: int, gx, upstream, truth=None,
                 else:
                     p.grad.add_(pg)
 
-            del self.out_activation[stage_id][mb_idx]
+            del activation
+            del self.inp_activation[stage_id][mb_idx]
 
             if self.tracing:
                 end_event.record()

From 78a61d504569d54a25b599a2e00a5c73638d0a3d Mon Sep 17 00:00:00 2001
From: Andy Ruan <andyubryh@gmail.com>
Date: Sun, 22 Feb 2026 13:46:19 +0000
Subject: [PATCH 043/100] p2p ordering test v0

Signed-off-by: Andy Ruan <andyubryh@gmail.com>
---
 scripts/compare_p2p_tensors.py     | 90 ++++++++++++++++++++++++++++++
 scripts/run_p2p_tensor_workflow.sh | 29 ++++++++++
 src/piper_actor.py                 | 77 +++++++++++++++++++++++++
 test/test_p2p_tensor_compare.py    | 61 ++++++++++++++++++++
 4 files changed, 257 insertions(+)
 create mode 100755 scripts/compare_p2p_tensors.py
 create mode 100755 scripts/run_p2p_tensor_workflow.sh
 create mode 100644 test/test_p2p_tensor_compare.py

diff --git a/scripts/compare_p2p_tensors.py b/scripts/compare_p2p_tensors.py
new file mode 100755
index 0000000..4514df1
--- /dev/null
+++ b/scripts/compare_p2p_tensors.py
@@ -0,0 +1,90 @@
+#!/usr/bin/env python3
+import argparse
+import glob
+import os
+from collections import defaultdict
+
+import torch
+
+
+def _parse_key(meta: dict) -> tuple:
+    return (
+        meta["pass"],
+        meta["src_stage"],
+        meta["dst_stage"],
+        meta["mb_idx"],
+        meta["tensor_idx"],
+        meta.get("dp_rank", 0),
+    )
+
+
+def compare_logged_tensors(log_dir: str):
+    tensor_files = glob.glob(os.path.join(log_dir, "*.pt"))
+    if not tensor_files:
+        raise RuntimeError(f"No tensor logs found in {log_dir}")
+
+    sends = defaultdict(list)
+    recvs = defaultdict(list)
+
+    for path in tensor_files:
+        payload = torch.load(path, map_location="cpu")
+        meta = payload["meta"]
+        key = _parse_key(meta)
+        item = (path, payload["tensor"])
+        if meta["direction"] == "send":
+            sends[key].append(item)
+        elif meta["direction"] == "recv":
+            recvs[key].append(item)
+        else:
+            raise RuntimeError(f"Unknown direction {meta['direction']} for {path}")
+
+    errors = []
+    checked_pairs = 0
+
+    for key in sorted(set(sends.keys()) | set(recvs.keys())):
+        send_items = sends.get(key, [])
+        recv_items = recvs.get(key, [])
+        if len(send_items) != 1 or len(recv_items) != 1:
+            errors.append(
+                f"Key {key}: expected 1 send and 1 recv, got {len(send_items)} send / {len(recv_items)} recv"
+            )
+            continue
+
+        send_path, send_tensor = send_items[0]
+        recv_path, recv_tensor = recv_items[0]
+        checked_pairs += 1
+
+        if send_tensor.shape != recv_tensor.shape:
+            errors.append(
+                f"Key {key}: shape mismatch send {tuple(send_tensor.shape)} ({send_path}) recv {tuple(recv_tensor.shape)} ({recv_path})"
+            )
+            continue
+
+        diff = (send_tensor - recv_tensor).abs()
+        max_diff = float(diff.max().item()) if diff.numel() > 0 else 0.0
+        if not torch.equal(send_tensor, recv_tensor):
+            errors.append(
+                f"Key {key}: tensor mismatch max_diff={max_diff:.8e} send={send_path} recv={recv_path}"
+            )
+
+    return checked_pairs, errors
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Compare Piper P2P sent and received tensor logs.")
+    parser.add_argument("--log-dir", required=True, help="Directory containing *.pt P2P tensor logs.")
+    args = parser.parse_args()
+
+    checked_pairs, errors = compare_logged_tensors(args.log_dir)
+
+    if errors:
+        print("P2P tensor comparison failed:")
+        for err in errors:
+            print(f" - {err}")
+        raise SystemExit(1)
+
+    print(f"P2P tensor comparison passed. Checked {checked_pairs} send/recv pairs.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/run_p2p_tensor_workflow.sh b/scripts/run_p2p_tensor_workflow.sh
new file mode 100755
index 0000000..f8f655e
--- /dev/null
+++ b/scripts/run_p2p_tensor_workflow.sh
@@ -0,0 +1,29 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+WARMUP="${WARMUP:-1}"
+ITERS="${ITERS:-1}"
+MBS="${MBS:-4}"
+SCHEDULE="${SCHEDULE:-interleaved-1f1b}"
+MODEL="${MODEL:-debug}"
+LOG_DIR="${LOG_DIR:-./logs}"
+P2P_TENSOR_LOG_DIR="${P2P_TENSOR_LOG_DIR:-$LOG_DIR/p2p_tensors}"
+
+mkdir -p "$LOG_DIR" "$P2P_TENSOR_LOG_DIR"
+
+export PIPER_P2P_TENSOR_LOG_DIR="$P2P_TENSOR_LOG_DIR"
+
+RUN_LOG="$LOG_DIR/${SCHEDULE}_${MODEL}-memopt.log"
+
+echo "Running Piper with warmup=$WARMUP iters=$ITERS mbs=$MBS schedule=$SCHEDULE model=$MODEL"
+python3 -m test.test_llama \
+    --warmup "$WARMUP" \
+    --iters "$ITERS" \
+    --schedule "$SCHEDULE" \
+    --mbs "$MBS" \
+    --model "$MODEL" > "$RUN_LOG" 2>&1
+
+echo "Comparing send/recv P2P tensors in $P2P_TENSOR_LOG_DIR"
+python3 scripts/compare_p2p_tensors.py --log-dir "$P2P_TENSOR_LOG_DIR"
+
+echo "Workflow completed successfully"
diff --git a/src/piper_actor.py b/src/piper_actor.py
index aa363ce..45d0c96 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -157,6 +157,10 @@ def __init__(
         self.trace_events = dict()
         self.trace_data = defaultdict(list)
 
+        self.p2p_tensor_log_dir = os.environ.get("PIPER_P2P_TENSOR_LOG_DIR")
+        if self.p2p_tensor_log_dir:
+            os.makedirs(self.p2p_tensor_log_dir, exist_ok=True)
+
         from .piper_utils import piper_metadata
 
         piper_metadata.actor_self = self
@@ -505,6 +509,14 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int):
         )
 
         self.p2p_cache[p2p_op] = inputs_to_recv
+        self._log_p2p_tensors(
+            tensors=inputs_to_recv,
+            direction="recv",
+            pass_name="fwd",
+            src_stage=stage_id - 1,
+            dst_stage=stage_id,
+            mb_idx=mb_idx,
+        )
 
     def _exec_fwd_send(self, stage_id: int, mb_idx: int):
         if stage_id == self.num_stages - 1:
@@ -527,6 +539,14 @@ def _exec_fwd_send(self, stage_id: int, mb_idx: int):
         self.logger.debug(
             f"Completed fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
         )
+        self._log_p2p_tensors(
+            tensors=output,
+            direction="send",
+            pass_name="fwd",
+            src_stage=stage_id,
+            dst_stage=stage_id + 1,
+            mb_idx=mb_idx,
+        )
 
     def _exec_bwd_recv(self, stage_id: int, mb_idx: int):
         if stage_id >= self.num_stages - 1:
@@ -548,6 +568,14 @@ def _exec_bwd_recv(self, stage_id: int, mb_idx: int):
         self.logger.debug(
             f"Completed bwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id+1} -> {stage_id}, mb {mb_idx})"
         )
+        self._log_p2p_tensors(
+            tensors=[input_grad],
+            direction="recv",
+            pass_name="bwd",
+            src_stage=stage_id + 1,
+            dst_stage=stage_id,
+            mb_idx=mb_idx,
+        )
 
         out_activation.backward(gradient=input_grad)
 
@@ -571,9 +599,58 @@ def _exec_bwd_send(self, stage_id: int, mb_idx: int):
         self.logger.debug(
             f"Completed bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
         )
+        self._log_p2p_tensors(
+            tensors=[output_grad],
+            direction="send",
+            pass_name="bwd",
+            src_stage=stage_id,
+            dst_stage=stage_id - 1,
+            mb_idx=mb_idx,
+        )
 
         self.inp_activation[stage_id][mb_idx] = None
 
+    def _log_p2p_tensors(
+        self,
+        tensors,
+        direction: str,
+        pass_name: str,
+        src_stage: int,
+        dst_stage: int,
+        mb_idx: int,
+    ):
+        if not self.p2p_tensor_log_dir:
+            return
+
+        for tensor_idx, tensor in enumerate(tensors):
+            tensor_to_log = tensor.detach().cpu()
+            key = {
+                "pass": pass_name,
+                "src_stage": src_stage,
+                "dst_stage": dst_stage,
+                "mb_idx": mb_idx,
+                "tensor_idx": tensor_idx,
+                "dp_rank": self.dp_rank,
+            }
+            key_text = "__".join(f"{k}-{v}" for k, v in key.items())
+            output_path = os.path.join(
+                self.p2p_tensor_log_dir,
+                f"{direction}__{key_text}__actor-{self.global_rank}.pt",
+            )
+            torch.save(
+                {
+                    "meta": {
+                        **key,
+                        "direction": direction,
+                        "global_rank": self.global_rank,
+                        "shape": list(tensor_to_log.shape),
+                        "dtype": str(tensor_to_log.dtype),
+                    },
+                    "tensor": tensor_to_log,
+                },
+                output_path,
+            )
+
     def _forward(self, stage_id: int, mb_idx: int):
 
         self.logger.debug(
diff --git a/test/test_p2p_tensor_compare.py b/test/test_p2p_tensor_compare.py
new file mode 100644
index 0000000..53ae69e
--- /dev/null
+++ b/test/test_p2p_tensor_compare.py
@@ -0,0 +1,61 @@
+import tempfile
+import unittest
+from pathlib import Path
+
+import torch
+
+from scripts.compare_p2p_tensors import compare_logged_tensors
+
+
+class TestP2PTensorCompare(unittest.TestCase):
+    def _write_tensor_log(self, folder: Path, direction: str, key: dict, tensor: torch.Tensor):
+        payload = {
+            "meta": {**key, "direction": direction, "global_rank": 0},
+            "tensor": tensor,
+        }
+        file_name = f"{direction}__{key['pass']}__s{key['src_stage']}__d{key['dst_stage']}__mb{key['mb_idx']}__t{key['tensor_idx']}.pt"
+        torch.save(payload, folder / file_name)
+
+    def test_compare_logged_tensors_success(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            folder = Path(tmpdir)
+            key = {
+                "pass": "fwd",
+                "src_stage": 0,
+                "dst_stage": 1,
+                "mb_idx": 0,
+                "tensor_idx": 0,
+                "dp_rank": 0,
+            }
+            tensor = torch.tensor([1.0, 2.0, 3.0])
+            self._write_tensor_log(folder, "send", key, tensor)
+            self._write_tensor_log(folder, "recv", key, tensor.clone())
+
+            checked_pairs, errors = compare_logged_tensors(str(folder))
+
+            self.assertEqual(checked_pairs, 1)
+            self.assertEqual(errors, [])
+
+    def test_compare_logged_tensors_reports_max_diff(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            folder = Path(tmpdir)
+            key = {
+                "pass": "bwd",
+                "src_stage": 1,
+                "dst_stage": 0,
+                "mb_idx": 2,
+                "tensor_idx": 0,
+                "dp_rank": 0,
+            }
+            self._write_tensor_log(folder, "send", key, torch.tensor([1.0, 2.0]))
+            self._write_tensor_log(folder, "recv", key, torch.tensor([1.0, 2.5]))
+
+            checked_pairs, errors = compare_logged_tensors(str(folder))
+
+            self.assertEqual(checked_pairs, 1)
+            self.assertEqual(len(errors), 1)
+            self.assertIn("max_diff=5.00000000e-01", errors[0])
+
+
+if __name__ == "__main__":
+    unittest.main()

From 033f2410edf4f6bd5e13cc594b79968757c95094 Mon Sep 17 00:00:00 2001
From: Andy Ruan <andyubryh@gmail.com>
Date: Mon, 23 Feb 2026 09:46:54 +0000
Subject: [PATCH 044/100] fix: sync streams for p2p ops

Signed-off-by: Andy Ruan <andyubryh@gmail.com>
---
 logs/interleaved-1f1b_debug-memopt.log | 97 ++++++++++++++++++++++++++
 logs/piper_parity.log                  |  4 ++
 run_test.sh                            | 14 ++++
 src/piper_actor.py                     | 16 ++++-
 4 files changed, 130 insertions(+), 1 deletion(-)
 create mode 100644 logs/interleaved-1f1b_debug-memopt.log
 create mode 100644 logs/piper_parity.log
 create mode 100644 run_test.sh

diff --git a/logs/interleaved-1f1b_debug-memopt.log b/logs/interleaved-1f1b_debug-memopt.log
new file mode 100644
index 0000000..ae8b183
--- /dev/null
+++ b/logs/interleaved-1f1b_debug-memopt.log
@@ -0,0 +1,97 @@
+2026-02-23 09:44:58,398	INFO worker.py:2007 -- Started a local Ray instance.
+/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+  warnings.warn(
+(run_dp_rank pid=3315950) 2026-02-23 09:45:11,701 - piper_coordinator - DEBUG - Running DP rank 1 of 1
+(run_dp_rank pid=3315950) 2026-02-23 09:45:11,723 - piper_compile - DEBUG - mbs: 4, stages: 4, devices: 2
+(run_dp_rank pid=3315950) 2026-02-23 09:45:11,723 - piper_compile - DEBUG - Fwd/bwd schedule: [[Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=2, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=0, stage_id=0, mb_idx=3, is_fwd=False, upd=False)], [Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=0, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=1, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=True, upd=False), Task(pp_rank=1, stage_id=3, mb_idx=3, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=2, is_fwd=False, upd=False), Task(pp_rank=1, stage_id=1, mb_idx=3, is_fwd=False, upd=False)]]
+(run_dp_rank pid=3315950) 2026-02-23 09:45:11,723 - piper_compile - DEBUG - P2P comms: [(0, 1, 0), (0, 1, 1), (1, 2, 0), (2, 3, 0), (1, 2, 1), (2, 3, 1), (0, 1, 2), (3, 2, 0), (0, 1, 3), (2, 1, 0), (3, 2, 1), (2, 1, 1), (1, 2, 2), (2, 3, 2), (1, 0, 0), (1, 2, 3), (2, 3, 3), (1, 0, 1), (3, 2, 2), (2, 1, 2), (3, 2, 3), (2, 1, 3), (1, 0, 2), (1, 0, 3)]
+(run_dp_rank pid=3315950) 2026-02-23 09:45:11,723 - piper_compile - DEBUG - P2P schedules: {0: [(0, 1, 0, True), (0, 1, 1, True), (1, 2, 0, False), (2, 3, 0, True), (1, 2, 1, False), (2, 3, 1, True), (0, 1, 2, True), (3, 2, 0, False), (0, 1, 3, True), (2, 1, 0, True), (3, 2, 1, False), (2, 1, 1, True), (1, 2, 2, False), (2, 3, 2, True), (1, 0, 0, False), (1, 2, 3, False), (2, 3, 3, True), (1, 0, 1, False), (3, 2, 2, False), (2, 1, 2, True), (3, 2, 3, False), (2, 1, 3, True), (1, 0, 2, False), (1, 0, 3, False)], 1: [(0, 1, 0, False), (0, 1, 1, False), (1, 2, 0, True), (2, 3, 0, False), (1, 2, 1, True), (2, 3, 1, False), (0, 1, 2, False), (3, 2, 0, True), (0, 1, 3, False), (2, 1, 0, False), (3, 2, 1, True), (2, 1, 1, False), (1, 2, 2, True), (2, 3, 2, False), (1, 0, 0, True), (1, 2, 3, True), (2, 3, 3, False), (1, 0, 1, True), (3, 2, 2, True), (2, 1, 2, False), (3, 2, 3, True), (2, 1, 3, False), (1, 0, 2, True), (1, 0, 3, True)]}
+(run_dp_rank pid=3315950) 2026-02-23 09:45:11,796 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, 9c128c7475d130471175682701000000) global rank 0
+(run_dp_rank pid=3315950) 2026-02-23 09:45:11,797 - piper_actor - DEBUG - DP rank 0 created actor Actor(PiperActor, 81aafcede4df004b4c5ae53001000000) global rank 1
+(PiperActor pid=3315991) 2026-02-23 09:45:14,600 - piper_actor - INFO - Initializing Ray actor 0 global rank 0 GPU 0
+(PiperActor pid=3315945) 2026-02-23 09:45:16,226 - piper_actor - DEBUG - Actor 1 has GPU 1, joined the global process group
+(PiperActor pid=3315945) 2026-02-23 09:45:16,227 - piper_actor - INFO - Global rank 1 joined its pp group 0 along with ranks [0, 1]
+(run_dp_rank pid=3315950) 2026-02-23 09:45:16,443 - piper_compile - INFO - DP rank 1 compiling...
+(PiperActor pid=3315991) 2026-02-23 09:45:18,948 - piper_actor - INFO - Loading stage 0 graph on actor 0
+(PiperActor pid=3315945) 2026-02-23 09:45:14,806 - piper_actor - INFO - Initializing Ray actor 1 global rank 1 GPU 1
+(run_dp_rank pid=3315950) 2026-02-23 09:45:20,627 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=3315950) 2026-02-23 09:45:27,130 - piper_compile - INFO - DP rank 1 done.
+(PiperActor pid=3315991) 2026-02-23 09:45:16,226 - piper_actor - DEBUG - Actor 0 has GPU 0, joined the global process group
+(PiperActor pid=3315991) 2026-02-23 09:45:16,227 - piper_actor - INFO - Global rank 0 joined its pp group 0 along with ranks [0, 1]
+(PiperActor pid=3315945) 2026-02-23 09:45:20,488 - piper_actor - INFO - Loading stage 3 graph on actor 1 [repeated 3x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.)
+(PiperActor pid=3315945) 2026-02-23 09:45:27,162 - piper_actor - DEBUG - Actor 1 loaded labels torch.Size([16, 256, 512])
+(PiperActor pid=3315945) 2026-02-23 09:45:27,168 - piper_actor - DEBUG - Calling forward 1 mb 0 on actor 1
+(PiperActor pid=3315945) 2026-02-23 09:45:27,169 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 0 -> 1, mb 0, is_sender=False
+(PiperActor pid=3315945) 2026-02-23 09:45:27,169 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (0 -> 1, mb 0)
+(PiperActor pid=3315945) [rank1]:[W223 09:45:27.651186640 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=3315991) 2026-02-23 09:45:27,151 - piper_actor - DEBUG - Actor 0 loaded inputs 1
+(PiperActor pid=3315991) 2026-02-23 09:45:27,505 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 0
+(PiperActor pid=3315991) 2026-02-23 09:45:27,505 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 0 -> 1, mb 0, is_sender=True
+(PiperActor pid=3315991) 2026-02-23 09:45:27,505 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 0)
+(PiperActor pid=3315945) 2026-02-23 09:45:30,155 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 0)
+(PiperActor pid=3315945) 2026-02-23 09:45:30,164 - piper_actor - DEBUG - Executed P2P(fwd_recv, stage 0 -> 1, mb 0) on actor 1
+(PiperActor pid=3315945) 2026-02-23 09:45:30,164 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 0
+(PiperActor pid=3315991) 2026-02-23 09:45:30,149 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 0)
+(PiperActor pid=3315991) 2026-02-23 09:45:30,161 - piper_actor - DEBUG - Forward 0 mb 0 on actor 0 returning [torch.Size([16, 256, 512]), torch.Size([256, 256]), torch.Size([256, 8])]
+(PiperActor pid=3315991) 2026-02-23 09:45:30,171 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 0 mb 1
+(PiperActor pid=3315991) 2026-02-23 09:45:30,171 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 0 -> 1, mb 1, is_sender=True
+(PiperActor pid=3315991) 2026-02-23 09:45:30,171 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (0 -> 1, mb 1)
+(PiperActor pid=3315991) 2026-02-23 09:45:30,171 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (0 -> 1, mb 1)
+(PiperActor pid=3315991) 2026-02-23 09:45:30,182 - piper_actor - DEBUG - Forward 0 mb 1 on actor 0 returning [torch.Size([16, 256, 512]), torch.Size([256, 256]), torch.Size([256, 8])]
+(PiperActor pid=3315945) 2026-02-23 09:45:30,455 - piper_actor - DEBUG - Completed fwd p2p recv on 1 from 0, op: (0 -> 1, mb 1)
+(PiperActor pid=3315945) 2026-02-23 09:45:32,171 - piper_actor - DEBUG - P2P op (0, 1, 1, False) already executed on actor 1, skipping
+(PiperActor pid=3315945) 2026-02-23 09:45:32,171 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 1 mb 1
+(PiperActor pid=3315945) 2026-02-23 09:45:32,171 - piper_actor - DEBUG - Calling forward 1 mb 1 on actor 1 [repeated 4x across cluster]
+(PiperActor pid=3315945) 2026-02-23 09:45:32,171 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 0 -> 1, mb 1, is_sender=False [repeated 2x across cluster]
+(PiperActor pid=3315945) 2026-02-23 09:45:32,180 - piper_actor - DEBUG - Dispatch fwd p2p recv on 1 from 0, op: (2 -> 3, mb 0) [repeated 3x across cluster]
+(PiperActor pid=3315945) [rank1]:[W223 09:45:30.952091798 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator()) [repeated 3x across cluster]
+(PiperActor pid=3315945) 2026-02-23 09:45:32,208 - piper_actor - DEBUG - P2P op (2, 3, 0, False) already executed on actor 1, skipping
+(PiperActor pid=3315945) 2026-02-23 09:45:32,262 - piper_actor - DEBUG - Calling backward 3 mb 0 on actor 1
+(PiperActor pid=3315945) 2026-02-23 09:45:32,387 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 0)
+(PiperActor pid=3315945) 2026-02-23 09:45:32,387 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 0)
+(PiperActor pid=3315945) 2026-02-23 09:45:32,395 - piper_actor - DEBUG - P2P op (2, 3, 1, False) already executed on actor 1, skipping
+(PiperActor pid=3315945) 2026-02-23 09:45:32,399 - piper_actor - DEBUG - Calling backward 3 mb 1 on actor 1
+(PiperActor pid=3315945) 2026-02-23 09:45:32,457 - piper_actor - DEBUG - Dispatch bwd p2p recv on 1 from 0, op: (2 -> 1, mb 0)
+(PiperActor pid=3315945) 2026-02-23 09:45:32,458 - piper_actor - DEBUG - Completed bwd p2p recv on 1 from 0, op: (2 -> 1, mb 0)
+(PiperActor pid=3315945) 2026-02-23 09:45:32,482 - piper_actor - DEBUG - Dispatch bwd p2p send on 1 to 0, op: (3 -> 2, mb 1)
+(PiperActor pid=3315945) 2026-02-23 09:45:32,483 - piper_actor - DEBUG - Completed bwd p2p send on 1 to 0, op: (3 -> 2, mb 1)
+(PiperActor pid=3315945) 2026-02-23 09:45:32,497 - piper_actor - DEBUG - P2P op (0, 1, 2, False) already executed on actor 1, skipping
+(PiperActor pid=3315991) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
+(PiperActor pid=3315991)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+(PiperActor pid=3315945) 2026-02-23 09:45:32,757 - piper_actor - DEBUG - Actor 1 waiting for backward sync events
+(PiperActor pid=3315945) 2026-02-23 09:45:32,758 - piper_actor - DEBUG - Actor 1 waiting for p2p ops
+(PiperActor pid=3315945) 2026-02-23 09:45:32,699 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3 [repeated 13x across cluster]
+(PiperActor pid=3315945) 2026-02-23 09:45:32,750 - piper_actor - DEBUG - [DEBUG] Actor 1 executing p2p op: 1 -> 0, mb 3, is_sender=True [repeated 19x across cluster]
+(PiperActor pid=3315945) 2026-02-23 09:45:32,562 - piper_actor - DEBUG - Dispatch fwd p2p send on 1 to 0, op: (1 -> 2, mb 3) [repeated 9x across cluster]
+(PiperActor pid=3315945) 2026-02-23 09:45:32,845 - piper_actor - INFO - Actor 1: Tracing disabled
+(PiperActor pid=3315945) [rank1]:[W223 09:45:34.272024998 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+(PiperActor pid=3315991) 2026-02-23 09:45:33,058 - piper_actor - DEBUG - Completed fwd p2p recv on 0 from 1, op: (1 -> 2, mb 3) [repeated 22x across cluster]
+(PiperActor pid=3315991) 2026-02-23 09:45:33,179 - piper_actor - DEBUG - Executed P2P(bwd_recv, stage 1 -> 0, mb 3) on actor 0 [repeated 95x across cluster]
+(PiperActor pid=3315945) 2026-02-23 09:45:33,108 - piper_actor - DEBUG - Saving input activation torch.Size([16, 256, 512]) for stage 3 mb 3 [repeated 22x across cluster]
+(PiperActor pid=3315991) 2026-02-23 09:45:33,069 - piper_actor - DEBUG - Completed fwd p2p send on 0 to 1, op: (2 -> 3, mb 3) [repeated 22x across cluster]
+(PiperActor pid=3315945) 2026-02-23 09:45:33,111 - piper_actor - DEBUG - Forward 3 mb 3 on actor 1 returning [torch.Size([16, 256, 512])] [repeated 30x across cluster]
+(PiperActor pid=3315945) 2026-02-23 09:45:33,163 - piper_actor - DEBUG - P2P op (2, 1, 3, False) already executed on actor 1, skipping [repeated 20x across cluster]
+(PiperActor pid=3315945) 2026-02-23 09:45:33,108 - piper_actor - DEBUG - Calling forward 3 mb 3 on actor 1 [repeated 27x across cluster]
+(PiperActor pid=3315991) 2026-02-23 09:45:33,168 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 1 -> 0, mb 3, is_sender=False [repeated 45x across cluster]
+(PiperActor pid=3315991) 2026-02-23 09:45:33,057 - piper_actor - DEBUG - Dispatch fwd p2p recv on 0 from 1, op: (1 -> 2, mb 3) [repeated 20x across cluster]
+(PiperActor pid=3315991) 2026-02-23 09:45:33,168 - piper_actor - DEBUG - Calling backward 0 mb 3 on actor 0 [repeated 30x across cluster]
+(PiperActor pid=3315991) 2026-02-23 09:45:33,141 - piper_actor - DEBUG - Dispatch bwd p2p send on 0 to 1, op: (2 -> 1, mb 3) [repeated 22x across cluster]
+(PiperActor pid=3315991) 2026-02-23 09:45:33,142 - piper_actor - DEBUG - Completed bwd p2p send on 0 to 1, op: (2 -> 1, mb 3) [repeated 22x across cluster]
+(PiperActor pid=3315991) 2026-02-23 09:45:33,168 - piper_actor - DEBUG - Dispatch bwd p2p recv on 0 from 1, op: (1 -> 0, mb 3) [repeated 23x across cluster]
+(PiperActor pid=3315991) 2026-02-23 09:45:33,169 - piper_actor - DEBUG - Completed bwd p2p recv on 0 from 1, op: (1 -> 0, mb 3) [repeated 23x across cluster]
+(PiperActor pid=3315991) 2026-02-23 09:45:33,180 - piper_actor - DEBUG - Actor 0 waiting for backward sync events [repeated 3x across cluster]
+(PiperActor pid=3315991) 2026-02-23 09:45:33,181 - piper_actor - DEBUG - Actor 0 waiting for p2p ops [repeated 3x across cluster]
+(PiperActor pid=3315945) 2026-02-23 09:45:33,111 - piper_actor - DEBUG - Saving output activation torch.Size([16, 256, 512]) for stage 3 mb 3 [repeated 17x across cluster]
+(PiperActor pid=3315991) 2026-02-23 09:45:33,141 - piper_actor - DEBUG - [DEBUG] Actor 0 executing p2p op: 2 -> 1, mb 3, is_sender=True [repeated 27x across cluster]
+(PiperActor pid=3315991) 2026-02-23 09:45:33,069 - piper_actor - DEBUG - Dispatch fwd p2p send on 0 to 1, op: (2 -> 3, mb 3) [repeated 13x across cluster]
+(PiperActor pid=3315991) 2026-02-23 09:45:32,845 - piper_actor - INFO - Actor 0: Tracing disabled
+(PiperActor pid=3315991) [rank0]:[W223 09:45:35.605370703 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+(run_dp_rank pid=3315950) Namespace(model='debug', schedule='interleaved-1f1b', dp=1, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=1, iters=1, tracing=False, naive_gradient_sync=False)
+(run_dp_rank pid=3315950) Schedule:
+(run_dp_rank pid=3315950) 0:0:f	0:1:f	2:0:f	2:1:f	0:2:f	0:3:f	2:0:b	 -- 	2:1:b	2:2:f	0:0:b	2:3:f	0:1:b	 -- 	2:2:b	2:3:b	0:2:b	0:3:b	0:0:u	
+(run_dp_rank pid=3315950)  -- 	1:0:f	1:1:f	3:0:f	3:0:b	3:1:f	3:1:b	1:2:f	1:0:b	1:3:f	1:1:b	3:2:f	3:2:b	3:3:f	3:3:b	1:2:b	1:3:b	1:0:u	 -- 	
+(run_dp_rank pid=3315950) Running 1 warmup iterations...
+(run_dp_rank pid=3315950) Running 1 timed iterations...
+(run_dp_rank pid=3315950) rank 0 iter time= 0.35 ± 0.00 s (1 samples)
+(run_dp_rank pid=3315950) rank 0 throughput= 46453.47 tokens/sec (1 samples)
+(run_dp_rank pid=3315950) Ray timeline saved to: out/debug-pp2-dp1-interleaved-1f1b.json
diff --git a/logs/piper_parity.log b/logs/piper_parity.log
new file mode 100644
index 0000000..a084bca
--- /dev/null
+++ b/logs/piper_parity.log
@@ -0,0 +1,4 @@
+Running Piper with warmup=1 iters=1 mbs=4 schedule=interleaved-1f1b model=debug
+Comparing send/recv P2P tensors in ./logs/p2p_tensors
+P2P tensor comparison passed. Checked 32 send/recv pairs.
+Workflow completed successfully
diff --git a/run_test.sh b/run_test.sh
new file mode 100644
index 0000000..9a7ded3
--- /dev/null
+++ b/run_test.sh
@@ -0,0 +1,14 @@
+#!/bin/bash
+LOG_DIR=logs
+mkdir -p $LOG_DIR
+# RAY_TMP=$HOME/ray_tmp
+RAY_TMP=/m-coriander/coriander/tmp/ray
+mkdir -p $RAY_TMP
+export RAY_TMPDIR="$RAY_TMP"
+export PYTHONUNBUFFERED=1
+
+export CUDA_VISIBLE_DEVICES=5,7
+export RAY_DEDUP_LOGS=0
+export PIPER_MASTER_PORT=12389
+
+python3 -m test.test_llama_piper_parity > $LOG_DIR/piper_parity.log 2>&1
\ No newline at end of file
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 45d0c96..a924541 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -49,7 +49,9 @@ def _create_actors(
         else:
             max_concurrency = 1
         p2p_schedule = p2p_schedules[pp_rank]
-        actor = PiperActor.options(num_gpus=1, max_concurrency=max_concurrency).remote(
+        actor = PiperActor.options(
+            num_gpus=0.8, max_concurrency=max_concurrency
+        ).remote(
             pp_rank,
             optim_class,
             world_size,
@@ -503,6 +505,8 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int):
                     src=global_src_rank,
                     group=self.pp_group,
                 )
+        # Ensure the default stream only consumes tensors after recv completes.
+        torch.cuda.current_stream().wait_stream(self.p2p_stream)
         self._stop_timing(self.p2p_stream, "fwd_p2p_recv")
         self.logger.debug(
             f"Completed fwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
@@ -531,11 +535,15 @@ def _exec_fwd_send(self, stage_id: int, mb_idx: int):
         self.logger.debug(
             f"Dispatch fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
         )
+        # Ensure send sees the latest writes from the default stream.
+        self.p2p_stream.wait_stream(torch.cuda.current_stream())
         self._start_timing(self.p2p_stream, "fwd_p2p_send")
         with torch.cuda.stream(self.p2p_stream):
             for i in range(len(output)):
                 dist.send(output[i], dst=global_dst_rank, group=self.pp_group)
         self._stop_timing(self.p2p_stream, "fwd_p2p_send")
+        # Ensure the default stream does not reuse/mutate buffers before send completes.
+        torch.cuda.current_stream().wait_stream(self.p2p_stream)
         self.logger.debug(
             f"Completed fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
         )
@@ -564,6 +572,8 @@ def _exec_bwd_recv(self, stage_id: int, mb_idx: int):
         self._start_timing(self.p2p_stream, "bwd_p2p_recv")
         with torch.cuda.stream(self.p2p_stream):
             dist.recv(input_grad, src=global_src_rank, group=self.pp_group)
+        # Ensure the default stream only consumes gradients after recv completes.
+        torch.cuda.current_stream().wait_stream(self.p2p_stream)
         self._stop_timing(self.p2p_stream, "bwd_p2p_recv")
         self.logger.debug(
             f"Completed bwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id+1} -> {stage_id}, mb {mb_idx})"
@@ -592,9 +602,13 @@ def _exec_bwd_send(self, stage_id: int, mb_idx: int):
         self.logger.debug(
             f"Dispatch bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
         )
+        # Ensure send sees the latest gradient writes from the default stream.
+        self.p2p_stream.wait_stream(torch.cuda.current_stream())
         self._start_timing(self.p2p_stream, "bwd_p2p_send")
         with torch.cuda.stream(self.p2p_stream):
             dist.send(output_grad, dst=global_src_rank, group=self.pp_group)
+        # Ensure the default stream does not reuse/mutate buffers before send completes.
+        torch.cuda.current_stream().wait_stream(self.p2p_stream)
         self._stop_timing(self.p2p_stream, "bwd_p2p_send")
         self.logger.debug(
             f"Completed bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"

From d3018b1b855eb750cd2fb84f42997d230e3a9ea9 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Mon, 23 Feb 2026 20:15:38 +0000
Subject: [PATCH 045/100] Add DualPipeV schedule, FWD_BWD tasks run
 sequentially

---
 src/piper_actor.py       |  86 +++--
 src/piper_compile.py     | 127 +++++--
 src/piper_exec.py        | 354 ++++++++++++------
 test/models/llama.py     |  78 ++--
 test/print_schedules.py  |  28 +-
 test/schedule_helpers.py | 762 ++++++++++++++++++++++++---------------
 test/test_llama.py       |  43 +--
 7 files changed, 934 insertions(+), 544 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index 92966a0..8f66e50 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -137,7 +137,6 @@ def __init__(
         self.inp_activation = defaultdict(dict)
         # map stage id -> mb_idx -> current activation
         self.out_activation = defaultdict(dict)
-        self.bwd_recv_buf = None
         # accumuate loss for each microbatch
         self.loss = []
         # map stage id -> data parallel communication operations
@@ -149,6 +148,8 @@ def __init__(
         # map stage id -> list of tensor ids that require communciation
         self.comm_op_tensor_ids = dict()
 
+        self.p2p_self_buf = None
+
         self.tracing = False
         self.trace_events = dict()
         self.trace_data = defaultdict(list)
@@ -424,15 +425,19 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int):
         self.logger.debug(
             f"Dispatch fwd p2p recv on {self.global_rank} from {global_src_rank}"
         )
-        self._start_timing(self.p2p_stream, "fwd_p2p_recv")
-        with torch.cuda.stream(self.p2p_stream):
+        if self.global_rank == global_src_rank:
             for i in self.input_idxs[stage_id]:
-                dist.recv(
-                    self.forward_args[stage_id][i],
-                    src=global_src_rank,
-                    group=self.pp_group,
-                )
-        self._stop_timing(self.p2p_stream, "fwd_p2p_recv")
+                self.forward_args[stage_id][i] = self.p2p_self_buf[i]
+        else:
+            self._start_timing(self.p2p_stream, "fwd_p2p_recv")
+            with torch.cuda.stream(self.p2p_stream):
+                for i in self.input_idxs[stage_id]:
+                    dist.recv(
+                        self.forward_args[stage_id][i],
+                        src=global_src_rank,
+                        group=self.pp_group,
+                    )
+            self._stop_timing(self.p2p_stream, "fwd_p2p_recv")
         self.logger.debug(
             f"Completed fwd p2p recv on {self.global_rank} from {global_src_rank}"
         )
@@ -448,11 +453,14 @@ def _exec_fwd_send(self, stage_id: int, mb_idx: int, output):
         self.logger.debug(
             f"Dispatch fwd p2p send on {self.global_rank} to {global_dst_rank}"
         )
-        self._start_timing(self.p2p_stream, "fwd_p2p_send")
-        with torch.cuda.stream(self.p2p_stream):
-            for i in range(len(output)):
-                dist.send(output[i], dst=global_dst_rank, group=self.pp_group)
-        self._stop_timing(self.p2p_stream, "fwd_p2p_send")
+        if self.global_rank == global_dst_rank:
+            self.p2p_self_buf = output
+        else:
+            self._start_timing(self.p2p_stream, "fwd_p2p_send")
+            with torch.cuda.stream(self.p2p_stream):
+                for i in range(len(output)):
+                    dist.send(output[i], dst=global_dst_rank, group=self.pp_group)
+            self._stop_timing(self.p2p_stream, "fwd_p2p_send")
         self.logger.debug(
             f"Completed fwd p2p send on {self.global_rank} to {global_dst_rank}"
         )
@@ -470,10 +478,13 @@ def _exec_bwd_recv(self, stage_id: int, mb_idx: int):
         self.logger.debug(
             f"Dispatch bwd p2p recv on {self.global_rank} from {global_src_rank}"
         )
-        self._start_timing(self.p2p_stream, "bwd_p2p_recv")
-        with torch.cuda.stream(self.p2p_stream):
-            dist.recv(input_grad, src=global_src_rank, group=self.pp_group)
-        self._stop_timing(self.p2p_stream, "bwd_p2p_recv")
+        if self.global_rank == global_src_rank:
+            input_grad = self.p2p_self_buf
+        else:
+            self._start_timing(self.p2p_stream, "bwd_p2p_recv")
+            with torch.cuda.stream(self.p2p_stream):
+                dist.recv(input_grad, src=global_src_rank, group=self.pp_group)
+            self._stop_timing(self.p2p_stream, "bwd_p2p_recv")
         self.logger.debug(
             f"Completed bwd p2p recv on {self.global_rank} from {global_src_rank}"
         )
@@ -489,24 +500,29 @@ def _exec_bwd_send(self, stage_id: int, mb_idx: int):
 
         # For non-first stages, send output gradients to the previous backward stage
         output_grad = self.inp_activation[stage_id][mb_idx].grad
-        assert output_grad is not None
+        if output_grad is None:
+            self.logger.warning(f"No output gradient found for stage {stage_id} mb {mb_idx} on actor {self.global_rank}")
+            assert False
         pp_rank = piper_metadata.stage_to_device[stage_id - 1]
         global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
         self.logger.debug(
             f"Dispatch bwd p2p send on {self.global_rank} to {global_src_rank}"
         )
-        self._start_timing(self.p2p_stream, "bwd_p2p_send")
-        with torch.cuda.stream(self.p2p_stream):
-            dist.send(output_grad, dst=global_src_rank, group=self.pp_group)
-        self._stop_timing(self.p2p_stream, "bwd_p2p_send")
+        if self.global_rank == global_src_rank:
+            self.p2p_self_buf = output_grad
+        else:
+            self._start_timing(self.p2p_stream, "bwd_p2p_send")
+            with torch.cuda.stream(self.p2p_stream):
+                dist.send(output_grad, dst=global_src_rank, group=self.pp_group)
+            self._stop_timing(self.p2p_stream, "bwd_p2p_send")
         self.logger.debug(
             f"Completed bwd p2p send on {self.global_rank} to {global_src_rank}"
         )
 
         self.inp_activation[stage_id][mb_idx] = None
 
-    def _forward(self, stage_id: int, mb_idx: int):
+    def _forward(self, stage_id: int, mb_idx: int, dep):
 
         self.logger.debug(
             f"Calling forward {stage_id} mb {mb_idx} on actor {self.global_rank}"
@@ -519,6 +535,12 @@ def _forward(self, stage_id: int, mb_idx: int):
         else:
             self._exec_p2p_op(stage_id - 1, stage_id, mb_idx, False)
 
+            # Detach the computation graph for cases where two consecutive stages are on the same actor
+            if stage_id > 0 and piper_metadata.stage_to_device[stage_id] == piper_metadata.stage_to_device[stage_id - 1]:
+                for i in self.input_idxs[stage_id]:
+                    if self.forward_args[stage_id][i].requires_grad:
+                        self.forward_args[stage_id][i] = self.forward_args[stage_id][i].detach().requires_grad_(True)
+
             # save first input that requires grad as input activation
             inp_with_grad = [
                 self.forward_args[stage_id][i]
@@ -568,18 +590,21 @@ def _forward(self, stage_id: int, mb_idx: int):
         )
         torch.cuda.synchronize()
 
-    def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
+        return 1
+
+    def _backward(self, stage_id: int, mb_idx: int, dep, loss_fn=None):
 
         self.logger.debug(
             f"Calling backward {stage_id} mb {mb_idx} on actor {self.global_rank}"
         )
 
+        if mb_idx not in self.out_activation[stage_id]:
+            self.logger.warning(f"No output activation found for stage {stage_id} mb {mb_idx} on actor {self.global_rank}")
         out_activation = self.out_activation[stage_id][mb_idx]
 
         if stage_id < self.num_stages - 1:
             self._exec_p2p_op(stage_id + 1, stage_id, mb_idx, False)
         else:
-            # For the final stage, wait for the forward pass to complete
             assert loss_fn is not None
             labels = self.labels
             assert out_activation.shape == labels.shape
@@ -606,6 +631,13 @@ def _backward(self, stage_id: int, mb_idx: int, loss_fn=None):
 
         torch.cuda.synchronize()
 
+        return 1
+
+    def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: int, bwd_mb_idx: int, dep, loss_fn=None):
+        self._forward(fwd_stage_id, fwd_mb_idx, dep)
+        self._backward(bwd_stage_id, bwd_mb_idx, dep, loss_fn=loss_fn)
+        return 1
+
     def _synchronize_gradients(self):
         self.logger.info(f"Actor {self.global_rank} synchronizing gradients")
         # Iterate over all stages on this actor and synchronize their parameters
@@ -617,7 +649,7 @@ def _synchronize_gradients(self):
                             param.grad, op=dist.ReduceOp.AVG, group=self.dp_group
                         )
 
-    def _update(self):
+    def _update(self, dep):
         self.logger.debug(f"Actor {self.global_rank} waiting for backward sync events")
 
         # if dp degree > 1, make sure all gradients are synchronized before optimizer step
diff --git a/src/piper_compile.py b/src/piper_compile.py
index c86c6a8..8396561 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -4,68 +4,137 @@
 import os
 import gc
 import copy
+import itertools
 
 from torch._dynamo.backends.debugging import eager
 
 from .piper_actor import _create_actors
 from .piper_utils import piper_metadata, create_logger, LOG_LEVEL
-from .piper_exec import CompType, Schedule, _topological_order
+from .piper_exec import CompType, Schedule2D, Task
 from .piper import piper
 
 logger = create_logger("piper_compile", LOG_LEVEL)
 
 
-def order_p2p_comms_from_dag(
-    schedule: Schedule,
-    num_devices: int,
-    num_stages: int,
-    stage_to_device: dict[int, int],
-) -> dict[int, list[tuple[int, int, int, bool]]]:
-    """Build P2P communication schedule from DAG in topological order."""
-    order = _topological_order(schedule)
-    p2p_comms: list[tuple[int, int, int]] = []
-    for ti in order:
-        task = schedule.tasks[ti]
-        for mb in task.microbatches:
-            if mb.comp_type == CompType.FWD and mb.stage_id < num_stages - 1:
-                p2p_comms.append((mb.stage_id, mb.stage_id + 1, mb.mb_idx))
-            elif mb.comp_type == CompType.BWD and mb.stage_id > 0:
-                p2p_comms.append((mb.stage_id, mb.stage_id - 1, mb.mb_idx))
+def _expand_task(task: Task | None) -> list[Task]:
+    if task is None or task.type == CompType.UPD:
+        return []
+    if task.type != CompType.FWD_BWD:
+        return [task]
+    return [
+        Task(pp_rank=task.pp_rank, batches=[task.batches[0]], type=CompType.FWD),
+        Task(pp_rank=task.pp_rank, batches=[task.batches[1]], type=CompType.BWD),
+    ]
+
+
+def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, stage_to_device: dict[int, int]):
+    schedule = schedule.grid
+    # Filter to fwd/bwd tasks and split FWD_BWD into two consecutive tasks (FWD then BWD).
+    schedule = [
+        list(itertools.chain.from_iterable(_expand_task(task) for task in row))
+        for row in schedule
+    ]
+
+    logger.debug(f"Fwd/bwd schedule: {schedule}")
+    assert num_devices == len(schedule), "Some rank has no fwd/bwd tasks"
+    # Tasks are guaranteed to be fwd or bwd
+    num_fwd_bwd_tasks = sum(len(rank_tasks) for rank_tasks in schedule)
+
+    num_executed_tasks = 0
+    # (src_stage, dst_stage, mb_idx) for each p2p comm in execution order
+    # is_fwd if src_stage < dst_stage else bwd
+    p2p_comms = []
+    rank_to_next_task = {rank: 0 for rank in range(len(schedule))}
+    # P2P comm is enqueued when sender is ready
+    # Pick the next executable task in a round-robin fashion
+    while True:
+        executable_tasks = []
+        for rank, time_step in rank_to_next_task.items():
+            if time_step < len(schedule[rank]):
+                task = schedule[rank][time_step]
+                assert len(task.batches) == 1, "FWD_BWD tasks should be expanded into two consecutive tasks"
+                batch = task.batches[0]
+                # Check the task's dependency is satisfied
+                # i.e., the sender has enqueued the comm
+                dependee = None
+                if task.type == CompType.FWD and batch.stage_id > 0:
+                    dependee = (batch.stage_id - 1, batch.stage_id, batch.mb_idx)
+                elif task.type == CompType.BWD and batch.stage_id < num_stages - 1:
+                    dependee = (batch.stage_id + 1, batch.stage_id, batch.mb_idx)
+
+                can_exec = dependee is None or dependee in p2p_comms
+                if can_exec:
+                    executable_tasks.append((rank, task))
+
+        if not executable_tasks:
+            break
+
+        for rank, task in executable_tasks:
+            assert len(task.batches) == 1, "FWD_BWD tasks should be expanded into two consecutive tasks"
+            batch = task.batches[0]
+            if task.type == CompType.FWD and batch.stage_id < num_stages - 1:
+                p2p_comms.append((batch.stage_id, batch.stage_id + 1, batch.mb_idx))
+            elif task.type == CompType.BWD and batch.stage_id > 0:
+                p2p_comms.append((batch.stage_id, batch.stage_id - 1, batch.mb_idx))
+            rank_to_next_task[rank] += 1
+
+        num_executed_tasks += len(executable_tasks)
+
+    if num_executed_tasks != num_fwd_bwd_tasks:
+        raise ValueError(
+            f"Only {num_executed_tasks} out of {num_fwd_bwd_tasks} "
+            f"fwd/bwd tasks can be executed"
+        )
+
     logger.debug(f"P2P comms: {p2p_comms}")
+    # (src_stage, dst_stage, mb_idx, is_sender)
     p2p_schedules = {rank: [] for rank in range(num_devices)}
     for src_stage, dst_stage, mb_idx in p2p_comms:
         src_rank = stage_to_device[src_stage]
         dst_rank = stage_to_device[dst_stage]
         p2p_schedules[src_rank].append((src_stage, dst_stage, mb_idx, True))
         p2p_schedules[dst_rank].append((src_stage, dst_stage, mb_idx, False))
+    logger.debug(f"P2P schedules: {p2p_schedules}")
+
     return p2p_schedules
 
 
+
 def piper_setup(
     model,
     optim_fn,
     example_inputs,
-    schedule: Schedule,
+    example_outputs,
+    schedule: Schedule2D,
     naive_gradient_sync=False,
 ):
     """
     Compile a model with the piper backend.
-    schedule: Schedule DAG (tasks and edges).
+    schedule: 2D schedule grid (rank x time_step).
     """
+
+    stage_to_device = schedule.stage_to_device()
+    piper_metadata.stage_to_device = stage_to_device
+    print(f"stage_to_device: {stage_to_device}")
+
     num_mbs = schedule.num_mbs()
     num_stages = schedule.num_stages()
-    stage_to_device = {}
-    for task in schedule.tasks:
-        for mb in task.microbatches:
-            stage_to_device[mb.stage_id] = task.pp_rank
-    num_devices = max(stage_to_device.values()) + 1 if stage_to_device else 0
-    piper_metadata.stage_to_device = stage_to_device
-    p2p_schedules = order_p2p_comms_from_dag(
-        schedule, num_devices, num_stages, stage_to_device
+    num_devices = schedule.num_ranks()
+
+    p2p_schedules = order_p2p_comms(
+        schedule=schedule,
+        num_devices=num_devices,
+        num_stages=num_stages,
+        stage_to_device=stage_to_device
     )
     _create_actors(
         num_devices, optim_fn, num_mbs, num_stages, p2p_schedules, naive_gradient_sync
     )
+
+    last_stage_rank = stage_to_device[num_stages - 1]
+    ray.get(piper_metadata.actors[0].load_input.remote(example_inputs))
+    ray.get(piper_metadata.actors[last_stage_rank].load_labels.remote(example_outputs))
+
     ray.get(
         [
             actor._join_process_groups.remote()
@@ -87,3 +156,7 @@ def piper_setup(
     gc.collect()
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
+    
+
+def piper_shutdown():
+    ray.get([actor.shutdown.remote() for actor in piper_metadata.actors.values()])
diff --git a/src/piper_exec.py b/src/piper_exec.py
index 14fa990..4c4548b 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -8,6 +8,7 @@
 import threading
 import time
 from collections import defaultdict
+import itertools
 
 from .piper_utils import piper_metadata, create_logger, LOG_LEVEL
 
@@ -20,17 +21,29 @@ class CompType(Enum):
     FWD_BWD = "forward_backward"
 
 
-class MicrobatchMetadata(NamedTuple):
+class BatchMeta(NamedTuple):
     """Metadata for one microbatch executed as part of a task."""
     stage_id: int
     mb_idx: int
-    comp_type: CompType
 
 
 class ScheduleTask(NamedTuple):
-    """A node in the schedule DAG: runs on pp_rank with one or more microbatches."""
+    """A node in the schedule DAG: runs on pp_rank with one microbatch."""
+
     pp_rank: int
-    microbatches: tuple[MicrobatchMetadata, ...]
+    microbatch: BatchMeta
+
+    def __repr__(self) -> str:
+        mb = self.microbatch
+        if mb.comp_type is None:
+            c = "?"
+        elif mb.comp_type == CompType.FWD:
+            c = "f"
+        elif mb.comp_type == CompType.BWD:
+            c = "b"
+        else:
+            c = "u"
+        return f"rank{self.pp_rank}_stage{mb.stage_id}_mb{mb.mb_idx}_{c}"
 
 
 class ScheduleEdge(NamedTuple):
@@ -53,28 +66,55 @@ class Schedule:
     temporal_edges: list[TemporalEdge] = field(default_factory=list)
 
     def num_stages(self) -> int:
-        stages = set()
-        for task in self.tasks:
-            for mb in task.microbatches:
-                stages.add(mb.stage_id)
+        stages = {task.microbatch.stage_id for task in self.tasks}
         return max(stages) + 1 if stages else 0
 
     def num_mbs(self) -> int:
-        mbs = set()
-        for task in self.tasks:
-            for mb in task.microbatches:
-                mbs.add(mb.mb_idx)
+        mbs = {task.microbatch.mb_idx for task in self.tasks}
         return max(mbs) + 1 if mbs else 0
 
 
-# Legacy single-microbatch task (for backward compatibility / conversion).
+# Task for 2D schedule grid: one cell (rank, time_step).
 class Task(NamedTuple):
     pp_rank: int
-    stage_id: int
-    mb_idx: int
+    batches: list[BatchMeta]
     type: CompType
-    second_stage_id: int | None = None
-    second_mb_idx: int | None = None
+
+
+@dataclass
+class Schedule2D:
+    """2D schedule grid: grid[rank][time_step] = Task | None. Execution order: by time_step, then rank descending."""
+    grid: list[list["Task | None"]]
+
+    def stage_to_device(self) -> dict[int, int]:
+        stage_to_device = {}
+        for row in self.grid:
+            for task in row:
+                if task is not None:
+                    for batch in task.batches:
+                        stage_to_device[batch.stage_id] = task.pp_rank
+        return stage_to_device
+    
+    def num_mbs(self) -> int:
+        mbs = set()
+        for row in self.grid:
+            for task in row:
+                if task is not None:
+                    for batch in task.batches:
+                        mbs.add(batch.mb_idx)
+        return len(mbs)
+    
+    def num_stages(self) -> int:
+        stages = set()
+        for row in self.grid:
+            for task in row:
+                if task is not None:
+                    for batch in task.batches:
+                        stages.add(batch.stage_id)
+        return len(stages)
+    
+    def num_ranks(self) -> int:
+        return len(self.grid)
 
 class DAGEdge(NamedTuple):
     from_stage: int
@@ -84,95 +124,137 @@ class DAGEdge(NamedTuple):
 def _get_backward_targets(stage_id: int, dag_edges: list[DAGEdge]):
     return [edge for edge in dag_edges if edge.to_stage == stage_id]
 
-
-def _validate_schedule_dag(
-    schedule: Schedule,
-    dag_edges: list[DAGEdge],
-    num_stages: int,
-    num_mbs: int,
-) -> None:
+def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdge], num_mbs: int) -> None:
     """
-    Validate that the schedule DAG has valid data dependencies for all tasks.
-    For each microbatch: FWD/BWD have correct input/output edges; UPD has none.
+    Validate that the schedule respects well-formedness rules and DAG dependencies.
+    
+    Args:
+        schedule: 2D array with one row per device and one column per time step
+        dag_edges: List of DAG edges defining stage dependencies
+        num_mbs: Number of microbatches in the schedule
+        
+    Raises:
+        ValueError: If the schedule violates any validation rules
     """
-    tasks = schedule.tasks
-    edges = schedule.edges
-    edges_into: dict[int, list[int]] = defaultdict(list)
-    edges_out_of: dict[int, list[int]] = defaultdict(list)
-    for e in edges:
-        edges_into[e.to_task_idx].append(e.from_task_idx)
-        edges_out_of[e.from_task_idx].append(e.to_task_idx)
-
+    num_devices, num_steps = len(schedule), len(schedule[0]) if schedule else 0
+    
+    for row in schedule:
+        assert len(row) == num_steps, "Each row must have the same number of time steps"
+    
+    # Check well-formedness: no duplicates, pp_rank matches row, and all stages present
+    all_tasks = set()
+    microbatch_tasks = {}  # mb_idx -> set of (stage_id, type)
+    
+    for stage_id in range(num_devices):
+        for time_step in range(num_steps):
+            task = schedule[stage_id][time_step]
+            if task is not None:
+                # Check pp_rank matches row
+                if task.pp_rank != stage_id:
+                    raise ValueError(
+                        f"Task pp_rank {task.pp_rank} does not match row {stage_id} "
+                        f"at time step {time_step}"
+                    )
+                
+                # Check for duplicates and track by (stage_id, mb_idx, type) per batch
+                for batch in task.batches:
+                    comp_type = (
+                        task.type
+                        if task.type != CompType.FWD_BWD
+                        else (CompType.FWD if batch is task.batches[0] else CompType.BWD)
+                    )
+                    task_key = (batch.stage_id, batch.mb_idx, comp_type)
+                    if task_key in all_tasks:
+                        raise ValueError(
+                            f"Duplicate task found: stage_id={batch.stage_id}, "
+                            f"mb_idx={batch.mb_idx}, type={comp_type}"
+                        )
+                    all_tasks.add(task_key)
+                    if batch.mb_idx not in microbatch_tasks:
+                        microbatch_tasks[batch.mb_idx] = set()
+                    microbatch_tasks[batch.mb_idx].add((batch.stage_id, comp_type))
+    
+    # Get all required stages from DAG edges
     all_required_stages = set()
     for edge in dag_edges:
         all_required_stages.add(edge.from_stage)
         all_required_stages.add(edge.to_stage)
-
-    task_produces: dict[int, set[tuple[int, int, CompType]]] = defaultdict(set)
-    for ti, task in enumerate(tasks):
-        for mb in task.microbatches:
-            task_produces[ti].add((mb.stage_id, mb.mb_idx, mb.comp_type))
-
-    for ti, task in enumerate(tasks):
-        predecessors = edges_into[ti]
-        successors = edges_out_of[ti]
-        for mb in task.microbatches:
-            s, m, c = mb.stage_id, mb.mb_idx, mb.comp_type
-            if c == CompType.UPD:
-                continue
-            if c == CompType.FWD:
-                if s > 0:
-                    required = (s - 1, m, c)
-                    if not any(required in task_produces[u] for u in predecessors):
-                        raise ValueError(
-                            f"Task {ti} microbatch (stage_id={s}, mb_idx={m}, FWD) missing input: "
-                            f"no predecessor produces {required}"
-                        )
-                if s < num_stages - 1:
-                    required = (s + 1, m, c)
-                    if not any(required in task_produces[v] for v in successors):
-                        raise ValueError(
-                            f"Task {ti} microbatch (stage_id={s}, mb_idx={m}, FWD) missing output: "
-                            f"no successor consumes {required}"
-                        )
-            else:  # BWD
-                if s == num_stages - 1:
-                    required = (s, m, CompType.FWD)
-                    if not any(required in task_produces[u] for u in predecessors):
-                        raise ValueError(
-                            f"Task {ti} microbatch (stage_id={s}, mb_idx={m}, BWD) missing FWD_BWD input: "
-                            f"no predecessor produces {required}"
-                        )
-                if s < num_stages - 1:
-                    required = (s + 1, m, c)
-                    if not any(required in task_produces[u] for u in predecessors):
-                        raise ValueError(
-                            f"Task {ti} microbatch (stage_id={s}, mb_idx={m}, BWD) missing BWD input: "
-                            f"no predecessor produces {required}"
-                        )
-                if s > 0:
-                    required = (s - 1, m, c)
-                    if not any(required in task_produces[v] for v in successors):
-                        raise ValueError(
-                            f"Task {ti} microbatch (stage_id={s}, mb_idx={m}, BWD) missing output: "
-                            f"no successor consumes {required}"
-                        )
-
-    mb_fwd_stages: dict[int, set[int]] = defaultdict(set)
-    mb_bwd_stages: dict[int, set[int]] = defaultdict(set)
-    for task in tasks:
-        for mb in task.microbatches:
-            if mb.comp_type == CompType.FWD:
-                mb_fwd_stages[mb.mb_idx].add(mb.stage_id)
-            elif mb.comp_type == CompType.BWD:
-                mb_bwd_stages[mb.mb_idx].add(mb.stage_id)
-    for mb_idx in range(num_mbs):
-        missing_fwd = all_required_stages - mb_fwd_stages.get(mb_idx, set())
+    
+    # Check that each microbatch has all required forward and backward stages
+    for mb_idx, tasks in microbatch_tasks.items():
+        # Find all stages that have forward/backward tasks for this microbatch
+        fwd_stages = {stage_id for stage_id, task_type in tasks if task_type == CompType.FWD}
+        bwd_stages = {stage_id for stage_id, task_type in tasks if task_type == CompType.BWD}
+        
+        # Check that all required stages have forward tasks
+        missing_fwd = all_required_stages - fwd_stages
         if missing_fwd:
-            raise ValueError(f"Microbatch {mb_idx} missing forward stages: {missing_fwd}")
-        missing_bwd = all_required_stages - mb_bwd_stages.get(mb_idx, set())
+            raise ValueError(
+                f"Microbatch {mb_idx} missing forward stages: {missing_fwd}"
+            )
+        
+        # Check that all required stages have backward tasks
+        missing_bwd = all_required_stages - bwd_stages
         if missing_bwd:
-            raise ValueError(f"Microbatch {mb_idx} missing backward stages: {missing_bwd}")
+            raise ValueError(
+                f"Microbatch {mb_idx} missing backward stages: {missing_bwd}"
+            )
+    
+    # Check pipeline stage dependencies
+    for mb_idx in range(num_mbs):
+        # Find all tasks for this microbatch
+        fwd_times = {}  # stage_id -> time_step
+        bwd_times = {}  # stage_id -> time_step
+        
+        for pp_rank in range(num_devices):
+            for time_step in range(num_steps):
+                task = schedule[pp_rank][time_step]
+                if task is None:
+                    continue
+                for batch in task.batches:
+                    if batch.mb_idx != mb_idx:
+                        continue
+                    comp_type = (
+                        task.type
+                        if task.type != CompType.FWD_BWD
+                        else (CompType.FWD if batch is task.batches[0] else CompType.BWD)
+                    )
+                    if comp_type == CompType.FWD:
+                        fwd_times[batch.stage_id] = time_step
+                    elif comp_type == CompType.BWD:
+                        bwd_times[batch.stage_id] = time_step
+        
+        # Check forward stage ordering: if A -> B, then fwd(A) < fwd(B)
+        for edge in dag_edges:
+            from_stage, to_stage = edge.from_stage, edge.to_stage
+            if from_stage in fwd_times and to_stage in fwd_times:
+                if fwd_times[from_stage] >= fwd_times[to_stage]:
+                    raise ValueError(
+                        f"Forward stage ordering violation for microbatch {mb_idx}: "
+                        f"forward stage {from_stage} (time {fwd_times[from_stage]}) must come "
+                        f"before forward stage {to_stage} (time {fwd_times[to_stage]})"
+                    )
+        
+        # Check forward-backward ordering: fwd(A) < bwd(A)
+        for stage_id in fwd_times:
+            if stage_id in bwd_times:
+                if fwd_times[stage_id] >= bwd_times[stage_id]:
+                    raise ValueError(
+                        f"Forward-backward ordering violation for microbatch {mb_idx}, "
+                        f"stage {stage_id}: forward (time {fwd_times[stage_id]}) must come "
+                        f"before backward (time {bwd_times[stage_id]})"
+                    )
+        
+        # Check backward stage ordering: if A -> B, then bwd(B) < bwd(A)
+        for edge in dag_edges:
+            from_stage, to_stage = edge.from_stage, edge.to_stage
+            if from_stage in bwd_times and to_stage in bwd_times:
+                if bwd_times[to_stage] >= bwd_times[from_stage]:
+                    raise ValueError(
+                        f"Backward stage ordering violation for microbatch {mb_idx}: "
+                        f"backward stage {to_stage} (time {bwd_times[to_stage]}) must come "
+                        f"before backward stage {from_stage} (time {bwd_times[from_stage]})"
+                    )
 
 
 def _topological_order(schedule: Schedule) -> list[int]:
@@ -190,6 +272,16 @@ def _topological_order(schedule: Schedule) -> list[int]:
     order = []
     while queue:
         u = queue.pop(0)
+        u_rank = tasks[u].pp_rank
+        if queue:
+            for v in queue:
+                if tasks[v].pp_rank == u_rank:
+                    logger.warning(
+                        "Topological order: ordering between task %s and task %s (same pp_rank %d) cannot be determined (no directed path in DAG)",
+                        tasks[u],
+                        tasks[v],
+                        u_rank,
+                    )
         order.append(u)
         for e in edges:
             if e.from_task_idx == u:
@@ -206,12 +298,17 @@ def _topological_order(schedule: Schedule) -> list[int]:
     return order
 
 
-def piper_exec(schedule: Schedule, inputs, truth, loss_fn, dp_degree=1, naive_gradient_sync=False):
+def piper_exec(
+    schedule: Schedule2D,
+    loss_fn,
+    dp_degree=1,
+    naive_gradient_sync=False,
+):
     """
     Execute one step of the pipeline schedule on the distributed model.
 
     Args:
-        schedule: Schedule DAG (tasks and edges).
+        schedule: 2D schedule grid (rank x time_step).
         inputs: Inputs to the model.
         truth: Ground-truth labels or targets.
         loss_fn: Loss function to be used for training.
@@ -219,22 +316,51 @@ def piper_exec(schedule: Schedule, inputs, truth, loss_fn, dp_degree=1, naive_gr
     Returns:
         List of losses per microbatch (from UPD tasks).
     """
-    dag_edges = [DAGEdge(e[0], e[1]) for e in piper_metadata.dag]
-    num_stages = schedule.num_stages()
     num_mbs = schedule.num_mbs()
-    _validate_schedule_dag(schedule, dag_edges, num_stages, num_mbs)
+    num_devices = schedule.num_ranks()
+    num_steps = len(schedule.grid[0])
+
+    schedule = schedule.grid
+
+    dag_edges = piper_metadata.dag
+    dag_edges = list(map(lambda e: (DAGEdge(e[0], e[1])), list(piper_metadata.dag)))
+    _validate_schedule(schedule, dag_edges, num_mbs)
 
-    order = _topological_order(schedule)
     actors = piper_metadata.actors
+
     ret = []
-    for ti in order:
-        task = schedule.tasks[ti]
-        actor = actors[task.pp_rank]
-        for mb in task.microbatches:
-            if mb.comp_type == CompType.UPD:
-                ret.append(actor._update.remote())
-            elif mb.comp_type == CompType.FWD:
-                actor._forward.remote(mb.stage_id, mb.mb_idx)
-            elif mb.comp_type == CompType.BWD:
-                actor._backward.remote(mb.stage_id, mb.mb_idx, loss_fn=loss_fn)
+    # map pp_rank -> latest dependency
+    deps = defaultdict(dict)
+    for i in range(num_steps):
+        for j in range(num_devices-1, -1, -1):
+            if not i < len(schedule[j]):
+                continue
+            task = schedule[j][i]
+            if task:
+                pp_rank, batches, task_type = task
+                actor_id = j
+                actor = actors[actor_id]
+                match task_type:
+                    case CompType.UPD:
+                        loss = actor._update.remote(deps[pp_rank])
+                        ret.append(loss)
+                    case CompType.FWD:
+                        stage_id, mb_idx = batches[0]
+                        # logger.info(f"Executing forward {stage_id} mb {mb_idx} on actor {actor_id}")
+                        if pp_rank not in deps:
+                            dep = actor._forward.remote(stage_id, mb_idx, None)
+                        else:
+                            dep = actor._forward.remote(stage_id, mb_idx, deps[pp_rank])
+                        deps[pp_rank] = dep
+                    case CompType.BWD:
+                        stage_id, mb_idx = batches[0]
+                        # logger.info(f"Executing backward {stage_id} mb {mb_idx} on actor {actor_id}")
+                        dep = actor._backward.remote(stage_id, mb_idx, deps[pp_rank], loss_fn=loss_fn)
+                        deps[pp_rank] = dep
+                    case CompType.FWD_BWD:
+                        fwd_stage_id, fwd_mb_idx = batches[0]
+                        bwd_stage_id, bwd_mb_idx = batches[1]
+                        # logger.info(f"Executing forward-backward {fwd_stage_id} mb {fwd_mb_idx} -> {bwd_stage_id} mb {bwd_mb_idx} on actor {actor_id}")
+                        dep = actor._forward_backward.remote(fwd_stage_id, fwd_mb_idx, bwd_stage_id, bwd_mb_idx, deps[pp_rank], loss_fn=loss_fn)
+                        deps[pp_rank] = dep
     return ray.get(ret)
\ No newline at end of file
diff --git a/test/models/llama.py b/test/models/llama.py
index 77719c6..5a64d7f 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -397,59 +397,59 @@ def log_size(layer, indent=0):
         mask = torch.triu(mask, diagonal=1)
         self.mask = torch.hstack([torch.zeros((self.seq_len, 0)), mask]).to('cuda')
 
-    # """
-    # forward method for pp4-1f1b or pp2-interleaved-1f1b
-    # requires:
-    # - 4 devices
-    # - 4 stages
-    # - n_layers is divisible by 4
-    # """
-    # def forward(self, tokens: torch.Tensor):
-
-    #     with torch.fx.traceback.annotate({"stage": 0}):
-    #         h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-    #         start_pos = 0
-    #         for layer in self.layers[:self.n_layers//4]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[self.n_layers//4:self.n_layers//2]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 2}):
-    #         for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 3}):
-    #         for layer in self.layers[3*self.n_layers//4:]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-    #         h = self.norm(h) if self.norm else h
-    #         output = self.output(h).float() if self.output else h
-
-    #     return output
-
     """
-    forward method for 1f1b schedule
+    forward method for pp4-1f1b or pp2-interleaved-1f1b
     requires:
-    - 2 stages
-    - n_layers is divisible by 2
+    - 4 devices
+    - 4 stages
+    - n_layers is divisible by 4
     """
     def forward(self, tokens: torch.Tensor):
 
         with torch.fx.traceback.annotate({"stage": 0}):
-            h = self.tok_embeddings(tokens)
+            h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
             start_pos = 0
-            for layer in self.layers[:self.n_layers//2]:
+            for layer in self.layers[:self.n_layers//4]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
 
         with torch.fx.traceback.annotate({"stage": 1}):
-            for layer in self.layers[self.n_layers//2:]:
+            for layer in self.layers[self.n_layers//4:self.n_layers//2]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
-            h = self.norm(h)
-            output = self.output(h).float()
+
+        with torch.fx.traceback.annotate({"stage": 2}):
+            for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
+                h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+        with torch.fx.traceback.annotate({"stage": 3}):
+            for layer in self.layers[3*self.n_layers//4:]:
+                h = layer(h, start_pos, self.freqs_cis, self.mask)
+            h = self.norm(h) if self.norm else h
+            output = self.output(h).float() if self.output else h
 
         return output
 
+    # """
+    # forward method for 1f1b schedule
+    # requires:
+    # - 2 stages
+    # - n_layers is divisible by 2
+    # """
+    # def forward(self, tokens: torch.Tensor):
+
+    #     with torch.fx.traceback.annotate({"stage": 0}):
+    #         h = self.tok_embeddings(tokens)
+    #         start_pos = 0
+    #         for layer in self.layers[:self.n_layers//2]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 1}):
+    #         for layer in self.layers[self.n_layers//2:]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+    #         h = self.norm(h)
+    #         output = self.output(h).float()
+
+    #     return output
+
     # """
     # forward method for no pp
     # requires:
diff --git a/test/print_schedules.py b/test/print_schedules.py
index f5159b1..4e37aac 100644
--- a/test/print_schedules.py
+++ b/test/print_schedules.py
@@ -1,21 +1,17 @@
 from .schedule_helpers import print_schedule, build_1f1b_schedule, build_gpipe_schedule
 
-num_mbs = 4
-num_stages = 2
-schedule = build_1f1b_schedule(num_mbs, num_stages)
-print("1F1B Schedule -> 1f1b_schedule.pdf")
-print_schedule(schedule, path="1f1b_schedule_pp2")
+schedule = build_1f1b_schedule(4, 2)
+print("1F1B Schedule")
+print_schedule(schedule)
 
-schedule = build_gpipe_schedule(num_mbs, num_stages)
-print("GPipe Schedule -> gpipe_schedule.pdf")
-print_schedule(schedule, path="gpipe_schedule_pp2")
+schedule = build_gpipe_schedule(4, 2)
+print("GPipe Schedule")
+print_schedule(schedule)
 
-num_mbs = 8
-num_stages = 4
-schedule = build_1f1b_schedule(num_mbs, num_stages)
-print("1F1B Schedule -> 1f1b_schedule.pdf")
-print_schedule(schedule, path="1f1b_schedule_pp4")
+schedule = build_1f1b_schedule(8, 4)
+print("1F1B Schedule")
+print_schedule(schedule)
 
-schedule = build_gpipe_schedule(num_mbs, num_stages)
-print("GPipe Schedule -> gpipe_schedule.pdf")
-print_schedule(schedule, path="gpipe_schedule_pp4")
\ No newline at end of file
+schedule = build_gpipe_schedule(8, 4)
+print("GPipe Schedule")
+print_schedule(schedule)
\ No newline at end of file
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index 68ad038..ccbddce 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -3,9 +3,13 @@
 from src.piper_exec import (
     Task,
     CompType,
+    Schedule2D,
+)
+# DAG types used only by legacy _dag builders (kept for reference)
+from src.piper_exec import (
     Schedule,
     ScheduleTask,
-    MicrobatchMetadata,
+    BatchMeta,
     ScheduleEdge,
     TemporalEdge,
     DAGEdge,
@@ -13,30 +17,58 @@
 )
 
 
-def print_schedule(schedule: Schedule, path: str = "schedule") -> None:
-    """Render Schedule DAG to a visual (graphviz) file. Default output: schedule.pdf."""
-    print_schedule_dag(schedule, path=path)
+def _task_label(task: Task) -> str:
+    """Short label for a task: stage:mb:f/b/u, or first+second for dual batches."""
+    c = (
+        "u"
+        if task.type == CompType.UPD
+        else "f"
+        if task.type == CompType.FWD
+        else "b"
+        if task.type == CompType.BWD
+        else "fb"
+    )
+    if task.type == CompType.UPD:
+        label = "   u    "
+    else:
+        first = task.batches[0]
+        label = f"{first.stage_id}:{first.mb_idx}:{c}"
+    if len(task.batches) > 1:
+        second = task.batches[1]
+        label += f"+{second.stage_id}:{second.mb_idx}"
+    return label
+
+
+def print_schedule(schedule: Schedule2D, path: str = "schedule") -> None:
+    """Print the 2D schedule grid to stdout. Rows = ranks, columns = time steps. None cells shown as ' --- '."""
+    for row in schedule.grid:
+        for task in row:
+            if task is None:
+                print(" ------ ", end="\t")
+            else:
+                print(_task_label(task), end="\t")
+        print()
 
 
 def print_schedule_dag(schedule: Schedule, path: str = "schedule") -> None:
     """
-    Use graphviz to produce a visual of the schedule DAG.
-    Tasks for the same pp_rank are drawn on the same row (one row per rank).
-    Creates path.pdf in the current directory.
+    Use graphviz to produce a visual of a Schedule DAG (legacy; not used in Piper workflow).
     """
     dot = Digraph(comment="Schedule DAG")
     dot.attr(rankdir="TB")
     dot.attr("node", shape="box", style="rounded,filled", fontname="sans")
 
+    topo_order = _topological_order(schedule)
+    task_to_order: dict[int, int] = {task_idx: order_idx for order_idx, task_idx in enumerate(topo_order)}
+
     rank_to_tasks: dict[int, list[int]] = {}
     task_labels: dict[int, str] = {}
     for ti, task in enumerate(schedule.tasks):
         rank_to_tasks.setdefault(task.pp_rank, []).append(ti)
-        mb_parts = []
-        for mb in task.microbatches:
-            c = "u" if mb.comp_type == CompType.UPD else "f" if mb.comp_type == CompType.FWD else "b"
-            mb_parts.append(f"{mb.stage_id}:{mb.mb_idx}:{c}")
-        task_labels[ti] = f"rank{task.pp_rank}\\n" + "\\n".join(mb_parts)
+        mb = task.microbatch
+        c = "u" if mb.comp_type == CompType.UPD else "f" if mb.comp_type == CompType.FWD else "b"
+        order_num = task_to_order.get(ti, -1)
+        task_labels[ti] = f"[{order_num}]\\nrank{task.pp_rank}\\n{mb.stage_id}:{mb.mb_idx}:{c}"
 
     for rank in sorted(rank_to_tasks.keys()):
         with dot.subgraph() as sub:
@@ -53,12 +85,86 @@ def print_schedule_dag(schedule: Schedule, path: str = "schedule") -> None:
     dot.render(path, format="pdf", cleanup=True)
 
 
-def build_gpipe_schedule(n_mbs: int, n_stages: int) -> Schedule:
+def build_gpipe_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
+    steps = n_mbs + n_stages - 1
+    schedule = [[None] * (steps * 2 + 1) for _ in range(n_stages)]
+    for step in range(steps):
+        for stage_id in range(n_stages):
+            mb_idx = step - stage_id
+            if mb_idx >= 0 and mb_idx < n_mbs:
+                schedule[stage_id][step] = Task(
+                    pp_rank=stage_id,
+                    batches=[BatchMeta(stage_id=stage_id, mb_idx=mb_idx)],
+                    type=CompType.FWD,
+                )
+
+    for step in range(steps, steps * 2):
+        for stage_id in reversed(range(n_stages)):
+            mb_idx = (step - steps) - (n_stages - stage_id - 1)
+            if mb_idx >= 0 and mb_idx < n_mbs:
+                schedule[stage_id][step] = Task(
+                    pp_rank=stage_id,
+                    batches=[BatchMeta(stage_id=stage_id, mb_idx=mb_idx)],
+                    type=CompType.BWD,
+                )
+
+    for i, stage in enumerate(range(n_stages)):
+        schedule[stage][-i-1] = Task(
+            pp_rank=stage,
+            batches=[BatchMeta(stage_id=stage, mb_idx=0)],
+            type=CompType.UPD,
+        )
+    return Schedule2D(grid=schedule)
+
+
+def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
+    steps = n_mbs + n_stages - 1
+    schedule = [[None] * (steps * 2 + 1) for _ in range(n_stages)]
+    stage_mb = [[0, 0] for _ in range(n_stages)]
+    for step in range(n_stages):
+        for stage_id in range(n_stages):
+            if step >= stage_id:
+                mb_idx = stage_mb[stage_id][0]
+                if mb_idx >= 0 and mb_idx < n_mbs:
+                    schedule[stage_id][step] = Task(
+                        pp_rank=stage_id,
+                        batches=[BatchMeta(stage_id=stage_id, mb_idx=mb_idx)],
+                        type=CompType.FWD,
+                    )
+                    stage_mb[stage_id][0] += 1
+    for step in range(n_stages, 2 * steps):
+        relative_step = step - n_stages
+        for stage_id in range(n_stages):
+            inv_stage = n_stages - stage_id - 1
+            if relative_step >= inv_stage:
+                fwd_or_bwd = 1 - (relative_step + inv_stage) % 2
+                task_type = CompType.FWD if fwd_or_bwd == 0 else CompType.BWD
+                mb_idx = stage_mb[stage_id][fwd_or_bwd]
+                if mb_idx >= 0 and mb_idx < n_mbs:
+                    schedule[stage_id][step] = Task(
+                        pp_rank=stage_id,
+                        batches=[BatchMeta(stage_id=stage_id, mb_idx=mb_idx)],
+                        type=task_type,
+                    )
+                    stage_mb[stage_id][fwd_or_bwd] += 1
+    for i, stage in enumerate(range(n_stages)):
+        schedule[stage][-i-1] = Task(
+            pp_rank=stage,
+            batches=[BatchMeta(stage_id=stage, mb_idx=n_mbs - 1)],
+            type=CompType.UPD,
+        )
+    return Schedule2D(grid=schedule)
+
+
+# --- DAG-based schedule builders (kept for reference) ---
+
+
+def build_gpipe_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
     """
     Build a GPipe schedule as a DAG: all FWD, then all BWD, then UPD.
     One task per (stage_id, mb_idx, comp_type); edges follow pipeline data dependencies.
     Temporal: forwards ordered by microbatch per stage (s:m:f → s:m+1:f); all forwards before
-    backwards (last fwd → first bwd on last stage); backwards in ascending microbatch per stage
+    backwards (last fwd → first bwd on every stage); backwards in ascending microbatch per stage
     (s:m:b → s:m+1:b); update after final backward (s:M-1:b → s:UPD).
     """
     tasks: list[ScheduleTask] = []
@@ -68,18 +174,18 @@ def build_gpipe_schedule(n_mbs: int, n_stages: int) -> Schedule:
 
     for mb_idx in range(n_mbs):
         for stage_id in range(n_stages):
-            mb = MicrobatchMetadata(stage_id=stage_id, mb_idx=mb_idx, comp_type=CompType.FWD)
+            mb = BatchMeta(stage_id=stage_id, mb_idx=mb_idx, comp_type=CompType.FWD)
             ti = len(tasks)
-            tasks.append(ScheduleTask(pp_rank=stage_id, microbatches=(mb,)))
+            tasks.append(ScheduleTask(pp_rank=stage_id, microbatch=mb))
             if stage_id > 0:
                 edges.append(ScheduleEdge(producer[(stage_id - 1, mb_idx, CompType.FWD)], ti))
             producer[(stage_id, mb_idx, CompType.FWD)] = ti
 
     for mb_idx in range(n_mbs):
         for stage_id in range(n_stages - 1, -1, -1):
-            mb = MicrobatchMetadata(stage_id=stage_id, mb_idx=mb_idx, comp_type=CompType.BWD)
+            mb = BatchMeta(stage_id=stage_id, mb_idx=mb_idx, comp_type=CompType.BWD)
             ti = len(tasks)
-            tasks.append(ScheduleTask(pp_rank=stage_id, microbatches=(mb,)))
+            tasks.append(ScheduleTask(pp_rank=stage_id, microbatch=mb))
             # Same-stage fwd→bwd only on last stage (loss and backward there)
             if stage_id == n_stages - 1:
                 edges.append(ScheduleEdge(producer[(stage_id, mb_idx, CompType.FWD)], ti))
@@ -92,10 +198,10 @@ def build_gpipe_schedule(n_mbs: int, n_stages: int) -> Schedule:
             temporal_edges.append(
                 TemporalEdge(producer[(stage_id, m, CompType.FWD)], producer[(stage_id, m + 1, CompType.FWD)])
             )
-    last_stage = n_stages - 1
-    temporal_edges.append(
-        TemporalEdge(producer[(last_stage, n_mbs - 1, CompType.FWD)], producer[(last_stage, 0, CompType.BWD)])
-    )
+    for stage_id in range(n_stages):
+        temporal_edges.append(
+            TemporalEdge(producer[(stage_id, n_mbs - 1, CompType.FWD)], producer[(stage_id, 0, CompType.BWD)])
+        )
 
     for stage_id in range(n_stages):
         for m in range(n_mbs - 1):
@@ -105,297 +211,361 @@ def build_gpipe_schedule(n_mbs: int, n_stages: int) -> Schedule:
 
     for stage_id in range(n_stages):
         ti = len(tasks)
-        mb = MicrobatchMetadata(stage_id=stage_id, mb_idx=0, comp_type=CompType.UPD)
-        tasks.append(ScheduleTask(pp_rank=stage_id, microbatches=(mb,)))
+        mb = BatchMeta(stage_id=stage_id, mb_idx=0, comp_type=CompType.UPD)
+        tasks.append(ScheduleTask(pp_rank=stage_id, microbatch=mb))
         temporal_edges.append(TemporalEdge(producer[(stage_id, n_mbs - 1, CompType.BWD)], ti))
 
     return Schedule(tasks=tasks, edges=edges, temporal_edges=temporal_edges)
 
 
-def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule:
+def _dualpipe_pp_rank(stage_id: int, mb_idx: int) -> int:
+    """Stage 0 appears on both ranks; mb 0 uses rank 0 for stage 0, mb 1 uses rank 1 for stage 0."""
+    return (stage_id + mb_idx) % 2
+
+
+def build_1f1b_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
     """
     Build a 1F1B (one forward one backward) schedule as a DAG.
     FWD and BWD are interleaved per stage; one UPD per stage at the end.
-    Temporal (per rank): backwards in ascending microbatch order (s:m:b → s:m+1:b);
-    update after final backward (s:M-1:b → s:UPD).
+    Temporal dependencies per rank (R = number of ranks):
+    - First rank: first R forwards, then alternate backward/forward for the remainder
+    - Rank r: first (R - r) forwards, then alternate backward/forward
+    - Last rank: alternate forward/backward from the beginning (f0, b0, f1, b1, ...)
     """
-    steps = n_mbs + n_stages - 1
-    num_steps = steps * 2 + 1
-    schedule_2d: list[list[Task | None]] = [[None] * num_steps for _ in range(n_stages)]
-    stage_mb = [[0, 0] for _ in range(n_stages)]
-
-    for step in range(n_stages):
-        for stage_id in range(n_stages):
-            if step >= stage_id:
-                mb_idx = stage_mb[stage_id][0]
-                if 0 <= mb_idx < n_mbs:
-                    schedule_2d[stage_id][step] = Task(
-                        pp_rank=stage_id, stage_id=stage_id, mb_idx=mb_idx, type=CompType.FWD
-                    )
-                    stage_mb[stage_id][0] += 1
+    R = n_stages
 
-    for step in range(n_stages, 2 * steps):
-        relative_step = step - n_stages
-        for stage_id in range(n_stages):
-            inv_stage = n_stages - stage_id - 1
-            if relative_step >= inv_stage:
-                fwd_or_bwd = 1 - (relative_step + inv_stage) % 2
-                task_type = CompType.FWD if fwd_or_bwd == 0 else CompType.BWD
-                mb_idx = stage_mb[stage_id][fwd_or_bwd]
-                if 0 <= mb_idx < n_mbs:
-                    schedule_2d[stage_id][step] = Task(
-                        pp_rank=stage_id, stage_id=stage_id, mb_idx=mb_idx, type=task_type
-                    )
-                    stage_mb[stage_id][fwd_or_bwd] += 1
-
-    for i, stage in enumerate(range(n_stages)):
-        schedule_2d[stage][-i - 1] = Task(
-            stage_id=stage, pp_rank=stage, mb_idx=n_mbs - 1, type=CompType.UPD
-        )
-
-    tasks = []
-    edges = []
+    # Build all tasks first (one task per stage_id, mb_idx, comp_type)
+    tasks: list[ScheduleTask] = []
+    edges: list[ScheduleEdge] = []
     temporal_edges: list[TemporalEdge] = []
     producer: dict[tuple[int, int, CompType], int] = {}
-    for time_step in range(num_steps):
-        for rank in range(n_stages - 1, -1, -1):
-            cell = schedule_2d[rank][time_step]
-            if cell is None:
-                continue
-            mb = MicrobatchMetadata(
-                stage_id=cell.stage_id, mb_idx=cell.mb_idx, comp_type=cell.type
-            )
+    
+    # Create all forward tasks
+    for mb_idx in range(n_mbs):
+        for stage_id in range(n_stages):
+            mb = BatchMeta(stage_id=stage_id, mb_idx=mb_idx, comp_type=CompType.FWD)
             ti = len(tasks)
-            tasks.append(ScheduleTask(pp_rank=cell.pp_rank, microbatches=(mb,)))
-            c, s, m = cell.type, cell.stage_id, cell.mb_idx
-            if c == CompType.FWD:
-                if s > 0:
-                    edges.append(ScheduleEdge(producer[(s - 1, m, c)], ti))
-                producer[(s, m, c)] = ti
-            elif c == CompType.BWD:
-                # Same-stage fwd→bwd only on last stage (loss and backward there)
-                if s == n_stages - 1:
-                    edges.append(ScheduleEdge(producer[(s, m, CompType.FWD)], ti))
-                if s < n_stages - 1:
-                    edges.append(ScheduleEdge(producer[(s + 1, m, c)], ti))
-                producer[(s, m, c)] = ti
-            elif c == CompType.UPD:
-                temporal_edges.append(TemporalEdge(producer[(s, n_mbs - 1, CompType.BWD)], ti))
-
-    for s in range(n_stages):
-        for m in range(n_mbs - 1):
-            temporal_edges.append(
-                TemporalEdge(producer[(s, m, CompType.BWD)], producer[(s, m + 1, CompType.BWD)])
-            )
-
+            tasks.append(ScheduleTask(pp_rank=stage_id, microbatch=mb))
+            if stage_id > 0:
+                edges.append(ScheduleEdge(producer[(stage_id - 1, mb_idx, CompType.FWD)], ti))
+            producer[(stage_id, mb_idx, CompType.FWD)] = ti
+    
+    # Create all backward tasks (in reverse stage order to handle dependencies)
+    for mb_idx in range(n_mbs):
+        for stage_id in range(n_stages - 1, -1, -1):
+            mb = BatchMeta(stage_id=stage_id, mb_idx=mb_idx, comp_type=CompType.BWD)
+            ti = len(tasks)
+            tasks.append(ScheduleTask(pp_rank=stage_id, microbatch=mb))
+            # Same-stage fwd→bwd only on last stage (loss and backward there)
+            if stage_id == n_stages - 1:
+                edges.append(ScheduleEdge(producer[(stage_id, mb_idx, CompType.FWD)], ti))
+            if stage_id < n_stages - 1:
+                edges.append(ScheduleEdge(producer[(stage_id + 1, mb_idx, CompType.BWD)], ti))
+            producer[(stage_id, mb_idx, CompType.BWD)] = ti
+    
+    # Create update tasks
+    for stage_id in range(n_stages):
+        mb = BatchMeta(stage_id=stage_id, mb_idx=0, comp_type=CompType.UPD)
+        ti = len(tasks)
+        tasks.append(ScheduleTask(pp_rank=stage_id, microbatch=mb))
+        temporal_edges.append(TemporalEdge(producer[(stage_id, n_mbs - 1, CompType.BWD)], ti))
+    
+    # Build temporal edges per rank according to the specified pattern
+    for rank in range(n_stages):
+        stage_id = rank
+        
+        if rank == n_stages - 1:
+            # Last rank: alternate forward/backward (f0, b0, f1, b1, ...)
+            for mb_idx in range(n_mbs):
+                if mb_idx == 0:
+                    # First forward doesn't have a predecessor
+                    pass
+                else:
+                    # Forward follows previous backward
+                    temporal_edges.append(
+                        TemporalEdge(
+                            producer[(stage_id, mb_idx - 1, CompType.BWD)],
+                            producer[(stage_id, mb_idx, CompType.FWD)]
+                        )
+                    )
+                # Backward follows its corresponding forward
+                temporal_edges.append(
+                    TemporalEdge(
+                        producer[(stage_id, mb_idx, CompType.FWD)],
+                        producer[(stage_id, mb_idx, CompType.BWD)]
+                    )
+                )
+        else:
+            # Rank r: first (R - r) forwards, then alternate backward/forward
+            num_initial_fwds = R - rank
+            # Initial forwards in sequence: f0 → f1 → ... → f(num_initial_fwds - 1)
+            for mb_idx in range(num_initial_fwds):
+                if mb_idx > 0:
+                    temporal_edges.append(
+                        TemporalEdge(
+                            producer[(stage_id, mb_idx - 1, CompType.FWD)],
+                            producer[(stage_id, mb_idx, CompType.FWD)]
+                        )
+                    )
+            # Then alternate: b0, f(num_initial_fwds), b1, f(num_initial_fwds+1), ...
+            bwd_idx = 0
+            fwd_idx = num_initial_fwds
+            # First in alternation is b0, following last initial forward
+            prev_mb_idx = num_initial_fwds - 1
+            prev_type = CompType.FWD
+
+            while bwd_idx < n_mbs or fwd_idx < n_mbs:
+                if prev_type == CompType.FWD:
+                    # Next is backward(mb bwd_idx)
+                    if bwd_idx < n_mbs:
+                        temporal_edges.append(
+                            TemporalEdge(
+                                producer[(stage_id, prev_mb_idx, CompType.FWD)],
+                                producer[(stage_id, bwd_idx, CompType.BWD)]
+                            )
+                        )
+                        prev_mb_idx = bwd_idx
+                        prev_type = CompType.BWD
+                        bwd_idx += 1
+                    elif fwd_idx < n_mbs:
+                        temporal_edges.append(
+                            TemporalEdge(
+                                producer[(stage_id, prev_mb_idx, CompType.FWD)],
+                                producer[(stage_id, fwd_idx, CompType.FWD)]
+                            )
+                        )
+                        prev_mb_idx = fwd_idx
+                        fwd_idx += 1
+                else:  # prev_type == CompType.BWD
+                    # Next is forward(mb fwd_idx)
+                    if fwd_idx < n_mbs:
+                        temporal_edges.append(
+                            TemporalEdge(
+                                producer[(stage_id, prev_mb_idx, CompType.BWD)],
+                                producer[(stage_id, fwd_idx, CompType.FWD)]
+                            )
+                        )
+                        prev_mb_idx = fwd_idx
+                        prev_type = CompType.FWD
+                        fwd_idx += 1
+                    elif bwd_idx < n_mbs:
+                        temporal_edges.append(
+                            TemporalEdge(
+                                producer[(stage_id, prev_mb_idx, CompType.BWD)],
+                                producer[(stage_id, bwd_idx, CompType.BWD)]
+                            )
+                        )
+                        prev_mb_idx = bwd_idx
+                        bwd_idx += 1
+    
     return Schedule(tasks=tasks, edges=edges, temporal_edges=temporal_edges)
 
 
-def _grid_to_dag(
-    schedule_2d: list[list[Task | None]],
-    n_stages: int,
-) -> Schedule:
-    """Convert a 2D grid (device x time_step) of Tasks into a Schedule DAG. Sequential pipeline."""
-    dag_edges = [DAGEdge(i, i + 1) for i in range(n_stages - 1)]
-    num_devices = len(schedule_2d)
-    num_steps = len(schedule_2d[0]) if schedule_2d else 0
-    tasks_list: list[ScheduleTask] = []
-    cell_to_task: dict[tuple[int, int], int] = {}
-    for j in range(num_devices):
-        for i in range(num_steps):
-            task = schedule_2d[j][i] if j < len(schedule_2d) and i < len(schedule_2d[j]) else None
-            if task is not None:
-                mb = MicrobatchMetadata(
-                    stage_id=task.stage_id, mb_idx=task.mb_idx, comp_type=task.type
-                )
-                tasks_list.append(ScheduleTask(pp_rank=task.pp_rank, microbatches=(mb,)))
-                cell_to_task[(j, i)] = len(tasks_list) - 1
-    order = []
-    for i in range(num_steps):
-        for j in range(num_devices - 1, -1, -1):
-            if (j, i) in cell_to_task:
-                order.append((j, i))
-    edges_list: list[ScheduleEdge] = []
-    producer: dict[tuple[int, int, CompType], int] = {}
-    for (j, i) in order:
-        ti = cell_to_task[(j, i)]
-        st = tasks_list[ti]
-        for mb in st.microbatches:
-            s, m, c = mb.stage_id, mb.mb_idx, mb.comp_type
-            if c == CompType.UPD:
-                continue
-            if c == CompType.FWD and s > 0:
-                pred_key = (s - 1, m, c)
-                if pred_key in producer:
-                    edges_list.append(ScheduleEdge(producer[pred_key], ti))
-            if c == CompType.BWD:
-                if s == n_stages - 1 and (s, m, CompType.FWD) in producer:
-                    edges_list.append(ScheduleEdge(producer[(s, m, CompType.FWD)], ti))
-                if s < n_stages - 1:
-                    pred_key = (s + 1, m, c)
-                    if pred_key in producer:
-                        edges_list.append(ScheduleEdge(producer[pred_key], ti))
-            producer[(s, m, c)] = ti
-    return Schedule(tasks=tasks_list, edges=edges_list, temporal_edges=[])
-
-
-no_pp_schedule = Schedule(
-    tasks=[
-        ScheduleTask(0, (MicrobatchMetadata(0, 0, CompType.FWD),)),
-        ScheduleTask(0, (MicrobatchMetadata(0, 0, CompType.BWD),)),
-        ScheduleTask(0, (MicrobatchMetadata(0, 0, CompType.UPD),)),
+NO_PP_SCHEDULE = Schedule2D(
+    grid=[
+        [
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.UPD),
+        ]
     ],
-    edges=[],
-    temporal_edges=[],
 )
 
+DUALPIPEV_SCHEDULE = Schedule2D(
+    grid=[
+        [
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2), BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD_BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4), BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD_BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3), BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD_BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5), BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD_BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4), BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD_BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5), BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD_BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD),
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[], type=CompType.UPD),
+        ],
+        [
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2), BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD_BWD),
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2), BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3), BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3), BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4), BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4), BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5), BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5), BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[], type=CompType.UPD),
+            None,
+        ]
+    ])
+
 # 2D grid literals for interleaved 1F1B (used only to build DAGs below)
-_PP2_GRID = [
-    [
-        Task(pp_rank=0, stage_id=0, mb_idx=0, type=CompType.FWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=1, type=CompType.FWD),
-        Task(pp_rank=0, stage_id=2, mb_idx=0, type=CompType.FWD),
-        Task(pp_rank=0, stage_id=2, mb_idx=1, type=CompType.FWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=2, type=CompType.FWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=3, type=CompType.FWD),
-        Task(pp_rank=0, stage_id=2, mb_idx=0, type=CompType.BWD),
-        None,
-        Task(pp_rank=0, stage_id=2, mb_idx=1, type=CompType.BWD),
-        Task(pp_rank=0, stage_id=2, mb_idx=2, type=CompType.FWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=0, type=CompType.BWD),
-        Task(pp_rank=0, stage_id=2, mb_idx=3, type=CompType.FWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=1, type=CompType.BWD),
-        None,
-        Task(pp_rank=0, stage_id=2, mb_idx=2, type=CompType.BWD),
-        Task(pp_rank=0, stage_id=2, mb_idx=3, type=CompType.BWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=2, type=CompType.BWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=3, type=CompType.BWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=0, type=CompType.UPD),
-    ],
-    [
-        None,
-        Task(pp_rank=1, stage_id=1, mb_idx=0, type=CompType.FWD),
-        Task(pp_rank=1, stage_id=1, mb_idx=1, type=CompType.FWD),
-        Task(pp_rank=1, stage_id=3, mb_idx=0, type=CompType.FWD),
-        Task(pp_rank=1, stage_id=3, mb_idx=0, type=CompType.BWD),
-        Task(pp_rank=1, stage_id=3, mb_idx=1, type=CompType.FWD),
-        Task(pp_rank=1, stage_id=3, mb_idx=1, type=CompType.BWD),
-        Task(pp_rank=1, stage_id=1, mb_idx=2, type=CompType.FWD),
-        Task(pp_rank=1, stage_id=1, mb_idx=0, type=CompType.BWD),
-        Task(pp_rank=1, stage_id=1, mb_idx=3, type=CompType.FWD),
-        Task(pp_rank=1, stage_id=1, mb_idx=1, type=CompType.BWD),
-        Task(pp_rank=1, stage_id=3, mb_idx=2, type=CompType.FWD),
-        Task(pp_rank=1, stage_id=3, mb_idx=2, type=CompType.BWD),
-        Task(pp_rank=1, stage_id=3, mb_idx=3, type=CompType.FWD),
-        Task(pp_rank=1, stage_id=3, mb_idx=3, type=CompType.BWD),
-        Task(pp_rank=1, stage_id=1, mb_idx=2, type=CompType.BWD),
-        Task(pp_rank=1, stage_id=1, mb_idx=3, type=CompType.BWD),
-        Task(pp_rank=1, stage_id=1, mb_idx=0, type=CompType.UPD),
-        None,
-    ],
-]
-pp2_interleaved_1f1b_grid_schedule = _grid_to_dag(_PP2_GRID, 4)
-
-_PP4_GRID = [
-    [
-        Task(pp_rank=0, stage_id=0, mb_idx=0, type=CompType.FWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=1, type=CompType.FWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=2, type=CompType.FWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=3, type=CompType.FWD),
-        Task(pp_rank=0, stage_id=4, mb_idx=0, type=CompType.FWD),
-        Task(pp_rank=0, stage_id=4, mb_idx=1, type=CompType.FWD),
-        Task(pp_rank=0, stage_id=4, mb_idx=2, type=CompType.FWD),
-        Task(pp_rank=0, stage_id=4, mb_idx=3, type=CompType.FWD),
-        None,
-        None,
-        None,
-        Task(pp_rank=0, stage_id=4, mb_idx=0, type=CompType.BWD),
-        None,
-        Task(pp_rank=0, stage_id=4, mb_idx=1, type=CompType.BWD),
-        None,
-        Task(pp_rank=0, stage_id=4, mb_idx=2, type=CompType.BWD),
-        None,
-        Task(pp_rank=0, stage_id=4, mb_idx=3, type=CompType.BWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=0, type=CompType.BWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=1, type=CompType.BWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=2, type=CompType.BWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=3, type=CompType.BWD),
-        Task(pp_rank=0, stage_id=0, mb_idx=0, type=CompType.UPD),
-    ],
-    [
-        None,
-        Task(pp_rank=1, stage_id=1, mb_idx=0, type=CompType.FWD),
-        Task(pp_rank=1, stage_id=1, mb_idx=1, type=CompType.FWD),
-        Task(pp_rank=1, stage_id=1, mb_idx=2, type=CompType.FWD),
-        Task(pp_rank=1, stage_id=1, mb_idx=3, type=CompType.FWD),
-        Task(pp_rank=1, stage_id=5, mb_idx=0, type=CompType.FWD),
-        Task(pp_rank=1, stage_id=5, mb_idx=1, type=CompType.FWD),
-        Task(pp_rank=1, stage_id=5, mb_idx=2, type=CompType.FWD),
-        Task(pp_rank=1, stage_id=5, mb_idx=3, type=CompType.FWD),
-        None,
-        Task(pp_rank=1, stage_id=5, mb_idx=0, type=CompType.BWD),
-        None,
-        Task(pp_rank=1, stage_id=5, mb_idx=1, type=CompType.BWD),
-        None,
-        Task(pp_rank=1, stage_id=5, mb_idx=2, type=CompType.BWD),
-        None,
-        Task(pp_rank=1, stage_id=5, mb_idx=3, type=CompType.BWD),
-        Task(pp_rank=1, stage_id=1, mb_idx=0, type=CompType.BWD),
-        Task(pp_rank=1, stage_id=1, mb_idx=1, type=CompType.BWD),
-        Task(pp_rank=1, stage_id=1, mb_idx=2, type=CompType.BWD),
-        Task(pp_rank=1, stage_id=1, mb_idx=3, type=CompType.BWD),
-        Task(pp_rank=1, stage_id=1, mb_idx=0, type=CompType.UPD),
-        None,
-    ],
-    [
-        None,
-        None,
-        Task(pp_rank=2, stage_id=2, mb_idx=0, type=CompType.FWD),
-        Task(pp_rank=2, stage_id=2, mb_idx=1, type=CompType.FWD),
-        Task(pp_rank=2, stage_id=2, mb_idx=2, type=CompType.FWD),
-        Task(pp_rank=2, stage_id=2, mb_idx=3, type=CompType.FWD),
-        Task(pp_rank=2, stage_id=6, mb_idx=0, type=CompType.FWD),
-        Task(pp_rank=2, stage_id=6, mb_idx=1, type=CompType.FWD),
-        Task(pp_rank=2, stage_id=6, mb_idx=2, type=CompType.FWD),
-        Task(pp_rank=2, stage_id=6, mb_idx=0, type=CompType.BWD),
-        Task(pp_rank=2, stage_id=6, mb_idx=3, type=CompType.FWD),
-        Task(pp_rank=2, stage_id=6, mb_idx=1, type=CompType.BWD),
-        None,
-        Task(pp_rank=2, stage_id=6, mb_idx=2, type=CompType.BWD),
-        None,
-        Task(pp_rank=2, stage_id=6, mb_idx=3, type=CompType.BWD),
-        Task(pp_rank=2, stage_id=2, mb_idx=0, type=CompType.BWD),
-        Task(pp_rank=2, stage_id=2, mb_idx=1, type=CompType.BWD),
-        Task(pp_rank=2, stage_id=2, mb_idx=2, type=CompType.BWD),
-        Task(pp_rank=2, stage_id=2, mb_idx=3, type=CompType.BWD),
-        Task(pp_rank=2, stage_id=2, mb_idx=0, type=CompType.UPD),
-        None,
-        None,
-    ],
-    [
-        None,
-        None,
-        None,
-        Task(pp_rank=3, stage_id=3, mb_idx=0, type=CompType.FWD),
-        Task(pp_rank=3, stage_id=3, mb_idx=1, type=CompType.FWD),
-        Task(pp_rank=3, stage_id=3, mb_idx=2, type=CompType.FWD),
-        Task(pp_rank=3, stage_id=3, mb_idx=3, type=CompType.FWD),
-        Task(pp_rank=3, stage_id=7, mb_idx=0, type=CompType.FWD),
-        Task(pp_rank=3, stage_id=7, mb_idx=0, type=CompType.BWD),
-        Task(pp_rank=3, stage_id=7, mb_idx=1, type=CompType.FWD),
-        Task(pp_rank=3, stage_id=7, mb_idx=1, type=CompType.BWD),
-        Task(pp_rank=3, stage_id=7, mb_idx=2, type=CompType.FWD),
-        Task(pp_rank=3, stage_id=7, mb_idx=2, type=CompType.BWD),
-        Task(pp_rank=3, stage_id=7, mb_idx=3, type=CompType.FWD),
-        Task(pp_rank=3, stage_id=7, mb_idx=3, type=CompType.BWD),
-        Task(pp_rank=3, stage_id=3, mb_idx=0, type=CompType.BWD),
-        Task(pp_rank=3, stage_id=3, mb_idx=1, type=CompType.BWD),
-        Task(pp_rank=3, stage_id=3, mb_idx=2, type=CompType.BWD),
-        Task(pp_rank=3, stage_id=3, mb_idx=3, type=CompType.BWD),
-        Task(pp_rank=3, stage_id=3, mb_idx=0, type=CompType.UPD),
-        None,
-        None,
-        None,
-    ],
-]
-pp4_interleaved_1f1b_grid_schedule = _grid_to_dag(_PP4_GRID, 8)
\ No newline at end of file
+INTERLEAVED_1F1B_PP2_SCHEDULE = Schedule2D(
+    grid=[
+        [
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.UPD),
+        ],
+        [
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.UPD),
+            None,
+        ],
+    ])
+
+INTERLEAVED_1F1B_PP4_SCHEDULE = Schedule2D(
+    grid=[
+        [
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.FWD),
+            None,
+            None,
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.BWD),
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.BWD),
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.BWD),
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.UPD),
+        ],
+        [
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.FWD),
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.BWD),
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.BWD),
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.BWD),
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.UPD),
+            None,
+        ],
+        [
+            None,
+            None,
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.BWD),
+            None,
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.BWD),
+            None,
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.UPD),
+            None,
+            None,
+        ],
+        [
+            None,
+            None,
+            None,
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.UPD),
+            None,
+            None,
+            None,
+        ],
+    ])
diff --git a/test/test_llama.py b/test/test_llama.py
index ce108d8..f67cc6c 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -3,16 +3,11 @@
 import time
 import argparse
 import os
-from torch import nn, optim
-from torch.profiler import profile, record_function, ProfilerActivity
 import numpy as np
-import itertools
 import gc
 
-from src.piper_exec import Task, piper_exec
-from src.piper_compile import piper_setup
-from src.piper import piper
-from src.piper_utils import piper_metadata
+from src.piper_exec import piper_exec
+from src.piper_compile import piper_setup, piper_shutdown
 from src.piper_coordinator import PiperProgramCoordinator
 
 from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B
@@ -20,9 +15,10 @@
     build_1f1b_schedule, 
     build_gpipe_schedule, 
     print_schedule,
-    pp2_interleaved_1f1b_grid_schedule, 
-    pp4_interleaved_1f1b_grid_schedule,
-    no_pp_schedule
+    INTERLEAVED_1F1B_PP2_SCHEDULE, 
+    INTERLEAVED_1F1B_PP4_SCHEDULE,
+    NO_PP_SCHEDULE,
+    DUALPIPEV_SCHEDULE,
 )
 
 
@@ -51,29 +47,27 @@ def main(args):
     schedule = None
     match args.schedule:
         case "no-pp":
-            schedule = no_pp_schedule
+            schedule = NO_PP_SCHEDULE
         case "interleaved-1f1b":
-            schedule = pp2_interleaved_1f1b_grid_schedule if args.pp == 2 else pp4_interleaved_1f1b_grid_schedule
+            schedule = INTERLEAVED_1F1B_PP2_SCHEDULE if args.pp == 2 else INTERLEAVED_1F1B_PP4_SCHEDULE
         case "1f1b":
             schedule = build_1f1b_schedule(args.mbs, args.pp)
         case "gpipe":
             schedule = build_gpipe_schedule(args.mbs, args.pp)
+        case "dualpipev":
+            schedule = DUALPIPEV_SCHEDULE
     print("Schedule:")
     print_schedule(schedule)
 
     piper_setup(
         model,
         torch.optim.Adam, 
-        [x], 
+        [x],
+        y,
         schedule,
         args.naive_gradient_sync,
     )
 
-    # Send data to actors ahead of time
-    actors = piper_metadata.actors
-    ray.get(actors[0].load_input.remote([x]))
-    ray.get(actors[len(actors)-1].load_labels.remote(y))
-
     del model
     del x
     del y 
@@ -84,7 +78,7 @@ def main(args):
     # Warmup
     print(f"Running {args.warmup} warmup iterations...")
     for i in range(args.warmup):
-        piper_exec(schedule, [None], None, loss_fn, args.dp, args.naive_gradient_sync)
+        piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
         print(f"Warmup {i} completed")
 
     # Time training steps
@@ -92,7 +86,7 @@ def main(args):
     iter_times = []
     for _ in range(args.iters):
         start = time.perf_counter()
-        piper_exec(schedule, [None], None, loss_fn, args.dp, args.naive_gradient_sync)
+        piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
         end = time.perf_counter()
         iter_times.append(end - start)
     
@@ -107,7 +101,7 @@ def main(args):
 
         print(f"Running {args.warmup} tracing iterations...")
         for _ in range(args.warmup):
-            piper_exec(schedule, [None], None, loss_fn, args.dp, args.naive_gradient_sync)
+            piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
 
         trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
         for rank, trace_data in trace_data_ret:
@@ -123,15 +117,14 @@ def main(args):
     ray.timeline(timeline_filename)
     print(f"Ray timeline saved to: {timeline_filename}")
 
-    ray.get([actor.shutdown.remote() for actor in actors.values()])
-
+    piper_shutdown()
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
     parser.add_argument('--model', choices=['debug', '1b', '3b', '8b'], default='debug',
                         help='Model configuration: debug, 1b, 3b, or 8b (default: debug)')
-    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'no-pp'], default='1f1b',
-                        help='Schedule type: gpipe, 1f1b, or interleaved-1f1b (default: 1f1b)')
+    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'dualpipev', 'no-pp'], default='1f1b',
+                        help='Schedule type: gpipe, 1f1b, interleaved-1f1b, dualpipev, or no-pp (default: 1f1b)')
     parser.add_argument('--dp', type=int, default=1,
                         help='Number of data parallel degrees (default: 1)')
     parser.add_argument('--pp', type=int, default=2,

From 08733581de0d681cc4c16d39c356c6fe75262acd Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Mon, 23 Feb 2026 20:32:59 +0000
Subject: [PATCH 046/100] coordinator gets its own GPU, will change in future
 with meta compilation

---
 src/piper_actor.py | 2 +-
 src/piper_utils.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index 8f66e50..29b7d76 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -46,7 +46,7 @@ def _create_actors(
     for pp_rank in range(num_actors):
         global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
         p2p_schedule = p2p_schedules[pp_rank]
-        actor = PiperActor.options(num_gpus=0.8).remote(
+        actor = PiperActor.options(num_gpus=1).remote(
             pp_rank,
             optim_class,
             world_size,
diff --git a/src/piper_utils.py b/src/piper_utils.py
index eb9eb25..b489a56 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -12,7 +12,7 @@
 
 import src.piper_patches
 
-LOG_LEVEL = "DEBUG"
+LOG_LEVEL = "INFO"
 
 """ 
 Print the backward graph of a tensor

From 98adc2fd1c2b44ad8febd3f7e681dd28df84d8d4 Mon Sep 17 00:00:00 2001
From: Yi Pan <conlesspan@outlook.com>
Date: Tue, 24 Feb 2026 09:13:24 +0000
Subject: [PATCH 047/100] fix: rebase and update outdated p2p ops and mixtral
 test file.

---
 src/piper_actor.py     | 10 +++----
 test/models/mixtral.py | 60 +++++++++++++++++++++---------------------
 test/test_mixtral.py   | 48 ++++++++++++++++++---------------
 3 files changed, 62 insertions(+), 56 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index 4b4894e..e8711ea 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -778,7 +778,7 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             for i, inp in zip(self.input_idxs[fwd_stage_id], self.inputs):
                 self.forward_args[fwd_stage_id][i] = inp
         else:
-            self._exec_p2p_op(fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False, p2p_stream=fwd_p2p_stream)
+            self._exec_p2p_op(fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False, None, p2p_stream=fwd_p2p_stream)
             inputs_from_prev_stage = self.p2p_cache.pop(
                 (fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False)
             )
@@ -813,7 +813,7 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
         # PREPARE BACKWARD PASS
         out_activation = self.out_activation[bwd_stage_id][bwd_mb_idx]
         if bwd_stage_id < self.num_stages - 1:
-            self._exec_p2p_op(bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False, p2p_stream=bwd_p2p_stream)
+            self._exec_p2p_op(bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False, None, p2p_stream=bwd_p2p_stream)
 
             input_grad = self.p2p_cache.pop(
                 (bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False)
@@ -863,11 +863,11 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             send_p2p_op = (fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True)
             assert send_p2p_op not in self.p2p_cache
             self.p2p_cache[send_p2p_op] = output
-            self._exec_p2p_op(fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True, p2p_stream=fwd_p2p_stream)
+            self._exec_p2p_op(fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True, None, p2p_stream=fwd_p2p_stream)
 
         # POST BACKWARD P2P OPERATIONS
         if bwd_stage_id > 0:
-            self._exec_p2p_op(bwd_stage_id, bwd_stage_id - 1, bwd_mb_idx, True, p2p_stream=bwd_p2p_stream)
+            self._exec_p2p_op(bwd_stage_id, bwd_stage_id - 1, bwd_mb_idx, True, None, p2p_stream=bwd_p2p_stream)
 
         if CLEANUP_MEMORY:
             gc.collect()
@@ -890,7 +890,7 @@ def _synchronize_gradients(self):
                         )
 
     def _update(self, *deps):
-        self._wait_pending_sends()
+        # self._wait_pending_sends()
         self.logger.debug(f"Actor {self.global_rank} waiting for backward sync events")
 
         # if dp degree > 1, make sure all gradients are synchronized before optimizer step
diff --git a/test/models/mixtral.py b/test/models/mixtral.py
index 602ef99..3378f8c 100644
--- a/test/models/mixtral.py
+++ b/test/models/mixtral.py
@@ -115,52 +115,52 @@ def setup_caches(self, max_batch_size, max_seq_length):
         self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base)
         self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
 
-    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
-        """
-        2 Stages
-        """
-        self.first_arg = idx.shape
-        with torch.fx.traceback.annotate({"stage": 0}):
-            mask = self.causal_mask[None, None, input_pos]
-            freqs_cis = self.freqs_cis[input_pos]
-            x = self.tok_embeddings(idx)
-            for layer in self.layers[:len(self.layers)//2]:
-                x = layer(x, input_pos, freqs_cis, mask)
-        
-        with torch.fx.traceback.annotate({"stage": 1}):
-            for layer in self.layers[len(self.layers)//2:]:
-                x = layer(x, input_pos, freqs_cis, mask)
-            x = self.norm(x)
-            logits = self.output(x)
-        return logits
-
     # def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
     #     """
-    #     4 Stages
+    #     2 Stages
     #     """
     #     self.first_arg = idx.shape
     #     with torch.fx.traceback.annotate({"stage": 0}):
     #         mask = self.causal_mask[None, None, input_pos]
     #         freqs_cis = self.freqs_cis[input_pos]
     #         x = self.tok_embeddings(idx)
-    #         for layer in self.layers[:len(self.layers)//4]:
+    #         for layer in self.layers[:len(self.layers)//2]:
     #             x = layer(x, input_pos, freqs_cis, mask)
         
     #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[len(self.layers)//4:len(self.layers)//2]:
-    #             x = layer(x, input_pos, freqs_cis, mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 2}):
-    #         for layer in self.layers[len(self.layers)//2:3*len(self.layers)//4]:
-    #             x = layer(x, input_pos, freqs_cis, mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 3}):
-    #         for layer in self.layers[3*len(self.layers)//4:]:
+    #         for layer in self.layers[len(self.layers)//2:]:
     #             x = layer(x, input_pos, freqs_cis, mask)
     #         x = self.norm(x)
     #         logits = self.output(x)
     #     return logits
 
+    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        """
+        4 Stages
+        """
+        self.first_arg = idx.shape
+        with torch.fx.traceback.annotate({"stage": 0}):
+            mask = self.causal_mask[None, None, input_pos]
+            freqs_cis = self.freqs_cis[input_pos]
+            x = self.tok_embeddings(idx)
+            for layer in self.layers[:len(self.layers)//4]:
+                x = layer(x, input_pos, freqs_cis, mask)
+        
+        with torch.fx.traceback.annotate({"stage": 1}):
+            for layer in self.layers[len(self.layers)//4:len(self.layers)//2]:
+                x = layer(x, input_pos, freqs_cis, mask)
+
+        with torch.fx.traceback.annotate({"stage": 2}):
+            for layer in self.layers[len(self.layers)//2:3*len(self.layers)//4]:
+                x = layer(x, input_pos, freqs_cis, mask)
+
+        with torch.fx.traceback.annotate({"stage": 3}):
+            for layer in self.layers[3*len(self.layers)//4:]:
+                x = layer(x, input_pos, freqs_cis, mask)
+            x = self.norm(x)
+            logits = self.output(x)
+        return logits
+
     @classmethod
     def from_name(cls, name: str):
         return cls(ModelArgs.from_name(name))
diff --git a/test/test_mixtral.py b/test/test_mixtral.py
index e367150..2ceb69e 100644
--- a/test/test_mixtral.py
+++ b/test/test_mixtral.py
@@ -13,8 +13,15 @@
 from src.piper_utils import piper_metadata
 
 from .models.mixtral import Transformer, ModelArgs
-from .schedule_helpers import build_1f1b_schedule, print_schedule, no_pp_schedule, pp2_interleaved_1f1b_grid_schedule, pp4_interleaved_1f1b_grid_schedule, build_gpipe_schedule
-from torch._dynamo.backends.debugging import eager
+from .schedule_helpers import (
+    build_1f1b_schedule,
+    build_gpipe_schedule,
+    print_schedule,
+    NO_PP_SCHEDULE,
+    INTERLEAVED_1F1B_PP2_SCHEDULE,
+    INTERLEAVED_1F1B_PP4_SCHEDULE,
+    DUALPIPEV_SCHEDULE,
+)
 
 def main(args):
 
@@ -35,13 +42,15 @@ def main(args):
     schedule = None
     match args.schedule:
         case "no-pp":
-            schedule = no_pp_schedule
+            schedule = NO_PP_SCHEDULE
         case "interleaved-1f1b":
-            schedule = pp2_interleaved_1f1b_grid_schedule if args.pp == 2 else pp4_interleaved_1f1b_grid_schedule
+            schedule = INTERLEAVED_1F1B_PP2_SCHEDULE if args.pp == 2 else INTERLEAVED_1F1B_PP4_SCHEDULE
         case "1f1b":
             schedule = build_1f1b_schedule(args.mbs, args.pp)
         case "gpipe":
             schedule = build_gpipe_schedule(args.mbs, args.pp)
+        case "dualpipev":
+            schedule = DUALPIPEV_SCHEDULE
     print("Schedule:")
     print_schedule(schedule)
 
@@ -52,30 +61,26 @@ def main(args):
     input_pos = torch.arange(config.block_size).to('cuda')
     y = torch.randn(batch_size, config.block_size, config.vocab_size).to('cuda')
 
+    loss_fn = torch.nn.CrossEntropyLoss()
+
     piper_setup(
-        model, 
-        torch.optim.Adam, 
-        [x, input_pos], 
+        model,
+        torch.optim.Adam,
+        [x, input_pos],
+        y,
         schedule,
         args.naive_gradient_sync,
     )
 
-    # Send data to actors ahead of time
-    actors = piper_metadata.actors
-    ray.get(actors[0].load_input.remote([x]))
-    ray.get(actors[len(actors)-1].load_labels.remote(y))
-
-    loss_fn = torch.nn.CrossEntropyLoss()
-
     print(f"Running {args.warmup} warmup iterations...")
     for _ in range(args.warmup):
-        piper_exec(schedule, [x, input_pos], y, loss_fn, args.dp, args.naive_gradient_sync)
-    
+        piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
+
     print(f"Running {args.iters} timed iterations...")
     iter_times = []
     for _ in range(args.iters):
         start = time.perf_counter()
-        piper_exec(schedule, [x, input_pos], y, loss_fn, args.dp, args.naive_gradient_sync)
+        piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
         end = time.perf_counter()
         iter_times.append(end - start)
     
@@ -86,11 +91,12 @@ def main(args):
     )
 
     if args.tracing:
+        actors = piper_metadata.actors
         ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
 
         print(f"Running {args.warmup} tracing iterations...")
         for _ in range(args.warmup):
-            piper_exec(schedule, [x, input_pos], y, loss_fn, args.dp, args.naive_gradient_sync)
+            piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
 
         trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
         for rank, trace_data in trace_data_ret:
@@ -106,8 +112,8 @@ def parse_args():
     parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
     parser.add_argument('--model', choices=['tiny', 'small', 'medium', 'large'], default='tiny',
                         help='Model configuration: tiny, small, medium, or large (default: tiny)')
-    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'no-pp'], default='1f1b',
-                        help='Schedule type: gpipe, 1f1b, or interleaved-1f1b (default: 1f1b)')
+    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'dualpipev', 'no-pp'], default='1f1b',
+                        help='Schedule type: gpipe, 1f1b, interleaved-1f1b, dualpipev, or no-pp (default: 1f1b)')
     parser.add_argument('--dp', type=int, default=2,
                         help='Number of data parallel degrees (default: 2)')
     parser.add_argument('--pp', type=int, default=2,
@@ -127,7 +133,7 @@ def parse_args():
     return parser.parse_args()
 
 if __name__ == "__main__":
-    ray.init(include_dashboard=False, log_to_driver=True, namespace="llama", _temp_dir="/m-coriander/coriander/mfris/tmp/ray")
+    ray.init(include_dashboard=False, log_to_driver=True, namespace="mixtral")
     args = parse_args()
     piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
     handles = piper_coordinator.run_program.remote(main, args)

From fc1cbb40946710b163de89e93ef633b89d071b1c Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Tue, 24 Feb 2026 21:17:06 +0000
Subject: [PATCH 048/100] overlap p2p, batch p2p ops for outputs with multiple
 tensors. add overlapped _forward_backward task for dualpipev.

---
 src/piper.py         |  18 +-
 src/piper_actor.py   | 384 ++++++++++++++++++++++++++++++++++---------
 src/piper_compile.py |  68 ++++----
 src/piper_exec.py    |  21 ++-
 src/piper_utils.py   |   2 +-
 5 files changed, 366 insertions(+), 127 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index d09723b..25f562a 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -42,15 +42,15 @@ def piper(gm, example_inputs, **kwargs):
         if dp_degree > 1:
             stage_gm = _insert_a2a_ops(stage_gm)
 
-        # stage_gm(*graphargs)
-        # start = torch.cuda.Event(enable_timing=True)
-        # start.record()
-        # for i in range(10):
-        #     stage_gm(*graphargs)
-        # end = torch.cuda.Event(enable_timing=True)
-        # end.record()
-        # torch.cuda.synchronize()
-        # print(f"Stage {stage_id} time measured on controller: {start.elapsed_time(end) / 10:.2f} ms")
+        stage_gm(*graphargs)
+        start = torch.cuda.Event(enable_timing=True)
+        start.record()
+        for i in range(5):
+            stage_gm(*graphargs)
+        end = torch.cuda.Event(enable_timing=True)
+        end.record()
+        torch.cuda.synchronize()
+        print(f"Stage {stage_id} time measured on controller: {start.elapsed_time(end) / 5:.2f} ms")
 
         actor_id = piper_metadata.stage_to_device[stage_id]
         actor = _get_actor(actor_id)
diff --git a/src/piper_actor.py b/src/piper_actor.py
index c36a08a..0a2c945 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -19,7 +19,7 @@
     piper_metadata,
 )
 
-CLEANUP_MEMORY = True
+CLEANUP_MEMORY = False
 
 logger = create_logger("piper_actor", LOG_LEVEL)
 
@@ -120,6 +120,8 @@ def __init__(
         self.comm_stream = torch.cuda.Stream()
         self.a2a_stream = torch.cuda.Stream()
         self.p2p_stream = torch.cuda.Stream()
+        self.overlapped_comp_stream = torch.cuda.Stream()
+        self.overlapped_p2p_stream = torch.cuda.Stream()
 
         # map stage id -> compiled fx.Graph function
         self.forward_fns = dict()
@@ -139,6 +141,8 @@ def __init__(
         self.out_activation = defaultdict(dict)
         # map (src_stage, dst_stage, mb_idx, is_sender) -> tensor for p2p communication
         self.p2p_cache = dict()
+        # pending send operation handles; waited on before starting the next task
+        self.pending_send_handles: list = []
         # accumuate loss for each microbatch
         self.loss = []
         # map stage id -> data parallel communication operations
@@ -163,6 +167,12 @@ def reset_p2p_states(self):
         self.executed_p2ps = set()
         self.p2p_cache = dict()
 
+    def _wait_pending_sends(self) -> None:
+        """Wait on all pending send handles from previous task before starting the next."""
+        for handle in self.pending_send_handles:
+            handle.wait()
+        self.pending_send_handles.clear()
+
     def get_trace_data(self) -> dict:
         return self.global_rank, self.trace_data
 
@@ -216,7 +226,7 @@ def _stop_timing(self, stream, label):
             if label in self.trace_events:
                 start, stop = self.trace_events[label]
                 stop.record(stream)
-                torch.cuda.synchronize()
+                stop.synchronize()
                 self.trace_data[label].append(start.elapsed_time(stop))
 
     def _join_process_groups(self):
@@ -224,6 +234,9 @@ def _join_process_groups(self):
         master_port = os.environ.get("PIPER_MASTER_PORT", "10000")
         init_method = f"tcp://{master_addr}:{master_port}"
 
+        self.device = f"cuda:{self.global_rank % torch.cuda.device_count()}"
+        torch.cuda.set_device(self.device)
+
         dist.init_process_group(
             "nccl",
             init_method=init_method,
@@ -360,12 +373,73 @@ def move_to_device(idx, arg):
         del gm_data
 
     def _exec_p2p_op(
-        self, src_stage: int, dst_stage: int, mb_idx: int, is_sender: bool
+        self, src_stage: int, dst_stage: int, mb_idx: int, is_sender: bool, dep, p2p_stream=None
+    ):
+        p2p_op = (src_stage, dst_stage, mb_idx, is_sender)
+        if p2p_op in self.executed_p2ps:
+            return
+
+        if p2p_stream is None:
+            p2p_stream = self.p2p_stream
+
+        op_idx = self.next_p2p_idx
+        while op_idx < len(self.p2p_schedule):
+            op = self.p2p_schedule[op_idx]
+            if op == p2p_op:
+                break
+            op_idx += 1
+        assert op_idx < len(
+            self.p2p_schedule
+        ), f"P2P op {p2p_op} not found in schedule for actor {self.global_rank}"
+
+        # Execute everything before and including the given p2p op
+        for idx in range(self.next_p2p_idx, op_idx + 1):
+            op = self.p2p_schedule[idx]
+            src_stage, dst_stage, mb_idx, is_sender = op
+            is_fwd = src_stage == dst_stage - 1
+            is_bwd = src_stage == dst_stage + 1
+            assert is_fwd or is_bwd
+            is_recver = not is_sender
+
+            op_name = "P2P(unknown)"
+            if is_fwd and is_sender:
+                self._exec_fwd_send(src_stage, mb_idx, p2p_stream=p2p_stream)
+                op_name = (
+                    f"P2P(fwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
+                )
+            elif is_fwd and is_recver:
+                self._exec_fwd_recv(dst_stage, mb_idx, p2p_stream=p2p_stream)
+                op_name = (
+                    f"P2P(fwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
+                )
+            elif is_bwd and is_sender:
+                self._exec_bwd_send(src_stage, mb_idx, p2p_stream=p2p_stream)
+                op_name = (
+                    f"P2P(bwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
+                )
+            elif is_bwd and is_recver:
+                self._exec_bwd_recv(dst_stage, mb_idx, p2p_stream=p2p_stream)
+                op_name = (
+                    f"P2P(bwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
+                )
+            else:
+                raise ValueError(f"Invalid p2p op: {op}")
+
+            self.executed_p2ps.add(op)
+            logger.debug(f"Executed {op_name} on actor {self.global_rank}")
+
+        self.next_p2p_idx = op_idx + 1
+        
+    def _exec_p2p_op(
+        self, src_stage: int, dst_stage: int, mb_idx: int, is_sender: bool, dep, p2p_stream=None
     ):
         p2p_op = (src_stage, dst_stage, mb_idx, is_sender)
         if p2p_op in self.executed_p2ps:
             return
 
+        if p2p_stream is None:
+            p2p_stream = self.p2p_stream
+
         op_idx = self.next_p2p_idx
         while op_idx < len(self.p2p_schedule):
             op = self.p2p_schedule[op_idx]
@@ -387,22 +461,22 @@ def _exec_p2p_op(
 
             op_name = "P2P(unknown)"
             if is_fwd and is_sender:
-                self._exec_fwd_send(src_stage, mb_idx)
+                self._exec_fwd_send(src_stage, mb_idx, p2p_stream=p2p_stream)
                 op_name = (
                     f"P2P(fwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
                 )
             elif is_fwd and is_recver:
-                self._exec_fwd_recv(dst_stage, mb_idx)
+                self._exec_fwd_recv(dst_stage, mb_idx, p2p_stream=p2p_stream)
                 op_name = (
                     f"P2P(fwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
                 )
             elif is_bwd and is_sender:
-                self._exec_bwd_send(src_stage, mb_idx)
+                self._exec_bwd_send(src_stage, mb_idx, p2p_stream=p2p_stream)
                 op_name = (
                     f"P2P(bwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
                 )
             elif is_bwd and is_recver:
-                self._exec_bwd_recv(dst_stage, mb_idx)
+                self._exec_bwd_recv(dst_stage, mb_idx, p2p_stream=p2p_stream)
                 op_name = (
                     f"P2P(bwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
                 )
@@ -414,10 +488,15 @@ def _exec_p2p_op(
 
         self.next_p2p_idx = op_idx + 1
 
-    def _exec_fwd_recv(self, stage_id: int, mb_idx: int):
+    def _exec_fwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
         if stage_id == 0:
             return
 
+        if p2p_stream is None:
+            p2p_stream = self.p2p_stream
+        if comp_stream is None:
+            comp_stream = self.comp_stream
+
         p2p_op = (stage_id - 1, stage_id, mb_idx, False)
         assert p2p_op not in self.p2p_cache
 
@@ -441,27 +520,39 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int):
             self.logger.debug(
                 f"Dispatch fwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
             )
-            self._start_timing(self.p2p_stream, "fwd_p2p_recv")
-            with torch.cuda.stream(self.p2p_stream):
-                for i in self.input_idxs[stage_id]:
-                    dist.recv(
-                        inputs_to_recv[i],
-                        src=global_src_rank,
-                        group=self.pp_group,
-                    )
+            self._start_timing(p2p_stream, "fwd_p2p_recv")
+            p2p_op_list = [
+                dist.P2POp(
+                    dist.irecv,
+                    inputs_to_recv[i],
+                    global_src_rank,
+                    group=self.pp_group,
+                )
+                for i in self.input_idxs[stage_id]
+            ]
+            with torch.cuda.stream(p2p_stream):
+                recv_handles = dist.batch_isend_irecv(p2p_op_list)
+            for handle in recv_handles:
+                handle.wait()
             # Ensure the default stream only consumes tensors after recv completes.
-            torch.cuda.current_stream().wait_stream(self.p2p_stream)
-            self._stop_timing(self.p2p_stream, "fwd_p2p_recv")
+            comp_stream.wait_stream(p2p_stream)
+            torch.cuda.synchronize()
+            self._stop_timing(p2p_stream, "fwd_p2p_recv")
             self.logger.debug(
                 f"Completed fwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
             )
 
         self.p2p_cache[p2p_op] = inputs_to_recv
 
-    def _exec_fwd_send(self, stage_id: int, mb_idx: int):
+    def _exec_fwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
         if stage_id == self.num_stages - 1:
             return
 
+        if p2p_stream is None:
+            p2p_stream = self.p2p_stream
+        if comp_stream is None:
+            comp_stream = self.comp_stream
+
         p2p_op = (stage_id, stage_id + 1, mb_idx, True)
         output = self.p2p_cache.pop(p2p_op)
         # For non-final stages, send output tensors to the next stage
@@ -471,58 +562,74 @@ def _exec_fwd_send(self, stage_id: int, mb_idx: int):
         if self.global_rank == global_dst_rank:
             self.p2p_cache[p2p_op] = output
         else:
-            self.logger.debug(
-                f"Dispatch fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
-            )
             # Ensure send sees the latest writes from the default stream.
-            self.p2p_stream.wait_stream(torch.cuda.current_stream())
-            self._start_timing(self.p2p_stream, "fwd_p2p_send")
-            with torch.cuda.stream(self.p2p_stream):
-                for i in range(len(output)):
-                    dist.send(output[i], dst=global_dst_rank, group=self.pp_group)
-            # Ensure the default stream does not reuse/mutate buffers before send completes.
-            torch.cuda.current_stream().wait_stream(self.p2p_stream)
-            self._stop_timing(self.p2p_stream, "fwd_p2p_send")
+            self._start_timing(p2p_stream, "fwd_p2p_send")
+            p2p_stream.wait_stream(comp_stream)
+            p2p_op_list = [
+                dist.P2POp(
+                    dist.isend,
+                    output[i],
+                    global_dst_rank,
+                    group=self.pp_group,
+                )
+                for i in range(len(output))
+            ]
+            with torch.cuda.stream(p2p_stream):
+                send_handles = dist.batch_isend_irecv(p2p_op_list)
+            self.pending_send_handles.extend(send_handles)
+            self._stop_timing(p2p_stream, "fwd_p2p_send")
             self.logger.debug(
-                f"Completed fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
+                f"Dispatched fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
             )
 
-    def _exec_bwd_recv(self, stage_id: int, mb_idx: int):
+    def _exec_bwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
         if stage_id >= self.num_stages - 1:
             return
 
+        if p2p_stream is None:
+            p2p_stream = self.p2p_stream
+        if comp_stream is None:
+            comp_stream = self.comp_stream
+
         out_activation = self.out_activation[stage_id][mb_idx]
         # For non-final stages, recieve input gradients from the subsequent backward pass
         input_grad = torch.empty_like(out_activation)
         pp_rank = piper_metadata.stage_to_device[stage_id + 1]
         global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
+        p2p_op = (stage_id + 1, stage_id, mb_idx, False)
+
         if self.global_rank == global_src_rank:
-            p2p_op = (stage_id + 1, stage_id, mb_idx, True)
-            input_grad = self.p2p_cache.pop(p2p_op)
+            input_grad = self.p2p_cache.pop((stage_id + 1, stage_id, mb_idx, True))
         else:
             self.logger.debug(
                 f"Dispatch bwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id+1} -> {stage_id}, mb {mb_idx})"
             )
-            self._start_timing(self.p2p_stream, "bwd_p2p_recv")
-            with torch.cuda.stream(self.p2p_stream):
-                dist.recv(input_grad, src=global_src_rank, group=self.pp_group)
+            self._start_timing(p2p_stream, "bwd_p2p_recv")
+            with torch.cuda.stream(p2p_stream):
+                recv_handle = dist.irecv(
+                    input_grad, src=global_src_rank, group=self.pp_group
+                )
+            recv_handle.wait()
             # Ensure the default stream only consumes gradients after recv completes.
-            torch.cuda.current_stream().wait_stream(self.p2p_stream)
-            self._stop_timing(self.p2p_stream, "bwd_p2p_recv")
+            comp_stream.wait_stream(p2p_stream)
+            torch.cuda.synchronize()
+            self._stop_timing(p2p_stream, "bwd_p2p_recv")
             self.logger.debug(
                 f"Completed bwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id+1} -> {stage_id}, mb {mb_idx})"
             )
+        
+        self.p2p_cache[p2p_op] = input_grad
 
-        self._start_timing(self.comp_stream, "backward_comp")
-        with torch.cuda.stream(self.comp_stream):
-            out_activation.backward(gradient=input_grad)
-        self._stop_timing(self.comp_stream, "backward_comp")
-
-    def _exec_bwd_send(self, stage_id: int, mb_idx: int):
+    def _exec_bwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
         if stage_id <= 0:
             return
 
+        if p2p_stream is None:
+            p2p_stream = self.p2p_stream
+        if comp_stream is None:
+            comp_stream = self.comp_stream
+
         # For non-first stages, send output gradients to the previous backward stage
         output_grad = self.inp_activation[stage_id][mb_idx].grad
         if output_grad is None:
@@ -535,24 +642,25 @@ def _exec_bwd_send(self, stage_id: int, mb_idx: int):
             p2p_op = (stage_id, stage_id - 1, mb_idx, True)
             self.p2p_cache[p2p_op] = output_grad
         else:
-            self.logger.debug(
-                f"Dispatch bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
-            )
             # Ensure send sees the latest gradient writes from the default stream.
-            self.p2p_stream.wait_stream(torch.cuda.current_stream())
-            self._start_timing(self.p2p_stream, "bwd_p2p_send")
-            with torch.cuda.stream(self.p2p_stream):
-                dist.send(output_grad, dst=global_src_rank, group=self.pp_group)
-            # Ensure the default stream does not reuse/mutate buffers before send completes.
-            torch.cuda.current_stream().wait_stream(self.p2p_stream)
-            self._stop_timing(self.p2p_stream, "bwd_p2p_send")
+            self._start_timing(p2p_stream, "bwd_p2p_send")
+            p2p_stream.wait_stream(comp_stream)
+            with torch.cuda.stream(p2p_stream):
+                handle = dist.isend(
+                    output_grad, dst=global_src_rank, group=self.pp_group
+                )
+                self.pending_send_handles.append(handle)
+            self._stop_timing(p2p_stream, "bwd_p2p_send")
             self.logger.debug(
-                f"Completed bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
+                f"Dispatched bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
             )
 
         self.inp_activation[stage_id][mb_idx] = None
+    
+    def _forward(self, stage_id: int, mb_idx: int, *deps):
+        # self._wait_pending_sends()
 
-    def _forward(self, stage_id: int, mb_idx: int, dep):
+        comp_stream = self.comp_stream
 
         self.logger.debug(
             f"Calling forward {stage_id} mb {mb_idx} on actor {self.global_rank}"
@@ -563,7 +671,7 @@ def _forward(self, stage_id: int, mb_idx: int, dep):
             for i, inp in zip(self.input_idxs[stage_id], self.inputs):
                 self.forward_args[stage_id][i] = inp
         else:
-            self._exec_p2p_op(stage_id - 1, stage_id, mb_idx, False)
+            # self._exec_p2p_op(stage_id - 1, stage_id, mb_idx, False)
             inputs_from_prev_stage = self.p2p_cache.pop(
                 (stage_id - 1, stage_id, mb_idx, False)
             )
@@ -591,10 +699,10 @@ def _forward(self, stage_id: int, mb_idx: int, dep):
         
 
         # Run the forward pass
-        self._start_timing(self.comp_stream, "forward_comp")
-        with torch.cuda.stream(self.comp_stream):
+        self._start_timing(comp_stream, "forward_comp")
+        with torch.cuda.stream(comp_stream):
             output = self.forward_fns[stage_id](*self.forward_args[stage_id])
-        self._stop_timing(self.comp_stream, "forward_comp")
+        self._stop_timing(comp_stream, "forward_comp")
 
         # Save first output that requires grad as output activation
         # TODO: support multiple outputs
@@ -615,7 +723,7 @@ def _forward(self, stage_id: int, mb_idx: int, dep):
             send_p2p_op = (stage_id, stage_id + 1, mb_idx, True)
             assert send_p2p_op not in self.p2p_cache
             self.p2p_cache[send_p2p_op] = output
-            self._exec_p2p_op(stage_id, stage_id + 1, mb_idx, True)
+            # self._exec_p2p_op(stage_id, stage_id + 1, mb_idx, True)
 
         if CLEANUP_MEMORY:
             gc.collect()
@@ -625,32 +733,40 @@ def _forward(self, stage_id: int, mb_idx: int, dep):
         self.logger.debug(
             f"Forward {stage_id} mb {mb_idx} on actor {self.global_rank} returning {[out.shape for out in output]}"
         )
-        torch.cuda.synchronize()
 
         return 1
 
-    def _backward(self, stage_id: int, mb_idx: int, dep, loss_fn=None):
+    def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
+        # self._wait_pending_sends()
+
+        comp_stream = self.comp_stream
 
         self.logger.debug(
             f"Calling backward {stage_id} mb {mb_idx} on actor {self.global_rank}"
         )
-
-        if mb_idx not in self.out_activation[stage_id]:
-            self.logger.warning(f"No output activation found for stage {stage_id} mb {mb_idx} on actor {self.global_rank}")
         out_activation = self.out_activation[stage_id][mb_idx]
 
         if stage_id < self.num_stages - 1:
-            self._exec_p2p_op(stage_id + 1, stage_id, mb_idx, False)
+            # self._exec_p2p_op(stage_id + 1, stage_id, mb_idx, False)
+
+            input_grad = self.p2p_cache.pop(
+                (stage_id + 1, stage_id, mb_idx, False)
+            )
+
+            self._start_timing(comp_stream, "backward_comp")
+            with torch.cuda.stream(comp_stream):
+                out_activation.backward(gradient=input_grad)
+            self._stop_timing(comp_stream, "backward_comp")
         else:
             assert loss_fn is not None
             labels = self.labels
             assert out_activation.shape == labels.shape
 
-            self._start_timing(self.comp_stream, "backward_comp")
-            with torch.cuda.stream(self.comp_stream):
+            self._start_timing(comp_stream, "backward_comp")
+            with torch.cuda.stream(comp_stream):
                 loss = loss_fn(out_activation, labels)
                 loss.backward()
-            self._stop_timing(self.comp_stream, "backward_comp")
+            self._stop_timing(comp_stream, "backward_comp")
 
             self.loss.append(loss.item())
 
@@ -658,21 +774,130 @@ def _backward(self, stage_id: int, mb_idx: int, dep, loss_fn=None):
         self.out_activation[stage_id][mb_idx] = None
         del out_activation
 
-        if stage_id > 0:
-            self._exec_p2p_op(stage_id, stage_id - 1, mb_idx, True)
+        # if stage_id > 0:
+        #     self._exec_p2p_op(stage_id, stage_id - 1, mb_idx, True)
 
         if CLEANUP_MEMORY:
             gc.collect()
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
 
-        torch.cuda.synchronize()
-
         return 1
 
-    def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: int, bwd_mb_idx: int, dep, loss_fn=None):
-        self._forward(fwd_stage_id, fwd_mb_idx, dep)
-        self._backward(bwd_stage_id, bwd_mb_idx, dep, loss_fn=loss_fn)
+    def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: int, bwd_mb_idx: int, *deps, loss_fn=None):
+        # self._wait_pending_sends()
+
+        fwd_comp_stream = self.comp_stream
+        bwd_comp_stream = self.overlapped_comp_stream
+        fwd_p2p_stream = self.p2p_stream
+        bwd_p2p_stream = self.overlapped_p2p_stream
+
+        self.logger.debug(
+            f"Calling forward {fwd_stage_id} mb {fwd_mb_idx} backward {bwd_stage_id} mb {bwd_mb_idx} on actor {self.global_rank}"
+        )
+
+        # PREPARE FORWARD PASS
+        if fwd_stage_id == 0:
+            # For the first stage, load input tensors from self.inputs
+            for i, inp in zip(self.input_idxs[fwd_stage_id], self.inputs):
+                self.forward_args[fwd_stage_id][i] = inp
+        else:
+            self._exec_p2p_op(fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False, p2p_stream=fwd_p2p_stream)
+            inputs_from_prev_stage = self.p2p_cache.pop(
+                (fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False)
+            )
+            for i, tensor in zip(self.input_idxs[fwd_stage_id], inputs_from_prev_stage):
+                if isinstance(tensor, (tuple, list)):
+                    assert len(tensor) == 1
+                    tensor = tensor[0]
+                if fwd_stage_id > 0 and piper_metadata.stage_to_device[fwd_stage_id] == piper_metadata.stage_to_device[fwd_stage_id - 1] and tensor.requires_grad:
+                    tensor = tensor.detach().requires_grad_(True)
+                self.forward_args[fwd_stage_id][i] = tensor
+
+            # save first input that requires grad as input activation
+            inp_with_grad = [
+                self.forward_args[fwd_stage_id][i]
+                for i in self.input_idxs[fwd_stage_id]
+                if self.forward_args[fwd_stage_id][i].requires_grad
+            ]
+            assert (
+                len(inp_with_grad) == 1
+            ), "Exactly one input per stage should require a gradient"
+            self.logger.debug(
+                f"Saving input activation {inp_with_grad[0].shape} for stage {fwd_stage_id} mb {fwd_mb_idx}"
+            )
+            self.inp_activation[fwd_stage_id][fwd_mb_idx] = inp_with_grad[0]
+
+        # RUN FORWARD PASS
+        self._start_timing(fwd_comp_stream, "forward_comp")
+        with torch.cuda.stream(fwd_comp_stream):
+            output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
+        self._stop_timing(fwd_comp_stream, "forward_comp")
+        
+        # PREPARE BACKWARD PASS
+        out_activation = self.out_activation[bwd_stage_id][bwd_mb_idx]
+        if bwd_stage_id < self.num_stages - 1:
+            self._exec_p2p_op(bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False, p2p_stream=bwd_p2p_stream)
+
+            input_grad = self.p2p_cache.pop(
+                (bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False)
+            )
+        else:
+            assert loss_fn is not None
+            labels = self.labels
+            assert out_activation.shape == labels.shape
+
+        # RUN BACKWARD PASS
+        if bwd_stage_id < self.num_stages - 1:
+            self._start_timing(bwd_comp_stream, "backward_comp")
+            with torch.cuda.stream(bwd_comp_stream):
+                out_activation.backward(gradient=input_grad)
+            self._stop_timing(bwd_comp_stream, "backward_comp")
+        else:
+            self._start_timing(bwd_comp_stream, "backward_comp")
+            with torch.cuda.stream(bwd_comp_stream):
+                loss = loss_fn(out_activation, labels)
+                loss.backward()
+            self._stop_timing(bwd_comp_stream, "backward_comp")
+
+        # POST PROCESS FORWARD PASS
+        out_with_grad = [out for out in output if out.requires_grad]
+        assert (
+            len(out_with_grad) == 1
+        ), "Piper only supports one output per subgraph with requires_grad"
+        self.logger.debug(
+            f"Saving output activation {out_with_grad[0].shape} for stage {fwd_stage_id} mb {fwd_mb_idx}"
+        )
+        self.out_activation[fwd_stage_id][fwd_mb_idx] = out_with_grad[0]
+
+        # clear the input tensors
+        for i in self.input_idxs[fwd_stage_id]:
+            self.forward_args[fwd_stage_id][i] = None
+        
+        # POST PROCESS BACKWARD PASS
+        if bwd_stage_id == self.num_stages - 1:
+            self.loss.append(loss.item())
+
+        # Clear output activation after backward pass
+        self.out_activation[bwd_stage_id][bwd_mb_idx] = None
+        del out_activation
+
+        # POST FORWARD P2P OPERATIONS
+        if fwd_stage_id < self.num_stages - 1:
+            send_p2p_op = (fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True)
+            assert send_p2p_op not in self.p2p_cache
+            self.p2p_cache[send_p2p_op] = output
+            self._exec_p2p_op(fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True, p2p_stream=fwd_p2p_stream)
+
+        # POST BACKWARD P2P OPERATIONS
+        if bwd_stage_id > 0:
+            self._exec_p2p_op(bwd_stage_id, bwd_stage_id - 1, bwd_mb_idx, True, p2p_stream=bwd_p2p_stream)
+
+        if CLEANUP_MEMORY:
+            gc.collect()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
         return 1
 
     def _synchronize_gradients(self):
@@ -686,7 +911,8 @@ def _synchronize_gradients(self):
                             param.grad, op=dist.ReduceOp.AVG, group=self.dp_group
                         )
 
-    def _update(self, dep):
+    def _update(self, *deps):
+        self._wait_pending_sends()
         self.logger.debug(f"Actor {self.global_rank} waiting for backward sync events")
 
         # if dp degree > 1, make sure all gradients are synchronized before optimizer step
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 882d80b..b2bf643 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -16,26 +16,11 @@
 logger = create_logger("piper_compile", LOG_LEVEL)
 
 
-def _expand_task(task: Task | None) -> list[Task]:
-    if task is None or task.type == CompType.UPD:
-        return []
-    if task.type != CompType.FWD_BWD:
-        return [task]
-    return [
-        Task(pp_rank=task.pp_rank, batches=[task.batches[0]], type=CompType.FWD),
-        Task(pp_rank=task.pp_rank, batches=[task.batches[1]], type=CompType.BWD),
-    ]
-
-
 def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, stage_to_device: dict[int, int]):
     schedule = schedule.grid
     # Filter to fwd/bwd tasks and split FWD_BWD into two consecutive tasks (FWD then BWD).
-    schedule = [
-        list(itertools.chain.from_iterable(_expand_task(task) for task in row))
-        for row in schedule
-    ]
+    schedule = [[task for task in row if task is not None and task.type != CompType.UPD] for row in schedule]
 
-    logger.debug(f"Fwd/bwd schedule: {schedule}")
     assert num_devices == len(schedule), "Some rank has no fwd/bwd tasks"
     # Tasks are guaranteed to be fwd or bwd
     num_fwd_bwd_tasks = sum(len(rank_tasks) for rank_tasks in schedule)
@@ -52,17 +37,25 @@ def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, sta
         for rank, time_step in rank_to_next_task.items():
             if time_step < len(schedule[rank]):
                 task = schedule[rank][time_step]
-                assert len(task.batches) == 1, "FWD_BWD tasks should be expanded into two consecutive tasks"
-                batch = task.batches[0]
                 # Check the task's dependency is satisfied
                 # i.e., the sender has enqueued the comm
-                dependee = None
-                if task.type == CompType.FWD and batch.stage_id > 0:
-                    dependee = (batch.stage_id - 1, batch.stage_id, batch.mb_idx)
-                elif task.type == CompType.BWD and batch.stage_id < num_stages - 1:
-                    dependee = (batch.stage_id + 1, batch.stage_id, batch.mb_idx)
-
-                can_exec = dependee is None or dependee in p2p_comms
+                dependee = []
+                if task.type == CompType.FWD:
+                    batch = task.batches[0]
+                    if batch.stage_id > 0:
+                        dependee.append((batch.stage_id - 1, batch.stage_id, batch.mb_idx))
+                elif task.type == CompType.BWD:
+                    batch = task.batches[0]
+                    if batch.stage_id < num_stages - 1:
+                        dependee.append((batch.stage_id + 1, batch.stage_id, batch.mb_idx))
+                elif task.type == CompType.FWD_BWD:
+                    fwd_batch, bwd_batch = task.batches
+                    if fwd_batch.stage_id > 0:
+                        dependee.append((fwd_batch.stage_id - 1, fwd_batch.stage_id, fwd_batch.mb_idx))
+                    if bwd_batch.stage_id < num_stages - 1:
+                        dependee.append((bwd_batch.stage_id + 1, bwd_batch.stage_id, bwd_batch.mb_idx))
+
+                can_exec = dependee is [] or all(dependee in p2p_comms for dependee in dependee)
                 if can_exec:
                     executable_tasks.append((rank, task))
 
@@ -70,12 +63,20 @@ def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, sta
             break
 
         for rank, task in executable_tasks:
-            assert len(task.batches) == 1, "FWD_BWD tasks should be expanded into two consecutive tasks"
-            batch = task.batches[0]
-            if task.type == CompType.FWD and batch.stage_id < num_stages - 1:
-                p2p_comms.append((batch.stage_id, batch.stage_id + 1, batch.mb_idx))
-            elif task.type == CompType.BWD and batch.stage_id > 0:
-                p2p_comms.append((batch.stage_id, batch.stage_id - 1, batch.mb_idx))
+            if task.type == CompType.FWD:
+                batch = task.batches[0]
+                if batch.stage_id < num_stages - 1:
+                    p2p_comms.append((batch.stage_id, batch.stage_id + 1, batch.mb_idx))
+            elif task.type == CompType.BWD:
+                batch = task.batches[0]
+                if batch.stage_id > 0:
+                    p2p_comms.append((batch.stage_id, batch.stage_id - 1, batch.mb_idx))
+            elif task.type == CompType.FWD_BWD:
+                fwd_batch, bwd_batch = task.batches
+                if fwd_batch.stage_id < num_stages - 1:
+                    p2p_comms.append((fwd_batch.stage_id, fwd_batch.stage_id + 1, fwd_batch.mb_idx))
+                if bwd_batch.stage_id > 0:
+                    p2p_comms.append((bwd_batch.stage_id, bwd_batch.stage_id - 1, bwd_batch.mb_idx))
             rank_to_next_task[rank] += 1
 
         num_executed_tasks += len(executable_tasks)
@@ -86,7 +87,6 @@ def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, sta
             f"fwd/bwd tasks can be executed"
         )
 
-    logger.debug(f"P2P comms: {p2p_comms}")
     # (src_stage, dst_stage, mb_idx, is_sender)
     p2p_schedules = {rank: [] for rank in range(num_devices)}
     for src_stage, dst_stage, mb_idx in p2p_comms:
@@ -94,7 +94,9 @@ def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, sta
         dst_rank = stage_to_device[dst_stage]
         p2p_schedules[src_rank].append((src_stage, dst_stage, mb_idx, True))
         p2p_schedules[dst_rank].append((src_stage, dst_stage, mb_idx, False))
-    logger.debug(f"P2P schedules: {p2p_schedules}")
+    
+    for rank, schedule in p2p_schedules.items():
+        logger.info(f"P2P schedule for rank {rank}: {schedule}")
 
     return p2p_schedules
 
diff --git a/src/piper_exec.py b/src/piper_exec.py
index 4c4548b..118fe04 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -318,6 +318,7 @@ def piper_exec(
     """
     num_mbs = schedule.num_mbs()
     num_devices = schedule.num_ranks()
+    num_stages = schedule.num_stages()
     num_steps = len(schedule.grid[0])
 
     schedule = schedule.grid
@@ -330,7 +331,7 @@ def piper_exec(
 
     ret = []
     # map pp_rank -> latest dependency
-    deps = defaultdict(dict)
+    deps = {}
     for i in range(num_steps):
         for j in range(num_devices-1, -1, -1):
             if not i < len(schedule[j]):
@@ -347,20 +348,30 @@ def piper_exec(
                     case CompType.FWD:
                         stage_id, mb_idx = batches[0]
                         # logger.info(f"Executing forward {stage_id} mb {mb_idx} on actor {actor_id}")
-                        if pp_rank not in deps:
-                            dep = actor._forward.remote(stage_id, mb_idx, None)
+                        dep = deps[pp_rank] if pp_rank in deps else None
+                        if stage_id == 0:
+                            dep = actor._forward.remote(stage_id, mb_idx, dep)
                         else:
-                            dep = actor._forward.remote(stage_id, mb_idx, deps[pp_rank])
+                            dep = actor._exec_p2p_op.remote(stage_id - 1, stage_id, mb_idx, False, dep)
+                            dep = actor._forward.remote(stage_id, mb_idx, dep)
+                        if stage_id < num_stages - 1:
+                            dep = actor._exec_p2p_op.remote(stage_id, stage_id + 1, mb_idx, True, dep)
                         deps[pp_rank] = dep
                     case CompType.BWD:
                         stage_id, mb_idx = batches[0]
                         # logger.info(f"Executing backward {stage_id} mb {mb_idx} on actor {actor_id}")
-                        dep = actor._backward.remote(stage_id, mb_idx, deps[pp_rank], loss_fn=loss_fn)
+                        dep = deps[pp_rank]
+                        if stage_id < num_stages - 1:
+                            dep = actor._exec_p2p_op.remote(stage_id + 1, stage_id, mb_idx, False, dep)
+                        dep = actor._backward.remote(stage_id, mb_idx, dep, loss_fn=loss_fn)
+                        if stage_id > 0:
+                            dep = actor._exec_p2p_op.remote(stage_id, stage_id - 1, mb_idx, True, dep)
                         deps[pp_rank] = dep
                     case CompType.FWD_BWD:
                         fwd_stage_id, fwd_mb_idx = batches[0]
                         bwd_stage_id, bwd_mb_idx = batches[1]
                         # logger.info(f"Executing forward-backward {fwd_stage_id} mb {fwd_mb_idx} -> {bwd_stage_id} mb {bwd_mb_idx} on actor {actor_id}")
+                        # Don't explicitly schedule p2p ops -- overlapped in _forward_backward
                         dep = actor._forward_backward.remote(fwd_stage_id, fwd_mb_idx, bwd_stage_id, bwd_mb_idx, deps[pp_rank], loss_fn=loss_fn)
                         deps[pp_rank] = dep
     return ray.get(ret)
\ No newline at end of file
diff --git a/src/piper_utils.py b/src/piper_utils.py
index b489a56..eb9eb25 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -12,7 +12,7 @@
 
 import src.piper_patches
 
-LOG_LEVEL = "INFO"
+LOG_LEVEL = "DEBUG"
 
 """ 
 Print the backward graph of a tensor

From 05ea925427a1db3649072d0e5ec343c4241f47f3 Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Wed, 25 Feb 2026 00:12:53 +0000
Subject: [PATCH 049/100] test_tracing.py and Llama 70B config

---
 test/models/llama.py |  14 +++
 test/test_tracing.py | 240 +++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 254 insertions(+)
 create mode 100644 test/test_tracing.py

diff --git a/test/models/llama.py b/test/models/llama.py
index 5a64d7f..d401f59 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -99,6 +99,20 @@ class ModelArgs:
     max_seq_len=2048,
 )
 
+LLAMA_70B = ModelArgs(
+    dim=8192,
+    n_layers=80,
+    n_heads=64,
+    n_kv_heads=8,          # GQA (Grouped Query Attention)
+    vocab_size=128256,
+    multiple_of=4096,
+    ffn_dim_multiplier=1.3,
+    norm_eps=1e-5,
+    rope_theta=500000,
+    max_batch_size=32,
+    max_seq_len=8192,      # Llama 3 default context length
+)
+
 
 class RMSNorm(torch.nn.Module):
     def __init__(self, dim: int, eps: float = 1e-6):
diff --git a/test/test_tracing.py b/test/test_tracing.py
new file mode 100644
index 0000000..4f6876b
--- /dev/null
+++ b/test/test_tracing.py
@@ -0,0 +1,240 @@
+import torch
+import torch.fx as fx
+import time
+import logging
+from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_8B, LLAMA_70B
+from src.piper_compile import piper_setup
+from src import piper as piper_backend
+from src.piper_utils import piper_metadata
+import ray
+
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def test_meta_device_compile():
+    """Test torch.compile on Llama model using meta device."""
+    logger.info("Testing torch.compile on Llama model with meta device...")
+    
+    config = LLAMA_70B
+    seq_len = 256
+    batch_size = 16
+    
+    try:
+        # Create model on meta device to avoid allocating memory
+        with torch.device("meta"):
+            start = time.perf_counter()
+            model = Transformer(config, seq_len)
+            end = time.perf_counter()
+            logger.info("✓ Model created on meta device")
+            logger.info(f"Model creation time: {(end - start)*1e3:.0f} ms")
+            
+        # Create dummy inputs on meta device
+        tokens = torch.randint(0, config.vocab_size, (batch_size, seq_len), device="meta")
+        logger.info(f"✓ Dummy tokens created on meta device: shape {tokens.shape}")
+        
+        # Compile the model on meta device
+        start = time.perf_counter()
+        compiled_model = torch.compile(model)
+        end = time.perf_counter()
+        logger.info("✓ torch.compile succeeded on meta device model")
+        logger.info(f"Compilation time: {(end - start)*1e3:.0f} ms")
+                
+        try:
+            output = compiled_model(tokens)
+            logger.info(f"✓ Inference successful on meta device, output shape: {output.shape}")
+        except Exception as e:
+            import traceback
+            tb = traceback.format_exc()
+            err_type = type(e).__name__
+            err_msg = str(e)
+            # Log detailed information for debugging
+            logger.warning(
+                f"⚠ Forward pass on meta device produced error (expected): {err_type}: {err_msg}\n{tb}"
+            )
+            # Add short hints for common error types
+            if isinstance(e, KeyError):
+                logger.warning(f"  Missing key in mapping: {e.args}")
+            elif isinstance(e, TypeError):
+                logger.warning(f"  TypeError details: args={e.args}")
+            elif isinstance(e, RuntimeError):
+                logger.warning(f"  RuntimeError details: args={e.args}")
+            
+        return True
+    except Exception as e:
+        logger.error(f"✗ Meta device compile test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_meta_device_fx_trace():
+    """Test torch.fx.symbolic_trace on Llama model using meta device."""
+    logger.info("\nTesting torch.fx.symbolic_trace on Llama model with meta device...")
+    
+    config = LLAMA_DEBUG
+    seq_len = 128
+    batch_size = 1
+    
+    try:
+        # Create model on meta device
+        model = Transformer(config, seq_len).to("meta")
+        logger.info("✓ Model created on meta device")
+        model.eval()
+        
+        # Create dummy inputs on meta device
+        tokens = torch.randint(0, config.vocab_size, (batch_size, seq_len), device="meta")
+        logger.info(f"✓ Dummy tokens created on meta device: shape {tokens.shape}")
+        
+        # Attempt symbolic tracing with meta device
+        traced_model = fx.symbolic_trace(model)
+        logger.info("✓ torch.fx.symbolic_trace succeeded")
+        logger.info(f"✓ Graph has {len(list(traced_model.graph.nodes))} nodes")
+        
+        return True
+    except Exception as e:
+        logger.error(f"✗ torch.fx.symbolic_trace failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_meta_device_graph_module():
+    """Test creating a GraphModule from meta device model."""
+    logger.info("\nTesting GraphModule creation with meta device...")
+    
+    config = LLAMA_8B
+    seq_len = 128
+    batch_size = 1
+    
+    try:
+        # Create model on meta device
+        model = Transformer(config, seq_len).to("meta")
+        logger.info("✓ Model created on meta device")
+        model.eval()
+        
+        # Try to trace and examine the computation graph
+        tokens = torch.randint(0, config.vocab_size, (batch_size, seq_len), device="meta")
+        
+        # Use torch._dynamo to trace the model
+        traced = torch.compile(model, backend="eager", fullgraph=False)
+        logger.info("✓ Model compiled with torch.compile on meta device")
+        
+        # Get model info
+        num_params = sum(p.numel() for p in model.parameters())
+        logger.info(f"✓ Model has {num_params:,} parameters")
+        
+        return True
+    except Exception as e:
+        logger.error(f"✗ GraphModule test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+if __name__ == "__main__":
+    logger.info("Starting Llama symbolic tracing with meta device...\n")
+    
+    results = {
+        "torch.compile (meta)": test_meta_device_compile(),
+        # "torch.fx.symbolic_trace (meta)": test_meta_device_fx_trace(),
+        # "GraphModule (meta)": test_meta_device_graph_module(),
+        # "piper_backend (meta)": False,
+    }
+
+    def test_piper_backend_meta():
+        """Quick smoke test that calls the piper backend setup (no schedule execution)."""
+        logger.info("\nTesting piper backend setup with meta device...")
+        config = LLAMA_DEBUG
+        seq_len = 128
+        batch_size = 1
+        try:
+            # dummy input shapes (on cpu) are sufficient for piper_setup
+            x = torch.randint(0, config.vocab_size, (batch_size, seq_len))
+            # call piper setup which prepares the pipeline/actors
+            compiled = piper_setup(Transformer, (config, seq_len), torch.optim.Adam, [x], 2, 2)
+            logger.info("✓ piper_setup completed (no schedule run)")
+            return True
+        except Exception as e:
+            logger.error(f"✗ piper backend setup failed: {e}")
+            import traceback
+            traceback.print_exc()
+            return False
+
+    # run piper backend smoke test last (it may start actors)
+    # results["piper_backend (meta)"] = test_piper_backend_meta()
+
+    def test_piper_backend_meta_graph():
+        """Compile the model with the `piper` torch.compile backend on meta device.
+
+        This attempts to invoke the backend compilation path to produce serialized
+        graphmodules without requiring real Ray actors or GPUs by monkeypatching
+        a minimal actor handle and `ray.get`.
+        """
+        logger.info("\nTesting piper backend as torch.compile backend with meta device...")
+        config = LLAMA_DEBUG
+        seq_len = 128
+
+        # Build model on meta device
+        model = Transformer(config, seq_len).to("meta")
+
+        # Prepare example input on meta device
+        tokens = torch.randint(0, config.vocab_size, (1, seq_len), device="meta")
+
+        # Save original ray.get to restore later
+        orig_ray_get = ray.get
+        try:
+            # Set minimal piper metadata to allow backend to run
+            piper_metadata.currently_compiling = True
+            piper_metadata.current_stage = 0
+            piper_metadata.current_actor = 0
+            piper_metadata.first_graph_of_stage = True
+
+            # Create a minimal dummy actor handle with a .load_graph.remote
+            class _DummyLoad:
+                def remote(self, *a, **k):
+                    return None
+
+            class _DummyActor:
+                def __init__(self):
+                    self.load_graph = _DummyLoad()
+
+            piper_metadata.actors = {0: _DummyActor()}
+
+            # Monkeypatch ray.get so it accepts our dummy return value
+            ray.get = lambda x: x
+
+            # Compile with piper backend (this will call into src.piper.piper)
+            compiled = torch.compile(model, backend=piper_backend.piper)
+            logger.info("✓ torch.compile returned a compiled function (piper backend)")
+
+            # Call compiled to trigger backend handling (serialization/loading)
+            try:
+                _ = compiled(tokens)
+                logger.info("✓ Invoked compiled function (backend path exercised)")
+            except Exception:
+                # It's acceptable for the runtime invocation to raise when using meta tensors;
+                # the important part is that the backend path executed during compilation.
+                logger.info("⚠ Invocation raised during meta-device run (expected) — compilation path exercised")
+
+            return True
+        except Exception as e:
+            logger.error(f"✗ piper backend meta compile failed: {e}")
+            import traceback
+            traceback.print_exc()
+            return False
+        finally:
+            # restore
+            ray.get = orig_ray_get
+            piper_metadata.currently_compiling = False
+            piper_metadata.actors = {}
+
+    # results["piper_backend_graph (meta)"] = test_piper_backend_meta_graph()
+    
+    logger.info("\n" + "="*60)
+    logger.info("Test Results Summary:")
+    for test_name, result in results.items():
+        status = "✓ PASS" if result else "✗ FAIL"
+        logger.info(f"  {test_name}: {status}")
+    logger.info("="*60)
\ No newline at end of file

From 170941664a0ea65a122e2486919677c594716e58 Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Wed, 25 Feb 2026 00:16:36 +0000
Subject: [PATCH 050/100] Llama changes to allow for meta device tracing

---
 test/models/llama.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/test/models/llama.py b/test/models/llama.py
index d401f59..47b0c74 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -401,15 +401,18 @@ def log_size(layer, indent=0):
         )
         log_size(self.output)
 
-        self.freqs_cis = precompute_freqs_cis(
+        # Register freq_cis and mask as buffers so they are moved with the model
+        freqs_cis = precompute_freqs_cis(
             params.dim // params.n_heads,
             self.seq_len,
             params.rope_theta,
-        ).to('cuda')
+        )
+        self.register_buffer("freqs_cis", freqs_cis, persistent=False)
 
         mask = torch.full((self.seq_len, self.seq_len), float("-inf"))
         mask = torch.triu(mask, diagonal=1)
-        self.mask = torch.hstack([torch.zeros((self.seq_len, 0)), mask]).to('cuda')
+        mask = torch.hstack([torch.zeros((self.seq_len, 0)), mask])
+        self.register_buffer("mask", mask, persistent=False)
 
     """
     forward method for pp4-1f1b or pp2-interleaved-1f1b

From c41d7ca074d607718075e5ffca7ddea19839f5a0 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Wed, 25 Feb 2026 03:38:46 +0000
Subject: [PATCH 051/100] isend/irecv -> send/recv. in order_p2p_comm schedule
 FWD p2p ops before BWD p2p ops when there is a choice.

---
 requirements.txt         |  2 +-
 src/piper_actor.py       | 86 ++++++++++++++--------------------------
 src/piper_compile.py     | 11 ++++-
 src/piper_exec.py        |  3 ++
 src/piper_utils.py       |  2 +-
 test/models/llama.py     | 12 +++---
 test/schedule_helpers.py | 62 +++++++++++++++++++++++++++++
 test/test_llama.py       | 10 ++++-
 8 files changed, 120 insertions(+), 68 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index abc398c..c3060ad 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
-python==3.10
 numpy
 ray==2.53.0
 torch==2.9.1+cu128
 cupy-cuda12x
+graphviz
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 0a2c945..4b4894e 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -141,8 +141,6 @@ def __init__(
         self.out_activation = defaultdict(dict)
         # map (src_stage, dst_stage, mb_idx, is_sender) -> tensor for p2p communication
         self.p2p_cache = dict()
-        # pending send operation handles; waited on before starting the next task
-        self.pending_send_handles: list = []
         # accumuate loss for each microbatch
         self.loss = []
         # map stage id -> data parallel communication operations
@@ -167,12 +165,6 @@ def reset_p2p_states(self):
         self.executed_p2ps = set()
         self.p2p_cache = dict()
 
-    def _wait_pending_sends(self) -> None:
-        """Wait on all pending send handles from previous task before starting the next."""
-        for handle in self.pending_send_handles:
-            handle.wait()
-        self.pending_send_handles.clear()
-
     def get_trace_data(self) -> dict:
         return self.global_rank, self.trace_data
 
@@ -521,19 +513,13 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
                 f"Dispatch fwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
             )
             self._start_timing(p2p_stream, "fwd_p2p_recv")
-            p2p_op_list = [
-                dist.P2POp(
-                    dist.irecv,
-                    inputs_to_recv[i],
-                    global_src_rank,
-                    group=self.pp_group,
-                )
-                for i in self.input_idxs[stage_id]
-            ]
             with torch.cuda.stream(p2p_stream):
-                recv_handles = dist.batch_isend_irecv(p2p_op_list)
-            for handle in recv_handles:
-                handle.wait()
+                for i in self.input_idxs[stage_id]:
+                    dist.recv(
+                        inputs_to_recv[i],
+                        src=global_src_rank,
+                        group=self.pp_group,
+                    )
             # Ensure the default stream only consumes tensors after recv completes.
             comp_stream.wait_stream(p2p_stream)
             torch.cuda.synchronize()
@@ -563,23 +549,21 @@ def _exec_fwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
             self.p2p_cache[p2p_op] = output
         else:
             # Ensure send sees the latest writes from the default stream.
+            self.logger.debug(
+                f"Dispatch fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
+            )
             self._start_timing(p2p_stream, "fwd_p2p_send")
             p2p_stream.wait_stream(comp_stream)
-            p2p_op_list = [
-                dist.P2POp(
-                    dist.isend,
-                    output[i],
-                    global_dst_rank,
-                    group=self.pp_group,
-                )
-                for i in range(len(output))
-            ]
             with torch.cuda.stream(p2p_stream):
-                send_handles = dist.batch_isend_irecv(p2p_op_list)
-            self.pending_send_handles.extend(send_handles)
+                for i in range(len(output)):
+                    dist.send(
+                        output[i],
+                        dst=global_dst_rank,
+                        group=self.pp_group,
+                    )
             self._stop_timing(p2p_stream, "fwd_p2p_send")
             self.logger.debug(
-                f"Dispatched fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
+                f"Completed fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
             )
 
     def _exec_bwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
@@ -607,10 +591,9 @@ def _exec_bwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
             )
             self._start_timing(p2p_stream, "bwd_p2p_recv")
             with torch.cuda.stream(p2p_stream):
-                recv_handle = dist.irecv(
+                dist.recv(
                     input_grad, src=global_src_rank, group=self.pp_group
                 )
-            recv_handle.wait()
             # Ensure the default stream only consumes gradients after recv completes.
             comp_stream.wait_stream(p2p_stream)
             torch.cuda.synchronize()
@@ -642,24 +625,24 @@ def _exec_bwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
             p2p_op = (stage_id, stage_id - 1, mb_idx, True)
             self.p2p_cache[p2p_op] = output_grad
         else:
-            # Ensure send sees the latest gradient writes from the default stream.
+            self.logger.debug(
+                f"Dispatch bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
+            )
             self._start_timing(p2p_stream, "bwd_p2p_send")
+            # Ensure send sees the latest gradient writes from the default stream.
             p2p_stream.wait_stream(comp_stream)
             with torch.cuda.stream(p2p_stream):
-                handle = dist.isend(
+                dist.send(
                     output_grad, dst=global_src_rank, group=self.pp_group
                 )
-                self.pending_send_handles.append(handle)
             self._stop_timing(p2p_stream, "bwd_p2p_send")
             self.logger.debug(
-                f"Dispatched bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
+                f"Completed bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
             )
 
         self.inp_activation[stage_id][mb_idx] = None
     
     def _forward(self, stage_id: int, mb_idx: int, *deps):
-        # self._wait_pending_sends()
-
         comp_stream = self.comp_stream
 
         self.logger.debug(
@@ -671,7 +654,6 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
             for i, inp in zip(self.input_idxs[stage_id], self.inputs):
                 self.forward_args[stage_id][i] = inp
         else:
-            # self._exec_p2p_op(stage_id - 1, stage_id, mb_idx, False)
             inputs_from_prev_stage = self.p2p_cache.pop(
                 (stage_id - 1, stage_id, mb_idx, False)
             )
@@ -723,7 +705,6 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
             send_p2p_op = (stage_id, stage_id + 1, mb_idx, True)
             assert send_p2p_op not in self.p2p_cache
             self.p2p_cache[send_p2p_op] = output
-            # self._exec_p2p_op(stage_id, stage_id + 1, mb_idx, True)
 
         if CLEANUP_MEMORY:
             gc.collect()
@@ -734,11 +715,11 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
             f"Forward {stage_id} mb {mb_idx} on actor {self.global_rank} returning {[out.shape for out in output]}"
         )
 
+        torch.cuda.synchronize()
+
         return 1
 
     def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        # self._wait_pending_sends()
-
         comp_stream = self.comp_stream
 
         self.logger.debug(
@@ -747,8 +728,6 @@ def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         out_activation = self.out_activation[stage_id][mb_idx]
 
         if stage_id < self.num_stages - 1:
-            # self._exec_p2p_op(stage_id + 1, stage_id, mb_idx, False)
-
             input_grad = self.p2p_cache.pop(
                 (stage_id + 1, stage_id, mb_idx, False)
             )
@@ -774,19 +753,16 @@ def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         self.out_activation[stage_id][mb_idx] = None
         del out_activation
 
-        # if stage_id > 0:
-        #     self._exec_p2p_op(stage_id, stage_id - 1, mb_idx, True)
-
         if CLEANUP_MEMORY:
             gc.collect()
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
 
+        torch.cuda.synchronize()
+
         return 1
 
     def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: int, bwd_mb_idx: int, *deps, loss_fn=None):
-        # self._wait_pending_sends()
-
         fwd_comp_stream = self.comp_stream
         bwd_comp_stream = self.overlapped_comp_stream
         fwd_p2p_stream = self.p2p_stream
@@ -898,6 +874,8 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
 
+        torch.cuda.synchronize()
+
         return 1
 
     def _synchronize_gradients(self):
@@ -935,12 +913,6 @@ def _update(self, *deps):
         losses = self.loss
         self.loss.clear()
 
-        # Aggressive memory cleanup after optimizer step
-        # if CLEANUP_MEMORY:
-        #     gc.collect()
-        #     if torch.cuda.is_available():
-        #         torch.cuda.empty_cache()
-
         torch.cuda.synchronize()
 
         self.reset_p2p_states()
diff --git a/src/piper_compile.py b/src/piper_compile.py
index b2bf643..306385e 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -15,6 +15,14 @@
 
 logger = create_logger("piper_compile", LOG_LEVEL)
 
+def _sort_by_task(executable_tasks: list[tuple[int, Task]]) -> list[tuple[int, Task]]:
+    """Sort executable tasks by type so that BWD comes before FWD (then FWD_BWD)."""
+    type_order = {CompType.BWD: 2, CompType.FWD: 1, CompType.FWD_BWD: 0}
+    return sorted(
+        executable_tasks,
+        key=lambda item: type_order.get(item[1].type, 3),
+    )
+
 
 def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, stage_to_device: dict[int, int]):
     schedule = schedule.grid
@@ -62,6 +70,7 @@ def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, sta
         if not executable_tasks:
             break
 
+        executable_tasks = _sort_by_task(executable_tasks)
         for rank, task in executable_tasks:
             if task.type == CompType.FWD:
                 batch = task.batches[0]
@@ -96,7 +105,7 @@ def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, sta
         p2p_schedules[dst_rank].append((src_stage, dst_stage, mb_idx, False))
     
     for rank, schedule in p2p_schedules.items():
-        logger.info(f"P2P schedule for rank {rank}: {schedule}")
+        logger.debug(f"P2P schedule for rank {rank}: {schedule}")
 
     return p2p_schedules
 
diff --git a/src/piper_exec.py b/src/piper_exec.py
index 118fe04..b61c663 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -80,6 +80,9 @@ class Task(NamedTuple):
     batches: list[BatchMeta]
     type: CompType
 
+    def __repr__(self) -> str:
+        return f"Task(pp_rank={self.pp_rank}, batches={[(batch.stage_id, batch.mb_idx) for batch in self.batches]}, type={self.type})"
+
 
 @dataclass
 class Schedule2D:
diff --git a/src/piper_utils.py b/src/piper_utils.py
index eb9eb25..b489a56 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -12,7 +12,7 @@
 
 import src.piper_patches
 
-LOG_LEVEL = "DEBUG"
+LOG_LEVEL = "INFO"
 
 """ 
 Print the backward graph of a tensor
diff --git a/test/models/llama.py b/test/models/llama.py
index 5a64d7f..b1a24e8 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -409,19 +409,19 @@ def forward(self, tokens: torch.Tensor):
         with torch.fx.traceback.annotate({"stage": 0}):
             h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
             start_pos = 0
-            for layer in self.layers[:self.n_layers//4]:
+            for layer in self.layers[:self.n_layers//4+1]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
 
         with torch.fx.traceback.annotate({"stage": 1}):
-            for layer in self.layers[self.n_layers//4:self.n_layers//2]:
+            for layer in self.layers[self.n_layers//4+1:self.n_layers//2+2]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
 
         with torch.fx.traceback.annotate({"stage": 2}):
-            for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
+            for layer in self.layers[self.n_layers//2+2:3*self.n_layers//4+3]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
 
         with torch.fx.traceback.annotate({"stage": 3}):
-            for layer in self.layers[3*self.n_layers//4:]:
+            for layer in self.layers[3*self.n_layers//4+3:]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
             h = self.norm(h) if self.norm else h
             output = self.output(h).float() if self.output else h
@@ -439,11 +439,11 @@ def forward(self, tokens: torch.Tensor):
     #     with torch.fx.traceback.annotate({"stage": 0}):
     #         h = self.tok_embeddings(tokens)
     #         start_pos = 0
-    #         for layer in self.layers[:self.n_layers//2]:
+    #         for layer in self.layers[:self.n_layers//2+2]:
     #             h = layer(h, start_pos, self.freqs_cis, self.mask)
 
     #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[self.n_layers//2:]:
+    #         for layer in self.layers[self.n_layers//2+2:]:
     #             h = layer(h, start_pos, self.freqs_cis, self.mask)
     #         h = self.norm(h)
     #         output = self.output(h).float()
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index 9a9ccc8..5d8cdab 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -421,6 +421,68 @@ def build_1f1b_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
         ]
     ])
 
+INTERLEAVED_1F1B_PP2_MB6_SCHEDULE = Schedule2D(
+    grid=[
+        [
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[], type=CompType.UPD),
+        ],
+        [
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[], type=CompType.UPD),
+            None,
+        ],
+    ])
+
 # 2D grid literals for interleaved 1F1B (used only to build DAGs below)
 INTERLEAVED_1F1B_PP2_SCHEDULE = Schedule2D(
     grid=[
diff --git a/test/test_llama.py b/test/test_llama.py
index f67cc6c..6c6fc1c 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -15,7 +15,8 @@
     build_1f1b_schedule, 
     build_gpipe_schedule, 
     print_schedule,
-    INTERLEAVED_1F1B_PP2_SCHEDULE, 
+    INTERLEAVED_1F1B_PP2_SCHEDULE,
+    INTERLEAVED_1F1B_PP2_MB6_SCHEDULE,
     INTERLEAVED_1F1B_PP4_SCHEDULE,
     NO_PP_SCHEDULE,
     DUALPIPEV_SCHEDULE,
@@ -49,7 +50,10 @@ def main(args):
         case "no-pp":
             schedule = NO_PP_SCHEDULE
         case "interleaved-1f1b":
-            schedule = INTERLEAVED_1F1B_PP2_SCHEDULE if args.pp == 2 else INTERLEAVED_1F1B_PP4_SCHEDULE
+            if args.mbs == 6:
+                schedule = INTERLEAVED_1F1B_PP2_MB6_SCHEDULE
+            else:
+                schedule = INTERLEAVED_1F1B_PP2_SCHEDULE if args.pp == 2 else INTERLEAVED_1F1B_PP4_SCHEDULE
         case "1f1b":
             schedule = build_1f1b_schedule(args.mbs, args.pp)
         case "gpipe":
@@ -97,6 +101,8 @@ def main(args):
     )
 
     if args.tracing:
+        from src.piper_utils import piper_metadata
+        actors = piper_metadata.actors
         ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
 
         print(f"Running {args.warmup} tracing iterations...")

From f435e1e76f98fa5dc015c8316595bbb9a3200753 Mon Sep 17 00:00:00 2001
From: Yi Pan <conlesspan@outlook.com>
Date: Tue, 24 Feb 2026 04:27:51 +0000
Subject: [PATCH 052/100] feat: basic DualPipe-style overlapping.

TODO: enhanced comp/comm overlap
---
 src/piper.py                 |   6 +-
 src/piper_compile.py         |   6 +-
 src/piper_graph_transform.py |   2 +-
 src/piper_overlap_actor.py   | 989 +++++++++++++++++++++++++++++++++++
 4 files changed, 997 insertions(+), 6 deletions(-)
 create mode 100644 src/piper_overlap_actor.py

diff --git a/src/piper.py b/src/piper.py
index 25f562a..8239063 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -39,9 +39,6 @@ def piper(gm, example_inputs, **kwargs):
     refs = []
     actor_stages = []
     for (stage_id, stage_gm, input_idxs, graphargs, placeholders) in submodules:
-        if dp_degree > 1:
-            stage_gm = _insert_a2a_ops(stage_gm)
-
         stage_gm(*graphargs)
         start = torch.cuda.Event(enable_timing=True)
         start.record()
@@ -52,6 +49,9 @@ def piper(gm, example_inputs, **kwargs):
         torch.cuda.synchronize()
         print(f"Stage {stage_id} time measured on controller: {start.elapsed_time(end) / 5:.2f} ms")
 
+        if dp_degree > 1:
+            stage_gm = _insert_a2a_ops(stage_gm)
+
         actor_id = piper_metadata.stage_to_device[stage_id]
         actor = _get_actor(actor_id)
         actor_stages.append((actor, stage_id))
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 306385e..ffbdbad 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -8,7 +8,7 @@
 
 from torch._dynamo.backends.debugging import eager
 
-from .piper_actor import _create_actors
+from .piper_overlap_actor import _create_actors
 from .piper_utils import piper_metadata, create_logger, LOG_LEVEL
 from .piper_exec import CompType, Schedule2D, Task
 from .piper import piper
@@ -125,6 +125,7 @@ def piper_setup(
     """
 
     stage_to_device = schedule.stage_to_device()
+    assert len(stage_to_device) > 0
     piper_metadata.stage_to_device = stage_to_device
 
     num_mbs = schedule.num_mbs()
@@ -138,7 +139,8 @@ def piper_setup(
         stage_to_device=stage_to_device
     )
     _create_actors(
-        num_devices, optim_fn, num_mbs, num_stages, p2p_schedules, naive_gradient_sync
+        num_devices, optim_fn, num_mbs, num_stages, p2p_schedules, naive_gradient_sync,
+        profile=True, mode="sequential",
     )
 
     last_stage_rank = stage_to_device[num_stages - 1]
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index f9eec1e..965a16c 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -57,7 +57,7 @@ def forward(ctx, output, input_tensor):
         ctx.actor_self = piper_metadata.actor_self
         ctx.group = ctx.actor_self.dp_group
         ctx.global_rank = ctx.actor_self.global_rank
-        ctx.stream = ctx.actor_self.a2a_stream
+        ctx.stream = ctx.actor_self.comp_stream
 
         logger.debug(f"Dispatch AllToAllSingleFunction forward rank={ctx.global_rank}, shape={input_tensor.shape}")
         
diff --git a/src/piper_overlap_actor.py b/src/piper_overlap_actor.py
new file mode 100644
index 0000000..fb39762
--- /dev/null
+++ b/src/piper_overlap_actor.py
@@ -0,0 +1,989 @@
+import ray
+import torch
+import os
+import gc
+from collections import defaultdict
+import torch.distributed as dist
+
+from .piper_utils import (
+    _deserialize_graphmodule,
+    create_logger,
+    LOG_LEVEL,
+    piper_metadata,
+)
+
+CLEANUP_MEMORY = False
+
+logger = create_logger("piper_overlap_actor", LOG_LEVEL)
+
+
+def _get_rank(pp_rank, dp_rank, pp_degree):
+    return pp_rank + dp_rank * pp_degree
+
+
+def _create_actors(
+    num_actors,
+    optim_class,
+    num_mbs,
+    num_stages,
+    p2p_schedules,
+    naive_gradient_sync=False,
+    profile=False,
+    mode="sequential",
+):
+    dp_rank = int(os.environ["PIPER_DP_RANK"])
+    world_size = int(os.environ["PIPER_WORLD_SIZE"])
+    dp_degree = int(os.environ["PIPER_DP_DEGREE"])
+    pp_degree = int(os.environ["PIPER_PP_DEGREE"])
+
+    from .piper_utils import piper_metadata
+
+    for pp_rank in range(num_actors):
+        global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
+        p2p_schedule = p2p_schedules[pp_rank]
+        actor = PiperOverlapActor.options(num_gpus=1).remote(
+            pp_rank,
+            optim_class,
+            world_size,
+            num_mbs,
+            num_stages,
+            p2p_schedule,
+            naive_gradient_sync,
+            dp_rank=dp_rank,
+            dp_degree=dp_degree,
+            pp_degree=pp_degree,
+            profile=profile,
+            mode=mode,
+        )
+        piper_metadata.actors[pp_rank] = actor
+        logger.debug(
+            f"DP rank {dp_rank} created actor {actor} global rank {global_rank}"
+        )
+
+
+def _get_actor(pp_rank):
+    from .piper_utils import piper_metadata
+
+    return piper_metadata.actors[pp_rank]
+
+@ray.remote
+class PiperOverlapActor:
+    def __init__(
+        self,
+        pp_rank,
+        optim_class,
+        world_size,
+        num_mbs,
+        num_stages,
+        p2p_schedule,
+        naive_gradient_sync=False,
+        dp_rank=0,
+        dp_degree=1,
+        pp_degree=1,
+        profile=False,
+        mode="sequential",
+    ):
+        self.logger = create_logger("piper_overlap_actor", LOG_LEVEL)
+        self.profile = profile
+        self.mode = mode
+
+        self.pp_rank = pp_rank
+        self.optim_class = optim_class
+        self.naive_gradient_sync = naive_gradient_sync
+
+        self.dp_rank = dp_rank
+        self.dp_degree = dp_degree
+        self.pp_degree = pp_degree
+        self.world_size = world_size
+
+        self.num_mbs = num_mbs
+        self.num_stages = num_stages
+        # list of (src_stage, dst_stage, mb_idx, is_sender)
+        self.p2p_schedule = p2p_schedule
+        self.next_p2p_idx = 0
+        # set of (src_stage, dst_stage, mb_idx, is_sender) for completed p2p ops
+        self.executed_p2ps = set()
+        self.dp_group = None
+        self.pp_group = None
+        self.device = "cuda"
+
+        self.global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
+
+        self.logger.info(
+            f"Initializing Ray actor {self.global_rank} GPU {os.environ['CUDA_VISIBLE_DEVICES']}"
+        )
+
+        self.input = None
+        self.labels = None
+
+        self.comp_stream = torch.cuda.Stream()
+        self.comm_stream = torch.cuda.Stream()
+        self.a2a_stream = torch.cuda.Stream()
+        self.p2p_stream = torch.cuda.Stream()
+        self.overlapped_comp_stream = torch.cuda.Stream()
+        self.overlapped_p2p_stream = torch.cuda.Stream()
+        if mode == "naive":
+            self.per_mb_streams = [torch.cuda.Stream() for _ in range(2)]
+
+        # map stage id -> compiled fx.Graph function
+        self.forward_fns = dict()
+        # map stage id -> original GraphModule (for hook registration)
+        self.graph_modules = dict()
+        # map stage id -> model parameters used by the fx.Graph with holes (None values) for input tensors
+        self.forward_args = dict()
+        # map stage id -> input idx -> input tensor metadata
+        self.forward_input_meta = defaultdict(dict)
+        # map stage id -> indices of the input tensors (as opposed to model parameters) used by the fx.Graph
+        self.input_idxs = dict()
+        # map stage id -> optimizer for the fx.Graph
+        self.optims = dict()
+        # map stage id -> mb_idx -> previous activation (if this stage is not first)
+        self.inp_activation = defaultdict(dict)
+        # map stage id -> mb_idx -> current activation
+        self.out_activation = defaultdict(dict)
+        # map (src_stage, dst_stage, mb_idx, is_sender) -> tensor for p2p communication
+        self.p2p_cache = dict()
+        # accumuate loss for each microbatch
+        self.loss = []
+        # map stage id -> data parallel communication operations
+        self.comm_ops = dict()
+        # map stage id -> tensor id -> comm op status
+        self.comm_op_status = defaultdict(lambda: defaultdict(int))
+        # map stage id -> tensor id -> comm op handle
+        self.comm_op_handles = defaultdict(dict)
+        # map stage id -> list of tensor ids that require communciation
+        self.comm_op_tensor_ids = dict()
+
+        self.tracing = False
+        self.trace_events = dict()
+        self.trace_data = defaultdict(list)
+
+        from .piper_utils import piper_metadata
+
+        piper_metadata.actor_self = self
+
+    def reset_p2p_states(self):
+        self.next_p2p_idx = 0
+        self.executed_p2ps = set()
+        self.p2p_cache = dict()
+
+    def get_trace_data(self) -> dict:
+        return self.global_rank, self.trace_data
+
+    def clear_trace_data(self) -> None:
+        self.trace_data.clear()
+
+    def set_tracing(self, enabled: bool) -> None:
+        self.tracing = enabled
+        self.logger.info(
+            f"Actor {self.global_rank}: Tracing {'enabled' if enabled else 'disabled'}"
+        )
+
+    def start_mem_tracing(self) -> None:
+        torch.cuda.memory._record_memory_history()
+
+    def stop_mem_tracing(self) -> None:
+        torch.cuda.memory._dump_snapshot(
+            f"actor{self.global_rank}_memory_snapshot_mb4_gpipe.pickle"
+        )
+        self.logger.info(
+            f"Saved memory snapshot to actor{self.global_rank}_memory_snapshot_mb4_gpipe.pickle"
+        )
+        torch.cuda.memory._record_memory_history(enabled=None)
+
+    def reset_peak_memory(self):
+        torch.cuda.reset_peak_memory_stats()
+
+    def get_peak_memory(self):
+        return torch.cuda.max_memory_allocated() / (1024**3)
+
+    def load_input(self, inputs):
+        self.inputs = [inp.to(self.device) for inp in inputs]
+        self.logger.debug(f"Actor {self.global_rank} loaded inputs {len(self.inputs)}")
+
+    def load_labels(self, labels):
+        self.labels = labels.to(self.device)
+        self.logger.debug(f"Actor {self.global_rank} loaded labels {self.labels.shape}")
+
+    def _start_timing(self, stream, label):
+        if self.tracing:
+            if label not in self.trace_events:
+                self.trace_events[label] = (
+                    torch.cuda.Event(enable_timing=True),
+                    torch.cuda.Event(enable_timing=True),
+                )
+            start, _ = self.trace_events[label]
+            start.record(stream)
+
+    def _stop_timing(self, stream, label):
+        if self.tracing:
+            if label in self.trace_events:
+                start, stop = self.trace_events[label]
+                stop.record(stream)
+                stop.synchronize()
+                self.trace_data[label].append(start.elapsed_time(stop))
+
+    def _join_process_groups(self):
+        master_addr = os.environ.get("PIPER_MASTER_ADDR", "127.0.0.1")
+        master_port = os.environ.get("PIPER_MASTER_PORT", "10000")
+        init_method = f"tcp://{master_addr}:{master_port}"
+
+        self.device = f"cuda:{self.global_rank % torch.cuda.device_count()}"
+        torch.cuda.set_device(self.device)
+
+        dist.init_process_group(
+            "nccl",
+            init_method=init_method,
+            rank=self.global_rank,
+            world_size=self.world_size,
+        )
+        self.logger.debug(
+            f"Actor {self.global_rank} has GPU {os.environ['CUDA_VISIBLE_DEVICES']}, joined the global process group"
+        )
+
+        if self.dp_degree > 1:
+            self._join_dp_process_group()
+        if self.pp_degree > 1:
+            self._join_pp_process_group()
+
+    def _join_dp_process_group(self):
+        num_dp_groups = self.world_size // self.dp_degree
+        for dp_group_id in range(num_dp_groups):
+            group_ranks = [
+                (dp_group_id + num_dp_groups * i) for i in range(self.dp_degree)
+            ]
+            process_group = dist.new_group(ranks=group_ranks, backend="nccl")
+            if self.global_rank % num_dp_groups == dp_group_id:
+                self.dp_group = process_group
+                self.logger.debug(
+                    f"Global rank {self.global_rank} joined its dp group {dp_group_id} along with ranks {group_ranks}"
+                )
+
+    def _join_pp_process_group(self):
+        num_pp_groups = self.world_size // self.pp_degree
+        for pp_group_id in range(num_pp_groups):
+            group_ranks = [
+                (pp_group_id * self.pp_degree + i) for i in range(self.pp_degree)
+            ]
+            process_group = dist.new_group(ranks=group_ranks, backend="nccl")
+            if self.global_rank // self.pp_degree == pp_group_id:
+                self.pp_group = process_group
+                self.logger.debug(f"Global rank {self.global_rank} joined its pp group {pp_group_id} along with ranks {group_ranks}")
+
+    def shutdown(self):
+        dist.destroy_process_group()
+
+    def _prepare_dp_comm_ops(self, stage_id):
+        def hook_maker(tensor_id):
+            def post_backward_hook(grad):
+                self.comm_op_status[stage_id][tensor_id] += 1
+                self.logger.debug(
+                    f"Updating status on dp_rank: {self.dp_rank}, tensor={tensor_id}, status={self.comm_op_status[stage_id][tensor_id]}"
+                )
+                if self.comm_op_status[stage_id][tensor_id] == self.num_mbs:
+                    with torch.cuda.stream(self.comm_stream):
+                        handle = dist.all_reduce(
+                            grad,
+                            op=dist.ReduceOp.AVG,
+                            group=self.dp_group,
+                            async_op=True,
+                        )
+                    self.logger.debug(
+                        f"Allreduce on dp_rank: {self.dp_rank}, tensor={tensor_id}"
+                    )
+                    self.comm_op_status[stage_id][tensor_id] = 0
+                    self.comm_op_handles[stage_id][tensor_id] = handle
+                return grad
+            return post_backward_hook
+
+        ids = []
+        for t in self.forward_args[stage_id]:
+            if t is not None and t.requires_grad:
+                tid = id(t)
+                ids.append(tid)
+                t.register_post_accumulate_grad_hook(hook_maker(tid))
+        self.comm_op_tensor_ids[stage_id] = ids
+
+    def _wait_for_comm_ops(self):
+        self.logger.debug(f"Actor {self.global_rank} waiting for comm ops")
+        for stage_id, tids in self.comm_op_tensor_ids.items():
+            for tid in tids:
+                self.logger.debug(
+                    f"Waiting for comm op to be launched dp_rank: {self.dp_rank}, tensor={tid}"
+                )
+                done = False
+                while not done:
+                    if tid in self.comm_op_handles[stage_id]:
+                        self.logger.debug(
+                            f"Waiting for comm op to be finished dp_rank: {self.dp_rank}, tensor={tid}"
+                        )
+                        self.comm_op_handles[stage_id][tid].wait()
+                        done = True
+
+    def _load_stage(
+        self, stage_id: int, gm_data, forward_args,input_idxs
+    ):
+        self.logger.debug(f"Loading stage {stage_id} graph on actor {self.global_rank}")
+
+        # compile the graph with the given graphargs
+        gm = _deserialize_graphmodule(gm_data)
+
+        # Store GraphModule reference
+        self.graph_modules[stage_id] = gm
+        self.forward_fns[stage_id] = gm.forward
+
+        # place parameters on the device
+        def move_to_device(idx, arg):
+            if arg.requires_grad:
+                return arg.to(self.device).detach().requires_grad_(True)
+            else:
+                return arg.to(self.device)
+        forward_args = list(map(move_to_device, range(len(forward_args)), forward_args))
+
+        # save parameters
+        self.forward_args[stage_id] = forward_args
+        self.stage_id = stage_id
+        self.input_idxs[stage_id] = input_idxs
+
+        for i in self.input_idxs[stage_id]:
+            self.forward_input_meta[stage_id][i] = (
+                forward_args[i].shape,
+                forward_args[i].dtype,
+                forward_args[i].requires_grad,
+            )
+            self.forward_args[stage_id][i] = None
+
+        # prepare tensors with DP comm ops
+        if not self.naive_gradient_sync and self.dp_degree > 1:
+            self._prepare_dp_comm_ops(stage_id)
+
+        # add the parameters to the optimizer for this stage
+        params = [param for param in forward_args if param is not None and param.requires_grad]
+        if stage_id not in self.optims:
+            self.optims[stage_id] = self.optim_class(params)
+        else:
+            self.optims[stage_id].add_param_group({"params": params})
+
+        del gm_data
+
+    def _exec_p2p_op(
+        self, src_stage: int, dst_stage: int, mb_idx: int, is_sender: bool, dep, p2p_stream=None
+    ):
+        p2p_op = (src_stage, dst_stage, mb_idx, is_sender)
+        if p2p_op in self.executed_p2ps:
+            return
+
+        if p2p_stream is None:
+            p2p_stream = self.p2p_stream
+
+        op_idx = self.next_p2p_idx
+        while op_idx < len(self.p2p_schedule):
+            op = self.p2p_schedule[op_idx]
+            if op == p2p_op:
+                break
+            op_idx += 1
+        assert op_idx < len(
+            self.p2p_schedule
+        ), f"P2P op {p2p_op} not found in schedule for actor {self.global_rank}"
+
+        # Execute everything before and including the given p2p op
+        for idx in range(self.next_p2p_idx, op_idx + 1):
+            op = self.p2p_schedule[idx]
+            src_stage, dst_stage, mb_idx, is_sender = op
+            is_fwd = src_stage == dst_stage - 1
+            is_bwd = src_stage == dst_stage + 1
+            assert is_fwd or is_bwd
+            is_recver = not is_sender
+
+            op_name = "P2P(unknown)"
+            if is_fwd and is_sender:
+                self._exec_fwd_send(src_stage, mb_idx, p2p_stream=p2p_stream)
+                op_name = (
+                    f"P2P(fwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
+                )
+            elif is_fwd and is_recver:
+                self._exec_fwd_recv(dst_stage, mb_idx, p2p_stream=p2p_stream)
+                op_name = (
+                    f"P2P(fwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
+                )
+            elif is_bwd and is_sender:
+                self._exec_bwd_send(src_stage, mb_idx, p2p_stream=p2p_stream)
+                op_name = (
+                    f"P2P(bwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
+                )
+            elif is_bwd and is_recver:
+                self._exec_bwd_recv(dst_stage, mb_idx, p2p_stream=p2p_stream)
+                op_name = (
+                    f"P2P(bwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
+                )
+            else:
+                raise ValueError(f"Invalid p2p op: {op}")
+
+            self.executed_p2ps.add(op)
+            logger.debug(f"Executed {op_name} on actor {self.global_rank}")
+
+        self.next_p2p_idx = op_idx + 1
+        
+    def _exec_p2p_op(
+        self, src_stage: int, dst_stage: int, mb_idx: int, is_sender: bool, dep, p2p_stream=None
+    ):
+        p2p_op = (src_stage, dst_stage, mb_idx, is_sender)
+        if p2p_op in self.executed_p2ps:
+            return
+
+        if p2p_stream is None:
+            p2p_stream = self.p2p_stream
+
+        op_idx = self.next_p2p_idx
+        while op_idx < len(self.p2p_schedule):
+            op = self.p2p_schedule[op_idx]
+            if op == p2p_op:
+                break
+            op_idx += 1
+        assert op_idx < len(
+            self.p2p_schedule
+        ), f"P2P op {p2p_op} not found in schedule for actor {self.global_rank}"
+
+        # Execute everything before and including the given p2p op
+        for idx in range(self.next_p2p_idx, op_idx + 1):
+            op = self.p2p_schedule[idx]
+            src_stage, dst_stage, mb_idx, is_sender = op
+            is_fwd = src_stage == dst_stage - 1
+            is_bwd = src_stage == dst_stage + 1
+            assert is_fwd or is_bwd
+            is_recver = not is_sender
+
+            op_name = "P2P(unknown)"
+            if is_fwd and is_sender:
+                self._exec_fwd_send(src_stage, mb_idx, p2p_stream=p2p_stream)
+                op_name = (
+                    f"P2P(fwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
+                )
+            elif is_fwd and is_recver:
+                self._exec_fwd_recv(dst_stage, mb_idx, p2p_stream=p2p_stream)
+                op_name = (
+                    f"P2P(fwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
+                )
+            elif is_bwd and is_sender:
+                self._exec_bwd_send(src_stage, mb_idx, p2p_stream=p2p_stream)
+                op_name = (
+                    f"P2P(bwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
+                )
+            elif is_bwd and is_recver:
+                self._exec_bwd_recv(dst_stage, mb_idx, p2p_stream=p2p_stream)
+                op_name = (
+                    f"P2P(bwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
+                )
+            else:
+                raise ValueError(f"Invalid p2p op: {op}")
+
+            self.executed_p2ps.add(op)
+            logger.debug(f"Executed {op_name} on actor {self.global_rank}")
+
+        self.next_p2p_idx = op_idx + 1
+
+    def _exec_fwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
+        if stage_id == 0:
+            return
+
+        if p2p_stream is None:
+            p2p_stream = self.p2p_stream
+        if comp_stream is None:
+            comp_stream = self.comp_stream
+
+        p2p_op = (stage_id - 1, stage_id, mb_idx, False)
+        assert p2p_op not in self.p2p_cache
+
+        inputs_to_recv = []
+        for i in self.input_idxs[stage_id]:
+            shape, dtype, requires_grad = self.forward_input_meta[stage_id][i]
+            inputs_to_recv.append(
+                torch.empty(
+                    shape, dtype=dtype, requires_grad=requires_grad, device=self.device
+                )
+            )
+
+        # For non-first stages, receive input tensors from the previous stage
+        pp_rank = piper_metadata.stage_to_device[stage_id - 1]
+        global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+
+        if self.global_rank == global_src_rank:
+            for i in self.input_idxs[stage_id]:
+                inputs_to_recv[i] = self.p2p_cache.pop((stage_id-1, stage_id, mb_idx, True))
+        else:
+            self.logger.debug(
+                f"Dispatch fwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
+            )
+            self._start_timing(p2p_stream, "fwd_p2p_recv")
+            with torch.cuda.stream(p2p_stream):
+                for i in self.input_idxs[stage_id]:
+                    dist.recv(
+                        inputs_to_recv[i],
+                        src=global_src_rank,
+                        group=self.pp_group,
+                    )
+            # Ensure the default stream only consumes tensors after recv completes.
+            comp_stream.wait_stream(p2p_stream)
+            torch.cuda.synchronize()
+            self._stop_timing(p2p_stream, "fwd_p2p_recv")
+            self.logger.debug(
+                f"Completed fwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
+            )
+
+        self.p2p_cache[p2p_op] = inputs_to_recv
+
+    def _exec_fwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
+        if stage_id == self.num_stages - 1:
+            return
+
+        if p2p_stream is None:
+            p2p_stream = self.p2p_stream
+        if comp_stream is None:
+            comp_stream = self.comp_stream
+
+        p2p_op = (stage_id, stage_id + 1, mb_idx, True)
+        output = self.p2p_cache.pop(p2p_op)
+        # For non-final stages, send output tensors to the next stage
+        pp_rank = piper_metadata.stage_to_device[stage_id + 1]
+        global_dst_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+
+        if self.global_rank == global_dst_rank:
+            self.p2p_cache[p2p_op] = output
+        else:
+            # Ensure send sees the latest writes from the default stream.
+            self.logger.debug(
+                f"Dispatch fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
+            )
+            self._start_timing(p2p_stream, "fwd_p2p_send")
+            p2p_stream.wait_stream(comp_stream)
+            with torch.cuda.stream(p2p_stream):
+                for i in range(len(output)):
+                    dist.send(
+                        output[i],
+                        dst=global_dst_rank,
+                        group=self.pp_group,
+                    )
+            self._stop_timing(p2p_stream, "fwd_p2p_send")
+            self.logger.debug(
+                f"Completed fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
+            )
+
+    def _exec_bwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
+        if stage_id >= self.num_stages - 1:
+            return
+
+        if p2p_stream is None:
+            p2p_stream = self.p2p_stream
+        if comp_stream is None:
+            comp_stream = self.comp_stream
+
+        out_activation = self.out_activation[stage_id][mb_idx]
+        # For non-final stages, recieve input gradients from the subsequent backward pass
+        input_grad = torch.empty_like(out_activation)
+        pp_rank = piper_metadata.stage_to_device[stage_id + 1]
+        global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+
+        p2p_op = (stage_id + 1, stage_id, mb_idx, False)
+
+        if self.global_rank == global_src_rank:
+            input_grad = self.p2p_cache.pop((stage_id + 1, stage_id, mb_idx, True))
+        else:
+            self.logger.debug(
+                f"Dispatch bwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id+1} -> {stage_id}, mb {mb_idx})"
+            )
+            self._start_timing(p2p_stream, "bwd_p2p_recv")
+            with torch.cuda.stream(p2p_stream):
+                dist.recv(
+                    input_grad, src=global_src_rank, group=self.pp_group
+                )
+            # Ensure the default stream only consumes gradients after recv completes.
+            comp_stream.wait_stream(p2p_stream)
+            torch.cuda.synchronize()
+            self._stop_timing(p2p_stream, "bwd_p2p_recv")
+            self.logger.debug(
+                f"Completed bwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id+1} -> {stage_id}, mb {mb_idx})"
+            )
+        
+        self.p2p_cache[p2p_op] = input_grad
+
+    def _exec_bwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
+        if stage_id <= 0:
+            return
+
+        if p2p_stream is None:
+            p2p_stream = self.p2p_stream
+        if comp_stream is None:
+            comp_stream = self.comp_stream
+
+        # For non-first stages, send output gradients to the previous backward stage
+        output_grad = self.inp_activation[stage_id][mb_idx].grad
+        if output_grad is None:
+            self.logger.warning(f"No output gradient found for stage {stage_id} mb {mb_idx} on actor {self.global_rank}")
+            assert False
+        pp_rank = piper_metadata.stage_to_device[stage_id - 1]
+        global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
+
+        if self.global_rank == global_src_rank:
+            p2p_op = (stage_id, stage_id - 1, mb_idx, True)
+            self.p2p_cache[p2p_op] = output_grad
+        else:
+            self.logger.debug(
+                f"Dispatch bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
+            )
+            self._start_timing(p2p_stream, "bwd_p2p_send")
+            # Ensure send sees the latest gradient writes from the default stream.
+            p2p_stream.wait_stream(comp_stream)
+            with torch.cuda.stream(p2p_stream):
+                dist.send(
+                    output_grad, dst=global_src_rank, group=self.pp_group
+                )
+            self._stop_timing(p2p_stream, "bwd_p2p_send")
+            self.logger.debug(
+                f"Completed bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
+            )
+
+        self.inp_activation[stage_id][mb_idx] = None
+    
+    def _forward(self, stage_id: int, mb_idx: int, *deps):
+        if self.mode == "sequential":
+            comp_stream = self.comp_stream
+        elif self.mode == "naive":
+            comp_stream = self.per_mb_streams[(stage_id + mb_idx) % 2]
+        else:
+            raise ValueError(f"Invalid mode: {self.mode}")
+
+        self.logger.debug(
+            f"Calling forward {stage_id} mb {mb_idx} on actor {self.global_rank}"
+        )
+
+        if self.profile:
+            profiler = torch.profiler.profile(
+                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
+                with_stack=True,
+            )
+            profiler.__enter__()
+            forward_ctx_manager = torch.profiler.record_function(f"forward_stage_{stage_id}_mb_{mb_idx}")
+            forward_ctx_manager.__enter__()
+
+        if stage_id == 0:
+            # For the first stage, load input tensors from self.inputs
+            for i, inp in zip(self.input_idxs[stage_id], self.inputs):
+                self.forward_args[stage_id][i] = inp
+        else:
+            inputs_from_prev_stage = self.p2p_cache.pop(
+                (stage_id - 1, stage_id, mb_idx, False)
+            )
+            for i, tensor in zip(self.input_idxs[stage_id], inputs_from_prev_stage):
+                if isinstance(tensor, (tuple, list)):
+                    assert len(tensor) == 1
+                    tensor = tensor[0]
+                if stage_id > 0 and piper_metadata.stage_to_device[stage_id] == piper_metadata.stage_to_device[stage_id - 1] and tensor.requires_grad:
+                    tensor = tensor.detach().requires_grad_(True)
+                self.forward_args[stage_id][i] = tensor
+
+            # save first input that requires grad as input activation
+            inp_with_grad = [
+                self.forward_args[stage_id][i]
+                for i in self.input_idxs[stage_id]
+                if self.forward_args[stage_id][i].requires_grad
+            ]
+            assert (
+                len(inp_with_grad) == 1
+            ), "Exactly one input per stage should require a gradient"
+            self.logger.debug(
+                f"Saving input activation {inp_with_grad[0].shape} for stage {stage_id} mb {mb_idx}"
+            )
+            self.inp_activation[stage_id][mb_idx] = inp_with_grad[0]
+
+        # Run the forward pass
+        self._start_timing(comp_stream, "forward_comp")
+        with torch.cuda.stream(comp_stream):
+            output = self.forward_fns[stage_id](*self.forward_args[stage_id])
+        self._stop_timing(comp_stream, "forward_comp")
+
+        # Save first output that requires grad as output activation
+        # TODO: support multiple outputs
+        out_with_grad = [out for out in output if out.requires_grad]
+        assert (
+            len(out_with_grad) == 1
+        ), "Piper only supports one output per subgraph with requires_grad"
+        self.logger.debug(
+            f"Saving output activation {out_with_grad[0].shape} for stage {stage_id} mb {mb_idx}"
+        )
+        self.out_activation[stage_id][mb_idx] = out_with_grad[0]
+
+        # clear the input tensors
+        for i in self.input_idxs[stage_id]:
+            self.forward_args[stage_id][i] = None
+
+        if stage_id < self.num_stages - 1:
+            send_p2p_op = (stage_id, stage_id + 1, mb_idx, True)
+            assert send_p2p_op not in self.p2p_cache
+            self.p2p_cache[send_p2p_op] = output
+
+        if CLEANUP_MEMORY:
+            gc.collect()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+        self.logger.debug(
+            f"Forward {stage_id} mb {mb_idx} on actor {self.global_rank} returning {[out.shape for out in output]}"
+        )
+
+        torch.cuda.synchronize()
+
+        if self.profile:
+            forward_ctx_manager.__exit__(None, None, None)
+            profiler.__exit__(None, None, None)
+            profiler.export_chrome_trace(f"log/{self.mode}/actor{self.global_rank}_fwd{stage_id}mb{mb_idx}_trace.json")
+
+        return 1
+
+    def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
+        if self.mode == "sequential":
+            comp_stream = self.comp_stream
+        elif self.mode == "naive":
+            comp_stream = self.per_mb_streams[(stage_id + mb_idx) % 2]
+        else:
+            raise ValueError(f"Invalid mode: {self.mode}")
+
+        self.logger.debug(
+            f"Calling backward {stage_id} mb {mb_idx} on actor {self.global_rank}"
+        )
+
+        if self.profile:
+            profiler = torch.profiler.profile(
+                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
+                with_stack=True,
+            )
+            profiler.__enter__()
+            backward_ctx_manager = torch.profiler.record_function(f"backward_stage_{stage_id}_mb_{mb_idx}")
+            backward_ctx_manager.__enter__()
+
+        out_activation = self.out_activation[stage_id][mb_idx]
+
+        if stage_id < self.num_stages - 1:
+            input_grad = self.p2p_cache.pop(
+                (stage_id + 1, stage_id, mb_idx, False)
+            )
+
+            self._start_timing(comp_stream, "backward_comp")
+            with torch.cuda.stream(comp_stream):
+                out_activation.backward(gradient=input_grad)
+            self._stop_timing(comp_stream, "backward_comp")
+        else:
+            assert loss_fn is not None
+            labels = self.labels
+            assert out_activation.shape == labels.shape
+
+            self._start_timing(comp_stream, "backward_comp")
+            with torch.cuda.stream(comp_stream):
+                loss = loss_fn(out_activation, labels)
+                loss.backward()
+            self._stop_timing(comp_stream, "backward_comp")
+
+            self.loss.append(loss.item())
+
+        # Clear output activation after backward pass
+        self.out_activation[stage_id][mb_idx] = None
+        del out_activation
+
+        if CLEANUP_MEMORY:
+            gc.collect()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+        torch.cuda.synchronize()
+
+        if self.profile:
+            backward_ctx_manager.__exit__(None, None, None)
+            profiler.__exit__(None, None, None)
+            profiler.export_chrome_trace(f"log/{self.mode}/actor{self.global_rank}_bwd{stage_id}mb{mb_idx}_trace.json")
+
+        return 1
+
+    def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: int, bwd_mb_idx: int, *deps, loss_fn=None):
+        if self.mode == "sequential":
+            fwd_comp_stream = self.comp_stream
+            bwd_comp_stream = self.comp_stream
+        elif self.mode == "naive":
+            fwd_comp_stream = self.per_mb_streams[(fwd_stage_id + fwd_mb_idx) % 2]
+            bwd_comp_stream = self.per_mb_streams[(bwd_stage_id + bwd_mb_idx) % 2]
+        else:
+            raise ValueError(f"Invalid mode: {self.mode}")
+        fwd_p2p_stream = self.p2p_stream
+        bwd_p2p_stream = self.p2p_stream
+
+        self.logger.debug(
+            f"Calling forward {fwd_stage_id} mb {fwd_mb_idx} backward {bwd_stage_id} mb {bwd_mb_idx} on actor {self.global_rank}"
+        )
+        
+        if self.profile:
+            profiler = torch.profiler.profile(
+                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
+                with_stack=True,
+            )
+            profiler.__enter__()
+
+        # PREPARE FORWARD PASS
+        if fwd_stage_id == 0:
+            # For the first stage, load input tensors from self.inputs
+            for i, inp in zip(self.input_idxs[fwd_stage_id], self.inputs):
+                self.forward_args[fwd_stage_id][i] = inp
+        else:
+            self._exec_p2p_op(fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False, None, p2p_stream=fwd_p2p_stream)
+            inputs_from_prev_stage = self.p2p_cache.pop(
+                (fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False)
+            )
+            for i, tensor in zip(self.input_idxs[fwd_stage_id], inputs_from_prev_stage):
+                if isinstance(tensor, (tuple, list)):
+                    assert len(tensor) == 1
+                    tensor = tensor[0]
+                if fwd_stage_id > 0 and piper_metadata.stage_to_device[fwd_stage_id] == piper_metadata.stage_to_device[fwd_stage_id - 1] and tensor.requires_grad:
+                    tensor = tensor.detach().requires_grad_(True)
+                self.forward_args[fwd_stage_id][i] = tensor
+
+            # save first input that requires grad as input activation
+            inp_with_grad = [
+                self.forward_args[fwd_stage_id][i]
+                for i in self.input_idxs[fwd_stage_id]
+                if self.forward_args[fwd_stage_id][i].requires_grad
+            ]
+            assert (
+                len(inp_with_grad) == 1
+            ), "Exactly one input per stage should require a gradient"
+            self.logger.debug(
+                f"Saving input activation {inp_with_grad[0].shape} for stage {fwd_stage_id} mb {fwd_mb_idx}"
+            )
+            self.inp_activation[fwd_stage_id][fwd_mb_idx] = inp_with_grad[0]
+
+        # RUN FORWARD PASS
+        if self.profile:
+            forward_ctx_manager = torch.profiler.record_function(f"forward_stage_{fwd_stage_id}_mb_{fwd_mb_idx}")
+            forward_ctx_manager.__enter__()
+        self._start_timing(fwd_comp_stream, "forward_comp")
+        with torch.cuda.stream(fwd_comp_stream):
+            output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
+        self._stop_timing(fwd_comp_stream, "forward_comp")
+        
+        if self.profile:
+            forward_ctx_manager.__exit__(None, None, None)
+            backward_ctx_manager = torch.profiler.record_function(f"backward_stage_{bwd_stage_id}_mb_{bwd_mb_idx}")
+            backward_ctx_manager.__enter__()
+
+        # PREPARE BACKWARD PASS
+        out_activation = self.out_activation[bwd_stage_id][bwd_mb_idx]
+        if bwd_stage_id < self.num_stages - 1:
+            self._exec_p2p_op(bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False, None, p2p_stream=bwd_p2p_stream)
+
+            input_grad = self.p2p_cache.pop(
+                (bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False)
+            )
+        else:
+            assert loss_fn is not None
+            labels = self.labels
+            assert out_activation.shape == labels.shape
+
+        # RUN BACKWARD PASS
+        if bwd_stage_id < self.num_stages - 1:
+            self._start_timing(bwd_comp_stream, "backward_comp")
+            with torch.cuda.stream(bwd_comp_stream):
+                out_activation.backward(gradient=input_grad)
+            self._stop_timing(bwd_comp_stream, "backward_comp")
+        else:
+            self._start_timing(bwd_comp_stream, "backward_comp")
+            with torch.cuda.stream(bwd_comp_stream):
+                loss = loss_fn(out_activation, labels)
+                loss.backward()
+            self._stop_timing(bwd_comp_stream, "backward_comp")
+
+        # POST PROCESS FORWARD PASS
+        out_with_grad = [out for out in output if out.requires_grad]
+        assert (
+            len(out_with_grad) == 1
+        ), "Piper only supports one output per subgraph with requires_grad"
+        self.logger.debug(
+            f"Saving output activation {out_with_grad[0].shape} for stage {fwd_stage_id} mb {fwd_mb_idx}"
+        )
+        self.out_activation[fwd_stage_id][fwd_mb_idx] = out_with_grad[0]
+
+        # clear the input tensors
+        for i in self.input_idxs[fwd_stage_id]:
+            self.forward_args[fwd_stage_id][i] = None
+        
+        # POST PROCESS BACKWARD PASS
+        if bwd_stage_id == self.num_stages - 1:
+            self.loss.append(loss.item())
+
+        # Clear output activation after backward pass
+        self.out_activation[bwd_stage_id][bwd_mb_idx] = None
+        del out_activation
+
+        # POST FORWARD P2P OPERATIONS
+        if fwd_stage_id < self.num_stages - 1:
+            send_p2p_op = (fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True)
+            assert send_p2p_op not in self.p2p_cache
+            self.p2p_cache[send_p2p_op] = output
+            self._exec_p2p_op(fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True, None, p2p_stream=fwd_p2p_stream)
+
+        # POST BACKWARD P2P OPERATIONS
+        if bwd_stage_id > 0:
+            self._exec_p2p_op(bwd_stage_id, bwd_stage_id - 1, bwd_mb_idx, True, None, p2p_stream=bwd_p2p_stream)
+
+        if CLEANUP_MEMORY:
+            gc.collect()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+        torch.cuda.synchronize()
+
+        if self.profile:
+            backward_ctx_manager.__exit__(None, None, None)
+            profiler.__exit__(None, None, None)
+            profiler.export_chrome_trace(
+                f"log/{self.mode}/actor{self.global_rank}_"
+                f"fwd{fwd_stage_id}mb{fwd_mb_idx}_"
+                f"bwd{bwd_stage_id}mb{bwd_mb_idx}_trace.json")
+
+        return 1
+
+    def _synchronize_gradients(self):
+        self.logger.info(f"Actor {self.global_rank} synchronizing gradients")
+        # Iterate over all stages on this actor and synchronize their parameters
+        for stage_id, parameters in self.forward_args.items():
+            for param in parameters:
+                if param is not None and param.grad is not None:
+                    with torch.cuda.stream(self.comm_stream):
+                        dist.all_reduce(
+                            param.grad, op=dist.ReduceOp.AVG, group=self.dp_group
+                        )
+
+    def _update(self, *deps):
+        # self._wait_pending_sends()
+        self.logger.debug(f"Actor {self.global_rank} waiting for backward sync events")
+
+        # if dp degree > 1, make sure all gradients are synchronized before optimizer step
+        # TODO: this does not allow overlapping with the optimizer step
+        if self.dp_degree > 1:
+            self._start_timing(self.comm_stream, "backward_sync")
+            if self.naive_gradient_sync:
+                self._synchronize_gradients()
+            else:
+                self._wait_for_comm_ops()
+            self._stop_timing(self.comm_stream, "backward_sync")
+
+        # step the optimizer for each stage
+        self._start_timing(self.comp_stream, "optim_step")
+        for _, optim in self.optims.items():
+            optim.step()
+            optim.zero_grad()
+        self._stop_timing(self.comp_stream, "optim_step")
+
+        losses = self.loss
+        self.loss.clear()
+
+        torch.cuda.synchronize()
+
+        self.reset_p2p_states()
+
+        return losses

From 3c00543927693fa66d23dfae2b214fd1ade2167c Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Wed, 25 Feb 2026 18:52:27 +0000
Subject: [PATCH 053/100] schedule _forward_backward p2p comms on the
 coordinator (in piper_exec) instead of on the actor

---
 requirements.txt         |   5 +-
 src/piper_actor.py       |  46 ++++++-------
 src/piper_exec.py        |  12 +++-
 test/models/llama.py     |  44 +++++++++++++
 test/schedule_helpers.py | 135 +++++++++++++++++++++++++++++++++++----
 test/test_llama.py       |   5 +-
 6 files changed, 206 insertions(+), 41 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index c3060ad..e521807 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,4 @@
 numpy
-ray==2.53.0
+ray
 torch==2.9.1+cu128
-cupy-cuda12x
-graphviz
+cupy-cuda12x
\ No newline at end of file
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 4b4894e..c2ff8b8 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -46,7 +46,11 @@ def _create_actors(
     for pp_rank in range(num_actors):
         global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
         p2p_schedule = p2p_schedules[pp_rank]
-        actor = PiperActor.options(num_gpus=1).remote(
+        # if pp_rank == 0:
+        #     num_gpus = 0.3
+        # else:
+        num_gpus = 0.8
+        actor = PiperActor.options(num_gpus=num_gpus).remote(
             pp_rank,
             optim_class,
             world_size,
@@ -520,6 +524,7 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
                         src=global_src_rank,
                         group=self.pp_group,
                     )
+                    inputs_to_recv[i].record_stream(comp_stream)
             # Ensure the default stream only consumes tensors after recv completes.
             comp_stream.wait_stream(p2p_stream)
             torch.cuda.synchronize()
@@ -594,6 +599,7 @@ def _exec_bwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
                 dist.recv(
                     input_grad, src=global_src_rank, group=self.pp_group
                 )
+                input_grad.record_stream(comp_stream)
             # Ensure the default stream only consumes gradients after recv completes.
             comp_stream.wait_stream(p2p_stream)
             torch.cuda.synchronize()
@@ -773,12 +779,12 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
         )
 
         # PREPARE FORWARD PASS
+
         if fwd_stage_id == 0:
             # For the first stage, load input tensors from self.inputs
             for i, inp in zip(self.input_idxs[fwd_stage_id], self.inputs):
                 self.forward_args[fwd_stage_id][i] = inp
         else:
-            self._exec_p2p_op(fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False, p2p_stream=fwd_p2p_stream)
             inputs_from_prev_stage = self.p2p_cache.pop(
                 (fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False)
             )
@@ -804,17 +810,10 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             )
             self.inp_activation[fwd_stage_id][fwd_mb_idx] = inp_with_grad[0]
 
-        # RUN FORWARD PASS
-        self._start_timing(fwd_comp_stream, "forward_comp")
-        with torch.cuda.stream(fwd_comp_stream):
-            output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
-        self._stop_timing(fwd_comp_stream, "forward_comp")
-        
         # PREPARE BACKWARD PASS
+
         out_activation = self.out_activation[bwd_stage_id][bwd_mb_idx]
         if bwd_stage_id < self.num_stages - 1:
-            self._exec_p2p_op(bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False, p2p_stream=bwd_p2p_stream)
-
             input_grad = self.p2p_cache.pop(
                 (bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False)
             )
@@ -822,8 +821,16 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             assert loss_fn is not None
             labels = self.labels
             assert out_activation.shape == labels.shape
+            
+        # RUN FORWARD PASS
+
+        self._start_timing(fwd_comp_stream, "forward_comp")
+        with torch.cuda.stream(fwd_comp_stream):
+            output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
+        self._stop_timing(fwd_comp_stream, "forward_comp")
 
         # RUN BACKWARD PASS
+
         if bwd_stage_id < self.num_stages - 1:
             self._start_timing(bwd_comp_stream, "backward_comp")
             with torch.cuda.stream(bwd_comp_stream):
@@ -837,6 +844,7 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             self._stop_timing(bwd_comp_stream, "backward_comp")
 
         # POST PROCESS FORWARD PASS
+
         out_with_grad = [out for out in output if out.requires_grad]
         assert (
             len(out_with_grad) == 1
@@ -850,7 +858,13 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
         for i in self.input_idxs[fwd_stage_id]:
             self.forward_args[fwd_stage_id][i] = None
         
+        if fwd_stage_id < self.num_stages - 1:
+            send_p2p_op = (fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True)
+            assert send_p2p_op not in self.p2p_cache
+            self.p2p_cache[send_p2p_op] = output
+
         # POST PROCESS BACKWARD PASS
+
         if bwd_stage_id == self.num_stages - 1:
             self.loss.append(loss.item())
 
@@ -858,17 +872,6 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
         self.out_activation[bwd_stage_id][bwd_mb_idx] = None
         del out_activation
 
-        # POST FORWARD P2P OPERATIONS
-        if fwd_stage_id < self.num_stages - 1:
-            send_p2p_op = (fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True)
-            assert send_p2p_op not in self.p2p_cache
-            self.p2p_cache[send_p2p_op] = output
-            self._exec_p2p_op(fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True, p2p_stream=fwd_p2p_stream)
-
-        # POST BACKWARD P2P OPERATIONS
-        if bwd_stage_id > 0:
-            self._exec_p2p_op(bwd_stage_id, bwd_stage_id - 1, bwd_mb_idx, True, p2p_stream=bwd_p2p_stream)
-
         if CLEANUP_MEMORY:
             gc.collect()
             if torch.cuda.is_available():
@@ -890,7 +893,6 @@ def _synchronize_gradients(self):
                         )
 
     def _update(self, *deps):
-        self._wait_pending_sends()
         self.logger.debug(f"Actor {self.global_rank} waiting for backward sync events")
 
         # if dp degree > 1, make sure all gradients are synchronized before optimizer step
diff --git a/src/piper_exec.py b/src/piper_exec.py
index b61c663..ef874b7 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -374,7 +374,15 @@ def piper_exec(
                         fwd_stage_id, fwd_mb_idx = batches[0]
                         bwd_stage_id, bwd_mb_idx = batches[1]
                         # logger.info(f"Executing forward-backward {fwd_stage_id} mb {fwd_mb_idx} -> {bwd_stage_id} mb {bwd_mb_idx} on actor {actor_id}")
-                        # Don't explicitly schedule p2p ops -- overlapped in _forward_backward
-                        dep = actor._forward_backward.remote(fwd_stage_id, fwd_mb_idx, bwd_stage_id, bwd_mb_idx, deps[pp_rank], loss_fn=loss_fn)
+                        dep = deps[pp_rank]
+                        if fwd_stage_id > 0:
+                            dep = actor._exec_p2p_op.remote(fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False, dep)
+                        if bwd_stage_id < num_stages - 1:
+                            dep = actor._exec_p2p_op.remote(bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False, dep)
+                        dep = actor._forward_backward.remote(fwd_stage_id, fwd_mb_idx, bwd_stage_id, bwd_mb_idx, dep, loss_fn=loss_fn)
+                        if fwd_stage_id < num_stages - 1:
+                            dep = actor._exec_p2p_op.remote(fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True, dep)
+                        if bwd_stage_id > 0:
+                            dep = actor._exec_p2p_op.remote(bwd_stage_id, bwd_stage_id - 1, bwd_mb_idx, True, dep)
                         deps[pp_rank] = dep
     return ray.get(ret)
\ No newline at end of file
diff --git a/test/models/llama.py b/test/models/llama.py
index b1a24e8..427760a 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -397,6 +397,50 @@ def log_size(layer, indent=0):
         mask = torch.triu(mask, diagonal=1)
         self.mask = torch.hstack([torch.zeros((self.seq_len, 0)), mask]).to('cuda')
 
+    # """
+    # forward method for pp8-1f1b or pp4-interleaved-1f1b
+    # requires:
+    # - 8 stages
+    # - n_layers is divisible by 7
+    # """
+    # def forward(self, tokens: torch.Tensor):
+
+    #     with torch.fx.traceback.annotate({"stage": 0}):
+    #         h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
+    #         start_pos = 0
+    #         for layer in self.layers[:self.n_layers//7]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 1}):
+    #         for layer in self.layers[self.n_layers//7:2*self.n_layers//7]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 2}):
+    #         for layer in self.layers[2*self.n_layers//7:3*self.n_layers//7]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 3}):
+    #         for layer in self.layers[3*self.n_layers//7:4*self.n_layers//7]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 4}):
+    #         for layer in self.layers[4*self.n_layers//7:5*self.n_layers//7]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 5}):
+    #         for layer in self.layers[5*self.n_layers//7:6*self.n_layers//7]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 6}):
+    #         for layer in self.layers[6*self.n_layers//7:]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 7}):
+    #         h = self.norm(h) if self.norm else h
+    #         output = self.output(h).float() if self.output else h
+
+    #     return output
+
     """
     forward method for pp4-1f1b or pp2-interleaved-1f1b
     requires:
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index 5d8cdab..f450def 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -483,7 +483,6 @@ def build_1f1b_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
         ],
     ])
 
-# 2D grid literals for interleaved 1F1B (used only to build DAGs below)
 INTERLEAVED_1F1B_PP2_SCHEDULE = Schedule2D(
     grid=[
         [
@@ -530,6 +529,52 @@ def build_1f1b_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
         ],
     ])
 
+INTERLEAVED_GPIPE_PP2_SCHEDULE = Schedule2D(
+    grid=[
+        [
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
+            None,
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[], type=CompType.UPD),
+        ],
+        [
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[], type=CompType.UPD),
+            None,
+        ],
+    ])
+
 INTERLEAVED_1F1B_PP4_SCHEDULE = Schedule2D(
     grid=[
         [
@@ -541,21 +586,37 @@ def build_1f1b_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
             Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
             None,
             None,
             None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.BWD),
-            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.BWD),
-            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.BWD),
-            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
+            None,
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
+            None,
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.UPD),
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[], type=CompType.UPD),
         ],
         [
             None,
@@ -568,18 +629,34 @@ def build_1f1b_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
             Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.FWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.FWD),
             None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.BWD),
             None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.BWD),
-            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.FWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.BWD),
-            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.FWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.FWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.FWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.FWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.FWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.UPD),
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.BWD),
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[], type=CompType.UPD),
             None,
         ],
         [
@@ -591,19 +668,35 @@ def build_1f1b_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
             Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.FWD),
+            None,
             Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.BWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.BWD),
-            None,
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.BWD),
-            None,
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.UPD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.FWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.FWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.BWD),
+            None,
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[], type=CompType.UPD),
             None,
             None,
         ],
@@ -623,11 +716,27 @@ def build_1f1b_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
             Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.BWD),
             Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.FWD),
             Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
             Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
             Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.FWD),
             Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.FWD),
             Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.UPD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.FWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.FWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.FWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.FWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[], type=CompType.UPD),
             None,
             None,
             None,
diff --git a/test/test_llama.py b/test/test_llama.py
index 6c6fc1c..c6b82c7 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -20,6 +20,7 @@
     INTERLEAVED_1F1B_PP4_SCHEDULE,
     NO_PP_SCHEDULE,
     DUALPIPEV_SCHEDULE,
+    INTERLEAVED_GPIPE_PP2_SCHEDULE,
 )
 
 
@@ -58,6 +59,8 @@ def main(args):
             schedule = build_1f1b_schedule(args.mbs, args.pp)
         case "gpipe":
             schedule = build_gpipe_schedule(args.mbs, args.pp)
+        case "interleaved-gpipe":
+            schedule = INTERLEAVED_GPIPE_PP2_SCHEDULE
         case "dualpipev":
             schedule = DUALPIPEV_SCHEDULE
     print("Schedule:")
@@ -129,7 +132,7 @@ def parse_args():
     parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
     parser.add_argument('--model', choices=['debug', '1b', '3b', '8b'], default='debug',
                         help='Model configuration: debug, 1b, 3b, or 8b (default: debug)')
-    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'dualpipev', 'no-pp'], default='1f1b',
+    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'interleaved-gpipe', 'dualpipev', 'no-pp'], default='1f1b',
                         help='Schedule type: gpipe, 1f1b, interleaved-1f1b, dualpipev, or no-pp (default: 1f1b)')
     parser.add_argument('--dp', type=int, default=1,
                         help='Number of data parallel degrees (default: 1)')

From 6a322271c05b02fadc6060e048aae2aacb2956ab Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Wed, 25 Feb 2026 19:03:08 +0000
Subject: [PATCH 054/100] rever file names to piper_*

---
 {piper => src}/__init__.py                               | 0
 {piper => src}/backward_utils.py                         | 0
 {piper => src}/patches.py                                | 0
 {piper => src}/piper.py                                  | 0
 piper/actor.py => src/piper_actor.py                     | 0
 piper/compile.py => src/piper_compile.py                 | 0
 piper/coordinator.py => src/piper_coordinator.py         | 0
 piper/exec.py => src/piper_exec.py                       | 0
 piper/graph_transform.py => src/piper_graph_transform.py | 0
 piper/utils.py => src/piper_utils.py                     | 0
 10 files changed, 0 insertions(+), 0 deletions(-)
 rename {piper => src}/__init__.py (100%)
 rename {piper => src}/backward_utils.py (100%)
 rename {piper => src}/patches.py (100%)
 rename {piper => src}/piper.py (100%)
 rename piper/actor.py => src/piper_actor.py (100%)
 rename piper/compile.py => src/piper_compile.py (100%)
 rename piper/coordinator.py => src/piper_coordinator.py (100%)
 rename piper/exec.py => src/piper_exec.py (100%)
 rename piper/graph_transform.py => src/piper_graph_transform.py (100%)
 rename piper/utils.py => src/piper_utils.py (100%)

diff --git a/piper/__init__.py b/src/__init__.py
similarity index 100%
rename from piper/__init__.py
rename to src/__init__.py
diff --git a/piper/backward_utils.py b/src/backward_utils.py
similarity index 100%
rename from piper/backward_utils.py
rename to src/backward_utils.py
diff --git a/piper/patches.py b/src/patches.py
similarity index 100%
rename from piper/patches.py
rename to src/patches.py
diff --git a/piper/piper.py b/src/piper.py
similarity index 100%
rename from piper/piper.py
rename to src/piper.py
diff --git a/piper/actor.py b/src/piper_actor.py
similarity index 100%
rename from piper/actor.py
rename to src/piper_actor.py
diff --git a/piper/compile.py b/src/piper_compile.py
similarity index 100%
rename from piper/compile.py
rename to src/piper_compile.py
diff --git a/piper/coordinator.py b/src/piper_coordinator.py
similarity index 100%
rename from piper/coordinator.py
rename to src/piper_coordinator.py
diff --git a/piper/exec.py b/src/piper_exec.py
similarity index 100%
rename from piper/exec.py
rename to src/piper_exec.py
diff --git a/piper/graph_transform.py b/src/piper_graph_transform.py
similarity index 100%
rename from piper/graph_transform.py
rename to src/piper_graph_transform.py
diff --git a/piper/utils.py b/src/piper_utils.py
similarity index 100%
rename from piper/utils.py
rename to src/piper_utils.py

From 15a7525f5f04d6a400c4e6a99bdf54865f2a0ab8 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Thu, 26 Feb 2026 09:29:06 +0000
Subject: [PATCH 055/100] DualPipeV schedule with ZB

---
 src/piper_actor.py       |  2 +-
 src/piper_compile.py     |  2 +-
 src/piper_coordinator.py |  2 +-
 src/piper_exec.py        |  3 +-
 src/piper_utils.py       |  2 +-
 test/models/llama.py     | 66 +++++++++++++++++++-------------------
 test/print_schedules.py  | 21 ++++---------
 test/schedule_helpers.py | 68 +++++++++++++++++++++++++++++++++++++---
 test/test_llama.py       | 45 ++++++++++++++++++--------
 9 files changed, 140 insertions(+), 71 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index b55dd37..a2d885c 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -206,7 +206,7 @@ def reset_peak_memory(self):
         torch.cuda.reset_peak_memory_stats()
 
     def get_peak_memory(self):
-        return torch.cuda.max_memory_allocated() / (1024**3)
+        return self.global_rank, torch.cuda.max_memory_allocated() / (1024**3)
 
     def load_input(self, inputs):
         self.inputs = [inp.to(self.device) for inp in inputs]
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 829cc3a..014587a 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -60,7 +60,7 @@ def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, sta
                     batch = task.batches[0]
                     if batch.stage_id > 0:
                         dependee.append((batch.stage_id - 1, batch.stage_id, batch.mb_idx))
-                elif task.type == CompType.BWD or task.type == CompType.BWD_I:
+                elif task.type in (CompType.BWD, CompType.BWD_I):
                     batch = task.batches[0]
                     if batch.stage_id < num_stages - 1:
                         dependee.append((batch.stage_id + 1, batch.stage_id, batch.mb_idx))
diff --git a/src/piper_coordinator.py b/src/piper_coordinator.py
index ed71060..6504330 100644
--- a/src/piper_coordinator.py
+++ b/src/piper_coordinator.py
@@ -7,7 +7,7 @@
 from .piper_utils import create_logger, LOG_LEVEL
 
 
-@ray.remote(num_gpus=0.1)
+@ray.remote(num_gpus=0.6)
 def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, master_addr, master_port, training_func: Callable, *args, **kwargs):
     logger = create_logger("piper_coordinator", LOG_LEVEL)
     logger.debug(f"Running DP rank {dp_rank+1} of {dp_degree}")
diff --git a/src/piper_exec.py b/src/piper_exec.py
index e1071dc..f34a3a3 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -158,7 +158,8 @@ def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdg
         missing_bwd = all_required_stages - bwd_stages
         if missing_bwd:
             raise ValueError(
-                f"Microbatch {mb_idx} missing backward stages: {missing_bwd}"
+                f"Microbatch {mb_idx} missing backward stages: {missing_bwd} "
+                f"(need BWD, BWD_I, or FWD_BWD backward for each stage)"
             )
     
     # Check pipeline stage dependencies
diff --git a/src/piper_utils.py b/src/piper_utils.py
index ce8e785..b15878d 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -10,7 +10,7 @@
 from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
 from typing import Any, Optional
 
-LOG_LEVEL = "DEBUG"
+LOG_LEVEL = "INFO"
 
 """ 
 Print the backward graph of a tensor
diff --git a/test/models/llama.py b/test/models/llama.py
index 79d0bc0..71d4dbb 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -438,52 +438,52 @@ def log_size(layer, indent=0):
 
     #     return output
 
-    # """
-    # 4 STAGES
-    # """
-    # def forward(self, tokens: torch.Tensor):
-
-    #     with torch.fx.traceback.annotate({"stage": 0}):
-    #         h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-    #         start_pos = 0
-    #         for layer in self.layers[:self.n_layers//4+1]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[self.n_layers//4+1:self.n_layers//2+2]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 2}):
-    #         for layer in self.layers[self.n_layers//2+2:3*self.n_layers//4+3]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 3}):
-    #         for layer in self.layers[3*self.n_layers//4+3:]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-    #         h = self.norm(h) if self.norm else h
-    #         output = self.output(h).float() if self.output else h
-
-    #     return output
-
     """
-    2 STAGES
+    4 STAGES
     """
     def forward(self, tokens: torch.Tensor):
 
         with torch.fx.traceback.annotate({"stage": 0}):
-            h = self.tok_embeddings(tokens)
+            h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
             start_pos = 0
-            for layer in self.layers[:self.n_layers//2+2]:
+            for layer in self.layers[:self.n_layers//4+1]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
 
         with torch.fx.traceback.annotate({"stage": 1}):
-            for layer in self.layers[self.n_layers//2+2:]:
+            for layer in self.layers[self.n_layers//4+1:self.n_layers//2+2]:
+                h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+        with torch.fx.traceback.annotate({"stage": 2}):
+            for layer in self.layers[self.n_layers//2+2:3*self.n_layers//4+3]:
+                h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+        with torch.fx.traceback.annotate({"stage": 3}):
+            for layer in self.layers[3*self.n_layers//4+3:]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
-            h = self.norm(h)
-            output = self.output(h).float()
+            h = self.norm(h) if self.norm else h
+            output = self.output(h).float() if self.output else h
 
         return output
 
+    # """
+    # 2 STAGES
+    # """
+    # def forward(self, tokens: torch.Tensor):
+
+    #     with torch.fx.traceback.annotate({"stage": 0}):
+    #         h = self.tok_embeddings(tokens)
+    #         start_pos = 0
+    #         for layer in self.layers[:self.n_layers//2+2]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 1}):
+    #         for layer in self.layers[self.n_layers//2+2:]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+    #         h = self.norm(h)
+    #         output = self.output(h).float()
+
+    #     return output
+
     # """
     # 1 STAGE
     # """
diff --git a/test/print_schedules.py b/test/print_schedules.py
index 4e37aac..b488d9c 100644
--- a/test/print_schedules.py
+++ b/test/print_schedules.py
@@ -1,17 +1,8 @@
 from .schedule_helpers import print_schedule, build_1f1b_schedule, build_gpipe_schedule
+from .schedule_helpers import DUALPIPEV_SCHEDULE, DUALPIPEV_NO_ZB_SCHEDULE
+from src.piper_exec import _validate_schedule, DAGEdge
 
-schedule = build_1f1b_schedule(4, 2)
-print("1F1B Schedule")
-print_schedule(schedule)
-
-schedule = build_gpipe_schedule(4, 2)
-print("GPipe Schedule")
-print_schedule(schedule)
-
-schedule = build_1f1b_schedule(8, 4)
-print("1F1B Schedule")
-print_schedule(schedule)
-
-schedule = build_gpipe_schedule(8, 4)
-print("GPipe Schedule")
-print_schedule(schedule)
\ No newline at end of file
+schedule = DUALPIPEV_SCHEDULE
+dag_edges = [DAGEdge(from_stage=0, to_stage=1), DAGEdge(from_stage=1, to_stage=2), DAGEdge(from_stage=2, to_stage=3)]
+num_mbs = 6
+_validate_schedule(schedule.grid, dag_edges, num_mbs)
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index 2e998ef..361250c 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -112,7 +112,7 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
     return Schedule2D(grid=schedule)
 
 
-ZEROBUBBLE_SCHEDULE = Schedule2D(
+ZEROBUBBLE_MB4_SCHEDULE = Schedule2D(
     grid=[
         [
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),          # t0:  F0
@@ -158,7 +158,65 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
     ],
 )
 
-DUALPIPEV_SCHEDULE = Schedule2D(
+DUALPIPEV_MB6_SCHEDULE = Schedule2D(
+    grid=[
+        [
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_I),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_W),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_I),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_W),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2), BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD_BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4), BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD_BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3), BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD_BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5), BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD_BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4), BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD_BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5), BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD_BWD),
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_I),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_I),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_W),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_W),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[], type=CompType.UPD),
+            None,
+        ],
+        [
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2), BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD_BWD),
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2), BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD_BWD),
+            None,
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3), BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3), BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4), BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4), BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5), BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5), BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_I),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_W),
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_I),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_I),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_W),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_W),
+            Task(pp_rank=1, batches=[], type=CompType.UPD),
+        ]
+    ])
+
+DUALPIPEV_NOZB_MB6_SCHEDULE = Schedule2D(
     grid=[
         [
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
@@ -268,7 +326,7 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
         ],
     ])
 
-INTERLEAVED_1F1B_PP2_SCHEDULE = Schedule2D(
+INTERLEAVED_1F1B_PP2_MB4_SCHEDULE = Schedule2D(
     grid=[
         [
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
@@ -314,7 +372,7 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
         ],
     ])
 
-INTERLEAVED_GPIPE_PP2_SCHEDULE = Schedule2D(
+INTERLEAVED_GPIPE_PP2_MB4_SCHEDULE = Schedule2D(
     grid=[
         [
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
@@ -360,7 +418,7 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
         ],
     ])
 
-INTERLEAVED_1F1B_PP4_SCHEDULE = Schedule2D(
+INTERLEAVED_1F1B_PP4_MB8_SCHEDULE = Schedule2D(
     grid=[
         [
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
diff --git a/test/test_llama.py b/test/test_llama.py
index aa04b58..c295114 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -15,13 +15,14 @@
     build_1f1b_schedule, 
     build_gpipe_schedule, 
     print_schedule,
-    INTERLEAVED_1F1B_PP2_SCHEDULE,
+    INTERLEAVED_1F1B_PP2_MB4_SCHEDULE,
     INTERLEAVED_1F1B_PP2_MB6_SCHEDULE,
-    INTERLEAVED_1F1B_PP4_SCHEDULE,
+    INTERLEAVED_1F1B_PP4_MB8_SCHEDULE,
+    INTERLEAVED_GPIPE_PP2_MB4_SCHEDULE,
     NO_PP_SCHEDULE,
-    DUALPIPEV_SCHEDULE,
-    INTERLEAVED_GPIPE_PP2_SCHEDULE,
-    ZEROBUBBLE_SCHEDULE,
+    DUALPIPEV_MB6_SCHEDULE,
+    DUALPIPEV_NOZB_MB6_SCHEDULE,
+    ZEROBUBBLE_MB4_SCHEDULE,
 )
 
 
@@ -53,20 +54,32 @@ def main(args):
         case "no-pp":
             schedule = NO_PP_SCHEDULE
         case "interleaved-1f1b":
-            if args.mbs == 6:
-                schedule = INTERLEAVED_1F1B_PP2_MB6_SCHEDULE
-            else:
-                schedule = INTERLEAVED_1F1B_PP2_SCHEDULE if args.pp == 2 else INTERLEAVED_1F1B_PP4_SCHEDULE
+            if args.pp == 2:
+                if args.mbs == 6:
+                    schedule = INTERLEAVED_1F1B_PP2_MB6_SCHEDULE
+                elif args.mbs == 4:
+                    schedule = INTERLEAVED_1F1B_PP2_MB4_SCHEDULE
+                else:
+                    raise ValueError(f"Unsupported number of microbatches: for interleaved-1f1b with PP={args.pp}: {args.mbs}")
+            elif args.pp == 4:
+                assert args.mbs == 8
+                schedule = INTERLEAVED_1F1B_PP4_MB8_SCHEDULE
         case "1f1b":
             schedule = build_1f1b_schedule(args.mbs, args.pp)
         case "gpipe":
             schedule = build_gpipe_schedule(args.mbs, args.pp)
         case "interleaved-gpipe":
-            schedule = INTERLEAVED_GPIPE_PP2_SCHEDULE
+            assert args.pp == 2 and args.mbs == 4
+            schedule = INTERLEAVED_GPIPE_PP2_MB4_SCHEDULE
         case "dualpipev":
-            schedule = DUALPIPEV_SCHEDULE
+            assert args.pp == 2 and args.mbs == 6
+            schedule = DUALPIPEV_MB6_SCHEDULE
+        case "dualpipev-nozb":
+            assert args.pp == 2 and args.mbs == 6
+            schedule = DUALPIPEV_NOZB_MB6_SCHEDULE
         case "zerobubble":
-            schedule = ZEROBUBBLE_SCHEDULE
+            assert args.pp == 2 and args.mbs == 4
+            schedule = ZEROBUBBLE_MB4_SCHEDULE
     print("Schedule:")
     print_schedule(schedule)
 
@@ -111,11 +124,17 @@ def main(args):
         from src.piper_utils import piper_metadata
         actors = piper_metadata.actors
         ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
+        ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
+        ray.get([actor.start_mem_tracing.remote() for actor in actors.values()])
 
         print(f"Running {args.warmup} tracing iterations...")
         for _ in range(args.warmup):
             piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
 
+        mem_data_ret = ray.get([actor.get_peak_memory.remote() for actor in actors.values()])
+        for rank, peak_mem in mem_data_ret:
+            print(f"rank {rank} peak memory= {peak_mem:.3f} GB")
+
         trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
         for rank, trace_data in trace_data_ret:
             for key in trace_data:
@@ -136,7 +155,7 @@ def parse_args():
     parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
     parser.add_argument('--model', choices=['debug', '1b', '3b', '8b'], default='debug',
                         help='Model configuration: debug, 1b, 3b, or 8b (default: debug)')
-    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'interleaved-gpipe', 'dualpipev', 'zerobubble', 'no-pp'], default='1f1b',
+    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'interleaved-gpipe', 'dualpipev-nozb', 'dualpipev', 'zerobubble', 'no-pp'], default='1f1b',
                         help='Schedule type: gpipe, 1f1b, interleaved-1f1b, dualpipev, zerobubble, or no-pp (default: 1f1b)')
     parser.add_argument('--dp', type=int, default=1,
                         help='Number of data parallel degrees (default: 1)')

From 0e22ccaaa64f141744be8a160d26f76579f7726c Mon Sep 17 00:00:00 2001
From: Yi Pan <conlesspan@outlook.com>
Date: Thu, 26 Feb 2026 16:04:41 +0000
Subject: [PATCH 056/100] fix: stream wait logic.

---
 src/piper_compile.py         |  2 +-
 src/piper_graph_transform.py | 60 ++++++++++++++++++++++++++----------
 2 files changed, 45 insertions(+), 17 deletions(-)

diff --git a/src/piper_compile.py b/src/piper_compile.py
index ffbdbad..5f28750 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -140,7 +140,7 @@ def piper_setup(
     )
     _create_actors(
         num_devices, optim_fn, num_mbs, num_stages, p2p_schedules, naive_gradient_sync,
-        profile=True, mode="sequential",
+        profile=True, mode="overlapped",
     )
 
     last_stage_rank = stage_to_device[num_stages - 1]
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index 965a16c..bfca861 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -45,7 +45,7 @@ class AllToAllSingleFunction(torch.autograd.Function):
     def forward(ctx, output, input_tensor):
         """
         Forward pass: performs all_to_all_single communication.
-        
+
         Args:
             ctx: Context for storing information for backward pass
             output: Output buffer (will be modified in-place)
@@ -57,53 +57,81 @@ def forward(ctx, output, input_tensor):
         ctx.actor_self = piper_metadata.actor_self
         ctx.group = ctx.actor_self.dp_group
         ctx.global_rank = ctx.actor_self.global_rank
-        ctx.stream = ctx.actor_self.comp_stream
+        ctx.stream = ctx.actor_self.a2a_stream
+
+        comp_stream = torch.cuda.current_stream()
 
         logger.debug(f"Dispatch AllToAllSingleFunction forward rank={ctx.global_rank}, shape={input_tensor.shape}")
-        
+
+        # PRE-HOOK: record event so bwd knows fwd comp reached this A2A
+        if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.n_a2a_ops > 0:
+            idx = ctx.actor_self.fwd_a2a_counter
+            ctx.actor_self.fwd_a2a_pre_events[idx].record(comp_stream)
+
         ctx.actor_self._start_timing(ctx.stream, "fwd_a2a")
 
         # Ensure input_tensor is contiguous (all_to_all_single requires contiguous tensors)
-        # TODO: performance cost?
         input_tensor = input_tensor.contiguous()
-        
-        # Perform the communication (modifies output in-place)
+
+        # Execute A2A on a2a_stream
+        ctx.stream.wait_stream(comp_stream)
         with torch.cuda.stream(ctx.stream):
             dist.all_to_all_single(output, input_tensor, group=ctx.group)
+        comp_stream.wait_stream(ctx.stream)
 
         ctx.actor_self._stop_timing(ctx.stream, "fwd_a2a")
 
+        # POST-HOOK: wait for bwd to reach its corresponding A2A
+        if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.n_a2a_ops > 0:
+            idx = ctx.actor_self.fwd_a2a_counter
+            comp_stream.wait_event(ctx.actor_self.bwd_a2a_pre_events[idx])
+            ctx.actor_self.fwd_a2a_counter += 1
+
         return output
     
     @staticmethod
     def backward(ctx, grad_output):
         """
         Backward pass: performs reverse all_to_all_single to propagate gradients.
-        
+
         The backward of all_to_all_single is another all_to_all_single operation
         that reverses the communication pattern.
         """
         logger.debug(f"Dispatch AllToAllSingleFunction backward rank={ctx.global_rank}, shape={grad_output.shape}")
 
-        ctx.actor_self._start_timing(ctx.stream, "bwd_a2a")
-
         if grad_output is None:
             return None, None, None
-        
+
+        comp_stream = torch.cuda.current_stream()
+
+        # PRE-HOOK: record event so fwd post-hook knows bwd comp reached this A2A
+        if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.n_a2a_ops > 0:
+            idx = ctx.actor_self.bwd_a2a_counter
+            ctx.actor_self.bwd_a2a_pre_events[idx].record(comp_stream)
+
+        ctx.actor_self._start_timing(ctx.stream, "bwd_a2a")
+
         # Ensure grad_output is contiguous (all_to_all_single requires contiguous tensors)
-        # TODO: performance cost?
         grad_output = grad_output.contiguous()
-        
+
         # Create a buffer for the gradient input
         grad_input = torch.empty_like(grad_output)
-        
-        # Reverse the communication: all_to_all_single in backward
-        # This propagates gradients from output back to input
+
+        # Execute A2A on a2a_stream
+        ctx.stream.wait_stream(comp_stream)
         with torch.cuda.stream(ctx.stream):
             dist.all_to_all_single(grad_input, grad_output, group=ctx.group)
+        comp_stream.wait_stream(ctx.stream)
 
         ctx.actor_self._stop_timing(ctx.stream, "bwd_a2a")
-        
+
+        # POST-HOOK: wait for fwd to reach the next A2A
+        if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.n_a2a_ops > 0:
+            idx = ctx.actor_self.bwd_a2a_counter
+            if idx + 1 < ctx.actor_self.n_a2a_ops:
+                comp_stream.wait_event(ctx.actor_self.fwd_a2a_pre_events[idx + 1])
+            ctx.actor_self.bwd_a2a_counter += 1
+
         # Return gradients: grad_output flows to grad_input, None for group
         return grad_input, grad_input, None
 

From 18b28762abc6645fa6e754f01630c31390dd3930 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Fri, 27 Feb 2026 00:14:18 +0000
Subject: [PATCH 057/100] merge zb into dualpipe overlapping

---
 requirements.txt                              |   5 +-
 src/__init__.py                               |   0
 src/backward_utils.py                         | 169 +++
 src/piper.py                                  |   9 +-
 src/piper_actor.py                            | 419 +++++++-
 src/piper_compile.py                          |  27 +-
 src/piper_coordinator.py                      |   2 +-
 src/piper_exec.py                             | 173 ++-
 src/piper_graph_transform.py                  |  95 +-
 src/piper_overlap_actor.py                    | 989 ------------------
 src/piper_patches.py                          | 166 ---
 src/piper_utils.py                            |   4 +-
 test/models/llama.py                          |  57 +-
 test/models/mixtral.py                        |   6 +-
 test/pippy_profiling/1f1b_event_profile.py    | 300 ++++++
 .../1f1b_expected_schedule.dot                | 118 +++
 .../1f1b_expected_schedule.png                | Bin 0 -> 60730 bytes
 .../zb1p_expected_schedule.dot                | 122 +++
 .../zb1p_expected_schedule.png                | Bin 0 -> 100276 bytes
 .../zb_1f1b_memory_profiling.py               | 206 ++++
 .../zerobubble_event_profile.py               | 318 ++++++
 test/pippy_profiling/zerobubble_example.py    | 270 +++++
 test/print_schedules.py                       |  21 +-
 test/schedule_helpers.py                      | 500 ++++-----
 test/test_backward_split.py                   | 144 +++
 test/test_llama.py                            |  46 +-
 test/test_mixtral.py                          |  58 +-
 27 files changed, 2527 insertions(+), 1697 deletions(-)
 create mode 100644 src/__init__.py
 create mode 100644 src/backward_utils.py
 delete mode 100644 src/piper_overlap_actor.py
 delete mode 100644 src/piper_patches.py
 create mode 100644 test/pippy_profiling/1f1b_event_profile.py
 create mode 100644 test/pippy_profiling/1f1b_expected_schedule.dot
 create mode 100644 test/pippy_profiling/1f1b_expected_schedule.png
 create mode 100644 test/pippy_profiling/zb1p_expected_schedule.dot
 create mode 100644 test/pippy_profiling/zb1p_expected_schedule.png
 create mode 100644 test/pippy_profiling/zb_1f1b_memory_profiling.py
 create mode 100644 test/pippy_profiling/zerobubble_event_profile.py
 create mode 100644 test/pippy_profiling/zerobubble_example.py
 create mode 100644 test/test_backward_split.py

diff --git a/requirements.txt b/requirements.txt
index c3060ad..e521807 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,4 @@
 numpy
-ray==2.53.0
+ray
 torch==2.9.1+cu128
-cupy-cuda12x
-graphviz
+cupy-cuda12x
\ No newline at end of file
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/backward_utils.py b/src/backward_utils.py
new file mode 100644
index 0000000..fb5bf56
--- /dev/null
+++ b/src/backward_utils.py
@@ -0,0 +1,169 @@
+# ===========================================================================================
+# This file contains helper functions to implement the split backward pass.
+#
+# These helper functions were copied from the PyTorch pipelining backward implementation.
+#
+#   PyTorch Team,
+#   "torch.distributed.pipelining._backward",
+#   https://github.com/pytorch/pytorch/blob/main/torch/distributed/pipelining/_backward.py
+#
+# There are some minor implementation differences, but these functions are nearly identical.
+#
+# ===========================================================================================
+
+
+from __future__ import annotations
+
+import collections
+from typing import Any, Dict, Iterator, List, Optional, Set, Tuple
+
+import torch
+from torch.autograd.graph import GradientEdge, Node
+from torch.nn import Parameter
+
+
+def _get_grad_fn_or_grad_acc(t: torch.Tensor) -> Optional[Node]:
+    """
+    Return the autograd Node for a tensor:
+      - non-leaf tensors: t.grad_fn
+      - leaf tensors requiring grad (Parameters): AccumulateGrad node (created lazily)
+    """
+    if not t.requires_grad:
+        return None
+    if t.grad_fn is not None:
+        return t.grad_fn
+
+    # If t is a leaf tensor, we create a view to generate its AccumulateGrad node
+    viewed = t.view_as(t)
+    grad_fn = viewed.grad_fn
+    if grad_fn is None:
+        raise RuntimeError(
+            "Tried to get grad accumulator but got None. "
+            "Are you in a no-grad context?"
+        )
+    return grad_fn.next_functions[0][0]
+
+
+def reverse_closure(
+    roots: List[Node],
+    target_nodes: Set[Node],
+    reverse_edges: Dict[Node, List[Node]],
+) -> Tuple[Set[Node], Set[Node]]:
+    """
+    From roots, follow reverse_edges to collect reachable nodes, but stop traversal
+    when hitting any node in target_nodes. Returns the closure (set of reachable nodes) and
+    the set of target nodes that were able to be visited.
+    """
+    closure: Set[Node] = set()
+    visited_targets: Set[Node] = set()
+    q: collections.deque[Node] = collections.deque()
+
+    for node in roots:
+        if node is None or node in closure:
+            continue
+        closure.add(node)
+        q.append(node)
+
+    while q:
+        currNode = q.popleft()
+        for node in reverse_edges.get(currNode, []):
+            if node is None or node in closure:
+                continue
+            if node in target_nodes:
+                visited_targets.add(node)
+                continue
+            closure.add(node)
+            q.append(node)
+
+    return closure, visited_targets
+
+
+def construct_reverse_graph(roots: List[Node]) -> Dict[Node, List[Node]]:
+    """
+    Builds a reverse graph. In a backward graph, for each node X, 
+    reverse_edges[X] is the list of nodes that X sends gradients to.
+    """
+    q: collections.deque[Node] = collections.deque()
+    seen: Set[Node] = set()
+    reverse_edges: Dict[Node, List[Node]] = collections.defaultdict(list)
+
+    for node in roots:
+        if node is not None and node not in seen:
+            seen.add(node)
+            q.append(node)
+
+    while q:
+        node = q.popleft()
+        for fn, _ in node.next_functions:
+            if fn is None:
+                continue
+
+            # fn -> node in reverse graph
+            reverse_edges[fn].append(node)
+            if fn not in seen:
+                seen.add(fn)
+                q.append(fn)
+
+    return reverse_edges
+
+
+def get_param_groups(
+    inputs: List[Node],
+    params: List[Node],
+    reverse_edges: Dict[Node, List[Node]],
+) -> List[Dict[str, Any]]:
+    """
+    Returns a list of parameter groups. Parameters are in the same group if they share the same 
+    intermediate boundary nodes where the 'param-side' graph intersects the input closure.
+    """
+    inputs_closure, _ = reverse_closure(inputs, set(), reverse_edges)
+
+    intermediate_to_param_group: Dict[Node, Dict[str, Any]] = {}
+    solo_groups: List[Dict[str, Any]] = []
+
+    for p in params:
+        if p is None:
+            continue
+
+        _, intersected_nodes = reverse_closure([p], inputs_closure, reverse_edges)
+
+        if not intersected_nodes:
+            # if the parameter doesn't intersect inputs (unused or disconnected), then it 
+            # we place it in its own group
+            solo_groups.append({"params": {p}, "intermediates": []})
+            continue
+
+        # Merge into an existing group if any intersected intermediate node already has one
+        group: Optional[Dict[str, Any]] = None
+        for inter_node in intersected_nodes:
+            if inter_node in intermediate_to_param_group:
+                group = intermediate_to_param_group[inter_node]
+                break
+
+        if group is None:
+            group = {"params": set(), "intermediates": []}
+
+        group["params"].add(p)
+
+        # remap all intersected intermediates to this group
+        for inter_node in intersected_nodes:
+            intermediate_to_param_group[inter_node] = group
+
+    # add unique groups to param_groups
+    param_groups: List[Dict[str, Any]] = []
+    seen_ids: Set[int] = set()
+    for params in intermediate_to_param_group.values():
+        if id(params) in seen_ids:
+            continue
+        seen_ids.add(id(params))
+        param_groups.append(params)
+
+    for params in param_groups:
+        inters = [k for k, v in intermediate_to_param_group.items() if v is params]
+        # sort nodes to maintain deterministic order when iterating over them in backward_weight
+        inters.sort(key=id)
+        params["intermediates"] = inters
+
+    param_groups.extend(solo_groups)
+
+    return param_groups
diff --git a/src/piper.py b/src/piper.py
index 8239063..c21cd32 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -1,5 +1,3 @@
-from . import piper_patches
-
 import ray
 import torch
 import os
@@ -38,7 +36,7 @@ def piper(gm, example_inputs, **kwargs):
 
     refs = []
     actor_stages = []
-    for (stage_id, stage_gm, input_idxs, graphargs, placeholders) in submodules:
+    for (stage_id, stage_gm, input_idxs, param_idxs, graphargs, placeholders) in submodules:
         stage_gm(*graphargs)
         start = torch.cuda.Event(enable_timing=True)
         start.record()
@@ -49,8 +47,9 @@ def piper(gm, example_inputs, **kwargs):
         torch.cuda.synchronize()
         print(f"Stage {stage_id} time measured on controller: {start.elapsed_time(end) / 5:.2f} ms")
 
+        n_a2a_ops = 0
         if dp_degree > 1:
-            stage_gm = _insert_a2a_ops(stage_gm)
+            stage_gm, n_a2a_ops = _insert_a2a_ops(stage_gm)
 
         actor_id = piper_metadata.stage_to_device[stage_id]
         actor = _get_actor(actor_id)
@@ -62,6 +61,8 @@ def piper(gm, example_inputs, **kwargs):
             stage_gm_data,
             graphargs,
             input_idxs,
+            param_idxs,
+            n_a2a_ops,
         ))
 
         del stage_gm
diff --git a/src/piper_actor.py b/src/piper_actor.py
index e8711ea..b3fe374 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -3,14 +3,13 @@
 import logging
 import os
 import time
+from typing import Any, Dict, List, Set, Tuple
 import gc
 from torch._guards import CompileId
 from torch.nn import Parameter
-from collections import defaultdict
+from torch.autograd.graph import GradientEdge, Node
 import torch.distributed as dist
-import threading
-from typing import Callable
-import asyncio
+from collections import defaultdict
 
 from .piper_utils import (
     _deserialize_graphmodule,
@@ -18,6 +17,7 @@
     LOG_LEVEL,
     piper_metadata,
 )
+from .backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
 
 CLEANUP_MEMORY = False
 
@@ -35,6 +35,8 @@ def _create_actors(
     num_stages,
     p2p_schedules,
     naive_gradient_sync=False,
+    profile=False,
+    mode="sequential",
 ):
     dp_rank = int(os.environ["PIPER_DP_RANK"])
     world_size = int(os.environ["PIPER_WORLD_SIZE"])
@@ -57,6 +59,8 @@ def _create_actors(
             dp_rank=dp_rank,
             dp_degree=dp_degree,
             pp_degree=pp_degree,
+            profile=profile,
+            mode=mode,
         )
         piper_metadata.actors[pp_rank] = actor
         logger.debug(
@@ -69,7 +73,6 @@ def _get_actor(pp_rank):
 
     return piper_metadata.actors[pp_rank]
 
-
 @ray.remote
 class PiperActor:
     def __init__(
@@ -84,8 +87,12 @@ def __init__(
         dp_rank=0,
         dp_degree=1,
         pp_degree=1,
+        profile=False,
+        mode="sequential",
     ):
         self.logger = create_logger("piper_actor", LOG_LEVEL)
+        self.profile = profile
+        self.mode = mode
 
         self.pp_rank = pp_rank
         self.optim_class = optim_class
@@ -122,6 +129,10 @@ def __init__(
         self.p2p_stream = torch.cuda.Stream()
         self.overlapped_comp_stream = torch.cuda.Stream()
         self.overlapped_p2p_stream = torch.cuda.Stream()
+        if mode == "naive":
+            self.per_mb_streams = [torch.cuda.Stream() for _ in range(2)]
+        self.n_a2a_ops = dict()
+        self.overlap_a2a_ops = False
 
         # map stage id -> compiled fx.Graph function
         self.forward_fns = dict()
@@ -133,6 +144,8 @@ def __init__(
         self.forward_input_meta = defaultdict(dict)
         # map stage id -> indices of the input tensors (as opposed to model parameters) used by the fx.Graph
         self.input_idxs = dict()
+        # map stage id -> indices of the model parameters used by the fx.Graph
+        self.param_idxs = dict()
         # map stage id -> optimizer for the fx.Graph
         self.optims = dict()
         # map stage id -> mb_idx -> previous activation (if this stage is not first)
@@ -156,6 +169,13 @@ def __init__(
         self.trace_events = dict()
         self.trace_data = defaultdict(list)
 
+        # map stage id -> mb_idx -> parameter groups for backward pass
+        self.bw_param_groups = defaultdict(dict)
+        # map stage id -> mb_idx -> gradient cache for backward pass
+        self.bw_grad_cache = defaultdict(dict)
+        # map stage id -> mb_idx -> upstream gradient cache for backward pass
+        self.upstream_grad_cache = defaultdict(dict)
+
         from .piper_utils import piper_metadata
 
         piper_metadata.actor_self = self
@@ -276,7 +296,7 @@ def hook_maker(tensor_id):
             def post_backward_hook(grad):
                 self.comm_op_status[stage_id][tensor_id] += 1
                 self.logger.debug(
-                    f"Updating status on dp_rank: {self.dp_rank}, tensor={tensor_id}, status={self.comm_op_status[stage_id][tensor_id]}"
+                    f"Updating status on actor: {self.global_rank}, tensor={tensor_id}, status={self.comm_op_status[stage_id][tensor_id]}/{self.num_mbs}"
                 )
                 if self.comm_op_status[stage_id][tensor_id] == self.num_mbs:
                     with torch.cuda.stream(self.comm_stream):
@@ -287,7 +307,7 @@ def post_backward_hook(grad):
                             async_op=True,
                         )
                     self.logger.debug(
-                        f"Allreduce on dp_rank: {self.dp_rank}, tensor={tensor_id}"
+                        f"Allreduce on actor: {self.global_rank}, tensor={tensor_id}"
                     )
                     self.comm_op_status[stage_id][tensor_id] = 0
                     self.comm_op_handles[stage_id][tensor_id] = handle
@@ -319,7 +339,7 @@ def _wait_for_comm_ops(self):
                         done = True
 
     def _load_stage(
-        self, stage_id: int, gm_data, forward_args,input_idxs
+        self, stage_id: int, gm_data, forward_args, input_idxs, param_idxs, n_a2a_ops
     ):
         self.logger.debug(f"Loading stage {stage_id} graph on actor {self.global_rank}")
 
@@ -330,6 +350,9 @@ def _load_stage(
         self.graph_modules[stage_id] = gm
         self.forward_fns[stage_id] = gm.forward
 
+        # initialize A2A states
+        self.n_a2a_ops[stage_id] = n_a2a_ops
+
         # place parameters on the device
         def move_to_device(idx, arg):
             if arg.requires_grad:
@@ -342,6 +365,7 @@ def move_to_device(idx, arg):
         self.forward_args[stage_id] = forward_args
         self.stage_id = stage_id
         self.input_idxs[stage_id] = input_idxs
+        self.param_idxs[stage_id] = param_idxs
 
         for i in self.input_idxs[stage_id]:
             self.forward_input_meta[stage_id][i] = (
@@ -643,12 +667,30 @@ def _exec_bwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
         self.inp_activation[stage_id][mb_idx] = None
     
     def _forward(self, stage_id: int, mb_idx: int, *deps):
-        comp_stream = self.comp_stream
+        if self.mode == "sequential":
+            comp_stream = self.comp_stream
+        elif self.mode == "naive":
+            comp_stream = self.per_mb_streams[(stage_id + mb_idx) % 2]
+        elif self.mode == "overlapped":
+            comp_stream = self.comp_stream
+        else:
+            raise ValueError(f"Invalid mode: {self.mode}")
+        
+        self.overlap_a2a_ops = False
 
         self.logger.debug(
             f"Calling forward {stage_id} mb {mb_idx} on actor {self.global_rank}"
         )
 
+        if self.profile:
+            profiler = torch.profiler.profile(
+                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
+                with_stack=True,
+            )
+            profiler.__enter__()
+            forward_ctx_manager = torch.profiler.record_function(f"forward_stage_{stage_id}_mb_{mb_idx}")
+            forward_ctx_manager.__enter__()
+
         if stage_id == 0:
             # For the first stage, load input tensors from self.inputs
             for i, inp in zip(self.input_idxs[stage_id], self.inputs):
@@ -678,7 +720,6 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
                 f"Saving input activation {inp_with_grad[0].shape} for stage {stage_id} mb {mb_idx}"
             )
             self.inp_activation[stage_id][mb_idx] = inp_with_grad[0]
-        
 
         # Run the forward pass
         self._start_timing(comp_stream, "forward_comp")
@@ -717,14 +758,38 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
 
         torch.cuda.synchronize()
 
+        if self.profile:
+            forward_ctx_manager.__exit__(None, None, None)
+            profiler.__exit__(None, None, None)
+            profiler.export_chrome_trace(f"out/{self.mode}/actor{self.global_rank}_fwd{stage_id}mb{mb_idx}_trace.json")
+
         return 1
 
     def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        comp_stream = self.comp_stream
+        if self.mode == "sequential":
+            comp_stream = self.comp_stream
+        elif self.mode == "naive":
+            comp_stream = self.per_mb_streams[(stage_id + mb_idx) % 2]
+        elif self.mode == "overlapped":
+            comp_stream = self.comp_stream
+        else:
+            raise ValueError(f"Invalid mode: {self.mode}")
+
+        self.overlap_a2a_ops = False
 
         self.logger.debug(
             f"Calling backward {stage_id} mb {mb_idx} on actor {self.global_rank}"
         )
+
+        if self.profile:
+            profiler = torch.profiler.profile(
+                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
+                with_stack=True,
+            )
+            profiler.__enter__()
+            backward_ctx_manager = torch.profiler.record_function(f"backward_stage_{stage_id}_mb_{mb_idx}")
+            backward_ctx_manager.__enter__()
+
         out_activation = self.out_activation[stage_id][mb_idx]
 
         if stage_id < self.num_stages - 1:
@@ -760,17 +825,295 @@ def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
 
         torch.cuda.synchronize()
 
+        if self.profile:
+            backward_ctx_manager.__exit__(None, None, None)
+            profiler.__exit__(None, None, None)
+            profiler.export_chrome_trace(f"out/{self.mode}/actor{self.global_rank}_bwd{stage_id}mb{mb_idx}_trace.json")
+
+        return 1
+
+
+    def _backward_input(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
+        comp_stream = self.comp_stream
+
+        self.logger.debug(
+            f"Calling backward I {stage_id} mb {mb_idx} on actor {self.global_rank}"
+        )
+
+        out_activation = self.out_activation[stage_id][mb_idx]
+
+        activation_or_loss = None
+        upstream_grad = None
+        if stage_id < self.num_stages - 1:
+            activation_or_loss = out_activation
+            upstream_grad = self.p2p_cache.pop(
+                (stage_id + 1, stage_id, mb_idx, False)
+            )
+        else:
+            assert loss_fn is not None
+            labels = self.labels
+            assert out_activation.shape == labels.shape
+            with torch.cuda.stream(comp_stream):
+                loss = loss_fn(out_activation, labels)
+                self.loss.append(loss.item())
+            activation_or_loss = loss
+            upstream_grad = torch.ones_like(loss)
+
+        # no-op for the first stage
+        if stage_id == 0:
+            self.upstream_grad_cache[stage_id][mb_idx] = upstream_grad
+            self.logger.debug(
+                f"Saving upstream gradient {upstream_grad.shape} for stage {stage_id} mb {mb_idx}"
+            )
+            return 1
+
+        stage_input = self.inp_activation[stage_id][mb_idx]
+        stage_params = [self.forward_args[stage_id][i] for i in self.param_idxs[stage_id]]
+
+        output_nodes = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [activation_or_loss]) if n is not None]
+        input_nodes  = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [stage_input]) if n is not None]
+        param_nodes   = [n for n in (_get_grad_fn_or_grad_acc(p) for p in stage_params) if n is not None]
+
+        # Use the autograd graph with edges reversed to compute parameter groups, which are groups 
+        # of parameters that share the same intermediate nodes. Intermediate nodes are the nodes that
+        # lie on both (1) a backward path from the output node(s) to the stage input nodes and 
+        # (2) in a path from the output node(s) a parameter node/gradient accumulator
+        reverse_edges = construct_reverse_graph(output_nodes)
+        param_groups = get_param_groups(input_nodes, param_nodes, reverse_edges)
+
+        # Hooks to capture grads at intermediate nodes. In backward_weight,
+        # we'll backprop from these intermediate values
+        handles = []
+        for pg in param_groups:
+            intermediates = pg["intermediates"]
+            if not intermediates:
+                continue
+
+            pg["grads"] = [None] * len(intermediates)
+
+            for i, intermediate_node in enumerate(intermediates):
+                def make_hook(group: Dict[str, Any], idx: int):
+                    def hook(grad_inputs):
+                        group["grads"][idx] = grad_inputs
+                    return hook
+                handles.append(intermediate_node.register_prehook(make_hook(pg, i)))
+
+        self._start_timing(comp_stream, "backward_input")
+        with torch.cuda.stream(comp_stream):
+            gx = torch.autograd.grad(
+                outputs=activation_or_loss,
+                inputs=stage_input,
+                grad_outputs=upstream_grad,
+                retain_graph=True,
+                allow_unused=True,
+            )
+        self._stop_timing(comp_stream, "backward_input")
+
+        gx = gx[0] # Take the gx gradient out of the tuple returned by autograd.grad
+        if gx is not None and stage_input.requires_grad:
+            if stage_input.grad is None:
+                stage_input.grad = gx
+            else:
+                stage_input.grad.add_(gx)
+
+        # Free output tensors between the output nodes and the intermediate nodes
+        if not isinstance(activation_or_loss, list):
+            activation_or_loss = [activation_or_loss]
+
+        for t in activation_or_loss:
+            t.detach_()
+
+        for h in handles:
+            h.remove()
+
+        del activation_or_loss
+        del stage_input
+
+        # Save parameter groups for use in backward_weight
+        self.bw_param_groups[stage_id][mb_idx] = param_groups
+
+        if CLEANUP_MEMORY:
+            gc.collect()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+        torch.cuda.synchronize()
+
+        return 1
+
+    def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
+        comp_stream = self.comp_stream
+
+        self.logger.debug(
+            f"Calling backward W {stage_id} mb {mb_idx} on actor {self.global_rank}"
+        )
+
+        stage_params = [self.forward_args[stage_id][i] for i in self.param_idxs[stage_id]]
+
+        # Special case to handle stage 0 since backward_input is a NOOP, 
+        # meaning no parameter groups are created
+        if stage_id == 0:
+            upstream_grad = self.upstream_grad_cache[stage_id][mb_idx]
+            out_activation = self.out_activation[stage_id][mb_idx]
+            self._start_timing(comp_stream, "backward_weight")
+            if stage_id < self.num_stages - 1:
+                with torch.cuda.stream(comp_stream):
+                    gparams = torch.autograd.grad(
+                        outputs=out_activation,
+                        inputs=stage_params,
+                        grad_outputs=upstream_grad,
+                        retain_graph=False,
+                    )
+            else:
+                assert loss_fn is not None
+                labels = self.labels
+                assert out_activation.shape == labels.shape
+                with torch.cuda.stream(comp_stream):
+                    loss = loss_fn(out_activation, labels)
+                    gparams = torch.autograd.grad(
+                        outputs=loss,
+                        inputs=stage_params,
+                        retain_graph=False,
+                    )
+            self._stop_timing(comp_stream, "backward_weight")
+
+            assert len(gparams) == len(stage_params), (
+                f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(stage_params)}"
+            )
+            
+            for p, pg in zip(stage_params, gparams):
+                if p.grad is None:
+                    p.grad = pg.clone()
+                else:
+                    p.grad.add_(pg)
+        else:
+            # Create mapping from autograd nodes -> parameters
+            grad_acc_to_weight: Dict[Node, Tuple[Parameter, int]] = {}
+            for param in stage_params:
+                node = _get_grad_fn_or_grad_acc(param)
+                grad_acc_to_weight[node] = param
+
+            param_groups = self.bw_param_groups[stage_id][mb_idx]
+
+            # Perform the weight updates separately for each param_group, beginning
+            # backprop from each the intermediate node(s) of each group
+            for pg in param_groups:
+                intermediates: List[Node] = pg.get("intermediates", [])
+                intermediate_grads = pg.get("grads", None) # List of intermediate node gradients, captured by the hooks
+
+                # Skip groups without intermediate nodes (could happen in weird cases
+                # where one node is disconnected from the rest of the autograd graph for some reason)
+                if not intermediates or intermediate_grads is None:
+                    continue
+
+                intermediate_edges: List[GradientEdge] = []
+                intermediate_edge_grads: List[torch.Tensor] = []
+
+                for intermediate_node, grad_inputs in zip(intermediates, intermediate_grads):
+                    if grad_inputs is None:
+                        continue
+
+                    gs = [x for x in grad_inputs if x is not None]
+                    if not gs:
+                        continue
+                    
+                    # Sum all gradients arriving at the current intermediate node
+                    # in case the node has multiple source of gradients
+                    summed = sum(gs)
+
+                    # Create a GradientEdge for each intermediate node (we can backprop with respect to these)
+                    # and store the summed gradient for that node
+                    intermediate_edges.append(GradientEdge(intermediate_node, 0))
+                    intermediate_edge_grads.append(summed)
+
+                del pg["intermediates"]
+
+                if not intermediate_edges:
+                    continue
+
+                # Grab params for the param_nodes in this param group using our grad_acc_to_weight map from earlier
+                mapped_param_nodes = [p for p in pg["params"] if p in grad_acc_to_weight]
+                if not mapped_param_nodes:
+                    continue
+
+                # Use these parameters to create a GradientEdge that we'll use as our input to autograd.grad
+                weight_edges = tuple(GradientEdge(p, 0) for p in mapped_param_nodes)
+
+                self._start_timing(comp_stream, "backward_weight")
+                with torch.cuda.stream(comp_stream):
+                    gparams = torch.autograd.grad(
+                        outputs=intermediate_edges,
+                        inputs=weight_edges,
+                        grad_outputs=intermediate_edge_grads,
+                        retain_graph=False,
+                    )
+                self._stop_timing(comp_stream, "backward_weight")
+
+                del pg["grads"]
+
+                assert len(gparams) == len(mapped_param_nodes), (
+                    f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(mapped_param_nodes)}"
+                )
+                
+                # Finally, update gradients for the params in this param_group
+                for param_node, dw in zip(pg["params"], gparams):
+                    if dw is None:
+                        continue
+
+                    weight = grad_acc_to_weight[param_node]
+
+                    if weight.grad is None:
+                        weight.grad = dw
+                    else:
+                        weight.grad.add_(dw)
+
+        self.bw_grad_cache[stage_id][mb_idx] = None
+        self.upstream_grad_cache[stage_id][mb_idx] = None
+        self.bw_param_groups[stage_id][mb_idx] = None
+        self.out_activation[stage_id][mb_idx] = None
+
+        if CLEANUP_MEMORY:
+            gc.collect()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+        torch.cuda.synchronize()
+
         return 1
 
     def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: int, bwd_mb_idx: int, *deps, loss_fn=None):
-        fwd_comp_stream = self.comp_stream
-        bwd_comp_stream = self.overlapped_comp_stream
+        if self.mode == "sequential":
+            fwd_comp_stream = self.comp_stream
+            bwd_comp_stream = self.comp_stream
+            self.overlap_a2a_ops = False
+        elif self.mode == "naive":
+            fwd_comp_stream = self.per_mb_streams[(fwd_stage_id + fwd_mb_idx) % 2]
+            bwd_comp_stream = self.per_mb_streams[(bwd_stage_id + bwd_mb_idx) % 2]
+            self.overlap_a2a_ops = False
+        elif self.mode == "overlapped":
+            fwd_comp_stream = self.comp_stream
+            bwd_comp_stream = self.overlapped_comp_stream
+            self.overlap_a2a_ops = True
+            # initialize A2A states
+            self.fwd_a2a_pre_events = [torch.cuda.Event() for _ in range(self.n_a2a_ops[fwd_stage_id])]
+            self.bwd_a2a_pre_events = [torch.cuda.Event() for _ in range(self.n_a2a_ops[bwd_stage_id])]
+            self.fwd_a2a_counter = 0
+            self.bwd_a2a_counter = 0
+        else:
+            raise ValueError(f"Invalid mode: {self.mode}")
         fwd_p2p_stream = self.p2p_stream
-        bwd_p2p_stream = self.overlapped_p2p_stream
+        bwd_p2p_stream = self.p2p_stream
 
         self.logger.debug(
             f"Calling forward {fwd_stage_id} mb {fwd_mb_idx} backward {bwd_stage_id} mb {bwd_mb_idx} on actor {self.global_rank}"
         )
+        
+        if self.profile:
+            profiler = torch.profiler.profile(
+                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
+                with_stack=True,
+            )
+            profiler.__enter__()
 
         # PREPARE FORWARD PASS
         if fwd_stage_id == 0:
@@ -778,7 +1121,6 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             for i, inp in zip(self.input_idxs[fwd_stage_id], self.inputs):
                 self.forward_args[fwd_stage_id][i] = inp
         else:
-            self._exec_p2p_op(fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False, None, p2p_stream=fwd_p2p_stream)
             inputs_from_prev_stage = self.p2p_cache.pop(
                 (fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False)
             )
@@ -804,17 +1146,9 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             )
             self.inp_activation[fwd_stage_id][fwd_mb_idx] = inp_with_grad[0]
 
-        # RUN FORWARD PASS
-        self._start_timing(fwd_comp_stream, "forward_comp")
-        with torch.cuda.stream(fwd_comp_stream):
-            output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
-        self._stop_timing(fwd_comp_stream, "forward_comp")
-        
         # PREPARE BACKWARD PASS
         out_activation = self.out_activation[bwd_stage_id][bwd_mb_idx]
         if bwd_stage_id < self.num_stages - 1:
-            self._exec_p2p_op(bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False, None, p2p_stream=bwd_p2p_stream)
-
             input_grad = self.p2p_cache.pop(
                 (bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False)
             )
@@ -823,18 +1157,28 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             labels = self.labels
             assert out_activation.shape == labels.shape
 
+        # RUN FORWARD PASS
+        if self.profile:
+            forward_ctx_manager = torch.profiler.record_function(f"forward_stage_{fwd_stage_id}_mb_{fwd_mb_idx}")
+            forward_ctx_manager.__enter__()
+
+        with torch.cuda.stream(fwd_comp_stream):
+            output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
+
         # RUN BACKWARD PASS
+        if self.profile:
+            forward_ctx_manager.__exit__(None, None, None)
+            backward_ctx_manager = torch.profiler.record_function(f"backward_stage_{bwd_stage_id}_mb_{bwd_mb_idx}")
+            backward_ctx_manager.__enter__()
+        if self.overlap_a2a_ops and self.dp_degree > 1:
+            bwd_comp_stream.wait_event(self.fwd_a2a_pre_events[0])
         if bwd_stage_id < self.num_stages - 1:
-            self._start_timing(bwd_comp_stream, "backward_comp")
             with torch.cuda.stream(bwd_comp_stream):
                 out_activation.backward(gradient=input_grad)
-            self._stop_timing(bwd_comp_stream, "backward_comp")
         else:
-            self._start_timing(bwd_comp_stream, "backward_comp")
             with torch.cuda.stream(bwd_comp_stream):
                 loss = loss_fn(out_activation, labels)
                 loss.backward()
-            self._stop_timing(bwd_comp_stream, "backward_comp")
 
         # POST PROCESS FORWARD PASS
         out_with_grad = [out for out in output if out.requires_grad]
@@ -863,11 +1207,6 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             send_p2p_op = (fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True)
             assert send_p2p_op not in self.p2p_cache
             self.p2p_cache[send_p2p_op] = output
-            self._exec_p2p_op(fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True, None, p2p_stream=fwd_p2p_stream)
-
-        # POST BACKWARD P2P OPERATIONS
-        if bwd_stage_id > 0:
-            self._exec_p2p_op(bwd_stage_id, bwd_stage_id - 1, bwd_mb_idx, True, None, p2p_stream=bwd_p2p_stream)
 
         if CLEANUP_MEMORY:
             gc.collect()
@@ -876,6 +1215,21 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
 
         torch.cuda.synchronize()
 
+        # clear A2A states
+        if self.overlap_a2a_ops and self.dp_degree > 1:
+            self.fwd_a2a_pre_events.clear()
+            self.bwd_a2a_pre_events.clear()
+            self.fwd_a2a_counter = 0
+            self.bwd_a2a_counter = 0
+
+        if self.profile:
+            backward_ctx_manager.__exit__(None, None, None)
+            profiler.__exit__(None, None, None)
+            profiler.export_chrome_trace(
+                f"out/{self.mode}/actor{self.global_rank}_"
+                f"fwd{fwd_stage_id}mb{fwd_mb_idx}_"
+                f"bwd{bwd_stage_id}mb{bwd_mb_idx}_trace.json")
+
         return 1
 
     def _synchronize_gradients(self):
@@ -890,7 +1244,6 @@ def _synchronize_gradients(self):
                         )
 
     def _update(self, *deps):
-        # self._wait_pending_sends()
         self.logger.debug(f"Actor {self.global_rank} waiting for backward sync events")
 
         # if dp degree > 1, make sure all gradients are synchronized before optimizer step
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 5f28750..d99642e 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -1,5 +1,7 @@
 import ray
 import torch
+from torch._dynamo.backends.debugging import eager
+import torch.distributed as dist
 import threading
 import os
 import gc
@@ -8,7 +10,7 @@
 
 from torch._dynamo.backends.debugging import eager
 
-from .piper_overlap_actor import _create_actors
+from .piper_actor import _create_actors
 from .piper_utils import piper_metadata, create_logger, LOG_LEVEL
 from .piper_exec import CompType, Schedule2D, Task
 from .piper import piper
@@ -16,17 +18,23 @@
 logger = create_logger("piper_compile", LOG_LEVEL)
 
 def _sort_by_task(executable_tasks: list[tuple[int, Task]]) -> list[tuple[int, Task]]:
-    """Sort executable tasks by type so that BWD comes before FWD (then FWD_BWD)."""
-    type_order = {CompType.BWD: 2, CompType.FWD: 1, CompType.FWD_BWD: 0}
+    """Sort executable tasks by type so that BWD/BWD_I come before FWD (then BWD_W, FWD_BWD)."""
+    type_order = {
+        CompType.BWD: 0,
+        CompType.BWD_I: 0,
+        CompType.FWD_BWD: 1,
+        CompType.FWD: 2,
+        CompType.BWD_W: 3,
+    }
     return sorted(
         executable_tasks,
-        key=lambda item: type_order.get(item[1].type, 3),
+        key=lambda item: type_order.get(item[1].type, 4),
     )
 
 
 def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, stage_to_device: dict[int, int]):
     schedule = schedule.grid
-    # Filter to fwd/bwd tasks and split FWD_BWD into two consecutive tasks (FWD then BWD).
+    # Filter to fwd/bwd tasks (exclude UPD). Include BWD_I, BWD_W, FWD_BWD.
     schedule = [[task for task in row if task is not None and task.type != CompType.UPD] for row in schedule]
 
     assert num_devices == len(schedule), "Some rank has no fwd/bwd tasks"
@@ -52,7 +60,7 @@ def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, sta
                     batch = task.batches[0]
                     if batch.stage_id > 0:
                         dependee.append((batch.stage_id - 1, batch.stage_id, batch.mb_idx))
-                elif task.type == CompType.BWD:
+                elif task.type in (CompType.BWD, CompType.BWD_I):
                     batch = task.batches[0]
                     if batch.stage_id < num_stages - 1:
                         dependee.append((batch.stage_id + 1, batch.stage_id, batch.mb_idx))
@@ -63,7 +71,7 @@ def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, sta
                     if bwd_batch.stage_id < num_stages - 1:
                         dependee.append((bwd_batch.stage_id + 1, bwd_batch.stage_id, bwd_batch.mb_idx))
 
-                can_exec = dependee is [] or all(dependee in p2p_comms for dependee in dependee)
+                can_exec = dependee == [] or all(dep in p2p_comms for dep in dependee)
                 if can_exec:
                     executable_tasks.append((rank, task))
 
@@ -76,7 +84,7 @@ def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, sta
                 batch = task.batches[0]
                 if batch.stage_id < num_stages - 1:
                     p2p_comms.append((batch.stage_id, batch.stage_id + 1, batch.mb_idx))
-            elif task.type == CompType.BWD:
+            elif task.type == CompType.BWD or task.type == CompType.BWD_I:
                 batch = task.batches[0]
                 if batch.stage_id > 0:
                     p2p_comms.append((batch.stage_id, batch.stage_id - 1, batch.mb_idx))
@@ -118,6 +126,7 @@ def piper_setup(
     example_outputs,
     schedule: Schedule2D,
     naive_gradient_sync=False,
+    mode="overlapped",
 ):
     """
     Compile a model with the piper backend.
@@ -140,7 +149,7 @@ def piper_setup(
     )
     _create_actors(
         num_devices, optim_fn, num_mbs, num_stages, p2p_schedules, naive_gradient_sync,
-        profile=True, mode="overlapped",
+        profile=True, mode=mode,
     )
 
     last_stage_rank = stage_to_device[num_stages - 1]
diff --git a/src/piper_coordinator.py b/src/piper_coordinator.py
index ed71060..6504330 100644
--- a/src/piper_coordinator.py
+++ b/src/piper_coordinator.py
@@ -7,7 +7,7 @@
 from .piper_utils import create_logger, LOG_LEVEL
 
 
-@ray.remote(num_gpus=0.1)
+@ray.remote(num_gpus=0.6)
 def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, master_addr, master_port, training_func: Callable, *args, **kwargs):
     logger = create_logger("piper_coordinator", LOG_LEVEL)
     logger.debug(f"Running DP rank {dp_rank+1} of {dp_degree}")
diff --git a/src/piper_exec.py b/src/piper_exec.py
index b61c663..f34a3a3 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -1,6 +1,8 @@
 import os
-import ray
 import torch
+import threading
+import time
+import ray
 import torch.distributed as dist
 from typing import NamedTuple
 from enum import Enum
@@ -18,6 +20,8 @@ class CompType(Enum):
     FWD = "forward"
     BWD = "backward"
     UPD = "update"
+    BWD_I = "backward_input"
+    BWD_W = "backward_weight"
     FWD_BWD = "forward_backward"
 
 
@@ -26,54 +30,6 @@ class BatchMeta(NamedTuple):
     stage_id: int
     mb_idx: int
 
-
-class ScheduleTask(NamedTuple):
-    """A node in the schedule DAG: runs on pp_rank with one microbatch."""
-
-    pp_rank: int
-    microbatch: BatchMeta
-
-    def __repr__(self) -> str:
-        mb = self.microbatch
-        if mb.comp_type is None:
-            c = "?"
-        elif mb.comp_type == CompType.FWD:
-            c = "f"
-        elif mb.comp_type == CompType.BWD:
-            c = "b"
-        else:
-            c = "u"
-        return f"rank{self.pp_rank}_stage{mb.stage_id}_mb{mb.mb_idx}_{c}"
-
-
-class ScheduleEdge(NamedTuple):
-    """Data dependency: to_task consumes output of from_task (source and destination only)."""
-    from_task_idx: int
-    to_task_idx: int
-
-
-class TemporalEdge(NamedTuple):
-    """Temporal dependency: to_task must run after from_task (ordering only)."""
-    from_task_idx: int
-    to_task_idx: int
-
-
-@dataclass
-class Schedule:
-    """DAG schedule: tasks (nodes), data edges, and temporal edges."""
-    tasks: list[ScheduleTask]
-    edges: list[ScheduleEdge]
-    temporal_edges: list[TemporalEdge] = field(default_factory=list)
-
-    def num_stages(self) -> int:
-        stages = {task.microbatch.stage_id for task in self.tasks}
-        return max(stages) + 1 if stages else 0
-
-    def num_mbs(self) -> int:
-        mbs = {task.microbatch.mb_idx for task in self.tasks}
-        return max(mbs) + 1 if mbs else 0
-
-
 # Task for 2D schedule grid: one cell (rank, time_step).
 class Task(NamedTuple):
     pp_rank: int
@@ -148,14 +104,14 @@ def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdg
     all_tasks = set()
     microbatch_tasks = {}  # mb_idx -> set of (stage_id, type)
     
-    for stage_id in range(num_devices):
+    for pp_rank in range(num_devices):
         for time_step in range(num_steps):
-            task = schedule[stage_id][time_step]
+            task = schedule[pp_rank][time_step]
             if task is not None:
                 # Check pp_rank matches row
-                if task.pp_rank != stage_id:
+                if task.pp_rank != pp_rank:
                     raise ValueError(
-                        f"Task pp_rank {task.pp_rank} does not match row {stage_id} "
+                        f"Task pp_rank {task.pp_rank} does not match row {pp_rank} "
                         f"at time step {time_step}"
                     )
                 
@@ -187,27 +143,31 @@ def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdg
     for mb_idx, tasks in microbatch_tasks.items():
         # Find all stages that have forward/backward tasks for this microbatch
         fwd_stages = {stage_id for stage_id, task_type in tasks if task_type == CompType.FWD}
-        bwd_stages = {stage_id for stage_id, task_type in tasks if task_type == CompType.BWD}
+        bwd_stages = {
+            stage_id
+            for stage_id, task_type in tasks
+            if task_type in (CompType.BWD, CompType.BWD_I)
+        }
         
         # Check that all required stages have forward tasks
         missing_fwd = all_required_stages - fwd_stages
         if missing_fwd:
-            raise ValueError(
-                f"Microbatch {mb_idx} missing forward stages: {missing_fwd}"
-            )
-        
-        # Check that all required stages have backward tasks
+            raise ValueError(f"Microbatch {mb_idx} missing forward stages: {missing_fwd}")
+
+        # Check that all required stages have backward tasks (BWD or BWD_I)
         missing_bwd = all_required_stages - bwd_stages
         if missing_bwd:
             raise ValueError(
-                f"Microbatch {mb_idx} missing backward stages: {missing_bwd}"
+                f"Microbatch {mb_idx} missing backward stages: {missing_bwd} "
+                f"(need BWD, BWD_I, or FWD_BWD backward for each stage)"
             )
     
     # Check pipeline stage dependencies
     for mb_idx in range(num_mbs):
         # Find all tasks for this microbatch
-        fwd_times = {}  # stage_id -> time_step
-        bwd_times = {}  # stage_id -> time_step
+        fwd_times: dict[int, int] = {}  # stage_id -> time_step
+        bwd_times: dict[int, int] = {}  # stage_id -> time_step (BWD or BWD_I)
+        bwd_w_times: dict[int, int] = {}  # stage_id -> time_step (BWD_W)
         
         for pp_rank in range(num_devices):
             for time_step in range(num_steps):
@@ -224,8 +184,10 @@ def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdg
                     )
                     if comp_type == CompType.FWD:
                         fwd_times[batch.stage_id] = time_step
-                    elif comp_type == CompType.BWD:
+                    elif comp_type in (CompType.BWD, CompType.BWD_I):
                         bwd_times[batch.stage_id] = time_step
+                    elif comp_type == CompType.BWD_W:
+                        bwd_w_times[batch.stage_id] = time_step
         
         # Check forward stage ordering: if A -> B, then fwd(A) < fwd(B)
         for edge in dag_edges:
@@ -258,47 +220,20 @@ def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdg
                         f"backward stage {to_stage} (time {bwd_times[to_stage]}) must come "
                         f"before backward stage {from_stage} (time {bwd_times[from_stage]})"
                     )
-
-
-def _topological_order(schedule: Schedule) -> list[int]:
-    """Return task indices in topological order (data + temporal predecessors)."""
-    tasks = schedule.tasks
-    edges = schedule.edges
-    temporal = schedule.temporal_edges or []
-    n = len(tasks)
-    in_degree = [0] * n
-    for e in edges:
-        in_degree[e.to_task_idx] += 1
-    for e in temporal:
-        in_degree[e.to_task_idx] += 1
-    queue = [i for i in range(n) if in_degree[i] == 0]
-    order = []
-    while queue:
-        u = queue.pop(0)
-        u_rank = tasks[u].pp_rank
-        if queue:
-            for v in queue:
-                if tasks[v].pp_rank == u_rank:
-                    logger.warning(
-                        "Topological order: ordering between task %s and task %s (same pp_rank %d) cannot be determined (no directed path in DAG)",
-                        tasks[u],
-                        tasks[v],
-                        u_rank,
-                    )
-        order.append(u)
-        for e in edges:
-            if e.from_task_idx == u:
-                in_degree[e.to_task_idx] -= 1
-                if in_degree[e.to_task_idx] == 0:
-                    queue.append(e.to_task_idx)
-        for e in temporal:
-            if e.from_task_idx == u:
-                in_degree[e.to_task_idx] -= 1
-                if in_degree[e.to_task_idx] == 0:
-                    queue.append(e.to_task_idx)
-    if len(order) != n:
-        raise ValueError("Schedule DAG has a cycle")
-    return order
+        
+        # Check BWD_W must come after BWD_I for same (stage_id, mb_idx)
+        for stage_id in bwd_w_times:
+            if stage_id not in bwd_times:
+                raise ValueError(
+                    f"BWD_W for microbatch {mb_idx} stage {stage_id} has no corresponding "
+                    f"BWD_I or BWD task"
+                )
+            if bwd_w_times[stage_id] <= bwd_times[stage_id]:
+                raise ValueError(
+                    f"BWD_W ordering violation for microbatch {mb_idx}, stage {stage_id}: "
+                    f"BWD_W (time {bwd_w_times[stage_id]}) must come after "
+                    f"BWD_I/BWD (time {bwd_times[stage_id]})"
+                )
 
 
 def piper_exec(
@@ -370,11 +305,35 @@ def piper_exec(
                         if stage_id > 0:
                             dep = actor._exec_p2p_op.remote(stage_id, stage_id - 1, mb_idx, True, dep)
                         deps[pp_rank] = dep
+                    case CompType.BWD_I:
+                        stage_id, mb_idx = batches[0]
+                        # logger.info(f"Executing backward I {stage_id} mb {mb_idx} on actor {actor_id}")
+                        dep = deps[pp_rank]
+                        if stage_id < num_stages - 1:
+                            dep = actor._exec_p2p_op.remote(stage_id + 1, stage_id, mb_idx, False, dep)
+                        dep = actor._backward_input.remote(stage_id, mb_idx, dep, loss_fn=loss_fn)
+                        if stage_id > 0:
+                            dep = actor._exec_p2p_op.remote(stage_id, stage_id - 1, mb_idx, True, dep)
+                        deps[pp_rank] = dep
+                    case CompType.BWD_W:
+                        stage_id, mb_idx = batches[0]
+                        # logger.info(f"Executing backward W {stage_id} mb {mb_idx} on actor {actor_id}")
+                        dep = deps[pp_rank]
+                        dep = actor._backward_weight.remote(stage_id, mb_idx, dep)
+                        deps[pp_rank] = dep
                     case CompType.FWD_BWD:
                         fwd_stage_id, fwd_mb_idx = batches[0]
                         bwd_stage_id, bwd_mb_idx = batches[1]
                         # logger.info(f"Executing forward-backward {fwd_stage_id} mb {fwd_mb_idx} -> {bwd_stage_id} mb {bwd_mb_idx} on actor {actor_id}")
-                        # Don't explicitly schedule p2p ops -- overlapped in _forward_backward
-                        dep = actor._forward_backward.remote(fwd_stage_id, fwd_mb_idx, bwd_stage_id, bwd_mb_idx, deps[pp_rank], loss_fn=loss_fn)
+                        dep = deps[pp_rank]
+                        if fwd_stage_id > 0:
+                            dep = actor._exec_p2p_op.remote(fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False, dep)
+                        if bwd_stage_id < num_stages - 1:
+                            dep = actor._exec_p2p_op.remote(bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False, dep)
+                        dep = actor._forward_backward.remote(fwd_stage_id, fwd_mb_idx, bwd_stage_id, bwd_mb_idx, dep, loss_fn=loss_fn)
+                        if fwd_stage_id < num_stages - 1:
+                            dep = actor._exec_p2p_op.remote(fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True, dep)
+                        if bwd_stage_id > 0:
+                            dep = actor._exec_p2p_op.remote(bwd_stage_id, bwd_stage_id - 1, bwd_mb_idx, True, dep)
                         deps[pp_rank] = dep
-    return ray.get(ret)
\ No newline at end of file
+    return ray.get(ret)
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index bfca861..e59c166 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -57,34 +57,35 @@ def forward(ctx, output, input_tensor):
         ctx.actor_self = piper_metadata.actor_self
         ctx.group = ctx.actor_self.dp_group
         ctx.global_rank = ctx.actor_self.global_rank
-        ctx.stream = ctx.actor_self.a2a_stream
+        
+        stream = ctx.actor_self.comp_stream
 
-        comp_stream = torch.cuda.current_stream()
+        # comp_stream = torch.cuda.current_stream()
 
         logger.debug(f"Dispatch AllToAllSingleFunction forward rank={ctx.global_rank}, shape={input_tensor.shape}")
 
         # PRE-HOOK: record event so bwd knows fwd comp reached this A2A
-        if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.n_a2a_ops > 0:
+        if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.overlap_a2a_ops:
             idx = ctx.actor_self.fwd_a2a_counter
-            ctx.actor_self.fwd_a2a_pre_events[idx].record(comp_stream)
+            ctx.actor_self.fwd_a2a_pre_events[idx].record(stream)
 
-        ctx.actor_self._start_timing(ctx.stream, "fwd_a2a")
+        ctx.actor_self._start_timing(stream, "fwd_a2a")
 
         # Ensure input_tensor is contiguous (all_to_all_single requires contiguous tensors)
         input_tensor = input_tensor.contiguous()
 
-        # Execute A2A on a2a_stream
-        ctx.stream.wait_stream(comp_stream)
-        with torch.cuda.stream(ctx.stream):
+        # Execute A2A
+        # stream.wait_stream(comp_stream)
+        with torch.cuda.stream(stream):
             dist.all_to_all_single(output, input_tensor, group=ctx.group)
-        comp_stream.wait_stream(ctx.stream)
+        # comp_stream.wait_stream(stream)
 
-        ctx.actor_self._stop_timing(ctx.stream, "fwd_a2a")
+        ctx.actor_self._stop_timing(stream, "fwd_a2a")
 
         # POST-HOOK: wait for bwd to reach its corresponding A2A
-        if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.n_a2a_ops > 0:
+        if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.overlap_a2a_ops:
             idx = ctx.actor_self.fwd_a2a_counter
-            comp_stream.wait_event(ctx.actor_self.bwd_a2a_pre_events[idx])
+            stream.wait_event(ctx.actor_self.bwd_a2a_pre_events[idx])
             ctx.actor_self.fwd_a2a_counter += 1
 
         return output
@@ -102,14 +103,14 @@ def backward(ctx, grad_output):
         if grad_output is None:
             return None, None, None
 
-        comp_stream = torch.cuda.current_stream()
+        stream = ctx.actor_self.overlapped_comp_stream
 
         # PRE-HOOK: record event so fwd post-hook knows bwd comp reached this A2A
-        if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.n_a2a_ops > 0:
+        if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.overlap_a2a_ops:
             idx = ctx.actor_self.bwd_a2a_counter
-            ctx.actor_self.bwd_a2a_pre_events[idx].record(comp_stream)
+            ctx.actor_self.bwd_a2a_pre_events[idx].record(stream)
 
-        ctx.actor_self._start_timing(ctx.stream, "bwd_a2a")
+        ctx.actor_self._start_timing(stream, "bwd_a2a")
 
         # Ensure grad_output is contiguous (all_to_all_single requires contiguous tensors)
         grad_output = grad_output.contiguous()
@@ -117,19 +118,19 @@ def backward(ctx, grad_output):
         # Create a buffer for the gradient input
         grad_input = torch.empty_like(grad_output)
 
-        # Execute A2A on a2a_stream
-        ctx.stream.wait_stream(comp_stream)
-        with torch.cuda.stream(ctx.stream):
+        # Execute A2A
+        # stream.wait_stream(comp_stream)
+        with torch.cuda.stream(stream):
             dist.all_to_all_single(grad_input, grad_output, group=ctx.group)
-        comp_stream.wait_stream(ctx.stream)
+        # comp_stream.wait_stream(stream)
 
-        ctx.actor_self._stop_timing(ctx.stream, "bwd_a2a")
+        ctx.actor_self._stop_timing(stream, "bwd_a2a")
 
         # POST-HOOK: wait for fwd to reach the next A2A
-        if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.n_a2a_ops > 0:
+        if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.overlap_a2a_ops:
             idx = ctx.actor_self.bwd_a2a_counter
-            if idx + 1 < ctx.actor_self.n_a2a_ops:
-                comp_stream.wait_event(ctx.actor_self.fwd_a2a_pre_events[idx + 1])
+            if idx + 1 < len(ctx.actor_self.fwd_a2a_pre_events):
+                stream.wait_event(ctx.actor_self.fwd_a2a_pre_events[idx + 1])
             ctx.actor_self.bwd_a2a_counter += 1
 
         # Return gradients: grad_output flows to grad_input, None for group
@@ -196,7 +197,7 @@ def _split_gm_by_stages(gm) -> tuple[fx.GraphModule, list[tuple[int, fx.GraphMod
         prev_stage_outputs = set()
         for prev_stage_id in range(stage_annotation_id):
             if prev_stage_id in stage_modules:
-                _, _, _, prev_outputs, _, _, _, _, _ = stage_modules[prev_stage_id]
+                _, _, _, prev_outputs, _, _, _, _, _, _ = stage_modules[prev_stage_id]
                 prev_stage_outputs.update(prev_outputs)
         
         # Find all inputs needed by this stage (nodes that are not in the stage set)
@@ -281,7 +282,7 @@ def extract_nodes_from_arg(arg):
         if stage_annotation_id > 0:
             prev_stage_id = stage_annotation_id - 1
             if prev_stage_id in stage_modules:
-                _, _, _, prev_stage_outputs_list, _, _, _, _, _ = stage_modules[prev_stage_id]
+                _, _, _, prev_stage_outputs_list, _, _, _, _, _, _ = stage_modules[prev_stage_id]
                 prev_stage_outputs_ordered = prev_stage_outputs_list
         
         # For stages > 0, first add inputs that come from previous stage outputs
@@ -328,7 +329,7 @@ def extract_nodes_from_arg(arg):
         prev_stage_outputs = set()
         for prev_stage_id in range(stage_annotation_id):
             if prev_stage_id in stage_modules:
-                _, _, _, prev_outputs, _, _, _, _, _ = stage_modules[prev_stage_id]
+                _, _, _, prev_outputs, _, _, _, _, _, _ = stage_modules[prev_stage_id]
                 prev_stage_outputs.update(prev_outputs)
         
         # Find all nodes computed in this stage (dependencies of stage_nodes)
@@ -409,7 +410,7 @@ def collect_dependencies(node):
                 if not user_in_other_stage:
                     for other_stage_id in range(stage_annotation_id):
                         if other_stage_id in stage_modules:
-                            _, _, _, _, _, other_ordered_nodes, _, _, _ = stage_modules[other_stage_id]
+                            _, _, _, _, _, other_ordered_nodes, _, _, _, _ = stage_modules[other_stage_id]
                             if user in other_ordered_nodes:
                                 user_in_other_stage = True
                                 break
@@ -469,6 +470,7 @@ def collect_dependencies(node):
         # Load the module on the corresponding actor
         # Track which inputs come from previous stage outputs
         input_idxs = []
+        param_idxs = []
         graphargs = []
         placeholders = stage_gm.graph.find_nodes(op="placeholder")
         
@@ -479,14 +481,21 @@ def collect_dependencies(node):
         prev_stage_outputs = set()
         prev_stage_id = stage_annotation_id - 1
         if prev_stage_id in stage_modules:
-            _, _, _, prev_stage_outputs_list, _, _, _, _, _ = stage_modules[prev_stage_id]
+            _, _, _, prev_stage_outputs_list, _, _, _, _, _, _ = stage_modules[prev_stage_id]
             prev_stage_outputs = set(prev_stage_outputs_list)
         
         for i, placeholder in enumerate(placeholders):
             if "grapharg" in placeholder.meta:
-                graphargs.append(placeholder.meta["grapharg"]._example())
+                example = placeholder.meta["grapharg"]._example()
             else:
-                graphargs.append(torch.zeros(placeholder.meta["example_value"].shape, dtype=placeholder.meta["example_value"].dtype, requires_grad=placeholder.meta["example_value"].requires_grad, device=placeholder.meta["example_value"].device))
+                example = torch.zeros(
+                    placeholder.meta["example_value"].shape, 
+                    dtype=placeholder.meta["example_value"].dtype, 
+                    requires_grad=placeholder.meta["example_value"].requires_grad, 
+                    device=placeholder.meta["example_value"].device)
+            graphargs.append(example)
+            if isinstance(example, torch.nn.Parameter):
+                param_idxs.append(i)
             # For the first stage, the input indices are everything that's not an attribute
             if stage_annotation_id == 0:
                 if 'self' not in placeholder.name:
@@ -498,6 +507,8 @@ def collect_dependencies(node):
                 if is_from_prev_stage:
                     input_idxs.append(i)
 
+        assert set(input_idxs) & set(param_idxs) == set(), "Input and parameter indices should be disjoint"
+
         stage_modules[stage_annotation_id] = (
             stage_gm,
             input_placeholders,
@@ -507,6 +518,7 @@ def collect_dependencies(node):
             ordered_stage_nodes,
             stage_annotation_id,
             input_idxs,
+            param_idxs,
             graphargs,
         )
 
@@ -555,7 +567,7 @@ def collect_dependencies(node):
                 in_stage = True
                 
                 # Get the stage module info
-                stage_gm, input_placeholders, input_order, stage_outputs, stage_nodes, ordered_stage_nodes, _, _, _ = stage_modules[stage_annotation_id]
+                stage_gm, input_placeholders, input_order, stage_outputs, stage_nodes, ordered_stage_nodes, _, _, _, _= stage_modules[stage_annotation_id]
                 
                 # Check if we've already created the call_module for this stage call
                 if stage_annotation_id not in stage_call_replaced:
@@ -628,10 +640,10 @@ def collect_dependencies(node):
     # Create root module and add stage modules as submodules
     root_module = torch.nn.Module()
     submodule_list = []
-    for stage_annotation_id, (stage_gm, placeholders, _, _, _, _, _, input_idxs, params) in stage_modules.items():
+    for stage_annotation_id, (stage_gm, placeholders, _, _, _, _, _, input_idxs, param_idxs, params) in stage_modules.items():
         module_name = f"stage_{stage_annotation_id}"
         root_module.add_module(module_name, stage_gm)
-        submodule_list.append((stage_annotation_id, stage_gm, input_idxs, params, placeholders))
+        submodule_list.append((stage_annotation_id, stage_gm, input_idxs, param_idxs, params, placeholders))
     
     new_gm = fx.GraphModule(root_module, new_graph)
     
@@ -654,7 +666,7 @@ def collect_dependencies(node):
 
 
 
-def _insert_a2a_ops(gm: fx.GraphModule) -> fx.GraphModule:
+def _insert_a2a_ops(gm: fx.GraphModule) -> tuple[fx.GraphModule, int]:
     """
     Transform a graph module by inserting communication operations on annotated nodes.
     
@@ -698,8 +710,7 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> fx.GraphModule:
                     annotated_nodes.append((idx, node, annotation_key, reshape))
     
     if not annotated_nodes:
-        logger.debug("No communication annotations found in graph")
-        return gm
+        return gm, 0
     
     # Group annotated nodes into contiguous blocks
     # A contiguous block is a sequence of nodes with the same annotation_key
@@ -726,7 +737,7 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> fx.GraphModule:
 
     annotation_blocks = list(blocks_by_key.values())
     
-    logger.debug(f"Found {len(annotation_blocks)} comm annotation blocks")
+    n_a2a_ops = len(annotation_blocks)
     
     # For each contiguous block, find the output node (the one used outside the annotation)
     # This is the node that should have communication applied to it
@@ -753,13 +764,11 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> fx.GraphModule:
             if output_candidates:
                 # Use the first output candidate (should be the output of the annotated block)
                 annotated_output_nodes.append(output_candidates[0])
-                logger.debug(f"Block {block_idx}: Using output node {output_candidates[0][0].name}")
             else:
                 # No external users found, use the last node in topological order within the block
                 # This happens if the annotated block's output isn't used yet
                 last_node = max(nodes_in_block, key=lambda x: node_list.index(x[0]))
                 annotated_output_nodes.append(last_node)
-                logger.debug(f"Block {block_idx}: No external users found, using last node: {last_node[0].name}")
     
     # Create a new graph to build the transformed version
     new_graph = fx.Graph()
@@ -819,7 +828,6 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> fx.GraphModule:
                         torch.reshape,
                         (new_node, reshape_shape)
                     )
-                    logger.debug(f"Applied input reshape {reshape_shape} for node {node.name}")
             
             # Insert communication operations after this node
             # Following the exact pattern from the comments in mixtral.py:
@@ -853,13 +861,10 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> fx.GraphModule:
                         torch.reshape,
                         (buf_node, reshape_shape)
                     )
-                    logger.debug(f"Applied output reshape {reshape_shape} for node {node.name}")
             
             # 3. Replace all uses of the original node with the buffer
             # The buffer now contains the result after communication (and optional reshape)
             node_mapping[node] = buf_node
-            
-            logger.debug(f"Inserted all_to_all_single communication for node {node.name}")
         else:
             # Regular node, just copy it
             new_node = new_graph.node_copy(
@@ -899,7 +904,7 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> fx.GraphModule:
     
     new_gm.recompile()
     
-    return new_gm
+    return new_gm, n_a2a_ops
 
 def _insert_p2p_ops(
     gm: fx.GraphModule,
diff --git a/src/piper_overlap_actor.py b/src/piper_overlap_actor.py
deleted file mode 100644
index fb39762..0000000
--- a/src/piper_overlap_actor.py
+++ /dev/null
@@ -1,989 +0,0 @@
-import ray
-import torch
-import os
-import gc
-from collections import defaultdict
-import torch.distributed as dist
-
-from .piper_utils import (
-    _deserialize_graphmodule,
-    create_logger,
-    LOG_LEVEL,
-    piper_metadata,
-)
-
-CLEANUP_MEMORY = False
-
-logger = create_logger("piper_overlap_actor", LOG_LEVEL)
-
-
-def _get_rank(pp_rank, dp_rank, pp_degree):
-    return pp_rank + dp_rank * pp_degree
-
-
-def _create_actors(
-    num_actors,
-    optim_class,
-    num_mbs,
-    num_stages,
-    p2p_schedules,
-    naive_gradient_sync=False,
-    profile=False,
-    mode="sequential",
-):
-    dp_rank = int(os.environ["PIPER_DP_RANK"])
-    world_size = int(os.environ["PIPER_WORLD_SIZE"])
-    dp_degree = int(os.environ["PIPER_DP_DEGREE"])
-    pp_degree = int(os.environ["PIPER_PP_DEGREE"])
-
-    from .piper_utils import piper_metadata
-
-    for pp_rank in range(num_actors):
-        global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
-        p2p_schedule = p2p_schedules[pp_rank]
-        actor = PiperOverlapActor.options(num_gpus=1).remote(
-            pp_rank,
-            optim_class,
-            world_size,
-            num_mbs,
-            num_stages,
-            p2p_schedule,
-            naive_gradient_sync,
-            dp_rank=dp_rank,
-            dp_degree=dp_degree,
-            pp_degree=pp_degree,
-            profile=profile,
-            mode=mode,
-        )
-        piper_metadata.actors[pp_rank] = actor
-        logger.debug(
-            f"DP rank {dp_rank} created actor {actor} global rank {global_rank}"
-        )
-
-
-def _get_actor(pp_rank):
-    from .piper_utils import piper_metadata
-
-    return piper_metadata.actors[pp_rank]
-
-@ray.remote
-class PiperOverlapActor:
-    def __init__(
-        self,
-        pp_rank,
-        optim_class,
-        world_size,
-        num_mbs,
-        num_stages,
-        p2p_schedule,
-        naive_gradient_sync=False,
-        dp_rank=0,
-        dp_degree=1,
-        pp_degree=1,
-        profile=False,
-        mode="sequential",
-    ):
-        self.logger = create_logger("piper_overlap_actor", LOG_LEVEL)
-        self.profile = profile
-        self.mode = mode
-
-        self.pp_rank = pp_rank
-        self.optim_class = optim_class
-        self.naive_gradient_sync = naive_gradient_sync
-
-        self.dp_rank = dp_rank
-        self.dp_degree = dp_degree
-        self.pp_degree = pp_degree
-        self.world_size = world_size
-
-        self.num_mbs = num_mbs
-        self.num_stages = num_stages
-        # list of (src_stage, dst_stage, mb_idx, is_sender)
-        self.p2p_schedule = p2p_schedule
-        self.next_p2p_idx = 0
-        # set of (src_stage, dst_stage, mb_idx, is_sender) for completed p2p ops
-        self.executed_p2ps = set()
-        self.dp_group = None
-        self.pp_group = None
-        self.device = "cuda"
-
-        self.global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
-
-        self.logger.info(
-            f"Initializing Ray actor {self.global_rank} GPU {os.environ['CUDA_VISIBLE_DEVICES']}"
-        )
-
-        self.input = None
-        self.labels = None
-
-        self.comp_stream = torch.cuda.Stream()
-        self.comm_stream = torch.cuda.Stream()
-        self.a2a_stream = torch.cuda.Stream()
-        self.p2p_stream = torch.cuda.Stream()
-        self.overlapped_comp_stream = torch.cuda.Stream()
-        self.overlapped_p2p_stream = torch.cuda.Stream()
-        if mode == "naive":
-            self.per_mb_streams = [torch.cuda.Stream() for _ in range(2)]
-
-        # map stage id -> compiled fx.Graph function
-        self.forward_fns = dict()
-        # map stage id -> original GraphModule (for hook registration)
-        self.graph_modules = dict()
-        # map stage id -> model parameters used by the fx.Graph with holes (None values) for input tensors
-        self.forward_args = dict()
-        # map stage id -> input idx -> input tensor metadata
-        self.forward_input_meta = defaultdict(dict)
-        # map stage id -> indices of the input tensors (as opposed to model parameters) used by the fx.Graph
-        self.input_idxs = dict()
-        # map stage id -> optimizer for the fx.Graph
-        self.optims = dict()
-        # map stage id -> mb_idx -> previous activation (if this stage is not first)
-        self.inp_activation = defaultdict(dict)
-        # map stage id -> mb_idx -> current activation
-        self.out_activation = defaultdict(dict)
-        # map (src_stage, dst_stage, mb_idx, is_sender) -> tensor for p2p communication
-        self.p2p_cache = dict()
-        # accumuate loss for each microbatch
-        self.loss = []
-        # map stage id -> data parallel communication operations
-        self.comm_ops = dict()
-        # map stage id -> tensor id -> comm op status
-        self.comm_op_status = defaultdict(lambda: defaultdict(int))
-        # map stage id -> tensor id -> comm op handle
-        self.comm_op_handles = defaultdict(dict)
-        # map stage id -> list of tensor ids that require communciation
-        self.comm_op_tensor_ids = dict()
-
-        self.tracing = False
-        self.trace_events = dict()
-        self.trace_data = defaultdict(list)
-
-        from .piper_utils import piper_metadata
-
-        piper_metadata.actor_self = self
-
-    def reset_p2p_states(self):
-        self.next_p2p_idx = 0
-        self.executed_p2ps = set()
-        self.p2p_cache = dict()
-
-    def get_trace_data(self) -> dict:
-        return self.global_rank, self.trace_data
-
-    def clear_trace_data(self) -> None:
-        self.trace_data.clear()
-
-    def set_tracing(self, enabled: bool) -> None:
-        self.tracing = enabled
-        self.logger.info(
-            f"Actor {self.global_rank}: Tracing {'enabled' if enabled else 'disabled'}"
-        )
-
-    def start_mem_tracing(self) -> None:
-        torch.cuda.memory._record_memory_history()
-
-    def stop_mem_tracing(self) -> None:
-        torch.cuda.memory._dump_snapshot(
-            f"actor{self.global_rank}_memory_snapshot_mb4_gpipe.pickle"
-        )
-        self.logger.info(
-            f"Saved memory snapshot to actor{self.global_rank}_memory_snapshot_mb4_gpipe.pickle"
-        )
-        torch.cuda.memory._record_memory_history(enabled=None)
-
-    def reset_peak_memory(self):
-        torch.cuda.reset_peak_memory_stats()
-
-    def get_peak_memory(self):
-        return torch.cuda.max_memory_allocated() / (1024**3)
-
-    def load_input(self, inputs):
-        self.inputs = [inp.to(self.device) for inp in inputs]
-        self.logger.debug(f"Actor {self.global_rank} loaded inputs {len(self.inputs)}")
-
-    def load_labels(self, labels):
-        self.labels = labels.to(self.device)
-        self.logger.debug(f"Actor {self.global_rank} loaded labels {self.labels.shape}")
-
-    def _start_timing(self, stream, label):
-        if self.tracing:
-            if label not in self.trace_events:
-                self.trace_events[label] = (
-                    torch.cuda.Event(enable_timing=True),
-                    torch.cuda.Event(enable_timing=True),
-                )
-            start, _ = self.trace_events[label]
-            start.record(stream)
-
-    def _stop_timing(self, stream, label):
-        if self.tracing:
-            if label in self.trace_events:
-                start, stop = self.trace_events[label]
-                stop.record(stream)
-                stop.synchronize()
-                self.trace_data[label].append(start.elapsed_time(stop))
-
-    def _join_process_groups(self):
-        master_addr = os.environ.get("PIPER_MASTER_ADDR", "127.0.0.1")
-        master_port = os.environ.get("PIPER_MASTER_PORT", "10000")
-        init_method = f"tcp://{master_addr}:{master_port}"
-
-        self.device = f"cuda:{self.global_rank % torch.cuda.device_count()}"
-        torch.cuda.set_device(self.device)
-
-        dist.init_process_group(
-            "nccl",
-            init_method=init_method,
-            rank=self.global_rank,
-            world_size=self.world_size,
-        )
-        self.logger.debug(
-            f"Actor {self.global_rank} has GPU {os.environ['CUDA_VISIBLE_DEVICES']}, joined the global process group"
-        )
-
-        if self.dp_degree > 1:
-            self._join_dp_process_group()
-        if self.pp_degree > 1:
-            self._join_pp_process_group()
-
-    def _join_dp_process_group(self):
-        num_dp_groups = self.world_size // self.dp_degree
-        for dp_group_id in range(num_dp_groups):
-            group_ranks = [
-                (dp_group_id + num_dp_groups * i) for i in range(self.dp_degree)
-            ]
-            process_group = dist.new_group(ranks=group_ranks, backend="nccl")
-            if self.global_rank % num_dp_groups == dp_group_id:
-                self.dp_group = process_group
-                self.logger.debug(
-                    f"Global rank {self.global_rank} joined its dp group {dp_group_id} along with ranks {group_ranks}"
-                )
-
-    def _join_pp_process_group(self):
-        num_pp_groups = self.world_size // self.pp_degree
-        for pp_group_id in range(num_pp_groups):
-            group_ranks = [
-                (pp_group_id * self.pp_degree + i) for i in range(self.pp_degree)
-            ]
-            process_group = dist.new_group(ranks=group_ranks, backend="nccl")
-            if self.global_rank // self.pp_degree == pp_group_id:
-                self.pp_group = process_group
-                self.logger.debug(f"Global rank {self.global_rank} joined its pp group {pp_group_id} along with ranks {group_ranks}")
-
-    def shutdown(self):
-        dist.destroy_process_group()
-
-    def _prepare_dp_comm_ops(self, stage_id):
-        def hook_maker(tensor_id):
-            def post_backward_hook(grad):
-                self.comm_op_status[stage_id][tensor_id] += 1
-                self.logger.debug(
-                    f"Updating status on dp_rank: {self.dp_rank}, tensor={tensor_id}, status={self.comm_op_status[stage_id][tensor_id]}"
-                )
-                if self.comm_op_status[stage_id][tensor_id] == self.num_mbs:
-                    with torch.cuda.stream(self.comm_stream):
-                        handle = dist.all_reduce(
-                            grad,
-                            op=dist.ReduceOp.AVG,
-                            group=self.dp_group,
-                            async_op=True,
-                        )
-                    self.logger.debug(
-                        f"Allreduce on dp_rank: {self.dp_rank}, tensor={tensor_id}"
-                    )
-                    self.comm_op_status[stage_id][tensor_id] = 0
-                    self.comm_op_handles[stage_id][tensor_id] = handle
-                return grad
-            return post_backward_hook
-
-        ids = []
-        for t in self.forward_args[stage_id]:
-            if t is not None and t.requires_grad:
-                tid = id(t)
-                ids.append(tid)
-                t.register_post_accumulate_grad_hook(hook_maker(tid))
-        self.comm_op_tensor_ids[stage_id] = ids
-
-    def _wait_for_comm_ops(self):
-        self.logger.debug(f"Actor {self.global_rank} waiting for comm ops")
-        for stage_id, tids in self.comm_op_tensor_ids.items():
-            for tid in tids:
-                self.logger.debug(
-                    f"Waiting for comm op to be launched dp_rank: {self.dp_rank}, tensor={tid}"
-                )
-                done = False
-                while not done:
-                    if tid in self.comm_op_handles[stage_id]:
-                        self.logger.debug(
-                            f"Waiting for comm op to be finished dp_rank: {self.dp_rank}, tensor={tid}"
-                        )
-                        self.comm_op_handles[stage_id][tid].wait()
-                        done = True
-
-    def _load_stage(
-        self, stage_id: int, gm_data, forward_args,input_idxs
-    ):
-        self.logger.debug(f"Loading stage {stage_id} graph on actor {self.global_rank}")
-
-        # compile the graph with the given graphargs
-        gm = _deserialize_graphmodule(gm_data)
-
-        # Store GraphModule reference
-        self.graph_modules[stage_id] = gm
-        self.forward_fns[stage_id] = gm.forward
-
-        # place parameters on the device
-        def move_to_device(idx, arg):
-            if arg.requires_grad:
-                return arg.to(self.device).detach().requires_grad_(True)
-            else:
-                return arg.to(self.device)
-        forward_args = list(map(move_to_device, range(len(forward_args)), forward_args))
-
-        # save parameters
-        self.forward_args[stage_id] = forward_args
-        self.stage_id = stage_id
-        self.input_idxs[stage_id] = input_idxs
-
-        for i in self.input_idxs[stage_id]:
-            self.forward_input_meta[stage_id][i] = (
-                forward_args[i].shape,
-                forward_args[i].dtype,
-                forward_args[i].requires_grad,
-            )
-            self.forward_args[stage_id][i] = None
-
-        # prepare tensors with DP comm ops
-        if not self.naive_gradient_sync and self.dp_degree > 1:
-            self._prepare_dp_comm_ops(stage_id)
-
-        # add the parameters to the optimizer for this stage
-        params = [param for param in forward_args if param is not None and param.requires_grad]
-        if stage_id not in self.optims:
-            self.optims[stage_id] = self.optim_class(params)
-        else:
-            self.optims[stage_id].add_param_group({"params": params})
-
-        del gm_data
-
-    def _exec_p2p_op(
-        self, src_stage: int, dst_stage: int, mb_idx: int, is_sender: bool, dep, p2p_stream=None
-    ):
-        p2p_op = (src_stage, dst_stage, mb_idx, is_sender)
-        if p2p_op in self.executed_p2ps:
-            return
-
-        if p2p_stream is None:
-            p2p_stream = self.p2p_stream
-
-        op_idx = self.next_p2p_idx
-        while op_idx < len(self.p2p_schedule):
-            op = self.p2p_schedule[op_idx]
-            if op == p2p_op:
-                break
-            op_idx += 1
-        assert op_idx < len(
-            self.p2p_schedule
-        ), f"P2P op {p2p_op} not found in schedule for actor {self.global_rank}"
-
-        # Execute everything before and including the given p2p op
-        for idx in range(self.next_p2p_idx, op_idx + 1):
-            op = self.p2p_schedule[idx]
-            src_stage, dst_stage, mb_idx, is_sender = op
-            is_fwd = src_stage == dst_stage - 1
-            is_bwd = src_stage == dst_stage + 1
-            assert is_fwd or is_bwd
-            is_recver = not is_sender
-
-            op_name = "P2P(unknown)"
-            if is_fwd and is_sender:
-                self._exec_fwd_send(src_stage, mb_idx, p2p_stream=p2p_stream)
-                op_name = (
-                    f"P2P(fwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
-                )
-            elif is_fwd and is_recver:
-                self._exec_fwd_recv(dst_stage, mb_idx, p2p_stream=p2p_stream)
-                op_name = (
-                    f"P2P(fwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
-                )
-            elif is_bwd and is_sender:
-                self._exec_bwd_send(src_stage, mb_idx, p2p_stream=p2p_stream)
-                op_name = (
-                    f"P2P(bwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
-                )
-            elif is_bwd and is_recver:
-                self._exec_bwd_recv(dst_stage, mb_idx, p2p_stream=p2p_stream)
-                op_name = (
-                    f"P2P(bwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
-                )
-            else:
-                raise ValueError(f"Invalid p2p op: {op}")
-
-            self.executed_p2ps.add(op)
-            logger.debug(f"Executed {op_name} on actor {self.global_rank}")
-
-        self.next_p2p_idx = op_idx + 1
-        
-    def _exec_p2p_op(
-        self, src_stage: int, dst_stage: int, mb_idx: int, is_sender: bool, dep, p2p_stream=None
-    ):
-        p2p_op = (src_stage, dst_stage, mb_idx, is_sender)
-        if p2p_op in self.executed_p2ps:
-            return
-
-        if p2p_stream is None:
-            p2p_stream = self.p2p_stream
-
-        op_idx = self.next_p2p_idx
-        while op_idx < len(self.p2p_schedule):
-            op = self.p2p_schedule[op_idx]
-            if op == p2p_op:
-                break
-            op_idx += 1
-        assert op_idx < len(
-            self.p2p_schedule
-        ), f"P2P op {p2p_op} not found in schedule for actor {self.global_rank}"
-
-        # Execute everything before and including the given p2p op
-        for idx in range(self.next_p2p_idx, op_idx + 1):
-            op = self.p2p_schedule[idx]
-            src_stage, dst_stage, mb_idx, is_sender = op
-            is_fwd = src_stage == dst_stage - 1
-            is_bwd = src_stage == dst_stage + 1
-            assert is_fwd or is_bwd
-            is_recver = not is_sender
-
-            op_name = "P2P(unknown)"
-            if is_fwd and is_sender:
-                self._exec_fwd_send(src_stage, mb_idx, p2p_stream=p2p_stream)
-                op_name = (
-                    f"P2P(fwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
-                )
-            elif is_fwd and is_recver:
-                self._exec_fwd_recv(dst_stage, mb_idx, p2p_stream=p2p_stream)
-                op_name = (
-                    f"P2P(fwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
-                )
-            elif is_bwd and is_sender:
-                self._exec_bwd_send(src_stage, mb_idx, p2p_stream=p2p_stream)
-                op_name = (
-                    f"P2P(bwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
-                )
-            elif is_bwd and is_recver:
-                self._exec_bwd_recv(dst_stage, mb_idx, p2p_stream=p2p_stream)
-                op_name = (
-                    f"P2P(bwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
-                )
-            else:
-                raise ValueError(f"Invalid p2p op: {op}")
-
-            self.executed_p2ps.add(op)
-            logger.debug(f"Executed {op_name} on actor {self.global_rank}")
-
-        self.next_p2p_idx = op_idx + 1
-
-    def _exec_fwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
-        if stage_id == 0:
-            return
-
-        if p2p_stream is None:
-            p2p_stream = self.p2p_stream
-        if comp_stream is None:
-            comp_stream = self.comp_stream
-
-        p2p_op = (stage_id - 1, stage_id, mb_idx, False)
-        assert p2p_op not in self.p2p_cache
-
-        inputs_to_recv = []
-        for i in self.input_idxs[stage_id]:
-            shape, dtype, requires_grad = self.forward_input_meta[stage_id][i]
-            inputs_to_recv.append(
-                torch.empty(
-                    shape, dtype=dtype, requires_grad=requires_grad, device=self.device
-                )
-            )
-
-        # For non-first stages, receive input tensors from the previous stage
-        pp_rank = piper_metadata.stage_to_device[stage_id - 1]
-        global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
-
-        if self.global_rank == global_src_rank:
-            for i in self.input_idxs[stage_id]:
-                inputs_to_recv[i] = self.p2p_cache.pop((stage_id-1, stage_id, mb_idx, True))
-        else:
-            self.logger.debug(
-                f"Dispatch fwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
-            )
-            self._start_timing(p2p_stream, "fwd_p2p_recv")
-            with torch.cuda.stream(p2p_stream):
-                for i in self.input_idxs[stage_id]:
-                    dist.recv(
-                        inputs_to_recv[i],
-                        src=global_src_rank,
-                        group=self.pp_group,
-                    )
-            # Ensure the default stream only consumes tensors after recv completes.
-            comp_stream.wait_stream(p2p_stream)
-            torch.cuda.synchronize()
-            self._stop_timing(p2p_stream, "fwd_p2p_recv")
-            self.logger.debug(
-                f"Completed fwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
-            )
-
-        self.p2p_cache[p2p_op] = inputs_to_recv
-
-    def _exec_fwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
-        if stage_id == self.num_stages - 1:
-            return
-
-        if p2p_stream is None:
-            p2p_stream = self.p2p_stream
-        if comp_stream is None:
-            comp_stream = self.comp_stream
-
-        p2p_op = (stage_id, stage_id + 1, mb_idx, True)
-        output = self.p2p_cache.pop(p2p_op)
-        # For non-final stages, send output tensors to the next stage
-        pp_rank = piper_metadata.stage_to_device[stage_id + 1]
-        global_dst_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
-
-        if self.global_rank == global_dst_rank:
-            self.p2p_cache[p2p_op] = output
-        else:
-            # Ensure send sees the latest writes from the default stream.
-            self.logger.debug(
-                f"Dispatch fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
-            )
-            self._start_timing(p2p_stream, "fwd_p2p_send")
-            p2p_stream.wait_stream(comp_stream)
-            with torch.cuda.stream(p2p_stream):
-                for i in range(len(output)):
-                    dist.send(
-                        output[i],
-                        dst=global_dst_rank,
-                        group=self.pp_group,
-                    )
-            self._stop_timing(p2p_stream, "fwd_p2p_send")
-            self.logger.debug(
-                f"Completed fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
-            )
-
-    def _exec_bwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
-        if stage_id >= self.num_stages - 1:
-            return
-
-        if p2p_stream is None:
-            p2p_stream = self.p2p_stream
-        if comp_stream is None:
-            comp_stream = self.comp_stream
-
-        out_activation = self.out_activation[stage_id][mb_idx]
-        # For non-final stages, recieve input gradients from the subsequent backward pass
-        input_grad = torch.empty_like(out_activation)
-        pp_rank = piper_metadata.stage_to_device[stage_id + 1]
-        global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
-
-        p2p_op = (stage_id + 1, stage_id, mb_idx, False)
-
-        if self.global_rank == global_src_rank:
-            input_grad = self.p2p_cache.pop((stage_id + 1, stage_id, mb_idx, True))
-        else:
-            self.logger.debug(
-                f"Dispatch bwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id+1} -> {stage_id}, mb {mb_idx})"
-            )
-            self._start_timing(p2p_stream, "bwd_p2p_recv")
-            with torch.cuda.stream(p2p_stream):
-                dist.recv(
-                    input_grad, src=global_src_rank, group=self.pp_group
-                )
-            # Ensure the default stream only consumes gradients after recv completes.
-            comp_stream.wait_stream(p2p_stream)
-            torch.cuda.synchronize()
-            self._stop_timing(p2p_stream, "bwd_p2p_recv")
-            self.logger.debug(
-                f"Completed bwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id+1} -> {stage_id}, mb {mb_idx})"
-            )
-        
-        self.p2p_cache[p2p_op] = input_grad
-
-    def _exec_bwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
-        if stage_id <= 0:
-            return
-
-        if p2p_stream is None:
-            p2p_stream = self.p2p_stream
-        if comp_stream is None:
-            comp_stream = self.comp_stream
-
-        # For non-first stages, send output gradients to the previous backward stage
-        output_grad = self.inp_activation[stage_id][mb_idx].grad
-        if output_grad is None:
-            self.logger.warning(f"No output gradient found for stage {stage_id} mb {mb_idx} on actor {self.global_rank}")
-            assert False
-        pp_rank = piper_metadata.stage_to_device[stage_id - 1]
-        global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
-
-        if self.global_rank == global_src_rank:
-            p2p_op = (stage_id, stage_id - 1, mb_idx, True)
-            self.p2p_cache[p2p_op] = output_grad
-        else:
-            self.logger.debug(
-                f"Dispatch bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
-            )
-            self._start_timing(p2p_stream, "bwd_p2p_send")
-            # Ensure send sees the latest gradient writes from the default stream.
-            p2p_stream.wait_stream(comp_stream)
-            with torch.cuda.stream(p2p_stream):
-                dist.send(
-                    output_grad, dst=global_src_rank, group=self.pp_group
-                )
-            self._stop_timing(p2p_stream, "bwd_p2p_send")
-            self.logger.debug(
-                f"Completed bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
-            )
-
-        self.inp_activation[stage_id][mb_idx] = None
-    
-    def _forward(self, stage_id: int, mb_idx: int, *deps):
-        if self.mode == "sequential":
-            comp_stream = self.comp_stream
-        elif self.mode == "naive":
-            comp_stream = self.per_mb_streams[(stage_id + mb_idx) % 2]
-        else:
-            raise ValueError(f"Invalid mode: {self.mode}")
-
-        self.logger.debug(
-            f"Calling forward {stage_id} mb {mb_idx} on actor {self.global_rank}"
-        )
-
-        if self.profile:
-            profiler = torch.profiler.profile(
-                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
-                with_stack=True,
-            )
-            profiler.__enter__()
-            forward_ctx_manager = torch.profiler.record_function(f"forward_stage_{stage_id}_mb_{mb_idx}")
-            forward_ctx_manager.__enter__()
-
-        if stage_id == 0:
-            # For the first stage, load input tensors from self.inputs
-            for i, inp in zip(self.input_idxs[stage_id], self.inputs):
-                self.forward_args[stage_id][i] = inp
-        else:
-            inputs_from_prev_stage = self.p2p_cache.pop(
-                (stage_id - 1, stage_id, mb_idx, False)
-            )
-            for i, tensor in zip(self.input_idxs[stage_id], inputs_from_prev_stage):
-                if isinstance(tensor, (tuple, list)):
-                    assert len(tensor) == 1
-                    tensor = tensor[0]
-                if stage_id > 0 and piper_metadata.stage_to_device[stage_id] == piper_metadata.stage_to_device[stage_id - 1] and tensor.requires_grad:
-                    tensor = tensor.detach().requires_grad_(True)
-                self.forward_args[stage_id][i] = tensor
-
-            # save first input that requires grad as input activation
-            inp_with_grad = [
-                self.forward_args[stage_id][i]
-                for i in self.input_idxs[stage_id]
-                if self.forward_args[stage_id][i].requires_grad
-            ]
-            assert (
-                len(inp_with_grad) == 1
-            ), "Exactly one input per stage should require a gradient"
-            self.logger.debug(
-                f"Saving input activation {inp_with_grad[0].shape} for stage {stage_id} mb {mb_idx}"
-            )
-            self.inp_activation[stage_id][mb_idx] = inp_with_grad[0]
-
-        # Run the forward pass
-        self._start_timing(comp_stream, "forward_comp")
-        with torch.cuda.stream(comp_stream):
-            output = self.forward_fns[stage_id](*self.forward_args[stage_id])
-        self._stop_timing(comp_stream, "forward_comp")
-
-        # Save first output that requires grad as output activation
-        # TODO: support multiple outputs
-        out_with_grad = [out for out in output if out.requires_grad]
-        assert (
-            len(out_with_grad) == 1
-        ), "Piper only supports one output per subgraph with requires_grad"
-        self.logger.debug(
-            f"Saving output activation {out_with_grad[0].shape} for stage {stage_id} mb {mb_idx}"
-        )
-        self.out_activation[stage_id][mb_idx] = out_with_grad[0]
-
-        # clear the input tensors
-        for i in self.input_idxs[stage_id]:
-            self.forward_args[stage_id][i] = None
-
-        if stage_id < self.num_stages - 1:
-            send_p2p_op = (stage_id, stage_id + 1, mb_idx, True)
-            assert send_p2p_op not in self.p2p_cache
-            self.p2p_cache[send_p2p_op] = output
-
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-        self.logger.debug(
-            f"Forward {stage_id} mb {mb_idx} on actor {self.global_rank} returning {[out.shape for out in output]}"
-        )
-
-        torch.cuda.synchronize()
-
-        if self.profile:
-            forward_ctx_manager.__exit__(None, None, None)
-            profiler.__exit__(None, None, None)
-            profiler.export_chrome_trace(f"log/{self.mode}/actor{self.global_rank}_fwd{stage_id}mb{mb_idx}_trace.json")
-
-        return 1
-
-    def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        if self.mode == "sequential":
-            comp_stream = self.comp_stream
-        elif self.mode == "naive":
-            comp_stream = self.per_mb_streams[(stage_id + mb_idx) % 2]
-        else:
-            raise ValueError(f"Invalid mode: {self.mode}")
-
-        self.logger.debug(
-            f"Calling backward {stage_id} mb {mb_idx} on actor {self.global_rank}"
-        )
-
-        if self.profile:
-            profiler = torch.profiler.profile(
-                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
-                with_stack=True,
-            )
-            profiler.__enter__()
-            backward_ctx_manager = torch.profiler.record_function(f"backward_stage_{stage_id}_mb_{mb_idx}")
-            backward_ctx_manager.__enter__()
-
-        out_activation = self.out_activation[stage_id][mb_idx]
-
-        if stage_id < self.num_stages - 1:
-            input_grad = self.p2p_cache.pop(
-                (stage_id + 1, stage_id, mb_idx, False)
-            )
-
-            self._start_timing(comp_stream, "backward_comp")
-            with torch.cuda.stream(comp_stream):
-                out_activation.backward(gradient=input_grad)
-            self._stop_timing(comp_stream, "backward_comp")
-        else:
-            assert loss_fn is not None
-            labels = self.labels
-            assert out_activation.shape == labels.shape
-
-            self._start_timing(comp_stream, "backward_comp")
-            with torch.cuda.stream(comp_stream):
-                loss = loss_fn(out_activation, labels)
-                loss.backward()
-            self._stop_timing(comp_stream, "backward_comp")
-
-            self.loss.append(loss.item())
-
-        # Clear output activation after backward pass
-        self.out_activation[stage_id][mb_idx] = None
-        del out_activation
-
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-        torch.cuda.synchronize()
-
-        if self.profile:
-            backward_ctx_manager.__exit__(None, None, None)
-            profiler.__exit__(None, None, None)
-            profiler.export_chrome_trace(f"log/{self.mode}/actor{self.global_rank}_bwd{stage_id}mb{mb_idx}_trace.json")
-
-        return 1
-
-    def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: int, bwd_mb_idx: int, *deps, loss_fn=None):
-        if self.mode == "sequential":
-            fwd_comp_stream = self.comp_stream
-            bwd_comp_stream = self.comp_stream
-        elif self.mode == "naive":
-            fwd_comp_stream = self.per_mb_streams[(fwd_stage_id + fwd_mb_idx) % 2]
-            bwd_comp_stream = self.per_mb_streams[(bwd_stage_id + bwd_mb_idx) % 2]
-        else:
-            raise ValueError(f"Invalid mode: {self.mode}")
-        fwd_p2p_stream = self.p2p_stream
-        bwd_p2p_stream = self.p2p_stream
-
-        self.logger.debug(
-            f"Calling forward {fwd_stage_id} mb {fwd_mb_idx} backward {bwd_stage_id} mb {bwd_mb_idx} on actor {self.global_rank}"
-        )
-        
-        if self.profile:
-            profiler = torch.profiler.profile(
-                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
-                with_stack=True,
-            )
-            profiler.__enter__()
-
-        # PREPARE FORWARD PASS
-        if fwd_stage_id == 0:
-            # For the first stage, load input tensors from self.inputs
-            for i, inp in zip(self.input_idxs[fwd_stage_id], self.inputs):
-                self.forward_args[fwd_stage_id][i] = inp
-        else:
-            self._exec_p2p_op(fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False, None, p2p_stream=fwd_p2p_stream)
-            inputs_from_prev_stage = self.p2p_cache.pop(
-                (fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False)
-            )
-            for i, tensor in zip(self.input_idxs[fwd_stage_id], inputs_from_prev_stage):
-                if isinstance(tensor, (tuple, list)):
-                    assert len(tensor) == 1
-                    tensor = tensor[0]
-                if fwd_stage_id > 0 and piper_metadata.stage_to_device[fwd_stage_id] == piper_metadata.stage_to_device[fwd_stage_id - 1] and tensor.requires_grad:
-                    tensor = tensor.detach().requires_grad_(True)
-                self.forward_args[fwd_stage_id][i] = tensor
-
-            # save first input that requires grad as input activation
-            inp_with_grad = [
-                self.forward_args[fwd_stage_id][i]
-                for i in self.input_idxs[fwd_stage_id]
-                if self.forward_args[fwd_stage_id][i].requires_grad
-            ]
-            assert (
-                len(inp_with_grad) == 1
-            ), "Exactly one input per stage should require a gradient"
-            self.logger.debug(
-                f"Saving input activation {inp_with_grad[0].shape} for stage {fwd_stage_id} mb {fwd_mb_idx}"
-            )
-            self.inp_activation[fwd_stage_id][fwd_mb_idx] = inp_with_grad[0]
-
-        # RUN FORWARD PASS
-        if self.profile:
-            forward_ctx_manager = torch.profiler.record_function(f"forward_stage_{fwd_stage_id}_mb_{fwd_mb_idx}")
-            forward_ctx_manager.__enter__()
-        self._start_timing(fwd_comp_stream, "forward_comp")
-        with torch.cuda.stream(fwd_comp_stream):
-            output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
-        self._stop_timing(fwd_comp_stream, "forward_comp")
-        
-        if self.profile:
-            forward_ctx_manager.__exit__(None, None, None)
-            backward_ctx_manager = torch.profiler.record_function(f"backward_stage_{bwd_stage_id}_mb_{bwd_mb_idx}")
-            backward_ctx_manager.__enter__()
-
-        # PREPARE BACKWARD PASS
-        out_activation = self.out_activation[bwd_stage_id][bwd_mb_idx]
-        if bwd_stage_id < self.num_stages - 1:
-            self._exec_p2p_op(bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False, None, p2p_stream=bwd_p2p_stream)
-
-            input_grad = self.p2p_cache.pop(
-                (bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False)
-            )
-        else:
-            assert loss_fn is not None
-            labels = self.labels
-            assert out_activation.shape == labels.shape
-
-        # RUN BACKWARD PASS
-        if bwd_stage_id < self.num_stages - 1:
-            self._start_timing(bwd_comp_stream, "backward_comp")
-            with torch.cuda.stream(bwd_comp_stream):
-                out_activation.backward(gradient=input_grad)
-            self._stop_timing(bwd_comp_stream, "backward_comp")
-        else:
-            self._start_timing(bwd_comp_stream, "backward_comp")
-            with torch.cuda.stream(bwd_comp_stream):
-                loss = loss_fn(out_activation, labels)
-                loss.backward()
-            self._stop_timing(bwd_comp_stream, "backward_comp")
-
-        # POST PROCESS FORWARD PASS
-        out_with_grad = [out for out in output if out.requires_grad]
-        assert (
-            len(out_with_grad) == 1
-        ), "Piper only supports one output per subgraph with requires_grad"
-        self.logger.debug(
-            f"Saving output activation {out_with_grad[0].shape} for stage {fwd_stage_id} mb {fwd_mb_idx}"
-        )
-        self.out_activation[fwd_stage_id][fwd_mb_idx] = out_with_grad[0]
-
-        # clear the input tensors
-        for i in self.input_idxs[fwd_stage_id]:
-            self.forward_args[fwd_stage_id][i] = None
-        
-        # POST PROCESS BACKWARD PASS
-        if bwd_stage_id == self.num_stages - 1:
-            self.loss.append(loss.item())
-
-        # Clear output activation after backward pass
-        self.out_activation[bwd_stage_id][bwd_mb_idx] = None
-        del out_activation
-
-        # POST FORWARD P2P OPERATIONS
-        if fwd_stage_id < self.num_stages - 1:
-            send_p2p_op = (fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True)
-            assert send_p2p_op not in self.p2p_cache
-            self.p2p_cache[send_p2p_op] = output
-            self._exec_p2p_op(fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True, None, p2p_stream=fwd_p2p_stream)
-
-        # POST BACKWARD P2P OPERATIONS
-        if bwd_stage_id > 0:
-            self._exec_p2p_op(bwd_stage_id, bwd_stage_id - 1, bwd_mb_idx, True, None, p2p_stream=bwd_p2p_stream)
-
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-        torch.cuda.synchronize()
-
-        if self.profile:
-            backward_ctx_manager.__exit__(None, None, None)
-            profiler.__exit__(None, None, None)
-            profiler.export_chrome_trace(
-                f"log/{self.mode}/actor{self.global_rank}_"
-                f"fwd{fwd_stage_id}mb{fwd_mb_idx}_"
-                f"bwd{bwd_stage_id}mb{bwd_mb_idx}_trace.json")
-
-        return 1
-
-    def _synchronize_gradients(self):
-        self.logger.info(f"Actor {self.global_rank} synchronizing gradients")
-        # Iterate over all stages on this actor and synchronize their parameters
-        for stage_id, parameters in self.forward_args.items():
-            for param in parameters:
-                if param is not None and param.grad is not None:
-                    with torch.cuda.stream(self.comm_stream):
-                        dist.all_reduce(
-                            param.grad, op=dist.ReduceOp.AVG, group=self.dp_group
-                        )
-
-    def _update(self, *deps):
-        # self._wait_pending_sends()
-        self.logger.debug(f"Actor {self.global_rank} waiting for backward sync events")
-
-        # if dp degree > 1, make sure all gradients are synchronized before optimizer step
-        # TODO: this does not allow overlapping with the optimizer step
-        if self.dp_degree > 1:
-            self._start_timing(self.comm_stream, "backward_sync")
-            if self.naive_gradient_sync:
-                self._synchronize_gradients()
-            else:
-                self._wait_for_comm_ops()
-            self._stop_timing(self.comm_stream, "backward_sync")
-
-        # step the optimizer for each stage
-        self._start_timing(self.comp_stream, "optim_step")
-        for _, optim in self.optims.items():
-            optim.step()
-            optim.zero_grad()
-        self._stop_timing(self.comp_stream, "optim_step")
-
-        losses = self.loss
-        self.loss.clear()
-
-        torch.cuda.synchronize()
-
-        self.reset_p2p_states()
-
-        return losses
diff --git a/src/piper_patches.py b/src/piper_patches.py
deleted file mode 100644
index 18e0958..0000000
--- a/src/piper_patches.py
+++ /dev/null
@@ -1,166 +0,0 @@
-"""
-Runtime monkey patches for PyTorch and Ray dependencies.
-
-These patches enable Piper's RemoteTensor to work correctly with TorchDynamo
-and Ray's tensor transport backends.
-
-Import this module early in your application to apply patches:
-    import src.piper_patches
-"""
-
-import functools
-import logging
-
-logger = logging.getLogger(__name__)
-
-_patches_applied = False
-
-
-def apply_patches():
-    """Apply all required monkey patches to PyTorch and Ray."""
-    global _patches_applied
-    if _patches_applied:
-        return
-    _patches_applied = True
-
-    _patch_ray_actor_method()
-
-    logger.info("Piper runtime patches applied successfully")
-
-
-def _patch_ray_actor_method():
-    """
-    Patch ray.actor.ActorMethod._remote to support multiple return values
-    with tensor transport backends.
-
-    The default Ray implementation only supports 1 return value per task
-    when using tensor transport. This patch removes that restriction and
-    properly registers multiple ObjectRefs with the GPU object manager.
-    """
-    try:
-        import ray
-        import ray.actor as actor_module
-        from ray._raylet import ObjectRef
-    except ImportError as e:
-        logger.warning(f"Could not import ray.actor: {e}")
-        return
-
-    # Get TensorTransportEnum for comparison
-    try:
-        from ray.actor import TensorTransportEnum
-    except ImportError:
-        logger.warning("Could not import TensorTransportEnum, skipping Ray patch")
-        return
-
-    original_remote = actor_module.ActorMethod._remote
-
-    @functools.wraps(original_remote)
-    def patched_remote(
-        self,
-        args=None,
-        kwargs=None,
-        name="",
-        num_returns=None,
-        max_task_retries=None,
-        retry_exceptions=None,
-        concurrency_group=None,
-        _generator_backpressure_num_objects=None,
-        enable_task_events=None,
-        tensor_transport=None,
-    ):
-        if num_returns is None:
-            num_returns = self._num_returns
-        if max_task_retries is None:
-            max_task_retries = self._max_task_retries
-        if max_task_retries is None:
-            max_task_retries = 0
-        if retry_exceptions is None:
-            retry_exceptions = self._retry_exceptions
-        if enable_task_events is None:
-            enable_task_events = self._enable_task_events
-        if _generator_backpressure_num_objects is None:
-            _generator_backpressure_num_objects = (
-                self._generator_backpressure_num_objects
-            )
-
-        if tensor_transport is None:
-            tensor_transport = self._tensor_transport
-
-        # PIPER MODIFICATION: Remove the num_returns != 1 check for tensor_transport
-        # Original code would raise ValueError here if num_returns != 1
-        if tensor_transport != TensorTransportEnum.OBJECT_STORE.name:
-            # Skip the num_returns check - allow multiple returns
-            if not self._actor._ray_enable_tensor_transport:
-                raise ValueError(
-                    f'Currently, methods with .options(tensor_transport="{tensor_transport}") are not supported when enable_tensor_transport=False. '
-                    "Please set @ray.remote(enable_tensor_transport=True) on the actor class definition."
-                )
-            gpu_object_manager = ray._private.worker.global_worker.gpu_object_manager
-            if not gpu_object_manager.actor_has_tensor_transport(
-                self._actor, tensor_transport
-            ):
-                raise ValueError(
-                    f'{self._actor} does not have tensor transport {tensor_transport} available. If using a collective-based transport ("nccl" or "gloo"), please create a communicator with '
-                    "`ray.experimental.collective.create_collective_group` "
-                    "before calling actor tasks with non-default tensor_transport."
-                )
-
-        args = args or []
-        kwargs = kwargs or {}
-
-        def invocation(args, kwargs):
-            dst_actor = self._actor
-            if dst_actor is None:
-                raise RuntimeError(
-                    "Lost reference to actor. Actor handles must be stored as variables, e.g. `actor = MyActor.remote()` before calling methods."
-                )
-
-            gpu_object_manager = ray._private.worker.global_worker.gpu_object_manager
-            gpu_object_manager.trigger_out_of_band_tensor_transfer(dst_actor, args)
-
-            return dst_actor._actor_method_call(
-                self._method_name,
-                args=args,
-                kwargs=kwargs,
-                name=name,
-                num_returns=num_returns,
-                max_task_retries=max_task_retries,
-                retry_exceptions=retry_exceptions,
-                concurrency_group_name=concurrency_group,
-                generator_backpressure_num_objects=(
-                    _generator_backpressure_num_objects
-                ),
-                enable_task_events=enable_task_events,
-                tensor_transport=tensor_transport,
-            )
-
-        # Apply the decorator if there is one.
-        if self._decorator is not None:
-            invocation = self._decorator(invocation)
-
-        object_refs = invocation(args, kwargs)
-
-        # PIPER MODIFICATION: Handle multiple return values with GPU object manager
-        if tensor_transport != TensorTransportEnum.OBJECT_STORE.name:
-            gpu_object_manager = ray._private.worker.global_worker.gpu_object_manager
-            if isinstance(object_refs, ObjectRef):
-                # Single return value
-                object_ref = object_refs
-                gpu_object_manager.add_gpu_object_ref(
-                    object_ref, self._actor, tensor_transport
-                )
-            else:
-                # Multiple return values - register each ObjectRef
-                for object_ref in object_refs:
-                    if isinstance(object_ref, ObjectRef):
-                        gpu_object_manager.add_gpu_object_ref(
-                            object_ref, self._actor, tensor_transport
-                        )
-
-        return object_refs
-
-    actor_module.ActorMethod._remote = patched_remote
-    logger.debug("Patched ray.actor.ActorMethod._remote")
-
-
-apply_patches()
diff --git a/src/piper_utils.py b/src/piper_utils.py
index b489a56..b15878d 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -10,8 +10,6 @@
 from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
 from typing import Any, Optional
 
-import src.piper_patches
-
 LOG_LEVEL = "INFO"
 
 """ 
@@ -353,4 +351,4 @@ def _deserialize_graphmodule(s: str) -> fx.GraphModule:
     gm = fx.GraphModule(root_module, g)
     state = {k: torch.tensor(v) for k, v in data["state_dict"].items()}
     gm.load_state_dict(state, strict=False)
-    return gm
\ No newline at end of file
+    return gm
diff --git a/test/models/llama.py b/test/models/llama.py
index b1a24e8..71d4dbb 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -397,12 +397,49 @@ def log_size(layer, indent=0):
         mask = torch.triu(mask, diagonal=1)
         self.mask = torch.hstack([torch.zeros((self.seq_len, 0)), mask]).to('cuda')
 
+    # """
+    # 8 STAGES
+    # """
+    # def forward(self, tokens: torch.Tensor):
+
+    #     with torch.fx.traceback.annotate({"stage": 0}):
+    #         h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
+    #         start_pos = 0
+    #         for layer in self.layers[:self.n_layers//7]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 1}):
+    #         for layer in self.layers[self.n_layers//7:2*self.n_layers//7]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 2}):
+    #         for layer in self.layers[2*self.n_layers//7:3*self.n_layers//7]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 3}):
+    #         for layer in self.layers[3*self.n_layers//7:4*self.n_layers//7]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 4}):
+    #         for layer in self.layers[4*self.n_layers//7:5*self.n_layers//7]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 5}):
+    #         for layer in self.layers[5*self.n_layers//7:6*self.n_layers//7]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 6}):
+    #         for layer in self.layers[6*self.n_layers//7:]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 7}):
+    #         h = self.norm(h) if self.norm else h
+    #         output = self.output(h).float() if self.output else h
+
+    #     return output
+
     """
-    forward method for pp4-1f1b or pp2-interleaved-1f1b
-    requires:
-    - 4 devices
-    - 4 stages
-    - n_layers is divisible by 4
+    4 STAGES
     """
     def forward(self, tokens: torch.Tensor):
 
@@ -429,10 +466,7 @@ def forward(self, tokens: torch.Tensor):
         return output
 
     # """
-    # forward method for 1f1b schedule
-    # requires:
-    # - 2 stages
-    # - n_layers is divisible by 2
+    # 2 STAGES
     # """
     # def forward(self, tokens: torch.Tensor):
 
@@ -451,10 +485,7 @@ def forward(self, tokens: torch.Tensor):
     #     return output
 
     # """
-    # forward method for no pp
-    # requires:
-    # - 1 device
-    # - 1 stages
+    # 1 STAGE
     # """
     # def forward(self, tokens: torch.Tensor):
 
diff --git a/test/models/mixtral.py b/test/models/mixtral.py
index 3378f8c..8656103 100644
--- a/test/models/mixtral.py
+++ b/test/models/mixtral.py
@@ -56,9 +56,9 @@ def from_name(cls, name: str):
 
 transformer_configs = {
     "tiny": dict(block_size=128, n_layer=4, n_head=2, n_local_heads=1, dim=256, intermediate_size=512, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "small": dict(block_size=512, n_layer=8, n_head=8, n_local_heads=2, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "medium": dict(block_size=512, n_layer=24, n_head=32, n_local_heads=8, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "Mixtral-8x7B-v0.1": dict(block_size=32768, n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "small": dict(block_size=512, n_layer=4, n_head=32, n_local_heads=8, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "medium": dict(block_size=512, n_layer=4, n_head=8, n_local_heads=2, dim=2048, intermediate_size=7168, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "Mixtral-8x7B-v0.1": dict(block_size=32768, n_layer=4, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
 }
 
 class KVCache(nn.Module):
diff --git a/test/pippy_profiling/1f1b_event_profile.py b/test/pippy_profiling/1f1b_event_profile.py
new file mode 100644
index 0000000..5904935
--- /dev/null
+++ b/test/pippy_profiling/1f1b_event_profile.py
@@ -0,0 +1,300 @@
+"""
+CUDA-event timing for pipeline parallel Schedule1F1B.
+Measures per-rank forward and backward timings (backward not split) without using torch.profiler.
+"""
+
+import argparse
+from collections import defaultdict
+
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+import torch.nn as nn
+from torch.cuda import Event
+from torch.distributed.pipelining import (
+    SplitPoint,
+    Schedule1F1B,
+    pipeline,
+)
+
+from model import Transformer, LLAMA_3B
+
+
+def op_key(op) -> str:
+    """Normalize schedule op to key 'F' | 'B'."""
+    try:
+        s = str(op)
+    except Exception:
+        s = f"{op}"
+    s = s.upper()
+    if "FORWARD" in s or s == "F":
+        return "F"
+    if "BACKWARD" in s or s == "B":
+        return "B"
+    return s  # fallback
+
+
+class TimingTracker:
+    """Collect CUDA event pairs for forward/backward timings per microbatch."""
+
+    def __init__(self, device: torch.device):
+        self.device = device
+        self.reset_iteration()
+
+    def reset_iteration(self):
+        self.fwd_pairs = []  # list of (mb, start, end)
+        self.bwd_pairs = []
+        self._fwd_ctr = 0
+        self._bwd_ctr = 0
+
+    def next_fwd_mb(self):
+        mb = self._fwd_ctr
+        self._fwd_ctr += 1
+        return mb
+
+    def next_bwd_input_mb(self):
+        mb = self._bwd_ctr
+        self._bwd_ctr += 1
+        return mb
+
+    # alias for clarity
+    next_bwd_weight_mb = next_bwd_input_mb
+
+    def add_fwd(self, mb: int):
+        torch.cuda.set_device(self.device)
+        start, end = Event(enable_timing=True), Event(enable_timing=True)
+        self.fwd_pairs.append((mb, start, end))
+        return start, end
+
+    def add_bwd(self, mb: int):
+        torch.cuda.set_device(self.device)
+        start, end = Event(enable_timing=True), Event(enable_timing=True)
+        self.bwd_pairs.append((mb, start, end))
+        return start, end
+
+    def summarize_iteration(self):
+        """Return per-op ordered lists of seconds: {'F': [...], 'B': [...]}."""
+        if not torch.cuda.is_available():
+            return {"F": [], "B": []}
+        torch.cuda.set_device(self.device)
+        torch.cuda.synchronize(self.device)
+
+        def to_list(pairs):
+            return [s.elapsed_time(e) / 1000.0 for _, s, e in pairs]
+
+        return {
+            "F": to_list(self.fwd_pairs),
+            "B": to_list(self.bwd_pairs),
+        }
+
+
+def wrap_stage_forward(stage, tracker: TimingTracker):
+    """Wrap forward to record CUDA event timing."""
+    orig_forward = stage.submod.forward
+
+    def wrapped_forward(self, *args, **kwargs):
+        mb = int(tracker.next_fwd_mb())
+        f_start, f_end = tracker.add_fwd(mb)
+        f_start.record()
+        out = orig_forward(self, *args, **kwargs)
+        f_end.record()
+        return out
+
+    stage.submod.forward = wrapped_forward
+
+
+def wrap_stage_backward(stage, tracker: TimingTracker):
+    """Wrap backward (activation/weight together in 1f1b) to time gradients."""
+    orig_bwd_one = stage.__class__.backward_one_chunk
+
+    def _normalize_mb(mb_val):
+        try:
+            return int(mb_val)
+        except Exception:
+            try:
+                return int(mb_val.item())
+            except Exception:
+                return int(tracker.next_bwd_input_mb())
+
+    def wrapped_bwd_one(self, *args, **kwargs):
+        raw_mb = kwargs.get("bwd_chunk_id", args[0] if args else tracker.next_bwd_input_mb())
+        mb = _normalize_mb(raw_mb)
+        b_start, b_end = tracker.add_bwd(mb)
+        b_start.record()
+        result = orig_bwd_one(self, *args, **kwargs)
+        b_end.record()
+        return result
+
+    stage.backward_one_chunk = wrapped_bwd_one.__get__(stage, stage.__class__)
+
+
+def setup(rank, world_size):
+    import os
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "29500"
+    dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
+
+
+def cleanup():
+    dist.destroy_process_group()
+
+
+def run_pipeline(rank, world_size, mb_size, num_mbs, seq_len, model_cfg):
+    device = torch.device(f"cuda:{rank}")
+    torch.cuda.set_device(device)
+
+    setup(rank, world_size)
+    dist.barrier()
+    print(f"[Rank {rank}] using torch device {device}")
+
+    model = Transformer(model_cfg).to(device)
+
+    def tokenwise_loss_fn(outputs, targets):
+        loss_fn = nn.CrossEntropyLoss()
+        outputs = outputs.reshape(-1, model_cfg.vocab_size)
+        targets = targets.reshape(-1)
+        return loss_fn(outputs, targets)
+
+    layers_per_rank = model_cfg.n_layers // world_size
+    split_spec = {
+        f"layers.{i * layers_per_rank}": SplitPoint.BEGINNING for i in range(1, world_size)
+    }
+
+    example_input = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size, seq_len),
+        dtype=torch.long,
+        device=device,
+    )
+
+    pipe = pipeline(module=model, mb_args=(example_input,), split_spec=split_spec)
+    stage = pipe.build_stage(rank, device=device)
+
+    tracker = TimingTracker(device)
+    wrap_stage_forward(stage, tracker)
+    wrap_stage_backward(stage, tracker)
+
+    optimizer = torch.optim.Adam(stage.submod.parameters(), lr=1e-3)
+    pipe_schedule = Schedule1F1B(stage, n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn)
+
+    x = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size * num_mbs, seq_len),
+        dtype=torch.long,
+        device=device,
+    )
+    y = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size * num_mbs, seq_len),
+        dtype=torch.long,
+        device=device,
+    )
+
+    warmup_steps = 2
+    measure_steps = 1
+    total_tokens = mb_size * num_mbs * seq_len
+
+    # Warmup
+    for _ in range(warmup_steps):
+        optimizer.zero_grad(set_to_none=True)
+        if rank != world_size - 1:
+            pipe_schedule.step(x)
+        else:
+            _ = pipe_schedule.step(target=y, losses=[])
+        optimizer.step()
+        torch.cuda.synchronize()
+        dist.barrier()
+
+    iter_times = []
+    avg_accum = {"F": defaultdict(list), "B": defaultdict(list)}
+
+    for _ in range(measure_steps):
+        dist.barrier()
+        torch.cuda.synchronize()
+
+        tracker.reset_iteration()
+
+        iter_start = Event(enable_timing=True)
+        iter_end = Event(enable_timing=True)
+        iter_start.record()
+
+        optimizer.zero_grad(set_to_none=True)
+
+        if rank != world_size - 1:
+            pipe_schedule.step(x)
+        else:
+            losses = []
+            _ = pipe_schedule.step(target=y, losses=losses)
+
+        optimizer.step()
+        torch.cuda.synchronize()
+        iter_end.record()
+        torch.cuda.synchronize()
+        dist.barrier()
+
+        per_op_lists = tracker.summarize_iteration()
+        for key_in, times in per_op_lists.items():
+            norm_key = op_key(key_in)
+            for idx, t in enumerate(times):
+                avg_accum[norm_key][idx].append(t)
+
+        if rank == world_size - 1:
+            iter_times.append(iter_start.elapsed_time(iter_end) / 1000.0)
+
+    # Compute per-op per-occurrence averages across measured steps
+    op_idx_avg = {
+        op: {idx: sum(times) / len(times) for idx, times in idx_dict.items()}
+        for op, idx_dict in avg_accum.items()
+    }
+
+    if rank == world_size - 1 and iter_times:
+        avg_iter = sum(iter_times) / len(iter_times)
+        tput = total_tokens / avg_iter
+
+        print(f"[Rank {rank}] Total iter time: {sum(iter_times):.6f}s")
+        print(f"[Rank {rank}] Avg iter time:   {avg_iter:.6f}s")
+        print(f"[Rank {rank}] Throughput:      {tput:.2f} tokens/s")
+        if 'losses' in locals():
+            print(f"[Rank {rank}] final losses: {losses}")
+
+    # Print pipeline order with average op timings for this rank
+    print(f"\n=== Pipeline order for rank {rank} (avg over {measure_steps} steps) ===")
+    op_counters = defaultdict(int)
+    for step, action in enumerate(pipe_schedule.pipeline_order[rank]):
+        if action is None:
+            continue
+        stage_idx, op, mb = action
+        key = op_key(op)
+        idx = op_counters[key]
+        op_counters[key] += 1
+        t_val = op_idx_avg.get(key, {}).get(idx)
+        t_str = f"{t_val:.6f}s" if t_val is not None else "n/a"
+        print(f"t={step:03d} | stage={stage_idx} | op={op} | microbatch={mb} | time={t_str}")
+
+    dist.barrier()
+    cleanup()
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--microbatch_size", type=int, default=16)
+    parser.add_argument("--num_microbatches", type=int, default=4)
+    parser.add_argument("--seq_len", type=int, default=256)
+    args = parser.parse_args()
+
+    world_size = 2
+    mp.set_start_method("spawn", force=True)
+
+    mp.spawn(
+        run_pipeline,
+        args=(world_size, args.microbatch_size, args.num_microbatches, args.seq_len, LLAMA_3B),
+        nprocs=world_size,
+        join=True,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/test/pippy_profiling/1f1b_expected_schedule.dot b/test/pippy_profiling/1f1b_expected_schedule.dot
new file mode 100644
index 0000000..cab5522
--- /dev/null
+++ b/test/pippy_profiling/1f1b_expected_schedule.dot
@@ -0,0 +1,118 @@
+digraph PipelineDAG_1F1B {
+  rankdir=LR;
+  node [shape=box, fontname="Helvetica", fontsize=10];
+
+  // -----------------------
+  // Nodes (durations used in calculations)
+  // -----------------------
+  subgraph cluster_s0 {
+    label="Stage 0 (rank 0)";
+    style=rounded;
+
+    S0_F0 [label="S0 F mb0\nt=000 274.75ms"];
+    S0_F1 [label="S0 F mb1\nt=003 274.75ms"];
+    S0_B0 [label="S0 B mb0\nt=004 551ms"];
+    S0_F2 [label="S0 F mb2\nt=005 274.75ms"];
+    S0_B1 [label="S0 B mb1\nt=006 551ms"];
+    S0_F3 [label="S0 F mb3\nt=007 274.75ms"];
+    S0_B2 [label="S0 B mb2\nt=008 551ms"];
+    S0_B3 [label="S0 B mb3\nt=010 551ms"];
+
+    Update0 [shape=box, style="rounded", label="Update (rank 0)\n40ms"];
+  }
+
+  subgraph cluster_s1 {
+    label="Stage 1 (rank 1)";
+    style=rounded;
+
+    S1_F1 [label="S1 F mb1\nt=001 274.75ms"];
+    S1_B0 [label="S1 B mb0\nt=002 551ms"];
+    S1_F2 [label="S1 F mb2\nt=003 274.75ms"];
+    S1_B1 [label="S1 B mb1\nt=004 551ms"];
+    S1_F3 [label="S1 F mb3\nt=005 274.75ms"];
+    S1_B2 [label="S1 B mb2\nt=006 551ms"];
+    S1_F4 [label="S1 F mb4\nt=007 274.75ms"];
+    S1_B3 [label="S1 B mb3\nt=008 551ms"];
+
+    Update1 [shape=box, style="rounded", label="Update (rank 1)\n40ms"];
+  }
+
+  Done [shape=oval, label="Done\n0ms"];
+
+  // -----------------------
+  // Temporal edges (serialization on each stage)
+  // -----------------------
+  edge [color=black, penwidth=1.2];
+
+  // Stage 0 order
+  S0_F0 -> S0_F1 -> S0_B0 -> S0_F2 -> S0_B1 -> S0_F3 -> S0_B2 -> S0_B3 -> Update0;
+
+  // Stage 1 order
+  S1_F1 -> S1_B0 -> S1_F2 -> S1_B1 -> S1_F3 -> S1_B2 -> S1_F4 -> S1_B3 -> Update1;
+
+  // -----------------------
+  // Intra-microbatch logical deps (within a stage)
+  // -----------------------
+  edge [color=gray50, style=dashed, penwidth=1.0];
+
+  // Stage 0: Fm -> Bm
+  S0_F0 -> S0_B0;
+  S0_F1 -> S0_B1;
+  S0_F2 -> S0_B2;
+  S0_F3 -> S0_B3;
+
+  // Stage 1: F(m+1) -> Bm (offseted microbatch labeling in your trace)
+  S1_F1 -> S1_B0;
+  S1_F2 -> S1_B1;
+  S1_F3 -> S1_B2;
+  S1_F4 -> S1_B3;
+
+  // -----------------------
+  // Cross-stage data deps
+  // -----------------------
+  // Forward activations (offset mapping): S0_Fm -> S1_F(m+1)
+  edge [color=blue, penwidth=2.0];
+  S0_F0 -> S1_F1;
+  S0_F1 -> S1_F2;
+  S0_F2 -> S1_F3;
+  S0_F3 -> S1_F4;
+
+  // Backward grads: S1_Bm -> S0_Bm
+  edge [color=red, penwidth=2.0];
+  S1_B0 -> S0_B0;
+  S1_B1 -> S0_B1;
+  S1_B2 -> S0_B2;
+  S1_B3 -> S0_B3;
+
+  // -----------------------
+  // Done joins both updates
+  // -----------------------
+  edge [color=purple, style=dashed, penwidth=1.5];
+  Update0 -> Done;
+  Update1 -> Done;
+
+  // ============================================================
+  // Highlight critical path edges (longest path to Done)
+  // Chain sums to 4168.75ms with F=274.75, B=551, U=40
+  // ============================================================
+  edge [color=green, penwidth=4.0];
+
+  // activation + stage1 compute + grad back
+  S0_F0 -> S1_F1;
+  S1_F1 -> S1_B0;
+  S1_B0 -> S0_B0;
+
+  // stage0 tail (forced by temporal order + future grad dependency)
+  S0_B0 -> S0_F2;
+  S0_F2 -> S0_B1;
+  S0_B1 -> S0_F3;
+
+  // activation to stage1 last forward, then last backward, then final stage0 backward
+  S0_F3 -> S1_F4;
+  S1_F4 -> S1_B3;
+  S1_B3 -> S0_B3;
+
+  // update + done
+  S0_B3 -> Update0;
+  Update0 -> Done;
+}
diff --git a/test/pippy_profiling/1f1b_expected_schedule.png b/test/pippy_profiling/1f1b_expected_schedule.png
new file mode 100644
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diff --git a/test/pippy_profiling/zb1p_expected_schedule.dot b/test/pippy_profiling/zb1p_expected_schedule.dot
new file mode 100644
index 0000000..a119c49
--- /dev/null
+++ b/test/pippy_profiling/zb1p_expected_schedule.dot
@@ -0,0 +1,122 @@
+digraph PipelineDAG {
+  rankdir=LR;
+  node [shape=box, fontname="Helvetica", fontsize=10];
+
+  // -----------------------
+  // Nodes (durations used in calculations)
+  // -----------------------
+  subgraph cluster_s0 {
+    label="Stage 0 (rank 0)";
+    style=rounded;
+
+    S0_F0 [label="S0 F mb0\nt=000 274.75ms"];
+    S0_F1 [label="S0 F mb1\nt=002 274.75ms"];
+    S0_I0 [label="S0 I mb0\nt=003 0ms"];
+    S0_W0 [label="S0 W mb0\nt=004 551ms"];
+    S0_F2 [label="S0 F mb2\nt=005 274.75ms"];
+    S0_I1 [label="S0 I mb1\nt=006 0ms"];
+    S0_W1 [label="S0 W mb1\nt=007 551ms"];
+    S0_F3 [label="S0 F mb3\nt=008 274.75ms"];
+    S0_I2 [label="S0 I mb2\nt=009 0ms"];
+    S0_W2 [label="S0 W mb2\nt=010 551ms"];
+    S0_I3 [label="S0 I mb3\nt=011 0ms"];
+    S0_W3 [label="S0 W mb3\nt=012 551ms"];
+  }
+
+  subgraph cluster_s1 {
+    label="Stage 1 (rank 1)";
+    style=rounded;
+
+    S1_F0 [label="S1 F mb0\nt=001 274.75ms"];
+    S1_I0 [label="S1 I mb0\nt=002 275.5ms"];
+    S1_F1 [label="S1 F mb1\nt=003 274.75ms"];
+    S1_I1 [label="S1 I mb1\nt=004 275.5ms"];
+    S1_W0 [label="S1 W mb0\nt=005 275.5ms"];
+    S1_F2 [label="S1 F mb2\nt=006 274.75ms"];
+    S1_I2 [label="S1 I mb2\nt=007 275.5ms"];
+    S1_W1 [label="S1 W mb1\nt=008 275.5ms"];
+    S1_F3 [label="S1 F mb3\nt=009 274.75ms"];
+    S1_I3 [label="S1 I mb3\nt=010 275.5ms"];
+    S1_W2 [label="S1 W mb2\nt=011 275.5ms"];
+    S1_W3 [label="S1 W mb3\nt=012 275.5ms"];
+  }
+
+  // Iteration end when both ranks complete their last op
+  Done [shape=oval, label="Done\n0ms"];
+
+  // -----------------------
+  // Temporal edges (serialization on each stage)
+  // -----------------------
+  edge [color=black, penwidth=1.2];
+
+  // Stage 0 order
+  S0_F0 -> S0_F1 -> S0_I0 -> S0_W0 -> S0_F2 -> S0_I1 -> S0_W1 -> S0_F3 -> S0_I2 -> S0_W2 -> S0_I3 -> S0_W3;
+
+  // Stage 1 order
+  S1_F0 -> S1_I0 -> S1_F1 -> S1_I1 -> S1_W0 -> S1_F2 -> S1_I2 -> S1_W1 -> S1_F3 -> S1_I3 -> S1_W2 -> S1_W3;
+
+  // -----------------------
+  // Intra-microbatch logical deps (within a stage)
+  // -----------------------
+  edge [color=gray50, style=dashed, penwidth=1.0];
+
+  // Stage 0: F -> I -> W
+  S0_F0 -> S0_I0 -> S0_W0;
+  S0_F1 -> S0_I1 -> S0_W1;
+  S0_F2 -> S0_I2 -> S0_W2;
+  S0_F3 -> S0_I3 -> S0_W3;
+
+  // Stage 1: F -> I -> W
+  S1_F0 -> S1_I0 -> S1_W0;
+  S1_F1 -> S1_I1 -> S1_W1;
+  S1_F2 -> S1_I2 -> S1_W2;
+  S1_F3 -> S1_I3 -> S1_W3;
+
+  // -----------------------
+  // Cross-stage data deps
+  // -----------------------
+  edge [color=blue, penwidth=2.0];
+  S0_F0 -> S1_F0;
+  S0_F1 -> S1_F1;
+  S0_F2 -> S1_F2;
+  S0_F3 -> S1_F3;
+
+  edge [color=red, penwidth=2.0];
+  S1_I0 -> S0_I0;
+  S1_I1 -> S0_I1;
+  S1_I2 -> S0_I2;
+  S1_I3 -> S0_I3;
+
+  // -----------------------
+  // Join (Done)
+  // -----------------------
+  edge [color=purple, style=dashed, penwidth=1.5];
+  S0_W3 -> Done;
+  S1_W3 -> Done;
+
+  // ============================================================
+  // Highlight critical path edges (longest path to Done)
+  // ============================================================
+  edge [color=green, penwidth=4.0];
+
+  // activation dep mb0
+  S0_F0 -> S1_F0;
+
+  // stage1 temporal + grad dep back to stage0
+  S1_F0 -> S1_I0;
+  S1_I0 -> S0_I0;
+
+  // stage0 temporal tail to end (critical)
+  S0_I0 -> S0_W0;
+  S0_W0 -> S0_F2;
+  S0_F2 -> S0_I1;
+  S0_I1 -> S0_W1;
+  S0_W1 -> S0_F3;
+  S0_F3 -> S0_I2;
+  S0_I2 -> S0_W2;
+  S0_W2 -> S0_I3;
+  S0_I3 -> S0_W3;
+
+  // done
+  S0_W3 -> Done;
+}
diff --git a/test/pippy_profiling/zb1p_expected_schedule.png b/test/pippy_profiling/zb1p_expected_schedule.png
new file mode 100644
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HcmV?d00001

diff --git a/test/pippy_profiling/zb_1f1b_memory_profiling.py b/test/pippy_profiling/zb_1f1b_memory_profiling.py
new file mode 100644
index 0000000..6da4fe5
--- /dev/null
+++ b/test/pippy_profiling/zb_1f1b_memory_profiling.py
@@ -0,0 +1,206 @@
+import os
+import torch
+import torch.multiprocessing as mp
+import argparse
+import torch.distributed as dist
+import torch.nn as nn
+import subprocess
+from torch.distributed.pipelining import pipeline, SplitPoint, ScheduleInterleavedZeroBubble, Schedule1F1B
+from torch.profiler import profile, ProfilerActivity, record_function, schedule
+from model import Transformer, LLAMA_DEBUG, LLAMA_3B, LLAMA_1B
+
+def setup(rank, world_size):
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "29500"
+
+    backend = "nccl" if torch.cuda.is_available() else "gloo"
+    dist.init_process_group(backend=backend, rank=rank, world_size=world_size)
+    if backend == "nccl":
+        torch.cuda.set_device(rank)
+
+def cleanup():
+    dist.destroy_process_group()
+
+def wrap_stage_forward(stage, name):
+    orig_forward = stage.submod.forward
+    mb_counter = {"i": 0}
+
+    def wrapped_forward(self, *args, **kwargs):
+        i = mb_counter["i"]
+        mb_counter["i"] += 1
+
+        with record_function(f"{name}_F_mb{i}"):
+            return orig_forward(self, *args, **kwargs)
+
+    stage.submod.forward = wrapped_forward
+
+def wrap_stage_backward_ZB(stage, name):
+    orig_bwd_one = stage.__class__.backward_one_chunk
+    def wrapped_bwd_one(self, *args, **kwargs):
+        mb = kwargs.get("bwd_chunk_id", args[0] if args else "unk")
+        with record_function(f"{name}_B_mb{mb}"):
+            return orig_bwd_one(self, *args, **kwargs)
+    stage.backward_one_chunk = wrapped_bwd_one.__get__(stage, stage.__class__)
+
+    orig_bwd_w = stage.__class__.backward_weight_one_chunk
+    def wrapped_bwd_w(self, *args, **kwargs):
+       mb = kwargs.get("bwd_chunk_id", args[0] if args else "unk")
+       with record_function(f"{name}_W_mb{mb}"):
+            return orig_bwd_w(self, *args, **kwargs)
+    stage.backward_weight_one_chunk = wrapped_bwd_w.__get__(stage, stage.__class__)
+
+def wrap_stage_backward_1f1b(stage, name):
+    orig_bwd_one = stage.__class__.backward_one_chunk
+    def wrapped_bwd_one(self, *args, **kwargs):
+        mb = kwargs.get("bwd_chunk_id", args[0] if args else "unk")
+        with record_function(f"{name}_B_mb{mb}"):
+            return orig_bwd_one(self, *args, **kwargs)
+    stage.backward_one_chunk = wrapped_bwd_one.__get__(stage, stage.__class__)
+
+
+def run_pipeline(rank, world_size, schedule_name, mb_size, num_mbs, result_queue, model_cfg=LLAMA_DEBUG):
+    setup(rank, world_size)
+
+    device = torch.device(f"cuda:{rank}") if torch.cuda.is_available() else torch.device("cpu")
+
+    model = Transformer(model_cfg)
+    model.to(device)
+
+    # Helper loss function that automatically reshapes output and targets tensors
+    def tokenwise_loss_fn(outputs, targets):
+        loss_fn = nn.CrossEntropyLoss()
+        outputs = outputs.reshape(-1, model_cfg.vocab_size)
+        targets = targets.reshape(-1)
+        return loss_fn(outputs, targets)
+
+    # Partition the model into world_size stages and create the stage for this rank
+    layers_per_rank = model_cfg.n_layers // world_size
+
+    split_spec = {
+        f"layers.{i * layers_per_rank}": SplitPoint.BEGINNING
+        for i in range(1, world_size)
+    }
+
+    if rank == 0:
+        print(model_cfg.n_layers)
+        print(world_size)
+        print(layers_per_rank)
+        print(split_spec)
+
+    example_input = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size, model_cfg.max_seq_len),
+        dtype=torch.long,
+        device=device
+    )
+    
+    pipe = pipeline(
+        module=model,
+        mb_args=(example_input,),
+        split_spec=split_spec,
+    )
+    
+    stage = pipe.build_stage(rank, device=device) # Currently assumes one stage per rank  
+    schedule = None      
+    wrap_stage_forward(stage, f"stage{rank}")
+    if schedule_name == "1f1b":
+        wrap_stage_backward_1f1b(stage, f"stage{rank}")
+        schedule = Schedule1F1B(stage, n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn)
+    else:
+        wrap_stage_backward_ZB(stage, f"stage{rank}")
+        schedule = ScheduleInterleavedZeroBubble([stage], n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn)
+
+    # Print the pipeline schedule
+    print(f"\n=== Pipeline order for rank {rank} ===")
+    for step, action in enumerate(schedule.pipeline_order[rank]):
+        if action is None:
+            continue
+        stage_idx, op, mb = action
+        print(f"t={step:03d} | stage={stage_idx} | op={op} | microbatch={mb}")
+
+    prof_schedule = torch.profiler.schedule(wait=0, warmup=0, active=1, repeat=1)
+
+    # Generate random training tensors
+    x = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size * num_mbs, model_cfg.max_seq_len),
+        dtype=torch.long,
+        device=device                 
+    )
+
+    y = torch.randint(
+        low=0, 
+        high=model_cfg.vocab_size, 
+        size=(mb_size * num_mbs, model_cfg.max_seq_len), 
+        dtype=torch.long,
+        device=device
+    )
+
+    # Initialize profiler activity based on device availability
+    activities = [ProfilerActivity.CPU]
+    if torch.cuda.is_available():
+        activities += [ProfilerActivity.CUDA]
+
+    with profile(
+        activities=activities, 
+        schedule=prof_schedule,
+        profile_memory=True,
+        with_stack=True,
+        record_shapes=True,
+        experimental_config=torch._C._profiler._ExperimentalConfig(
+            verbose=True,
+            profile_all_threads=True,
+            capture_overload_names=True,
+        )) as prof:
+        if rank == 0:
+            schedule.step(x)
+        elif rank == 1:
+            losses = []
+            output = schedule.step(target=y, losses=losses)
+            print(f"final losses: {losses}")
+        prof.step()
+
+    trace_dir = os.path.join(os.path.dirname(__file__), "traces")
+    out_path = os.path.join(trace_dir, f"trace_{rank}.json")
+    prof.export_chrome_trace(out_path)
+    
+    dist.barrier()
+    if rank == 1:
+        # Create traces folder if it doesn't exist
+        os.makedirs(trace_dir, exist_ok=True)
+
+        merged_out = os.path.join(trace_dir, "merged_trace.json")
+        cmd = ["python", "filter_trace.py", os.path.join(trace_dir,"trace_0.json"), os.path.join(trace_dir,"user_only_trace_0.json")]
+        subprocess.run(cmd, check=True)
+
+        cmd = ["python", "filter_trace.py", os.path.join(trace_dir, "trace_1.json"),  os.path.join(trace_dir,"user_only_trace_1.json")]
+        subprocess.run(cmd, check=True)
+
+        cmd = ["python", "merge_traces.py", os.path.join(trace_dir,"user_only_trace_0.json"), os.path.join(trace_dir,"user_only_trace_1.json"), merged_out]
+        subprocess.run(cmd, check=True)
+        print(f"Merged trace available at {merged_out}")
+    
+    cleanup()
+   
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--microbatch_size', type=int, default=64)
+    parser.add_argument('--num_microbatches', type=int, default=256)
+    parser.add_argument("--schedule", type=str, default="1f1b", choices=["1f1b", "zb"])
+    args = parser.parse_args()
+    
+    world_size = 4
+
+    mp.set_start_method("spawn", force=True)
+    manager = mp.Manager()
+    result_queue = manager.Queue()
+
+    mp.spawn(
+        run_pipeline,
+        args=(world_size, args.schedule, args.microbatch_size, args.num_microbatches, result_queue),
+        nprocs=world_size,
+        join=True,
+    )
diff --git a/test/pippy_profiling/zerobubble_event_profile.py b/test/pippy_profiling/zerobubble_event_profile.py
new file mode 100644
index 0000000..804eecc
--- /dev/null
+++ b/test/pippy_profiling/zerobubble_event_profile.py
@@ -0,0 +1,318 @@
+import os
+import argparse
+from collections import defaultdict
+
+import torch
+import torch.multiprocessing as mp
+import torch.distributed as dist
+import torch.nn as nn
+from torch.cuda import Event
+from torch.distributed.pipelining import pipeline, SplitPoint, ScheduleInterleavedZeroBubble
+
+from model import Transformer, LLAMA_3B
+
+
+def op_key(op) -> str:
+    """Normalize schedule op to string key 'F' | 'I' | 'W'."""
+    try:
+        s = str(op)
+    except Exception:
+        s = f"{op}"
+    s = s.upper()
+    if "FORWARD" in s:
+        return "F"
+    if "BACKWARD_INPUT" in s or s == "I":
+        return "I"
+    if "BACKWARD_WEIGHT" in s or s == "W":
+        return "W"
+    return s  # fall back (will not match counters)
+
+
+class TimingTracker:
+    """Collect CUDA event pairs for forward/backward timings per microbatch."""
+
+    def __init__(self, device: torch.device):
+        self.device = device
+        self.reset_iteration()
+
+    def reset_iteration(self):
+        self.fwd_pairs = []  # list of (mb, start, end)
+        self.bwd_input_pairs = []
+        self.bwd_weight_pairs = []
+        self._fwd_ctr = 0
+        self._bwd_input_ctr = 0
+        self._bwd_weight_ctr = 0
+
+    def next_fwd_mb(self):
+        mb = self._fwd_ctr
+        self._fwd_ctr += 1
+        return mb
+
+    def next_bwd_input_mb(self):
+        mb = self._bwd_input_ctr
+        self._bwd_input_ctr += 1
+        return mb
+
+    def next_bwd_weight_mb(self):
+        mb = self._bwd_weight_ctr
+        self._bwd_weight_ctr += 1
+        return mb
+
+    def add_fwd(self, mb: int):
+        torch.cuda.set_device(self.device)
+        start, end = Event(enable_timing=True), Event(enable_timing=True)
+        self.fwd_pairs.append((mb, start, end))
+        return start, end
+
+    def add_bwd_input(self, mb: int):
+        torch.cuda.set_device(self.device)
+        start, end = Event(enable_timing=True), Event(enable_timing=True)
+        self.bwd_input_pairs.append((mb, start, end))
+        return start, end
+
+    def add_bwd_weight(self, mb: int):
+        torch.cuda.set_device(self.device)
+        start, end = Event(enable_timing=True), Event(enable_timing=True)
+        self.bwd_weight_pairs.append((mb, start, end))
+        return start, end
+
+    def summarize_iteration(self):
+        """Return per-op ordered lists of seconds: {'F': [...], 'I': [...], 'W': [...]}."""
+        if not torch.cuda.is_available():
+            return {"F": [], "I": [], "W": []}
+        torch.cuda.set_device(self.device)
+        torch.cuda.synchronize(self.device)
+
+        def to_list(pairs):
+            return [s.elapsed_time(e) / 1000.0 for _, s, e in pairs]
+
+        return {
+            "F": to_list(self.fwd_pairs),
+            "I": to_list(self.bwd_input_pairs),
+            "W": to_list(self.bwd_weight_pairs),
+        }
+
+
+def wrap_stage_forward(stage, tracker: TimingTracker):
+    """Wrap forward to record CUDA event timing."""
+    orig_forward = stage.submod.forward
+
+    def wrapped_forward(self, *args, **kwargs):
+        mb = int(tracker.next_fwd_mb())
+        f_start, f_end = tracker.add_fwd(mb)
+        f_start.record()
+        out = orig_forward(self, *args, **kwargs)
+        f_end.record()
+        return out
+
+    stage.submod.forward = wrapped_forward
+
+def wrap_stage_backward(stage, tracker: TimingTracker):
+    """Wrap backward passes to time activation and weight gradients separately."""
+    orig_bwd_one = stage.__class__.backward_one_chunk
+    def _normalize_mb(mb_val):
+        try:
+            return int(mb_val)
+        except Exception:
+            try:
+                return int(mb_val.item())
+            except Exception:
+                return int(tracker.next_bwd_input_mb())
+
+    def wrapped_bwd_one(self, *args, **kwargs):
+        raw_mb = kwargs.get("bwd_chunk_id", args[0] if args else tracker.next_bwd_input_mb())
+        mb = _normalize_mb(raw_mb)
+        b_start, b_end = tracker.add_bwd_input(mb)
+        b_start.record()
+        result = orig_bwd_one(self, *args, **kwargs)
+        b_end.record()
+        return result
+    
+    stage.backward_one_chunk = wrapped_bwd_one.__get__(stage, stage.__class__)
+
+    orig_bwd_w = stage.__class__.backward_weight_one_chunk
+
+    def wrapped_bwd_w(self, *args, **kwargs):
+        raw_mb = kwargs.get("bwd_chunk_id", args[0] if args else tracker.next_bwd_weight_mb())
+        mb = _normalize_mb(raw_mb)
+        w_start, w_end = tracker.add_bwd_weight(mb)
+        w_start.record()
+        result = orig_bwd_w(self, *args, **kwargs)
+        w_end.record()
+        return result
+
+    stage.backward_weight_one_chunk = wrapped_bwd_w.__get__(stage, stage.__class__)
+
+
+def setup(rank, world_size):
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "29500"
+    dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
+
+
+def cleanup():
+    dist.destroy_process_group()
+
+
+def run_pipeline(rank, world_size, mb_size, num_mbs, seq_len, model_cfg):
+    setup(rank, world_size)
+    dist.barrier()
+    device = torch.device(f"cuda:{rank}")
+    torch.cuda.set_device(device)
+    print(f"[Rank {rank}] using torch device {device}")
+
+    model = Transformer(model_cfg).to(device)
+
+    def tokenwise_loss_fn(outputs, targets):
+        loss_fn = nn.CrossEntropyLoss()
+        outputs = outputs.reshape(-1, model_cfg.vocab_size)
+        targets = targets.reshape(-1)
+        return loss_fn(outputs, targets)
+
+    layers_per_rank = model_cfg.n_layers // world_size
+    split_spec = {
+        f"layers.{i * layers_per_rank}": SplitPoint.BEGINNING for i in range(1, world_size)
+    }
+
+    example_input = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size, seq_len),
+        dtype=torch.long,
+        device=device,
+    )
+
+    pipe = pipeline(module=model, mb_args=(example_input,), split_spec=split_spec)
+    stage = pipe.build_stage(rank, device=device)
+
+    tracker = TimingTracker(device)
+    wrap_stage_forward(stage, tracker)
+    wrap_stage_backward(stage, tracker)
+
+    optimizer = torch.optim.Adam(stage.submod.parameters(), lr=1e-3)
+    pipe_schedule = ScheduleInterleavedZeroBubble(
+        [stage], n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn
+    )
+
+    x = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size * num_mbs, seq_len),
+        dtype=torch.long,
+        device=device,
+    )
+    y = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size * num_mbs, seq_len),
+        dtype=torch.long,
+        device=device,
+    )
+
+    warmup_steps = 2
+    measure_steps = 1
+    total_tokens = mb_size * num_mbs * seq_len
+
+    # Warmup
+    for _ in range(warmup_steps):
+        optimizer.zero_grad(set_to_none=True)
+        if rank != world_size - 1:
+            pipe_schedule.step(x)
+        else:
+            _ = pipe_schedule.step(target=y, losses=[])
+        optimizer.step()
+        torch.cuda.synchronize()
+        dist.barrier()
+
+    iter_times = []
+    avg_accum = {"F": defaultdict(list), "I": defaultdict(list), "W": defaultdict(list)}
+
+    for _ in range(measure_steps):
+        dist.barrier()
+        torch.cuda.synchronize()
+
+        tracker.reset_iteration()
+
+        iter_start = Event(enable_timing=True)
+        iter_end = Event(enable_timing=True)
+        iter_start.record()
+
+        optimizer.zero_grad(set_to_none=True)
+
+        if rank != world_size - 1:
+            pipe_schedule.step(x)
+        else:
+            losses = []
+            _ = pipe_schedule.step(target=y, losses=losses)
+
+        optimizer.step()
+
+        torch.cuda.synchronize()
+        iter_end.record()
+        torch.cuda.synchronize()
+        dist.barrier()
+
+        per_op_lists = tracker.summarize_iteration()
+        # accumulate by occurrence index (per op) to align with pipeline order
+        for key_in, times in per_op_lists.items():
+            norm_key = op_key(key_in)
+            for idx, t in enumerate(times):
+                avg_accum[norm_key][idx].append(t)
+
+        if rank == world_size - 1:
+            iter_times.append(iter_start.elapsed_time(iter_end) / 1000.0)
+
+    # Compute per-op per-occurrence averages across measured steps
+    op_idx_avg = {
+        op: {idx: sum(times) / len(times) for idx, times in idx_dict.items()}
+        for op, idx_dict in avg_accum.items()
+    }
+
+    if rank == world_size - 1 and iter_times:
+        avg_iter = sum(iter_times) / len(iter_times)
+        tput = total_tokens / avg_iter
+
+        print(f"[Rank {rank}] Total iter time: {sum(iter_times):.6f}s")
+        print(f"[Rank {rank}] Avg iter time:   {avg_iter:.6f}s")
+        print(f"[Rank {rank}] Throughput:      {tput:.2f} tokens/s")
+        if rank == world_size - 1 and 'losses' in locals():
+            print(f"[Rank {rank}] final losses: {losses}")
+
+    # Print pipeline order with average op timings for this rank
+    print(f"\n=== Pipeline order for rank {rank} (avg over {measure_steps} steps) ===")
+    op_counters = defaultdict(int)
+    for step, action in enumerate(pipe_schedule.pipeline_order[rank]):
+        if action is None:
+            continue
+        stage_idx, op, mb = action
+        key = op_key(op)
+        idx = op_counters[key]
+        op_counters[key] += 1
+        t_val = op_idx_avg.get(key, {}).get(idx)
+        t_str = f"{t_val:.6f}s" if t_val is not None else "n/a"
+        print(f"t={step:03d} | stage={stage_idx} | op={op} | microbatch={mb} | time={t_str}")
+
+    dist.barrier()
+    cleanup()
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--microbatch_size", type=int, default=16)
+    parser.add_argument("--num_microbatches", type=int, default=4)
+    parser.add_argument("--seq_len", type=int, default=256)
+    args = parser.parse_args()
+
+    world_size = 2
+    mp.set_start_method("spawn", force=True)
+
+    mp.spawn(
+        run_pipeline,
+        args=(world_size, args.microbatch_size, args.num_microbatches, args.seq_len, LLAMA_3B),
+        nprocs=world_size,
+        join=True,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/test/pippy_profiling/zerobubble_example.py b/test/pippy_profiling/zerobubble_example.py
new file mode 100644
index 0000000..0d3a189
--- /dev/null
+++ b/test/pippy_profiling/zerobubble_example.py
@@ -0,0 +1,270 @@
+import os
+import torch
+import torch.multiprocessing as mp
+import argparse
+import torch.distributed as dist
+import torch.nn as nn
+import time
+import subprocess
+from torch.cuda import Event
+from torch.distributed.pipelining import pipeline, SplitPoint, ScheduleInterleavedZeroBubble, Schedule1F1B, ScheduleInterleaved1F1B
+from torch.profiler import profile, ProfilerActivity, record_function, schedule as profiler_schedule
+from model import Transformer, LLAMA_DEBUG, LLAMA_3B, LLAMA_1B
+
+
+def wrap_stage_forward(stage, name):
+    """Tag each microbatch forward pass for profiling."""
+    orig_forward = stage.submod.forward
+    mb_counter = {"i": 0}
+
+    def wrapped_forward(self, *args, **kwargs):
+        i = mb_counter["i"]
+        mb_counter["i"] += 1
+
+        with record_function(f"{name}_F_mb{i}"):
+            return orig_forward(self, *args, **kwargs)
+
+    stage.submod.forward = wrapped_forward
+
+
+def wrap_stage_backward(stage, name):
+    """Tag activation and weight backward passes for profiling."""
+    orig_bwd_one = stage.__class__.backward_one_chunk
+
+    def wrapped_bwd_one(self, *args, **kwargs):
+        mb = kwargs.get("bwd_chunk_id", args[0] if args else "unk")
+        with record_function(f"{name}_B_mb{mb}"):
+            return orig_bwd_one(self, *args, **kwargs)
+
+    stage.backward_one_chunk = wrapped_bwd_one.__get__(stage, stage.__class__)
+
+    orig_bwd_w = stage.__class__.backward_weight_one_chunk
+
+    def wrapped_bwd_w(self, *args, **kwargs):
+        mb = kwargs.get("bwd_chunk_id", args[0] if args else "unk")
+        with record_function(f"{name}_W_mb{mb}"):
+            return orig_bwd_w(self, *args, **kwargs)
+
+    stage.backward_weight_one_chunk = wrapped_bwd_w.__get__(stage, stage.__class__)
+
+def setup(rank, world_size):
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "29500"
+
+    backend = "nccl"
+    dist.init_process_group(backend=backend, rank=rank, world_size=world_size)
+
+
+def cleanup():
+    dist.destroy_process_group()
+
+
+def run_pipeline(rank, world_size, mb_size, num_mbs, seq_len, model_cfg):
+    setup(rank, world_size)
+    dist.barrier()
+    device = torch.device(f"cuda:{rank}")
+    torch.cuda.set_device(device)
+    print(f"[Rank {rank}] using torch device {device}")
+
+    model = Transformer(model_cfg)
+    model.to(device)
+
+    # Helper loss function that automatically reshapes output and targets tensors
+    def tokenwise_loss_fn(outputs, targets):
+        loss_fn = nn.CrossEntropyLoss()
+        outputs = outputs.reshape(-1, model_cfg.vocab_size)
+        targets = targets.reshape(-1)
+        return loss_fn(outputs, targets)
+
+    # Partition the model into world_size stages and create the stage for this rank
+    layers_per_rank = model_cfg.n_layers // world_size
+
+    split_spec = {
+        f"layers.{i * layers_per_rank}": SplitPoint.BEGINNING
+        for i in range(1, world_size)
+    }
+
+    example_input = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size, seq_len),
+        dtype=torch.long,
+        device=device
+    )
+    
+    pipe = pipeline(
+        module=model,
+        mb_args=(example_input,),
+        split_spec=split_spec,
+    )
+
+    stage = pipe.build_stage(rank, device=device)  # Currently assumes one stage per rank
+
+    # Add profiling markers to forward / backward passes (names align with zerobubble.py)
+    wrap_stage_forward(stage, f"stage{rank}")
+    wrap_stage_backward(stage, f"stage{rank}")
+
+    # Optimizer for this stage's parameters; profiled per-iteration
+    optimizer = torch.optim.Adam(stage.submod.parameters(), lr=1e-3)
+
+    # pipe_schedule = ScheduleInterleavedZeroBubble([stage], n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn)
+    pipe_schedule = Schedule1F1B(stage, n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn)
+
+    # Print the pipeline schedule
+    print(f"\n=== Pipeline order for rank {rank} ===")
+    for step, action in enumerate(pipe_schedule.pipeline_order[rank]):
+        if action is None:
+            continue
+        stage_idx, op, mb = action
+        print(f"t={step:03d} | stage={stage_idx} | op={op} | microbatch={mb}")
+
+    # Generate random training tensors
+    x = torch.randint(
+        low=0,
+        high=model_cfg.vocab_size,
+        size=(mb_size * num_mbs, seq_len),
+        dtype=torch.long,
+        device=device                 
+    )
+
+    y = torch.randint(
+        low=0, 
+        high=model_cfg.vocab_size, 
+        size=(mb_size * num_mbs, seq_len), 
+        dtype=torch.long,
+        device=device
+    )
+
+    total_tokens = mb_size * num_mbs * seq_len
+
+    warmup_steps = 2
+    measure_steps = 4
+
+    for _ in range(warmup_steps):
+        optimizer.zero_grad(set_to_none=True)
+        if rank != world_size - 1:
+            pipe_schedule.step(x)
+        else:
+            _losses = []
+            _ = pipe_schedule.step(target=y, losses=_losses)
+        optimizer.step()
+        torch.cuda.synchronize()
+        dist.barrier()
+
+    elapsed_times = []
+
+    # ---------------------------
+    # Profiling + timing section
+    # ---------------------------
+
+    activities = [ProfilerActivity.CPU]
+    if torch.cuda.is_available():
+        activities.append(ProfilerActivity.CUDA)
+
+    # Profile the full measure_steps window (after warmup) and export chrome trace.
+    prof_schedule = profiler_schedule(wait=0, warmup=0, active=measure_steps, repeat=1)
+
+    trace_dir = os.path.join(os.path.dirname(__file__), "traces")
+    os.makedirs(trace_dir, exist_ok=True)
+    trace_path = os.path.join(trace_dir, f"trace_{rank}.json")
+
+    def _remove_if_exists(path: str):
+        try:
+            os.remove(path)
+        except FileNotFoundError:
+            pass
+
+    with profile(
+        activities=activities,
+        schedule=prof_schedule,
+        with_stack=False,
+        record_shapes=False,
+    ) as prof:
+        for _ in range(measure_steps):
+            dist.barrier()
+            torch.cuda.synchronize()
+
+            start = torch.cuda.Event(enable_timing=True)
+            end = torch.cuda.Event(enable_timing=True)
+
+            start.record()
+
+            optimizer.zero_grad(set_to_none=True)
+
+            if rank != world_size - 1:
+                pipe_schedule.step(x)
+            else:
+                losses = []
+                _ = pipe_schedule.step(target=y, losses=losses)
+
+            with record_function(f"optimizer_step_rank{rank}"):
+                optimizer.step()
+            
+            torch.cuda.synchronize()
+            end.record()
+            torch.cuda.synchronize()
+            dist.barrier()
+
+            if rank == world_size - 1:
+                elapsed_ms = start.elapsed_time(end)
+                elapsed_times.append(elapsed_ms / 1000.0)
+
+            prof.step()
+
+    _remove_if_exists(trace_path)
+    prof.export_chrome_trace(trace_path)
+    dist.barrier()  # ensure both traces are written before merging
+
+    if rank == world_size - 1:
+        print(f"[Rank {rank}] chrome trace saved to {trace_path}")
+
+        # Create user-only traces and merge (mirrors zerobubble.py workflow)
+        trace0 = os.path.join(trace_dir, "trace_0.json")
+        trace1 = os.path.join(trace_dir, "trace_1.json")
+        user0 = os.path.join(trace_dir, "user_only_trace_0.json")
+        user1 = os.path.join(trace_dir, "user_only_trace_1.json")
+        merged = os.path.join(trace_dir, "merged_trace.json")
+        merged_raw = os.path.join(trace_dir, "merged_trace_raw.json")
+
+        for p in (user0, user1, merged, merged_raw):
+            _remove_if_exists(p)
+
+        subprocess.run(["python", "filter_trace.py", trace0, user0], check=True)
+        subprocess.run(["python", "filter_trace.py", trace1, user1], check=True)
+        subprocess.run(["python", "merge_traces.py", user0, user1, merged], check=True)
+        subprocess.run(["python", "merge_traces.py", trace0, trace1, merged_raw], check=True)
+        print(f"[Rank {rank}] merged trace available at {merged}")
+        print(f"[Rank {rank}] raw merged trace available at {merged_raw}")
+
+    if rank == world_size - 1:
+        avg_elapsed = sum(elapsed_times) / len(elapsed_times)
+        tokens_per_sec = total_tokens / avg_elapsed
+        print(f"[Rank {rank}] Total elapsed: {sum(elapsed_times):.6f}s (CUDA events)")
+        print(f"[Rank {rank}] Avg elapsed: {avg_elapsed:.6f}s (CUDA events)")
+        print(f"[Rank {rank}] Throughput: {tokens_per_sec:.2f} tokens/s")
+        print(f"[Rank {rank}] final losses: {losses}")
+
+    dist.barrier()
+    cleanup()
+
+   
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--microbatch_size', type=int, default=16)
+    parser.add_argument('--num_microbatches', type=int, default=4)
+    parser.add_argument('--seq_len', type=int, default=256)
+    # parser.add_argument("--schedule", type=str, default="1f1b", choices=["1f1b", "zb"])
+    args = parser.parse_args()
+    
+    world_size = 2
+
+    mp.set_start_method("spawn", force=True)
+    manager = mp.Manager()
+
+    mp.spawn(
+        run_pipeline,
+        args=(world_size, args.microbatch_size, args.num_microbatches, args.seq_len, LLAMA_3B),
+        nprocs=world_size,
+        join=True,
+    )
diff --git a/test/print_schedules.py b/test/print_schedules.py
index 4e37aac..b488d9c 100644
--- a/test/print_schedules.py
+++ b/test/print_schedules.py
@@ -1,17 +1,8 @@
 from .schedule_helpers import print_schedule, build_1f1b_schedule, build_gpipe_schedule
+from .schedule_helpers import DUALPIPEV_SCHEDULE, DUALPIPEV_NO_ZB_SCHEDULE
+from src.piper_exec import _validate_schedule, DAGEdge
 
-schedule = build_1f1b_schedule(4, 2)
-print("1F1B Schedule")
-print_schedule(schedule)
-
-schedule = build_gpipe_schedule(4, 2)
-print("GPipe Schedule")
-print_schedule(schedule)
-
-schedule = build_1f1b_schedule(8, 4)
-print("1F1B Schedule")
-print_schedule(schedule)
-
-schedule = build_gpipe_schedule(8, 4)
-print("GPipe Schedule")
-print_schedule(schedule)
\ No newline at end of file
+schedule = DUALPIPEV_SCHEDULE
+dag_edges = [DAGEdge(from_stage=0, to_stage=1), DAGEdge(from_stage=1, to_stage=2), DAGEdge(from_stage=2, to_stage=3)]
+num_mbs = 6
+_validate_schedule(schedule.grid, dag_edges, num_mbs)
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index 5d8cdab..361250c 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -1,32 +1,22 @@
-from graphviz import Digraph
 
 from src.piper_exec import (
     Task,
+    BatchMeta,
     CompType,
     Schedule2D,
 )
-# DAG types used only by legacy _dag builders (kept for reference)
-from src.piper_exec import (
-    Schedule,
-    ScheduleTask,
-    BatchMeta,
-    ScheduleEdge,
-    TemporalEdge,
-    DAGEdge,
-    _topological_order,
-)
 
 
 def _task_label(task: Task) -> str:
     """Short label for a task: stage:mb:f/b/u, or first+second for dual batches."""
     c = (
-        "u"
-        if task.type == CompType.UPD
-        else "f"
-        if task.type == CompType.FWD
-        else "b"
-        if task.type == CompType.BWD
-        else "fb"
+        "u" if task.type == CompType.UPD
+        else "f" if task.type == CompType.FWD
+        else "b" if task.type == CompType.BWD
+        else "fb" if task.type == CompType.FWD_BWD
+        else "bi" if task.type == CompType.BWD_I
+        else "bw" if task.type == CompType.BWD_W
+        else "?"
     )
     if task.type == CompType.UPD:
         label = "   u    "
@@ -51,42 +41,6 @@ def print_schedule(schedule: Schedule2D, path: str = "schedule") -> None:
                 print(_task_label(task), end="\t")
         print()
 
-
-def print_schedule_dag(schedule: Schedule, path: str = "schedule") -> None:
-    """
-    Use graphviz to produce a visual of a Schedule DAG (legacy; not used in Piper workflow).
-    """
-    dot = Digraph(comment="Schedule DAG")
-    dot.attr(rankdir="TB")
-    dot.attr("node", shape="box", style="rounded,filled", fontname="sans")
-
-    topo_order = _topological_order(schedule)
-    task_to_order: dict[int, int] = {task_idx: order_idx for order_idx, task_idx in enumerate(topo_order)}
-
-    rank_to_tasks: dict[int, list[int]] = {}
-    task_labels: dict[int, str] = {}
-    for ti, task in enumerate(schedule.tasks):
-        rank_to_tasks.setdefault(task.pp_rank, []).append(ti)
-        mb = task.microbatch
-        c = "u" if mb.comp_type == CompType.UPD else "f" if mb.comp_type == CompType.FWD else "b"
-        order_num = task_to_order.get(ti, -1)
-        task_labels[ti] = f"[{order_num}]\\nrank{task.pp_rank}\\n{mb.stage_id}:{mb.mb_idx}:{c}"
-
-    for rank in sorted(rank_to_tasks.keys()):
-        with dot.subgraph() as sub:
-            sub.attr(rank="same")
-            for ti in rank_to_tasks[rank]:
-                sub.node(str(ti), label=task_labels[ti])
-
-    for e in schedule.edges:
-        dot.edge(str(e.from_task_idx), str(e.to_task_idx), color="red")
-
-    for e in schedule.temporal_edges or []:
-        dot.edge(str(e.from_task_idx), str(e.to_task_idx), style="dotted", color="blue")
-
-    dot.render(path, format="pdf", cleanup=True)
-
-
 def build_gpipe_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
     steps = n_mbs + n_stages - 1
     schedule = [[None] * (steps * 2 + 1) for _ in range(n_stages)]
@@ -158,210 +112,41 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
     return Schedule2D(grid=schedule)
 
 
-# --- DAG-based schedule builders (kept for reference) ---
-
-
-def build_gpipe_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
-    """
-    Build a GPipe schedule as a DAG: all FWD, then all BWD, then UPD.
-    One task per (stage_id, mb_idx, comp_type); edges follow pipeline data dependencies.
-    Temporal: forwards ordered by microbatch per stage (s:m:f → s:m+1:f); all forwards before
-    backwards (last fwd → first bwd on every stage); backwards in ascending microbatch per stage
-    (s:m:b → s:m+1:b); update after final backward (s:M-1:b → s:UPD).
-    """
-    tasks: list[ScheduleTask] = []
-    edges: list[ScheduleEdge] = []
-    temporal_edges: list[TemporalEdge] = []
-    producer: dict[tuple[int, int, CompType], int] = {}
-
-    for mb_idx in range(n_mbs):
-        for stage_id in range(n_stages):
-            mb = BatchMeta(stage_id=stage_id, mb_idx=mb_idx, comp_type=CompType.FWD)
-            ti = len(tasks)
-            tasks.append(ScheduleTask(pp_rank=stage_id, microbatch=mb))
-            if stage_id > 0:
-                edges.append(ScheduleEdge(producer[(stage_id - 1, mb_idx, CompType.FWD)], ti))
-            producer[(stage_id, mb_idx, CompType.FWD)] = ti
-
-    for mb_idx in range(n_mbs):
-        for stage_id in range(n_stages - 1, -1, -1):
-            mb = BatchMeta(stage_id=stage_id, mb_idx=mb_idx, comp_type=CompType.BWD)
-            ti = len(tasks)
-            tasks.append(ScheduleTask(pp_rank=stage_id, microbatch=mb))
-            # Same-stage fwd→bwd only on last stage (loss and backward there)
-            if stage_id == n_stages - 1:
-                edges.append(ScheduleEdge(producer[(stage_id, mb_idx, CompType.FWD)], ti))
-            if stage_id < n_stages - 1:
-                edges.append(ScheduleEdge(producer[(stage_id + 1, mb_idx, CompType.BWD)], ti))
-            producer[(stage_id, mb_idx, CompType.BWD)] = ti
-
-    for stage_id in range(n_stages):
-        for m in range(n_mbs - 1):
-            temporal_edges.append(
-                TemporalEdge(producer[(stage_id, m, CompType.FWD)], producer[(stage_id, m + 1, CompType.FWD)])
-            )
-    for stage_id in range(n_stages):
-        temporal_edges.append(
-            TemporalEdge(producer[(stage_id, n_mbs - 1, CompType.FWD)], producer[(stage_id, 0, CompType.BWD)])
-        )
-
-    for stage_id in range(n_stages):
-        for m in range(n_mbs - 1):
-            temporal_edges.append(
-                TemporalEdge(producer[(stage_id, m, CompType.BWD)], producer[(stage_id, m + 1, CompType.BWD)])
-            )
-
-    for stage_id in range(n_stages):
-        ti = len(tasks)
-        mb = BatchMeta(stage_id=stage_id, mb_idx=0, comp_type=CompType.UPD)
-        tasks.append(ScheduleTask(pp_rank=stage_id, microbatch=mb))
-        temporal_edges.append(TemporalEdge(producer[(stage_id, n_mbs - 1, CompType.BWD)], ti))
-
-    return Schedule(tasks=tasks, edges=edges, temporal_edges=temporal_edges)
-
-
-def _dualpipe_pp_rank(stage_id: int, mb_idx: int) -> int:
-    """Stage 0 appears on both ranks; mb 0 uses rank 0 for stage 0, mb 1 uses rank 1 for stage 0."""
-    return (stage_id + mb_idx) % 2
-
-
-def build_1f1b_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
-    """
-    Build a 1F1B (one forward one backward) schedule as a DAG.
-    FWD and BWD are interleaved per stage; one UPD per stage at the end.
-    Temporal dependencies per rank (R = number of ranks):
-    - First rank: first R forwards, then alternate backward/forward for the remainder
-    - Rank r: first (R - r) forwards, then alternate backward/forward
-    - Last rank: alternate forward/backward from the beginning (f0, b0, f1, b1, ...)
-    """
-    R = n_stages
-
-    # Build all tasks first (one task per stage_id, mb_idx, comp_type)
-    tasks: list[ScheduleTask] = []
-    edges: list[ScheduleEdge] = []
-    temporal_edges: list[TemporalEdge] = []
-    producer: dict[tuple[int, int, CompType], int] = {}
-    
-    # Create all forward tasks
-    for mb_idx in range(n_mbs):
-        for stage_id in range(n_stages):
-            mb = BatchMeta(stage_id=stage_id, mb_idx=mb_idx, comp_type=CompType.FWD)
-            ti = len(tasks)
-            tasks.append(ScheduleTask(pp_rank=stage_id, microbatch=mb))
-            if stage_id > 0:
-                edges.append(ScheduleEdge(producer[(stage_id - 1, mb_idx, CompType.FWD)], ti))
-            producer[(stage_id, mb_idx, CompType.FWD)] = ti
-    
-    # Create all backward tasks (in reverse stage order to handle dependencies)
-    for mb_idx in range(n_mbs):
-        for stage_id in range(n_stages - 1, -1, -1):
-            mb = BatchMeta(stage_id=stage_id, mb_idx=mb_idx, comp_type=CompType.BWD)
-            ti = len(tasks)
-            tasks.append(ScheduleTask(pp_rank=stage_id, microbatch=mb))
-            # Same-stage fwd→bwd only on last stage (loss and backward there)
-            if stage_id == n_stages - 1:
-                edges.append(ScheduleEdge(producer[(stage_id, mb_idx, CompType.FWD)], ti))
-            if stage_id < n_stages - 1:
-                edges.append(ScheduleEdge(producer[(stage_id + 1, mb_idx, CompType.BWD)], ti))
-            producer[(stage_id, mb_idx, CompType.BWD)] = ti
-    
-    # Create update tasks
-    for stage_id in range(n_stages):
-        mb = BatchMeta(stage_id=stage_id, mb_idx=0, comp_type=CompType.UPD)
-        ti = len(tasks)
-        tasks.append(ScheduleTask(pp_rank=stage_id, microbatch=mb))
-        temporal_edges.append(TemporalEdge(producer[(stage_id, n_mbs - 1, CompType.BWD)], ti))
-    
-    # Build temporal edges per rank according to the specified pattern
-    for rank in range(n_stages):
-        stage_id = rank
-        
-        if rank == n_stages - 1:
-            # Last rank: alternate forward/backward (f0, b0, f1, b1, ...)
-            for mb_idx in range(n_mbs):
-                if mb_idx == 0:
-                    # First forward doesn't have a predecessor
-                    pass
-                else:
-                    # Forward follows previous backward
-                    temporal_edges.append(
-                        TemporalEdge(
-                            producer[(stage_id, mb_idx - 1, CompType.BWD)],
-                            producer[(stage_id, mb_idx, CompType.FWD)]
-                        )
-                    )
-                # Backward follows its corresponding forward
-                temporal_edges.append(
-                    TemporalEdge(
-                        producer[(stage_id, mb_idx, CompType.FWD)],
-                        producer[(stage_id, mb_idx, CompType.BWD)]
-                    )
-                )
-        else:
-            # Rank r: first (R - r) forwards, then alternate backward/forward
-            num_initial_fwds = R - rank
-            # Initial forwards in sequence: f0 → f1 → ... → f(num_initial_fwds - 1)
-            for mb_idx in range(num_initial_fwds):
-                if mb_idx > 0:
-                    temporal_edges.append(
-                        TemporalEdge(
-                            producer[(stage_id, mb_idx - 1, CompType.FWD)],
-                            producer[(stage_id, mb_idx, CompType.FWD)]
-                        )
-                    )
-            # Then alternate: b0, f(num_initial_fwds), b1, f(num_initial_fwds+1), ...
-            bwd_idx = 0
-            fwd_idx = num_initial_fwds
-            # First in alternation is b0, following last initial forward
-            prev_mb_idx = num_initial_fwds - 1
-            prev_type = CompType.FWD
-
-            while bwd_idx < n_mbs or fwd_idx < n_mbs:
-                if prev_type == CompType.FWD:
-                    # Next is backward(mb bwd_idx)
-                    if bwd_idx < n_mbs:
-                        temporal_edges.append(
-                            TemporalEdge(
-                                producer[(stage_id, prev_mb_idx, CompType.FWD)],
-                                producer[(stage_id, bwd_idx, CompType.BWD)]
-                            )
-                        )
-                        prev_mb_idx = bwd_idx
-                        prev_type = CompType.BWD
-                        bwd_idx += 1
-                    elif fwd_idx < n_mbs:
-                        temporal_edges.append(
-                            TemporalEdge(
-                                producer[(stage_id, prev_mb_idx, CompType.FWD)],
-                                producer[(stage_id, fwd_idx, CompType.FWD)]
-                            )
-                        )
-                        prev_mb_idx = fwd_idx
-                        fwd_idx += 1
-                else:  # prev_type == CompType.BWD
-                    # Next is forward(mb fwd_idx)
-                    if fwd_idx < n_mbs:
-                        temporal_edges.append(
-                            TemporalEdge(
-                                producer[(stage_id, prev_mb_idx, CompType.BWD)],
-                                producer[(stage_id, fwd_idx, CompType.FWD)]
-                            )
-                        )
-                        prev_mb_idx = fwd_idx
-                        prev_type = CompType.FWD
-                        fwd_idx += 1
-                    elif bwd_idx < n_mbs:
-                        temporal_edges.append(
-                            TemporalEdge(
-                                producer[(stage_id, prev_mb_idx, CompType.BWD)],
-                                producer[(stage_id, bwd_idx, CompType.BWD)]
-                            )
-                        )
-                        prev_mb_idx = bwd_idx
-                        bwd_idx += 1
-    
-    return Schedule(tasks=tasks, edges=edges, temporal_edges=temporal_edges)
-
+ZEROBUBBLE_MB4_SCHEDULE = Schedule2D(
+    grid=[
+        [
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),          # t0:  F0
+            None,                                                                                   # t1:   -
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),          # t2:  F1
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD_I),        # t3:  I0
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD_W),        # t4:  W0
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),          # t5:  F2
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD_I),        # t6:  I1
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD_W),        # t7:  W1
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),          # t8:  F3
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD_I),        # t9:  I2
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD_W),        # t10: W2
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD_I),        # t11: I3
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD_W),        # t12: W3
+            Task(pp_rank=0, batches=[], type=CompType.UPD),                                         # t13: U0
+        ],
+        [
+            None,                                                                                   # t0:  -
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),          # t1:  F0
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD_I),        # t2:  I0
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),          # t3:  F1
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD_I),        # t4:  I1
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD_W),        # t5:  W0
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),          # t6:  F2
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD_I),        # t7:  I2
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD_W),        # t8:  W1
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),          # t9:  F3
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD_I),        # t10: I3
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD_W),        # t11: W2
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD_W),        # t12: W3
+            Task(pp_rank=1, batches=[], type=CompType.UPD),                                         # t13: U0
+        ],
+    ])
 
 NO_PP_SCHEDULE = Schedule2D(
     grid=[
@@ -373,7 +158,65 @@ def build_1f1b_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
     ],
 )
 
-DUALPIPEV_SCHEDULE = Schedule2D(
+DUALPIPEV_MB6_SCHEDULE = Schedule2D(
+    grid=[
+        [
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_I),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_W),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_I),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_W),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2), BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD_BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4), BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD_BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3), BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD_BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5), BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD_BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4), BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD_BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5), BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD_BWD),
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_I),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_I),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_W),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_W),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[], type=CompType.UPD),
+            None,
+        ],
+        [
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2), BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD_BWD),
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2), BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD_BWD),
+            None,
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3), BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3), BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4), BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4), BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5), BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5), BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD_BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_I),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_W),
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_I),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_I),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_W),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_W),
+            Task(pp_rank=1, batches=[], type=CompType.UPD),
+        ]
+    ])
+
+DUALPIPEV_NOZB_MB6_SCHEDULE = Schedule2D(
     grid=[
         [
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
@@ -483,8 +326,7 @@ def build_1f1b_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
         ],
     ])
 
-# 2D grid literals for interleaved 1F1B (used only to build DAGs below)
-INTERLEAVED_1F1B_PP2_SCHEDULE = Schedule2D(
+INTERLEAVED_1F1B_PP2_MB4_SCHEDULE = Schedule2D(
     grid=[
         [
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
@@ -530,7 +372,53 @@ def build_1f1b_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
         ],
     ])
 
-INTERLEAVED_1F1B_PP4_SCHEDULE = Schedule2D(
+INTERLEAVED_GPIPE_PP2_MB4_SCHEDULE = Schedule2D(
+    grid=[
+        [
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
+            None,
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[], type=CompType.UPD),
+        ],
+        [
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[], type=CompType.UPD),
+            None,
+        ],
+    ])
+
+INTERLEAVED_1F1B_PP4_MB8_SCHEDULE = Schedule2D(
     grid=[
         [
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
@@ -541,21 +429,37 @@ def build_1f1b_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
             Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
             None,
             None,
             None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.BWD),
-            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.BWD),
-            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.BWD),
-            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
+            None,
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
+            None,
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.UPD),
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[], type=CompType.UPD),
         ],
         [
             None,
@@ -568,18 +472,34 @@ def build_1f1b_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
             Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.FWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.FWD),
             None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.BWD),
             None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.BWD),
-            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.FWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.BWD),
-            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.FWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.FWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.FWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.FWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.FWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.UPD),
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.BWD),
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[], type=CompType.UPD),
             None,
         ],
         [
@@ -591,19 +511,35 @@ def build_1f1b_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
             Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.FWD),
+            None,
             Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.BWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.BWD),
-            None,
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.BWD),
-            None,
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.FWD),
             Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.UPD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.FWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.FWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.BWD),
+            None,
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.BWD),
+            Task(pp_rank=2, batches=[], type=CompType.UPD),
             None,
             None,
         ],
@@ -623,11 +559,27 @@ def build_1f1b_schedule_dag(n_mbs: int, n_stages: int) -> Schedule:
             Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.BWD),
             Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.FWD),
             Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
             Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
             Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.FWD),
             Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.FWD),
             Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.UPD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.FWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.FWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.FWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.FWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.BWD),
+            Task(pp_rank=3, batches=[], type=CompType.UPD),
             None,
             None,
             None,
diff --git a/test/test_backward_split.py b/test/test_backward_split.py
new file mode 100644
index 0000000..edc0890
--- /dev/null
+++ b/test/test_backward_split.py
@@ -0,0 +1,144 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import ray
+
+from piper.piper import piper, distributed_stage
+from piper.exec import piper_exec, Task, TaskType
+from piper.utils import piper_metadata
+from piper.compile import piper_setup 
+from .schedule_helpers import build_zb1p_schedule, print_schedule
+
+
+class MyModelVanilla(nn.Module):
+    """
+    Simple 2-layer MLP: 1024 -> 512 -> 512
+    Used as the baseline (non-pipelined) model.
+    """
+    def __init__(self, in_dim=1024, hidden_dim=512, out_dim=512):
+        super().__init__()
+        self.fc1 = nn.Linear(in_dim, hidden_dim)
+        self.fc2 = nn.Linear(hidden_dim, out_dim)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        return x
+
+
+class MyModelPipelined(nn.Module):
+    def __init__(self, in_dim=1024, hidden_dim=512, out_dim=512):
+        super().__init__()
+        self.fc1 = nn.Linear(in_dim, hidden_dim)
+        self.fc2 = nn.Linear(hidden_dim, out_dim)
+
+    def forward(self, x, dynamo_mb: int = 0):
+        distributed_stage(0, actor_id=0, mb=dynamo_mb, optim=torch.optim.Adam)
+        x = F.relu(self.fc1(x))
+
+        distributed_stage(1, actor_id=1, mb=dynamo_mb, optim=torch.optim.Adam)
+        x = F.relu(self.fc2(x))
+
+        return x
+
+
+def main():
+    torch.manual_seed(0)
+    ray.init(ignore_reinit_error=True)
+
+    device = "cuda"
+
+    init_model = MyModelVanilla().to(device)
+    init_state = init_model.state_dict()
+
+    model_base = MyModelVanilla().to(device)
+    model_base.load_state_dict(init_state)
+    opt_base = torch.optim.Adam(model_base.parameters(), lr=1e-3)
+
+    model_pipe = MyModelPipelined().to(device)
+    model_pipe.load_state_dict(init_state)
+
+    batch_size = 8
+    in_dim = 1024
+    out_dim = 512
+    num_mbs = 4
+
+    x = torch.randn(batch_size, in_dim, device=device)
+    y = torch.randn(batch_size, out_dim, device=device)
+    loss_fn = nn.MSELoss(reduction="mean")
+
+    compiled = piper_setup(
+        model_pipe,
+        example_inputs=[x],
+        backend=piper,
+    )
+
+    actors = piper_metadata["actors"]
+    num_actors = len(actors)
+    assert num_actors == 2, f"Expected 2 actors, got {num_actors}"
+
+    ray.get(actors[0].send_input.remote(x))
+    ray.get(actors[num_actors - 1].send_truth.remote(y))
+
+    schedule = build_zb1p_schedule(n_mbs=num_mbs, n_stages=2)
+    print("Schedule:")
+    print_schedule(schedule)
+
+    def iter_schedule():
+        out = piper_exec(compiled, schedule, [x], y, loss_fn, num_mbs)
+        return ray.get(out)
+
+    iter_schedule()
+
+    piper_grads = []
+    for stage_id in range(num_actors):
+        stage_grads = ray.get(actors[stage_id].get_param_grads.remote(stage_id))
+        piper_grads.extend(stage_grads)
+
+    print(piper_grads)
+
+    piper_params_after = []
+    for stage_id in range(num_actors):
+        stage_params = ray.get(actors[stage_id].get_params.remote(stage_id))
+        piper_params_after.extend(stage_params)
+
+    opt_base.zero_grad(set_to_none=True)
+
+    for _ in range(num_mbs):
+        out_base = model_base(x)
+        loss_base = loss_fn(out_base, y)
+        loss_base.backward()
+
+    print(opt_base)
+    opt_base.step()
+
+    base_params_after = [
+        p.detach().clone().cpu() for p in model_base.parameters()
+    ]
+    base_grads = [p.grad.detach().clone().cpu() for p in model_base.parameters()]
+
+    assert len(piper_params_after) == len(base_params_after), \
+        "Mismatch in # of parameters between pipeline and baseline models"
+
+    # for i, (g_pipe, g_base) in enumerate(zip(piper_grads, base_grads)):
+    #     if g_pipe is None and g_base is None:
+    #         print(f"Grad {i}: both None")
+    #         continue
+    #     if g_pipe is None or g_base is None:
+    #         print(f"Grad {i}: one is None, one not")
+    #         continue
+    #     diff = (g_pipe - g_base).abs().max().item()
+    #     print(f"Grad {i}: max |Δ| = {diff:.6e}")
+
+
+    max_abs_diff = 0.0
+    for i, (p_pipe, p_base) in enumerate(zip(piper_params_after, base_params_after)):
+        diff = (p_pipe - p_base).abs().max().item()
+        max_abs_diff = max(max_abs_diff, diff)
+        print(f"Param {i}: max |Δ| = {diff:.6e}")
+
+    print(f"\n[RESULT] Max param diff after one step: {max_abs_diff:.6e}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/test/test_llama.py b/test/test_llama.py
index 6c6fc1c..c295114 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -15,11 +15,14 @@
     build_1f1b_schedule, 
     build_gpipe_schedule, 
     print_schedule,
-    INTERLEAVED_1F1B_PP2_SCHEDULE,
+    INTERLEAVED_1F1B_PP2_MB4_SCHEDULE,
     INTERLEAVED_1F1B_PP2_MB6_SCHEDULE,
-    INTERLEAVED_1F1B_PP4_SCHEDULE,
+    INTERLEAVED_1F1B_PP4_MB8_SCHEDULE,
+    INTERLEAVED_GPIPE_PP2_MB4_SCHEDULE,
     NO_PP_SCHEDULE,
-    DUALPIPEV_SCHEDULE,
+    DUALPIPEV_MB6_SCHEDULE,
+    DUALPIPEV_NOZB_MB6_SCHEDULE,
+    ZEROBUBBLE_MB4_SCHEDULE,
 )
 
 
@@ -46,20 +49,37 @@ def main(args):
     y = torch.randn((args.batch_size, args.seq_len, llama_config.vocab_size)).to('cuda')
 
     schedule = None
+
     match args.schedule:
         case "no-pp":
             schedule = NO_PP_SCHEDULE
         case "interleaved-1f1b":
-            if args.mbs == 6:
-                schedule = INTERLEAVED_1F1B_PP2_MB6_SCHEDULE
-            else:
-                schedule = INTERLEAVED_1F1B_PP2_SCHEDULE if args.pp == 2 else INTERLEAVED_1F1B_PP4_SCHEDULE
+            if args.pp == 2:
+                if args.mbs == 6:
+                    schedule = INTERLEAVED_1F1B_PP2_MB6_SCHEDULE
+                elif args.mbs == 4:
+                    schedule = INTERLEAVED_1F1B_PP2_MB4_SCHEDULE
+                else:
+                    raise ValueError(f"Unsupported number of microbatches: for interleaved-1f1b with PP={args.pp}: {args.mbs}")
+            elif args.pp == 4:
+                assert args.mbs == 8
+                schedule = INTERLEAVED_1F1B_PP4_MB8_SCHEDULE
         case "1f1b":
             schedule = build_1f1b_schedule(args.mbs, args.pp)
         case "gpipe":
             schedule = build_gpipe_schedule(args.mbs, args.pp)
+        case "interleaved-gpipe":
+            assert args.pp == 2 and args.mbs == 4
+            schedule = INTERLEAVED_GPIPE_PP2_MB4_SCHEDULE
         case "dualpipev":
-            schedule = DUALPIPEV_SCHEDULE
+            assert args.pp == 2 and args.mbs == 6
+            schedule = DUALPIPEV_MB6_SCHEDULE
+        case "dualpipev-nozb":
+            assert args.pp == 2 and args.mbs == 6
+            schedule = DUALPIPEV_NOZB_MB6_SCHEDULE
+        case "zerobubble":
+            assert args.pp == 2 and args.mbs == 4
+            schedule = ZEROBUBBLE_MB4_SCHEDULE
     print("Schedule:")
     print_schedule(schedule)
 
@@ -104,11 +124,17 @@ def main(args):
         from src.piper_utils import piper_metadata
         actors = piper_metadata.actors
         ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
+        ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
+        ray.get([actor.start_mem_tracing.remote() for actor in actors.values()])
 
         print(f"Running {args.warmup} tracing iterations...")
         for _ in range(args.warmup):
             piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
 
+        mem_data_ret = ray.get([actor.get_peak_memory.remote() for actor in actors.values()])
+        for rank, peak_mem in mem_data_ret:
+            print(f"rank {rank} peak memory= {peak_mem:.3f} GB")
+
         trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
         for rank, trace_data in trace_data_ret:
             for key in trace_data:
@@ -129,8 +155,8 @@ def parse_args():
     parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
     parser.add_argument('--model', choices=['debug', '1b', '3b', '8b'], default='debug',
                         help='Model configuration: debug, 1b, 3b, or 8b (default: debug)')
-    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'dualpipev', 'no-pp'], default='1f1b',
-                        help='Schedule type: gpipe, 1f1b, interleaved-1f1b, dualpipev, or no-pp (default: 1f1b)')
+    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'interleaved-gpipe', 'dualpipev-nozb', 'dualpipev', 'zerobubble', 'no-pp'], default='1f1b',
+                        help='Schedule type: gpipe, 1f1b, interleaved-1f1b, dualpipev, zerobubble, or no-pp (default: 1f1b)')
     parser.add_argument('--dp', type=int, default=1,
                         help='Number of data parallel degrees (default: 1)')
     parser.add_argument('--pp', type=int, default=2,
diff --git a/test/test_mixtral.py b/test/test_mixtral.py
index 2ceb69e..a82ad00 100644
--- a/test/test_mixtral.py
+++ b/test/test_mixtral.py
@@ -1,3 +1,4 @@
+import logging
 import ray
 import torch
 import torch.nn as nn
@@ -7,6 +8,8 @@
 import numpy as np
 import itertools
 
+logger = logging.getLogger(__name__)
+
 from src.piper_coordinator import PiperProgramCoordinator
 from src.piper_compile import piper_setup
 from src.piper_exec import piper_exec
@@ -18,9 +21,10 @@
     build_gpipe_schedule,
     print_schedule,
     NO_PP_SCHEDULE,
-    INTERLEAVED_1F1B_PP2_SCHEDULE,
-    INTERLEAVED_1F1B_PP4_SCHEDULE,
-    DUALPIPEV_SCHEDULE,
+    INTERLEAVED_1F1B_PP2_MB4_SCHEDULE,
+    INTERLEAVED_1F1B_PP4_MB8_SCHEDULE,
+    DUALPIPEV_NOZB_MB6_SCHEDULE,
+    DUALPIPEV_MB6_SCHEDULE,
 )
 
 def main(args):
@@ -44,18 +48,23 @@ def main(args):
         case "no-pp":
             schedule = NO_PP_SCHEDULE
         case "interleaved-1f1b":
-            schedule = INTERLEAVED_1F1B_PP2_SCHEDULE if args.pp == 2 else INTERLEAVED_1F1B_PP4_SCHEDULE
+            schedule = INTERLEAVED_1F1B_PP2_MB4_SCHEDULE if args.pp == 2 else INTERLEAVED_1F1B_PP4_MB8_SCHEDULE
         case "1f1b":
             schedule = build_1f1b_schedule(args.mbs, args.pp)
         case "gpipe":
             schedule = build_gpipe_schedule(args.mbs, args.pp)
         case "dualpipev":
-            schedule = DUALPIPEV_SCHEDULE
+            schedule = DUALPIPEV_MB6_SCHEDULE
+        case "dualpipev-nozb":
+            schedule = DUALPIPEV_NOZB_MB6_SCHEDULE
     print("Schedule:")
     print_schedule(schedule)
-
+    
     model = Transformer(config)
     model.to('cuda')
+    num_params = sum(p.numel() for p in model.parameters())
+    param_size_mb = num_params * 4 / (1024**3)  # float32
+    print(f"Model size: {num_params/(1e6):.0f} M parameters ({param_size_mb:.2f} GB)")
 
     x = torch.randint(0, config.vocab_size, (batch_size, config.block_size)).to('cuda')
     input_pos = torch.arange(config.block_size).to('cuda')
@@ -69,34 +78,35 @@ def main(args):
         [x, input_pos],
         y,
         schedule,
-        args.naive_gradient_sync,
+        args.naive_grad_sync,
+        mode=args.mode,
     )
 
     print(f"Running {args.warmup} warmup iterations...")
     for _ in range(args.warmup):
-        piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
+        piper_exec(schedule, loss_fn, args.dp, args.naive_grad_sync)
 
     print(f"Running {args.iters} timed iterations...")
     iter_times = []
     for _ in range(args.iters):
         start = time.perf_counter()
-        piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
+        piper_exec(schedule, loss_fn, args.dp, args.naive_grad_sync)
         end = time.perf_counter()
         iter_times.append(end - start)
     
     dp_rank = int(os.environ['PIPER_DP_RANK'])
     print(
-        f"rank {dp_rank} iter time= {np.mean(iter_times):.2f} ± {np.std(iter_times):.2f} s ({len(iter_times)} samples)\n"
-        f"rank {dp_rank} throughput= {(args.batch_size * args.mbs * config.block_size)/np.mean(iter_times):.2f} tokens/s ({len(iter_times)} samples)"
+        f"rank {dp_rank} iter time= {np.mean(iter_times):.5f} ± {np.std(iter_times):.5f} s ({len(iter_times)} samples)\n"
+        f"rank {dp_rank} throughput= {(args.batch_size * args.mbs * config.block_size)/np.mean(iter_times):.3f} tokens/s ({len(iter_times)} samples)"
     )
 
     if args.tracing:
         actors = piper_metadata.actors
         ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
 
-        print(f"Running {args.warmup} tracing iterations...")
-        for _ in range(args.warmup):
-            piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
+        print(f"Running {args.trace_iters} tracing iterations...")
+        for _ in range(args.trace_iters):
+            piper_exec(schedule, loss_fn, args.dp, args.naive_grad_sync)
 
         trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
         for rank, trace_data in trace_data_ret:
@@ -104,7 +114,7 @@ def main(args):
                 all_times = trace_data[key]
                 print(f"rank {rank} {key} time= {np.mean(all_times):.3f} ± {np.std(all_times):.3f} ms ({len(all_times)} samples)")
 
-    timeline_filename = f"out/mixtral-pp{args.pp}-dp{args.dp}"
+    timeline_filename = f"out/mixtral-pp{args.pp}-dp{args.dp}-{args.schedule}-{args.mode}"
     ray.timeline(timeline_filename)
     print(f"Ray timeline saved to: {timeline_filename}")
 
@@ -112,8 +122,8 @@ def parse_args():
     parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
     parser.add_argument('--model', choices=['tiny', 'small', 'medium', 'large'], default='tiny',
                         help='Model configuration: tiny, small, medium, or large (default: tiny)')
-    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'dualpipev', 'no-pp'], default='1f1b',
-                        help='Schedule type: gpipe, 1f1b, interleaved-1f1b, dualpipev, or no-pp (default: 1f1b)')
+    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'dualpipev', 'dualpipev-nozb', 'no-pp'], default='1f1b',
+                        help='Schedule type: gpipe, 1f1b, interleaved-1f1b, dualpipev, dualpipev-nozb, or no-pp (default: 1f1b)')
     parser.add_argument('--dp', type=int, default=2,
                         help='Number of data parallel degrees (default: 2)')
     parser.add_argument('--pp', type=int, default=2,
@@ -122,14 +132,18 @@ def parse_args():
                         help='Batch size (default: 16)')
     parser.add_argument('--mbs', type=int, default=4,
                         help='Number of microbatches (default: 4)')
-    parser.add_argument('--warmup', type=int, default=5,
-                        help='Number of warmup iterations (default: 5)')
-    parser.add_argument('--iters', type=int, default=10,
-                        help='Number of timing iterations (default: 10)')
+    parser.add_argument('--warmup', type=int, default=3,
+                        help='Number of warmup iterations (default: 3)')
+    parser.add_argument('--iters', type=int, default=5,
+                        help='Number of timing iterations (default: 5)')
+    parser.add_argument('--trace-iters', type=int, default=3,
+                        help='Number of trace iterations (default: 3)')
     parser.add_argument('--tracing', action='store_true', default=False,
                         help='Enable tracing')
-    parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
+    parser.add_argument('--naive-grad-sync', action='store_true', default=False,
                         help='Enable naive gradient sync')
+    parser.add_argument('--mode', choices=['sequential', 'naive', 'overlapped'], default='overlapped',
+                        help='Mode: sequential, naive, or overlapped (default: overlapped)')
     return parser.parse_args()
 
 if __name__ == "__main__":

From 08f1b63702d70d7832dfaf01146aa4f35175a04e Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Fri, 27 Feb 2026 07:39:04 +0000
Subject: [PATCH 058/100] [Buggy] Initial deferred init implementation

---
 src/piper.py                 |  35 +++++++---
 src/piper_actor.py           | 127 +++++++++++++++++++++++++++++------
 src/piper_compile.py         |  55 ++++++++++++---
 src/piper_graph_transform.py |  24 +++++--
 test/models/llama.py         | 103 +++++++++++++++++-----------
 test/test_llama.py           |  19 +++---
 test/test_tracing.py         |  50 ++++++++++++++
 7 files changed, 319 insertions(+), 94 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index 25f562a..9fc86c9 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -25,8 +25,18 @@ def piper(gm, example_inputs, **kwargs):
     # end.record()
     # torch.cuda.synchronize()
     # print(f"Entire model time measured on controller: {start.elapsed_time(end) / 10:.2f} ms")
+    def _dump_get_attrs(tag, gm_):
+        attrs = [n.target for n in gm_.graph.nodes if n.op == "get_attr"]
+        print(f"[{tag}] get_attr targets:", attrs)
+
+    # print("gm code:")
+    # gm.print_readable()
+    # print("nodes:", [(n.op, n.target) for n in gm.graph.nodes])
 
     original_gm = gm
+
+    # _dump_get_attrs("original gm", original_gm)
+
     top_level_gm, submodules = _split_gm_by_stages(gm)
     num_stages = len(piper_metadata.stage_to_device.keys())
     dp_rank = int(os.environ['PIPER_DP_RANK'])
@@ -42,19 +52,22 @@ def piper(gm, example_inputs, **kwargs):
         if dp_degree > 1:
             stage_gm = _insert_a2a_ops(stage_gm)
 
-        stage_gm(*graphargs)
-        start = torch.cuda.Event(enable_timing=True)
-        start.record()
-        for i in range(5):
-            stage_gm(*graphargs)
-        end = torch.cuda.Event(enable_timing=True)
-        end.record()
-        torch.cuda.synchronize()
-        print(f"Stage {stage_id} time measured on controller: {start.elapsed_time(end) / 5:.2f} ms")
+        # stage_gm(*graphargs)
+        # start = torch.cuda.Event(enable_timing=True)
+        # start.record()
+        # for i in range(5):
+        #     stage_gm(*graphargs)
+        # end = torch.cuda.Event(enable_timing=True)
+        # end.record()
+        # torch.cuda.synchronize()
+        # print(f"Stage {stage_id} time measured on controller: {start.elapsed_time(end) / 5:.2f} ms")
 
         actor_id = piper_metadata.stage_to_device[stage_id]
         actor = _get_actor(actor_id)
         actor_stages.append((actor, stage_id))
+        
+
+        # _dump_get_attrs(f"stage{stage_id}-pre-serialize", stage_gm)
 
         stage_gm_data = _serialize_graphmodule(stage_gm)
         refs.append(actor._load_stage.remote(
@@ -79,11 +92,11 @@ def piper(gm, example_inputs, **kwargs):
         if stage_id < len(piper_metadata.stage_to_device.keys()) - 1:
             piper_metadata.dag.add((stage_id, stage_id+1))
     
-    example_outputs = original_gm(*example_inputs)
+    # example_outputs = original_gm(*example_inputs)
 
     def callback(*args):
         logger.warning("Should not directly call compiled module, running non-distributed execution")
-        return example_outputs
+        # return example_outputs
 
     del original_gm
     del example_inputs
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 0a2c945..bf8fe8f 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -123,6 +123,10 @@ def __init__(
         self.overlapped_comp_stream = torch.cuda.Stream()
         self.overlapped_p2p_stream = torch.cuda.Stream()
 
+        # fields for deferred initialization
+        self.forward_args_meta = dict()
+        self.stage_materialized = defaultdict(bool)
+
         # map stage id -> compiled fx.Graph function
         self.forward_fns = dict()
         # map stage id -> original GraphModule (for hook registration)
@@ -326,51 +330,123 @@ def _wait_for_comm_ops(self):
                         self.comm_op_handles[stage_id][tid].wait()
                         done = True
 
-    def _load_stage(
-        self, stage_id: int, gm_data, forward_args,input_idxs
-    ):
+    # def _load_stage(
+    #     self, stage_id: int, gm_data, forward_args,input_idxs
+    # ):
+    #     self.logger.debug(f"Loading stage {stage_id} graph on actor {self.global_rank}")
+
+    #     # compile the graph with the given graphargs
+    #     gm = _deserialize_graphmodule(gm_data)
+
+    #     # Store GraphModule reference
+    #     self.graph_modules[stage_id] = gm
+    #     self.forward_fns[stage_id] = gm.forward
+
+    #     # place parameters on the device
+    #     def move_to_device(idx, arg):
+    #         if arg.requires_grad:
+    #             return arg.to(self.device).detach().requires_grad_(True)
+    #         else:
+    #             return arg.to(self.device)
+    #     forward_args = list(map(move_to_device, range(len(forward_args)), forward_args))
+
+    #     # save parameters
+    #     self.forward_args[stage_id] = forward_args
+    #     self.stage_id = stage_id
+    #     self.input_idxs[stage_id] = input_idxs
+
+    #     for i in self.input_idxs[stage_id]:
+    #         self.forward_input_meta[stage_id][i] = (
+    #             forward_args[i].shape,
+    #             forward_args[i].dtype,
+    #             forward_args[i].requires_grad,
+    #         )
+    #         self.forward_args[stage_id][i] = None
+
+    #     # prepare tensors with DP comm ops
+    #     if not self.naive_gradient_sync and self.dp_degree > 1:
+    #         self._prepare_dp_comm_ops(stage_id)
+
+    #     # add the parameters to the optimizer for this stage
+    #     params = [param for param in forward_args if param is not None and param.requires_grad]
+    #     if stage_id not in self.optims:
+    #         self.optims[stage_id] = self.optim_class(params)
+    #     else:
+    #         self.optims[stage_id].add_param_group({"params": params})
+
+    #     del gm_data
+    def _load_stage(self, stage_id: int, gm_data, forward_args, input_idxs):
         self.logger.debug(f"Loading stage {stage_id} graph on actor {self.global_rank}")
 
-        # compile the graph with the given graphargs
         gm = _deserialize_graphmodule(gm_data)
 
-        # Store GraphModule reference
         self.graph_modules[stage_id] = gm
         self.forward_fns[stage_id] = gm.forward
 
-        # place parameters on the device
-        def move_to_device(idx, arg):
-            if arg.requires_grad:
-                return arg.to(self.device).detach().requires_grad_(True)
-            else:
-                return arg.to(self.device)
-        forward_args = list(map(move_to_device, range(len(forward_args)), forward_args))
-
-        # save parameters
-        self.forward_args[stage_id] = forward_args
-        self.stage_id = stage_id
+        forward_args = list(forward_args)
         self.input_idxs[stage_id] = input_idxs
 
+        # Save input meta and punch holes
         for i in self.input_idxs[stage_id]:
             self.forward_input_meta[stage_id][i] = (
                 forward_args[i].shape,
                 forward_args[i].dtype,
                 forward_args[i].requires_grad,
             )
-            self.forward_args[stage_id][i] = None
+            forward_args[i] = None
+
+        self.forward_args_meta[stage_id] = forward_args
+        self.forward_args[stage_id] = list(forward_args)
+        self.stage_materialized[stage_id] = False
+
+        del gm_data
+
+    def materialize_stage(self, stage_id: int, init="random", seed=0):
+        if self.stage_materialized[stage_id]:
+            return 1
+
+        meta_args = self.forward_args_meta[stage_id]
+        realized = [None] * len(meta_args)
+
+        g = torch.Generator(device=self.device)
+        g.manual_seed(seed + 1000 * self.global_rank + stage_id)
+
+        for i, arg in enumerate(meta_args):
+            if arg is None:
+                continue
+
+            t = torch.empty(arg.shape, dtype=arg.dtype, device=self.device)
+
+            if arg.requires_grad:
+                t.requires_grad_(True)
+                if init == "random":
+                    torch.nn.init.normal_(t, mean=0.0, std=0.02, generator=g)
+            else:
+                t.zero_()
+
+            realized[i] = t
+
+        self.forward_args[stage_id] = realized
+        self.stage_materialized[stage_id] = True
 
-        # prepare tensors with DP comm ops
+        # Now safe to setup hooks + optimizer
         if not self.naive_gradient_sync and self.dp_degree > 1:
             self._prepare_dp_comm_ops(stage_id)
 
-        # add the parameters to the optimizer for this stage
-        params = [param for param in forward_args if param is not None and param.requires_grad]
+        params = [t for t in realized if t is not None and t.requires_grad]
+
         if stage_id not in self.optims:
             self.optims[stage_id] = self.optim_class(params)
         else:
             self.optims[stage_id].add_param_group({"params": params})
 
-        del gm_data
+        return 1
+
+
+    def materialize_all_stages(self, init="random", seed=0):
+        for stage_id in list(self.forward_args_meta.keys()):
+            self.materialize_stage(stage_id, init=init, seed=seed)
+        return 1
 
     def _exec_p2p_op(
         self, src_stage: int, dst_stage: int, mb_idx: int, is_sender: bool, dep, p2p_stream=None
@@ -666,6 +742,10 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
             f"Calling forward {stage_id} mb {mb_idx} on actor {self.global_rank}"
         )
 
+        assert (
+            self.stage_materialized[stage_id]
+        ), f"Stage {stage_id} not materialized on actor {self.global_rank}"
+
         if stage_id == 0:
             # For the first stage, load input tensors from self.inputs
             for i, inp in zip(self.input_idxs[stage_id], self.inputs):
@@ -744,6 +824,11 @@ def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         self.logger.debug(
             f"Calling backward {stage_id} mb {mb_idx} on actor {self.global_rank}"
         )
+
+        assert (
+            self.stage_materialized[stage_id]
+        ), f"Stage {stage_id} not materialized on actor {self.global_rank}"
+
         out_activation = self.out_activation[stage_id][mb_idx]
 
         if stage_id < self.num_stages - 1:
diff --git a/src/piper_compile.py b/src/piper_compile.py
index b2bf643..ba24d2b 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -103,11 +103,13 @@ def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, sta
 
 
 def piper_setup(
-    model,
-    optim_fn,
-    example_inputs,
-    example_outputs,
-    schedule: Schedule2D,
+    model_class,
+    model_args=(),
+    model_kwargs={},
+    optim_fn=None,
+    example_inputs=None,
+    example_outputs=None,
+    schedule: Schedule2D=None,
     naive_gradient_sync=False,
 ):
     """
@@ -132,9 +134,9 @@ def piper_setup(
         num_devices, optim_fn, num_mbs, num_stages, p2p_schedules, naive_gradient_sync
     )
 
-    last_stage_rank = stage_to_device[num_stages - 1]
-    ray.get(piper_metadata.actors[0].load_input.remote(example_inputs))
-    ray.get(piper_metadata.actors[last_stage_rank].load_labels.remote(example_outputs))
+    # last_stage_rank = stage_to_device[num_stages - 1]
+    # ray.get(piper_metadata.actors[0].load_input.remote(example_inputs))
+    # ray.get(piper_metadata.actors[last_stage_rank].load_labels.remote(example_outputs))
 
     ray.get(
         [
@@ -143,12 +145,45 @@ def piper_setup(
         ]
     )
 
+    # Build the model on meta device
+    with torch.device("meta"):
+        model = model_class(*model_args, **model_kwargs)
+
     compiled = torch.compile(model, backend=piper)
 
     dp_rank = int(os.environ["PIPER_DP_RANK"])
-    logger.info(f"DP rank {dp_rank+1} compiling...")
+    logger.info(f"DP rank {dp_rank+1} compiling (meta)...")
+
+    # Create meta tensors from our example_inputs to pass to the compiled graph
+    # meta_inputs = [torch.empty_like(t, device="meta") for t in example_inputs]
+    meta_inputs = [x.to(device="meta") for x in example_inputs]
+
+    # # 1) Verify meta model params are actually meta
+    # for n, p in model.named_parameters():
+    #     assert p.device.type == "meta", (n, p.device)
+
+    # for n, b in model.named_buffers():
+    #     assert b.device.type == "meta", (n, b.device)
 
-    output = compiled(*example_inputs)
+    # # 2) Verify meta inputs
+    # for i, x in enumerate(meta_inputs):
+    #     assert isinstance(x, torch.Tensor)
+    #     assert x.device.type == "meta", (i, x.device)
+
+    _ = compiled(*meta_inputs)
+
+    logger.info(f"DP rank {dp_rank+1} stage graphs loaded onto actors.")
+
+    ray.get([
+        actor.materialize_all_stages.remote(init=init, seed=seed)
+        for actor in piper_metadata.actors.values()
+    ])
+    logger.info(f"DP rank {dp_rank+1} stages materialized on actors.")
+
+    last_stage_rank = stage_to_device[num_stages - 1]
+    ray.get(piper_metadata.actors[0].load_input.remote(example_inputs))
+    ray.get(piper_metadata.actors[last_stage_rank].load_labels.remote(example_outputs))
+    logger.info(f"DP rank {dp_rank+1} real inputs/labels loaded onto actors.")
 
     logger.info(f"DP rank {dp_rank+1} done.")
 
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index f9eec1e..c04b597 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -118,6 +118,13 @@ def _dispatch_a2a_single(output: torch.Tensor, input_tensor: torch.Tensor) -> to
 # Allow the dispatch function in the graph
 torch.compiler.allow_in_graph(_dispatch_a2a_single)
 
+def _meta_tensor_like(example: torch.Tensor, *, requires_grad: bool, as_parameter: bool):
+    t = torch.empty(example.shape, dtype=example.dtype, device="meta")
+    t.requires_grad_(requires_grad)
+    if as_parameter:
+        return torch.nn.Parameter(t, requires_grad=requires_grad)
+    return t
+
 def _split_gm_by_stages(gm) -> tuple[fx.GraphModule, list[tuple[int, fx.GraphModule, list[int], list]]]:
     """
     Transform a graph module with stage annotations by extracting
@@ -455,10 +462,19 @@ def collect_dependencies(node):
             prev_stage_outputs = set(prev_stage_outputs_list)
         
         for i, placeholder in enumerate(placeholders):
-            if "grapharg" in placeholder.meta:
-                graphargs.append(placeholder.meta["grapharg"]._example())
-            else:
-                graphargs.append(torch.zeros(placeholder.meta["example_value"].shape, dtype=placeholder.meta["example_value"].dtype, requires_grad=placeholder.meta["example_value"].requires_grad, device=placeholder.meta["example_value"].device))
+            ex = placeholder.meta["example_value"]
+
+            # Parameter-like placeholders
+            is_param_like = ("grapharg" in placeholder.meta) or ("self" in placeholder.name)
+
+            graphargs.append(
+                _meta_tensor_like(
+                    ex,
+                    requires_grad=bool(getattr(ex, "requires_grad", False)),
+                    as_parameter=is_param_like,
+                )
+            )
+
             # For the first stage, the input indices are everything that's not an attribute
             if stage_annotation_id == 0:
                 if 'self' not in placeholder.name:
diff --git a/test/models/llama.py b/test/models/llama.py
index 47b0c74..304c964 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -248,6 +248,8 @@ def forward(
 
         xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis)
 
+        # print(f"xq is None: {xq is None}, xk is None: {xk is None}, xv is None: {xv is None}")
+
         # [NOTE] Disable KV cache during training.
 
         # self.cache_k = self.cache_k.to(xq)
@@ -275,7 +277,9 @@ def forward(
         values = values.transpose(
             1, 2
         )  # (bs, n_local_heads, cache_len + seqlen, head_dim)
+        # print("Calculating scores")
         scores = torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(self.head_dim)
+        # print("Calculated scores")
         if mask is not None:
             scores = scores + mask  # (bs, n_local_heads, seqlen, cache_len + seqlen)
         scores = F.softmax(scores.float(), dim=-1).type_as(xq)
@@ -352,8 +356,11 @@ def forward(
         freqs_cis: torch.Tensor,
         mask: Optional[torch.Tensor],
     ):
+        # print(f"TransformerBlock {self.layer_id} forward:")
         h = x + self.attention(self.attention_norm(x), start_pos, freqs_cis, mask)
+        # print(f"After attention: {h.shape}")
         out = h + self.feed_forward(self.ffn_norm(h))
+        # print(f"After feed forward: {out.shape}")
         return out
 
 
@@ -414,59 +421,77 @@ def log_size(layer, indent=0):
         mask = torch.hstack([torch.zeros((self.seq_len, 0)), mask])
         self.register_buffer("mask", mask, persistent=False)
 
-    """
-    forward method for pp4-1f1b or pp2-interleaved-1f1b
-    requires:
-    - 4 devices
-    - 4 stages
-    - n_layers is divisible by 4
-    """
-    def forward(self, tokens: torch.Tensor):
-
-        with torch.fx.traceback.annotate({"stage": 0}):
-            h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-            start_pos = 0
-            for layer in self.layers[:self.n_layers//4]:
-                h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-        with torch.fx.traceback.annotate({"stage": 1}):
-            for layer in self.layers[self.n_layers//4:self.n_layers//2]:
-                h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-        with torch.fx.traceback.annotate({"stage": 2}):
-            for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
-                h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-        with torch.fx.traceback.annotate({"stage": 3}):
-            for layer in self.layers[3*self.n_layers//4:]:
-                h = layer(h, start_pos, self.freqs_cis, self.mask)
-            h = self.norm(h) if self.norm else h
-            output = self.output(h).float() if self.output else h
-
-        return output
-
     # """
-    # forward method for 1f1b schedule
+    # forward method for pp4-1f1b or pp2-interleaved-1f1b
     # requires:
-    # - 2 stages
-    # - n_layers is divisible by 2
+    # - 4 devices
+    # - 4 stages
+    # - n_layers is divisible by 4
     # """
     # def forward(self, tokens: torch.Tensor):
 
     #     with torch.fx.traceback.annotate({"stage": 0}):
-    #         h = self.tok_embeddings(tokens)
+    #         h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
     #         start_pos = 0
-    #         for layer in self.layers[:self.n_layers//2]:
+    #         for layer in self.layers[:self.n_layers//4]:
     #             h = layer(h, start_pos, self.freqs_cis, self.mask)
 
     #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[self.n_layers//2:]:
+    #         for layer in self.layers[self.n_layers//4:self.n_layers//2]:
     #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-    #         h = self.norm(h)
-    #         output = self.output(h).float()
+
+    #     with torch.fx.traceback.annotate({"stage": 2}):
+    #         for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 3}):
+    #         for layer in self.layers[3*self.n_layers//4:]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+    #         h = self.norm(h) if self.norm else h
+    #         output = self.output(h).float() if self.output else h
 
     #     return output
 
+    """
+    forward method for 1f1b schedule
+    requires:
+    - 2 stages
+    - n_layers is divisible by 2
+    """
+    def forward(self, tokens: torch.Tensor):
+        assert self.freqs_cis is not None, "freqs_cis is None"
+        assert self.mask is not None, "mask is None"
+        assert self.tok_embeddings is not None, "tok_embeddings is None"
+        assert self.layers is not None, "layers is None"
+        assert self.norm is not None, "norm is None"
+        assert self.output is not None, "output is None"
+        assert tokens is not None, "tokens is None"
+
+        # print(self.layers)
+
+        with torch.fx.traceback.annotate({"stage": 0}):
+            h = self.tok_embeddings(tokens)
+            start_pos = 0
+            for i, layer in enumerate(self.layers[:self.n_layers//2]):
+                # print(f"Stage 0, Layer {i}:")
+                # print(layer)
+                # print(f"freqs_cis shape: {self.freqs_cis.shape}")
+                # print(f"mask shape: {self.mask.shape}")
+                h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+        # print("Made it past stage 0 layers")
+        with torch.fx.traceback.annotate({"stage": 1}):
+            # print("Iterating through stage 1")
+            # print(self.layers[self.n_layers//2:])
+            for layer in self.layers[self.n_layers//2:]:
+                # print(layer)
+                h = layer(h, start_pos, self.freqs_cis, self.mask)
+            # print(self.norm(h))
+            h = self.norm(h)
+            output = self.output(h).float()
+
+        return output
+
     # """
     # forward method for no pp
     # requires:
diff --git a/test/test_llama.py b/test/test_llama.py
index f67cc6c..da4eaa5 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -38,8 +38,8 @@ def main(args):
 
     loss_fn = torch.nn.CrossEntropyLoss()
     
-    model = Transformer(llama_config, args.seq_len)
-    model.to('cuda')
+    # model = Transformer(llama_config, args.seq_len)
+    # model.to('cuda')
 
     x = torch.randint(0, llama_config.vocab_size, (args.batch_size, args.seq_len)).to('cuda')
     y = torch.randn((args.batch_size, args.seq_len, llama_config.vocab_size)).to('cuda')
@@ -60,15 +60,16 @@ def main(args):
     print_schedule(schedule)
 
     piper_setup(
-        model,
-        torch.optim.Adam, 
-        [x],
-        y,
-        schedule,
-        args.naive_gradient_sync,
+        Transformer,
+        model_args=(llama_config, args.seq_len),
+        optim_fn=torch.optim.Adam,
+        example_inputs=[x],
+        example_outputs=y,
+        schedule=schedule,
+        naive_gradient_sync=args.naive_gradient_sync,
     )
 
-    del model
+    # del model
     del x
     del y 
     gc.collect()
diff --git a/test/test_tracing.py b/test/test_tracing.py
index 4f6876b..bd1731a 100644
--- a/test/test_tracing.py
+++ b/test/test_tracing.py
@@ -68,6 +68,55 @@ def test_meta_device_compile():
         traceback.print_exc()
         return False
 
+def test_meta_device_fake_tensor():
+    """Test torch.compile on Llama model using meta params + fake inputs."""
+    logger.info("Testing torch.compile on Llama model with meta device + fake inputs...")
+
+    config = LLAMA_70B
+    seq_len = 256
+    batch_size = 16
+
+    try:
+        # Create model on meta device to avoid allocating memory
+        with torch.device("meta"):
+            start = time.perf_counter()
+            model = Transformer(config, seq_len)
+            end = time.perf_counter()
+            logger.info("✓ Model created on meta device")
+            logger.info(f"Model creation time: {(end - start)*1e3:.0f} ms")
+
+        from torch._subclasses.fake_tensor import FakeTensorMode
+        fake_mode = FakeTensorMode(allow_non_fake_inputs=False)
+
+        trace_device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+        # Create fake tokens (no storage) that pretend to be on trace_device
+        with fake_mode:
+            tokens = torch.empty((batch_size, seq_len), dtype=torch.long, device=trace_device)
+            logger.info(f"✓ Fake tokens created: shape={tuple(tokens.shape)} device={tokens.device} type={type(tokens)}")
+
+            start = time.perf_counter()
+            compiled_model = torch.compile(model)
+            end = time.perf_counter()
+            logger.info("✓ torch.compile succeeded (meta model + fake inputs)")
+            logger.info(f"Compilation time: {(end - start)*1e3:.0f} ms")
+
+            try:
+                output = compiled_model(tokens)
+                # output may also be fake
+                logger.info(f"✓ Forward pass succeeded, output shape: {tuple(output.shape)}")
+            except Exception as e:
+                import traceback
+                tb = traceback.format_exc()
+                logger.warning(f"⚠ Forward pass produced error (still useful): {type(e).__name__}: {e}\n{tb}")
+
+        return True
+
+    except Exception as e:
+        logger.error(f"✗ Meta+fake compile test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
 
 def test_meta_device_fx_trace():
     """Test torch.fx.symbolic_trace on Llama model using meta device."""
@@ -138,6 +187,7 @@ def test_meta_device_graph_module():
     
     results = {
         "torch.compile (meta)": test_meta_device_compile(),
+        # "torch.compile + fake tensors (meta)": test_meta_device_fake_tensor(),
         # "torch.fx.symbolic_trace (meta)": test_meta_device_fx_trace(),
         # "GraphModule (meta)": test_meta_device_graph_module(),
         # "piper_backend (meta)": False,

From 165d8df1c534f0a8f9dec129600c73464043397d Mon Sep 17 00:00:00 2001
From: Yi Pan <conlesspan@outlook.com>
Date: Fri, 27 Feb 2026 13:05:10 +0000
Subject: [PATCH 059/100] fix: put paired fwd/bwd on specific streams and fix
 stream wait.

---
 src/piper_actor.py           | 25 ++++++++-----------------
 src/piper_graph_transform.py | 15 ++++++++-------
 2 files changed, 16 insertions(+), 24 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index b3fe374..9e8c9e7 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -129,7 +129,7 @@ def __init__(
         self.p2p_stream = torch.cuda.Stream()
         self.overlapped_comp_stream = torch.cuda.Stream()
         self.overlapped_p2p_stream = torch.cuda.Stream()
-        if mode == "naive":
+        if mode == "naive" or mode == "overlapped":
             self.per_mb_streams = [torch.cuda.Stream() for _ in range(2)]
         self.n_a2a_ops = dict()
         self.overlap_a2a_ops = False
@@ -669,15 +669,11 @@ def _exec_bwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
     def _forward(self, stage_id: int, mb_idx: int, *deps):
         if self.mode == "sequential":
             comp_stream = self.comp_stream
-        elif self.mode == "naive":
+        elif self.mode == "naive" or self.mode == "overlapped":
             comp_stream = self.per_mb_streams[(stage_id + mb_idx) % 2]
-        elif self.mode == "overlapped":
-            comp_stream = self.comp_stream
         else:
             raise ValueError(f"Invalid mode: {self.mode}")
         
-        self.overlap_a2a_ops = False
-
         self.logger.debug(
             f"Calling forward {stage_id} mb {mb_idx} on actor {self.global_rank}"
         )
@@ -768,15 +764,11 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
     def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         if self.mode == "sequential":
             comp_stream = self.comp_stream
-        elif self.mode == "naive":
+        elif self.mode == "naive" or self.mode == "overlapped":
             comp_stream = self.per_mb_streams[(stage_id + mb_idx) % 2]
-        elif self.mode == "overlapped":
-            comp_stream = self.comp_stream
         else:
             raise ValueError(f"Invalid mode: {self.mode}")
 
-        self.overlap_a2a_ops = False
-
         self.logger.debug(
             f"Calling backward {stage_id} mb {mb_idx} on actor {self.global_rank}"
         )
@@ -834,7 +826,7 @@ def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
 
 
     def _backward_input(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        comp_stream = self.comp_stream
+        comp_stream = self.per_mb_streams[(stage_id + mb_idx) % 2]
 
         self.logger.debug(
             f"Calling backward I {stage_id} mb {mb_idx} on actor {self.global_rank}"
@@ -1091,8 +1083,8 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             bwd_comp_stream = self.per_mb_streams[(bwd_stage_id + bwd_mb_idx) % 2]
             self.overlap_a2a_ops = False
         elif self.mode == "overlapped":
-            fwd_comp_stream = self.comp_stream
-            bwd_comp_stream = self.overlapped_comp_stream
+            fwd_comp_stream = self.per_mb_streams[(fwd_stage_id + fwd_mb_idx) % 2]
+            bwd_comp_stream = self.per_mb_streams[(bwd_stage_id + bwd_mb_idx) % 2]
             self.overlap_a2a_ops = True
             # initialize A2A states
             self.fwd_a2a_pre_events = [torch.cuda.Event() for _ in range(self.n_a2a_ops[fwd_stage_id])]
@@ -1101,8 +1093,6 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             self.bwd_a2a_counter = 0
         else:
             raise ValueError(f"Invalid mode: {self.mode}")
-        fwd_p2p_stream = self.p2p_stream
-        bwd_p2p_stream = self.p2p_stream
 
         self.logger.debug(
             f"Calling forward {fwd_stage_id} mb {fwd_mb_idx} backward {bwd_stage_id} mb {bwd_mb_idx} on actor {self.global_rank}"
@@ -1216,7 +1206,8 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
         torch.cuda.synchronize()
 
         # clear A2A states
-        if self.overlap_a2a_ops and self.dp_degree > 1:
+        if self.overlap_a2a_ops:
+            self.overlap_a2a_ops = False
             self.fwd_a2a_pre_events.clear()
             self.bwd_a2a_pre_events.clear()
             self.fwd_a2a_counter = 0
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index e59c166..d2c52e1 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -60,12 +60,12 @@ def forward(ctx, output, input_tensor):
         
         stream = ctx.actor_self.comp_stream
 
-        # comp_stream = torch.cuda.current_stream()
+        comp_stream = torch.cuda.current_stream()
 
         logger.debug(f"Dispatch AllToAllSingleFunction forward rank={ctx.global_rank}, shape={input_tensor.shape}")
 
         # PRE-HOOK: record event so bwd knows fwd comp reached this A2A
-        if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.overlap_a2a_ops:
+        if ctx.actor_self.overlap_a2a_ops:
             idx = ctx.actor_self.fwd_a2a_counter
             ctx.actor_self.fwd_a2a_pre_events[idx].record(stream)
 
@@ -75,18 +75,18 @@ def forward(ctx, output, input_tensor):
         input_tensor = input_tensor.contiguous()
 
         # Execute A2A
-        # stream.wait_stream(comp_stream)
+        stream.wait_stream(comp_stream)
         with torch.cuda.stream(stream):
             dist.all_to_all_single(output, input_tensor, group=ctx.group)
-        # comp_stream.wait_stream(stream)
 
         ctx.actor_self._stop_timing(stream, "fwd_a2a")
 
         # POST-HOOK: wait for bwd to reach its corresponding A2A
-        if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.overlap_a2a_ops:
+        if ctx.actor_self.overlap_a2a_ops:
             idx = ctx.actor_self.fwd_a2a_counter
             stream.wait_event(ctx.actor_self.bwd_a2a_pre_events[idx])
             ctx.actor_self.fwd_a2a_counter += 1
+        comp_stream.wait_stream(stream)
 
         return output
     
@@ -104,6 +104,7 @@ def backward(ctx, grad_output):
             return None, None, None
 
         stream = ctx.actor_self.overlapped_comp_stream
+        comp_stream = torch.cuda.current_stream()
 
         # PRE-HOOK: record event so fwd post-hook knows bwd comp reached this A2A
         if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.overlap_a2a_ops:
@@ -119,10 +120,9 @@ def backward(ctx, grad_output):
         grad_input = torch.empty_like(grad_output)
 
         # Execute A2A
-        # stream.wait_stream(comp_stream)
+        stream.wait_stream(comp_stream)
         with torch.cuda.stream(stream):
             dist.all_to_all_single(grad_input, grad_output, group=ctx.group)
-        # comp_stream.wait_stream(stream)
 
         ctx.actor_self._stop_timing(stream, "bwd_a2a")
 
@@ -132,6 +132,7 @@ def backward(ctx, grad_output):
             if idx + 1 < len(ctx.actor_self.fwd_a2a_pre_events):
                 stream.wait_event(ctx.actor_self.fwd_a2a_pre_events[idx + 1])
             ctx.actor_self.bwd_a2a_counter += 1
+        comp_stream.wait_stream(stream)
 
         # Return gradients: grad_output flows to grad_input, None for group
         return grad_input, grad_input, None

From 6c291460ef9285dc3eaba3233832ffc9b13d11ee Mon Sep 17 00:00:00 2001
From: Yi Pan <conlesspan@outlook.com>
Date: Fri, 27 Feb 2026 15:55:34 +0000
Subject: [PATCH 060/100] feat: comm/comp overlap working with thread-based
 approach.

---
 src/piper_actor.py           | 89 +++++++++++++++++++++++-------------
 src/piper_graph_transform.py | 62 ++++++++++---------------
 2 files changed, 81 insertions(+), 70 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index 9e8c9e7..031baf5 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -1,8 +1,11 @@
+from contextlib import nullcontext
+
 import ray
 import torch
 import logging
 import os
 import time
+import threading
 from typing import Any, Dict, List, Set, Tuple
 import gc
 from torch._guards import CompileId
@@ -667,9 +670,9 @@ def _exec_bwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
         self.inp_activation[stage_id][mb_idx] = None
     
     def _forward(self, stage_id: int, mb_idx: int, *deps):
-        if self.mode == "sequential":
+        if self.mode == "sequential" or self.mode == "overlapped":
             comp_stream = self.comp_stream
-        elif self.mode == "naive" or self.mode == "overlapped":
+        elif self.mode == "naive":
             comp_stream = self.per_mb_streams[(stage_id + mb_idx) % 2]
         else:
             raise ValueError(f"Invalid mode: {self.mode}")
@@ -762,9 +765,9 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
         return 1
 
     def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        if self.mode == "sequential":
+        if self.mode == "sequential" or self.mode == "overlapped":
             comp_stream = self.comp_stream
-        elif self.mode == "naive" or self.mode == "overlapped":
+        elif self.mode == "naive":
             comp_stream = self.per_mb_streams[(stage_id + mb_idx) % 2]
         else:
             raise ValueError(f"Invalid mode: {self.mode}")
@@ -1083,14 +1086,9 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             bwd_comp_stream = self.per_mb_streams[(bwd_stage_id + bwd_mb_idx) % 2]
             self.overlap_a2a_ops = False
         elif self.mode == "overlapped":
-            fwd_comp_stream = self.per_mb_streams[(fwd_stage_id + fwd_mb_idx) % 2]
-            bwd_comp_stream = self.per_mb_streams[(bwd_stage_id + bwd_mb_idx) % 2]
+            fwd_comp_stream = self.comp_stream
+            bwd_comp_stream = self.comp_stream
             self.overlap_a2a_ops = True
-            # initialize A2A states
-            self.fwd_a2a_pre_events = [torch.cuda.Event() for _ in range(self.n_a2a_ops[fwd_stage_id])]
-            self.bwd_a2a_pre_events = [torch.cuda.Event() for _ in range(self.n_a2a_ops[bwd_stage_id])]
-            self.fwd_a2a_counter = 0
-            self.bwd_a2a_counter = 0
         else:
             raise ValueError(f"Invalid mode: {self.mode}")
 
@@ -1146,29 +1144,58 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             assert loss_fn is not None
             labels = self.labels
             assert out_activation.shape == labels.shape
-
-        # RUN FORWARD PASS
+            
         if self.profile:
             forward_ctx_manager = torch.profiler.record_function(f"forward_stage_{fwd_stage_id}_mb_{fwd_mb_idx}")
-            forward_ctx_manager.__enter__()
+            backward_ctx_manager = torch.profiler.record_function(f"backward_stage_{bwd_stage_id}_mb_{bwd_mb_idx}")
+        else:
+            forward_ctx_manager = nullcontext()
+            backward_ctx_manager = nullcontext()
 
-        with torch.cuda.stream(fwd_comp_stream):
-            output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
+        # RUN FORWARD AND BACKWARD PASSES
+        if self.overlap_a2a_ops:
+            output = None
+            loss = None
+            # threading.Events for CPU-side thread coordination
+            n_a2a = self.n_a2a_ops[fwd_stage_id]
+            self.fwd_a2a_submitted = [threading.Event() for _ in range(n_a2a)]
+            self.bwd_a2a_submitted = [threading.Event() for _ in range(n_a2a)]
+            self.fwd_a2a_counter = 0
+            self.bwd_a2a_counter = 0
 
-        # RUN BACKWARD PASS
-        if self.profile:
-            forward_ctx_manager.__exit__(None, None, None)
-            backward_ctx_manager = torch.profiler.record_function(f"backward_stage_{bwd_stage_id}_mb_{bwd_mb_idx}")
-            backward_ctx_manager.__enter__()
-        if self.overlap_a2a_ops and self.dp_degree > 1:
-            bwd_comp_stream.wait_event(self.fwd_a2a_pre_events[0])
-        if bwd_stage_id < self.num_stages - 1:
-            with torch.cuda.stream(bwd_comp_stream):
-                out_activation.backward(gradient=input_grad)
+            def run_fwd():
+                nonlocal output
+                with torch.cuda.stream(fwd_comp_stream), forward_ctx_manager:
+                    output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
+
+            def run_bwd():
+                nonlocal loss
+                self.fwd_a2a_submitted[0].wait()
+                if bwd_stage_id < self.num_stages - 1:
+                    with torch.cuda.stream(bwd_comp_stream), backward_ctx_manager:
+                        out_activation.backward(gradient=input_grad)
+                else:
+                    with torch.cuda.stream(bwd_comp_stream), backward_ctx_manager:
+                        loss = loss_fn(out_activation, labels)
+                        loss.backward()
+
+            fwd_thread = threading.Thread(target=run_fwd)
+            bwd_thread = threading.Thread(target=run_bwd)
+            fwd_thread.start()
+            bwd_thread.start()
+            fwd_thread.join()
+            bwd_thread.join()
         else:
-            with torch.cuda.stream(bwd_comp_stream):
-                loss = loss_fn(out_activation, labels)
-                loss.backward()
+            with torch.cuda.stream(fwd_comp_stream), forward_ctx_manager:
+                output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
+
+            if bwd_stage_id < self.num_stages - 1:
+                with torch.cuda.stream(bwd_comp_stream), backward_ctx_manager:
+                    out_activation.backward(gradient=input_grad)
+            else:
+                with torch.cuda.stream(bwd_comp_stream), backward_ctx_manager:
+                    loss = loss_fn(out_activation, labels)
+                    loss.backward()
 
         # POST PROCESS FORWARD PASS
         out_with_grad = [out for out in output if out.requires_grad]
@@ -1208,8 +1235,8 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
         # clear A2A states
         if self.overlap_a2a_ops:
             self.overlap_a2a_ops = False
-            self.fwd_a2a_pre_events.clear()
-            self.bwd_a2a_pre_events.clear()
+            self.fwd_a2a_submitted.clear()
+            self.bwd_a2a_submitted.clear()
             self.fwd_a2a_counter = 0
             self.bwd_a2a_counter = 0
 
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index d2c52e1..50dd08c 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -57,36 +57,28 @@ def forward(ctx, output, input_tensor):
         ctx.actor_self = piper_metadata.actor_self
         ctx.group = ctx.actor_self.dp_group
         ctx.global_rank = ctx.actor_self.global_rank
-        
-        stream = ctx.actor_self.comp_stream
+        ctx.stream = ctx.actor_self.a2a_stream
 
         comp_stream = torch.cuda.current_stream()
+        comm_finished_event = torch.cuda.Event()
+        ctx.stream.wait_stream(comp_stream)
 
-        logger.debug(f"Dispatch AllToAllSingleFunction forward rank={ctx.global_rank}, shape={input_tensor.shape}")
-
-        # PRE-HOOK: record event so bwd knows fwd comp reached this A2A
-        if ctx.actor_self.overlap_a2a_ops:
-            idx = ctx.actor_self.fwd_a2a_counter
-            ctx.actor_self.fwd_a2a_pre_events[idx].record(stream)
-
-        ctx.actor_self._start_timing(stream, "fwd_a2a")
+        ctx.actor_self._start_timing(ctx.stream, "fwd_a2a")
 
-        # Ensure input_tensor is contiguous (all_to_all_single requires contiguous tensors)
         input_tensor = input_tensor.contiguous()
+        with torch.cuda.stream(ctx.stream):
+            dist.all_to_all_single(output, input_tensor, group=ctx.group, async_op=True).wait()
+            comm_finished_event.record()
 
-        # Execute A2A
-        stream.wait_stream(comp_stream)
-        with torch.cuda.stream(stream):
-            dist.all_to_all_single(output, input_tensor, group=ctx.group)
-
-        ctx.actor_self._stop_timing(stream, "fwd_a2a")
+        ctx.actor_self._stop_timing(ctx.stream, "fwd_a2a")
 
         # POST-HOOK: wait for bwd to reach its corresponding A2A
         if ctx.actor_self.overlap_a2a_ops:
             idx = ctx.actor_self.fwd_a2a_counter
-            stream.wait_event(ctx.actor_self.bwd_a2a_pre_events[idx])
+            ctx.actor_self.fwd_a2a_submitted[idx].set()
+            ctx.actor_self.bwd_a2a_submitted[idx].wait()
             ctx.actor_self.fwd_a2a_counter += 1
-        comp_stream.wait_stream(stream)
+        comp_stream.wait_event(comm_finished_event)
 
         return output
     
@@ -103,36 +95,28 @@ def backward(ctx, grad_output):
         if grad_output is None:
             return None, None, None
 
-        stream = ctx.actor_self.overlapped_comp_stream
         comp_stream = torch.cuda.current_stream()
+        comm_finished_event = torch.cuda.Event()
+        ctx.stream.wait_stream(comp_stream)
 
-        # PRE-HOOK: record event so fwd post-hook knows bwd comp reached this A2A
-        if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.overlap_a2a_ops:
-            idx = ctx.actor_self.bwd_a2a_counter
-            ctx.actor_self.bwd_a2a_pre_events[idx].record(stream)
-
-        ctx.actor_self._start_timing(stream, "bwd_a2a")
+        ctx.actor_self._start_timing(ctx.stream, "bwd_a2a")
 
-        # Ensure grad_output is contiguous (all_to_all_single requires contiguous tensors)
         grad_output = grad_output.contiguous()
-
-        # Create a buffer for the gradient input
         grad_input = torch.empty_like(grad_output)
+        with torch.cuda.stream(ctx.stream):
+            dist.all_to_all_single(grad_input, grad_output, group=ctx.group, async_op=True).wait()
+            comm_finished_event.record()
 
-        # Execute A2A
-        stream.wait_stream(comp_stream)
-        with torch.cuda.stream(stream):
-            dist.all_to_all_single(grad_input, grad_output, group=ctx.group)
-
-        ctx.actor_self._stop_timing(stream, "bwd_a2a")
+        ctx.actor_self._stop_timing(ctx.stream, "bwd_a2a")
 
         # POST-HOOK: wait for fwd to reach the next A2A
-        if getattr(ctx.actor_self, 'mode', None) == "overlapped" and ctx.actor_self.overlap_a2a_ops:
+        if ctx.actor_self.overlap_a2a_ops:
             idx = ctx.actor_self.bwd_a2a_counter
-            if idx + 1 < len(ctx.actor_self.fwd_a2a_pre_events):
-                stream.wait_event(ctx.actor_self.fwd_a2a_pre_events[idx + 1])
+            ctx.actor_self.bwd_a2a_submitted[idx].set()
+            if idx < len(ctx.actor_self.fwd_a2a_submitted) - 1:
+                ctx.actor_self.fwd_a2a_submitted[idx + 1].wait()
             ctx.actor_self.bwd_a2a_counter += 1
-        comp_stream.wait_stream(stream)
+        comp_stream.wait_event(comm_finished_event)
 
         # Return gradients: grad_output flows to grad_input, None for group
         return grad_input, grad_input, None

From 778beda9ecb7da4d3c030543b2ed6e062ab72376 Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Sat, 28 Feb 2026 07:57:35 +0000
Subject: [PATCH 061/100] Deferred Init Working

---
 src/piper.py         |  22 ++++----
 src/piper_actor.py   |  11 ++--
 src/piper_compile.py |   2 +-
 test/models/llama.py | 122 +++++++++++++++++++++----------------------
 test/test_llama.py   |  13 +++--
 5 files changed, 86 insertions(+), 84 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index 9fc86c9..bc363fd 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -52,15 +52,15 @@ def _dump_get_attrs(tag, gm_):
         if dp_degree > 1:
             stage_gm = _insert_a2a_ops(stage_gm)
 
-        # stage_gm(*graphargs)
-        # start = torch.cuda.Event(enable_timing=True)
-        # start.record()
-        # for i in range(5):
-        #     stage_gm(*graphargs)
-        # end = torch.cuda.Event(enable_timing=True)
-        # end.record()
-        # torch.cuda.synchronize()
-        # print(f"Stage {stage_id} time measured on controller: {start.elapsed_time(end) / 5:.2f} ms")
+        stage_gm(*graphargs)
+        start = torch.cuda.Event(enable_timing=True)
+        start.record()
+        for i in range(5):
+            stage_gm(*graphargs)
+        end = torch.cuda.Event(enable_timing=True)
+        end.record()
+        torch.cuda.synchronize()
+        print(f"Stage {stage_id} time measured on controller: {start.elapsed_time(end) / 5:.2f} ms")
 
         actor_id = piper_metadata.stage_to_device[stage_id]
         actor = _get_actor(actor_id)
@@ -92,11 +92,11 @@ def _dump_get_attrs(tag, gm_):
         if stage_id < len(piper_metadata.stage_to_device.keys()) - 1:
             piper_metadata.dag.add((stage_id, stage_id+1))
     
-    # example_outputs = original_gm(*example_inputs)
+    example_outputs = original_gm(*example_inputs)
 
     def callback(*args):
         logger.warning("Should not directly call compiled module, running non-distributed execution")
-        # return example_outputs
+        return example_outputs
 
     del original_gm
     del example_inputs
diff --git a/src/piper_actor.py b/src/piper_actor.py
index bf8fe8f..c4fb97d 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -401,7 +401,7 @@ def _load_stage(self, stage_id: int, gm_data, forward_args, input_idxs):
 
         del gm_data
 
-    def materialize_stage(self, stage_id: int, init="random", seed=0):
+    def materialize_stage(self, stage_id: int):
         if self.stage_materialized[stage_id]:
             return 1
 
@@ -409,7 +409,7 @@ def materialize_stage(self, stage_id: int, init="random", seed=0):
         realized = [None] * len(meta_args)
 
         g = torch.Generator(device=self.device)
-        g.manual_seed(seed + 1000 * self.global_rank + stage_id)
+        g.manual_seed(0 + 1000 * self.global_rank + stage_id)
 
         for i, arg in enumerate(meta_args):
             if arg is None:
@@ -419,8 +419,7 @@ def materialize_stage(self, stage_id: int, init="random", seed=0):
 
             if arg.requires_grad:
                 t.requires_grad_(True)
-                if init == "random":
-                    torch.nn.init.normal_(t, mean=0.0, std=0.02, generator=g)
+                torch.nn.init.normal_(t, mean=0.0, std=0.02, generator=g)
             else:
                 t.zero_()
 
@@ -443,9 +442,9 @@ def materialize_stage(self, stage_id: int, init="random", seed=0):
         return 1
 
 
-    def materialize_all_stages(self, init="random", seed=0):
+    def materialize_all_stages(self):
         for stage_id in list(self.forward_args_meta.keys()):
-            self.materialize_stage(stage_id, init=init, seed=seed)
+            self.materialize_stage(stage_id)
         return 1
 
     def _exec_p2p_op(
diff --git a/src/piper_compile.py b/src/piper_compile.py
index ba24d2b..430a21f 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -175,7 +175,7 @@ def piper_setup(
     logger.info(f"DP rank {dp_rank+1} stage graphs loaded onto actors.")
 
     ray.get([
-        actor.materialize_all_stages.remote(init=init, seed=seed)
+        actor.materialize_all_stages.remote()
         for actor in piper_metadata.actors.values()
     ])
     logger.info(f"DP rank {dp_rank+1} stages materialized on actors.")
diff --git a/test/models/llama.py b/test/models/llama.py
index 304c964..ce26dd3 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -409,89 +409,89 @@ def log_size(layer, indent=0):
         log_size(self.output)
 
         # Register freq_cis and mask as buffers so they are moved with the model
-        freqs_cis = precompute_freqs_cis(
+        self.freqs_cis = precompute_freqs_cis(
             params.dim // params.n_heads,
             self.seq_len,
             params.rope_theta,
         )
-        self.register_buffer("freqs_cis", freqs_cis, persistent=False)
+        # self.register_buffer("freqs_cis", freqs_cis, persistent=False)
 
         mask = torch.full((self.seq_len, self.seq_len), float("-inf"))
         mask = torch.triu(mask, diagonal=1)
-        mask = torch.hstack([torch.zeros((self.seq_len, 0)), mask])
-        self.register_buffer("mask", mask, persistent=False)
-
-    # """
-    # forward method for pp4-1f1b or pp2-interleaved-1f1b
-    # requires:
-    # - 4 devices
-    # - 4 stages
-    # - n_layers is divisible by 4
-    # """
-    # def forward(self, tokens: torch.Tensor):
-
-    #     with torch.fx.traceback.annotate({"stage": 0}):
-    #         h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-    #         start_pos = 0
-    #         for layer in self.layers[:self.n_layers//4]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[self.n_layers//4:self.n_layers//2]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 2}):
-    #         for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 3}):
-    #         for layer in self.layers[3*self.n_layers//4:]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-    #         h = self.norm(h) if self.norm else h
-    #         output = self.output(h).float() if self.output else h
-
-    #     return output
+        self.mask = torch.hstack([torch.zeros((self.seq_len, 0)), mask])
+        # self.register_buffer("mask", mask, persistent=False)
 
     """
-    forward method for 1f1b schedule
+    forward method for pp4-1f1b or pp2-interleaved-1f1b
     requires:
-    - 2 stages
-    - n_layers is divisible by 2
+    - 4 devices
+    - 4 stages
+    - n_layers is divisible by 4
     """
     def forward(self, tokens: torch.Tensor):
-        assert self.freqs_cis is not None, "freqs_cis is None"
-        assert self.mask is not None, "mask is None"
-        assert self.tok_embeddings is not None, "tok_embeddings is None"
-        assert self.layers is not None, "layers is None"
-        assert self.norm is not None, "norm is None"
-        assert self.output is not None, "output is None"
-        assert tokens is not None, "tokens is None"
-
-        # print(self.layers)
 
         with torch.fx.traceback.annotate({"stage": 0}):
-            h = self.tok_embeddings(tokens)
+            h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
             start_pos = 0
-            for i, layer in enumerate(self.layers[:self.n_layers//2]):
-                # print(f"Stage 0, Layer {i}:")
-                # print(layer)
-                # print(f"freqs_cis shape: {self.freqs_cis.shape}")
-                # print(f"mask shape: {self.mask.shape}")
+            for layer in self.layers[:self.n_layers//4]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
 
-        # print("Made it past stage 0 layers")
         with torch.fx.traceback.annotate({"stage": 1}):
-            # print("Iterating through stage 1")
-            # print(self.layers[self.n_layers//2:])
-            for layer in self.layers[self.n_layers//2:]:
-                # print(layer)
+            for layer in self.layers[self.n_layers//4:self.n_layers//2]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
-            # print(self.norm(h))
-            h = self.norm(h)
-            output = self.output(h).float()
+
+        with torch.fx.traceback.annotate({"stage": 2}):
+            for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
+                h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+        with torch.fx.traceback.annotate({"stage": 3}):
+            for layer in self.layers[3*self.n_layers//4:]:
+                h = layer(h, start_pos, self.freqs_cis, self.mask)
+            h = self.norm(h) if self.norm else h
+            output = self.output(h).float() if self.output else h
 
         return output
 
+    # """
+    # forward method for 1f1b schedule
+    # requires:
+    # - 2 stages
+    # - n_layers is divisible by 2
+    # """
+    # def forward(self, tokens: torch.Tensor):
+    #     assert self.freqs_cis is not None, "freqs_cis is None"
+    #     assert self.mask is not None, "mask is None"
+    #     assert self.tok_embeddings is not None, "tok_embeddings is None"
+    #     assert self.layers is not None, "layers is None"
+    #     assert self.norm is not None, "norm is None"
+    #     assert self.output is not None, "output is None"
+    #     assert tokens is not None, "tokens is None"
+
+    #     # print(self.layers)
+
+    #     with torch.fx.traceback.annotate({"stage": 0}):
+    #         h = self.tok_embeddings(tokens)
+    #         start_pos = 0
+    #         for i, layer in enumerate(self.layers[:self.n_layers//2]):
+    #             # print(f"Stage 0, Layer {i}:")
+    #             # print(layer)
+    #             # print(f"freqs_cis shape: {self.freqs_cis.shape}")
+    #             # print(f"mask shape: {self.mask.shape}")
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+
+    #     # print("Made it past stage 0 layers")
+    #     with torch.fx.traceback.annotate({"stage": 1}):
+    #         # print("Iterating through stage 1")
+    #         # print(self.layers[self.n_layers//2:])
+    #         for layer in self.layers[self.n_layers//2:]:
+    #             # print(layer)
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+    #         # print(self.norm(h))
+    #         h = self.norm(h)
+    #         output = self.output(h).float()
+
+    #     return output
+
     # """
     # forward method for no pp
     # requires:
diff --git a/test/test_llama.py b/test/test_llama.py
index da4eaa5..9e5745b 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -9,8 +9,9 @@
 from src.piper_exec import piper_exec
 from src.piper_compile import piper_setup, piper_shutdown
 from src.piper_coordinator import PiperProgramCoordinator
+from src.piper_utils import piper_metadata
 
-from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B
+from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B, LLAMA_70B
 from .schedule_helpers import (
     build_1f1b_schedule, 
     build_gpipe_schedule, 
@@ -34,6 +35,8 @@ def main(args):
             llama_config = LLAMA_3B
         case '8b':
             llama_config = LLAMA_8B
+        case '70b':
+            llama_config = LLAMA_70B
     print(args) 
 
     loss_fn = torch.nn.CrossEntropyLoss()
@@ -98,13 +101,13 @@ def main(args):
     )
 
     if args.tracing:
-        ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
+        ray.get([actor.set_tracing.remote(args.tracing) for actor in piper_metadata.actors.values()])
 
         print(f"Running {args.warmup} tracing iterations...")
         for _ in range(args.warmup):
             piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
 
-        trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
+        trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in piper_metadata.actors.values()])
         for rank, trace_data in trace_data_ret:
             for key in trace_data:
                 all_times = trace_data[key]
@@ -122,8 +125,8 @@ def main(args):
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
-    parser.add_argument('--model', choices=['debug', '1b', '3b', '8b'], default='debug',
-                        help='Model configuration: debug, 1b, 3b, or 8b (default: debug)')
+    parser.add_argument('--model', choices=['debug', '1b', '3b', '8b', '70b'], default='debug',
+                        help='Model configuration: debug, 1b, 3b, 8b, or 70b (default: debug)')
     parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'dualpipev', 'no-pp'], default='1f1b',
                         help='Schedule type: gpipe, 1f1b, interleaved-1f1b, dualpipev, or no-pp (default: 1f1b)')
     parser.add_argument('--dp', type=int, default=1,

From 542413dea5ce1554326292681d8945f8299dc6ec Mon Sep 17 00:00:00 2001
From: Parker Gustafson <pgusto34@gmail.com>
Date: Sat, 28 Feb 2026 20:33:49 +0000
Subject: [PATCH 062/100] Code cleanup for PR

---
 src/piper.py                 | 25 +-----------------
 src/piper_actor.py           | 51 +++---------------------------------
 src/piper_compile.py         | 19 +-------------
 src/piper_graph_transform.py |  2 ++
 test/models/llama.py         | 33 ++---------------------
 test/test_llama.py           |  3 ---
 6 files changed, 9 insertions(+), 124 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index bc363fd..c044661 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -16,34 +16,14 @@
 @register_backend
 def piper(gm, example_inputs, **kwargs):
 
-    # gm(*example_inputs)
-    # start = torch.cuda.Event(enable_timing=True)
-    # start.record()
-    # for i in range(10):
-    #     gm(*example_inputs)
-    # end = torch.cuda.Event(enable_timing=True)
-    # end.record()
-    # torch.cuda.synchronize()
-    # print(f"Entire model time measured on controller: {start.elapsed_time(end) / 10:.2f} ms")
-    def _dump_get_attrs(tag, gm_):
-        attrs = [n.target for n in gm_.graph.nodes if n.op == "get_attr"]
-        print(f"[{tag}] get_attr targets:", attrs)
-
-    # print("gm code:")
-    # gm.print_readable()
-    # print("nodes:", [(n.op, n.target) for n in gm.graph.nodes])
-
     original_gm = gm
-
-    # _dump_get_attrs("original gm", original_gm)
-
     top_level_gm, submodules = _split_gm_by_stages(gm)
+    
     num_stages = len(piper_metadata.stage_to_device.keys())
     dp_rank = int(os.environ['PIPER_DP_RANK'])
     pp_degree = int(os.environ['PIPER_PP_DEGREE'])
     dp_degree = int(os.environ['PIPER_DP_DEGREE'])
 
-    # top_level_gm.print_readable()
     del top_level_gm
 
     refs = []
@@ -66,9 +46,6 @@ def _dump_get_attrs(tag, gm_):
         actor = _get_actor(actor_id)
         actor_stages.append((actor, stage_id))
         
-
-        # _dump_get_attrs(f"stage{stage_id}-pre-serialize", stage_gm)
-
         stage_gm_data = _serialize_graphmodule(stage_gm)
         refs.append(actor._load_stage.remote(
             stage_id, 
diff --git a/src/piper_actor.py b/src/piper_actor.py
index c4fb97d..b031283 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -123,7 +123,7 @@ def __init__(
         self.overlapped_comp_stream = torch.cuda.Stream()
         self.overlapped_p2p_stream = torch.cuda.Stream()
 
-        # fields for deferred initialization
+        # maps for deferred initialization
         self.forward_args_meta = dict()
         self.stage_materialized = defaultdict(bool)
 
@@ -330,51 +330,6 @@ def _wait_for_comm_ops(self):
                         self.comm_op_handles[stage_id][tid].wait()
                         done = True
 
-    # def _load_stage(
-    #     self, stage_id: int, gm_data, forward_args,input_idxs
-    # ):
-    #     self.logger.debug(f"Loading stage {stage_id} graph on actor {self.global_rank}")
-
-    #     # compile the graph with the given graphargs
-    #     gm = _deserialize_graphmodule(gm_data)
-
-    #     # Store GraphModule reference
-    #     self.graph_modules[stage_id] = gm
-    #     self.forward_fns[stage_id] = gm.forward
-
-    #     # place parameters on the device
-    #     def move_to_device(idx, arg):
-    #         if arg.requires_grad:
-    #             return arg.to(self.device).detach().requires_grad_(True)
-    #         else:
-    #             return arg.to(self.device)
-    #     forward_args = list(map(move_to_device, range(len(forward_args)), forward_args))
-
-    #     # save parameters
-    #     self.forward_args[stage_id] = forward_args
-    #     self.stage_id = stage_id
-    #     self.input_idxs[stage_id] = input_idxs
-
-    #     for i in self.input_idxs[stage_id]:
-    #         self.forward_input_meta[stage_id][i] = (
-    #             forward_args[i].shape,
-    #             forward_args[i].dtype,
-    #             forward_args[i].requires_grad,
-    #         )
-    #         self.forward_args[stage_id][i] = None
-
-    #     # prepare tensors with DP comm ops
-    #     if not self.naive_gradient_sync and self.dp_degree > 1:
-    #         self._prepare_dp_comm_ops(stage_id)
-
-    #     # add the parameters to the optimizer for this stage
-    #     params = [param for param in forward_args if param is not None and param.requires_grad]
-    #     if stage_id not in self.optims:
-    #         self.optims[stage_id] = self.optim_class(params)
-    #     else:
-    #         self.optims[stage_id].add_param_group({"params": params})
-
-    #     del gm_data
     def _load_stage(self, stage_id: int, gm_data, forward_args, input_idxs):
         self.logger.debug(f"Loading stage {stage_id} graph on actor {self.global_rank}")
 
@@ -409,7 +364,7 @@ def materialize_stage(self, stage_id: int):
         realized = [None] * len(meta_args)
 
         g = torch.Generator(device=self.device)
-        g.manual_seed(0 + 1000 * self.global_rank + stage_id)
+        g.manual_seed(1000 * self.global_rank + stage_id)
 
         for i, arg in enumerate(meta_args):
             if arg is None:
@@ -428,7 +383,7 @@ def materialize_stage(self, stage_id: int):
         self.forward_args[stage_id] = realized
         self.stage_materialized[stage_id] = True
 
-        # Now safe to setup hooks + optimizer
+        # It's safe to setup hooks + optimizer after tensors are materialized
         if not self.naive_gradient_sync and self.dp_degree > 1:
             self._prepare_dp_comm_ops(stage_id)
 
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 430a21f..e9081e6 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -134,10 +134,6 @@ def piper_setup(
         num_devices, optim_fn, num_mbs, num_stages, p2p_schedules, naive_gradient_sync
     )
 
-    # last_stage_rank = stage_to_device[num_stages - 1]
-    # ray.get(piper_metadata.actors[0].load_input.remote(example_inputs))
-    # ray.get(piper_metadata.actors[last_stage_rank].load_labels.remote(example_outputs))
-
     ray.get(
         [
             actor._join_process_groups.remote()
@@ -145,7 +141,7 @@ def piper_setup(
         ]
     )
 
-    # Build the model on meta device
+    # Build the model directly on meta device
     with torch.device("meta"):
         model = model_class(*model_args, **model_kwargs)
 
@@ -155,21 +151,8 @@ def piper_setup(
     logger.info(f"DP rank {dp_rank+1} compiling (meta)...")
 
     # Create meta tensors from our example_inputs to pass to the compiled graph
-    # meta_inputs = [torch.empty_like(t, device="meta") for t in example_inputs]
     meta_inputs = [x.to(device="meta") for x in example_inputs]
 
-    # # 1) Verify meta model params are actually meta
-    # for n, p in model.named_parameters():
-    #     assert p.device.type == "meta", (n, p.device)
-
-    # for n, b in model.named_buffers():
-    #     assert b.device.type == "meta", (n, b.device)
-
-    # # 2) Verify meta inputs
-    # for i, x in enumerate(meta_inputs):
-    #     assert isinstance(x, torch.Tensor)
-    #     assert x.device.type == "meta", (i, x.device)
-
     _ = compiled(*meta_inputs)
 
     logger.info(f"DP rank {dp_rank+1} stage graphs loaded onto actors.")
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index c04b597..3418420 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -467,6 +467,8 @@ def collect_dependencies(node):
             # Parameter-like placeholders
             is_param_like = ("grapharg" in placeholder.meta) or ("self" in placeholder.name)
 
+            # we may not actually need to call _metathis if we're already placing example inputs on meta, 
+            # but it's guaranteed to be safe with this function call
             graphargs.append(
                 _meta_tensor_like(
                     ex,
diff --git a/test/models/llama.py b/test/models/llama.py
index ce26dd3..8c88109 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -103,14 +103,14 @@ class ModelArgs:
     dim=8192,
     n_layers=80,
     n_heads=64,
-    n_kv_heads=8,          # GQA (Grouped Query Attention)
+    n_kv_heads=8,
     vocab_size=128256,
     multiple_of=4096,
     ffn_dim_multiplier=1.3,
     norm_eps=1e-5,
     rope_theta=500000,
     max_batch_size=32,
-    max_seq_len=8192,      # Llama 3 default context length
+    max_seq_len=8192,
 )
 
 
@@ -248,8 +248,6 @@ def forward(
 
         xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis)
 
-        # print(f"xq is None: {xq is None}, xk is None: {xk is None}, xv is None: {xv is None}")
-
         # [NOTE] Disable KV cache during training.
 
         # self.cache_k = self.cache_k.to(xq)
@@ -277,9 +275,7 @@ def forward(
         values = values.transpose(
             1, 2
         )  # (bs, n_local_heads, cache_len + seqlen, head_dim)
-        # print("Calculating scores")
         scores = torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(self.head_dim)
-        # print("Calculated scores")
         if mask is not None:
             scores = scores + mask  # (bs, n_local_heads, seqlen, cache_len + seqlen)
         scores = F.softmax(scores.float(), dim=-1).type_as(xq)
@@ -356,11 +352,8 @@ def forward(
         freqs_cis: torch.Tensor,
         mask: Optional[torch.Tensor],
     ):
-        # print(f"TransformerBlock {self.layer_id} forward:")
         h = x + self.attention(self.attention_norm(x), start_pos, freqs_cis, mask)
-        # print(f"After attention: {h.shape}")
         out = h + self.feed_forward(self.ffn_norm(h))
-        # print(f"After feed forward: {out.shape}")
         return out
 
 
@@ -414,12 +407,10 @@ def log_size(layer, indent=0):
             self.seq_len,
             params.rope_theta,
         )
-        # self.register_buffer("freqs_cis", freqs_cis, persistent=False)
 
         mask = torch.full((self.seq_len, self.seq_len), float("-inf"))
         mask = torch.triu(mask, diagonal=1)
         self.mask = torch.hstack([torch.zeros((self.seq_len, 0)), mask])
-        # self.register_buffer("mask", mask, persistent=False)
 
     """
     forward method for pp4-1f1b or pp2-interleaved-1f1b
@@ -459,34 +450,14 @@ def forward(self, tokens: torch.Tensor):
     # - n_layers is divisible by 2
     # """
     # def forward(self, tokens: torch.Tensor):
-    #     assert self.freqs_cis is not None, "freqs_cis is None"
-    #     assert self.mask is not None, "mask is None"
-    #     assert self.tok_embeddings is not None, "tok_embeddings is None"
-    #     assert self.layers is not None, "layers is None"
-    #     assert self.norm is not None, "norm is None"
-    #     assert self.output is not None, "output is None"
-    #     assert tokens is not None, "tokens is None"
-
-    #     # print(self.layers)
-
     #     with torch.fx.traceback.annotate({"stage": 0}):
     #         h = self.tok_embeddings(tokens)
     #         start_pos = 0
     #         for i, layer in enumerate(self.layers[:self.n_layers//2]):
-    #             # print(f"Stage 0, Layer {i}:")
-    #             # print(layer)
-    #             # print(f"freqs_cis shape: {self.freqs_cis.shape}")
-    #             # print(f"mask shape: {self.mask.shape}")
     #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     # print("Made it past stage 0 layers")
     #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         # print("Iterating through stage 1")
-    #         # print(self.layers[self.n_layers//2:])
     #         for layer in self.layers[self.n_layers//2:]:
-    #             # print(layer)
     #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-    #         # print(self.norm(h))
     #         h = self.norm(h)
     #         output = self.output(h).float()
 
diff --git a/test/test_llama.py b/test/test_llama.py
index 9e5745b..0f27b6d 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -40,9 +40,6 @@ def main(args):
     print(args) 
 
     loss_fn = torch.nn.CrossEntropyLoss()
-    
-    # model = Transformer(llama_config, args.seq_len)
-    # model.to('cuda')
 
     x = torch.randint(0, llama_config.vocab_size, (args.batch_size, args.seq_len)).to('cuda')
     y = torch.randn((args.batch_size, args.seq_len, llama_config.vocab_size)).to('cuda')

From 6faf0cdbef651de70f3013710403e909885757fb Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Mon, 2 Mar 2026 18:52:51 +0000
Subject: [PATCH 063/100] backward hook fix for split backwards pass

---
 src/piper_actor.py     | 163 +++++++++++++++++++++++++----------------
 test/models/mixtral.py |   2 +-
 test/test_mixtral.py   |   1 +
 3 files changed, 103 insertions(+), 63 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index 031baf5..6ff4167 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -1,11 +1,8 @@
-from contextlib import nullcontext
-
 import ray
 import torch
 import logging
 import os
 import time
-import threading
 from typing import Any, Dict, List, Set, Tuple
 import gc
 from torch._guards import CompileId
@@ -51,7 +48,7 @@ def _create_actors(
     for pp_rank in range(num_actors):
         global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
         p2p_schedule = p2p_schedules[pp_rank]
-        actor = PiperActor.options(num_gpus=1).remote(
+        actor = PiperActor.options(num_gpus=0.8).remote(
             pp_rank,
             optim_class,
             world_size,
@@ -132,7 +129,7 @@ def __init__(
         self.p2p_stream = torch.cuda.Stream()
         self.overlapped_comp_stream = torch.cuda.Stream()
         self.overlapped_p2p_stream = torch.cuda.Stream()
-        if mode == "naive" or mode == "overlapped":
+        if mode == "naive":
             self.per_mb_streams = [torch.cuda.Stream() for _ in range(2)]
         self.n_a2a_ops = dict()
         self.overlap_a2a_ops = False
@@ -294,6 +291,54 @@ def _join_pp_process_group(self):
     def shutdown(self):
         dist.destroy_process_group()
 
+    def _maybe_trigger_grad_allreduce(
+        self, stage_id: int, param: torch.nn.Parameter
+    ) -> None:
+        """
+        Preserve `_prepare_dp_comm_ops` semantics for split backward (BWD_I/BWD_W).
+
+        `_prepare_dp_comm_ops` attaches `register_post_accumulate_grad_hook` to params.
+        Those hooks fire only when autograd's AccumulateGrad runs. In ZeroBubble's
+        split backward, we compute grads via `torch.autograd.grad` and manually
+        accumulate into `param.grad`, so AccumulateGrad (and thus the hooks) never run.
+
+        This helper mirrors the hook behavior: once a parameter has had gradients
+        accumulated for all microbatches (`num_mbs`), launch an async all-reduce on
+        the accumulated `param.grad` and store the handle so `_wait_for_comm_ops()`
+        can wait on it before the optimizer step.
+        """
+        if self.naive_gradient_sync or self.dp_degree <= 1:
+            return
+        if param.grad is None:
+            return
+
+        tid = id(param)
+        self.logger.debug(f"Split bwd: Updating status on actor {self.global_rank}, tensor={tid}")
+        if stage_id not in self.comm_op_tensor_ids:
+            return
+        if tid not in self.comm_op_tensor_ids[stage_id]:
+            return
+
+        self.comm_op_status[stage_id][tid] += 1
+        self.logger.debug(
+            f"Split bwd: Updating status on actor {self.global_rank}, tensor={tid}, "
+            f"status={self.comm_op_status[stage_id][tid]}/{self.num_mbs}"
+        )
+
+        if self.comm_op_status[stage_id][tid] == self.num_mbs:
+            with torch.cuda.stream(self.comm_stream):
+                handle = dist.all_reduce(
+                    param.grad,
+                    op=dist.ReduceOp.AVG,
+                    group=self.dp_group,
+                    async_op=True,
+                )
+            self.logger.debug(
+                f"Split bwd: Allreduce launched on actor {self.global_rank}, tensor={tid}"
+            )
+            self.comm_op_status[stage_id][tid] = 0
+            self.comm_op_handles[stage_id][tid] = handle
+
     def _prepare_dp_comm_ops(self, stage_id):
         def hook_maker(tensor_id):
             def post_backward_hook(grad):
@@ -670,13 +715,17 @@ def _exec_bwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
         self.inp_activation[stage_id][mb_idx] = None
     
     def _forward(self, stage_id: int, mb_idx: int, *deps):
-        if self.mode == "sequential" or self.mode == "overlapped":
+        if self.mode == "sequential":
             comp_stream = self.comp_stream
         elif self.mode == "naive":
             comp_stream = self.per_mb_streams[(stage_id + mb_idx) % 2]
+        elif self.mode == "overlapped":
+            comp_stream = self.comp_stream
         else:
             raise ValueError(f"Invalid mode: {self.mode}")
         
+        self.overlap_a2a_ops = False
+
         self.logger.debug(
             f"Calling forward {stage_id} mb {mb_idx} on actor {self.global_rank}"
         )
@@ -765,13 +814,17 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
         return 1
 
     def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        if self.mode == "sequential" or self.mode == "overlapped":
+        if self.mode == "sequential":
             comp_stream = self.comp_stream
         elif self.mode == "naive":
             comp_stream = self.per_mb_streams[(stage_id + mb_idx) % 2]
+        elif self.mode == "overlapped":
+            comp_stream = self.comp_stream
         else:
             raise ValueError(f"Invalid mode: {self.mode}")
 
+        self.overlap_a2a_ops = False
+
         self.logger.debug(
             f"Calling backward {stage_id} mb {mb_idx} on actor {self.global_rank}"
         )
@@ -829,7 +882,7 @@ def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
 
 
     def _backward_input(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        comp_stream = self.per_mb_streams[(stage_id + mb_idx) % 2]
+        comp_stream = self.comp_stream
 
         self.logger.debug(
             f"Calling backward I {stage_id} mb {mb_idx} on actor {self.global_rank}"
@@ -944,6 +997,7 @@ def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         )
 
         stage_params = [self.forward_args[stage_id][i] for i in self.param_idxs[stage_id]]
+        updated_params: dict[int, torch.nn.Parameter] = {}
 
         # Special case to handle stage 0 since backward_input is a NOOP, 
         # meaning no parameter groups are created
@@ -977,10 +1031,13 @@ def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
             )
             
             for p, pg in zip(stage_params, gparams):
+                if pg is None:
+                    continue
                 if p.grad is None:
                     p.grad = pg.clone()
                 else:
                     p.grad.add_(pg)
+                updated_params[id(p)] = p
         else:
             # Create mapping from autograd nodes -> parameters
             grad_acc_to_weight: Dict[Node, Tuple[Parameter, int]] = {}
@@ -1051,7 +1108,7 @@ def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
                 )
                 
                 # Finally, update gradients for the params in this param_group
-                for param_node, dw in zip(pg["params"], gparams):
+                for param_node, dw in zip(mapped_param_nodes, gparams):
                     if dw is None:
                         continue
 
@@ -1061,6 +1118,11 @@ def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
                         weight.grad = dw
                     else:
                         weight.grad.add_(dw)
+                    updated_params[id(weight)] = weight
+
+        # Mirror post-accumulate hook behavior for split backward.
+        for p in updated_params.values():
+            self._maybe_trigger_grad_allreduce(stage_id, p)
 
         self.bw_grad_cache[stage_id][mb_idx] = None
         self.upstream_grad_cache[stage_id][mb_idx] = None
@@ -1087,10 +1149,17 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             self.overlap_a2a_ops = False
         elif self.mode == "overlapped":
             fwd_comp_stream = self.comp_stream
-            bwd_comp_stream = self.comp_stream
+            bwd_comp_stream = self.overlapped_comp_stream
             self.overlap_a2a_ops = True
+            # initialize A2A states
+            self.fwd_a2a_pre_events = [torch.cuda.Event() for _ in range(self.n_a2a_ops[fwd_stage_id])]
+            self.bwd_a2a_pre_events = [torch.cuda.Event() for _ in range(self.n_a2a_ops[bwd_stage_id])]
+            self.fwd_a2a_counter = 0
+            self.bwd_a2a_counter = 0
         else:
             raise ValueError(f"Invalid mode: {self.mode}")
+        fwd_p2p_stream = self.p2p_stream
+        bwd_p2p_stream = self.p2p_stream
 
         self.logger.debug(
             f"Calling forward {fwd_stage_id} mb {fwd_mb_idx} backward {bwd_stage_id} mb {bwd_mb_idx} on actor {self.global_rank}"
@@ -1144,58 +1213,29 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             assert loss_fn is not None
             labels = self.labels
             assert out_activation.shape == labels.shape
-            
+
+        # RUN FORWARD PASS
         if self.profile:
             forward_ctx_manager = torch.profiler.record_function(f"forward_stage_{fwd_stage_id}_mb_{fwd_mb_idx}")
-            backward_ctx_manager = torch.profiler.record_function(f"backward_stage_{bwd_stage_id}_mb_{bwd_mb_idx}")
-        else:
-            forward_ctx_manager = nullcontext()
-            backward_ctx_manager = nullcontext()
-
-        # RUN FORWARD AND BACKWARD PASSES
-        if self.overlap_a2a_ops:
-            output = None
-            loss = None
-            # threading.Events for CPU-side thread coordination
-            n_a2a = self.n_a2a_ops[fwd_stage_id]
-            self.fwd_a2a_submitted = [threading.Event() for _ in range(n_a2a)]
-            self.bwd_a2a_submitted = [threading.Event() for _ in range(n_a2a)]
-            self.fwd_a2a_counter = 0
-            self.bwd_a2a_counter = 0
+            forward_ctx_manager.__enter__()
 
-            def run_fwd():
-                nonlocal output
-                with torch.cuda.stream(fwd_comp_stream), forward_ctx_manager:
-                    output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
-
-            def run_bwd():
-                nonlocal loss
-                self.fwd_a2a_submitted[0].wait()
-                if bwd_stage_id < self.num_stages - 1:
-                    with torch.cuda.stream(bwd_comp_stream), backward_ctx_manager:
-                        out_activation.backward(gradient=input_grad)
-                else:
-                    with torch.cuda.stream(bwd_comp_stream), backward_ctx_manager:
-                        loss = loss_fn(out_activation, labels)
-                        loss.backward()
-
-            fwd_thread = threading.Thread(target=run_fwd)
-            bwd_thread = threading.Thread(target=run_bwd)
-            fwd_thread.start()
-            bwd_thread.start()
-            fwd_thread.join()
-            bwd_thread.join()
-        else:
-            with torch.cuda.stream(fwd_comp_stream), forward_ctx_manager:
-                output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
+        with torch.cuda.stream(fwd_comp_stream):
+            output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
 
-            if bwd_stage_id < self.num_stages - 1:
-                with torch.cuda.stream(bwd_comp_stream), backward_ctx_manager:
-                    out_activation.backward(gradient=input_grad)
-            else:
-                with torch.cuda.stream(bwd_comp_stream), backward_ctx_manager:
-                    loss = loss_fn(out_activation, labels)
-                    loss.backward()
+        # RUN BACKWARD PASS
+        if self.profile:
+            forward_ctx_manager.__exit__(None, None, None)
+            backward_ctx_manager = torch.profiler.record_function(f"backward_stage_{bwd_stage_id}_mb_{bwd_mb_idx}")
+            backward_ctx_manager.__enter__()
+        if self.overlap_a2a_ops and self.dp_degree > 1:
+            bwd_comp_stream.wait_event(self.fwd_a2a_pre_events[0])
+        if bwd_stage_id < self.num_stages - 1:
+            with torch.cuda.stream(bwd_comp_stream):
+                out_activation.backward(gradient=input_grad)
+        else:
+            with torch.cuda.stream(bwd_comp_stream):
+                loss = loss_fn(out_activation, labels)
+                loss.backward()
 
         # POST PROCESS FORWARD PASS
         out_with_grad = [out for out in output if out.requires_grad]
@@ -1233,10 +1273,9 @@ def run_bwd():
         torch.cuda.synchronize()
 
         # clear A2A states
-        if self.overlap_a2a_ops:
-            self.overlap_a2a_ops = False
-            self.fwd_a2a_submitted.clear()
-            self.bwd_a2a_submitted.clear()
+        if self.overlap_a2a_ops and self.dp_degree > 1:
+            self.fwd_a2a_pre_events.clear()
+            self.bwd_a2a_pre_events.clear()
             self.fwd_a2a_counter = 0
             self.bwd_a2a_counter = 0
 
diff --git a/test/models/mixtral.py b/test/models/mixtral.py
index 8656103..a89f23f 100644
--- a/test/models/mixtral.py
+++ b/test/models/mixtral.py
@@ -57,7 +57,7 @@ def from_name(cls, name: str):
 transformer_configs = {
     "tiny": dict(block_size=128, n_layer=4, n_head=2, n_local_heads=1, dim=256, intermediate_size=512, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
     "small": dict(block_size=512, n_layer=4, n_head=32, n_local_heads=8, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "medium": dict(block_size=512, n_layer=4, n_head=8, n_local_heads=2, dim=2048, intermediate_size=7168, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "medium": dict(block_size=512, n_layer=4, n_head=8, n_local_heads=2, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=4, num_activated_experts=2),
     "Mixtral-8x7B-v0.1": dict(block_size=32768, n_layer=4, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
 }
 
diff --git a/test/test_mixtral.py b/test/test_mixtral.py
index a82ad00..2c5c043 100644
--- a/test/test_mixtral.py
+++ b/test/test_mixtral.py
@@ -149,6 +149,7 @@ def parse_args():
 if __name__ == "__main__":
     ray.init(include_dashboard=False, log_to_driver=True, namespace="mixtral")
     args = parse_args()
+    print(args)
     piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
     handles = piper_coordinator.run_program.remote(main, args)
     ray.get(handles)

From 4a52dfe0a57ac1ccfbe160b7812d99e08a64c0a0 Mon Sep 17 00:00:00 2001
From: Shubham Tiwari <shubhamtiwaribits@gmail.com>
Date: Tue, 3 Mar 2026 21:02:54 +0000
Subject: [PATCH 064/100] integrate qwen with torch.ops workaround

---
 src/piper_actor.py   | 103 ++++++++++++-
 src/piper_utils.py   |  69 ++++++++-
 test/models/qwen3.py | 180 ++++++++++++++++++++++
 test/test_qwen.py    | 345 +++++++++++++++++++++++++++++++++++++++++++
 4 files changed, 692 insertions(+), 5 deletions(-)
 create mode 100644 test/models/qwen3.py
 create mode 100644 test/test_qwen.py

diff --git a/src/piper_actor.py b/src/piper_actor.py
index a2d885c..f2bfdb5 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -331,11 +331,110 @@ def _wait_for_comm_ops(self):
                         self.comm_op_handles[stage_id][tid].wait()
                         done = True
 
+    def _load_stage_kernels(self):
+        # Ensure Triton kernel modules are imported before deserialization
+        # This ensures the kernel registration mechanism is available
+        try:
+            import torchtitan.models.common.moe.kernels
+            import torchtitan.models.common.moe.utils
+            import torch._higher_order_ops.triton_kernel_wrap
+
+            # Trigger module initialization by accessing a function
+            from torchtitan.models.common.moe.kernels import generate_permute_indices
+            from torchtitan.models.common.moe.kernels import fill_indices_wrapper
+            self.logger.info(f"Actor {self.global_rank} imported and initialized Triton kernel modules")
+        except ImportError as e:
+            self.logger.warning(
+                f"Actor {self.global_rank} failed to import Triton kernel modules: {e}. "
+                f"This may cause issues with Triton kernels in the graph."
+            )
+        
+        # HACK: Manually register Triton kernel in the side table
+        # The graph expects kernel_idx=0, so we need to register it at that index
+        self.logger.info(f"Manually registering Triton kernel at index 0 on actor {self.global_rank}...")
+        try:
+            # Import the kernel wrapper
+            from torchtitan.models.common.moe.kernels import fill_indices_wrapper
+            
+            # Create a dummy function that uses the Triton kernel
+            # This needs to be compiled with torch.compile to register the kernel
+            def dummy_kernel_user(tokens, starts, offsets):
+                return fill_indices_wrapper(
+                    tokens, starts, offsets,
+                    experts_per_rank=1, num_ranks=1, max_len=8, block_size=128
+                )
+            
+            # Compile the function to trigger kernel registration
+            device_idx = int(self.device.split(":")[1]) if ":" in self.device else 0
+            with torch.cuda.device(device_idx):
+                # Create dummy inputs
+                dummy_tokens = torch.zeros(8, dtype=torch.int32, device=self.device)
+                dummy_start = torch.zeros(8, dtype=torch.int64, device=self.device)
+                dummy_offsets = torch.zeros(1, dtype=torch.int64, device=self.device)
+                
+                # Compile and run to register the kernel
+                compiled_dummy = torch.compile(dummy_kernel_user, mode="reduce-overhead")
+                _ = compiled_dummy(dummy_tokens, dummy_start, dummy_offsets)
+            
+            # Now try to access the kernel side table and register at index 0
+            from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
+            
+            # The kernel should now be registered, but we need to find it and move it to index 0
+            if hasattr(kernel_side_table, 'id_to_kernel') and hasattr(kernel_side_table, 'constant_args'):
+                registered_kernels = list(kernel_side_table.id_to_kernel.items())
+                registered_constants = list(kernel_side_table.constant_args.items())
+                
+                if len(registered_kernels) > 0:
+                    # Get the most recently registered kernel (highest index)
+                    max_idx, kernel = max(registered_kernels, key=lambda x: x[0])
+                    # Manually register it at index 0
+                    kernel_side_table.id_to_kernel[0] = kernel
+                    
+                    # Also register constant args if available
+                    # The graph might expect constant_args_idx=1, so we need to check what was registered
+                    if len(registered_constants) > 0:
+                        # Get the constant args that correspond to our kernel
+                        # Usually they're at the same index, but let's be safe
+                        max_const_idx, const_args = max(registered_constants, key=lambda x: x[0])
+                        for idx in range(10):
+                            kernel_side_table.constant_args[idx] = const_args
+                        self.logger.info(
+                            f"Actor {self.global_rank} manually registered kernel at index 0 "
+                            f"(copied from index {max_idx}, total kernels: {len(registered_kernels)}) "
+                            f"and constant_args at index 1 (copied from index {max_const_idx}, total constants: {len(registered_constants)})"
+                        )
+                    else:
+                        # If no constant args were registered, we might need to create empty ones
+                        # But let's see if the graph actually needs them
+                        self.logger.info(
+                            f"Actor {self.global_rank} manually registered kernel at index 0 "
+                            f"(copied from index {max_idx}, total kernels: {len(registered_kernels)}) "
+                            f"but no constant_args found"
+                        )
+                else:
+                    self.logger.warning(
+                        f"Actor {self.global_rank} no kernels found in side table after compilation"
+                    )
+            else:
+                # Try accessing via different attribute names
+                attrs = [attr for attr in dir(kernel_side_table) if not attr.startswith('_')]
+                self.logger.warning(
+                    f"Actor {self.global_rank} kernel_side_table doesn't have expected attributes. "
+                    f"Available attributes: {attrs}"
+                )
+        except Exception as e:
+            self.logger.warning(
+                f"Actor {self.global_rank} failed to manually register kernel: {e}. "
+                f"This may cause Triton kernel errors."
+            )
+            import traceback
+            self.logger.info(f"Traceback: {traceback.format_exc()}")
+
     def _load_stage(
         self, stage_id: int, gm_data, forward_args, input_idxs, param_idxs, 
     ):
-        self.logger.debug(f"Loading stage {stage_id} graph on actor {self.global_rank}")
-
+        self.logger.info(f"Loading stage {stage_id} graph on actor {self.global_rank}")
+        self._load_stage_kernels()
         # compile the graph with the given graphargs
         gm = _deserialize_graphmodule(gm_data)
 
diff --git a/src/piper_utils.py b/src/piper_utils.py
index b15878d..2a64802 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -139,6 +139,12 @@ def _serialize_target(t):
     if isinstance(t, str):
         return {"kind": "string", "value": t}
 
+    target_str = str(t)
+    if target_str.startswith("torch.ops."):
+            # Remove "torch.ops." prefix to get the path
+            path = target_str[len("torch.ops."):]
+            return {"kind": "torch_op", "path": path}
+
     # Handle _VariableFunctionsClass
     if getattr(t, "__module__", "") == "torch._VariableFunctionsClass":
         public_name = t.__name__
@@ -153,6 +159,25 @@ def _serialize_target(t):
 
     # regular python function or built-in
     if inspect.isfunction(t) or inspect.isbuiltin(t):
+        mod = inspect.getmodule(t)
+        # Check if inspect.getmodule returned torch.ops.* namespace
+        # These should be serialized as torch_op, not py_func
+        if mod is not None:
+            mod_name = mod.__name__
+            if mod_name.startswith("torch.ops"):
+                # This is a torch.ops function, serialize as torch_op
+                target_str = str(t)
+                if target_str.startswith("torch.ops."):
+                    path = target_str[len("torch.ops."):]
+                    return {"kind": "torch_op", "path": path}
+                # If str(t) doesn't work, try to construct path from module and name
+                # e.g., module="torch.ops.higher_order", name="triton_kernel_wrapper_mutation"
+                # -> path="higher_order.triton_kernel_wrapper_mutation"
+                module_parts = mod_name.split(".")
+                if len(module_parts) >= 3:
+                    namespace = module_parts[2]  # e.g., "higher_order"
+                    path = f"{namespace}.{t.__name__}"
+                    return {"kind": "torch_op", "path": path}
         # Special handling for torch.autograd.Function.apply methods
         # These are static methods that may have the wrong module in inspect.getmodule
         if t.__name__ == "apply":
@@ -225,12 +250,25 @@ def _resolve_qualname(mod, qualname):
     return obj
 
 def _deserialize_target(payload):
+    # print("DESERIALIZING", payload, flush=True)
     kind = payload["kind"]
 
     if kind == "string":
         return payload["value"]
 
     if kind == "py_func":
+        # Workaround: if module is torch.ops.*, redirect to torch_op deserialization
+        if payload["module"].startswith("torch.ops"):
+            # Reconstruct the path from module and qualname
+            # e.g., module="torch.ops.higher_order", qualname="triton_kernel_wrapper_mutation"
+            # -> path="higher_order.triton_kernel_wrapper_mutation"
+            module_parts = payload["module"].split(".")
+            if len(module_parts) >= 3:
+                namespace = module_parts[2]  # e.g., "higher_order"
+                path = f"{namespace}.{payload['qualname']}"
+                # Recursively call with torch_op payload
+                torch_op_payload = {"kind": "torch_op", "path": path}
+                return _deserialize_target(torch_op_payload)
         mod = importlib.import_module(payload["module"])
         return _resolve_qualname(mod, payload["qualname"])
 
@@ -246,9 +284,34 @@ def _deserialize_target(payload):
 
     if kind == "torch_op":
         obj = torch.ops
-        for part in payload["path"].split("."):
-            obj = getattr(obj, part)
-        return obj
+        path = payload["path"]
+        try:
+            for part in payload["path"].split("."):
+                obj = getattr(obj, part)
+            return obj
+        except AttributeError:
+                # If operator doesn't exist, try importing implementation module to trigger registration
+                if path.startswith("higher_order."):
+                    op_name = path.split(".", 1)[1]
+                    op_to_module = {
+                        "triton_kernel_wrapper_mutation": "torch._higher_order_ops.triton_kernel_wrap",
+                        "triton_kernel_wrapper_functional": "torch._higher_order_ops.triton_kernel_wrap",
+                    }
+                    if op_name in op_to_module:
+                        try:
+                            # Import to trigger registration
+                            importlib.import_module(op_to_module[op_name])
+                            # Try again
+                            obj = torch.ops
+                            for part in path.split("."):
+                                obj = getattr(obj, part)
+                            return obj
+                        except (ImportError, AttributeError):
+                            pass
+                raise AttributeError(
+                    f"Failed to resolve torch.ops path '{path}'. "
+                    f"Available attributes: {[x for x in dir(obj) if not x.startswith('_')][:20]}"
+                )
 
     raise NotImplementedError(f"Unknown target kind: {kind}")
 
diff --git a/test/models/qwen3.py b/test/models/qwen3.py
new file mode 100644
index 0000000..0ad5154
--- /dev/null
+++ b/test/models/qwen3.py
@@ -0,0 +1,180 @@
+from dataclasses import dataclass
+from typing import Optional
+import torch
+import dataclasses
+from torch import nn
+
+# from torchtitan.models.qwen3 import Qwen3Model
+from torchtitan.models.qwen3 import Qwen3Model, Qwen3TransformerBlock
+from torchtitan.models.common.attention import AttentionMasksType
+from torchtitan.models.common.moe.moe import MoE
+
+
+class AnnotatedMoE(MoE):
+    
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        bs, slen, dim = x.shape
+        x = x.view(-1, dim)
+        
+        (
+            top_scores,
+            selected_experts_indices,
+            num_tokens_per_expert,
+        ) = self.router(x, self.expert_bias)
+        
+        with torch.no_grad():
+            self.tokens_per_expert.add_(num_tokens_per_expert)
+        
+        (
+            top_scores_experts_sorted,
+            token_indices_experts_sorted,
+            num_tokens_per_expert,
+        ) = self.reorderer(top_scores, selected_experts_indices)
+        
+        # shape (bs*slen*top_k, dim)
+        routed_input = x[token_indices_experts_sorted // self.router.top_k]
+        
+        if self.score_before_experts:
+            routed_input = (
+                routed_input.to(torch.float32)
+                * top_scores_experts_sorted.reshape(-1, 1)
+            ).to(x.dtype)
+        
+        # PIPER ANNOTATION 1: Dispatch tokens to experts via all_to_all
+        with torch.fx.traceback.annotate({
+            "collective": "all_to_all_single",
+            "group": "ep",
+            "reshape": ("output", (self.experts.num_experts, -1, dim))
+        }):
+            # The routed_input will be dispatched to experts on different ranks
+            dispatched_input = routed_input
+        
+        routed_output = self.experts(dispatched_input, num_tokens_per_expert)
+        
+        # PIPER ANNOTATION 2: Gather expert outputs back via all_to_all
+        with torch.fx.traceback.annotate({
+            "collective": "all_to_all_single",
+            "group": "ep",
+            "reshape": ("input", (self.experts.num_experts, -1, dim))
+        }):
+            # The routed_output will be gathered back from expert ranks
+            gathered_output = routed_output
+        
+        # Rest of the forward pass (same as parent)
+        out = self.shared_experts(x) if self.shared_experts is not None else None
+        
+        # Unsort routed outputs
+        routed_output_unsorted = torch.zeros(
+            (bs * slen * self.router.top_k, dim),
+            dtype=gathered_output.dtype,
+            device=gathered_output.device,
+        )
+        routed_output_unsorted[token_indices_experts_sorted] = gathered_output
+        routed_output_unsorted = routed_output_unsorted.reshape(
+            -1, self.router.top_k, dim
+        )
+        
+        if not self.score_before_experts:
+            out_experts = (
+                torch.bmm(
+                    top_scores.reshape(-1, 1, self.router.top_k),
+                    routed_output_unsorted.float(),
+                )
+                .to(x.dtype)
+                .squeeze(1)
+            )
+        else:
+            out_experts = routed_output_unsorted.sum(dim=1)
+        
+        if out is None:
+            return out_experts.reshape(bs, slen, dim)
+        return (out + out_experts).reshape(bs, slen, dim)
+
+
+class AnnotatedQwen3TransformerBlock(Qwen3TransformerBlock):
+    def __init__(self, config, *, layer_id: int, dim: int, n_layers: int):
+        # Call parent init
+        super().__init__(config, layer_id=layer_id, dim=dim, n_layers=n_layers)
+        
+        # Replace MoE with annotated version if MoE is enabled
+        if self.moe_enabled and hasattr(self, 'moe'):
+            # Create AnnotatedMoE using the config (simpler and correct)
+            annotated_moe = AnnotatedMoE(config.moe, dim=dim)
+            
+            # Copy weights from original MoE if already initialized
+            if hasattr(self.moe, 'experts'):
+                annotated_moe.experts.load_state_dict(self.moe.experts.state_dict())
+                annotated_moe.router.load_state_dict(self.moe.router.state_dict())
+                if self.moe.shared_experts is not None:
+                    annotated_moe.shared_experts.load_state_dict(
+                        self.moe.shared_experts.state_dict()
+                    )
+            self.moe = annotated_moe
+
+
+class PiperQwen3Model(Qwen3Model):
+    """
+    Qwen3Model with pipeline stage annotations.
+    
+    This wraps TorchTitan's Qwen3Model and adds stage boundaries
+    in the forward pass for pipeline parallelism.
+    """
+    
+    def __init__(self, config, num_stages: int = 2):
+        # Temporarily replace layer config to use AnnotatedQwen3TransformerBlock
+        original_layer_config = config.layer
+        
+        # Create a new config that uses AnnotatedQwen3TransformerBlock
+        # We'll override the build method to use our annotated block
+        class AnnotatedLayerConfig(original_layer_config.__class__):
+            def build(self, *, layer_id: int, dim: int, n_layers: int):
+                return AnnotatedQwen3TransformerBlock(
+                    self, layer_id=layer_id, dim=dim, n_layers=n_layers
+                )
+        
+        # Extract fields from the original config (works with slots=True)
+        field_dict = {field.name: getattr(original_layer_config, field.name) 
+                     for field in dataclasses.fields(original_layer_config)}
+        annotated_layer_config = AnnotatedLayerConfig(**field_dict)
+        config.layer = annotated_layer_config
+        
+        # Call parent init
+        super().__init__(config)
+        self.num_stages = num_stages
+    
+    def forward(
+        self,
+        tokens: torch.Tensor,
+        attention_masks: Optional[AttentionMasksType] = None,
+        positions: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """
+        Forward with pipeline stage annotations.
+        
+        The model is split into num_stages stages. Each stage processes
+        a subset of layers.
+        """
+        num_layers = len(self.layers)
+        layers_per_stage = num_layers // self.num_stages
+        
+        # Stage 0: Embedding + first N layers
+        with torch.fx.traceback.annotate({"stage": 0}):
+            h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
+            
+            # Process first set of layers
+            for i in range(layers_per_stage * (self.num_stages - 1)):
+                layer = self.layers[str(i)]
+                h = layer(h, self.freqs_cis, attention_masks, positions)
+        
+        # Stage 1 (or last stage): Remaining layers + norm + output
+        with torch.fx.traceback.annotate({"stage": self.num_stages - 1}):
+            # Process remaining layers
+            for i in range(layers_per_stage * (self.num_stages - 1), num_layers):
+                layer = self.layers[str(i)]
+                h = layer(h, self.freqs_cis, attention_masks, positions)
+            
+            # Final norm and output
+            h = self.norm(h) if self.norm is not None else h
+            output = self.output(h) if self.output is not None else h
+        
+        return output
\ No newline at end of file
diff --git a/test/test_qwen.py b/test/test_qwen.py
new file mode 100644
index 0000000..ed094af
--- /dev/null
+++ b/test/test_qwen.py
@@ -0,0 +1,345 @@
+import ray
+import torch
+import torch.nn as nn
+import argparse
+import time
+import os
+import numpy as np
+
+from src.piper_coordinator import PiperProgramCoordinator
+from src.piper_compile import piper_setup
+from src.piper_exec import piper_exec
+from src.piper_utils import piper_metadata
+
+from .models.qwen3 import PiperQwen3Model
+from torchtitan.models.qwen3 import Qwen3Model, Qwen3TransformerBlock
+from torchtitan.models.common import GQAttention, RoPE, FeedForward
+from torchtitan.models.common.moe import MoE
+from .schedule_helpers import (
+    build_1f1b_schedule, 
+    build_gpipe_schedule, 
+    print_schedule,
+    INTERLEAVED_1F1B_PP2_MB4_SCHEDULE,
+    INTERLEAVED_1F1B_PP2_MB6_SCHEDULE,
+    INTERLEAVED_1F1B_PP4_MB8_SCHEDULE,
+    INTERLEAVED_GPIPE_PP2_MB4_SCHEDULE,
+    NO_PP_SCHEDULE,
+    DUALPIPEV_MB6_SCHEDULE,
+    DUALPIPEV_NOZB_MB6_SCHEDULE,
+    ZEROBUBBLE_MB4_SCHEDULE,
+)
+
+
+def create_qwen3_config(name: str) -> Qwen3Model.Config:
+    """Create Qwen3 model config based on name."""
+    match name:
+        case 'tiny':
+            return Qwen3Model.Config(
+                vocab_size=2048,
+                dim=256,
+                n_layers=8,
+                norm_eps=1e-6,
+                enable_weight_tying=False,
+                layer=Qwen3TransformerBlock.Config(
+                    depth_init=True,
+                    moe_enabled=True,
+                    norm_eps=1e-6,
+                    moe=MoE.Config(
+                        hidden_dim=128,
+                        num_experts=8,
+                        top_k=2,
+                        use_grouped_mm=True,
+                        num_expert_groups=None,
+                        num_limited_groups=None,
+                        score_func="softmax",
+                        route_norm=False,
+                        route_scale=1.0,
+                        gate_bias=False,
+                        score_before_experts=False,
+                        num_shared_experts=0,
+                        load_balance_coeff=None,
+                        _debug_force_load_balance=False,
+                    ),
+                    feed_forward=FeedForward.Config(hidden_dim=512),
+                    attention=GQAttention.Config(
+                        n_heads=8,
+                        n_kv_heads=4,
+                        head_dim=32,
+                        qk_norm=True,
+                        norm_eps=1e-6,
+                        attn_backend="sdpa",
+                        rope_backend="cos_sin",
+                    ),
+                ),
+                rope=RoPE.Config(
+                    dim=32,
+                    max_seq_len=2048,
+                    theta=1000000.0,
+                    backend="cos_sin",
+                ),
+            )
+        case 'small':
+            return Qwen3Model.Config(
+                vocab_size=151936,
+                dim=1024,
+                n_layers=16,
+                norm_eps=1e-6,
+                enable_weight_tying=False,
+                layer=Qwen3TransformerBlock.Config(
+                    depth_init=True,
+                    moe_enabled=True,
+                    norm_eps=1e-6,
+                    moe=MoE.Config(
+                        hidden_dim=3584,
+                        num_experts=2,
+                        top_k=2,
+                        use_grouped_mm=True,
+                        num_expert_groups=None,
+                        num_limited_groups=None,
+                        score_func="softmax",
+                        route_norm=False,
+                        route_scale=1.0,
+                        gate_bias=False,
+                        score_before_experts=False,
+                        num_shared_experts=0,
+                        load_balance_coeff=None,
+                        _debug_force_load_balance=False,
+                    ),
+                    attention=GQAttention.Config(
+                        n_heads=16,
+                        n_kv_heads=8,
+                        head_dim=64,
+                        qk_norm=True,
+                        norm_eps=1e-6,
+                        attn_backend="sdpa",
+                        rope_backend="cos_sin",
+                    ),
+                ),
+                rope=RoPE.Config(
+                    dim=64,
+                    max_seq_len=2048,
+                    theta=1000000.0,
+                    backend="cos_sin",
+                ),
+            )
+        case 'medium':
+            return Qwen3Model.Config(
+                vocab_size=151936,
+                dim=2048,
+                n_layers=24,
+                norm_eps=1e-6,
+                enable_weight_tying=False,
+                layer=Qwen3TransformerBlock.Config(
+                    depth_init=True,
+                    moe_enabled=True,
+                    norm_eps=1e-6,
+                    moe=MoE.Config(
+                        hidden_dim=7168,
+                        num_experts=8,
+                        top_k=2,
+                        use_grouped_mm=True,
+                        num_expert_groups=None,
+                        num_limited_groups=None,
+                        score_func="softmax",
+                        route_norm=False,
+                        route_scale=1.0,
+                        gate_bias=False,
+                        score_before_experts=False,
+                        num_shared_experts=0,
+                        load_balance_coeff=None,
+                        _debug_force_load_balance=False,
+                    ),
+                    attention=GQAttention.Config(
+                        n_heads=32,
+                        n_kv_heads=8,
+                        head_dim=64,
+                        qk_norm=True,
+                        norm_eps=1e-6,
+                        attn_backend="sdpa",
+                        rope_backend="cos_sin",
+                    ),
+                ),
+                rope=RoPE.Config(
+                    dim=64,
+                    max_seq_len=2048,
+                    theta=1000000.0,
+                    backend="cos_sin",
+                ),
+            )
+        case 'large':
+            return Qwen3Model.Config(
+                vocab_size=151936,
+                dim=4096,
+                n_layers=32,
+                norm_eps=1e-6,
+                enable_weight_tying=False,
+                layer=Qwen3TransformerBlock.Config(
+                    depth_init=True,
+                    moe_enabled=True,
+                    norm_eps=1e-6,
+                    moe=MoE.Config(
+                        hidden_dim=14336,
+                        num_experts=8,
+                        top_k=2,
+                        use_grouped_mm=True,
+                        num_expert_groups=None,
+                        num_limited_groups=None,
+                        score_func="softmax",
+                        route_norm=False,
+                        route_scale=1.0,
+                        gate_bias=False,
+                        score_before_experts=False,
+                        num_shared_experts=0,
+                        load_balance_coeff=None,
+                        _debug_force_load_balance=False,
+                    ),
+                    attention=GQAttention.Config(
+                        n_heads=32,
+                        n_kv_heads=8,
+                        head_dim=128,
+                        qk_norm=True,
+                        norm_eps=1e-6,
+                        attn_backend="sdpa",
+                        rope_backend="cos_sin",
+                    ),
+                ),
+                rope=RoPE.Config(
+                    dim=128,
+                    max_seq_len=2048,
+                    theta=1000000.0,
+                    backend="cos_sin",
+                ),
+            )
+        case _:
+            raise ValueError(f"Unknown model config: {name}")
+
+
+def main(args):
+    world_size = args.dp * args.pp
+    mbs = args.mbs
+    batch_size = args.batch_size
+    seq_len = args.seq_len
+
+    # Create model config
+    config = create_qwen3_config(args.model)
+
+    # Select schedule
+    schedule = None
+    match args.schedule:
+        case "no-pp":
+            schedule = NO_PP_SCHEDULE
+        case "interleaved-1f1b":
+            schedule = INTERLEAVED_1F1B_PP2_MB4_SCHEDULE if args.pp == 2 else INTERLEAVED_1F1B_PP4_MB8_SCHEDULE
+        case "1f1b":
+            schedule = build_1f1b_schedule(args.mbs, args.pp)
+        case "gpipe":
+            schedule = build_gpipe_schedule(args.mbs, args.pp)
+    
+    print("Schedule:")
+    print_schedule(schedule)
+
+    # Create model with pipeline stages
+    model = PiperQwen3Model(config, num_stages=args.pp)
+    model.to('cuda')
+    
+    # Initialize weights using TorchTitan's method
+    with torch.no_grad():
+        model.init_weights(buffer_device=torch.device("cuda:0"))
+    
+    # Create input tensors
+    # Qwen3 forward signature: forward(tokens, attention_masks=None, positions=None)
+    x = torch.randint(0, config.vocab_size, (batch_size, seq_len)).to('cuda')
+    # For Qwen3, we don't need input_pos like Mixtral - positions can be None
+    # attention_masks can also be None for causal attention
+    
+    # Create labels for loss (vocab_size)
+    y = torch.randn(batch_size, seq_len, config.vocab_size).to('cuda')
+
+    # Setup Piper
+    piper_setup(
+        model, 
+        torch.optim.Adam, 
+        [x],  # Only tokens, not [x, input_pos] like Mixtral
+        y,
+        schedule,
+        args.naive_gradient_sync,
+    )
+
+    # Send data to actors ahead of time
+    actors = piper_metadata.actors
+    ray.get(actors[0].load_input.remote([x]))
+    ray.get(actors[len(actors)-1].load_labels.remote(y))
+
+    loss_fn = torch.nn.CrossEntropyLoss()
+
+    print(f"Running {args.warmup} warmup iterations...")
+    for _ in range(args.warmup):
+        piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
+        # piper_exec(schedule, [x], y, loss_fn, args.dp, args.naive_gradient_sync)
+    
+    print(f"Running {args.iters} timed iterations...")
+    iter_times = []
+    for _ in range(args.iters):
+        start = time.perf_counter()
+        piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
+        end = time.perf_counter()
+        iter_times.append(end - start)
+    
+    dp_rank = int(os.environ['PIPER_DP_RANK'])
+    print(
+        f"rank {dp_rank} iter time= {np.mean(iter_times):.2f} ± {np.std(iter_times):.2f} s ({len(iter_times)} samples)\n"
+        f"rank {dp_rank} throughput= {(args.batch_size * args.mbs * seq_len)/np.mean(iter_times):.2f} tokens/s ({len(iter_times)} samples)"
+    )
+
+    if args.tracing:
+        ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
+
+        print(f"Running {args.warmup} tracing iterations...")
+        for _ in range(args.warmup):
+            piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
+
+        trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
+        for rank, trace_data in trace_data_ret:
+            for key in trace_data:
+                all_times = trace_data[key]
+                print(f"rank {rank} {key} time= {np.mean(all_times):.3f} ± {np.std(all_times):.3f} ms ({len(all_times)} samples)")
+
+    timeline_filename = f"out/qwen3-pp{args.pp}-dp{args.dp}"
+    ray.timeline(timeline_filename)
+    print(f"Ray timeline saved to: {timeline_filename}")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Run Qwen3 model with pipeline parallelism')
+    parser.add_argument('--model', choices=['tiny', 'small', 'medium', 'large'], default='small',
+                        help='Model configuration: tiny, small, medium, or large (default: tiny)')
+    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'no-pp'], default='1f1b',
+                        help='Schedule type: gpipe, 1f1b, or interleaved-1f1b (default: 1f1b)')
+    parser.add_argument('--dp', type=int, default=1,
+                        help='Number of data parallel degrees (default: 1)')
+    parser.add_argument('--pp', type=int, default=2,
+                        help='Number of pipeline parallel degrees (default: 2)')
+    parser.add_argument('--batch_size', type=int, default=16,
+                        help='Batch size (default: 16)')
+    parser.add_argument('--seq_len', type=int, default=2048,
+                        help='Sequence length (default: 2048)')
+    parser.add_argument('--mbs', type=int, default=4,
+                        help='Number of microbatches (default: 4)')
+    parser.add_argument('--warmup', type=int, default=5,
+                        help='Number of warmup iterations (default: 5)')
+    parser.add_argument('--iters', type=int, default=10,
+                        help='Number of timing iterations (default: 10)')
+    parser.add_argument('--tracing', action='store_true', default=False,
+                        help='Enable tracing')
+    parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
+                        help='Enable naive gradient sync')
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    ray.init(include_dashboard=False, log_to_driver=True, namespace="qwen3", _temp_dir="/m-coriander/coriander/mfris/tmp/ray")
+    args = parse_args()
+    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
+    handles = piper_coordinator.run_program.remote(main, args)
+    ray.get(handles)
+    ray.shutdown()
\ No newline at end of file

From 70fe692cb685e8e1a15bdd3347ba398a35c25e8a Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Thu, 5 Mar 2026 18:55:49 +0000
Subject: [PATCH 065/100] use cuda events to wait for recv in compute tasks
 rather than p2p tasks, separate p2p send vs recv thread (on a single
 communicator does not actually run on separate streams)

---
 src/piper_actor.py     | 183 ++++++++++++++++++++---------------------
 src/piper_compile.py   |   3 +-
 test/models/llama.py   |   8 +-
 test/models/mixtral.py |  60 +++++++-------
 test/test_llama.py     |  16 +++-
 test/test_mixtral.py   |  11 +++
 6 files changed, 148 insertions(+), 133 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index 2cc3e3b..7407e17 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -2,7 +2,6 @@
 import torch
 import logging
 import os
-import time
 from typing import Any, Dict, List, Set, Tuple
 import gc
 import threading
@@ -128,6 +127,8 @@ def __init__(
         self.comm_stream = torch.cuda.Stream()
         self.a2a_stream = torch.cuda.Stream()
         self.p2p_stream = torch.cuda.Stream()
+        self.p2p_send_stream = torch.cuda.Stream()
+        self.p2p_recv_stream = torch.cuda.Stream()
         self.overlapped_comp_stream = torch.cuda.Stream()
         self.overlapped_p2p_stream = torch.cuda.Stream()
         if mode == "naive":
@@ -155,6 +156,8 @@ def __init__(
         self.out_activation = defaultdict(dict)
         # map (src_stage, dst_stage, mb_idx, is_sender) -> tensor for p2p communication
         self.p2p_cache = dict()
+        # map recv p2p_op -> CUDA event recorded after the recv completes on p2p_stream
+        self.p2p_events = dict()
         # accumuate loss for each microbatch
         self.loss = []
         # map stage id -> data parallel communication operations
@@ -169,6 +172,7 @@ def __init__(
         self.tracing = False
         self.trace_events = dict()
         self.trace_data = defaultdict(list)
+        self.task_labels = dict()
 
         # map stage id -> mb_idx -> parameter groups for backward pass
         self.bw_param_groups = defaultdict(dict)
@@ -181,10 +185,19 @@ def __init__(
 
         piper_metadata.actor_self = self
 
+    def _label_task(self, label: str):
+        task_id = ray.get_runtime_context().get_task_id()
+        if task_id:
+            self.task_labels[task_id] = label
+
+    def get_task_labels(self) -> dict:
+        return self.task_labels
+
     def reset_p2p_states(self):
         self.next_p2p_idx = 0
         self.executed_p2ps = set()
         self.p2p_cache = dict()
+        self.p2p_events = dict()
 
     def get_trace_data(self) -> dict:
         return self.global_rank, self.trace_data
@@ -449,6 +462,7 @@ def _load_stage(
     def _exec_p2p_op(
         self, src_stage: int, dst_stage: int, mb_idx: int, is_sender: bool, dep, p2p_stream=None
     ):
+        self._label_task("P2P")
         p2p_op = (src_stage, dst_stage, mb_idx, is_sender)
         if p2p_op in self.executed_p2ps:
             return
@@ -477,80 +491,22 @@ def _exec_p2p_op(
 
             op_name = "P2P(unknown)"
             if is_fwd and is_sender:
-                self._exec_fwd_send(src_stage, mb_idx, p2p_stream=p2p_stream)
+                self._exec_fwd_send(src_stage, mb_idx, p2p_stream=self.p2p_send_stream)
                 op_name = (
                     f"P2P(fwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
                 )
             elif is_fwd and is_recver:
-                self._exec_fwd_recv(dst_stage, mb_idx, p2p_stream=p2p_stream)
+                self._exec_fwd_recv(dst_stage, mb_idx, p2p_stream=self.p2p_recv_stream)
                 op_name = (
                     f"P2P(fwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
                 )
             elif is_bwd and is_sender:
-                self._exec_bwd_send(src_stage, mb_idx, p2p_stream=p2p_stream)
+                self._exec_bwd_send(src_stage, mb_idx, p2p_stream=self.p2p_send_stream)
                 op_name = (
                     f"P2P(bwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
                 )
             elif is_bwd and is_recver:
-                self._exec_bwd_recv(dst_stage, mb_idx, p2p_stream=p2p_stream)
-                op_name = (
-                    f"P2P(bwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
-                )
-            else:
-                raise ValueError(f"Invalid p2p op: {op}")
-
-            self.executed_p2ps.add(op)
-            logger.debug(f"Executed {op_name} on actor {self.global_rank}")
-
-        self.next_p2p_idx = op_idx + 1
-        
-    def _exec_p2p_op(
-        self, src_stage: int, dst_stage: int, mb_idx: int, is_sender: bool, dep, p2p_stream=None
-    ):
-        p2p_op = (src_stage, dst_stage, mb_idx, is_sender)
-        if p2p_op in self.executed_p2ps:
-            return
-
-        if p2p_stream is None:
-            p2p_stream = self.p2p_stream
-
-        op_idx = self.next_p2p_idx
-        while op_idx < len(self.p2p_schedule):
-            op = self.p2p_schedule[op_idx]
-            if op == p2p_op:
-                break
-            op_idx += 1
-        assert op_idx < len(
-            self.p2p_schedule
-        ), f"P2P op {p2p_op} not found in schedule for actor {self.global_rank}"
-
-        # Execute everything before and including the given p2p op
-        for idx in range(self.next_p2p_idx, op_idx + 1):
-            op = self.p2p_schedule[idx]
-            src_stage, dst_stage, mb_idx, is_sender = op
-            is_fwd = src_stage == dst_stage - 1
-            is_bwd = src_stage == dst_stage + 1
-            assert is_fwd or is_bwd
-            is_recver = not is_sender
-
-            op_name = "P2P(unknown)"
-            if is_fwd and is_sender:
-                self._exec_fwd_send(src_stage, mb_idx, p2p_stream=p2p_stream)
-                op_name = (
-                    f"P2P(fwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
-                )
-            elif is_fwd and is_recver:
-                self._exec_fwd_recv(dst_stage, mb_idx, p2p_stream=p2p_stream)
-                op_name = (
-                    f"P2P(fwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
-                )
-            elif is_bwd and is_sender:
-                self._exec_bwd_send(src_stage, mb_idx, p2p_stream=p2p_stream)
-                op_name = (
-                    f"P2P(bwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
-                )
-            elif is_bwd and is_recver:
-                self._exec_bwd_recv(dst_stage, mb_idx, p2p_stream=p2p_stream)
+                self._exec_bwd_recv(dst_stage, mb_idx, p2p_stream=self.p2p_recv_stream)
                 op_name = (
                     f"P2P(bwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
                 )
@@ -602,9 +558,9 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
                         src=global_src_rank,
                         group=self.pp_group,
                     )
-            # Ensure the default stream only consumes tensors after recv completes.
-            comp_stream.wait_stream(p2p_stream)
-            torch.cuda.synchronize()
+            recv_event = torch.cuda.Event()
+            recv_event.record(p2p_stream)
+            self.p2p_events[p2p_op] = recv_event
             self._stop_timing(p2p_stream, "fwd_p2p_recv")
             self.logger.debug(
                 f"Completed fwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
@@ -676,9 +632,9 @@ def _exec_bwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
                 dist.recv(
                     input_grad, src=global_src_rank, group=self.pp_group
                 )
-            # Ensure the default stream only consumes gradients after recv completes.
-            comp_stream.wait_stream(p2p_stream)
-            torch.cuda.synchronize()
+            recv_event = torch.cuda.Event()
+            recv_event.record(p2p_stream)
+            self.p2p_events[p2p_op] = recv_event
             self._stop_timing(p2p_stream, "bwd_p2p_recv")
             self.logger.debug(
                 f"Completed bwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id+1} -> {stage_id}, mb {mb_idx})"
@@ -725,6 +681,7 @@ def _exec_bwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
         self.inp_activation[stage_id][mb_idx] = None
     
     def _forward(self, stage_id: int, mb_idx: int, *deps):
+        self._label_task(f"{stage_id}:{mb_idx}")
         if self.mode == "sequential":
             comp_stream = self.comp_stream
         elif self.mode == "naive":
@@ -754,9 +711,11 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
             for i, inp in zip(self.input_idxs[stage_id], self.inputs):
                 self.forward_args[stage_id][i] = inp
         else:
-            inputs_from_prev_stage = self.p2p_cache.pop(
-                (stage_id - 1, stage_id, mb_idx, False)
-            )
+            recv_key = (stage_id - 1, stage_id, mb_idx, False)
+            recv_event = self.p2p_events.pop(recv_key, None)
+            if recv_event is not None:
+                comp_stream.wait_event(recv_event)
+            inputs_from_prev_stage = self.p2p_cache.pop(recv_key)
             for i, tensor in zip(self.input_idxs[stage_id], inputs_from_prev_stage):
                 if isinstance(tensor, (tuple, list)):
                     assert len(tensor) == 1
@@ -782,7 +741,9 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
         # Run the forward pass
         self._start_timing(comp_stream, "forward_comp")
         with torch.cuda.stream(comp_stream):
+            torch.cuda.nvtx.range_push(f"forward_stage_{stage_id}_mb_{mb_idx}")
             output = self.forward_fns[stage_id](*self.forward_args[stage_id])
+            torch.cuda.nvtx.range_pop()
         self._stop_timing(comp_stream, "forward_comp")
 
         # Save first output that requires grad as output activation
@@ -814,16 +775,17 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
             f"Forward {stage_id} mb {mb_idx} on actor {self.global_rank} returning {[out.shape for out in output]}"
         )
 
-        torch.cuda.synchronize()
+        comp_stream.synchronize()
 
         if self.profile:
             forward_ctx_manager.__exit__(None, None, None)
             profiler.__exit__(None, None, None)
             profiler.export_chrome_trace(f"out/{self.mode}/actor{self.global_rank}_fwd{stage_id}mb{mb_idx}_trace.json")
 
-        return 1
+        return 0
 
     def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
+        self._label_task(f"{stage_id}:{mb_idx}")
         if self.mode == "sequential":
             comp_stream = self.comp_stream
         elif self.mode == "naive":
@@ -851,13 +813,17 @@ def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         out_activation = self.out_activation[stage_id][mb_idx]
 
         if stage_id < self.num_stages - 1:
-            input_grad = self.p2p_cache.pop(
-                (stage_id + 1, stage_id, mb_idx, False)
-            )
+            recv_key = (stage_id + 1, stage_id, mb_idx, False)
+            recv_event = self.p2p_events.pop(recv_key, None)
+            if recv_event is not None:
+                comp_stream.wait_event(recv_event)
+            input_grad = self.p2p_cache.pop(recv_key)
 
             self._start_timing(comp_stream, "backward_comp")
             with torch.cuda.stream(comp_stream):
+                torch.cuda.nvtx.range_push(f"backward_stage_{stage_id}_mb_{mb_idx}")
                 out_activation.backward(gradient=input_grad)
+                torch.cuda.nvtx.range_pop()
             self._stop_timing(comp_stream, "backward_comp")
         else:
             assert loss_fn is not None
@@ -866,8 +832,10 @@ def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
 
             self._start_timing(comp_stream, "backward_comp")
             with torch.cuda.stream(comp_stream):
+                torch.cuda.nvtx.range_push(f"backward_stage_{stage_id}_mb_{mb_idx}")
                 loss = loss_fn(out_activation, labels)
                 loss.backward()
+                torch.cuda.nvtx.range_pop()
             self._stop_timing(comp_stream, "backward_comp")
 
             self.loss.append(loss.item())
@@ -881,17 +849,18 @@ def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
 
-        torch.cuda.synchronize()
+        comp_stream.synchronize()
 
         if self.profile:
             backward_ctx_manager.__exit__(None, None, None)
             profiler.__exit__(None, None, None)
             profiler.export_chrome_trace(f"out/{self.mode}/actor{self.global_rank}_bwd{stage_id}mb{mb_idx}_trace.json")
 
-        return 1
+        return 0
 
 
     def _backward_input(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
+        self._label_task(f"{stage_id}:{mb_idx}")
         comp_stream = self.comp_stream
 
         self.logger.debug(
@@ -904,16 +873,20 @@ def _backward_input(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         upstream_grad = None
         if stage_id < self.num_stages - 1:
             activation_or_loss = out_activation
-            upstream_grad = self.p2p_cache.pop(
-                (stage_id + 1, stage_id, mb_idx, False)
-            )
+            recv_key = (stage_id + 1, stage_id, mb_idx, False)
+            recv_event = self.p2p_events.pop(recv_key, None)
+            if recv_event is not None:
+                comp_stream.wait_event(recv_event)
+            upstream_grad = self.p2p_cache.pop(recv_key)
         else:
             assert loss_fn is not None
             labels = self.labels
             assert out_activation.shape == labels.shape
             with torch.cuda.stream(comp_stream):
+                torch.cuda.nvtx.range_push(f"backward_loss_stage_{stage_id}_mb_{mb_idx}")
                 loss = loss_fn(out_activation, labels)
                 self.loss.append(loss.item())
+                torch.cuda.nvtx.range_pop()
             activation_or_loss = loss
             upstream_grad = torch.ones_like(loss)
 
@@ -923,7 +896,7 @@ def _backward_input(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
             self.logger.debug(
                 f"Saving upstream gradient {upstream_grad.shape} for stage {stage_id} mb {mb_idx}"
             )
-            return 1
+            return 0
 
         stage_input = self.inp_activation[stage_id][mb_idx]
         stage_params = [self.forward_args[stage_id][i] for i in self.param_idxs[stage_id]]
@@ -958,6 +931,7 @@ def hook(grad_inputs):
 
         self._start_timing(comp_stream, "backward_input")
         with torch.cuda.stream(comp_stream):
+            torch.cuda.nvtx.range_push(f"backward_input_stage_{stage_id}_mb_{mb_idx}")
             gx = torch.autograd.grad(
                 outputs=activation_or_loss,
                 inputs=stage_input,
@@ -965,6 +939,7 @@ def hook(grad_inputs):
                 retain_graph=True,
                 allow_unused=True,
             )
+            torch.cuda.nvtx.range_pop()
         self._stop_timing(comp_stream, "backward_input")
 
         gx = gx[0] # Take the gx gradient out of the tuple returned by autograd.grad
@@ -995,11 +970,12 @@ def hook(grad_inputs):
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
 
-        torch.cuda.synchronize()
+        comp_stream.synchronize()
 
-        return 1
+        return 0
 
     def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
+        self._label_task(f"{stage_id}:{mb_idx}")
         comp_stream = self.comp_stream
 
         self.logger.debug(
@@ -1017,23 +993,27 @@ def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
             self._start_timing(comp_stream, "backward_weight")
             if stage_id < self.num_stages - 1:
                 with torch.cuda.stream(comp_stream):
+                    torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
                     gparams = torch.autograd.grad(
                         outputs=out_activation,
                         inputs=stage_params,
                         grad_outputs=upstream_grad,
                         retain_graph=False,
                     )
+                    torch.cuda.nvtx.range_pop()
             else:
                 assert loss_fn is not None
                 labels = self.labels
                 assert out_activation.shape == labels.shape
                 with torch.cuda.stream(comp_stream):
+                    torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
                     loss = loss_fn(out_activation, labels)
                     gparams = torch.autograd.grad(
                         outputs=loss,
                         inputs=stage_params,
                         retain_graph=False,
                     )
+                    torch.cuda.nvtx.range_pop()
             self._stop_timing(comp_stream, "backward_weight")
 
             assert len(gparams) == len(stage_params), (
@@ -1103,12 +1083,14 @@ def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
 
                 self._start_timing(comp_stream, "backward_weight")
                 with torch.cuda.stream(comp_stream):
+                    torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
                     gparams = torch.autograd.grad(
                         outputs=intermediate_edges,
                         inputs=weight_edges,
                         grad_outputs=intermediate_edge_grads,
                         retain_graph=False,
                     )
+                    torch.cuda.nvtx.range_pop()
                 self._stop_timing(comp_stream, "backward_weight")
 
                 del pg["grads"]
@@ -1144,11 +1126,12 @@ def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
 
-        torch.cuda.synchronize()
+        comp_stream.synchronize()
 
-        return 1
+        return 0
 
     def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: int, bwd_mb_idx: int, *deps, loss_fn=None):
+        self._label_task(f"{fwd_stage_id}:{fwd_mb_idx}|{bwd_stage_id}:{bwd_mb_idx}")
         if self.mode == "sequential":
             fwd_comp_stream = self.comp_stream
             bwd_comp_stream = self.comp_stream
@@ -1181,9 +1164,11 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             for i, inp in zip(self.input_idxs[fwd_stage_id], self.inputs):
                 self.forward_args[fwd_stage_id][i] = inp
         else:
-            inputs_from_prev_stage = self.p2p_cache.pop(
-                (fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False)
-            )
+            fwd_recv_key = (fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False)
+            fwd_recv_event = self.p2p_events.pop(fwd_recv_key, None)
+            if fwd_recv_event is not None:
+                fwd_comp_stream.wait_event(fwd_recv_event)
+            inputs_from_prev_stage = self.p2p_cache.pop(fwd_recv_key)
             for i, tensor in zip(self.input_idxs[fwd_stage_id], inputs_from_prev_stage):
                 if isinstance(tensor, (tuple, list)):
                     assert len(tensor) == 1
@@ -1209,9 +1194,11 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
         # PREPARE BACKWARD PASS
         out_activation = self.out_activation[bwd_stage_id][bwd_mb_idx]
         if bwd_stage_id < self.num_stages - 1:
-            input_grad = self.p2p_cache.pop(
-                (bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False)
-            )
+            bwd_recv_key = (bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False)
+            bwd_recv_event = self.p2p_events.pop(bwd_recv_key, None)
+            if bwd_recv_event is not None:
+                bwd_comp_stream.wait_event(bwd_recv_event)
+            input_grad = self.p2p_cache.pop(bwd_recv_key)
         else:
             assert loss_fn is not None
             labels = self.labels
@@ -1302,7 +1289,9 @@ def run_bwd():
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
 
-        torch.cuda.synchronize()
+        fwd_comp_stream.synchronize()
+        if bwd_comp_stream is not fwd_comp_stream:
+            bwd_comp_stream.synchronize()
 
         # clear A2A states
         if self.overlap_a2a_ops:
@@ -1320,7 +1309,7 @@ def run_bwd():
                 f"fwd{fwd_stage_id}mb{fwd_mb_idx}_"
                 f"bwd{bwd_stage_id}mb{bwd_mb_idx}_trace.json")
 
-        return 1
+        return 0
 
     def _synchronize_gradients(self):
         self.logger.info(f"Actor {self.global_rank} synchronizing gradients")
@@ -1334,6 +1323,7 @@ def _synchronize_gradients(self):
                         )
 
     def _update(self, *deps):
+        self._label_task("update")
         self.logger.debug(f"Actor {self.global_rank} waiting for backward sync events")
 
         # if dp degree > 1, make sure all gradients are synchronized before optimizer step
@@ -1356,7 +1346,8 @@ def _update(self, *deps):
         losses = self.loss
         self.loss.clear()
 
-        torch.cuda.synchronize()
+        self.comp_stream.synchronize()
+        self.comm_stream.synchronize()
 
         self.reset_p2p_states()
 
diff --git a/src/piper_compile.py b/src/piper_compile.py
index e0277e8..428dec7 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -109,6 +109,7 @@ def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, sta
     for src_stage, dst_stage, mb_idx in p2p_comms:
         src_rank = stage_to_device[src_stage]
         dst_rank = stage_to_device[dst_stage]
+        logger.info(f"P2P comm: {src_stage}:{src_rank}->{dst_stage}:{dst_rank} MB {mb_idx}")
         p2p_schedules[src_rank].append((src_stage, dst_stage, mb_idx, True))
         p2p_schedules[dst_rank].append((src_stage, dst_stage, mb_idx, False))
     
@@ -128,7 +129,7 @@ def piper_setup(
     example_outputs=None,
     schedule: Schedule2D=None,
     naive_gradient_sync=False,
-    mode="overlapped",
+    mode="sequential",
 ):
     """
     Compile a model with the piper backend.
diff --git a/test/models/llama.py b/test/models/llama.py
index f23b3f2..5f95623 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -461,19 +461,19 @@ def forward(self, tokens: torch.Tensor):
         with torch.fx.traceback.annotate({"stage": 0}):
             h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
             start_pos = 0
-            for layer in self.layers[:self.n_layers//4+1]:
+            for layer in self.layers[:self.n_layers//4]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
 
         with torch.fx.traceback.annotate({"stage": 1}):
-            for layer in self.layers[self.n_layers//4+1:self.n_layers//2+2]:
+            for layer in self.layers[self.n_layers//4:self.n_layers//2]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
 
         with torch.fx.traceback.annotate({"stage": 2}):
-            for layer in self.layers[self.n_layers//2+2:3*self.n_layers//4+3]:
+            for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
 
         with torch.fx.traceback.annotate({"stage": 3}):
-            for layer in self.layers[3*self.n_layers//4+3:]:
+            for layer in self.layers[3*self.n_layers//4:]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
             h = self.norm(h) if self.norm else h
             output = self.output(h).float() if self.output else h
diff --git a/test/models/mixtral.py b/test/models/mixtral.py
index 82f6673..b1eda19 100644
--- a/test/models/mixtral.py
+++ b/test/models/mixtral.py
@@ -115,52 +115,52 @@ def setup_caches(self, max_batch_size, max_seq_length):
         self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base)
         self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
 
-    # def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
-    #     """
-    #     2 Stages
-    #     """
-    #     self.first_arg = idx.shape
-    #     with torch.fx.traceback.annotate({"stage": 0}):
-    #         mask = self.causal_mask[None, None, input_pos]
-    #         freqs_cis = self.freqs_cis[input_pos]
-    #         x = self.tok_embeddings(idx)
-    #         for layer in self.layers[:len(self.layers)//2]:
-    #             x = layer(x, input_pos, freqs_cis, mask)
-        
-    #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[len(self.layers)//2:]:
-    #             x = layer(x, input_pos, freqs_cis, mask)
-    #         x = self.norm(x)
-    #         logits = self.output(x)
-    #     return logits
-
     def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
         """
-        4 Stages
+        2 Stages
         """
         self.first_arg = idx.shape
         with torch.fx.traceback.annotate({"stage": 0}):
             mask = self.causal_mask[None, None, input_pos]
             freqs_cis = self.freqs_cis[input_pos]
             x = self.tok_embeddings(idx)
-            for layer in self.layers[:len(self.layers)//4]:
+            for layer in self.layers[:len(self.layers)//2]:
                 x = layer(x, input_pos, freqs_cis, mask)
         
         with torch.fx.traceback.annotate({"stage": 1}):
-            for layer in self.layers[len(self.layers)//4:len(self.layers)//2]:
-                x = layer(x, input_pos, freqs_cis, mask)
-
-        with torch.fx.traceback.annotate({"stage": 2}):
-            for layer in self.layers[len(self.layers)//2:3*len(self.layers)//4]:
-                x = layer(x, input_pos, freqs_cis, mask)
-
-        with torch.fx.traceback.annotate({"stage": 3}):
-            for layer in self.layers[3*len(self.layers)//4:]:
+            for layer in self.layers[len(self.layers)//2:]:
                 x = layer(x, input_pos, freqs_cis, mask)
             x = self.norm(x)
             logits = self.output(x)
         return logits
 
+    # def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+    #     """
+    #     4 Stages
+    #     """
+    #     self.first_arg = idx.shape
+    #     with torch.fx.traceback.annotate({"stage": 0}):
+    #         mask = self.causal_mask[None, None, input_pos]
+    #         freqs_cis = self.freqs_cis[input_pos]
+    #         x = self.tok_embeddings(idx)
+    #         for layer in self.layers[:len(self.layers)//4]:
+    #             x = layer(x, input_pos, freqs_cis, mask)
+        
+    #     with torch.fx.traceback.annotate({"stage": 1}):
+    #         for layer in self.layers[len(self.layers)//4:len(self.layers)//2]:
+    #             x = layer(x, input_pos, freqs_cis, mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 2}):
+    #         for layer in self.layers[len(self.layers)//2:3*len(self.layers)//4]:
+    #             x = layer(x, input_pos, freqs_cis, mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 3}):
+    #         for layer in self.layers[3*len(self.layers)//4:]:
+    #             x = layer(x, input_pos, freqs_cis, mask)
+    #         x = self.norm(x)
+    #         logits = self.output(x)
+    #     return logits
+
     @classmethod
     def from_name(cls, name: str):
         return cls(ModelArgs.from_name(name))
diff --git a/test/test_llama.py b/test/test_llama.py
index c11bf9b..876060c 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -2,6 +2,7 @@
 import torch
 import time
 import argparse
+import json
 import os
 import numpy as np
 import gc
@@ -102,11 +103,12 @@ def main(args):
     # Time training steps
     print(f"Running {args.iters} timed iterations...")
     iter_times = []
-    for _ in range(args.iters):
+    for i in range(args.iters):
         start = time.perf_counter()
         piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
         end = time.perf_counter()
         iter_times.append(end - start)
+        print(f"Iter {i} completed")
     
     dp_rank = int(os.environ['PIPER_DP_RANK'])
     print(
@@ -115,7 +117,6 @@ def main(args):
     )
 
     if args.tracing:
-        from src.piper_utils import piper_metadata
         actors = piper_metadata.actors
         ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
         ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
@@ -139,9 +140,20 @@ def main(args):
         suffix = "-naive-sync"
     else:
         suffix = ""
+    # Collect task_id -> label mappings from all actors
+    all_task_labels = {}
+    actors = piper_metadata.actors
+    for labels in ray.get([actor.get_task_labels.remote() for actor in actors.values()]):
+        all_task_labels.update(labels)
+
+    os.makedirs("out", exist_ok=True)
     timeline_filename = f"out/{args.model}-pp{args.pp}-dp{args.dp}-{args.schedule}{suffix}.json"
     ray.timeline(timeline_filename)
+    labels_filename = timeline_filename.replace(".json", "-labels.json")
+    with open(labels_filename, "w") as f:
+        json.dump(all_task_labels, f)
     print(f"Ray timeline saved to: {timeline_filename}")
+    print(f"Task labels saved to: {labels_filename}")
 
     piper_shutdown()
 
diff --git a/test/test_mixtral.py b/test/test_mixtral.py
index ee51711..4a1186a 100644
--- a/test/test_mixtral.py
+++ b/test/test_mixtral.py
@@ -3,6 +3,7 @@
 import torch
 import torch.nn as nn
 import argparse
+import json
 import time
 import os
 import numpy as np
@@ -109,9 +110,19 @@ def main(args):
                 all_times = trace_data[key]
                 print(f"rank {rank} {key} time= {np.mean(all_times):.3f} ± {np.std(all_times):.3f} ms ({len(all_times)} samples)")
 
+    # Collect task_id -> label mappings from all actors
+    all_task_labels = {}
+    for labels in ray.get([actor.get_task_labels.remote() for actor in actors.values()]):
+        all_task_labels.update(labels)
+
+    os.makedirs("out", exist_ok=True)
     timeline_filename = f"out/mixtral-pp{args.pp}-dp{args.dp}-{args.schedule}-{args.mode}"
     ray.timeline(timeline_filename)
+    labels_filename = timeline_filename + "-labels.json"
+    with open(labels_filename, "w") as f:
+        json.dump(all_task_labels, f)
     print(f"Ray timeline saved to: {timeline_filename}")
+    print(f"Task labels saved to: {labels_filename}")
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')

From d3914f51703fab69577921fb89336a155d7dfd67 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Fri, 6 Mar 2026 09:50:52 +0000
Subject: [PATCH 066/100] 1. order p2p operations by compute task dispatch
 order to avoid deadlocks 2. use separate nccl communicators for each p2p
 direction for each pair of ranks in a PP group to avoid send/recv blocking
 each other 3. Auto infer stage boundaries to address bubbles coming from
 unevenly distributed compute. original annotation method still works but
 stage annotations are no longer required

---
 src/piper.py                 |  30 ++--
 src/piper_actor.py           | 321 ++++++++++++++++-------------------
 src/piper_compile.py         | 115 +------------
 src/piper_exec.py            | 123 +++++++++++---
 src/piper_graph_transform.py | 244 ++++++++++++++++++++++++++
 test/models/llama.py         |  72 ++++----
 test/schedule_helpers.py     |   4 +-
 test/test_llama.py           |  16 +-
 8 files changed, 552 insertions(+), 373 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index b3aad0e..721ded7 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -5,7 +5,7 @@
 from torch._dynamo.backends.registry import register_backend
 
 from .piper_utils import _serialize_graphmodule, piper_metadata, create_logger, LOG_LEVEL
-from .piper_graph_transform import _get_dp_comm_ops, _split_gm_by_stages, _insert_a2a_ops, _insert_p2p_ops
+from .piper_graph_transform import _get_dp_comm_ops, _split_gm_by_stages, _profile_and_split_gm, _insert_a2a_ops, _insert_p2p_ops
 from .piper_actor import _get_actor
 
 logger = create_logger("piper_backend", LOG_LEVEL)
@@ -13,30 +13,34 @@
 
 @register_backend
 def piper(gm, example_inputs, **kwargs):
+    
+    # gm.print_readable()
 
     original_gm = gm
-    top_level_gm, submodules = _split_gm_by_stages(gm)
-    
     num_stages = len(piper_metadata.stage_to_device.keys())
+
+    # Check if the graph has stage annotations
+    has_annotations = any(
+        isinstance(node.meta.get('custom'), dict) and node.meta['custom'].get('stage') is not None
+        for node in gm.graph.nodes
+    )
+
+    if has_annotations:
+        top_level_gm, submodules = _split_gm_by_stages(gm)
+    else:
+        logger.info(f"No stage annotations found, profiling graph to split into {num_stages} stages")
+        top_level_gm, submodules = _profile_and_split_gm(gm, num_stages)
     dp_rank = int(os.environ['PIPER_DP_RANK'])
     pp_degree = int(os.environ['PIPER_PP_DEGREE'])
     dp_degree = int(os.environ['PIPER_DP_DEGREE'])
 
+    # top_level_gm.print_readable()
+
     del top_level_gm
 
     refs = []
     actor_stages = []
     for (stage_id, stage_gm, input_idxs, param_idxs, graphargs, placeholders) in submodules:
-        stage_gm(*graphargs)
-        start = torch.cuda.Event(enable_timing=True)
-        start.record()
-        for i in range(5):
-            stage_gm(*graphargs)
-        end = torch.cuda.Event(enable_timing=True)
-        end.record()
-        torch.cuda.synchronize()
-        print(f"Stage {stage_id} time measured on controller: {start.elapsed_time(end) / 5:.2f} ms")
-
         n_a2a_ops = 0
         if dp_degree > 1:
             stage_gm, n_a2a_ops = _insert_a2a_ops(stage_gm)
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 7407e17..3e72f48 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -33,10 +33,10 @@ def _create_actors(
     optim_class,
     num_mbs,
     num_stages,
-    p2p_schedules,
     naive_gradient_sync=False,
     profile=False,
     mode="sequential",
+    stage_to_device=None,
 ):
     dp_rank = int(os.environ["PIPER_DP_RANK"])
     world_size = int(os.environ["PIPER_WORLD_SIZE"])
@@ -47,20 +47,23 @@ def _create_actors(
 
     for pp_rank in range(num_actors):
         global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
-        p2p_schedule = p2p_schedules[pp_rank]
-        actor = PiperActor.options(num_gpus=0.8).remote(
+        nsight_env = {"nsight": {
+            "t": "cuda,cudnn,cublas,nvtx",
+            "cuda-event-trace": "false",
+            "stop-on-exit": "true",
+        }} if profile else {}
+        actor = PiperActor.options(num_gpus=0.8, runtime_env=nsight_env).remote(
             pp_rank,
             optim_class,
             world_size,
             num_mbs,
             num_stages,
-            p2p_schedule,
             naive_gradient_sync,
             dp_rank=dp_rank,
             dp_degree=dp_degree,
             pp_degree=pp_degree,
-            profile=profile,
             mode=mode,
+            stage_to_device=stage_to_device,
         )
         piper_metadata.actors[pp_rank] = actor
         logger.debug(
@@ -82,16 +85,14 @@ def __init__(
         world_size,
         num_mbs,
         num_stages,
-        p2p_schedule,
         naive_gradient_sync=False,
         dp_rank=0,
         dp_degree=1,
         pp_degree=1,
-        profile=False,
         mode="sequential",
+        stage_to_device=None,
     ):
         self.logger = create_logger("piper_actor", LOG_LEVEL)
-        self.profile = profile
         self.mode = mode
 
         self.pp_rank = pp_rank
@@ -105,13 +106,10 @@ def __init__(
 
         self.num_mbs = num_mbs
         self.num_stages = num_stages
-        # list of (src_stage, dst_stage, mb_idx, is_sender)
-        self.p2p_schedule = p2p_schedule
-        self.next_p2p_idx = 0
-        # set of (src_stage, dst_stage, mb_idx, is_sender) for completed p2p ops
-        self.executed_p2ps = set()
+        self.stage_to_device = stage_to_device or {}
         self.dp_group = None
-        self.pp_group = None
+        # Per-direction communicators: (src_global_rank, dst_global_rank) -> ProcessGroup
+        self.pp_groups = {}
         self.device = "cuda"
 
         self.global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
@@ -194,10 +192,33 @@ def get_task_labels(self) -> dict:
         return self.task_labels
 
     def reset_p2p_states(self):
-        self.next_p2p_idx = 0
-        self.executed_p2ps = set()
         self.p2p_cache = dict()
         self.p2p_events = dict()
+        self.p2p_cursor = 0
+
+    def set_p2p_schedule(self, schedule):
+        """Set the per-rank p2p schedule. Each entry: (op_type, stage_from, stage_to, mb_idx, is_fwd)."""
+        self.p2p_rank_schedule = schedule
+        self.p2p_cursor = 0
+        self.logger.debug(
+            f"Global rank {self.global_rank} set p2p schedule with {len(schedule)} ops"
+        )
+
+    def _exec_p2p_op(self, target_rank_idx, *deps):
+        """Execute all p2p ops from cursor up to and including target_rank_idx."""
+        while self.p2p_cursor <= target_rank_idx:
+            op_type, stage_from, stage_to, mb_idx, is_fwd = self.p2p_rank_schedule[self.p2p_cursor]
+            if is_fwd:
+                if op_type == "recv":
+                    self._exec_fwd_recv(stage_to, mb_idx)
+                else:
+                    self._exec_fwd_send(stage_from, mb_idx)
+            else:
+                if op_type == "recv":
+                    self._exec_bwd_recv(stage_to, mb_idx)
+                else:
+                    self._exec_bwd_send(stage_from, mb_idx)
+            self.p2p_cursor += 1
 
     def get_trace_data(self) -> dict:
         return self.global_rank, self.trace_data
@@ -293,14 +314,35 @@ def _join_dp_process_group(self):
 
     def _join_pp_process_group(self):
         num_pp_groups = self.world_size // self.pp_degree
+        my_pp_group_id = self.global_rank // self.pp_degree
+
+        # One communicator per direction per rank pair. The global p2p schedule
+        # (built in piper_exec) ensures both ranks issue ops in the same FIFO order.
         for pp_group_id in range(num_pp_groups):
             group_ranks = [
                 (pp_group_id * self.pp_degree + i) for i in range(self.pp_degree)
             ]
-            process_group = dist.new_group(ranks=group_ranks, backend="nccl")
-            if self.global_rank // self.pp_degree == pp_group_id:
-                self.pp_group = process_group
-                self.logger.debug(f"Global rank {self.global_rank} joined its pp group {pp_group_id} along with ranks {group_ranks}")
+            for i in range(len(group_ranks)):
+                for j in range(i + 1, len(group_ranks)):
+                    rank_lo, rank_hi = group_ranks[i], group_ranks[j]
+                    pg_lo_to_hi = dist.new_group(ranks=[rank_lo, rank_hi], backend="nccl")
+                    pg_hi_to_lo = dist.new_group(ranks=[rank_lo, rank_hi], backend="nccl")
+                    if pp_group_id == my_pp_group_id:
+                        self.pp_groups[(rank_lo, rank_hi)] = pg_lo_to_hi
+                        self.pp_groups[(rank_hi, rank_lo)] = pg_hi_to_lo
+            if pp_group_id == my_pp_group_id:
+                self.logger.debug(
+                    f"Global rank {self.global_rank} joined pp group {pp_group_id} "
+                    f"with communicators for ranks {group_ranks}"
+                )
+
+        # Warm up all communicators to force eager NCCL initialization.
+        dummy = torch.zeros(1, device=self.device)
+        for key, pg in self.pp_groups.items():
+            self.logger.debug(f"Global rank {self.global_rank} warming up pp communicator {key}")
+            dist.all_reduce(dummy, group=pg)
+        torch.cuda.synchronize()
+        self.logger.debug(f"Global rank {self.global_rank} warmed up {len(self.pp_groups)} pp communicators")
 
     def shutdown(self):
         dist.destroy_process_group()
@@ -459,76 +501,17 @@ def _load_stage(
 
         del gm_data
 
-    def _exec_p2p_op(
-        self, src_stage: int, dst_stage: int, mb_idx: int, is_sender: bool, dep, p2p_stream=None
-    ):
-        self._label_task("P2P")
-        p2p_op = (src_stage, dst_stage, mb_idx, is_sender)
-        if p2p_op in self.executed_p2ps:
-            return
-
-        if p2p_stream is None:
-            p2p_stream = self.p2p_stream
-
-        op_idx = self.next_p2p_idx
-        while op_idx < len(self.p2p_schedule):
-            op = self.p2p_schedule[op_idx]
-            if op == p2p_op:
-                break
-            op_idx += 1
-        assert op_idx < len(
-            self.p2p_schedule
-        ), f"P2P op {p2p_op} not found in schedule for actor {self.global_rank}"
-
-        # Execute everything before and including the given p2p op
-        for idx in range(self.next_p2p_idx, op_idx + 1):
-            op = self.p2p_schedule[idx]
-            src_stage, dst_stage, mb_idx, is_sender = op
-            is_fwd = src_stage == dst_stage - 1
-            is_bwd = src_stage == dst_stage + 1
-            assert is_fwd or is_bwd
-            is_recver = not is_sender
-
-            op_name = "P2P(unknown)"
-            if is_fwd and is_sender:
-                self._exec_fwd_send(src_stage, mb_idx, p2p_stream=self.p2p_send_stream)
-                op_name = (
-                    f"P2P(fwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
-                )
-            elif is_fwd and is_recver:
-                self._exec_fwd_recv(dst_stage, mb_idx, p2p_stream=self.p2p_recv_stream)
-                op_name = (
-                    f"P2P(fwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
-                )
-            elif is_bwd and is_sender:
-                self._exec_bwd_send(src_stage, mb_idx, p2p_stream=self.p2p_send_stream)
-                op_name = (
-                    f"P2P(bwd_send, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
-                )
-            elif is_bwd and is_recver:
-                self._exec_bwd_recv(dst_stage, mb_idx, p2p_stream=self.p2p_recv_stream)
-                op_name = (
-                    f"P2P(bwd_recv, stage {src_stage} -> {dst_stage}, mb {mb_idx})"
-                )
-            else:
-                raise ValueError(f"Invalid p2p op: {op}")
-
-            self.executed_p2ps.add(op)
-            logger.debug(f"Executed {op_name} on actor {self.global_rank}")
-
-        self.next_p2p_idx = op_idx + 1
-
-    def _exec_fwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
+    def _exec_fwd_recv(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, comp_stream=None):
         if stage_id == 0:
             return
 
         if p2p_stream is None:
-            p2p_stream = self.p2p_stream
+            p2p_stream = self.p2p_recv_stream
         if comp_stream is None:
             comp_stream = self.comp_stream
 
         p2p_op = (stage_id - 1, stage_id, mb_idx, False)
-        assert p2p_op not in self.p2p_cache
+        # assert p2p_op not in self.p2p_cache
 
         inputs_to_recv = []
         for i in self.input_idxs[stage_id]:
@@ -548,7 +531,7 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
                 inputs_to_recv[i] = self.p2p_cache.pop((stage_id-1, stage_id, mb_idx, True))
         else:
             self.logger.debug(
-                f"Dispatch fwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
+                f"Dispatch fwd p2p recv on communicator ({global_src_rank}, {self.global_rank}) to {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
             )
             self._start_timing(p2p_stream, "fwd_p2p_recv")
             with torch.cuda.stream(p2p_stream):
@@ -556,7 +539,7 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
                     dist.recv(
                         inputs_to_recv[i],
                         src=global_src_rank,
-                        group=self.pp_group,
+                        group=self.pp_groups[(global_src_rank, self.global_rank)],
                     )
             recv_event = torch.cuda.Event()
             recv_event.record(p2p_stream)
@@ -568,12 +551,12 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
 
         self.p2p_cache[p2p_op] = inputs_to_recv
 
-    def _exec_fwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
+    def _exec_fwd_send(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, comp_stream=None):
         if stage_id == self.num_stages - 1:
             return
 
         if p2p_stream is None:
-            p2p_stream = self.p2p_stream
+            p2p_stream = self.p2p_send_stream
         if comp_stream is None:
             comp_stream = self.comp_stream
 
@@ -588,7 +571,7 @@ def _exec_fwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
         else:
             # Ensure send sees the latest writes from the default stream.
             self.logger.debug(
-                f"Dispatch fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
+                f"Dispatch fwd p2p send on communicator ({self.global_rank}, {global_dst_rank}) from {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
             )
             self._start_timing(p2p_stream, "fwd_p2p_send")
             p2p_stream.wait_stream(comp_stream)
@@ -597,19 +580,19 @@ def _exec_fwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
                     dist.send(
                         output[i],
                         dst=global_dst_rank,
-                        group=self.pp_group,
+                        group=self.pp_groups[(self.global_rank, global_dst_rank)],
                     )
             self._stop_timing(p2p_stream, "fwd_p2p_send")
             self.logger.debug(
                 f"Completed fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
             )
 
-    def _exec_bwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
+    def _exec_bwd_recv(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, comp_stream=None):
         if stage_id >= self.num_stages - 1:
             return
 
         if p2p_stream is None:
-            p2p_stream = self.p2p_stream
+            p2p_stream = self.p2p_recv_stream
         if comp_stream is None:
             comp_stream = self.comp_stream
 
@@ -625,12 +608,12 @@ def _exec_bwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
             input_grad = self.p2p_cache.pop((stage_id + 1, stage_id, mb_idx, True))
         else:
             self.logger.debug(
-                f"Dispatch bwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id+1} -> {stage_id}, mb {mb_idx})"
+                f"Dispatch bwd p2p recv on communicator ({global_src_rank}, {self.global_rank}) to {self.global_rank} from {global_src_rank}, op: ({stage_id+1} -> {stage_id}, mb {mb_idx})"
             )
             self._start_timing(p2p_stream, "bwd_p2p_recv")
             with torch.cuda.stream(p2p_stream):
                 dist.recv(
-                    input_grad, src=global_src_rank, group=self.pp_group
+                    input_grad, src=global_src_rank, group=self.pp_groups[(global_src_rank, self.global_rank)]
                 )
             recv_event = torch.cuda.Event()
             recv_event.record(p2p_stream)
@@ -642,12 +625,12 @@ def _exec_bwd_recv(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
         
         self.p2p_cache[p2p_op] = input_grad
 
-    def _exec_bwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_stream=None):
+    def _exec_bwd_send(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, comp_stream=None):
         if stage_id <= 0:
             return
 
         if p2p_stream is None:
-            p2p_stream = self.p2p_stream
+            p2p_stream = self.p2p_send_stream
         if comp_stream is None:
             comp_stream = self.comp_stream
 
@@ -664,14 +647,13 @@ def _exec_bwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
             self.p2p_cache[p2p_op] = output_grad
         else:
             self.logger.debug(
-                f"Dispatch bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
+                f"Dispatch bwd p2p send on communicator ({self.global_rank}, {global_src_rank}) from {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
             )
             self._start_timing(p2p_stream, "bwd_p2p_send")
-            # Ensure send sees the latest gradient writes from the default stream.
             p2p_stream.wait_stream(comp_stream)
             with torch.cuda.stream(p2p_stream):
                 dist.send(
-                    output_grad, dst=global_src_rank, group=self.pp_group
+                    output_grad, dst=global_src_rank, group=self.pp_groups[(self.global_rank, global_src_rank)]
                 )
             self._stop_timing(p2p_stream, "bwd_p2p_send")
             self.logger.debug(
@@ -681,6 +663,9 @@ def _exec_bwd_send(self, stage_id: int, mb_idx: int, p2p_stream=None, comp_strea
         self.inp_activation[stage_id][mb_idx] = None
     
     def _forward(self, stage_id: int, mb_idx: int, *deps):
+        return self._forward_impl(stage_id, mb_idx, *deps)
+
+    def _forward_impl(self, stage_id: int, mb_idx: int, *deps):
         self._label_task(f"{stage_id}:{mb_idx}")
         if self.mode == "sequential":
             comp_stream = self.comp_stream
@@ -690,32 +675,28 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
             comp_stream = self.comp_stream
         else:
             raise ValueError(f"Invalid mode: {self.mode}")
-        
+
         self.overlap_a2a_ops = False
 
         self.logger.debug(
             f"Calling forward {stage_id} mb {mb_idx} on actor {self.global_rank}"
         )
 
-        if self.profile:
-            profiler = torch.profiler.profile(
-                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
-                with_stack=True,
-            )
-            profiler.__enter__()
-            forward_ctx_manager = torch.profiler.record_function(f"forward_stage_{stage_id}_mb_{mb_idx}")
-            forward_ctx_manager.__enter__()
-
         if stage_id == 0:
             # For the first stage, load input tensors from self.inputs
             for i, inp in zip(self.input_idxs[stage_id], self.inputs):
                 self.forward_args[stage_id][i] = inp
         else:
+            # Wait for recv to complete if this stage receives from the previous stage
+            self._start_timing(comp_stream, "fwd_recv_wait")
             recv_key = (stage_id - 1, stage_id, mb_idx, False)
             recv_event = self.p2p_events.pop(recv_key, None)
             if recv_event is not None:
                 comp_stream.wait_event(recv_event)
             inputs_from_prev_stage = self.p2p_cache.pop(recv_key)
+            self._stop_timing(comp_stream, "fwd_recv_wait")
+
+            # Detach to avoid double-backprop if the previous stage is co-located
             for i, tensor in zip(self.input_idxs[stage_id], inputs_from_prev_stage):
                 if isinstance(tensor, (tuple, list)):
                     assert len(tensor) == 1
@@ -741,17 +722,19 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
         # Run the forward pass
         self._start_timing(comp_stream, "forward_comp")
         with torch.cuda.stream(comp_stream):
-            torch.cuda.nvtx.range_push(f"forward_stage_{stage_id}_mb_{mb_idx}")
+            # torch.cuda.nvtx.range_push(f"forward_stage_{stage_id}_mb_{mb_idx}")
             output = self.forward_fns[stage_id](*self.forward_args[stage_id])
-            torch.cuda.nvtx.range_pop()
+            # torch.cuda.nvtx.range_pop()
         self._stop_timing(comp_stream, "forward_comp")
 
         # Save first output that requires grad as output activation
         # TODO: support multiple outputs
         out_with_grad = [out for out in output if out.requires_grad]
-        assert (
-            len(out_with_grad) == 1
-        ), "Piper only supports one output per subgraph with requires_grad"
+        if len(out_with_grad) != 1:
+            self.logger.warning(
+                f"Expected exactly one output with requires_grad in stage {stage_id} mb {mb_idx} on actor {self.global_rank}, but found {len(out_with_grad)}. Output shapes: {[out.shape for out in output]}"
+            )
+            assert False
         self.logger.debug(
             f"Saving output activation {out_with_grad[0].shape} for stage {stage_id} mb {mb_idx}"
         )
@@ -777,14 +760,12 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
 
         comp_stream.synchronize()
 
-        if self.profile:
-            forward_ctx_manager.__exit__(None, None, None)
-            profiler.__exit__(None, None, None)
-            profiler.export_chrome_trace(f"out/{self.mode}/actor{self.global_rank}_fwd{stage_id}mb{mb_idx}_trace.json")
-
         return 0
 
     def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
+        return self._backward_impl(stage_id, mb_idx, *deps, loss_fn=loss_fn)
+
+    def _backward_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         self._label_task(f"{stage_id}:{mb_idx}")
         if self.mode == "sequential":
             comp_stream = self.comp_stream
@@ -801,29 +782,22 @@ def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
             f"Calling backward {stage_id} mb {mb_idx} on actor {self.global_rank}"
         )
 
-        if self.profile:
-            profiler = torch.profiler.profile(
-                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
-                with_stack=True,
-            )
-            profiler.__enter__()
-            backward_ctx_manager = torch.profiler.record_function(f"backward_stage_{stage_id}_mb_{mb_idx}")
-            backward_ctx_manager.__enter__()
-
         out_activation = self.out_activation[stage_id][mb_idx]
 
+        # torch.cuda.nvtx.range_push(f"backward_stage_{stage_id}_mb_{mb_idx}")
         if stage_id < self.num_stages - 1:
+            # Wait for recv to complete if this stage receives from the next stage
+            self._start_timing(comp_stream, "bwd_recv_wait")
             recv_key = (stage_id + 1, stage_id, mb_idx, False)
             recv_event = self.p2p_events.pop(recv_key, None)
             if recv_event is not None:
                 comp_stream.wait_event(recv_event)
             input_grad = self.p2p_cache.pop(recv_key)
+            self._stop_timing(comp_stream, "bwd_recv_wait")
 
             self._start_timing(comp_stream, "backward_comp")
             with torch.cuda.stream(comp_stream):
-                torch.cuda.nvtx.range_push(f"backward_stage_{stage_id}_mb_{mb_idx}")
                 out_activation.backward(gradient=input_grad)
-                torch.cuda.nvtx.range_pop()
             self._stop_timing(comp_stream, "backward_comp")
         else:
             assert loss_fn is not None
@@ -832,13 +806,12 @@ def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
 
             self._start_timing(comp_stream, "backward_comp")
             with torch.cuda.stream(comp_stream):
-                torch.cuda.nvtx.range_push(f"backward_stage_{stage_id}_mb_{mb_idx}")
                 loss = loss_fn(out_activation, labels)
                 loss.backward()
-                torch.cuda.nvtx.range_pop()
             self._stop_timing(comp_stream, "backward_comp")
 
-            self.loss.append(loss.item())
+            # self.loss.append(loss.item())
+        # torch.cuda.nvtx.range_pop()
 
         # Clear output activation after backward pass
         self.out_activation[stage_id][mb_idx] = None
@@ -851,15 +824,13 @@ def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
 
         comp_stream.synchronize()
 
-        if self.profile:
-            backward_ctx_manager.__exit__(None, None, None)
-            profiler.__exit__(None, None, None)
-            profiler.export_chrome_trace(f"out/{self.mode}/actor{self.global_rank}_bwd{stage_id}mb{mb_idx}_trace.json")
-
         return 0
 
 
     def _backward_input(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
+        return self._backward_input_impl(stage_id, mb_idx, *deps, loss_fn=loss_fn)
+
+    def _backward_input_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         self._label_task(f"{stage_id}:{mb_idx}")
         comp_stream = self.comp_stream
 
@@ -872,21 +843,22 @@ def _backward_input(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         activation_or_loss = None
         upstream_grad = None
         if stage_id < self.num_stages - 1:
-            activation_or_loss = out_activation
+            # Wait for recv to complete if this stage receives from the next stage
             recv_key = (stage_id + 1, stage_id, mb_idx, False)
             recv_event = self.p2p_events.pop(recv_key, None)
             if recv_event is not None:
                 comp_stream.wait_event(recv_event)
             upstream_grad = self.p2p_cache.pop(recv_key)
+            activation_or_loss = out_activation
         else:
             assert loss_fn is not None
             labels = self.labels
             assert out_activation.shape == labels.shape
             with torch.cuda.stream(comp_stream):
-                torch.cuda.nvtx.range_push(f"backward_loss_stage_{stage_id}_mb_{mb_idx}")
+                # torch.cuda.nvtx.range_push(f"backward_loss_stage_{stage_id}_mb_{mb_idx}")
                 loss = loss_fn(out_activation, labels)
-                self.loss.append(loss.item())
-                torch.cuda.nvtx.range_pop()
+                # self.loss.append(loss.item())
+                # torch.cuda.nvtx.range_pop()
             activation_or_loss = loss
             upstream_grad = torch.ones_like(loss)
 
@@ -931,7 +903,7 @@ def hook(grad_inputs):
 
         self._start_timing(comp_stream, "backward_input")
         with torch.cuda.stream(comp_stream):
-            torch.cuda.nvtx.range_push(f"backward_input_stage_{stage_id}_mb_{mb_idx}")
+            # torch.cuda.nvtx.range_push(f"backward_input_stage_{stage_id}_mb_{mb_idx}")
             gx = torch.autograd.grad(
                 outputs=activation_or_loss,
                 inputs=stage_input,
@@ -939,7 +911,7 @@ def hook(grad_inputs):
                 retain_graph=True,
                 allow_unused=True,
             )
-            torch.cuda.nvtx.range_pop()
+            # torch.cuda.nvtx.range_pop()
         self._stop_timing(comp_stream, "backward_input")
 
         gx = gx[0] # Take the gx gradient out of the tuple returned by autograd.grad
@@ -975,6 +947,9 @@ def hook(grad_inputs):
         return 0
 
     def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
+        return self._backward_weight_impl(stage_id, mb_idx, *deps, loss_fn=loss_fn)
+
+    def _backward_weight_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         self._label_task(f"{stage_id}:{mb_idx}")
         comp_stream = self.comp_stream
 
@@ -993,27 +968,27 @@ def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
             self._start_timing(comp_stream, "backward_weight")
             if stage_id < self.num_stages - 1:
                 with torch.cuda.stream(comp_stream):
-                    torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
+                    # torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
                     gparams = torch.autograd.grad(
                         outputs=out_activation,
                         inputs=stage_params,
                         grad_outputs=upstream_grad,
                         retain_graph=False,
                     )
-                    torch.cuda.nvtx.range_pop()
+                    # torch.cuda.nvtx.range_pop()
             else:
                 assert loss_fn is not None
                 labels = self.labels
                 assert out_activation.shape == labels.shape
                 with torch.cuda.stream(comp_stream):
-                    torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
+                    # torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
                     loss = loss_fn(out_activation, labels)
                     gparams = torch.autograd.grad(
                         outputs=loss,
                         inputs=stage_params,
                         retain_graph=False,
                     )
-                    torch.cuda.nvtx.range_pop()
+                    # torch.cuda.nvtx.range_pop()
             self._stop_timing(comp_stream, "backward_weight")
 
             assert len(gparams) == len(stage_params), (
@@ -1083,14 +1058,14 @@ def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
 
                 self._start_timing(comp_stream, "backward_weight")
                 with torch.cuda.stream(comp_stream):
-                    torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
+                    # torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
                     gparams = torch.autograd.grad(
                         outputs=intermediate_edges,
                         inputs=weight_edges,
                         grad_outputs=intermediate_edge_grads,
                         retain_graph=False,
                     )
-                    torch.cuda.nvtx.range_pop()
+                    # torch.cuda.nvtx.range_pop()
                 self._stop_timing(comp_stream, "backward_weight")
 
                 del pg["grads"]
@@ -1131,6 +1106,9 @@ def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         return 0
 
     def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: int, bwd_mb_idx: int, *deps, loss_fn=None):
+        return self._forward_backward_impl(fwd_stage_id, fwd_mb_idx, bwd_stage_id, bwd_mb_idx, *deps, loss_fn=loss_fn)
+
+    def _forward_backward_impl(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: int, bwd_mb_idx: int, *deps, loss_fn=None):
         self._label_task(f"{fwd_stage_id}:{fwd_mb_idx}|{bwd_stage_id}:{bwd_mb_idx}")
         if self.mode == "sequential":
             fwd_comp_stream = self.comp_stream
@@ -1151,13 +1129,6 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             f"Calling forward {fwd_stage_id} mb {fwd_mb_idx} backward {bwd_stage_id} mb {bwd_mb_idx} on actor {self.global_rank}"
         )
         
-        if self.profile:
-            profiler = torch.profiler.profile(
-                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
-                with_stack=True,
-            )
-            profiler.__enter__()
-
         # PREPARE FORWARD PASS
         if fwd_stage_id == 0:
             # For the first stage, load input tensors from self.inputs
@@ -1165,10 +1136,14 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
                 self.forward_args[fwd_stage_id][i] = inp
         else:
             fwd_recv_key = (fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False)
+
+            # Wait for recv to complete if this stage receives from the previous stage
             fwd_recv_event = self.p2p_events.pop(fwd_recv_key, None)
             if fwd_recv_event is not None:
                 fwd_comp_stream.wait_event(fwd_recv_event)
             inputs_from_prev_stage = self.p2p_cache.pop(fwd_recv_key)
+
+            # Detach to avoid double-backprop if the previous stage is co-located
             for i, tensor in zip(self.input_idxs[fwd_stage_id], inputs_from_prev_stage):
                 if isinstance(tensor, (tuple, list)):
                     assert len(tensor) == 1
@@ -1195,6 +1170,8 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
         out_activation = self.out_activation[bwd_stage_id][bwd_mb_idx]
         if bwd_stage_id < self.num_stages - 1:
             bwd_recv_key = (bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False)
+
+            # Wait for recv to complete if this stage receives from the next stage
             bwd_recv_event = self.p2p_events.pop(bwd_recv_key, None)
             if bwd_recv_event is not None:
                 bwd_comp_stream.wait_event(bwd_recv_event)
@@ -1204,13 +1181,6 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
             labels = self.labels
             assert out_activation.shape == labels.shape
             
-        if self.profile:
-            forward_ctx_manager = torch.profiler.record_function(f"forward_stage_{fwd_stage_id}_mb_{fwd_mb_idx}")
-            backward_ctx_manager = torch.profiler.record_function(f"backward_stage_{bwd_stage_id}_mb_{bwd_mb_idx}")
-        else:
-            forward_ctx_manager = nullcontext()
-            backward_ctx_manager = nullcontext()
-
         # RUN FORWARD AND BACKWARD PASSES
         if self.overlap_a2a_ops:
             output = None
@@ -1224,17 +1194,17 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
 
             def run_fwd():
                 nonlocal output
-                with torch.cuda.stream(fwd_comp_stream), forward_ctx_manager:
+                with torch.cuda.stream(fwd_comp_stream):
                     output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
 
             def run_bwd():
                 nonlocal loss
                 self.fwd_a2a_submitted[0].wait()
                 if bwd_stage_id < self.num_stages - 1:
-                    with torch.cuda.stream(bwd_comp_stream), backward_ctx_manager:
+                    with torch.cuda.stream(bwd_comp_stream):
                         out_activation.backward(gradient=input_grad)
                 else:
-                    with torch.cuda.stream(bwd_comp_stream), backward_ctx_manager:
+                    with torch.cuda.stream(bwd_comp_stream):
                         loss = loss_fn(out_activation, labels)
                         loss.backward()
 
@@ -1245,14 +1215,14 @@ def run_bwd():
             fwd_thread.join()
             bwd_thread.join()
         else:
-            with torch.cuda.stream(fwd_comp_stream), forward_ctx_manager:
+            with torch.cuda.stream(fwd_comp_stream):
                 output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
 
             if bwd_stage_id < self.num_stages - 1:
-                with torch.cuda.stream(bwd_comp_stream), backward_ctx_manager:
+                with torch.cuda.stream(bwd_comp_stream):
                     out_activation.backward(gradient=input_grad)
             else:
-                with torch.cuda.stream(bwd_comp_stream), backward_ctx_manager:
+                with torch.cuda.stream(bwd_comp_stream):
                     loss = loss_fn(out_activation, labels)
                     loss.backward()
 
@@ -1271,8 +1241,8 @@ def run_bwd():
             self.forward_args[fwd_stage_id][i] = None
         
         # POST PROCESS BACKWARD PASS
-        if bwd_stage_id == self.num_stages - 1:
-            self.loss.append(loss.item())
+        # if bwd_stage_id == self.num_stages - 1:
+        #     self.loss.append(loss.item())
 
         # Clear output activation after backward pass
         self.out_activation[bwd_stage_id][bwd_mb_idx] = None
@@ -1301,14 +1271,6 @@ def run_bwd():
             self.fwd_a2a_counter = 0
             self.bwd_a2a_counter = 0
 
-        if self.profile:
-            backward_ctx_manager.__exit__(None, None, None)
-            profiler.__exit__(None, None, None)
-            profiler.export_chrome_trace(
-                f"out/{self.mode}/actor{self.global_rank}_"
-                f"fwd{fwd_stage_id}mb{fwd_mb_idx}_"
-                f"bwd{bwd_stage_id}mb{bwd_mb_idx}_trace.json")
-
         return 0
 
     def _synchronize_gradients(self):
@@ -1323,6 +1285,9 @@ def _synchronize_gradients(self):
                         )
 
     def _update(self, *deps):
+        return self._update_impl(*deps)
+
+    def _update_impl(self, *deps):
         self._label_task("update")
         self.logger.debug(f"Actor {self.global_rank} waiting for backward sync events")
 
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 428dec7..7f29176 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -12,114 +12,11 @@
 
 from .piper_actor import _create_actors
 from .piper_utils import piper_metadata, create_logger, LOG_LEVEL
-from .piper_exec import CompType, Schedule2D, Task
+from .piper_exec import Schedule2D
 from .piper import piper
 
 logger = create_logger("piper_compile", LOG_LEVEL)
 
-def _sort_by_task(executable_tasks: list[tuple[int, Task]]) -> list[tuple[int, Task]]:
-    """Sort executable tasks by type so that BWD/BWD_I come before FWD (then BWD_W, FWD_BWD)."""
-    type_order = {
-        CompType.BWD: 0,
-        CompType.BWD_I: 0,
-        CompType.FWD_BWD: 1,
-        CompType.FWD: 2,
-        CompType.BWD_W: 3,
-    }
-    return sorted(
-        executable_tasks,
-        key=lambda item: type_order.get(item[1].type, 4),
-    )
-
-
-def order_p2p_comms(schedule: Schedule2D, num_devices: int, num_stages: int, stage_to_device: dict[int, int]):
-    schedule = schedule.grid
-    # Filter to fwd/bwd tasks (exclude UPD). Include BWD_I, BWD_W, FWD_BWD.
-    schedule = [[task for task in row if task is not None and task.type != CompType.UPD] for row in schedule]
-
-    assert num_devices == len(schedule), "Some rank has no fwd/bwd tasks"
-    # Tasks are guaranteed to be fwd or bwd
-    num_fwd_bwd_tasks = sum(len(rank_tasks) for rank_tasks in schedule)
-
-    num_executed_tasks = 0
-    # (src_stage, dst_stage, mb_idx) for each p2p comm in execution order
-    # is_fwd if src_stage < dst_stage else bwd
-    p2p_comms = []
-    rank_to_next_task = {rank: 0 for rank in range(len(schedule))}
-    # P2P comm is enqueued when sender is ready
-    # Pick the next executable task in a round-robin fashion
-    while True:
-        executable_tasks = []
-        for rank, time_step in rank_to_next_task.items():
-            if time_step < len(schedule[rank]):
-                task = schedule[rank][time_step]
-                # Check the task's dependency is satisfied
-                # i.e., the sender has enqueued the comm
-                dependee = []
-                if task.type == CompType.FWD:
-                    batch = task.batches[0]
-                    if batch.stage_id > 0:
-                        dependee.append((batch.stage_id - 1, batch.stage_id, batch.mb_idx))
-                elif task.type in (CompType.BWD, CompType.BWD_I):
-                    batch = task.batches[0]
-                    if batch.stage_id < num_stages - 1:
-                        dependee.append((batch.stage_id + 1, batch.stage_id, batch.mb_idx))
-                elif task.type == CompType.FWD_BWD:
-                    fwd_batch, bwd_batch = task.batches
-                    if fwd_batch.stage_id > 0:
-                        dependee.append((fwd_batch.stage_id - 1, fwd_batch.stage_id, fwd_batch.mb_idx))
-                    if bwd_batch.stage_id < num_stages - 1:
-                        dependee.append((bwd_batch.stage_id + 1, bwd_batch.stage_id, bwd_batch.mb_idx))
-
-                can_exec = dependee == [] or all(dep in p2p_comms for dep in dependee)
-                if can_exec:
-                    executable_tasks.append((rank, task))
-
-        if not executable_tasks:
-            break
-
-        executable_tasks = _sort_by_task(executable_tasks)
-        for rank, task in executable_tasks:
-            if task.type == CompType.FWD:
-                batch = task.batches[0]
-                if batch.stage_id < num_stages - 1:
-                    p2p_comms.append((batch.stage_id, batch.stage_id + 1, batch.mb_idx))
-            elif task.type == CompType.BWD or task.type == CompType.BWD_I:
-                batch = task.batches[0]
-                if batch.stage_id > 0:
-                    p2p_comms.append((batch.stage_id, batch.stage_id - 1, batch.mb_idx))
-            elif task.type == CompType.FWD_BWD:
-                fwd_batch, bwd_batch = task.batches
-                if fwd_batch.stage_id < num_stages - 1:
-                    p2p_comms.append((fwd_batch.stage_id, fwd_batch.stage_id + 1, fwd_batch.mb_idx))
-                if bwd_batch.stage_id > 0:
-                    p2p_comms.append((bwd_batch.stage_id, bwd_batch.stage_id - 1, bwd_batch.mb_idx))
-            rank_to_next_task[rank] += 1
-
-        num_executed_tasks += len(executable_tasks)
-
-    if num_executed_tasks != num_fwd_bwd_tasks:
-        raise ValueError(
-            f"Only {num_executed_tasks} out of {num_fwd_bwd_tasks} "
-            f"fwd/bwd tasks can be executed"
-        )
-
-    # (src_stage, dst_stage, mb_idx, is_sender)
-    p2p_schedules = {rank: [] for rank in range(num_devices)}
-    for src_stage, dst_stage, mb_idx in p2p_comms:
-        src_rank = stage_to_device[src_stage]
-        dst_rank = stage_to_device[dst_stage]
-        logger.info(f"P2P comm: {src_stage}:{src_rank}->{dst_stage}:{dst_rank} MB {mb_idx}")
-        p2p_schedules[src_rank].append((src_stage, dst_stage, mb_idx, True))
-        p2p_schedules[dst_rank].append((src_stage, dst_stage, mb_idx, False))
-    
-    for rank, schedule in p2p_schedules.items():
-        logger.debug(f"P2P schedule for rank {rank}: {schedule}")
-
-    return p2p_schedules
-
-
-
 def piper_setup(
     model_class,
     model_args=(),
@@ -144,15 +41,9 @@ def piper_setup(
     num_stages = schedule.num_stages()
     num_devices = schedule.num_ranks()
 
-    p2p_schedules = order_p2p_comms(
-        schedule=schedule,
-        num_devices=num_devices,
-        num_stages=num_stages,
-        stage_to_device=stage_to_device
-    )
     _create_actors(
-        num_devices, optim_fn, num_mbs, num_stages, p2p_schedules, naive_gradient_sync,
-        profile=True, mode=mode,
+        num_devices, optim_fn, num_mbs, num_stages, naive_gradient_sync,
+        profile=True, mode=mode, stage_to_device=stage_to_device,
     )
 
     ray.get(
diff --git a/src/piper_exec.py b/src/piper_exec.py
index f34a3a3..107c6f6 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -236,6 +236,62 @@ def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdg
                 )
 
 
+def _build_p2p_schedule(schedule, num_stages, num_devices, num_steps, stage_to_device):
+    """
+    Build a global p2p schedule by iterating the compute schedule in execution order.
+
+    Returns:
+        rank_p2p: dict mapping pp_rank -> list of (op_type, stage_from, stage_to, mb_idx, is_fwd)
+        p2p_idx_map: dict mapping (pp_rank, stage_from, stage_to, mb_idx, is_fwd, op_type) -> rank_local_idx
+    """
+    # Global p2p ops in schedule iteration order. Each entry: (stage_from, stage_to, mb_idx, is_fwd).
+    # Only cross-rank ops are included. Each communication is added exactly once,
+    # from the SENDER's compute task, to avoid duplicates.
+    p2p_ops = []
+
+    for i in range(num_steps):
+        for j in range(num_devices - 1, -1, -1):
+            if i >= len(schedule[j]):
+                continue
+            task = schedule[j][i]
+            if task is None:
+                continue
+            pp_rank, batches, task_type = task
+
+            # Forward send (added after forward compute produces output)
+            if task_type in (CompType.FWD, CompType.FWD_BWD):
+                stage_id, mb_idx = batches[0]
+                if stage_id < num_stages - 1 and stage_to_device[stage_id] != stage_to_device[stage_id + 1]:
+                    p2p_ops.append((stage_id, stage_id + 1, mb_idx, True))
+
+            # Backward send (added after backward compute produces gradients)
+            if task_type in (CompType.BWD, CompType.BWD_I, CompType.FWD_BWD):
+                if task_type == CompType.FWD_BWD:
+                    stage_id, mb_idx = batches[1]
+                else:
+                    stage_id, mb_idx = batches[0]
+                if stage_id > 0 and stage_to_device[stage_id] != stage_to_device[stage_id - 1]:
+                    p2p_ops.append((stage_id, stage_id - 1, mb_idx, False))
+
+    # Build per-rank schedules and index map
+    rank_p2p = defaultdict(list)
+    p2p_idx_map = {}
+
+    for stage_from, stage_to, mb_idx, is_fwd in p2p_ops:
+        sender_pp = stage_to_device[stage_from]
+        receiver_pp = stage_to_device[stage_to]
+
+        send_idx = len(rank_p2p[sender_pp])
+        rank_p2p[sender_pp].append(("send", stage_from, stage_to, mb_idx, is_fwd))
+        p2p_idx_map[(sender_pp, stage_from, stage_to, mb_idx, is_fwd, "send")] = send_idx
+
+        recv_idx = len(rank_p2p[receiver_pp])
+        rank_p2p[receiver_pp].append(("recv", stage_from, stage_to, mb_idx, is_fwd))
+        p2p_idx_map[(receiver_pp, stage_from, stage_to, mb_idx, is_fwd, "recv")] = recv_idx
+
+    return rank_p2p, p2p_idx_map
+
+
 def piper_exec(
     schedule: Schedule2D,
     loss_fn,
@@ -266,10 +322,41 @@ def piper_exec(
     _validate_schedule(schedule, dag_edges, num_mbs)
 
     actors = piper_metadata.actors
+    stage_to_device = piper_metadata.stage_to_device
+
+    # Build p2p schedule and send to actors
+    rank_p2p, p2p_idx_map = _build_p2p_schedule(
+        schedule, num_stages, num_devices, num_steps, stage_to_device
+    )
+
+    def _p2p_recv(actor, pp_rank, stage_from, stage_to, mb_idx, is_fwd, dep):
+        """Dispatch a recv: _exec_p2p_op for cross-rank, old method for local."""
+        key = (pp_rank, stage_from, stage_to, mb_idx, is_fwd, "recv")
+        if key in p2p_idx_map:
+            return actor._exec_p2p_op.remote(p2p_idx_map[key], dep)
+        if is_fwd:
+            return actor._exec_fwd_recv.remote(stage_to, mb_idx, dep)
+        return actor._exec_bwd_recv.remote(stage_to, mb_idx, dep)
+
+    def _p2p_send(actor, pp_rank, stage_from, stage_to, mb_idx, is_fwd, dep):
+        """Dispatch a send: _exec_p2p_op for cross-rank, old method for local."""
+        key = (pp_rank, stage_from, stage_to, mb_idx, is_fwd, "send")
+        if key in p2p_idx_map:
+            return actor._exec_p2p_op.remote(p2p_idx_map[key], dep)
+        if is_fwd:
+            return actor._exec_fwd_send.remote(stage_from, mb_idx, dep)
+        return actor._exec_bwd_send.remote(stage_from, mb_idx, dep)
 
     ret = []
     # map pp_rank -> latest dependency
     deps = {}
+
+    # Send p2p schedules to actors before dispatch
+    for pp_rank in range(num_devices):
+        deps[pp_rank] = actors[pp_rank].set_p2p_schedule.remote(
+            rank_p2p.get(pp_rank, [])
+        )
+
     for i in range(num_steps):
         for j in range(num_devices-1, -1, -1):
             if not i < len(schedule[j]):
@@ -277,63 +364,55 @@ def piper_exec(
             task = schedule[j][i]
             if task:
                 pp_rank, batches, task_type = task
-                actor_id = j
-                actor = actors[actor_id]
+                actor = actors[j]
                 match task_type:
                     case CompType.UPD:
                         loss = actor._update.remote(deps[pp_rank])
                         ret.append(loss)
                     case CompType.FWD:
                         stage_id, mb_idx = batches[0]
-                        # logger.info(f"Executing forward {stage_id} mb {mb_idx} on actor {actor_id}")
-                        dep = deps[pp_rank] if pp_rank in deps else None
-                        if stage_id == 0:
-                            dep = actor._forward.remote(stage_id, mb_idx, dep)
-                        else:
-                            dep = actor._exec_p2p_op.remote(stage_id - 1, stage_id, mb_idx, False, dep)
-                            dep = actor._forward.remote(stage_id, mb_idx, dep)
+                        dep = deps[pp_rank]
+                        if stage_id > 0:
+                            dep = _p2p_recv(actor, pp_rank, stage_id - 1, stage_id, mb_idx, True, dep)
+                        dep = actor._forward.remote(stage_id, mb_idx, dep)
                         if stage_id < num_stages - 1:
-                            dep = actor._exec_p2p_op.remote(stage_id, stage_id + 1, mb_idx, True, dep)
+                            dep = _p2p_send(actor, pp_rank, stage_id, stage_id + 1, mb_idx, True, dep)
                         deps[pp_rank] = dep
                     case CompType.BWD:
                         stage_id, mb_idx = batches[0]
-                        # logger.info(f"Executing backward {stage_id} mb {mb_idx} on actor {actor_id}")
                         dep = deps[pp_rank]
                         if stage_id < num_stages - 1:
-                            dep = actor._exec_p2p_op.remote(stage_id + 1, stage_id, mb_idx, False, dep)
+                            dep = _p2p_recv(actor, pp_rank, stage_id + 1, stage_id, mb_idx, False, dep)
                         dep = actor._backward.remote(stage_id, mb_idx, dep, loss_fn=loss_fn)
                         if stage_id > 0:
-                            dep = actor._exec_p2p_op.remote(stage_id, stage_id - 1, mb_idx, True, dep)
+                            dep = _p2p_send(actor, pp_rank, stage_id, stage_id - 1, mb_idx, False, dep)
                         deps[pp_rank] = dep
                     case CompType.BWD_I:
                         stage_id, mb_idx = batches[0]
-                        # logger.info(f"Executing backward I {stage_id} mb {mb_idx} on actor {actor_id}")
                         dep = deps[pp_rank]
                         if stage_id < num_stages - 1:
-                            dep = actor._exec_p2p_op.remote(stage_id + 1, stage_id, mb_idx, False, dep)
+                            dep = _p2p_recv(actor, pp_rank, stage_id + 1, stage_id, mb_idx, False, dep)
                         dep = actor._backward_input.remote(stage_id, mb_idx, dep, loss_fn=loss_fn)
                         if stage_id > 0:
-                            dep = actor._exec_p2p_op.remote(stage_id, stage_id - 1, mb_idx, True, dep)
+                            dep = _p2p_send(actor, pp_rank, stage_id, stage_id - 1, mb_idx, False, dep)
                         deps[pp_rank] = dep
                     case CompType.BWD_W:
                         stage_id, mb_idx = batches[0]
-                        # logger.info(f"Executing backward W {stage_id} mb {mb_idx} on actor {actor_id}")
                         dep = deps[pp_rank]
                         dep = actor._backward_weight.remote(stage_id, mb_idx, dep)
                         deps[pp_rank] = dep
                     case CompType.FWD_BWD:
                         fwd_stage_id, fwd_mb_idx = batches[0]
                         bwd_stage_id, bwd_mb_idx = batches[1]
-                        # logger.info(f"Executing forward-backward {fwd_stage_id} mb {fwd_mb_idx} -> {bwd_stage_id} mb {bwd_mb_idx} on actor {actor_id}")
                         dep = deps[pp_rank]
                         if fwd_stage_id > 0:
-                            dep = actor._exec_p2p_op.remote(fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False, dep)
+                            dep = _p2p_recv(actor, pp_rank, fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, True, dep)
                         if bwd_stage_id < num_stages - 1:
-                            dep = actor._exec_p2p_op.remote(bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False, dep)
+                            dep = _p2p_recv(actor, pp_rank, bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False, dep)
                         dep = actor._forward_backward.remote(fwd_stage_id, fwd_mb_idx, bwd_stage_id, bwd_mb_idx, dep, loss_fn=loss_fn)
                         if fwd_stage_id < num_stages - 1:
-                            dep = actor._exec_p2p_op.remote(fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True, dep)
+                            dep = _p2p_send(actor, pp_rank, fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True, dep)
                         if bwd_stage_id > 0:
-                            dep = actor._exec_p2p_op.remote(bwd_stage_id, bwd_stage_id - 1, bwd_mb_idx, True, dep)
+                            dep = _p2p_send(actor, pp_rank, bwd_stage_id, bwd_stage_id - 1, bwd_mb_idx, False, dep)
                         deps[pp_rank] = dep
     return ray.get(ret)
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index 07128fc..abb815b 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -132,6 +132,245 @@ def _dispatch_a2a_single(output: torch.Tensor, input_tensor: torch.Tensor) -> to
 # Allow the dispatch function in the graph
 torch.compiler.allow_in_graph(_dispatch_a2a_single)
 
+def _profile_and_split_gm(gm, num_stages) -> tuple[fx.GraphModule, list[tuple[int, fx.GraphModule, list[int], list]]]:
+    """
+    Profile each node in the graph module, then split into num_stages stages
+    of roughly equal execution time. This replaces annotation-based splitting
+    when users don't provide stage annotations.
+
+    Returns the same format as _split_gm_by_stages:
+        (top_level_gm, [(stage_id, stage_gm, input_idxs, param_idxs, graphargs, placeholders), ...])
+    """
+    # Step 1: Collect compute nodes (everything that isn't placeholder, get_attr, or output)
+    compute_nodes = []
+    for node in gm.graph.nodes:
+        if node.op in ("placeholder", "get_attr", "output"):
+            continue
+        compute_nodes.append(node)
+
+    if not compute_nodes:
+        return gm, []
+
+    # Step 2: Build environment mapping for executing individual nodes
+    # We need real tensors on device to profile
+    env = {}
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    # Populate env with placeholder values (from example_value metadata)
+    for node in gm.graph.nodes:
+        if node.op == "placeholder":
+            ex = node.meta.get("example_value")
+            if ex is not None:
+                if isinstance(ex, torch.Tensor):
+                    # example_value may be on meta device, so create with explicit device
+                    if ex.is_floating_point():
+                        t = torch.randn(ex.shape, dtype=ex.dtype, device=device)
+                    else:
+                        t = torch.ones(ex.shape, dtype=ex.dtype, device=device)
+                    if isinstance(ex, torch.nn.Parameter):
+                        t = torch.nn.Parameter(t, requires_grad=ex.requires_grad)
+                    env[node.name] = t
+                else:
+                    env[node.name] = ex
+        elif node.op == "get_attr":
+            # Fetch the attribute from the graph module
+            target_atoms = node.target.split(".")
+            attr = gm
+            for atom in target_atoms:
+                attr = getattr(attr, atom)
+            if isinstance(attr, torch.Tensor):
+                if attr.device.type == "meta":
+                    if attr.is_floating_point():
+                        env[node.name] = torch.randn(attr.shape, dtype=attr.dtype, device=device)
+                    else:
+                        env[node.name] = torch.ones(attr.shape, dtype=attr.dtype, device=device)
+                else:
+                    env[node.name] = attr.to(device)
+            else:
+                env[node.name] = attr
+
+    def _fetch_arg(arg):
+        if isinstance(arg, fx.Node):
+            return env.get(arg.name)
+        elif isinstance(arg, (list, tuple)):
+            vals = [_fetch_arg(a) for a in arg]
+            return type(arg)(vals)
+        elif isinstance(arg, dict):
+            return {k: _fetch_arg(v) for k, v in arg.items()}
+        return arg
+
+    # Step 3: Profile each compute node
+    node_times = {}  # node -> time in ms
+
+    # Warmup + profile
+    num_warmup = 3
+    num_profile = 5
+    for node in compute_nodes:
+        args = _fetch_arg(node.args)
+        kwargs = _fetch_arg(node.kwargs)
+
+        # Execute the node
+        if node.op == "call_function":
+            fn = node.target
+        elif node.op == "call_method":
+            fn = getattr(type(args[0]), node.target) if args else None
+        elif node.op == "call_module":
+            fn = gm.get_submodule(node.target)
+        else:
+            node_times[node.name] = 0.0
+            env[node.name] = None
+            continue
+
+        # Try to execute and profile
+        try:
+            # Warmup
+            for _ in range(num_warmup):
+                if node.op == "call_method":
+                    result = getattr(args[0], node.target)(*args[1:], **kwargs)
+                elif node.op == "call_module":
+                    result = fn(*args, **kwargs)
+                else:
+                    result = fn(*args, **kwargs)
+
+            if device == "cuda":
+                torch.cuda.synchronize()
+
+            start_event = torch.cuda.Event(enable_timing=True)
+            end_event = torch.cuda.Event(enable_timing=True)
+            start_event.record()
+            for _ in range(num_profile):
+                if node.op == "call_method":
+                    result = getattr(args[0], node.target)(*args[1:], **kwargs)
+                elif node.op == "call_module":
+                    result = fn(*args, **kwargs)
+                else:
+                    result = fn(*args, **kwargs)
+            end_event.record()
+            torch.cuda.synchronize()
+            elapsed = start_event.elapsed_time(end_event) / num_profile
+            node_times[node.name] = elapsed
+            env[node.name] = result
+        except Exception:
+            node_times[node.name] = 0.0
+            # Try to get a result for downstream nodes
+            try:
+                if node.op == "call_method":
+                    result = getattr(args[0], node.target)(*args[1:], **kwargs)
+                elif node.op == "call_module":
+                    result = fn(*args, **kwargs)
+                else:
+                    result = fn(*args, **kwargs)
+                env[node.name] = result
+            except Exception:
+                env[node.name] = None
+
+    # Step 4: Partition compute nodes into num_stages groups of roughly equal time
+    total_time = sum(node_times.get(n.name, 0.0) for n in compute_nodes)
+    target_time = total_time / num_stages
+
+    # Precompute prefix sums for fast time range queries
+    prefix_times = [0.0]
+    for n in compute_nodes:
+        prefix_times.append(prefix_times[-1] + node_times.get(n.name, 0.0))
+
+    def _count_cross_boundary_deps(split_idx):
+        """Count how many compute nodes before split_idx are used by nodes at or after split_idx."""
+        before_set = set(compute_nodes[:split_idx])
+        cross = set()
+        for node in compute_nodes[split_idx:]:
+            for arg in node.all_input_nodes:
+                if arg in before_set and arg.op not in ("placeholder", "get_attr"):
+                    cross.add(arg)
+        return len(cross)
+
+    def _find_best_split(ideal_idx, stage_start_idx):
+        """Search all valid split points and return the one closest to ideal_idx in time."""
+        ideal_time = prefix_times[ideal_idx]
+        best_split = None
+        best_time_dist = float('inf')
+        for candidate in range(stage_start_idx + 1, len(compute_nodes)):
+            n_deps = _count_cross_boundary_deps(candidate)
+            if n_deps <= 1:
+                time_dist = abs(prefix_times[candidate] - ideal_time)
+                if time_dist < best_time_dist:
+                    best_time_dist = time_dist
+                    best_split = candidate
+        return best_split
+
+    # Find split points: we need (num_stages - 1) splits
+    split_indices = []
+    stage_start_idx = 0
+    for s in range(num_stages - 1):
+        # Target cumulative time for end of this stage
+        remaining_time = prefix_times[-1] - prefix_times[stage_start_idx]
+        remaining_stages = num_stages - s
+        stage_target = remaining_time / remaining_stages
+
+        target_cum_time = prefix_times[stage_start_idx] + stage_target
+        # Find the ideal split index (first node where cumulative time exceeds target)
+        ideal_idx = stage_start_idx + 1
+        for i in range(stage_start_idx + 1, len(compute_nodes)):
+            if prefix_times[i] >= target_cum_time:
+                ideal_idx = i
+                break
+        else:
+            ideal_idx = len(compute_nodes) - 1
+
+        best_split = _find_best_split(ideal_idx, stage_start_idx)
+        if best_split is not None:
+            split_indices.append(best_split)
+            time_offset = prefix_times[best_split] - prefix_times[ideal_idx]
+            logger.info(
+                f"Stage {s}/{s+1} boundary: split before node {compute_nodes[best_split].name} "
+                f"(index {best_split}, time offset from ideal: {time_offset:+.3f} ms)"
+            )
+            stage_start_idx = best_split
+        else:
+            logger.warning(f"Could not find valid split point near node {ideal_idx}, skipping")
+
+    # Assign stages based on split indices
+    stage_assignments = {}
+    current_stage = 0
+    split_iter = iter(split_indices)
+    next_split = next(split_iter, len(compute_nodes))
+    for idx, node in enumerate(compute_nodes):
+        if idx == next_split:
+            current_stage += 1
+            next_split = next(split_iter, len(compute_nodes))
+        stage_assignments[node] = current_stage
+
+    # Ensure example_value metadata matches the profiled result's requires_grad.
+    # This prevents assertions in _forward_impl from failing when non-grad tensors
+    # (e.g., masks, positional encodings) are passed between stages as outputs.
+    for node in compute_nodes:
+        result = env.get(node.name)
+        if result is not None and isinstance(result, torch.Tensor) and not result.requires_grad:
+            ex = node.meta.get("example_value")
+            if ex is not None and isinstance(ex, torch.Tensor) and ex.requires_grad:
+                node.meta["example_value"] = ex.detach()
+
+    # Inject stage annotations into node metadata so _split_gm_by_stages can handle it
+    for node, stage_id in stage_assignments.items():
+        if 'custom' not in node.meta:
+            node.meta['custom'] = {}
+        node.meta['custom']['stage'] = stage_id
+
+    # Log stage times
+    stage_times = defaultdict(float)
+    stage_node_counts = defaultdict(int)
+    for node, stage_id in stage_assignments.items():
+        stage_times[stage_id] += node_times.get(node.name, 0.0)
+        stage_node_counts[stage_id] += 1
+    for stage_id in sorted(stage_times.keys()):
+        logger.info(
+            f"Stage {stage_id}: {stage_times[stage_id]:.3f} ms "
+            f"({stage_node_counts[stage_id]} nodes)"
+        )
+
+    # Delegate to _split_gm_by_stages which handles all the submodule creation
+    return _split_gm_by_stages(gm)
+
+
 def _meta_tensor_like(example: torch.Tensor, *, requires_grad: bool, as_parameter: bool):
     t = torch.empty(example.shape, dtype=example.dtype, device="meta")
     t.requires_grad_(requires_grad)
@@ -386,8 +625,13 @@ def collect_dependencies(node):
         
         # Find outputs: nodes computed in this stage that are used by other stages
         # Check if users have stage annotations from other stages, or if they're in already-processed stages
+        # Only consider non-placeholder nodes as outputs — placeholders (e.g. model attributes
+        # like freqs_cis, mask) are copied into each stage via get_attr or re-created as
+        # placeholders, so they don't need to be passed between stages.
         stage_outputs = []
         for node in stage_computed_nodes:
+            if node.op == "placeholder":
+                continue
             for user in node.users:
                 # Check if user has a stage annotation from a different stage
                 user_in_other_stage = False
diff --git a/test/models/llama.py b/test/models/llama.py
index 5f95623..f579118 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -342,7 +342,7 @@ def __init__(self, layer_id: int, args: ModelArgs):
             ffn_dim_multiplier=args.ffn_dim_multiplier,
         )
         self.layer_id = layer_id
-        self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
+        self.  = RMSNorm(args.dim, eps=args.norm_eps)
         self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
 
     def forward(
@@ -453,32 +453,32 @@ def log_size(layer, indent=0):
 
     #     return output
 
-    """
-    4 STAGES
-    """
-    def forward(self, tokens: torch.Tensor):
+    # """
+    # 4 STAGES
+    # """
+    # def forward(self, tokens: torch.Tensor):
 
-        with torch.fx.traceback.annotate({"stage": 0}):
-            h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-            start_pos = 0
-            for layer in self.layers[:self.n_layers//4]:
-                h = layer(h, start_pos, self.freqs_cis, self.mask)
+    #     with torch.fx.traceback.annotate({"stage": 0}):
+    #         h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
+    #         start_pos = 0
+    #         for layer in self.layers[:self.n_layers//4]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
 
-        with torch.fx.traceback.annotate({"stage": 1}):
-            for layer in self.layers[self.n_layers//4:self.n_layers//2]:
-                h = layer(h, start_pos, self.freqs_cis, self.mask)
+    #     with torch.fx.traceback.annotate({"stage": 1}):
+    #         for layer in self.layers[self.n_layers//4:self.n_layers//2]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
 
-        with torch.fx.traceback.annotate({"stage": 2}):
-            for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
-                h = layer(h, start_pos, self.freqs_cis, self.mask)
+    #     with torch.fx.traceback.annotate({"stage": 2}):
+    #         for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
 
-        with torch.fx.traceback.annotate({"stage": 3}):
-            for layer in self.layers[3*self.n_layers//4:]:
-                h = layer(h, start_pos, self.freqs_cis, self.mask)
-            h = self.norm(h) if self.norm else h
-            output = self.output(h).float() if self.output else h
+    #     with torch.fx.traceback.annotate({"stage": 3}):
+    #         for layer in self.layers[3*self.n_layers//4:]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+    #         h = self.norm(h) if self.norm else h
+    #         output = self.output(h).float() if self.output else h
 
-        return output
+    #     return output
 
     # """
     # 2 STAGES
@@ -497,20 +497,14 @@ def forward(self, tokens: torch.Tensor):
 
     #     return output
 
-    # """
-    # 1 STAGE
-    # """
-    # def forward(self, tokens: torch.Tensor):
-
-    #     distributed_stage(0, actor_id=0)
-
-    #     h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-    #     start_pos = 0
-        
-    #     for layer in self.layers:
-    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     h = self.norm(h) if self.norm else h
-    #     output = self.output(h).float() if self.output else h
-
-    #     return output
\ No newline at end of file
+    """
+    No annotations: auto infer stages
+    """
+    def forward(self, tokens: torch.Tensor):
+        h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
+        start_pos = 0
+        for layer in self.layers:
+            h = layer(h, start_pos, self.freqs_cis, self.mask)
+        h = self.norm(h) if self.norm else h
+        output = self.output(h).float() if self.output else h
+        return output
\ No newline at end of file
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index 361250c..4bfe3c2 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -333,10 +333,10 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            None,
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
-            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
diff --git a/test/test_llama.py b/test/test_llama.py
index 876060c..bf26151 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -11,7 +11,6 @@
 from src.piper_compile import piper_setup, piper_shutdown
 from src.piper_coordinator import PiperProgramCoordinator
 from src.piper_utils import piper_metadata
-
 from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B, LLAMA_70B
 from .schedule_helpers import (
     build_1f1b_schedule, 
@@ -109,6 +108,7 @@ def main(args):
         end = time.perf_counter()
         iter_times.append(end - start)
         print(f"Iter {i} completed")
+        time.sleep(1) 
     
     dp_rank = int(os.environ['PIPER_DP_RANK'])
     print(
@@ -119,22 +119,24 @@ def main(args):
     if args.tracing:
         actors = piper_metadata.actors
         ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
-        ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
-        ray.get([actor.start_mem_tracing.remote() for actor in actors.values()])
+        # ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
+        # ray.get([actor.start_mem_tracing.remote() for actor in actors.values()])
 
         print(f"Running {args.warmup} tracing iterations...")
         for _ in range(args.warmup):
             piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
 
-        mem_data_ret = ray.get([actor.get_peak_memory.remote() for actor in actors.values()])
-        for rank, peak_mem in mem_data_ret:
-            print(f"rank {rank} peak memory= {peak_mem:.3f} GB")
+        # mem_data_ret = ray.get([actor.get_peak_memory.remote() for actor in actors.values()])
+        # for rank, peak_mem in mem_data_ret:
+        #     print(f"rank {rank} peak memory= {peak_mem:.3f} GB")
 
         trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
         for rank, trace_data in trace_data_ret:
             for key in trace_data:
                 all_times = trace_data[key]
                 print(f"rank {rank} {key} time= {np.mean(all_times):.3f} ± {np.std(all_times):.3f} ms ({len(all_times)} samples)")
+                if key == "fwd_recv_wait" or key == "bwd_recv_wait":
+                    print(f"rank {rank} {key} all times: {all_times}")
 
     if args.naive_gradient_sync:
         suffix = "-naive-sync"
@@ -185,7 +187,7 @@ def parse_args():
 
 
 if __name__ == "__main__":
-    ray.init(include_dashboard=False, log_to_driver=True, namespace="llama")
+    ray.init(log_to_driver=True, namespace="llama", include_dashboard=False, _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp")
     args = parse_args()
     piper_coordinator = PiperProgramCoordinator.remote(dp_degree=args.dp, pp_degree=args.pp)
     ray.get(piper_coordinator.run_program.remote(main, args))

From 483bb9b7d46cc993fd3e12d5413a2cb7889637cf Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Fri, 6 Mar 2026 23:01:32 +0000
Subject: [PATCH 067/100] Enable profiling models that don't fit into memory
 for auto stage splitting by only materializing meta tensors needed for the
 current computation and free when no longer needed

---
 src/piper_graph_transform.py | 240 ++++++++++++++++++++++++++---------
 test/models/llama.py         |   2 +-
 test/test_llama.py           |   2 +-
 3 files changed, 182 insertions(+), 62 deletions(-)

diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index abb815b..0e8195f 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -1,8 +1,11 @@
+import hashlib
+import json
 import ray
 import torch
 import torch.fx as fx
 import torch.distributed as dist
 from collections import defaultdict
+from pathlib import Path
 from typing import Optional
 import operator
 import os
@@ -151,46 +154,94 @@ def _profile_and_split_gm(gm, num_stages) -> tuple[fx.GraphModule, list[tuple[in
     if not compute_nodes:
         return gm, []
 
+    # Check for cached split from a previous profiling run
+    cache_dir = Path.home() / ".cache" / "piper" / "splits"
+    graph_signature = hashlib.sha256(
+        json.dumps([(n.name, n.op, str(n.target)) for n in compute_nodes] + [num_stages]).encode()
+    ).hexdigest()[:16]
+    cache_file = cache_dir / f"{graph_signature}.json"
+
+    if cache_file.exists():
+        try:
+            cached = json.loads(cache_file.read_text())
+            split_node_names = cached["split_node_names"]
+            # Rebuild split_indices from cached node names
+            node_name_to_idx = {n.name: i for i, n in enumerate(compute_nodes)}
+            split_indices = [node_name_to_idx[name] for name in split_node_names]
+            logger.info(f"Loaded cached split from {cache_file} (splits: {split_node_names})")
+
+            # Assign stages and inject annotations
+            stage_assignments = {}
+            current_stage = 0
+            split_iter = iter(split_indices)
+            next_split = next(split_iter, len(compute_nodes))
+            for idx, node in enumerate(compute_nodes):
+                if idx == next_split:
+                    current_stage += 1
+                    next_split = next(split_iter, len(compute_nodes))
+                stage_assignments[node] = current_stage
+
+            for node, stage_id in stage_assignments.items():
+                if 'custom' not in node.meta:
+                    node.meta['custom'] = {}
+                node.meta['custom']['stage'] = stage_id
+
+            return _split_gm_by_stages(gm)
+        except Exception as e:
+            logger.warning(f"Failed to load cached split from {cache_file}: {e}, re-profiling")
+
     # Step 2: Build environment mapping for executing individual nodes
-    # We need real tensors on device to profile
+    # We need real tensors on device to profile. To handle models too large for a single
+    # GPU, we eagerly free intermediate tensors once all their consumers have been profiled.
     env = {}
     device = "cuda" if torch.cuda.is_available() else "cpu"
 
-    # Populate env with placeholder values (from example_value metadata)
-    for node in gm.graph.nodes:
+    # Build reference counts: for each node, count how many compute nodes (+ the output node)
+    # consume it. When refcount hits 0 after profiling a consumer, we free the tensor.
+    compute_node_set = set(compute_nodes)
+    ref_counts = {}  # node.name -> int
+    for node in compute_nodes:
+        for inp in node.all_input_nodes:
+            ref_counts[inp.name] = ref_counts.get(inp.name, 0) + 1
+
+    def _materialize_tensor(ex, device):
+        """Create a real tensor on device from an example (possibly meta) tensor."""
+        if isinstance(ex, torch.Tensor):
+            shape = tuple(ex.shape)
+            if ex.device.type != "meta" and ex.device.type == device:
+                return ex
+            if ex.is_floating_point():
+                t = torch.randn(shape, dtype=ex.dtype, device=device)
+            else:
+                t = torch.ones(shape, dtype=ex.dtype, device=device)
+            if isinstance(ex, torch.nn.Parameter):
+                t = torch.nn.Parameter(t, requires_grad=ex.requires_grad)
+            return t
+        return ex
+
+    def _ensure_in_env(node):
+        """Lazily materialize a placeholder or get_attr node into env on first access."""
+        if node.name in env:
+            return
         if node.op == "placeholder":
             ex = node.meta.get("example_value")
             if ex is not None:
-                if isinstance(ex, torch.Tensor):
-                    # example_value may be on meta device, so create with explicit device
-                    if ex.is_floating_point():
-                        t = torch.randn(ex.shape, dtype=ex.dtype, device=device)
-                    else:
-                        t = torch.ones(ex.shape, dtype=ex.dtype, device=device)
-                    if isinstance(ex, torch.nn.Parameter):
-                        t = torch.nn.Parameter(t, requires_grad=ex.requires_grad)
-                    env[node.name] = t
-                else:
-                    env[node.name] = ex
+                env[node.name] = _materialize_tensor(ex, device)
+            else:
+                env[node.name] = None
         elif node.op == "get_attr":
-            # Fetch the attribute from the graph module
             target_atoms = node.target.split(".")
             attr = gm
             for atom in target_atoms:
                 attr = getattr(attr, atom)
             if isinstance(attr, torch.Tensor):
-                if attr.device.type == "meta":
-                    if attr.is_floating_point():
-                        env[node.name] = torch.randn(attr.shape, dtype=attr.dtype, device=device)
-                    else:
-                        env[node.name] = torch.ones(attr.shape, dtype=attr.dtype, device=device)
-                else:
-                    env[node.name] = attr.to(device)
+                env[node.name] = _materialize_tensor(attr, device)
             else:
                 env[node.name] = attr
 
     def _fetch_arg(arg):
         if isinstance(arg, fx.Node):
+            _ensure_in_env(arg)
             return env.get(arg.name)
         elif isinstance(arg, (list, tuple)):
             vals = [_fetch_arg(a) for a in arg]
@@ -199,71 +250,124 @@ def _fetch_arg(arg):
             return {k: _fetch_arg(v) for k, v in arg.items()}
         return arg
 
-    # Step 3: Profile each compute node
+    def _dec_ref(node):
+        """Decrement refcount for a node and free its env entry when it reaches 0."""
+        name = node.name
+        if name not in ref_counts:
+            return
+        ref_counts[name] -= 1
+        if ref_counts[name] <= 0 and name in env and isinstance(env[name], torch.Tensor):
+            del env[name]
+
+    def _exec_node(node, args, kwargs, fn):
+        """Execute a single graph node and return the result."""
+        if node.op == "call_method":
+            return getattr(args[0], node.target)(*args[1:], **kwargs)
+        elif node.op == "call_module":
+            return fn(*args, **kwargs)
+        else:
+            return fn(*args, **kwargs)
+
+    def _log_env_memory():
+        """Log all active tensors in env and their total memory usage."""
+        active = []
+        total_bytes = 0
+        for name, val in env.items():
+            if isinstance(val, torch.Tensor) and val.device.type != "meta":
+                nbytes = val.nelement() * val.element_size()
+                total_bytes += nbytes
+                active.append((name, tuple(val.shape), val.dtype, nbytes))
+        total_mb = total_bytes / (1024 * 1024)
+        cuda_allocated_mb = torch.cuda.memory_allocated() / (1024 * 1024) if device == "cuda" else 0
+        logger.debug(
+            f"Active env tensors: {len(active)}, env total: {total_mb:.1f} MB, "
+            f"CUDA allocated: {cuda_allocated_mb:.1f} MB"
+        )
+        # Log the top 10 largest tensors
+        active.sort(key=lambda x: x[3], reverse=True)
+        for name, shape, dtype, nbytes in active[:10]:
+            logger.debug(f"  {name}: {shape} {dtype} ({nbytes / (1024*1024):.1f} MB)")
+
+    # Step 3: Profile each compute node with eager memory freeing
+    # Use no_grad to prevent autograd graph accumulation — we only need forward timing.
     node_times = {}  # node -> time in ms
 
-    # Warmup + profile
     num_warmup = 3
     num_profile = 5
-    for node in compute_nodes:
+    log_interval = max(1, len(compute_nodes) // 20)  # Log ~20 times during profiling
+    for node_idx, node in enumerate(compute_nodes):
+        if node_idx % log_interval == 0:
+            _log_env_memory()
+
         args = _fetch_arg(node.args)
         kwargs = _fetch_arg(node.kwargs)
 
-        # Execute the node
+        # Check for None inputs from failed predecessor nodes
+        has_none_input = False
+        for inp in node.all_input_nodes:
+            if inp.op not in ("placeholder", "get_attr") and env.get(inp.name) is None:
+                has_none_input = True
+                break
+
+        if has_none_input:
+            logger.debug(f"Skipping node {node.name}: predecessor produced None")
+            node_times[node.name] = 0.0
+            env[node.name] = None
+            for inp in node.all_input_nodes:
+                _dec_ref(inp)
+            continue
+
+        # Resolve the callable
         if node.op == "call_function":
             fn = node.target
         elif node.op == "call_method":
             fn = getattr(type(args[0]), node.target) if args else None
         elif node.op == "call_module":
             fn = gm.get_submodule(node.target)
+            fn.to(device)
         else:
             node_times[node.name] = 0.0
             env[node.name] = None
+            for inp in node.all_input_nodes:
+                _dec_ref(inp)
             continue
 
-        # Try to execute and profile
+        # Execute and profile under no_grad to prevent autograd graph accumulation
         try:
-            # Warmup
-            for _ in range(num_warmup):
-                if node.op == "call_method":
-                    result = getattr(args[0], node.target)(*args[1:], **kwargs)
-                elif node.op == "call_module":
-                    result = fn(*args, **kwargs)
-                else:
-                    result = fn(*args, **kwargs)
-
-            if device == "cuda":
+            with torch.no_grad():
+                for _ in range(num_warmup):
+                    result = _exec_node(node, args, kwargs, fn)
+
+                if device == "cuda":
+                    torch.cuda.synchronize()
+
+                start_event = torch.cuda.Event(enable_timing=True)
+                end_event = torch.cuda.Event(enable_timing=True)
+                start_event.record()
+                for _ in range(num_profile):
+                    result = _exec_node(node, args, kwargs, fn)
+                end_event.record()
                 torch.cuda.synchronize()
-
-            start_event = torch.cuda.Event(enable_timing=True)
-            end_event = torch.cuda.Event(enable_timing=True)
-            start_event.record()
-            for _ in range(num_profile):
-                if node.op == "call_method":
-                    result = getattr(args[0], node.target)(*args[1:], **kwargs)
-                elif node.op == "call_module":
-                    result = fn(*args, **kwargs)
-                else:
-                    result = fn(*args, **kwargs)
-            end_event.record()
-            torch.cuda.synchronize()
-            elapsed = start_event.elapsed_time(end_event) / num_profile
+                elapsed = start_event.elapsed_time(end_event) / num_profile
             node_times[node.name] = elapsed
             env[node.name] = result
-        except Exception:
+        except Exception as e:
+            logger.warning(f"Failed to profile node {node.name} ({node.op}): {e}")
             node_times[node.name] = 0.0
-            # Try to get a result for downstream nodes
             try:
-                if node.op == "call_method":
-                    result = getattr(args[0], node.target)(*args[1:], **kwargs)
-                elif node.op == "call_module":
-                    result = fn(*args, **kwargs)
-                else:
-                    result = fn(*args, **kwargs)
-                env[node.name] = result
+                with torch.no_grad():
+                    env[node.name] = _exec_node(node, args, kwargs, fn)
             except Exception:
                 env[node.name] = None
 
+        # Move call_module submodules back to meta to free GPU memory
+        if node.op == "call_module":
+            fn.to("meta")
+
+        # Decrement refcounts for all inputs of this node
+        for inp in node.all_input_nodes:
+            _dec_ref(inp)
+
     # Step 4: Partition compute nodes into num_stages groups of roughly equal time
     total_time = sum(node_times.get(n.name, 0.0) for n in compute_nodes)
     target_time = total_time / num_stages
@@ -328,6 +432,15 @@ def _find_best_split(ideal_idx, stage_start_idx):
         else:
             logger.warning(f"Could not find valid split point near node {ideal_idx}, skipping")
 
+    # Save split to cache
+    try:
+        cache_dir.mkdir(parents=True, exist_ok=True)
+        split_node_names = [compute_nodes[i].name for i in split_indices]
+        cache_file.write_text(json.dumps({"split_node_names": split_node_names}, indent=2))
+        logger.info(f"Saved split cache to {cache_file}")
+    except Exception as e:
+        logger.warning(f"Failed to save split cache: {e}")
+
     # Assign stages based on split indices
     stage_assignments = {}
     current_stage = 0
@@ -367,6 +480,13 @@ def _find_best_split(ideal_idx, stage_start_idx):
             f"({stage_node_counts[stage_id]} nodes)"
         )
 
+    # Free all profiling tensors before training starts
+    import gc
+    env.clear()
+    del env
+    gc.collect()
+    torch.cuda.empty_cache()
+
     # Delegate to _split_gm_by_stages which handles all the submodule creation
     return _split_gm_by_stages(gm)
 
diff --git a/test/models/llama.py b/test/models/llama.py
index f579118..ce40cc1 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -342,7 +342,7 @@ def __init__(self, layer_id: int, args: ModelArgs):
             ffn_dim_multiplier=args.ffn_dim_multiplier,
         )
         self.layer_id = layer_id
-        self.  = RMSNorm(args.dim, eps=args.norm_eps)
+        self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
         self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
 
     def forward(
diff --git a/test/test_llama.py b/test/test_llama.py
index bf26151..4cb731b 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -161,7 +161,7 @@ def main(args):
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
-    parser.add_argument('--model', choices=['debug', '1b', '3b', '8b'], default='debug',
+    parser.add_argument('--model', choices=['debug', '1b', '3b', '8b', '70b'], default='debug',
                         help='Model configuration: debug, 1b, 3b, or 8b (default: debug)')
     parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'interleaved-gpipe', 'dualpipev-nozb', 'dualpipev', 'zerobubble', 'no-pp'], default='1f1b',
                         help='Schedule type: gpipe, 1f1b, interleaved-1f1b, dualpipev, zerobubble, or no-pp (default: 1f1b)')

From a8078dce573f7612c2803c0225a8a3d63574f059 Mon Sep 17 00:00:00 2001
From: Andy Ruan <andyubryh@gmail.com>
Date: Mon, 9 Mar 2026 14:17:57 +0000
Subject: [PATCH 068/100] (WIP) experimental: ZeRO1 with per-param sharding

Signed-off-by: Andy Ruan <andyubryh@gmail.com>
---
 docs/zero1_design.md | 178 +++++++++++++++
 src/piper_actor.py   |  94 ++++++--
 src/piper_compile.py |  17 +-
 src/piper_zero1.py   | 530 +++++++++++++++++++++++++++++++++++++++++++
 4 files changed, 794 insertions(+), 25 deletions(-)
 create mode 100644 docs/zero1_design.md
 create mode 100644 src/piper_zero1.py

diff --git a/docs/zero1_design.md b/docs/zero1_design.md
new file mode 100644
index 0000000..9f9c065
--- /dev/null
+++ b/docs/zero1_design.md
@@ -0,0 +1,178 @@
+# ZeRO-1 with Bucketed Reduce-Scatter for Piper
+
+## Overview
+
+ZeRO-1 (Zero Redundancy Optimizer Stage 1) shards optimizer states across data-parallel
+(DP) ranks. This design uses **per-parameter sharding** and **gradient-sync buckets**
+to overlap communication with backward computation, matching PyTorch DDP's bucketing
+approach but adapted for ZeRO's reduce-scatter + all-gather pattern.
+
+---
+
+## Memory Savings
+
+For Adam with fp32 states, optimizer state costs 12 bytes/element (fp32 master weight +
+momentum + variance). ZeRO-1 reduces this by `dp_degree`:
+
+| DP degree | Optimizer states per rank |
+|-----------|--------------------------|
+| 1 (DDP)   | 100%                     |
+| 2         | 50%                      |
+| 4         | 25%                      |
+| 8         | 12.5%                    |
+
+---
+
+## Per-Parameter Sharding
+
+Each trainable parameter `p` (numel = N) is divided into `dp_degree` contiguous shards
+of size `ceil(N / dp_degree)`. DP rank `r` owns the shard covering flat indices
+`[r * shard_size, (r+1) * shard_size)` (last rank may be padded with zeros).
+
+**Optimizer states** are stored only for the local shard — giving the memory savings.
+The **full parameter** remains on every rank (needed for forward/backward), but the
+optimizer (Adam's `m`, `v`, etc.) operates only on the local slice.
+
+---
+
+## Bucketing
+
+Parameters are grouped into fixed-size buckets so that a single collective covers
+multiple parameters. This amortises per-collective latency and improves bandwidth
+utilisation.
+
+### Construction Order
+
+Buckets are built in **reverse forward-use order** to approximate backward gradient
+arrival order:
+
+1. Extract the first-use position of each parameter in the FX graph (exact, no runtime
+   overhead — determined statically from graph node order).
+2. Reverse the list: parameters used last in forward produce gradients first in backward.
+3. Fill buckets by walking this reversed list, starting a new bucket when the running
+   size exceeds the 25 MB threshold (matching PyTorch DDP's default).
+
+Each bucket gets one flat gradient buffer, one flat parameter buffer, and one local
+shard buffer — all allocated once at setup and reused across steps.
+
+---
+
+## Communication Pattern per Training Step
+
+```
+Forward pass  ─── all ranks have full params (as in DDP) ───────────────────►
+
+Backward pass:
+  grad(p_k) ready ──► bucket B_i full? ──► async reduce_scatter(B_i)
+                                            ▲ overlaps with backward for B_{i+1},...
+
+_update():
+  for each bucket (in backward order):
+    wait reduce_scatter
+    optimizer step on local shard          ◄── overlaps with all_gather(B_{i-1})
+    async all_gather → flat_param_buf
+
+  for each bucket:
+    wait all_gather
+    copy flat_param_buf → p.data
+```
+
+### Collectives Used
+
+| Operation | API | Direction | Result |
+|-----------|-----|-----------|--------|
+| Gradient averaging | `dist.reduce_scatter_tensor` | N params → local shard | Rank r gets averaged grad for shard r |
+| Parameter restore  | `dist.all_gather_into_tensor` | local shard → full params | Every rank gets complete updated params |
+
+Both are launched with `async_op=True` on a dedicated `comm_stream`, overlapping with
+backward computation on `comp_stream`.
+
+---
+
+## Flat Buffer Layout
+
+For a bucket containing params `[p0, p1, p2]`:
+
+```
+flat_grad_buf / flat_param_buf (padded_numel = ceil(T / dp_degree) * dp_degree):
+┌─────────────┬─────────────┬─────────────┬──────────┐
+│  p0 elems   │  p1 elems   │  p2 elems   │ (padding)│
+└─────────────┴─────────────┴─────────────┴──────────┘
+               ◄──── shard for rank r ───►
+                 [r*shard_size, (r+1)*shard_size)
+```
+
+After `reduce_scatter`, rank r holds the averaged gradient for its shard.
+After the optimizer step, rank r holds the updated parameters for its shard.
+After `all_gather`, every rank has `flat_param_buf` fully reconstructed.
+
+---
+
+## Implementation Structure
+
+### `src/piper_zero1.py`
+
+| Symbol | Role |
+|--------|------|
+| `BUCKET_SIZE_BYTES_DEFAULT` | 25 MB threshold (same as DDP) |
+| `_get_param_forward_order(gm, param_idxs, params)` | Static FX graph analysis to sort params by first use |
+| `ZeROOneBucket` | One bucket: flat buffers, local shard, per-bucket optimizer, grad-ready counting |
+| `ZeROOneState` | Manages all buckets, registers hooks, runs pipelined `finalize_step` |
+
+### `src/piper_actor.py` changes
+
+| Location | Change |
+|----------|--------|
+| `_create_actors` | Accept `zero_stage: int = 0` |
+| `PiperActor.__init__` | Accept `zero_stage`, store `self.zero1_states: dict` |
+| `_load_stage` | When `zero_stage >= 1`: extract param order from FX graph, build `ZeROOneState` (which registers hooks); skip `_prepare_dp_comm_ops` |
+| `_update` | When `zero_stage >= 1`: call `zero1_state.finalize_step(comm_stream, comp_stream)` |
+
+### `src/piper_compile.py` changes
+
+`piper_setup` accepts and forwards `zero_stage: int = 0`.
+
+---
+
+## Hook Counting with Multiple Microbatches
+
+Piper supports `num_mbs` microbatches that accumulate gradients before a single
+optimizer step. `register_post_accumulate_grad_hook` fires once per microbatch per
+parameter. A bucket's reduce_scatter is launched only when every parameter in the
+bucket has accumulated gradients from **all** `num_mbs` microbatches.
+
+Per-step accumulation counts are tracked in `ZeROOneBucket._mb_accumulated` and reset
+in `reset_step_state()` at the end of `finalize_step`.
+
+---
+
+## Interaction with BWD_I / BWD_W (Split Backward)
+
+Split backward (`BWD_I` + `BWD_W`) writes gradients to `p.grad` via
+`torch.autograd.grad` and manual accumulation, identical to full backward.
+`register_post_accumulate_grad_hook` fires whenever `p.grad` is updated, so bucket
+counting works transparently across both backward modes.
+
+---
+
+## Comparison: Old vs. New Design
+
+| Aspect | Old (round-robin, all-reduce) | New (per-param shard, reduce_scatter + bucket) |
+|--------|-------------------------------|------------------------------------------------|
+| Param ownership | Whole params round-robin | Every rank co-owns every param (as a shard) |
+| Gradient sync | all-reduce (each rank gets full grad) | reduce_scatter (each rank gets its shard's grad) |
+| Gradient memory | Full gradient on every rank | Can free non-local gradient after reduce_scatter |
+| Optimizer memory | Saves 1 − 1/dp for locally-owned params only | Saves 1 − 1/dp for **all** params uniformly |
+| Load balance | Depends on per-param size distribution | Perfect: every rank has exactly `total/dp` elements |
+| Overlap | None | reduce_scatter overlaps with backward; all_gather with optimizer |
+
+---
+
+## Future Extensions
+
+- **ZeRO-2**: After reduce_scatter, free `p.grad` for non-owned elements to recover
+  gradient memory. Requires splitting grad buffers.
+- **ZeRO-3**: Shard the parameters themselves; fetch on-demand during forward/backward.
+- **Larger / adaptive buckets**: Tune bucket size based on observed communication costs.
+- **Optimizer overlap with all_gather**: Prefetch parameters for the next microbatch
+  during the all_gather of the current step.
diff --git a/src/piper_actor.py b/src/piper_actor.py
index a2d885c..5b82ff3 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -18,6 +18,7 @@
     piper_metadata,
 )
 from .backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
+from .piper_zero1 import ZeROOneState, _get_param_forward_order
 
 CLEANUP_MEMORY = False
 
@@ -35,6 +36,7 @@ def _create_actors(
     num_stages,
     p2p_schedules,
     naive_gradient_sync=False,
+    zero_stage: int = 0,
 ):
     dp_rank = int(os.environ["PIPER_DP_RANK"])
     world_size = int(os.environ["PIPER_WORLD_SIZE"])
@@ -61,6 +63,7 @@ def _create_actors(
             dp_rank=dp_rank,
             dp_degree=dp_degree,
             pp_degree=pp_degree,
+            zero_stage=zero_stage,
         )
         piper_metadata.actors[pp_rank] = actor
         logger.debug(
@@ -88,12 +91,15 @@ def __init__(
         dp_rank=0,
         dp_degree=1,
         pp_degree=1,
+        zero_stage: int = 0,
     ):
         self.logger = create_logger("piper_actor", LOG_LEVEL)
 
         self.pp_rank = pp_rank
         self.optim_class = optim_class
         self.naive_gradient_sync = naive_gradient_sync
+        # ZeRO optimisation stage (0 = disabled, 1 = optimizer state partitioning).
+        self.zero_stage = zero_stage
 
         self.dp_rank = dp_rank
         self.dp_degree = dp_degree
@@ -141,6 +147,8 @@ def __init__(
         self.param_idxs = dict()
         # map stage id -> optimizer for the fx.Graph
         self.optims = dict()
+        # map stage id -> ZeROOneState (populated when zero_stage >= 1 and dp_degree > 1)
+        self.zero1_states: dict[int, ZeROOneState] = dict()
         # map stage id -> mb_idx -> previous activation (if this stage is not first)
         self.inp_activation = defaultdict(dict)
         # map stage id -> mb_idx -> current activation
@@ -365,16 +373,46 @@ def move_to_device(idx, arg):
             )
             self.forward_args[stage_id][i] = None
 
-        # prepare tensors with DP comm ops
-        if not self.naive_gradient_sync and self.dp_degree > 1:
-            self._prepare_dp_comm_ops(stage_id)
-
-        # add the parameters to the optimizer for this stage
-        params = [param for param in forward_args if param is not None and param.requires_grad]
-        if stage_id not in self.optims:
-            self.optims[stage_id] = self.optim_class(params)
+        # Collect trainable parameters (input slots are already None at this point).
+        trainable_param_idxs = [
+            i for i in param_idxs
+            if forward_args[i] is not None and forward_args[i].requires_grad
+        ]
+        params = [forward_args[i] for i in trainable_param_idxs]
+
+        if self.zero_stage >= 1 and self.dp_degree > 1:
+            # ZeRO-1 path: gradient hooks and per-bucket optimizers are managed
+            # inside ZeROOneState; skip the per-param DDP hook setup.
+            params_in_fwd_order = _get_param_forward_order(gm, trainable_param_idxs, params)
+            if stage_id not in self.zero1_states:
+                self.zero1_states[stage_id] = ZeROOneState(
+                    all_params=params,
+                    params_in_forward_order=params_in_fwd_order,
+                    dp_rank=self.dp_rank,
+                    dp_degree=self.dp_degree,
+                    dp_group=self.dp_group,
+                    device=self.device,
+                    num_mbs=self.num_mbs,
+                    optim_class=self.optim_class,
+                    comm_stream=self.comm_stream,
+                    comp_stream=self.comp_stream,
+                )
+            else:
+                # A second graph loaded onto the same stage (shouldn't normally
+                # happen, but guard against it gracefully).
+                self.logger.warning(
+                    f"ZeROOneState already exists for stage {stage_id}; "
+                    "skipping re-initialisation."
+                )
         else:
-            self.optims[stage_id].add_param_group({"params": params})
+            # Standard DDP path: per-param post-accumulate hooks for gradient sync.
+            if not self.naive_gradient_sync and self.dp_degree > 1:
+                self._prepare_dp_comm_ops(stage_id)
+
+            if stage_id not in self.optims:
+                self.optims[stage_id] = self.optim_class(params)
+            else:
+                self.optims[stage_id].add_param_group({"params": params})
 
         del gm_data
 
@@ -1146,22 +1184,32 @@ def _synchronize_gradients(self):
     def _update(self, *deps):
         self.logger.debug(f"Actor {self.global_rank} waiting for backward sync events")
 
-        # if dp degree > 1, make sure all gradients are synchronized before optimizer step
-        # TODO: this does not allow overlapping with the optimizer step
-        if self.dp_degree > 1:
+        if self.zero_stage >= 1 and self.dp_degree > 1:
+            # ZeRO-1 path: hooks launched reduce_scatters during backward;
+            # finalize_step completes the pipelined reduce_scatter → optimizer →
+            # all_gather sequence for every bucket on every stage.
             self._start_timing(self.comm_stream, "backward_sync")
-            if self.naive_gradient_sync:
-                self._synchronize_gradients()
-            else:
-                self._wait_for_comm_ops()
+            for zero1_state in self.zero1_states.values():
+                zero1_state.finalize_step(self.comm_stream, self.comp_stream)
             self._stop_timing(self.comm_stream, "backward_sync")
-
-        # step the optimizer for each stage
-        self._start_timing(self.comp_stream, "optim_step")
-        for _, optim in self.optims.items():
-            optim.step()
-            optim.zero_grad()
-        self._stop_timing(self.comp_stream, "optim_step")
+        else:
+            # Standard DDP path.
+            if self.dp_degree > 1:
+                # if dp degree > 1, make sure all gradients are synchronized before optimizer step
+                # TODO: this does not allow overlapping with the optimizer step
+                self._start_timing(self.comm_stream, "backward_sync")
+                if self.naive_gradient_sync:
+                    self._synchronize_gradients()
+                else:
+                    self._wait_for_comm_ops()
+                self._stop_timing(self.comm_stream, "backward_sync")
+
+            # step the optimizer for each stage
+            self._start_timing(self.comp_stream, "optim_step")
+            for _, optim in self.optims.items():
+                optim.step()
+                optim.zero_grad()
+            self._stop_timing(self.comp_stream, "optim_step")
 
         losses = self.loss
         self.loss.clear()
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 014587a..6f7bc60 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -126,10 +126,22 @@ def piper_setup(
     example_outputs,
     schedule: Schedule2D,
     naive_gradient_sync=False,
+    zero_stage: int = 0,
 ):
     """
     Compile a model with the piper backend.
-    schedule: 2D schedule grid (rank x time_step).
+
+    Args:
+        model: The model to compile.
+        optim_fn: Callable ``(params) -> Optimizer`` used to create optimizers.
+        example_inputs: Example inputs for tracing.
+        example_outputs: Example outputs (labels) for tracing.
+        schedule: 2D schedule grid (rank x time_step).
+        naive_gradient_sync: Use a simple blocking all-reduce instead of
+            pipelined per-param hooks. Ignored when ``zero_stage >= 1``.
+        zero_stage: ZeRO optimisation stage.
+            0 = disabled (standard DDP).
+            1 = optimizer state partitioning with bucketed reduce-scatter.
     """
 
     stage_to_device = schedule.stage_to_device()
@@ -146,7 +158,8 @@ def piper_setup(
         stage_to_device=stage_to_device
     )
     _create_actors(
-        num_devices, optim_fn, num_mbs, num_stages, p2p_schedules, naive_gradient_sync
+        num_devices, optim_fn, num_mbs, num_stages, p2p_schedules,
+        naive_gradient_sync, zero_stage=zero_stage,
     )
 
     last_stage_rank = stage_to_device[num_stages - 1]
diff --git a/src/piper_zero1.py b/src/piper_zero1.py
new file mode 100644
index 0000000..493c99b
--- /dev/null
+++ b/src/piper_zero1.py
@@ -0,0 +1,530 @@
+"""
+ZeRO-1 with bucketed reduce-scatter for Piper.
+
+Each trainable parameter is divided into dp_degree contiguous shards; each DP rank
+owns and optimises one shard. Parameters are grouped into 25 MB gradient-sync buckets
+ordered by reverse forward-use (approximating backward arrival order), enabling
+overlap between communication and backward computation.
+
+Per-step communication pipeline (per bucket, pipelined across buckets):
+  1. reduce_scatter gradient flat buffer → local grad shard   (async, on comm_stream)
+  2. Optimizer step on local param shard                      (comp_stream)
+  3. all_gather local param shard → flat param buffer         (async, on comm_stream)
+  4. Scatter flat param buffer back to p.data
+
+See docs/zero1_design.md for the full design rationale.
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Callable, Optional, Type
+
+import torch
+import torch.distributed as dist
+import torch.fx
+from torch.nn import Parameter
+from torch.optim import Optimizer
+
+from .piper_utils import create_logger, LOG_LEVEL
+
+logger = create_logger("piper_zero1", LOG_LEVEL)
+
+# Default bucket size in bytes — matches PyTorch DDP's default of 25 MB.
+BUCKET_SIZE_BYTES_DEFAULT: int = 25 * 1024 * 1024
+
+
+# ---------------------------------------------------------------------------
+# FX graph analysis
+# ---------------------------------------------------------------------------
+
+def _get_param_forward_order(
+    gm: torch.fx.GraphModule,
+    param_idxs: list[int],
+    params: list[Parameter],
+) -> list[Parameter]:
+    """
+    Return ``params`` sorted by the first position in ``gm``'s FX graph where
+    each parameter placeholder is used as an input to a computation node.
+
+    Parameters used earlier in the forward graph (lower node index) come first.
+    Parameters never referenced as inputs are appended last in original order.
+
+    Args:
+        gm: The FX GraphModule for this pipeline stage.
+        param_idxs: Indices of parameter placeholders within the graph's full
+            placeholder list (matches the positional order of ``forward_args``).
+        params: Trainable parameter tensors, one per entry in ``param_idxs``.
+
+    Returns:
+        ``params`` in forward-use order.
+    """
+    placeholder_nodes = [n for n in gm.graph.nodes if n.op == "placeholder"]
+    param_idx_set = set(param_idxs)
+
+    # Build a set of placeholder nodes that correspond to parameters.
+    param_ph_nodes: set[torch.fx.Node] = {
+        placeholder_nodes[i]
+        for i in param_idxs
+        if i < len(placeholder_nodes)
+    }
+
+    # Walk graph nodes to find the first computation node that uses each param.
+    first_use_pos: dict[torch.fx.Node, int] = {}
+    for node_pos, node in enumerate(gm.graph.nodes):
+        if node.op == "placeholder":
+            continue
+        for inp in node.all_input_nodes:
+            if inp in param_ph_nodes and inp not in first_use_pos:
+                first_use_pos[inp] = node_pos
+
+    # Sort params by first-use position.
+    param_ph_by_idx = {
+        i: placeholder_nodes[i]
+        for i in param_idxs
+        if i < len(placeholder_nodes)
+    }
+    pairs: list[tuple[Parameter, float]] = []
+    for idx, p in zip(param_idxs, params):
+        ph = param_ph_by_idx.get(idx)
+        pos = first_use_pos.get(ph, math.inf) if ph is not None else math.inf
+        pairs.append((p, pos))
+
+    pairs.sort(key=lambda x: x[1])
+    return [p for p, _ in pairs]
+
+
+# ---------------------------------------------------------------------------
+# Bucket
+# ---------------------------------------------------------------------------
+
+class ZeROOneBucket:
+    """
+    One gradient-synchronisation bucket for ZeRO-1.
+
+    Holds a flat parameter buffer and a flat gradient buffer covering all params
+    in the bucket. The local shard (owned by this DP rank) is a contiguous slice
+    of those flat buffers. One optimizer instance manages the local param shard.
+
+    Lifecycle per training step:
+      ``on_grad_accumulated`` → (when bucket full) ``launch_reduce_scatter``
+      → (in finalize_step) ``optimizer_step`` → ``launch_all_gather``
+      → ``copy_flat_to_params`` → ``reset_step_state``
+    """
+
+    def __init__(
+        self,
+        bucket_id: int,
+        params: list[Parameter],
+        dp_rank: int,
+        dp_degree: int,
+        dp_group: dist.ProcessGroup,
+        device: str,
+        dtype: torch.dtype,
+        optim_class: Callable[[list[Parameter]], Optimizer],
+        num_mbs: int,
+    ) -> None:
+        self.bucket_id = bucket_id
+        self.params = params
+        self.dp_rank = dp_rank
+        self.dp_degree = dp_degree
+        self.dp_group = dp_group
+        self.num_mbs = num_mbs
+
+        # ------------------------------------------------------------------
+        # Flat buffer layout: [p0 elems | p1 elems | ... | padding]
+        # ------------------------------------------------------------------
+        self.param_offsets: list[int] = []
+        total_numel = 0
+        for p in params:
+            self.param_offsets.append(total_numel)
+            total_numel += p.numel()
+
+        # Pad so that padded_numel is divisible by dp_degree (for equal shards).
+        self.shard_numel: int = math.ceil(total_numel / dp_degree)
+        self.padded_numel: int = self.shard_numel * dp_degree
+        self.shard_start: int = dp_rank * self.shard_numel
+        self.shard_end: int = self.shard_start + self.shard_numel
+
+        # Persistent flat buffers (allocated once, reused each step).
+        self.flat_param_buf: torch.Tensor = torch.empty(
+            self.padded_numel, device=device, dtype=dtype
+        )
+        self.flat_grad_buf: torch.Tensor = torch.zeros(
+            self.padded_numel, device=device, dtype=dtype
+        )
+
+        # Initialise flat_param_buf from current parameter values.
+        for p, off in zip(params, self.param_offsets):
+            self.flat_param_buf[off : off + p.numel()].copy_(p.data.view(-1))
+
+        # Local shard: a separate tensor (not a view of flat_param_buf) to
+        # avoid aliasing when all_gather writes into flat_param_buf.
+        self.local_param_shard: Parameter = Parameter(
+            self.flat_param_buf[self.shard_start : self.shard_end].clone(),
+            requires_grad=True,
+        )
+        # Receives the reduce-scattered gradient for the local shard.
+        self.local_grad_shard: torch.Tensor = torch.zeros(
+            self.shard_numel, device=device, dtype=dtype
+        )
+
+        # One optimizer per bucket, managing only the local param shard.
+        self.optimizer: Optimizer = optim_class([self.local_param_shard])
+
+        # ------------------------------------------------------------------
+        # Per-step state (reset by reset_step_state)
+        # ------------------------------------------------------------------
+        # Counts how many microbatch backward passes have touched each param.
+        self._mb_accumulated: dict[int, int] = {id(p): 0 for p in params}
+        # Number of params that have fully accumulated all num_mbs microbatches.
+        self._ready_param_count: int = 0
+
+        # Async collective handles (None until launched).
+        self.reduce_scatter_handle: Optional[dist.Work] = None
+        self.all_gather_handle: Optional[dist.Work] = None
+
+        logger.debug(
+            f"ZeROOneBucket {bucket_id}: {len(params)} params, "
+            f"{total_numel} elems, shard [{self.shard_start}:{self.shard_end}]"
+        )
+
+    # ------------------------------------------------------------------
+    # Per-step trigger (called from grad hook)
+    # ------------------------------------------------------------------
+
+    def on_grad_accumulated(self, param: Parameter) -> bool:
+        """
+        Record one microbatch gradient accumulation for *param*.
+
+        Returns ``True`` when every parameter in this bucket has accumulated
+        gradients from all ``num_mbs`` microbatches, signalling that the bucket
+        is ready for reduce_scatter.
+        """
+        pid = id(param)
+        self._mb_accumulated[pid] += 1
+        if self._mb_accumulated[pid] == self.num_mbs:
+            self._ready_param_count += 1
+        return self._ready_param_count == len(self.params)
+
+    # ------------------------------------------------------------------
+    # Communication and computation
+    # ------------------------------------------------------------------
+
+    def launch_reduce_scatter(
+        self,
+        comm_stream: torch.cuda.Stream,
+        comp_stream: torch.cuda.Stream,
+    ) -> dist.Work:
+        """
+        Copy accumulated per-param gradients into the flat gradient buffer, then
+        launch an async ``reduce_scatter_tensor`` on *comm_stream*.
+
+        After completion, ``self.local_grad_shard`` holds the averaged gradient
+        for this rank's shard of the bucket.
+
+        Returns the ``dist.Work`` handle for the caller to wait on.
+        """
+        comm_stream.wait_stream(comp_stream)
+        with torch.cuda.stream(comm_stream):
+            for p, off in zip(self.params, self.param_offsets):
+                if p.grad is not None:
+                    self.flat_grad_buf[off : off + p.numel()].copy_(p.grad.view(-1))
+                else:
+                    self.flat_grad_buf[off : off + p.numel()].zero_()
+            handle = dist.reduce_scatter_tensor(
+                self.local_grad_shard,
+                self.flat_grad_buf,
+                op=dist.ReduceOp.AVG,
+                group=self.dp_group,
+                async_op=True,
+            )
+        return handle
+
+    def optimizer_step(self) -> None:
+        """
+        Attach the reduce-scattered gradient to ``local_param_shard`` and run
+        the optimizer.
+
+        Must be called after ``reduce_scatter_handle.wait()``.
+        """
+        if self.local_param_shard.grad is None:
+            self.local_param_shard.grad = self.local_grad_shard.clone()
+        else:
+            self.local_param_shard.grad.copy_(self.local_grad_shard)
+        self.optimizer.step()
+        self.optimizer.zero_grad(set_to_none=True)
+
+    def launch_all_gather(
+        self,
+        comm_stream: torch.cuda.Stream,
+        comp_stream: torch.cuda.Stream,
+    ) -> dist.Work:
+        """
+        Launch an async ``all_gather_into_tensor`` that reconstructs
+        ``flat_param_buf`` from all ranks' updated ``local_param_shard``.
+
+        Must be called after ``optimizer_step()``.
+        Returns the ``dist.Work`` handle.
+        """
+        comp_stream.wait_stream(comm_stream)
+        with torch.cuda.stream(comm_stream):
+            handle = dist.all_gather_into_tensor(
+                self.flat_param_buf,
+                self.local_param_shard.data,
+                group=self.dp_group,
+                async_op=True,
+            )
+        return handle
+
+    def copy_flat_to_params(self) -> None:
+        """
+        Scatter ``flat_param_buf`` back into each parameter's ``.data``.
+
+        Must be called after ``all_gather_handle.wait()``.
+        """
+        for p, off in zip(self.params, self.param_offsets):
+            p.data.copy_(
+                self.flat_param_buf[off : off + p.numel()].view(p.shape)
+            )
+
+    def reset_step_state(self) -> None:
+        """Reset per-step counters and handles for the next training step."""
+        for pid in self._mb_accumulated:
+            self._mb_accumulated[pid] = 0
+        self._ready_param_count = 0
+        self.reduce_scatter_handle = None
+        self.all_gather_handle = None
+        self.flat_grad_buf.zero_()
+
+    @property
+    def size_bytes(self) -> int:
+        """Total byte size of parameters in this bucket."""
+        return sum(p.numel() * p.element_size() for p in self.params)
+
+
+# ---------------------------------------------------------------------------
+# State manager
+# ---------------------------------------------------------------------------
+
+class ZeROOneState:
+    """
+    Manages ZeRO-1 optimizer state partitioning for one pipeline stage.
+
+    At construction time, parameters are grouped into gradient-sync buckets in
+    reverse forward-use order. Post-accumulate-grad hooks are registered on every
+    parameter; each hook decrements a per-bucket counter and launches an async
+    reduce_scatter when the bucket's counter reaches zero.
+
+    At the end of each training step, call ``finalize_step`` to complete the
+    pipelined reduce_scatter → optimizer step → all_gather sequence.
+
+    Args:
+        all_params: All trainable parameters for this stage.
+        params_in_forward_order: Same parameters sorted by first use in the
+            FX graph forward pass (from ``_get_param_forward_order``).
+        dp_rank: Data-parallel rank of this actor.
+        dp_degree: Total number of data-parallel replicas.
+        dp_group: ``torch.distributed`` process group for this DP replica set.
+        device: CUDA device string for the actor.
+        num_mbs: Number of microbatches per training step.
+        optim_class: Callable ``(params) -> Optimizer`` used per bucket.
+        comm_stream: CUDA stream for collective communication ops.
+        comp_stream: CUDA stream for computation (backward / optimizer).
+        bucket_size_bytes: Maximum byte size per bucket (default 25 MB).
+    """
+
+    def __init__(
+        self,
+        all_params: list[Parameter],
+        params_in_forward_order: list[Parameter],
+        dp_rank: int,
+        dp_degree: int,
+        dp_group: dist.ProcessGroup,
+        device: str,
+        num_mbs: int,
+        optim_class: Callable[[list[Parameter]], Optimizer],
+        comm_stream: torch.cuda.Stream,
+        comp_stream: torch.cuda.Stream,
+        bucket_size_bytes: int = BUCKET_SIZE_BYTES_DEFAULT,
+    ) -> None:
+        self.all_params = all_params
+        self.dp_rank = dp_rank
+        self.dp_degree = dp_degree
+        self.dp_group = dp_group
+        self._comm_stream = comm_stream
+        self._comp_stream = comp_stream
+
+        # Build buckets in reverse forward order ≈ backward arrival order.
+        self._buckets: list[ZeROOneBucket] = _build_buckets(
+            params_in_bwd_order=list(reversed(params_in_forward_order)),
+            dp_rank=dp_rank,
+            dp_degree=dp_degree,
+            dp_group=dp_group,
+            device=device,
+            num_mbs=num_mbs,
+            optim_class=optim_class,
+            bucket_size_bytes=bucket_size_bytes,
+        )
+
+        # Fast lookup: parameter id → owning bucket.
+        self._param_to_bucket: dict[int, ZeROOneBucket] = {
+            id(p): bucket
+            for bucket in self._buckets
+            for p in bucket.params
+        }
+
+        self._setup_grad_hooks()
+
+        logger.debug(
+            f"ZeROOneState: {len(self._buckets)} buckets, "
+            f"{len(all_params)} params, dp_rank={dp_rank}/{dp_degree}"
+        )
+
+    # ------------------------------------------------------------------
+    # Hook registration
+    # ------------------------------------------------------------------
+
+    def _setup_grad_hooks(self) -> None:
+        """Register a post-accumulate-grad hook on every tracked parameter."""
+        for p in self.all_params:
+            if id(p) in self._param_to_bucket:
+                p.register_post_accumulate_grad_hook(self._make_hook(p))
+
+    def _make_hook(self, param: Parameter) -> Callable[[Parameter], None]:
+        """Return a closure that fires when *param*'s gradient is accumulated."""
+        bucket = self._param_to_bucket[id(param)]
+
+        def _hook(_param: Parameter) -> None:
+            if bucket.on_grad_accumulated(_param):
+                # All params in bucket have full gradients: launch reduce_scatter.
+                bucket.reduce_scatter_handle = bucket.launch_reduce_scatter(
+                    self._comm_stream, self._comp_stream
+                )
+                logger.debug(
+                    f"Launched reduce_scatter for bucket {bucket.bucket_id}"
+                )
+
+        return _hook
+
+    # ------------------------------------------------------------------
+    # End-of-step pipeline
+    # ------------------------------------------------------------------
+
+    def finalize_step(
+        self,
+        comm_stream: torch.cuda.Stream,
+        comp_stream: torch.cuda.Stream,
+    ) -> None:
+        """
+        Complete the pipelined reduce_scatter → optimizer → all_gather sequence
+        for all buckets, then reset state for the next step.
+
+        Should be called in ``_update`` after all backward passes are complete.
+
+        Pipeline structure:
+          For bucket k: wait rs_k → optim_k → launch ag_k
+          The all_gather of bucket k-1 overlaps with optim_k and launch_ag_k.
+        """
+        # Ensure every bucket has a reduce_scatter in flight (handles buckets
+        # whose hooks were never triggered, e.g. params with no gradient).
+        for bucket in self._buckets:
+            if bucket.reduce_scatter_handle is None:
+                bucket.reduce_scatter_handle = bucket.launch_reduce_scatter(
+                    comm_stream, comp_stream
+                )
+
+        # Pipeline: per-bucket wait → optimizer → async all_gather.
+        for bucket in self._buckets:
+            bucket.reduce_scatter_handle.wait()
+            bucket.optimizer_step()
+            bucket.all_gather_handle = bucket.launch_all_gather(
+                comm_stream, comp_stream
+            )
+
+        # Wait for all all_gathers and write results back into p.data.
+        for bucket in self._buckets:
+            bucket.all_gather_handle.wait()
+            bucket.copy_flat_to_params()
+
+        # Zero all parameter gradients and reset per-step state.
+        for p in self.all_params:
+            p.grad = None
+        for bucket in self._buckets:
+            bucket.reset_step_state()
+
+    # ------------------------------------------------------------------
+    # Diagnostics
+    # ------------------------------------------------------------------
+
+    def num_buckets(self) -> int:
+        return len(self._buckets)
+
+    def bucket_sizes(self) -> list[int]:
+        return [b.size_bytes for b in self._buckets]
+
+    def __repr__(self) -> str:
+        return (
+            f"ZeROOneState(dp_rank={self.dp_rank}/{self.dp_degree}, "
+            f"buckets={self.num_buckets()}, "
+            f"params={len(self.all_params)})"
+        )
+
+
+# ---------------------------------------------------------------------------
+# Internal helpers
+# ---------------------------------------------------------------------------
+
+def _build_buckets(
+    params_in_bwd_order: list[Parameter],
+    dp_rank: int,
+    dp_degree: int,
+    dp_group: dist.ProcessGroup,
+    device: str,
+    num_mbs: int,
+    optim_class: Callable[[list[Parameter]], Optimizer],
+    bucket_size_bytes: int,
+) -> list[ZeROOneBucket]:
+    """
+    Fill buckets greedily: add parameters in *params_in_bwd_order* until a
+    bucket would exceed *bucket_size_bytes*, then start a new bucket.
+
+    The first parameter always starts a new bucket regardless of size (no
+    parameter is skipped).
+    """
+    buckets: list[ZeROOneBucket] = []
+    current: list[Parameter] = []
+    current_bytes = 0
+
+    for p in params_in_bwd_order:
+        p_bytes = p.numel() * p.element_size()
+        if current and current_bytes + p_bytes > bucket_size_bytes:
+            dtype = _bucket_dtype(current)
+            buckets.append(
+                ZeROOneBucket(
+                    len(buckets), current,
+                    dp_rank, dp_degree, dp_group,
+                    device, dtype, optim_class, num_mbs,
+                )
+            )
+            current = []
+            current_bytes = 0
+        current.append(p)
+        current_bytes += p_bytes
+
+    if current:
+        dtype = _bucket_dtype(current)
+        buckets.append(
+            ZeROOneBucket(
+                len(buckets), current,
+                dp_rank, dp_degree, dp_group,
+                device, dtype, optim_class, num_mbs,
+            )
+        )
+
+    return buckets
+
+
+def _bucket_dtype(params: list[Parameter]) -> torch.dtype:
+    """Return the dtype of the first parameter (all params in a bucket share dtype)."""
+    return params[0].dtype

From 8e002eb3e58c22cafc4946876c60b6bac4390995 Mon Sep 17 00:00:00 2001
From: Andy Ruan <andyubryh@gmail.com>
Date: Mon, 9 Mar 2026 22:08:59 +0000
Subject: [PATCH 069/100] (WIP) ZeRO1 initial profiling

Signed-off-by: Andy Ruan <andyubryh@gmail.com>
---
 logs/piper_zero0_20260309_220353.log | 49 ++++++++++++++++++++++++++++
 logs/piper_zero1_20260309_145713.log | 35 ++++++++++++++++++++
 logs/piper_zero1_20260309_215755.log | 46 ++++++++++++++++++++++++++
 run_zero0.sh                         | 29 ++++++++++++++++
 run_zero1.sh                         | 29 ++++++++++++++++
 test/test_llama.py                   |  5 ++-
 6 files changed, 192 insertions(+), 1 deletion(-)
 create mode 100644 logs/piper_zero0_20260309_220353.log
 create mode 100644 logs/piper_zero1_20260309_145713.log
 create mode 100644 logs/piper_zero1_20260309_215755.log
 create mode 100755 run_zero0.sh
 create mode 100755 run_zero1.sh

diff --git a/logs/piper_zero0_20260309_220353.log b/logs/piper_zero0_20260309_220353.log
new file mode 100644
index 0000000..1ac61fd
--- /dev/null
+++ b/logs/piper_zero0_20260309_220353.log
@@ -0,0 +1,49 @@
+2026-03-09 22:04:05,239	INFO worker.py:2007 -- Started a local Ray instance.
+/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+  warnings.warn(
+(raylet) [2026-03-09 22:04:15,210 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39113 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2199411) Namespace(model='3b', schedule='interleaved-1f1b', dp=1, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=0)
+(raylet) [2026-03-09 22:04:25,223 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.3912 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 22:04:35,236 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39117 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 22:04:45,248 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39119 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2199411) Schedule:
+(run_dp_rank pid=2199411) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
+(run_dp_rank pid=2199411)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
+(raylet) [2026-03-09 22:04:55,261 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.3911 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(PiperActor pid=2199505) 2026-03-09 22:04:57,202 - piper_actor - INFO - Initializing Ray actor 1 GPU 2
+(PiperActor pid=2199385) 2026-03-09 22:04:57,312 - piper_actor - INFO - Initializing Ray actor 0 GPU 1
+(raylet) [2026-03-09 22:05:05,273 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39103 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2199411) 2026-03-09 22:05:10,435 - piper_compile - INFO - DP rank 1 compiling...
+(raylet) [2026-03-09 22:05:15,286 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39008 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2199411) Stage 0 time measured on controller: 139.18 ms
+(raylet) [2026-03-09 22:05:25,303 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39006 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 22:05:35,317 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39005 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2199411) Stage 1 time measured on controller: 139.23 ms
+(raylet) [2026-03-09 22:05:45,352 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39004 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2199411) Stage 2 time measured on controller: 139.14 ms
+(raylet) [2026-03-09 22:05:55,365 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39005 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2199411) Stage 3 time measured on controller: 130.44 ms
+(raylet) [2026-03-09 22:06:05,381 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39001 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2199411) 2026-03-09 22:06:11,892 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=2199411) 2026-03-09 22:06:11,892 - piper_compile - INFO - DP rank 1 done.
+(run_dp_rank pid=2199411) Running 5 warmup iterations...
+(PiperActor pid=2199505) [rank1]:[W309 22:06:12.849148849 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2199385) [rank0]:[W309 22:06:12.238779712 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2199385) [rank0]:[W309 22:06:14.032574556 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2199505) [rank1]:[W309 22:06:14.450212103 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(raylet) [2026-03-09 22:06:15,403 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.38807 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2199411) Warmup 0 completed
+(run_dp_rank pid=2199411) Warmup 1 completed
+(raylet) [2026-03-09 22:06:25,422 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.38807 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2199411) Warmup 2 completed
+(run_dp_rank pid=2199411) Warmup 3 completed
+(raylet) [2026-03-09 22:06:35,435 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.38807 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2199411) Warmup 4 completed
+(run_dp_rank pid=2199411) Running 10 timed iterations...
+(raylet) [2026-03-09 22:06:45,449 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.38803 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 22:06:55,462 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.38803 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 22:07:05,475 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.38792 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 22:07:15,489 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.38627 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2199411) rank 0 iter time= 4.137 ± 0.176 s (10 samples)
+(run_dp_rank pid=2199411) rank 0 throughput= 3959.889 tokens/sec (10 samples)
+(run_dp_rank pid=2199411) Ray timeline saved to: out/3b-pp2-dp1-interleaved-1f1b.json
diff --git a/logs/piper_zero1_20260309_145713.log b/logs/piper_zero1_20260309_145713.log
new file mode 100644
index 0000000..225cf65
--- /dev/null
+++ b/logs/piper_zero1_20260309_145713.log
@@ -0,0 +1,35 @@
+2026-03-09 14:57:25,301	INFO worker.py:2007 -- Started a local Ray instance.
+/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+  warnings.warn(
+(raylet) [2026-03-09 14:57:34,292 E 2687383 2687424] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_14-57-18_705671_2685485 is over 95% full, available space: 1.66463 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2687594) Namespace(model='debug', schedule='interleaved-1f1b', dp=1, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=1)
+(run_dp_rank pid=2687594) Schedule:
+(run_dp_rank pid=2687594) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
+(run_dp_rank pid=2687594)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
+(PiperActor pid=2687577) 2026-03-09 14:57:43,760 - piper_actor - INFO - Initializing Ray actor 1 GPU 2
+(PiperActor pid=2687579) 2026-03-09 14:57:43,832 - piper_actor - INFO - Initializing Ray actor 0 GPU 1
+(raylet) [2026-03-09 14:57:44,307 E 2687383 2687424] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_14-57-18_705671_2685485 is over 95% full, available space: 1.66446 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2687594) 2026-03-09 14:57:46,381 - piper_compile - INFO - DP rank 1 compiling...
+(run_dp_rank pid=2687594) Stage 0 time measured on controller: 4.06 ms
+(run_dp_rank pid=2687594) Stage 1 time measured on controller: 4.02 ms
+(run_dp_rank pid=2687594) Stage 2 time measured on controller: 2.70 ms
+(run_dp_rank pid=2687594) Stage 3 time measured on controller: 0.19 ms
+(run_dp_rank pid=2687594) 2026-03-09 14:57:51,188 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=2687594) 2026-03-09 14:57:51,188 - piper_compile - INFO - DP rank 1 done.
+(run_dp_rank pid=2687594) Running 5 warmup iterations...
+(PiperActor pid=2687577) [rank1]:[W309 14:57:51.031928771 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2687579) [rank0]:[W309 14:57:51.293357587 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2687579) [rank0]:[W309 14:57:53.799416879 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2687577) [rank1]:[W309 14:57:53.989258762 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2687579) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
+(PiperActor pid=2687579)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+(raylet) [2026-03-09 14:57:54,325 E 2687383 2687424] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_14-57-18_705671_2685485 is over 95% full, available space: 1.66444 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2687594) Warmup 0 completed
+(run_dp_rank pid=2687594) Warmup 1 completed
+(run_dp_rank pid=2687594) Warmup 2 completed
+(run_dp_rank pid=2687594) Warmup 3 completed
+(run_dp_rank pid=2687594) Warmup 4 completed
+(run_dp_rank pid=2687594) Running 10 timed iterations...
+(run_dp_rank pid=2687594) rank 0 iter time= 0.141 ± 0.004 s (10 samples)
+(run_dp_rank pid=2687594) rank 0 throughput= 116525.096 tokens/sec (10 samples)
+(run_dp_rank pid=2687594) Ray timeline saved to: out/debug-pp2-dp1-interleaved-1f1b.json
diff --git a/logs/piper_zero1_20260309_215755.log b/logs/piper_zero1_20260309_215755.log
new file mode 100644
index 0000000..00fd4ad
--- /dev/null
+++ b/logs/piper_zero1_20260309_215755.log
@@ -0,0 +1,46 @@
+2026-03-09 21:58:07,111	INFO worker.py:2007 -- Started a local Ray instance.
+/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+  warnings.warn(
+(raylet) [2026-03-09 21:58:17,050 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39233 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2104175) Namespace(model='3b', schedule='interleaved-1f1b', dp=1, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=1)
+(raylet) [2026-03-09 21:58:27,062 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39219 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 21:58:37,074 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39219 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 21:58:47,089 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39672 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2104175) Schedule:
+(run_dp_rank pid=2104175) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
+(run_dp_rank pid=2104175)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
+(PiperActor pid=2103889) 2026-03-09 21:58:54,673 - piper_actor - INFO - Initializing Ray actor 0 GPU 1
+(PiperActor pid=2104562) 2026-03-09 21:58:54,700 - piper_actor - INFO - Initializing Ray actor 1 GPU 2
+(raylet) [2026-03-09 21:58:57,114 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39722 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2104175) 2026-03-09 21:59:02,110 - piper_compile - INFO - DP rank 1 compiling...
+(raylet) [2026-03-09 21:59:07,143 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39717 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2104175) Stage 0 time measured on controller: 139.14 ms
+(raylet) [2026-03-09 21:59:17,156 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39128 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2104175) Stage 1 time measured on controller: 139.16 ms
+(raylet) [2026-03-09 21:59:27,168 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39614 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2104175) Stage 2 time measured on controller: 139.19 ms
+(run_dp_rank pid=2104175) Stage 3 time measured on controller: 130.44 ms
+(raylet) [2026-03-09 21:59:37,181 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39616 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2104175) 2026-03-09 21:59:40,227 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=2104175) 2026-03-09 21:59:40,228 - piper_compile - INFO - DP rank 1 done.
+(run_dp_rank pid=2104175) Running 5 warmup iterations...
+(PiperActor pid=2104562) [rank1]:[W309 21:59:40.080240337 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2103889) [rank0]:[W309 21:59:41.541619488 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2103889) [rank0]:[W309 21:59:42.156095816 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2104562) [rank1]:[W309 21:59:42.264797203 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(raylet) [2026-03-09 21:59:47,195 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39615 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2104175) Warmup 0 completed
+(run_dp_rank pid=2104175) Warmup 1 completed
+(run_dp_rank pid=2104175) Warmup 2 completed
+(raylet) [2026-03-09 21:59:57,209 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39614 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2104175) Warmup 3 completed
+(run_dp_rank pid=2104175) Warmup 4 completed
+(run_dp_rank pid=2104175) Running 10 timed iterations...
+(raylet) [2026-03-09 22:00:07,223 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39604 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 22:00:17,236 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39409 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 22:00:27,249 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.3941 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 22:00:37,261 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.3941 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2104175) rank 0 iter time= 4.044 ± 0.003 s (10 samples)
+(run_dp_rank pid=2104175) rank 0 throughput= 4051.131 tokens/sec (10 samples)
+(run_dp_rank pid=2104175) Ray timeline saved to: out/3b-pp2-dp1-interleaved-1f1b.json
+(raylet) [2026-03-09 22:00:47,277 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39404 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
diff --git a/run_zero0.sh b/run_zero0.sh
new file mode 100755
index 0000000..c8cba90
--- /dev/null
+++ b/run_zero0.sh
@@ -0,0 +1,29 @@
+#!/bin/bash
+set -euo pipefail
+
+LOG_DIR="logs"
+mkdir -p "$LOG_DIR"
+
+TIMESTAMP="$(date +"%Y%m%d_%H%M%S")"
+LOG_FILE="$LOG_DIR/piper_zero1_${TIMESTAMP}.log"
+
+RAY_TMP="${RAY_TMP:-/tmp/ray}"
+mkdir -p "$RAY_TMP"
+
+export RAY_TMPDIR="$RAY_TMP"
+export CUDA_VISIBLE_DEVICES="0,1,2,3,4"
+export PYTHONUNBUFFERED=1
+export RAY_DEDUP_LOGS=0
+export PIPER_MASTER_PORT="${PIPER_MASTER_PORT:-12388}"
+
+echo "Logging to $LOG_FILE"
+python3 -m test.test_llama \
+  --dp 1 \
+  --pp 2 \
+  --warmup 5 \
+  --iters 10 \
+  --schedule interleaved-1f1b \
+  --mbs 4 \
+  --model 3b \
+  --zero_stage 0 \
+  >"$LOG_FILE" 2>&1
diff --git a/run_zero1.sh b/run_zero1.sh
new file mode 100755
index 0000000..05a203f
--- /dev/null
+++ b/run_zero1.sh
@@ -0,0 +1,29 @@
+#!/bin/bash
+set -euo pipefail
+
+LOG_DIR="logs"
+mkdir -p "$LOG_DIR"
+
+TIMESTAMP="$(date +"%Y%m%d_%H%M%S")"
+LOG_FILE="$LOG_DIR/piper_zero1_${TIMESTAMP}.log"
+
+RAY_TMP="${RAY_TMP:-/tmp/ray}"
+mkdir -p "$RAY_TMP"
+
+export RAY_TMPDIR="$RAY_TMP"
+export CUDA_VISIBLE_DEVICES="0,1,2,3,4"
+export PYTHONUNBUFFERED=1
+export RAY_DEDUP_LOGS=0
+export PIPER_MASTER_PORT="${PIPER_MASTER_PORT:-12388}"
+
+echo "Logging to $LOG_FILE"
+python3 -m test.test_llama \
+  --dp 1 \
+  --pp 2 \
+  --warmup 5 \
+  --iters 10 \
+  --schedule interleaved-1f1b \
+  --mbs 4 \
+  --model 3b \
+  --zero_stage 1 \
+  >"$LOG_FILE" 2>&1
diff --git a/test/test_llama.py b/test/test_llama.py
index c295114..9c4e069 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -90,6 +90,7 @@ def main(args):
         y,
         schedule,
         args.naive_gradient_sync,
+        zero_stage=args.zero_stage,
     )
 
     del model
@@ -175,6 +176,8 @@ def parse_args():
                         help='Enable tracing')
     parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
                         help='Enable naive gradient sync')
+    parser.add_argument('--zero_stage', type=int, choices=[0, 1], default=0,
+                        help='ZeRO optimization stage (0: disabled, 1: ZeRO-1)')
     return parser.parse_args()
 
 
@@ -184,4 +187,4 @@ def parse_args():
     piper_coordinator = PiperProgramCoordinator.remote(dp_degree=args.dp, pp_degree=args.pp)
     ray.get(piper_coordinator.run_program.remote(main, args))
     time.sleep(3)
-    ray.shutdown()
\ No newline at end of file
+    ray.shutdown()

From b709affd7b368631a01c3e1acd050bcc79ed2bbb Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Mon, 9 Mar 2026 22:50:41 +0000
Subject: [PATCH 070/100] AC implementation plus ray placement groups

---
 src/piper.py         | 12 +++++-----
 src/piper_actor.py   | 55 ++++++++++++++++++++++++++++++++++++--------
 src/piper_compile.py |  5 +++-
 src/piper_utils.py   |  1 +
 test/test_llama.py   | 31 +++++++++++++++++--------
 test/test_mixtral.py | 27 ++++++++++++++++++----
 6 files changed, 100 insertions(+), 31 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index 721ded7..7200840 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -3,7 +3,6 @@
 import os
 import gc
 from torch._dynamo.backends.registry import register_backend
-
 from .piper_utils import _serialize_graphmodule, piper_metadata, create_logger, LOG_LEVEL
 from .piper_graph_transform import _get_dp_comm_ops, _split_gm_by_stages, _profile_and_split_gm, _insert_a2a_ops, _insert_p2p_ops
 from .piper_actor import _get_actor
@@ -30,12 +29,11 @@ def piper(gm, example_inputs, **kwargs):
     else:
         logger.info(f"No stage annotations found, profiling graph to split into {num_stages} stages")
         top_level_gm, submodules = _profile_and_split_gm(gm, num_stages)
+
     dp_rank = int(os.environ['PIPER_DP_RANK'])
     pp_degree = int(os.environ['PIPER_PP_DEGREE'])
     dp_degree = int(os.environ['PIPER_DP_DEGREE'])
 
-    # top_level_gm.print_readable()
-
     del top_level_gm
 
     refs = []
@@ -45,18 +43,20 @@ def piper(gm, example_inputs, **kwargs):
         if dp_degree > 1:
             stage_gm, n_a2a_ops = _insert_a2a_ops(stage_gm)
 
+        stage_gm_data = _serialize_graphmodule(stage_gm)
+
         actor_id = piper_metadata.stage_to_device[stage_id]
         actor = _get_actor(actor_id)
         actor_stages.append((actor, stage_id))
-        
-        stage_gm_data = _serialize_graphmodule(stage_gm)
+
         refs.append(actor._load_stage.remote(
-            stage_id, 
+            stage_id,
             stage_gm_data,
             graphargs,
             input_idxs,
             param_idxs,
             n_a2a_ops,
+            use_activation_checkpointing=piper_metadata.use_activation_checkpointing,
         ))
 
         del stage_gm
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 3e72f48..6c74027 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -11,6 +11,8 @@
 import torch.distributed as dist
 from collections import defaultdict
 
+from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
+
 from .piper_utils import (
     _deserialize_graphmodule,
     create_logger,
@@ -37,6 +39,7 @@ def _create_actors(
     profile=False,
     mode="sequential",
     stage_to_device=None,
+    pg=None,
 ):
     dp_rank = int(os.environ["PIPER_DP_RANK"])
     world_size = int(os.environ["PIPER_WORLD_SIZE"])
@@ -52,7 +55,14 @@ def _create_actors(
             "cuda-event-trace": "false",
             "stop-on-exit": "true",
         }} if profile else {}
-        actor = PiperActor.options(num_gpus=0.8, runtime_env=nsight_env).remote(
+        actor = PiperActor.options(
+            num_gpus=0.8, 
+            runtime_env=nsight_env,
+            scheduling_strategy=PlacementGroupSchedulingStrategy(
+                placement_group=pg,
+                placement_group_bundle_index=dp_rank
+            ),
+        ).remote(
             pp_rank,
             optim_class,
             world_size,
@@ -248,7 +258,7 @@ def reset_peak_memory(self):
         torch.cuda.reset_peak_memory_stats()
 
     def get_peak_memory(self):
-        return torch.cuda.max_memory_allocated() / (1024**3)
+        return self.global_rank, torch.cuda.max_memory_allocated() / (1024**3)
 
     def load_input(self, inputs):
         self.inputs = [inp.to(self.device) for inp in inputs]
@@ -443,14 +453,21 @@ def _wait_for_comm_ops(self):
                         done = True
 
     def _load_stage(
-        self, stage_id: int, gm_data, forward_args, input_idxs, param_idxs, n_a2a_ops
+        self, stage_id: int, gm_data, forward_args, input_idxs, param_idxs, n_a2a_ops,
+        use_activation_checkpointing: bool = False,
     ):
         self.logger.debug(f"Loading stage {stage_id} graph on actor {self.global_rank}")
 
         gm = _deserialize_graphmodule(gm_data)
 
         self.graph_modules[stage_id] = gm
-        self.forward_fns[stage_id] = gm.forward
+
+        if use_activation_checkpointing:
+            forward_fn = gm.forward
+            self.forward_fns[stage_id] = lambda *args, _fn=forward_fn: torch.utils.checkpoint.checkpoint(_fn, *args, use_reentrant=False)
+            self.logger.debug(f"Applied activation checkpointing to stage {stage_id}")
+        else:
+            self.forward_fns[stage_id] = gm.forward
 
         # initialize A2A states
         self.n_a2a_ops[stage_id] = n_a2a_ops
@@ -663,7 +680,10 @@ def _exec_bwd_send(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, com
         self.inp_activation[stage_id][mb_idx] = None
     
     def _forward(self, stage_id: int, mb_idx: int, *deps):
-        return self._forward_impl(stage_id, mb_idx, *deps)
+        torch.cuda.nvtx.range_push(f"forward_stage_{stage_id}_mb_{mb_idx}")
+        ret = self._forward_impl(stage_id, mb_idx, *deps)
+        torch.cuda.nvtx.range_pop()
+        return ret
 
     def _forward_impl(self, stage_id: int, mb_idx: int, *deps):
         self._label_task(f"{stage_id}:{mb_idx}")
@@ -763,7 +783,10 @@ def _forward_impl(self, stage_id: int, mb_idx: int, *deps):
         return 0
 
     def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        return self._backward_impl(stage_id, mb_idx, *deps, loss_fn=loss_fn)
+        torch.cuda.nvtx.range_push(f"backward_stage_{stage_id}_mb_{mb_idx}")
+        ret = self._backward_impl(stage_id, mb_idx, *deps, loss_fn=loss_fn)
+        torch.cuda.nvtx.range_pop()
+        return ret
 
     def _backward_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         self._label_task(f"{stage_id}:{mb_idx}")
@@ -828,7 +851,10 @@ def _backward_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
 
 
     def _backward_input(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        return self._backward_input_impl(stage_id, mb_idx, *deps, loss_fn=loss_fn)
+        torch.cuda.nvtx.range_push(f"backward_input_stage_{stage_id}_mb_{mb_idx}")
+        ret = self._backward_input_impl(stage_id, mb_idx, *deps, loss_fn=loss_fn)
+        torch.cuda.nvtx.range_pop()
+        return ret
 
     def _backward_input_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         self._label_task(f"{stage_id}:{mb_idx}")
@@ -947,7 +973,10 @@ def hook(grad_inputs):
         return 0
 
     def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        return self._backward_weight_impl(stage_id, mb_idx, *deps, loss_fn=loss_fn)
+        torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
+        ret = self._backward_weight_impl(stage_id, mb_idx, *deps, loss_fn=loss_fn)
+        torch.cuda.nvtx.range_pop()
+        return ret
 
     def _backward_weight_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         self._label_task(f"{stage_id}:{mb_idx}")
@@ -1106,7 +1135,10 @@ def _backward_weight_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None)
         return 0
 
     def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: int, bwd_mb_idx: int, *deps, loss_fn=None):
-        return self._forward_backward_impl(fwd_stage_id, fwd_mb_idx, bwd_stage_id, bwd_mb_idx, *deps, loss_fn=loss_fn)
+        torch.cuda.nvtx.range_push(f"forward_stage_{fwd_stage_id}_mb_{fwd_mb_idx}_backward_stage_{bwd_stage_id}_mb_{bwd_mb_idx}")
+        ret = self._forward_backward_impl(fwd_stage_id, fwd_mb_idx, bwd_stage_id, bwd_mb_idx, *deps, loss_fn=loss_fn)
+        torch.cuda.nvtx.range_pop()
+        return ret
 
     def _forward_backward_impl(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: int, bwd_mb_idx: int, *deps, loss_fn=None):
         self._label_task(f"{fwd_stage_id}:{fwd_mb_idx}|{bwd_stage_id}:{bwd_mb_idx}")
@@ -1285,7 +1317,10 @@ def _synchronize_gradients(self):
                         )
 
     def _update(self, *deps):
-        return self._update_impl(*deps)
+        torch.cuda.nvtx.range_push(f"update")
+        ret = self._update_impl(*deps)
+        torch.cuda.nvtx.range_pop()
+        return ret
 
     def _update_impl(self, *deps):
         self._label_task("update")
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 7f29176..9e75579 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -26,7 +26,9 @@ def piper_setup(
     example_outputs=None,
     schedule: Schedule2D=None,
     naive_gradient_sync=False,
+    activation_checkpointing=False,
     mode="sequential",
+    pg=None,
 ):
     """
     Compile a model with the piper backend.
@@ -36,6 +38,7 @@ def piper_setup(
     stage_to_device = schedule.stage_to_device()
     assert len(stage_to_device) > 0
     piper_metadata.stage_to_device = stage_to_device
+    piper_metadata.use_activation_checkpointing = activation_checkpointing
 
     num_mbs = schedule.num_mbs()
     num_stages = schedule.num_stages()
@@ -43,7 +46,7 @@ def piper_setup(
 
     _create_actors(
         num_devices, optim_fn, num_mbs, num_stages, naive_gradient_sync,
-        profile=True, mode=mode, stage_to_device=stage_to_device,
+        profile=True, mode=mode, stage_to_device=stage_to_device, pg=pg,
     )
 
     ray.get(
diff --git a/src/piper_utils.py b/src/piper_utils.py
index b15878d..cdb5f25 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -75,6 +75,7 @@ class PiperMetadata:
     dag = set()
     stage_to_device = dict()
     naive_gradient_sync = False
+    use_activation_checkpointing = False
 
 piper_metadata = PiperMetadata()
 
diff --git a/test/test_llama.py b/test/test_llama.py
index 4cb731b..97b09f7 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -7,6 +7,12 @@
 import numpy as np
 import gc
 
+from ray.util.placement_group import (
+    placement_group,
+    placement_group_table,
+    remove_placement_group,
+)
+
 from src.piper_exec import piper_exec
 from src.piper_compile import piper_setup, piper_shutdown
 from src.piper_coordinator import PiperProgramCoordinator
@@ -27,7 +33,7 @@
 )
 
 
-def main(args):
+def main(args, pg):
     
     # Set model configuration based on argument
     match args.model:
@@ -91,6 +97,8 @@ def main(args):
         example_outputs=y,
         schedule=schedule,
         naive_gradient_sync=args.naive_gradient_sync,
+        activation_checkpointing=args.activation_checkpointing,
+        pg=pg,
     )
 
     # Warmup
@@ -119,24 +127,22 @@ def main(args):
     if args.tracing:
         actors = piper_metadata.actors
         ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
-        # ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
-        # ray.get([actor.start_mem_tracing.remote() for actor in actors.values()])
+        ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
+        ray.get([actor.start_mem_tracing.remote() for actor in actors.values()])
 
         print(f"Running {args.warmup} tracing iterations...")
         for _ in range(args.warmup):
             piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
 
-        # mem_data_ret = ray.get([actor.get_peak_memory.remote() for actor in actors.values()])
-        # for rank, peak_mem in mem_data_ret:
-        #     print(f"rank {rank} peak memory= {peak_mem:.3f} GB")
+        mem_data_ret = ray.get([actor.get_peak_memory.remote() for actor in actors.values()])
+        for rank, peak_mem in mem_data_ret:
+            print(f"rank {rank} peak memory= {peak_mem:.3f} GB")
 
         trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
         for rank, trace_data in trace_data_ret:
             for key in trace_data:
                 all_times = trace_data[key]
                 print(f"rank {rank} {key} time= {np.mean(all_times):.3f} ± {np.std(all_times):.3f} ms ({len(all_times)} samples)")
-                if key == "fwd_recv_wait" or key == "bwd_recv_wait":
-                    print(f"rank {rank} {key} all times: {all_times}")
 
     if args.naive_gradient_sync:
         suffix = "-naive-sync"
@@ -183,13 +189,18 @@ def parse_args():
                         help='Enable tracing')
     parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
                         help='Enable naive gradient sync')
+    parser.add_argument('--activation_checkpointing', action='store_true', default=False,
+                        help='Enable activation checkpointing')
     return parser.parse_args()
 
 
 if __name__ == "__main__":
-    ray.init(log_to_driver=True, namespace="llama", include_dashboard=False, _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp")
     args = parse_args()
+    ray.init(log_to_driver=True, namespace="llama", include_dashboard=False, _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp")
+    pg = placement_group([{"CPU": 32*args.pp, "GPU": args.pp}] * args.dp)
+    ray.get(pg.ready(), timeout=10)  # Wait for the placement group to be ready before proceeding
+    print(placement_group_table(pg))
     piper_coordinator = PiperProgramCoordinator.remote(dp_degree=args.dp, pp_degree=args.pp)
-    ray.get(piper_coordinator.run_program.remote(main, args))
+    ray.get(piper_coordinator.run_program.remote(main, args, pg))
     time.sleep(3)
     ray.shutdown()
\ No newline at end of file
diff --git a/test/test_mixtral.py b/test/test_mixtral.py
index 4a1186a..f4b524f 100644
--- a/test/test_mixtral.py
+++ b/test/test_mixtral.py
@@ -11,6 +11,12 @@
 
 logger = logging.getLogger(__name__)
 
+from ray.util.placement_group import (
+    placement_group,
+    placement_group_table,
+    remove_placement_group,
+)
+
 from src.piper_coordinator import PiperProgramCoordinator
 from src.piper_compile import piper_setup
 from src.piper_exec import piper_exec
@@ -28,7 +34,7 @@
     DUALPIPEV_MB6_SCHEDULE,
 )
 
-def main(args):
+def main(args, pg):
 
     world_size = args.dp * args.pp
     mbs = args.mbs
@@ -75,7 +81,9 @@ def main(args):
         example_outputs=y,
         schedule=schedule,
         naive_gradient_sync=args.naive_grad_sync,
+        activation_checkpointing=args.activation_checkpointing,
         mode=args.mode,
+        pg=pg,
     )
 
     print(f"Running {args.warmup} warmup iterations...")
@@ -96,8 +104,9 @@ def main(args):
         f"rank {dp_rank} throughput= {(args.batch_size * args.mbs * config.block_size)/np.mean(iter_times):.3f} tokens/s ({len(iter_times)} samples)"
     )
 
+    actors = piper_metadata.actors
+
     if args.tracing:
-        actors = piper_metadata.actors
         ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
 
         print(f"Running {args.trace_iters} tracing iterations...")
@@ -148,15 +157,25 @@ def parse_args():
                         help='Enable tracing')
     parser.add_argument('--naive-grad-sync', action='store_true', default=False,
                         help='Enable naive gradient sync')
+    parser.add_argument('--activation-checkpointing', action='store_true', default=False,
+                        help='Enable activation checkpointing')
     parser.add_argument('--mode', choices=['sequential', 'naive', 'overlapped'], default='overlapped',
                         help='Mode: sequential, naive, or overlapped (default: overlapped)')
     return parser.parse_args()
 
 if __name__ == "__main__":
-    ray.init(include_dashboard=False, log_to_driver=True, namespace="mixtral")
     args = parse_args()
     print(args)
+    ray.init(
+        log_to_driver=True,
+        namespace="mixtral",
+        include_dashboard=False,
+        _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp",
+    )
+    pg = placement_group([{"CPU": 32*args.pp, "GPU": args.pp}] * args.dp)
+    ray.get(pg.ready(), timeout=10)  # Wait for the placement group to be ready before proceeding
+    print(placement_group_table(pg))
     piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
-    handles = piper_coordinator.run_program.remote(main, args)
+    handles = piper_coordinator.run_program.remote(main, args, pg)
     ray.get(handles)
     ray.shutdown()

From aafe9c3f4bbf89df59b9ff5fa31c6f353a8663a6 Mon Sep 17 00:00:00 2001
From: Andy Ruan <andyubryh@gmail.com>
Date: Mon, 9 Mar 2026 23:02:20 +0000
Subject: [PATCH 071/100] (WIP) ZeRO1 DP2 PP2 profiling

Signed-off-by: Andy Ruan <andyubryh@gmail.com>
---
 logs/piper_zero1_20260309_224153.log | 77 ++++++++++++++++++++++++++++
 run_zero1.sh                         |  2 +-
 src/piper_coordinator.py             | 68 ++++++++++++++----------
 3 files changed, 119 insertions(+), 28 deletions(-)
 create mode 100644 logs/piper_zero1_20260309_224153.log

diff --git a/logs/piper_zero1_20260309_224153.log b/logs/piper_zero1_20260309_224153.log
new file mode 100644
index 0000000..319aeeb
--- /dev/null
+++ b/logs/piper_zero1_20260309_224153.log
@@ -0,0 +1,77 @@
+2026-03-09 22:42:05,154	INFO worker.py:2007 -- Started a local Ray instance.
+/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+  warnings.warn(
+(raylet) [2026-03-09 22:42:15,095 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47324 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2875347) Namespace(model='3b', schedule='interleaved-1f1b', dp=2, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=1)
+(run_dp_rank pid=2875351) Namespace(model='3b', schedule='interleaved-1f1b', dp=2, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=1)
+(raylet) [2026-03-09 22:42:25,109 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47821 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 22:42:35,124 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47819 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 22:42:45,139 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.4782 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2875351) Schedule:
+(run_dp_rank pid=2875351) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
+(run_dp_rank pid=2875351)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
+(run_dp_rank pid=2875347) Schedule:
+(run_dp_rank pid=2875347) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
+(run_dp_rank pid=2875347)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
+(PiperActor pid=2875383) 2026-03-09 22:42:52,618 - piper_actor - INFO - Initializing Ray actor 0 GPU 1
+(PiperActor pid=2875392) 2026-03-09 22:42:52,713 - piper_actor - INFO - Initializing Ray actor 1 GPU 2
+(PiperActor pid=2875350) 2026-03-09 22:42:52,729 - piper_actor - INFO - Initializing Ray actor 3 GPU 4
+(PiperActor pid=2875389) 2026-03-09 22:42:52,729 - piper_actor - INFO - Initializing Ray actor 2 GPU 3
+(raylet) [2026-03-09 22:42:55,154 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47817 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2875347) 2026-03-09 22:43:04,908 - piper_compile - INFO - DP rank 2 compiling...
+(run_dp_rank pid=2875351) 2026-03-09 22:43:04,907 - piper_compile - INFO - DP rank 1 compiling...
+(raylet) [2026-03-09 22:43:05,170 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47808 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2875347) Stage 0 time measured on controller: 307.10 ms
+(run_dp_rank pid=2875351) Stage 0 time measured on controller: 308.00 ms
+(raylet) [2026-03-09 22:43:15,185 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47709 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2875347) Stage 1 time measured on controller: 139.19 ms
+(run_dp_rank pid=2875351) Stage 1 time measured on controller: 139.20 ms
+(raylet) [2026-03-09 22:43:25,200 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47702 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2875347) Stage 2 time measured on controller: 139.17 ms
+(run_dp_rank pid=2875351) Stage 2 time measured on controller: 159.96 ms
+(run_dp_rank pid=2875347) Stage 3 time measured on controller: 130.45 ms
+(run_dp_rank pid=2875351) Stage 3 time measured on controller: 130.44 ms
+(raylet) [2026-03-09 22:43:35,215 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47704 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2875347) 2026-03-09 22:43:40,820 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=2875347) 2026-03-09 22:43:40,820 - piper_compile - INFO - DP rank 2 done.
+(run_dp_rank pid=2875347) Running 5 warmup iterations...
+(PiperActor pid=2875350) [rank3]:[W309 22:43:41.798146621 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2875389) [rank2]:[W309 22:43:42.757082704 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(run_dp_rank pid=2875351) 2026-03-09 22:43:44,807 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=2875351) 2026-03-09 22:43:44,808 - piper_compile - INFO - DP rank 1 done.
+(raylet) [2026-03-09 22:43:45,230 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47703 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2875351) Running 5 warmup iterations...
+(PiperActor pid=2875392) [rank1]:[W309 22:43:45.885325467 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2875383) [rank0]:[W309 22:43:46.907987648 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2875383) [rank0]:[W309 22:43:50.874415843 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2875392) [rank1]:[W309 22:43:50.264331469 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=2875389) [rank2]:[W309 22:43:54.931219822 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(raylet) [2026-03-09 22:43:55,246 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47702 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(PiperActor pid=2875350) [rank3]:[W309 22:43:55.821927540 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(raylet) [2026-03-09 22:44:05,262 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47699 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 22:44:15,282 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47501 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2875347) Warmup 0 completed
+(run_dp_rank pid=2875351) Warmup 0 completed
+(raylet) [2026-03-09 22:44:25,297 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47503 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2875347) Warmup 1 completed
+(run_dp_rank pid=2875351) Warmup 1 completed
+(run_dp_rank pid=2875347) Warmup 2 completed
+(run_dp_rank pid=2875351) Warmup 2 completed
+(run_dp_rank pid=2875347) Warmup 3 completed
+(run_dp_rank pid=2875351) Warmup 3 completed
+(raylet) [2026-03-09 22:44:35,312 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47501 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2875347) Warmup 4 completed
+(run_dp_rank pid=2875347) Running 10 timed iterations...
+(run_dp_rank pid=2875351) Warmup 4 completed
+(run_dp_rank pid=2875351) Running 10 timed iterations...
+(raylet) [2026-03-09 22:44:45,329 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47501 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 22:44:55,344 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47501 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 22:45:05,396 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47491 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(raylet) [2026-03-09 22:45:15,411 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47386 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
+(run_dp_rank pid=2875347) rank 1 iter time= 4.109 ± 0.021 s (10 samples)
+(run_dp_rank pid=2875347) rank 1 throughput= 3987.090 tokens/sec (10 samples)
+(run_dp_rank pid=2875351) rank 0 iter time= 4.110 ± 0.022 s (10 samples)
+(run_dp_rank pid=2875351) rank 0 throughput= 3986.833 tokens/sec (10 samples)
+(run_dp_rank pid=2875347) Ray timeline saved to: out/3b-pp2-dp2-interleaved-1f1b.json
+(run_dp_rank pid=2875351) Ray timeline saved to: out/3b-pp2-dp2-interleaved-1f1b.json
+(raylet) [2026-03-09 22:45:25,427 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47376 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
diff --git a/run_zero1.sh b/run_zero1.sh
index 05a203f..3a02940 100755
--- a/run_zero1.sh
+++ b/run_zero1.sh
@@ -18,7 +18,7 @@ export PIPER_MASTER_PORT="${PIPER_MASTER_PORT:-12388}"
 
 echo "Logging to $LOG_FILE"
 python3 -m test.test_llama \
-  --dp 1 \
+  --dp 2 \
   --pp 2 \
   --warmup 5 \
   --iters 10 \
diff --git a/src/piper_coordinator.py b/src/piper_coordinator.py
index 6504330..3bc8aee 100644
--- a/src/piper_coordinator.py
+++ b/src/piper_coordinator.py
@@ -7,50 +7,64 @@
 from .piper_utils import create_logger, LOG_LEVEL
 
 
-@ray.remote(num_gpus=0.6)
-def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, master_addr, master_port, training_func: Callable, *args, **kwargs):
+@ray.remote(num_gpus=0.5)
+def run_dp_rank(
+    dp_rank,
+    dp_degree,
+    pp_degree,
+    world_size,
+    master_addr,
+    master_port,
+    training_func: Callable,
+    *args,
+    **kwargs,
+):
     logger = create_logger("piper_coordinator", LOG_LEVEL)
     logger.debug(f"Running DP rank {dp_rank+1} of {dp_degree}")
 
-    os.environ['PIPER_DP_RANK'] = str(dp_rank)
-    os.environ['PIPER_DP_DEGREE'] = str(dp_degree)
-    os.environ['PIPER_PP_DEGREE'] = str(pp_degree)
-    os.environ['PIPER_WORLD_SIZE'] = str(world_size)
-    os.environ['PIPER_MASTER_ADDR'] = str(master_addr)
-    os.environ['PIPER_MASTER_PORT'] = str(master_port)
-    os.environ['TORCH_LOGS'] = "+graph_breaks"
+    os.environ["PIPER_DP_RANK"] = str(dp_rank)
+    os.environ["PIPER_DP_DEGREE"] = str(dp_degree)
+    os.environ["PIPER_PP_DEGREE"] = str(pp_degree)
+    os.environ["PIPER_WORLD_SIZE"] = str(world_size)
+    os.environ["PIPER_MASTER_ADDR"] = str(master_addr)
+    os.environ["PIPER_MASTER_PORT"] = str(master_port)
+    os.environ["TORCH_LOGS"] = "+graph_breaks"
     return training_func(*args, **kwargs)
 
+
 def find_free_port():
     import socket
+
     with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
         s.bind(("0.0.0.0", 0))
         port = s.getsockname()[1]
     return port
 
+
 @ray.remote
 class PiperProgramCoordinator:
-    """ Central Actor that Coordinates all the DP replicas of a single pipeline"""
+    """Central Actor that Coordinates all the DP replicas of a single pipeline"""
+
     def __init__(self, dp_degree, pp_degree):
         self.dp_degree = dp_degree
         self.pp_degree = pp_degree
         self.world_size = dp_degree * pp_degree
         self.master_port = find_free_port()
-    
-    def run_program(self, training_func: Callable, *args, **kwargs):
-        return ray.get([run_dp_rank.remote(
-                dp_rank, 
-                self.dp_degree, 
-                self.pp_degree,
-                self.world_size, 
-                "127.0.0.1", 
-                self.master_port, 
-                training_func,
-                *args, 
-                **kwargs) 
-            for dp_rank in range(self.dp_degree)])
-    
-
-            
-
 
+    def run_program(self, training_func: Callable, *args, **kwargs):
+        return ray.get(
+            [
+                run_dp_rank.remote(
+                    dp_rank,
+                    self.dp_degree,
+                    self.pp_degree,
+                    self.world_size,
+                    "127.0.0.1",
+                    self.master_port,
+                    training_func,
+                    *args,
+                    **kwargs,
+                )
+                for dp_rank in range(self.dp_degree)
+            ]
+        )

From bc80f90bd2e166a03432815fca238b823c82a3d9 Mon Sep 17 00:00:00 2001
From: Shubham Tiwari <shubhamtiwaribits@gmail.com>
Date: Tue, 10 Mar 2026 05:43:52 +0000
Subject: [PATCH 072/100] integrate qwen model from torchtitan with workarounds

---
 src/piper_actor.py           | 368 +++++++++++++++++++++++++++++++++--
 src/piper_graph_transform.py |  17 +-
 src/piper_utils.py           |   9 +-
 test/models/qwen3.py         |  61 +++---
 test/test_qwen.py            | 135 +++++++++++--
 5 files changed, 510 insertions(+), 80 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index e5a6a22..8e89d44 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -20,7 +20,7 @@
 )
 from .backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
 
-CLEANUP_MEMORY = False
+CLEANUP_MEMORY = True
 
 logger = create_logger("piper_actor", LOG_LEVEL)
 
@@ -169,6 +169,7 @@ def __init__(
         self.tracing = False
         self.trace_events = dict()
         self.trace_data = defaultdict(list)
+        self.memory_tracing_enabled = False
 
         # map stage id -> mb_idx -> parameter groups for backward pass
         self.bw_param_groups = defaultdict(dict)
@@ -181,6 +182,109 @@ def __init__(
 
         piper_metadata.actor_self = self
 
+
+        # In PiperActor.__init__, add:
+        self.pytorch_profiler = None
+        self.pytorch_profiler_output_dir = None
+        self.pytorch_profiler_iter_idx = 0
+        self.pytorch_profiler_running = False
+
+    def enable_pytorch_profiler(
+        self, 
+        enabled: bool = True, 
+        output_dir: str = "piper_profiling/chrome_traces/",
+        iter_idx: int = 0
+    ) -> None:
+        """Enable/disable PyTorch profiler on this actor."""
+        if enabled:
+            self.pytorch_profiler = torch.profiler.profile(
+                activities=[
+                    torch.profiler.ProfilerActivity.CPU,
+                    torch.profiler.ProfilerActivity.CUDA,
+                ],
+                record_shapes=True,
+                with_stack=True,
+            )
+            # Use context manager entry - this initializes the profiler
+            self.pytorch_profiler.__enter__()
+
+            # Force Kineto to initialize by doing a dummy GPU operation
+            if torch.cuda.is_available():
+                try:
+                    dummy = torch.zeros(1, device=self.device)
+                    _ = dummy + 1
+                    torch.cuda.synchronize()
+                    self.logger.debug(f"Actor {self.global_rank}: Initialized Kineto with dummy GPU op")
+                except Exception as e:
+                    self.logger.warning(f"Actor {self.global_rank}: Failed to initialize Kineto: {e}")
+
+            self.pytorch_profiler_output_dir = output_dir
+            self.pytorch_profiler_iter_idx = iter_idx
+            self.pytorch_profiler_running = True
+            self.logger.info(f"Actor {self.global_rank}: PyTorch profiler enabled, output directory: {self.pytorch_profiler_output_dir}, iteration index: {self.pytorch_profiler_iter_idx}")
+        else:
+            if hasattr(self, 'pytorch_profiler') and hasattr(self, 'pytorch_profiler_running') and self.pytorch_profiler_running:
+                try:
+                    self.pytorch_profiler.__exit__(None, None, None)
+                except RuntimeError:
+                    pass
+                self.pytorch_profiler_running = False
+            if hasattr(self, 'pytorch_profiler'):
+                del self.pytorch_profiler
+            self.logger.info(f"Actor {self.global_rank}: PyTorch profiler disabled")
+
+
+    def stop_pytorch_profiler(self) -> str:
+        """Stop PyTorch profiler and export trace. Returns path to trace file."""
+        if not hasattr(self, 'pytorch_profiler') or self.pytorch_profiler is None:
+            self.logger.warning(f"Actor {self.global_rank}: No profiler to stop")
+            return None
+        
+        if not hasattr(self, 'pytorch_profiler_running') or not self.pytorch_profiler_running:
+            self.logger.warning(f"Actor {self.global_rank}: Profiler not in running state")
+            return None
+        
+        # Stop using context manager exit - this properly stops Kineto
+        try:
+            self.pytorch_profiler.__exit__(None, None, None)
+            self.pytorch_profiler_running = False
+        except RuntimeError as e:
+            error_msg = str(e).lower()
+            if "not running" in error_msg or "not enabled" in error_msg or "can't disable" in error_msg:
+                self.logger.warning(f"Actor {self.global_rank}: Profiler was not running: {e}")
+                self.pytorch_profiler_running = False
+                if hasattr(self, 'pytorch_profiler'):
+                    del self.pytorch_profiler
+                return None
+            else:
+                raise
+        
+        # Export trace
+        if not hasattr(self, 'pytorch_profiler_output_dir') or self.pytorch_profiler_output_dir is None:
+            self.logger.warning(f"Actor {self.global_rank}: No profiler output dir set")
+            if hasattr(self, 'pytorch_profiler'):
+                del self.pytorch_profiler
+            return None
+        
+        try:
+            os.makedirs(self.pytorch_profiler_output_dir, exist_ok=True)
+            trace_path = os.path.join(
+                self.pytorch_profiler_output_dir,
+                f"actor_{self.global_rank}_iter_{self.pytorch_profiler_iter_idx}.pt.trace.json"
+            )
+            self.pytorch_profiler.export_chrome_trace(trace_path)
+            
+            self.logger.info(f"Actor {self.global_rank}: PyTorch profiler trace saved to {trace_path}")
+        except Exception as e:
+            self.logger.error(f"Actor {self.global_rank}: Failed to export profiler trace: {e}")
+            trace_path = None
+        finally:
+            # Cleanup
+            if hasattr(self, 'pytorch_profiler'):
+                del self.pytorch_profiler
+        
+        return trace_path
+
     def reset_p2p_states(self):
         self.next_p2p_idx = 0
         self.executed_p2ps = set()
@@ -209,6 +313,44 @@ def stop_mem_tracing(self) -> None:
             f"Saved memory snapshot to actor{self.global_rank}_memory_snapshot_mb4_gpipe.pickle"
         )
         torch.cuda.memory._record_memory_history(enabled=None)
+    
+    def enable_profiling(self, enabled: bool = True) -> None:
+        """Enable/disable per-task profiling (Chrome traces)."""
+        self.profile = enabled
+        self.logger.info(
+            f"Actor {self.global_rank}: Profiling {'enabled' if enabled else 'disabled'}"
+        )
+
+    def enable_memory_tracing(self, enabled: bool = True) -> None:
+        """Enable/disable memory tracing."""
+        self.memory_tracing_enabled = enabled
+        if enabled:
+            torch.cuda.memory._record_memory_history()
+            self.logger.info(f"Actor {self.global_rank}: Memory tracing enabled")
+        else:
+            if hasattr(self, 'memory_tracing_enabled') and self.memory_tracing_enabled:
+                # Dump snapshot before disabling
+                snapshot_path = f"actor{self.global_rank}_iter_memory.pickle"
+                os.makedirs(os.path.dirname(snapshot_path), exist_ok=True)
+                torch.cuda.memory._dump_snapshot(snapshot_path)
+                self.logger.info(f"Saved memory snapshot to {snapshot_path}")
+            torch.cuda.memory._record_memory_history(enabled=None)
+            self.memory_tracing_enabled = False
+            self.logger.info(f"Actor {self.global_rank}: Memory tracing disabled")
+
+    def dump_memory_snapshot(self, filename: str = None) -> str:
+        """Dump current memory snapshot. Returns snapshot path."""
+        if not (hasattr(self, 'memory_tracing_enabled') and self.memory_tracing_enabled):
+            self.logger.warning("Memory tracing not enabled, cannot dump snapshot")
+            return None
+        
+        if filename is None:
+            filename = f"actor{self.global_rank}_iter_memory.pickle"
+        
+        os.makedirs(os.path.dirname(filename), exist_ok=True)
+        torch.cuda.memory._dump_snapshot(filename)
+        self.logger.info(f"Saved memory snapshot to {filename}")
+        return filename
 
     def reset_peak_memory(self):
         torch.cuda.reset_peak_memory_stats()
@@ -387,9 +529,169 @@ def _wait_for_comm_ops(self):
                         self.comm_op_handles[stage_id][tid].wait()
                         done = True
 
+    @staticmethod
+    def _replace_meta_constants(gm, device):
+        """Replace any constants on meta device with tensors on the actual device."""
+        import torch.fx as fx
+        from .piper_utils import create_logger, LOG_LEVEL
+        from torch._subclasses.fake_tensor import FakeTensor
+        logger = create_logger("piper_actor", LOG_LEVEL)
+        
+        replaced_count = 0
+        params_count = 0
+        buffers_count = 0
+        get_attr_count = 0
+        constants_count = 0
+        attrs_count = 0
+        node_args_count = 0
+        node_meta_count = 0
+        submodule_count = 0
+        
+        def is_meta_or_fake(t):
+            """Check if tensor is on meta device or is a FakeTensor."""
+            if isinstance(t, FakeTensor):
+                return True
+            if isinstance(t, torch.Tensor):
+                return t.device.type == 'meta'
+            return False
+        
+        def replace_meta_tensor(t):
+            if is_meta_or_fake(t):
+                return torch.empty_like(t, device=device)
+            return t
+        
+        def replace_in_module(module, module_name=""):
+            """Recursively replace meta tensors in a module and all its submodules."""
+            nonlocal replaced_count, params_count, buffers_count, get_attr_count, constants_count, attrs_count, node_args_count, node_meta_count, submodule_count
+            
+            # replace all parameters that are on meta device or FakeTensor
+            for name, param in module.named_parameters(recurse=False):
+                if is_meta_or_fake(param):
+                    full_name = f"{module_name}.{name}" if module_name else name
+                    logger.info(f"Replacing parameter '{full_name}' from meta/fake to {device} (shape={param.shape}, dtype={param.dtype}, requires_grad={param.requires_grad})")
+                    new_param = torch.empty_like(param, device=device)
+                    new_param.requires_grad_(param.requires_grad)
+                    setattr(module, name, torch.nn.Parameter(new_param, requires_grad=param.requires_grad))
+                    params_count += 1
+                    replaced_count += 1
+            
+            # replace all buffers that are on meta device or FakeTensor
+            for name, buffer in module.named_buffers(recurse=False):
+                if is_meta_or_fake(buffer):
+                    full_name = f"{module_name}.{name}" if module_name else name
+                    logger.info(f"Replacing buffer '{full_name}' from meta/fake to {device} (shape={buffer.shape}, dtype={buffer.dtype})")
+                    new_buffer = torch.empty_like(buffer, device=device)
+                    setattr(module, name, new_buffer)
+                    buffers_count += 1
+                    replaced_count += 1
+            
+            # check gm 
+            if isinstance(module, fx.GraphModule):
+                # replace in get_attr nodes
+                for node in module.graph.nodes:
+                    if node.op == 'get_attr':
+                        try:
+                            attr_value = getattr(module, node.target)
+                            if is_meta_or_fake(attr_value):
+                                full_name = f"{module_name}.{node.target}" if module_name else node.target
+                                logger.info(f"Replacing get_attr '{full_name}' from meta/fake to {device} (shape={attr_value.shape}, dtype={attr_value.dtype})")
+                                new_tensor = torch.empty_like(attr_value, device=device)
+                                setattr(module, node.target, new_tensor)
+                                get_attr_count += 1
+                                replaced_count += 1
+                        except AttributeError:
+                            pass
+                    
+                    # check node.meta for tensor values
+                    if hasattr(node, 'meta') and isinstance(node.meta, dict):
+                        for key, value in node.meta.items():
+                            if is_meta_or_fake(value):
+                                full_name = f"{module_name}.{node.name}.meta['{key}']" if module_name else f"{node.name}.meta['{key}']"
+                                logger.info(f"Replacing node.meta['{key}'] for node '{node.name}' from meta/fake to {device} (shape={value.shape}, dtype={value.dtype})")
+                                node.meta[key] = torch.empty_like(value, device=device)
+                                node_meta_count += 1
+                                replaced_count += 1
+                
+                # replace in constants dict if it exists
+                if hasattr(module, '_constants'):
+                    for key, value in module._constants.items():
+                        if is_meta_or_fake(value):
+                            full_name = f"{module_name}._constants['{key}']" if module_name else f"_constants['{key}']"
+                            logger.info(f"Replacing constant '{full_name}' from meta/fake to {device} (shape={value.shape}, dtype={value.dtype})")
+                            module._constants[key] = torch.empty_like(value, device=device)
+                            constants_count += 1
+                            replaced_count += 1
+                
+                # traverse all nodes and replace meta tensors in args/kwargs
+                for node in module.graph.nodes:
+                    # replace in args
+                    new_args = []
+                    for arg in node.args:
+                        if is_meta_or_fake(arg):
+                            full_name = f"{module_name}.{node.name}" if module_name else node.name
+                            logger.info(f"Replacing meta/fake tensor in node '{full_name}' args (shape={arg.shape}, dtype={arg.dtype})")
+                            new_args.append(torch.empty_like(arg, device=device))
+                            node_args_count += 1
+                            replaced_count += 1
+                        elif isinstance(arg, torch.device) and arg.type == 'meta':
+                            # replace meta device objects with the actual device
+                            full_name = f"{module_name}.{node.name}" if module_name else node.name
+                            logger.info(f"Replacing meta device object in node '{full_name}' args with {device}")
+                            new_args.append(torch.device(device))
+                            node_args_count += 1
+                            replaced_count += 1
+                        elif isinstance(arg, (list, tuple)):
+                            new_args.append(type(arg)(replace_meta_tensor(x) for x in arg))
+                        else:
+                            new_args.append(arg)
+                    node.args = tuple(new_args)
+                    
+                    # replace in kwargs
+                    new_kwargs = {}
+                    for key, value in node.kwargs.items():
+                        if is_meta_or_fake(value):
+                            full_name = f"{module_name}.{node.name}" if module_name else node.name
+                            logger.info(f"Replacing meta/fake tensor in node '{full_name}' kwargs['{key}'] (shape={value.shape}, dtype={value.dtype})")
+                            new_kwargs[key] = torch.empty_like(value, device=device)
+                            node_args_count += 1
+                            replaced_count += 1
+                        elif isinstance(value, torch.device) and value.type == 'meta':
+                            # replace meta device objects with the actual device
+                            full_name = f"{module_name}.{node.name}" if module_name else node.name
+                            logger.info(f"Replacing meta device object in node '{full_name}' kwargs['{key}'] with {device}")
+                            new_kwargs[key] = torch.device(device)
+                            node_args_count += 1
+                            replaced_count += 1
+                        elif isinstance(value, (list, tuple)):
+                            new_kwargs[key] = type(value)(replace_meta_tensor(x) for x in value)
+                        else:
+                            new_kwargs[key] = value
+                    node.kwargs = new_kwargs
+                
+                # recompile the GraphModule after modifications
+                module.recompile()
+            
+            # recursively process all submodules
+            for name, submodule in module.named_children():
+                submodule_name = f"{module_name}.{name}" if module_name else name
+                replace_in_module(submodule, submodule_name)
+                submodule_count += 1
+        
+        replace_in_module(gm)
+        
+        if replaced_count > 0:
+            logger.info(
+                f"_replace_meta_constants: Replaced {replaced_count} meta device tensors "
+                f"(params: {params_count}, buffers: {buffers_count}, get_attr: {get_attr_count}, "
+                f"constants: {constants_count}, attrs: {attrs_count}, node_args: {node_args_count}, "
+                f"node_meta: {node_meta_count}, checked {submodule_count} submodules)"
+            )
+        else:
+            logger.warning(f"_replace_meta_constants: No meta device tensors found to replace! (checked {submodule_count} submodules)")
+        
+        return gm
+
     def _load_stage_kernels(self):
-        # Ensure Triton kernel modules are imported before deserialization
-        # This ensures the kernel registration mechanism is available
         try:
             import torchtitan.models.common.moe.kernels
             import torchtitan.models.common.moe.utils
@@ -404,15 +706,15 @@ def _load_stage_kernels(self):
                 f"Actor {self.global_rank} failed to import Triton kernel modules: {e}. "
                 f"This may cause issues with Triton kernels in the graph."
             )
-        
-        # HACK: Manually register Triton kernel in the side table
-        # The graph expects kernel_idx=0, so we need to register it at that index
+
+        # HACK: manually register Triton kernel in the side table
+        # the graph expects kernel_idx=0, so we need to register it at that index
         self.logger.info(f"Manually registering Triton kernel at index 0 on actor {self.global_rank}...")
         try:
             # Import the kernel wrapper
             from torchtitan.models.common.moe.kernels import fill_indices_wrapper
             
-            # Create a dummy function that uses the Triton kernel
+            # create a dummy function that uses the Triton kernel
             # This needs to be compiled with torch.compile to register the kernel
             def dummy_kernel_user(tokens, starts, offsets):
                 return fill_indices_wrapper(
@@ -432,27 +734,26 @@ def dummy_kernel_user(tokens, starts, offsets):
                 compiled_dummy = torch.compile(dummy_kernel_user, mode="reduce-overhead")
                 _ = compiled_dummy(dummy_tokens, dummy_start, dummy_offsets)
             
-            # Now try to access the kernel side table and register at index 0
+            # now try to access the kernel side table and register at index 0
             from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
             
-            # The kernel should now be registered, but we need to find it and move it to index 0
+            # move the kernel to index 0
             if hasattr(kernel_side_table, 'id_to_kernel') and hasattr(kernel_side_table, 'constant_args'):
                 registered_kernels = list(kernel_side_table.id_to_kernel.items())
                 registered_constants = list(kernel_side_table.constant_args.items())
                 
                 if len(registered_kernels) > 0:
-                    # Get the most recently registered kernel (highest index)
+                    # get the most recently registered kernel (highest index)
                     max_idx, kernel = max(registered_kernels, key=lambda x: x[0])
-                    # Manually register it at index 0
+                    # manually register it at index 0
                     kernel_side_table.id_to_kernel[0] = kernel
                     
-                    # Also register constant args if available
-                    # The graph might expect constant_args_idx=1, so we need to check what was registered
+                    # register constant args if available
+                    # the graph might expect constant_args_idx=1, so we need to check what was registered
                     if len(registered_constants) > 0:
-                        # Get the constant args that correspond to our kernel
-                        # Usually they're at the same index, but let's be safe
+                        # get the constant args that correspond to our kernel
                         max_const_idx, const_args = max(registered_constants, key=lambda x: x[0])
-                        for idx in range(10):
+                        for idx in range(20):
                             kernel_side_table.constant_args[idx] = const_args
                         self.logger.info(
                             f"Actor {self.global_rank} manually registered kernel at index 0 "
@@ -472,7 +773,7 @@ def dummy_kernel_user(tokens, starts, offsets):
                         f"Actor {self.global_rank} no kernels found in side table after compilation"
                     )
             else:
-                # Try accessing via different attribute names
+                # try accessing via different attribute names
                 attrs = [attr for attr in dir(kernel_side_table) if not attr.startswith('_')]
                 self.logger.warning(
                     f"Actor {self.global_rank} kernel_side_table doesn't have expected attributes. "
@@ -494,6 +795,9 @@ def _load_stage(
         # compile the graph with the given graphargs
         gm = _deserialize_graphmodule(gm_data)
 
+        # replace any meta device constants with tensors on the actual device
+        gm = self._replace_meta_constants(gm, self.device)
+
         self.graph_modules[stage_id] = gm
         self.forward_fns[stage_id] = gm.forward
 
@@ -530,6 +834,19 @@ def _load_stage(
                 t.zero_()
 
             realized[i] = t
+        
+        # Defensive check: ensure no realized tensors are on meta device
+        for i, t in enumerate(realized):
+            if t is not None and isinstance(t, torch.Tensor):
+                if t.device.type == 'meta':
+                    self.logger.error(
+                        f"CRITICAL: Realized tensor at index {i} is still on meta device! "
+                        f"shape={t.shape}, dtype={t.dtype}. Replacing..."
+                    )
+                    new_t = torch.empty_like(t, device=self.device)
+                    if t.requires_grad:
+                        new_t.requires_grad_(True)
+                    realized[i] = new_t
 
         self.forward_args[stage_id] = realized
 
@@ -921,7 +1238,7 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
         if self.profile:
             forward_ctx_manager.__exit__(None, None, None)
             profiler.__exit__(None, None, None)
-            profiler.export_chrome_trace(f"out/{self.mode}/actor{self.global_rank}_fwd{stage_id}mb{mb_idx}_trace.json")
+            profiler.export_chrome_trace(f"actor{self.global_rank}_fwd{stage_id}mb{mb_idx}_trace.json")
 
         return 1
 
@@ -964,8 +1281,17 @@ def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         else:
             assert loss_fn is not None
             labels = self.labels
+            # Debug: log shapes before loss computation
+            logger.info(f"out_activation shape: {out_activation.shape}, labels shape: {labels.shape}")
+            logger.info(f"Memory before loss: allocated={torch.cuda.memory_allocated()/1e9:.2f} GB, reserved={torch.cuda.memory_reserved()/1e9:.2f} GB")
             assert out_activation.shape == labels.shape
 
+            # Aggressively clear memory before loss computation
+            torch.cuda.synchronize()
+            gc.collect()
+            torch.cuda.empty_cache()
+            logger.info(f"Memory after cleanup: allocated={torch.cuda.memory_allocated()/1e9:.2f} GB, reserved={torch.cuda.memory_reserved()/1e9:.2f} GB")
+
             self._start_timing(comp_stream, "backward_comp")
             with torch.cuda.stream(comp_stream):
                 loss = loss_fn(out_activation, labels)
@@ -988,7 +1314,7 @@ def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         if self.profile:
             backward_ctx_manager.__exit__(None, None, None)
             profiler.__exit__(None, None, None)
-            profiler.export_chrome_trace(f"out/{self.mode}/actor{self.global_rank}_bwd{stage_id}mb{mb_idx}_trace.json")
+            profiler.export_chrome_trace(f"actor{self.global_rank}_bwd{stage_id}mb{mb_idx}_trace.json")
 
         return 1
 
@@ -1251,7 +1577,7 @@ def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         if self.profile:
             backward_ctx_manager.__exit__(None, None, None)
             profiler.__exit__(None, None, None)
-            profiler.export_chrome_trace(f"out/{self.mode}/actor{self.global_rank}_bwd{stage_id}mb{mb_idx}_trace.json")
+            profiler.export_chrome_trace(f"actor{self.global_rank}_bwd{stage_id}mb{mb_idx}_trace.json")
 
         return 1
 
@@ -1931,7 +2257,7 @@ def run_bwd():
             backward_ctx_manager.__exit__(None, None, None)
             profiler.__exit__(None, None, None)
             profiler.export_chrome_trace(
-                f"out/{self.mode}/actor{self.global_rank}_"
+                f"actor{self.global_rank}_"
                 f"fwd{fwd_stage_id}mb{fwd_mb_idx}_"
                 f"bwd{bwd_stage_id}mb{bwd_mb_idx}_trace.json")
 
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index 0b3d4c6..2b21d27 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -118,7 +118,12 @@ def backward(ctx, grad_output):
             ctx.actor_self.bwd_a2a_counter += 1
         comp_stream.wait_event(comm_finished_event)
 
-        logger.info(f"Completed AllToAllSingleFunction backward rank={ctx.global_rank} time={ctx.actor_self.trace_data['bwd_a2a'][-1]:.2f} ms")
+        # logger.info(f"Completed AllToAllSingleFunction backward rank={ctx.global_rank} time={ctx.actor_self.trace_data['bwd_a2a'][-1]:.2f} ms")
+        if ctx.actor_self.trace_data.get('bwd_a2a') and len(ctx.actor_self.trace_data['bwd_a2a']) > 0:
+            logger.info(f"Completed AllToAllSingleFunction backward rank={ctx.global_rank} time={ctx.actor_self.trace_data['bwd_a2a'][-1]:.2f} ms")
+        else:
+            logger.info(f"Completed AllToAllSingleFunction backward rank={ctx.global_rank}")
+        
         # Return gradients: grad_output flows to grad_input, None for group
         return grad_input, grad_input, None
 
@@ -713,7 +718,6 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> tuple[fx.GraphModule, int]:
                     annotated_nodes.append((idx, node, annotation_key, reshape))
     
     if not annotated_nodes:
-        logger.info("No communication annotations found in graph")
         return gm, 0
     
     # Group annotated nodes into contiguous blocks
@@ -830,6 +834,10 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> tuple[fx.GraphModule, int]:
             if reshape_info is not None:
                 reshape_type, reshape_shape = reshape_info
                 if reshape_type == "input":
+                    # DEBUG: Log reshape information
+                    logger.info(f"Applying input reshape {reshape_shape} to node {node.name}")
+                    if isinstance(reshape_shape, (list, tuple)) and -1 in reshape_shape:
+                        logger.warning(f"Input reshape shape contains -1: {reshape_shape}, this may cause shape inference issues")
                     # Reshape before communication
                     new_node = new_graph.call_function(
                         torch.reshape,
@@ -865,6 +873,11 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> tuple[fx.GraphModule, int]:
                 reshape_type, reshape_shape = reshape_info
                 if reshape_type == "output":
                     # Reshape after communication
+                    # DEBUG: Log reshape information
+                    logger.info(f"Applying output reshape {reshape_shape} to node {node.name}, buf_node shape inference needed")
+                    # Log the reshape shape for debugging
+                    if isinstance(reshape_shape, (list, tuple)) and -1 in reshape_shape:
+                        logger.warning(f"Reshape shape contains -1: {reshape_shape}, this may cause shape inference issues")
                     buf_node = new_graph.call_function(
                         torch.reshape,
                         (buf_node, reshape_shape)
diff --git a/src/piper_utils.py b/src/piper_utils.py
index 2a64802..ba9224b 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -160,6 +160,9 @@ def _serialize_target(t):
     # regular python function or built-in
     if inspect.isfunction(t) or inspect.isbuiltin(t):
         mod = inspect.getmodule(t)
+
+        # TODO: This may not be working as expected, since we use another 
+        # workaround during deserialization. 
         # Check if inspect.getmodule returned torch.ops.* namespace
         # These should be serialized as torch_op, not py_func
         if mod is not None:
@@ -257,7 +260,9 @@ def _deserialize_target(payload):
         return payload["value"]
 
     if kind == "py_func":
-        # Workaround: if module is torch.ops.*, redirect to torch_op deserialization
+        #TODO: this is a workaround to deserialize torch.ops.* functions
+        # if module is torch.ops.*, redirect to torch_op deserialization
+        # this needs to be dealt with during the serialization. 
         if payload["module"].startswith("torch.ops"):
             # Reconstruct the path from module and qualname
             # e.g., module="torch.ops.higher_order", qualname="triton_kernel_wrapper_mutation"
@@ -290,7 +295,7 @@ def _deserialize_target(payload):
                 obj = getattr(obj, part)
             return obj
         except AttributeError:
-                # If operator doesn't exist, try importing implementation module to trigger registration
+                # if operator doesn't exist, try importing implementation module to trigger registration
                 if path.startswith("higher_order."):
                     op_name = path.split(".", 1)[1]
                     op_to_module = {
diff --git a/test/models/qwen3.py b/test/models/qwen3.py
index 0ad5154..f473984 100644
--- a/test/models/qwen3.py
+++ b/test/models/qwen3.py
@@ -4,11 +4,14 @@
 import dataclasses
 from torch import nn
 
-# from torchtitan.models.qwen3 import Qwen3Model
 from torchtitan.models.qwen3 import Qwen3Model, Qwen3TransformerBlock
 from torchtitan.models.common.attention import AttentionMasksType
 from torchtitan.models.common.moe.moe import MoE
 
+from torchtitan.config import ActivationCheckpointConfig
+from torchtitan.distributed.activation_checkpoint import apply_ac
+
+from torch.utils.checkpoint import checkpoint
 
 class AnnotatedMoE(MoE):
     
@@ -40,30 +43,30 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
                 * top_scores_experts_sorted.reshape(-1, 1)
             ).to(x.dtype)
         
-        # PIPER ANNOTATION 1: Dispatch tokens to experts via all_to_all
+        routed_input = routed_input.reshape(
+            2, self.experts.num_experts, -1 , dim)
+
+        # dispatch tokens to experts via all_to_all
         with torch.fx.traceback.annotate({
             "collective": "all_to_all_single",
             "group": "ep",
-            "reshape": ("output", (self.experts.num_experts, -1, dim))
         }):
-            # The routed_input will be dispatched to experts on different ranks
-            dispatched_input = routed_input
+            dispatched_input = routed_input.contiguous()
         
+        dispatched_input = dispatched_input.reshape(-1, dim)
         routed_output = self.experts(dispatched_input, num_tokens_per_expert)
-        
-        # PIPER ANNOTATION 2: Gather expert outputs back via all_to_all
+        routed_output = routed_output.reshape(self.experts.num_experts, -1, dim)
+
+        # gather expert outputs back via all_to_all
         with torch.fx.traceback.annotate({
             "collective": "all_to_all_single",
             "group": "ep",
-            "reshape": ("input", (self.experts.num_experts, -1, dim))
         }):
-            # The routed_output will be gathered back from expert ranks
-            gathered_output = routed_output
+            gathered_output = routed_output.contiguous()
         
-        # Rest of the forward pass (same as parent)
+        gathered_output = gathered_output.reshape(-1, dim)
         out = self.shared_experts(x) if self.shared_experts is not None else None
         
-        # Unsort routed outputs
         routed_output_unsorted = torch.zeros(
             (bs * slen * self.router.top_k, dim),
             dtype=gathered_output.dtype,
@@ -93,23 +96,10 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 class AnnotatedQwen3TransformerBlock(Qwen3TransformerBlock):
     def __init__(self, config, *, layer_id: int, dim: int, n_layers: int):
-        # Call parent init
         super().__init__(config, layer_id=layer_id, dim=dim, n_layers=n_layers)
         
-        # Replace MoE with annotated version if MoE is enabled
-        if self.moe_enabled and hasattr(self, 'moe'):
-            # Create AnnotatedMoE using the config (simpler and correct)
-            annotated_moe = AnnotatedMoE(config.moe, dim=dim)
-            
-            # Copy weights from original MoE if already initialized
-            if hasattr(self.moe, 'experts'):
-                annotated_moe.experts.load_state_dict(self.moe.experts.state_dict())
-                annotated_moe.router.load_state_dict(self.moe.router.state_dict())
-                if self.moe.shared_experts is not None:
-                    annotated_moe.shared_experts.load_state_dict(
-                        self.moe.shared_experts.state_dict()
-                    )
-            self.moe = annotated_moe
+        if self.moe_enabled:
+            self.moe = AnnotatedMoE(config.moe, dim=dim)
 
 
 class PiperQwen3Model(Qwen3Model):
@@ -120,27 +110,24 @@ class PiperQwen3Model(Qwen3Model):
     in the forward pass for pipeline parallelism.
     """
     
-    def __init__(self, config, num_stages: int = 2):
-        # Temporarily replace layer config to use AnnotatedQwen3TransformerBlock
+    def __init__(self, config, num_stages: int = 2, use_checkpointing: bool = False):
         original_layer_config = config.layer
         
-        # Create a new config that uses AnnotatedQwen3TransformerBlock
-        # We'll override the build method to use our annotated block
         class AnnotatedLayerConfig(original_layer_config.__class__):
             def build(self, *, layer_id: int, dim: int, n_layers: int):
                 return AnnotatedQwen3TransformerBlock(
                     self, layer_id=layer_id, dim=dim, n_layers=n_layers
                 )
         
-        # Extract fields from the original config (works with slots=True)
         field_dict = {field.name: getattr(original_layer_config, field.name) 
                      for field in dataclasses.fields(original_layer_config)}
         annotated_layer_config = AnnotatedLayerConfig(**field_dict)
         config.layer = annotated_layer_config
         
-        # Call parent init
         super().__init__(config)
         self.num_stages = num_stages
+        self.use_checkpointing = use_checkpointing
+
     
     def forward(
         self,
@@ -157,23 +144,21 @@ def forward(
         num_layers = len(self.layers)
         layers_per_stage = num_layers // self.num_stages
         
-        # Stage 0: Embedding + first N layers
+        # stage 0: embedding + first N layers
         with torch.fx.traceback.annotate({"stage": 0}):
             h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
             
-            # Process first set of layers
             for i in range(layers_per_stage * (self.num_stages - 1)):
                 layer = self.layers[str(i)]
                 h = layer(h, self.freqs_cis, attention_masks, positions)
         
-        # Stage 1 (or last stage): Remaining layers + norm + output
+        # stage 1: remaining layers + norm + output
         with torch.fx.traceback.annotate({"stage": self.num_stages - 1}):
-            # Process remaining layers
             for i in range(layers_per_stage * (self.num_stages - 1), num_layers):
                 layer = self.layers[str(i)]
                 h = layer(h, self.freqs_cis, attention_masks, positions)
             
-            # Final norm and output
+            # final norm and output
             h = self.norm(h) if self.norm is not None else h
             output = self.output(h) if self.output is not None else h
         
diff --git a/test/test_qwen.py b/test/test_qwen.py
index ed094af..a1ea07f 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -15,6 +15,7 @@
 from torchtitan.models.qwen3 import Qwen3Model, Qwen3TransformerBlock
 from torchtitan.models.common import GQAttention, RoPE, FeedForward
 from torchtitan.models.common.moe import MoE
+
 from .schedule_helpers import (
     build_1f1b_schedule, 
     build_gpipe_schedule, 
@@ -91,7 +92,7 @@ def create_qwen3_config(name: str) -> Qwen3Model.Config:
                     norm_eps=1e-6,
                     moe=MoE.Config(
                         hidden_dim=3584,
-                        num_experts=2,
+                        num_experts=4,
                         top_k=2,
                         use_grouped_mm=True,
                         num_expert_groups=None,
@@ -210,6 +211,42 @@ def create_qwen3_config(name: str) -> Qwen3Model.Config:
                     backend="cos_sin",
                 ),
             )
+        case '30B-A3B':
+            return Qwen3Model.Config(
+                vocab_size=151936,
+                dim=2048,
+                n_layers=48,
+                layer=Qwen3TransformerBlock.Config(
+                    norm_eps=1e-6,
+                    moe_enabled=True,
+                    moe=MoE.Config(
+                        hidden_dim=768,
+                        num_experts=64,
+                        num_shared_experts=0,
+                        top_k=8,
+                        score_func="softmax",
+                        route_norm=True,
+                        route_scale=1.0,
+                        score_before_experts=False,
+                    ),
+                    feed_forward=FeedForward.Config(hidden_dim=6144),
+                    attention=GQAttention.Config(
+                        n_heads=32,
+                        n_kv_heads=4,
+                        head_dim=128,
+                        qk_norm=True,
+                        norm_eps=1e-6,
+                        attn_backend="sdpa",
+                        rope_backend="cos_sin",
+                    ),
+                ),
+                rope=RoPE.Config(
+                    dim=128,
+                    max_seq_len=262144,
+                    theta=1000000.0,
+                    backend="cos_sin",
+                ),
+            )
         case _:
             raise ValueError(f"Unknown model config: {name}")
 
@@ -242,9 +279,10 @@ def main(args):
     model = PiperQwen3Model(config, num_stages=args.pp)
     model.to('cuda')
     
-    # Initialize weights using TorchTitan's method
-    with torch.no_grad():
-        model.init_weights(buffer_device=torch.device("cuda:0"))
+
+    # # Initialize weights using TorchTitan's method
+    # with torch.no_grad():
+    #     model.init_weights(buffer_device=torch.device("cuda:0"))
     
     # Create input tensors
     # Qwen3 forward signature: forward(tokens, attention_masks=None, positions=None)
@@ -257,12 +295,13 @@ def main(args):
 
     # Setup Piper
     piper_setup(
-        model, 
-        torch.optim.Adam, 
-        [x],  # Only tokens, not [x, input_pos] like Mixtral
-        y,
-        schedule,
-        args.naive_gradient_sync,
+        PiperQwen3Model,  # model_class (not model instance)
+        model_args=(config, args.pp),  # arguments to pass to model class
+        optim_fn=torch.optim.Adam,
+        example_inputs=[x],
+        example_outputs=y,
+        schedule=schedule,
+        naive_gradient_sync=args.naive_gradient_sync,
     )
 
     # Send data to actors ahead of time
@@ -279,11 +318,67 @@ def main(args):
     
     print(f"Running {args.iters} timed iterations...")
     iter_times = []
-    for _ in range(args.iters):
+    profile_iter = args.profile_iter
+    
+
+    if profile_iter is not None:
+        os.makedirs("piper_profiling/memory_snapshots", exist_ok=True)
+        os.makedirs("piper_profiling/overlapped", exist_ok=True)
+        os.makedirs("./piper_profiling/tensorboard", exist_ok=True)
+        os.makedirs("./piper_profiling/chrome_traces", exist_ok=True)
+    
+    for iter_idx in range(args.iters):
+        # Enable profiling for the specified iteration
+        if profile_iter is not None and iter_idx == profile_iter:
+            print(f"\n=== Enabling profiling for iteration {iter_idx} ===")
+            ray.get([actor.enable_profiling.remote(True) for actor in actors.values()])
+            ray.get([actor.set_tracing.remote(True) for actor in actors.values()])
+            ray.get([actor.enable_memory_tracing.remote(True) for actor in actors.values()])
+            ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
+            ray.get([actor.clear_trace_data.remote() for actor in actors.values()])
+        
         start = time.perf_counter()
-        piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
+        if profile_iter is not None and iter_idx == profile_iter:
+            with torch.profiler.record_function("piper_exec_iteration"):
+                piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
+        else:
+            piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
+
         end = time.perf_counter()
         iter_times.append(end - start)
+
+        # Collect profiling data and disable profiling after the specified iteration
+        if profile_iter is not None and iter_idx == profile_iter:
+            print(f"\n=== Collecting profiling data for iteration {iter_idx} ===")
+            
+            # Memory profiling
+            snapshot_paths = ray.get([actor.dump_memory_snapshot.remote() for actor in actors.values()])
+            print("\nMemory snapshots:")
+            for rank, path in enumerate(snapshot_paths):
+                if path:
+                    print(f"Rank {rank}: {path}")
+            
+            # Peak memory
+            mem_data_ret = ray.get([actor.get_peak_memory.remote() for actor in actors.values()])
+            print("\nPeak memory usage:")
+            for rank, peak_mem in mem_data_ret:
+                print(f"  Rank {rank}: {peak_mem:.3f} GB")
+            
+            # Timing data
+            trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
+            print("\nTiming data:")
+            for rank, trace_data in trace_data_ret:
+                print(f"\n  Rank {rank}:")
+                for key in trace_data:
+                    all_times = trace_data[key]
+                    if all_times:
+                        print(f"    {key}: {np.mean(all_times):.3f} ± {np.std(all_times):.3f} ms ({len(all_times)} samples)")
+            
+            # Disable profiling
+            print("\n=== Disabling profiling ===")
+            ray.get([actor.enable_profiling.remote(False) for actor in actors.values()])
+            ray.get([actor.set_tracing.remote(False) for actor in actors.values()])
+            ray.get([actor.enable_memory_tracing.remote(False) for actor in actors.values()])
     
     dp_rank = int(os.environ['PIPER_DP_RANK'])
     print(
@@ -304,15 +399,15 @@ def main(args):
                 all_times = trace_data[key]
                 print(f"rank {rank} {key} time= {np.mean(all_times):.3f} ± {np.std(all_times):.3f} ms ({len(all_times)} samples)")
 
-    timeline_filename = f"out/qwen3-pp{args.pp}-dp{args.dp}"
+    timeline_filename = f"timeline/qwen3-pp{args.pp}-dp{args.dp}"
     ray.timeline(timeline_filename)
     print(f"Ray timeline saved to: {timeline_filename}")
 
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Run Qwen3 model with pipeline parallelism')
-    parser.add_argument('--model', choices=['tiny', 'small', 'medium', 'large'], default='small',
-                        help='Model configuration: tiny, small, medium, or large (default: tiny)')
+    parser.add_argument('--model', choices=['tiny', 'small', 'medium', 'large', '30B-A3B'], default='small',
+                        help='Model configuration: tiny, small, medium, large, or 30B-A3B (default: tiny)')
     parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'no-pp'], default='1f1b',
                         help='Schedule type: gpipe, 1f1b, or interleaved-1f1b (default: 1f1b)')
     parser.add_argument('--dp', type=int, default=1,
@@ -321,7 +416,7 @@ def parse_args():
                         help='Number of pipeline parallel degrees (default: 2)')
     parser.add_argument('--batch_size', type=int, default=16,
                         help='Batch size (default: 16)')
-    parser.add_argument('--seq_len', type=int, default=2048,
+    parser.add_argument('--seq_len', type=int, default=1024,
                         help='Sequence length (default: 2048)')
     parser.add_argument('--mbs', type=int, default=4,
                         help='Number of microbatches (default: 4)')
@@ -333,11 +428,17 @@ def parse_args():
                         help='Enable tracing')
     parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
                         help='Enable naive gradient sync')
+    parser.add_argument('--profile_iter', type=int, default=None,
+                        help='Iteration number to profile (0-indexed). If None, no profiling (default: None)')
     return parser.parse_args()
 
 
 if __name__ == "__main__":
-    ray.init(include_dashboard=False, log_to_driver=True, namespace="qwen3", _temp_dir="/m-coriander/coriander/mfris/tmp/ray")
+    ray.init(
+        include_dashboard=False, 
+        log_to_driver=True, 
+        namespace="qwen3", 
+        _temp_dir="/dev/shm/ray")
     args = parse_args()
     piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
     handles = piper_coordinator.run_program.remote(main, args)

From 752de6b2a740d9e3122c1e76a3dac596bc52632d Mon Sep 17 00:00:00 2001
From: Shubham Tiwari <shubhamtiwaribits@gmail.com>
Date: Tue, 10 Mar 2026 20:56:20 +0000
Subject: [PATCH 073/100] moe changes

---
 src/piper_actor.py | 20 +++++++++++++++-----
 test/test_qwen.py  |  8 ++++----
 2 files changed, 19 insertions(+), 9 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index 8e89d44..c5a9e21 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -307,7 +307,7 @@ def start_mem_tracing(self) -> None:
 
     def stop_mem_tracing(self) -> None:
         torch.cuda.memory._dump_snapshot(
-            f"actor{self.global_rank}_memory_snapshot_mb4_gpipe.pickle"
+            f"/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/actor{self.global_rank}_memory_snapshot_mb4_gpipe.pickle"
         )
         self.logger.info(
             f"Saved memory snapshot to actor{self.global_rank}_memory_snapshot_mb4_gpipe.pickle"
@@ -1238,7 +1238,7 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
         if self.profile:
             forward_ctx_manager.__exit__(None, None, None)
             profiler.__exit__(None, None, None)
-            profiler.export_chrome_trace(f"actor{self.global_rank}_fwd{stage_id}mb{mb_idx}_trace.json")
+            profiler.export_chrome_trace(f"/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/chrome_traces/{self.mode}/actor{self.global_rank}_fwd{stage_id}mb{mb_idx}_trace.json")
 
         return 1
 
@@ -1314,7 +1314,7 @@ def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         if self.profile:
             backward_ctx_manager.__exit__(None, None, None)
             profiler.__exit__(None, None, None)
-            profiler.export_chrome_trace(f"actor{self.global_rank}_bwd{stage_id}mb{mb_idx}_trace.json")
+            profiler.export_chrome_trace(f"/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/chrome_traces/{self.mode}/actor{self.global_rank}_bwd{stage_id}mb{mb_idx}_trace.json")
 
         return 1
 
@@ -1428,6 +1428,16 @@ def hook(grad_inputs):
         return 1
 
     def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
+        
+        if self.profile:
+            profiler = torch.profiler.profile(
+                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
+                with_stack=True,
+            )
+            profiler.__enter__()
+            backward_ctx_manager = torch.profiler.record_function(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
+            backward_ctx_manager.__enter__()
+        
         comp_stream = self.comp_stream
 
         self.logger.debug(
@@ -1577,7 +1587,7 @@ def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         if self.profile:
             backward_ctx_manager.__exit__(None, None, None)
             profiler.__exit__(None, None, None)
-            profiler.export_chrome_trace(f"actor{self.global_rank}_bwd{stage_id}mb{mb_idx}_trace.json")
+            profiler.export_chrome_trace(f"/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/chrome_traces/{self.mode}/actor{self.global_rank}_bwd{stage_id}mb{mb_idx}_trace.json")
 
         return 1
 
@@ -2257,7 +2267,7 @@ def run_bwd():
             backward_ctx_manager.__exit__(None, None, None)
             profiler.__exit__(None, None, None)
             profiler.export_chrome_trace(
-                f"actor{self.global_rank}_"
+                f"/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/chrome_traces/{self.mode}/actor{self.global_rank}_"
                 f"fwd{fwd_stage_id}mb{fwd_mb_idx}_"
                 f"bwd{bwd_stage_id}mb{bwd_mb_idx}_trace.json")
 
diff --git a/test/test_qwen.py b/test/test_qwen.py
index a1ea07f..cf06e9a 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -322,10 +322,10 @@ def main(args):
     
 
     if profile_iter is not None:
-        os.makedirs("piper_profiling/memory_snapshots", exist_ok=True)
-        os.makedirs("piper_profiling/overlapped", exist_ok=True)
-        os.makedirs("./piper_profiling/tensorboard", exist_ok=True)
-        os.makedirs("./piper_profiling/chrome_traces", exist_ok=True)
+        os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/memory_snapshots", exist_ok=True)
+        os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/overlapped", exist_ok=True)
+        os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/tensorboard", exist_ok=True)
+        os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/chrome_traces", exist_ok=True)
     
     for iter_idx in range(args.iters):
         # Enable profiling for the specified iteration

From 3de94cc89f7dc20e623de1c3d12d72bfb606227a Mon Sep 17 00:00:00 2001
From: Shubham Tiwari <shubhamtiwaribits@gmail.com>
Date: Tue, 10 Mar 2026 22:33:22 +0000
Subject: [PATCH 074/100] moe changes

---
 src/piper_actor.py       | 46 ++++++++++++++++++++++++++++--
 src/piper_coordinator.py |  2 +-
 test/test_qwen.py        | 60 ++++++++++++++++++++++++++++++++--------
 3 files changed, 92 insertions(+), 16 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index b86480e..4c6cc88 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -1103,6 +1103,16 @@ def _forward(self, stage_id: int, mb_idx: int, *deps):
         return ret
 
     def _forward_impl(self, stage_id: int, mb_idx: int, *deps):
+
+        if self.profile:
+            profiler = torch.profiler.profile(
+                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
+                with_stack=True,
+            )
+            profiler.__enter__()
+            forward_ctx_manager = torch.profiler.record_function(f"forward_stage_{stage_id}_mb_{mb_idx}")
+            forward_ctx_manager.__enter__()
+
         self._label_task(f"{stage_id}:{mb_idx}")
         if self.mode == "sequential":
             comp_stream = self.comp_stream
@@ -1212,6 +1222,16 @@ def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         return ret
 
     def _backward_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
+
+        if self.profile:
+            profiler = torch.profiler.profile(
+                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
+                with_stack=True,
+            )
+            profiler.__enter__()
+            backward_ctx_manager = torch.profiler.record_function(f"backward_stage_{stage_id}_mb_{mb_idx}")
+            backward_ctx_manager.__enter__()
+
         self._label_task(f"{stage_id}:{mb_idx}")
         if self.mode == "sequential":
             comp_stream = self.comp_stream
@@ -1265,7 +1285,7 @@ def _backward_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
                 loss.backward()
             self._stop_timing(comp_stream, "backward_comp")
 
-            # self.loss.append(loss.item())
+        # self.loss.append(loss.item())
         # torch.cuda.nvtx.range_pop()
 
         # Clear output activation after backward pass
@@ -1277,7 +1297,7 @@ def _backward_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
 
-        torch.cuda.synchronize()
+        comp_stream.synchronize()
 
         if self.profile:
             backward_ctx_manager.__exit__(None, None, None)
@@ -1704,6 +1724,16 @@ def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
         return ret
 
     def _backward_weight_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
+        
+        if self.profile:
+            profiler = torch.profiler.profile(
+                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
+                with_stack=True,
+            )
+            profiler.__enter__()
+            backward_ctx_manager = torch.profiler.record_function(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
+            backward_ctx_manager.__enter__()
+
         self._label_task(f"{stage_id}:{mb_idx}")
         comp_stream = self.comp_stream
 
@@ -2112,6 +2142,16 @@ def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: in
         return ret
 
     def _forward_backward_impl(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: int, bwd_mb_idx: int, *deps, loss_fn=None):
+        
+        if self.profile:
+            profiler = torch.profiler.profile(
+                activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
+                with_stack=True,
+            )
+            profiler.__enter__()
+            forward_backward_ctx_manager = torch.profiler.record_function(f"forward_backward_stage_{fwd_stage_id}_mb_{fwd_mb_idx}_backward_stage_{bwd_stage_id}_mb_{bwd_mb_idx}")
+            forward_backward_ctx_manager.__enter__()
+
         self._label_task(f"{fwd_stage_id}:{fwd_mb_idx}|{bwd_stage_id}:{bwd_mb_idx}")
         if self.mode == "sequential":
             fwd_comp_stream = self.comp_stream
@@ -2283,7 +2323,7 @@ def run_bwd():
             self.bwd_a2a_counter = 0
 
         if self.profile:
-            backward_ctx_manager.__exit__(None, None, None)
+            forward_backward_ctx_manager.__exit__(None, None, None)
             profiler.__exit__(None, None, None)
             profiler.export_chrome_trace(
                 f"/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/chrome_traces/{self.mode}/actor{self.global_rank}_"
diff --git a/src/piper_coordinator.py b/src/piper_coordinator.py
index 6504330..7d31f00 100644
--- a/src/piper_coordinator.py
+++ b/src/piper_coordinator.py
@@ -7,7 +7,7 @@
 from .piper_utils import create_logger, LOG_LEVEL
 
 
-@ray.remote(num_gpus=0.6)
+@ray.remote(num_gpus=1.0)
 def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, master_addr, master_port, training_func: Callable, *args, **kwargs):
     logger = create_logger("piper_coordinator", LOG_LEVEL)
     logger.debug(f"Running DP rank {dp_rank+1} of {dp_degree}")
diff --git a/test/test_qwen.py b/test/test_qwen.py
index cf06e9a..5b470b7 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -5,11 +5,17 @@
 import time
 import os
 import numpy as np
+import json
 
 from src.piper_coordinator import PiperProgramCoordinator
-from src.piper_compile import piper_setup
+from src.piper_compile import piper_setup, piper_shutdown
 from src.piper_exec import piper_exec
 from src.piper_utils import piper_metadata
+from ray.util.placement_group import (
+    placement_group,
+    placement_group_table,
+    remove_placement_group,
+)
 
 from .models.qwen3 import PiperQwen3Model
 from torchtitan.models.qwen3 import Qwen3Model, Qwen3TransformerBlock
@@ -251,7 +257,7 @@ def create_qwen3_config(name: str) -> Qwen3Model.Config:
             raise ValueError(f"Unknown model config: {name}")
 
 
-def main(args):
+def main(args, pg):
     world_size = args.dp * args.pp
     mbs = args.mbs
     batch_size = args.batch_size
@@ -295,13 +301,15 @@ def main(args):
 
     # Setup Piper
     piper_setup(
-        PiperQwen3Model,  # model_class (not model instance)
-        model_args=(config, args.pp),  # arguments to pass to model class
+        PiperQwen3Model,
+        model_args=(config, args.pp),
         optim_fn=torch.optim.Adam,
         example_inputs=[x],
         example_outputs=y,
         schedule=schedule,
         naive_gradient_sync=args.naive_gradient_sync,
+        activation_checkpointing=args.activation_checkpointing,
+        pg=pg,
     )
 
     # Send data to actors ahead of time
@@ -399,9 +407,30 @@ def main(args):
                 all_times = trace_data[key]
                 print(f"rank {rank} {key} time= {np.mean(all_times):.3f} ± {np.std(all_times):.3f} ms ({len(all_times)} samples)")
 
-    timeline_filename = f"timeline/qwen3-pp{args.pp}-dp{args.dp}"
+
+
+    if args.naive_gradient_sync:
+        suffix = "-naive-sync"
+    else:
+        suffix = ""
+    # Collect task_id -> label mappings from all actors
+    all_task_labels = {}
+    actors = piper_metadata.actors
+    for labels in ray.get([actor.get_task_labels.remote() for actor in actors.values()]):
+        all_task_labels.update(labels)
+
+
+    os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/timeline", exist_ok=True)
+    timeline_filename = f"/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/timeline/qwen3-pp{args.pp}-dp{args.dp}-{args.schedule}{suffix}.json"
     ray.timeline(timeline_filename)
+    labels_filename = timeline_filename.replace(".json", "-labels.json")
+    with open(labels_filename, "w") as f:
+        import json
+        json.dump(all_task_labels, f)
     print(f"Ray timeline saved to: {timeline_filename}")
+    print(f"Task labels saved to: {labels_filename}")
+
+    piper_shutdown()
 
 
 def parse_args():
@@ -428,19 +457,26 @@ def parse_args():
                         help='Enable tracing')
     parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
                         help='Enable naive gradient sync')
-    parser.add_argument('--profile_iter', type=int, default=None,
+    parser.add_argument('--profile_iter', type=int, default=5,
                         help='Iteration number to profile (0-indexed). If None, no profiling (default: None)')
+    parser.add_argument('--activation_checkpointing', action='store_true', default=True,
+                        help='Enable activation checkpointing')
     return parser.parse_args()
 
 
 if __name__ == "__main__":
-    ray.init(
-        include_dashboard=False, 
-        log_to_driver=True, 
-        namespace="qwen3", 
-        _temp_dir="/dev/shm/ray")
     args = parse_args()
+    ray.init(
+        include_dashboard=False,
+        log_to_driver=True,
+        namespace="qwen3",
+        _temp_dir="/dev/shm/ray"
+    )
+    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp)
+    ray.get(pg.ready(), timeout=10)
+    print(placement_group_table(pg))
     piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
-    handles = piper_coordinator.run_program.remote(main, args)
+    handles = piper_coordinator.run_program.remote(main, args, pg)
     ray.get(handles)
+    time.sleep(3)
     ray.shutdown()
\ No newline at end of file

From 77b4d77a768a946d858e9897d9712d28182c53d2 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Wed, 11 Mar 2026 02:33:01 +0000
Subject: [PATCH 075/100] enable bf16 support

---
 src/piper_compile.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/piper_compile.py b/src/piper_compile.py
index 9e75579..9733e12 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -29,6 +29,7 @@ def piper_setup(
     activation_checkpointing=False,
     mode="sequential",
     pg=None,
+    model_dtype=None,
 ):
     """
     Compile a model with the piper backend.
@@ -59,6 +60,8 @@ def piper_setup(
     # Build the model directly on meta device
     with torch.device("meta"):
         model = model_class(*model_args, **model_kwargs)
+    if model_dtype is not None:
+        model = model.to(model_dtype)
 
     num_params = sum(p.numel() for p in model.parameters())
     param_size_mb = num_params * 4 / (1024**3)  # float32

From da9c6200a1e14f473d57e208cf788462acde032c Mon Sep 17 00:00:00 2001
From: Shubham Tiwari <shubhamtiwaribits@gmail.com>
Date: Wed, 11 Mar 2026 05:20:07 +0000
Subject: [PATCH 076/100] enable bf16

---
 src/piper_actor.py   |   9 ++++
 src/piper_compile.py |  14 +++++-
 test/models/qwen3.py |  24 ++++++++--
 test/test_qwen.py    | 110 +++++++++++++++++++++++++++----------------
 4 files changed, 111 insertions(+), 46 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index 4c6cc88..ab22988 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -25,6 +25,7 @@
 
 logger = create_logger("piper_actor", LOG_LEVEL)
 
+torch.autograd.set_detect_anomaly(True, check_nan=False)
 
 def _get_rank(pp_rank, dp_rank, pp_degree):
     return pp_rank + dp_rank * pp_degree
@@ -194,6 +195,7 @@ def __init__(
 
         piper_metadata.actor_self = self
 
+        self.profile = False
 
         # In PiperActor.__init__, add:
         self.pytorch_profiler = None
@@ -1285,6 +1287,13 @@ def _backward_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
                 loss.backward()
             self._stop_timing(comp_stream, "backward_comp")
 
+
+        # gm = self.graph_modules.get(stage_id)
+        # if gm is not None:
+        #     for m in gm.modules():
+        #         if hasattr(m, 'flush_tokens_per_expert'):
+        #             m.flush_tokens_per_expert()
+
         # self.loss.append(loss.item())
         # torch.cuda.nvtx.range_pop()
 
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 9e75579..a69245c 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -21,6 +21,7 @@ def piper_setup(
     model_class,
     model_args=(),
     model_kwargs={},
+    model_dtype=torch.bfloat16,
     optim_fn=None,
     example_inputs=None,
     example_outputs=None,
@@ -59,10 +60,19 @@ def piper_setup(
     # Build the model directly on meta device
     with torch.device("meta"):
         model = model_class(*model_args, **model_kwargs)
+    
+    if model_dtype is not None:
+        model = model.to(model_dtype)
 
     num_params = sum(p.numel() for p in model.parameters())
-    param_size_mb = num_params * 4 / (1024**3)  # float32
-    print(f"Model size: {num_params/(1e6):.0f} M parameters ({param_size_mb:.2f} GB)")
+
+    if model_dtype == torch.bfloat16:
+        param_size_mb = num_params * 2 / (1024**3)  # bfloat16
+    elif model_dtype == torch.float32:
+        param_size_mb = num_params * 4 / (1024**3)  # float32
+    else:
+        raise ValueError(f"Unsupported model dtype: {model_dtype}")
+    print(f"Model size: {num_params/(1e6):.0f} M parameters ({param_size_mb:.2f} GB), dtype: {model_dtype}")
     
     compiled = torch.compile(model, backend=piper)
 
diff --git a/test/models/qwen3.py b/test/models/qwen3.py
index f473984..6c69bc3 100644
--- a/test/models/qwen3.py
+++ b/test/models/qwen3.py
@@ -14,7 +14,12 @@
 from torch.utils.checkpoint import checkpoint
 
 class AnnotatedMoE(MoE):
-    
+    def flush_tokens_per_expert(self) -> None:
+        if hasattr(self, '_tokens_per_expert_acc'):
+            with torch.no_grad():
+                self.tokens_per_expert.add_(self._tokens_per_expert_acc)
+                self._tokens_per_expert_acc.zero_()
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         bs, slen, dim = x.shape
         x = x.view(-1, dim)
@@ -25,9 +30,20 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             num_tokens_per_expert,
         ) = self.router(x, self.expert_bias)
         
-        with torch.no_grad():
-            self.tokens_per_expert.add_(num_tokens_per_expert)
-        
+        # with torch.no_grad():
+        #     # self.tokens_per_expert.add_(num_tokens_per_expert)
+        #     self.tokens_per_expert.add_(num_tokens_per_expert.detach().to(self.tokens_per_expert.device))
+        
+        # with torch.no_grad():
+        #     # Don't write to buffer in forward (breaks use_reentrant=False checkpoint).
+        #     # Accumulate; Piper must call flush_tokens_per_expert() after backward.
+        #     acc = num_tokens_per_expert.detach().to(
+        #         dtype=self.tokens_per_expert.dtype, device=self.tokens_per_expert.device
+        #     )
+        #     if not hasattr(self, '_tokens_per_expert_acc'):
+        #         self._tokens_per_expert_acc = torch.zeros_like(self.tokens_per_expert)
+        #     self._tokens_per_expert_acc = self._tokens_per_expert_acc + acc
+
         (
             top_scores_experts_sorted,
             token_indices_experts_sorted,
diff --git a/test/test_qwen.py b/test/test_qwen.py
index 5b470b7..73b5eff 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -253,6 +253,36 @@ def create_qwen3_config(name: str) -> Qwen3Model.Config:
                     backend="cos_sin",
                 ),
             )
+
+        case '72B':
+            return Qwen3Model.Config(
+                vocab_size=152064,
+                dim=8192,
+                n_layers=80,
+                norm_eps=1e-5,
+                enable_weight_tying=False,
+                layer=Qwen3TransformerBlock.Config(
+                    depth_init=True,
+                    moe_enabled=False,
+                    norm_eps=1e-5,
+                    feed_forward=FeedForward.Config(hidden_dim=29568),
+                    attention=GQAttention.Config(
+                        n_heads=64,
+                        n_kv_heads=8,
+                        head_dim=128,
+                        qk_norm=True,
+                        norm_eps=1e-5,
+                        attn_backend="sdpa",
+                        rope_backend="cos_sin",
+                    ),
+                ),
+                rope=RoPE.Config(
+                    dim=128,
+                    max_seq_len=131072,
+                    theta=1000000.0,
+                    backend="cos_sin",
+                ),
+            )
         case _:
             raise ValueError(f"Unknown model config: {name}")
 
@@ -329,21 +359,21 @@ def main(args, pg):
     profile_iter = args.profile_iter
     
 
-    if profile_iter is not None:
-        os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/memory_snapshots", exist_ok=True)
-        os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/overlapped", exist_ok=True)
-        os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/tensorboard", exist_ok=True)
-        os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/chrome_traces", exist_ok=True)
+    # if profile_iter is not None:
+    #     os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/memory_snapshots", exist_ok=True)
+    #     os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/overlapped", exist_ok=True)
+    #     os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/tensorboard", exist_ok=True)
+    #     os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/chrome_traces", exist_ok=True)
     
     for iter_idx in range(args.iters):
         # Enable profiling for the specified iteration
         if profile_iter is not None and iter_idx == profile_iter:
             print(f"\n=== Enabling profiling for iteration {iter_idx} ===")
-            ray.get([actor.enable_profiling.remote(True) for actor in actors.values()])
-            ray.get([actor.set_tracing.remote(True) for actor in actors.values()])
-            ray.get([actor.enable_memory_tracing.remote(True) for actor in actors.values()])
-            ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
-            ray.get([actor.clear_trace_data.remote() for actor in actors.values()])
+            # ray.get([actor.enable_profiling.remote(True) for actor in actors.values()])
+            # ray.get([actor.set_tracing.remote(True) for actor in actors.values()])
+            # ray.get([actor.enable_memory_tracing.remote(True) for actor in actors.values()])
+            # ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
+            # ray.get([actor.clear_trace_data.remote() for actor in actors.values()])
         
         start = time.perf_counter()
         if profile_iter is not None and iter_idx == profile_iter:
@@ -356,37 +386,37 @@ def main(args, pg):
         iter_times.append(end - start)
 
         # Collect profiling data and disable profiling after the specified iteration
-        if profile_iter is not None and iter_idx == profile_iter:
-            print(f"\n=== Collecting profiling data for iteration {iter_idx} ===")
+        # if profile_iter is not None and iter_idx == profile_iter:
+        #     print(f"\n=== Collecting profiling data for iteration {iter_idx} ===")
             
-            # Memory profiling
-            snapshot_paths = ray.get([actor.dump_memory_snapshot.remote() for actor in actors.values()])
-            print("\nMemory snapshots:")
-            for rank, path in enumerate(snapshot_paths):
-                if path:
-                    print(f"Rank {rank}: {path}")
+            # # Memory profiling
+            # snapshot_paths = ray.get([actor.dump_memory_snapshot.remote() for actor in actors.values()])
+            # print("\nMemory snapshots:")
+            # for rank, path in enumerate(snapshot_paths):
+            #     if path:
+            #         print(f"Rank {rank}: {path}")
             
-            # Peak memory
-            mem_data_ret = ray.get([actor.get_peak_memory.remote() for actor in actors.values()])
-            print("\nPeak memory usage:")
-            for rank, peak_mem in mem_data_ret:
-                print(f"  Rank {rank}: {peak_mem:.3f} GB")
+            # # Peak memory
+            # mem_data_ret = ray.get([actor.get_peak_memory.remote() for actor in actors.values()])
+            # print("\nPeak memory usage:")
+            # for rank, peak_mem in mem_data_ret:
+            #     print(f"  Rank {rank}: {peak_mem:.3f} GB")
             
-            # Timing data
-            trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
-            print("\nTiming data:")
-            for rank, trace_data in trace_data_ret:
-                print(f"\n  Rank {rank}:")
-                for key in trace_data:
-                    all_times = trace_data[key]
-                    if all_times:
-                        print(f"    {key}: {np.mean(all_times):.3f} ± {np.std(all_times):.3f} ms ({len(all_times)} samples)")
+            # # Timing data
+            # trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
+            # print("\nTiming data:")
+            # for rank, trace_data in trace_data_ret:
+            #     print(f"\n  Rank {rank}:")
+            #     for key in trace_data:
+            #         all_times = trace_data[key]
+            #         if all_times:
+            #             print(f"    {key}: {np.mean(all_times):.3f} ± {np.std(all_times):.3f} ms ({len(all_times)} samples)")
             
-            # Disable profiling
-            print("\n=== Disabling profiling ===")
-            ray.get([actor.enable_profiling.remote(False) for actor in actors.values()])
-            ray.get([actor.set_tracing.remote(False) for actor in actors.values()])
-            ray.get([actor.enable_memory_tracing.remote(False) for actor in actors.values()])
+            # # Disable profiling
+            # print("\n=== Disabling profiling ===")
+            # ray.get([actor.enable_profiling.remote(False) for actor in actors.values()])
+            # ray.get([actor.set_tracing.remote(False) for actor in actors.values()])
+            # ray.get([actor.enable_memory_tracing.remote(False) for actor in actors.values()])
     
     dp_rank = int(os.environ['PIPER_DP_RANK'])
     print(
@@ -435,8 +465,8 @@ def main(args, pg):
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Run Qwen3 model with pipeline parallelism')
-    parser.add_argument('--model', choices=['tiny', 'small', 'medium', 'large', '30B-A3B'], default='small',
-                        help='Model configuration: tiny, small, medium, large, or 30B-A3B (default: tiny)')
+    parser.add_argument('--model', choices=['tiny', 'small', 'medium', 'large', '30B-A3B', '72B'], default='small',
+                        help='Model configuration: tiny, small, medium, large, 30B-A3B, or 72B (default: tiny)')
     parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'no-pp'], default='1f1b',
                         help='Schedule type: gpipe, 1f1b, or interleaved-1f1b (default: 1f1b)')
     parser.add_argument('--dp', type=int, default=1,
@@ -459,7 +489,7 @@ def parse_args():
                         help='Enable naive gradient sync')
     parser.add_argument('--profile_iter', type=int, default=5,
                         help='Iteration number to profile (0-indexed). If None, no profiling (default: None)')
-    parser.add_argument('--activation_checkpointing', action='store_true', default=True,
+    parser.add_argument('--activation_checkpointing', action='store_true', default=False,
                         help='Enable activation checkpointing')
     return parser.parse_args()
 

From 3a39f53512a223f01a3bb5b4178ae4a0a778e8de Mon Sep 17 00:00:00 2001
From: Andy Ruan <andyubryh@gmail.com>
Date: Wed, 11 Mar 2026 17:24:40 +0000
Subject: [PATCH 077/100] (WIP) split ZeRO1 and ZeRO2

Signed-off-by: Andy Ruan <andyubryh@gmail.com>
---
 src/piper_actor.py   |  11 ++---
 src/piper_compile.py |   3 +-
 src/piper_zero1.py   | 102 +++++++++++++++++++++++++++++--------------
 test/test_llama.py   |   4 +-
 4 files changed, 80 insertions(+), 40 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index 5b82ff3..88b4fdc 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -381,8 +381,8 @@ def move_to_device(idx, arg):
         params = [forward_args[i] for i in trainable_param_idxs]
 
         if self.zero_stage >= 1 and self.dp_degree > 1:
-            # ZeRO-1 path: gradient hooks and per-bucket optimizers are managed
-            # inside ZeROOneState; skip the per-param DDP hook setup.
+            # ZeRO path (stage 1 or 2): gradient hooks and per-bucket optimizers
+            # are managed inside ZeROOneState; skip per-param DDP hook setup.
             params_in_fwd_order = _get_param_forward_order(gm, trainable_param_idxs, params)
             if stage_id not in self.zero1_states:
                 self.zero1_states[stage_id] = ZeROOneState(
@@ -396,6 +396,7 @@ def move_to_device(idx, arg):
                     optim_class=self.optim_class,
                     comm_stream=self.comm_stream,
                     comp_stream=self.comp_stream,
+                    zero_stage=self.zero_stage,
                 )
             else:
                 # A second graph loaded onto the same stage (shouldn't normally
@@ -1185,8 +1186,8 @@ def _update(self, *deps):
         self.logger.debug(f"Actor {self.global_rank} waiting for backward sync events")
 
         if self.zero_stage >= 1 and self.dp_degree > 1:
-            # ZeRO-1 path: hooks launched reduce_scatters during backward;
-            # finalize_step completes the pipelined reduce_scatter → optimizer →
+            # ZeRO path: hooks launch bucketed async gradient-sync collectives
+            # during backward; finalize_step completes grad-sync → optimizer →
             # all_gather sequence for every bucket on every stage.
             self._start_timing(self.comm_stream, "backward_sync")
             for zero1_state in self.zero1_states.values():
@@ -1218,4 +1219,4 @@ def _update(self, *deps):
 
         self.reset_p2p_states()
 
-        return losses
\ No newline at end of file
+        return losses
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 6f7bc60..c791b5a 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -141,7 +141,8 @@ def piper_setup(
             pipelined per-param hooks. Ignored when ``zero_stage >= 1``.
         zero_stage: ZeRO optimisation stage.
             0 = disabled (standard DDP).
-            1 = optimizer state partitioning with bucketed reduce-scatter.
+            1 = strict ZeRO-1 (optimizer sharding + bucketed all-reduce grads).
+            2 = strict ZeRO-2 (optimizer sharding + bucketed reduce-scatter grads).
     """
 
     stage_to_device = schedule.stage_to_device()
diff --git a/src/piper_zero1.py b/src/piper_zero1.py
index 493c99b..d702d89 100644
--- a/src/piper_zero1.py
+++ b/src/piper_zero1.py
@@ -1,5 +1,5 @@
 """
-ZeRO-1 with bucketed reduce-scatter for Piper.
+Bucketed ZeRO optimizer stages for Piper.
 
 Each trainable parameter is divided into dp_degree contiguous shards; each DP rank
 owns and optimises one shard. Parameters are grouped into 25 MB gradient-sync buckets
@@ -7,7 +7,8 @@
 overlap between communication and backward computation.
 
 Per-step communication pipeline (per bucket, pipelined across buckets):
-  1. reduce_scatter gradient flat buffer → local grad shard   (async, on comm_stream)
+  1. stage-1: all_reduce flat gradients (async, on comm_stream), or
+     stage-2: reduce_scatter gradient flat buffer → local grad shard (async)
   2. Optimizer step on local param shard                      (comp_stream)
   3. all_gather local param shard → flat param buffer         (async, on comm_stream)
   4. Scatter flat param buffer back to p.data
@@ -123,6 +124,7 @@ def __init__(
         dtype: torch.dtype,
         optim_class: Callable[[list[Parameter]], Optimizer],
         num_mbs: int,
+        zero_stage: int,
     ) -> None:
         self.bucket_id = bucket_id
         self.params = params
@@ -130,6 +132,7 @@ def __init__(
         self.dp_degree = dp_degree
         self.dp_group = dp_group
         self.num_mbs = num_mbs
+        self.zero_stage = zero_stage
 
         # ------------------------------------------------------------------
         # Flat buffer layout: [p0 elems | p1 elems | ... | padding]
@@ -181,7 +184,7 @@ def __init__(
         self._ready_param_count: int = 0
 
         # Async collective handles (None until launched).
-        self.reduce_scatter_handle: Optional[dist.Work] = None
+        self.grad_sync_handle: Optional[dist.Work] = None
         self.all_gather_handle: Optional[dist.Work] = None
 
         logger.debug(
@@ -211,36 +214,59 @@ def on_grad_accumulated(self, param: Parameter) -> bool:
     # Communication and computation
     # ------------------------------------------------------------------
 
-    def launch_reduce_scatter(
+    def launch_grad_sync(
         self,
         comm_stream: torch.cuda.Stream,
-        comp_stream: torch.cuda.Stream,
+        producer_stream: torch.cuda.Stream,
     ) -> dist.Work:
         """
         Copy accumulated per-param gradients into the flat gradient buffer, then
-        launch an async ``reduce_scatter_tensor`` on *comm_stream*.
+        launch stage-specific async gradient synchronisation on *comm_stream*.
 
-        After completion, ``self.local_grad_shard`` holds the averaged gradient
-        for this rank's shard of the bucket.
+        - stage 1: ``all_reduce`` on ``flat_grad_buf`` (full grads preserved)
+        - stage 2: ``reduce_scatter_tensor`` into ``local_grad_shard``
 
         Returns the ``dist.Work`` handle for the caller to wait on.
         """
-        comm_stream.wait_stream(comp_stream)
+        comm_stream.wait_stream(producer_stream)
         with torch.cuda.stream(comm_stream):
             for p, off in zip(self.params, self.param_offsets):
                 if p.grad is not None:
                     self.flat_grad_buf[off : off + p.numel()].copy_(p.grad.view(-1))
                 else:
                     self.flat_grad_buf[off : off + p.numel()].zero_()
-            handle = dist.reduce_scatter_tensor(
-                self.local_grad_shard,
-                self.flat_grad_buf,
-                op=dist.ReduceOp.AVG,
-                group=self.dp_group,
-                async_op=True,
-            )
+            if self.zero_stage == 1:
+                handle = dist.all_reduce(
+                    self.flat_grad_buf,
+                    op=dist.ReduceOp.AVG,
+                    group=self.dp_group,
+                    async_op=True,
+                )
+            else:
+                handle = dist.reduce_scatter_tensor(
+                    self.local_grad_shard,
+                    self.flat_grad_buf,
+                    op=dist.ReduceOp.AVG,
+                    group=self.dp_group,
+                    async_op=True,
+                )
+
+        # Bucket gradients are fully materialized in flat_grad_buf; free per-param
+        # gradients as soon as bucket sync is launched.
+        for p in self.params:
+            p.grad = None
+
         return handle
 
+    def prepare_local_grad_shard(self) -> None:
+        """Prepare local shard gradient after grad sync and free full flat grads."""
+        if self.zero_stage == 1:
+            self.local_grad_shard.copy_(
+                self.flat_grad_buf[self.shard_start : self.shard_end]
+            )
+        # In stage-2 local_grad_shard is already the communication output.
+        self.flat_grad_buf.zero_()
+
     def optimizer_step(self) -> None:
         """
         Attach the reduce-scattered gradient to ``local_param_shard`` and run
@@ -267,7 +293,7 @@ def launch_all_gather(
         Must be called after ``optimizer_step()``.
         Returns the ``dist.Work`` handle.
         """
-        comp_stream.wait_stream(comm_stream)
+        comm_stream.wait_stream(comp_stream)
         with torch.cuda.stream(comm_stream):
             handle = dist.all_gather_into_tensor(
                 self.flat_param_buf,
@@ -293,9 +319,8 @@ def reset_step_state(self) -> None:
         for pid in self._mb_accumulated:
             self._mb_accumulated[pid] = 0
         self._ready_param_count = 0
-        self.reduce_scatter_handle = None
+        self.grad_sync_handle = None
         self.all_gather_handle = None
-        self.flat_grad_buf.zero_()
 
     @property
     def size_bytes(self) -> int:
@@ -347,8 +372,13 @@ def __init__(
         comm_stream: torch.cuda.Stream,
         comp_stream: torch.cuda.Stream,
         bucket_size_bytes: int = BUCKET_SIZE_BYTES_DEFAULT,
+        zero_stage: int = 1,
     ) -> None:
+        if zero_stage not in (1, 2):
+            raise ValueError(f"ZeRO state must be 1 or 2, got {zero_stage}")
+
         self.all_params = all_params
+        self.zero_stage = zero_stage
         self.dp_rank = dp_rank
         self.dp_degree = dp_degree
         self.dp_group = dp_group
@@ -365,6 +395,7 @@ def __init__(
             num_mbs=num_mbs,
             optim_class=optim_class,
             bucket_size_bytes=bucket_size_bytes,
+            zero_stage=zero_stage,
         )
 
         # Fast lookup: parameter id → owning bucket.
@@ -378,7 +409,8 @@ def __init__(
 
         logger.debug(
             f"ZeROOneState: {len(self._buckets)} buckets, "
-            f"{len(all_params)} params, dp_rank={dp_rank}/{dp_degree}"
+            f"{len(all_params)} params, dp_rank={dp_rank}/{dp_degree}, "
+            f"zero_stage={zero_stage}"
         )
 
     # ------------------------------------------------------------------
@@ -398,11 +430,12 @@ def _make_hook(self, param: Parameter) -> Callable[[Parameter], None]:
         def _hook(_param: Parameter) -> None:
             if bucket.on_grad_accumulated(_param):
                 # All params in bucket have full gradients: launch reduce_scatter.
-                bucket.reduce_scatter_handle = bucket.launch_reduce_scatter(
-                    self._comm_stream, self._comp_stream
+                bucket.grad_sync_handle = bucket.launch_grad_sync(
+                    self._comm_stream,
+                    torch.cuda.current_stream(),
                 )
                 logger.debug(
-                    f"Launched reduce_scatter for bucket {bucket.bucket_id}"
+                    f"Launched grad sync for bucket {bucket.bucket_id}"
                 )
 
         return _hook
@@ -426,18 +459,21 @@ def finalize_step(
           For bucket k: wait rs_k → optim_k → launch ag_k
           The all_gather of bucket k-1 overlaps with optim_k and launch_ag_k.
         """
-        # Ensure every bucket has a reduce_scatter in flight (handles buckets
+        # Ensure every bucket has gradient sync in flight (handles buckets
         # whose hooks were never triggered, e.g. params with no gradient).
         for bucket in self._buckets:
-            if bucket.reduce_scatter_handle is None:
-                bucket.reduce_scatter_handle = bucket.launch_reduce_scatter(
-                    comm_stream, comp_stream
+            if bucket.grad_sync_handle is None:
+                bucket.grad_sync_handle = bucket.launch_grad_sync(
+                    comm_stream,
+                    torch.cuda.current_stream(),
                 )
 
         # Pipeline: per-bucket wait → optimizer → async all_gather.
         for bucket in self._buckets:
-            bucket.reduce_scatter_handle.wait()
-            bucket.optimizer_step()
+            bucket.grad_sync_handle.wait()
+            bucket.prepare_local_grad_shard()
+            with torch.cuda.stream(comp_stream):
+                bucket.optimizer_step()
             bucket.all_gather_handle = bucket.launch_all_gather(
                 comm_stream, comp_stream
             )
@@ -447,7 +483,7 @@ def finalize_step(
             bucket.all_gather_handle.wait()
             bucket.copy_flat_to_params()
 
-        # Zero all parameter gradients and reset per-step state.
+        # Ensure no stale parameter gradients remain.
         for p in self.all_params:
             p.grad = None
         for bucket in self._buckets:
@@ -466,6 +502,7 @@ def bucket_sizes(self) -> list[int]:
     def __repr__(self) -> str:
         return (
             f"ZeROOneState(dp_rank={self.dp_rank}/{self.dp_degree}, "
+            f"zero_stage={self.zero_stage}, "
             f"buckets={self.num_buckets()}, "
             f"params={len(self.all_params)})"
         )
@@ -484,6 +521,7 @@ def _build_buckets(
     num_mbs: int,
     optim_class: Callable[[list[Parameter]], Optimizer],
     bucket_size_bytes: int,
+    zero_stage: int,
 ) -> list[ZeROOneBucket]:
     """
     Fill buckets greedily: add parameters in *params_in_bwd_order* until a
@@ -504,7 +542,7 @@ def _build_buckets(
                 ZeROOneBucket(
                     len(buckets), current,
                     dp_rank, dp_degree, dp_group,
-                    device, dtype, optim_class, num_mbs,
+                    device, dtype, optim_class, num_mbs, zero_stage,
                 )
             )
             current = []
@@ -518,7 +556,7 @@ def _build_buckets(
             ZeROOneBucket(
                 len(buckets), current,
                 dp_rank, dp_degree, dp_group,
-                device, dtype, optim_class, num_mbs,
+                device, dtype, optim_class, num_mbs, zero_stage,
             )
         )
 
diff --git a/test/test_llama.py b/test/test_llama.py
index 9c4e069..19a55c9 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -176,8 +176,8 @@ def parse_args():
                         help='Enable tracing')
     parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
                         help='Enable naive gradient sync')
-    parser.add_argument('--zero_stage', type=int, choices=[0, 1], default=0,
-                        help='ZeRO optimization stage (0: disabled, 1: ZeRO-1)')
+    parser.add_argument('--zero_stage', type=int, choices=[0, 1, 2], default=0,
+                        help='ZeRO optimization stage (0: disabled, 1: strict ZeRO-1, 2: strict ZeRO-2)')
     return parser.parse_args()
 
 

From 2c85e9caf80ea9120b67dc7a0d86ae99d83a5530 Mon Sep 17 00:00:00 2001
From: Andy Ruan <andyubryh@gmail.com>
Date: Wed, 11 Mar 2026 17:39:56 +0000
Subject: [PATCH 078/100] run ZeRO1 and ZeRO2 on LLAMA_DEBUG

Signed-off-by: Andy Ruan <andyubryh@gmail.com>
---
 logs/piper_zero1_20260311_173310.log | 61 ++++++++++++++++++++++++++++
 logs/piper_zero2_20260311_173556.log | 61 ++++++++++++++++++++++++++++
 run_zero1.sh                         |  4 +-
 run_zero2.sh                         | 29 +++++++++++++
 4 files changed, 153 insertions(+), 2 deletions(-)
 create mode 100644 logs/piper_zero1_20260311_173310.log
 create mode 100644 logs/piper_zero2_20260311_173556.log
 create mode 100755 run_zero2.sh

diff --git a/logs/piper_zero1_20260311_173310.log b/logs/piper_zero1_20260311_173310.log
new file mode 100644
index 0000000..3afa87d
--- /dev/null
+++ b/logs/piper_zero1_20260311_173310.log
@@ -0,0 +1,61 @@
+2026-03-11 17:33:22,766	INFO worker.py:2007 -- Started a local Ray instance.
+/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+  warnings.warn(
+(run_dp_rank pid=4034041) Namespace(model='debug', schedule='interleaved-1f1b', dp=2, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=1)
+(run_dp_rank pid=4034042) Namespace(model='debug', schedule='interleaved-1f1b', dp=2, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=1)
+(run_dp_rank pid=4034042) Schedule:
+(run_dp_rank pid=4034042) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
+(run_dp_rank pid=4034042)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
+(run_dp_rank pid=4034041) Schedule:
+(run_dp_rank pid=4034041) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
+(run_dp_rank pid=4034041)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
+(PiperActor pid=4034029) 2026-03-11 17:33:48,959 - piper_actor - INFO - Initializing Ray actor 2 GPU 2
+(PiperActor pid=4034032) 2026-03-11 17:33:49,004 - piper_actor - INFO - Initializing Ray actor 0 GPU 1
+(PiperActor pid=4034044) 2026-03-11 17:33:48,998 - piper_actor - INFO - Initializing Ray actor 1 GPU 4
+(PiperActor pid=4034038) 2026-03-11 17:33:49,038 - piper_actor - INFO - Initializing Ray actor 3 GPU 3
+(run_dp_rank pid=4034042) 2026-03-11 17:33:53,980 - piper_compile - INFO - DP rank 2 compiling...
+(run_dp_rank pid=4034041) 2026-03-11 17:33:54,098 - piper_compile - INFO - DP rank 1 compiling...
+(run_dp_rank pid=4034042) Stage 0 time measured on controller: 5.30 ms
+(run_dp_rank pid=4034041) Stage 0 time measured on controller: 5.79 ms
+(run_dp_rank pid=4034042) Stage 1 time measured on controller: 4.97 ms
+(run_dp_rank pid=4034041) Stage 1 time measured on controller: 5.04 ms
+(run_dp_rank pid=4034042) Stage 2 time measured on controller: 5.24 ms
+(run_dp_rank pid=4034041) Stage 2 time measured on controller: 3.91 ms
+(run_dp_rank pid=4034041) Stage 3 time measured on controller: 0.09 ms
+(run_dp_rank pid=4034042) Stage 3 time measured on controller: 0.21 ms
+(run_dp_rank pid=4034042) 2026-03-11 17:33:58,667 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=4034042) 2026-03-11 17:33:58,667 - piper_compile - INFO - DP rank 2 done.
+(run_dp_rank pid=4034041) 2026-03-11 17:33:58,686 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=4034041) 2026-03-11 17:33:58,686 - piper_compile - INFO - DP rank 1 done.
+(run_dp_rank pid=4034042) Running 5 warmup iterations...
+(run_dp_rank pid=4034041) Running 5 warmup iterations...
+(PiperActor pid=4034038) [rank3]:[W311 17:33:58.492119629 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=4034044) [rank1]:[W311 17:33:58.504022547 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=4034029) [rank2]:[W311 17:33:59.876989456 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=4034032) [rank0]:[W311 17:33:59.882813165 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=4034029) [rank2]:[W311 17:34:03.718212031 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=4034038) [rank3]:[W311 17:34:03.017809408 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=4034032) [rank0]:[W311 17:34:05.838320263 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=4034029) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
+(PiperActor pid=4034029)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+(PiperActor pid=4034044) [rank1]:[W311 17:34:05.134825782 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=4034032) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
+(PiperActor pid=4034032)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+(run_dp_rank pid=4034042) Warmup 0 completed
+(run_dp_rank pid=4034041) Warmup 0 completed
+(run_dp_rank pid=4034042) Warmup 1 completed
+(run_dp_rank pid=4034041) Warmup 1 completed
+(run_dp_rank pid=4034042) Warmup 2 completed
+(run_dp_rank pid=4034041) Warmup 2 completed
+(run_dp_rank pid=4034042) Warmup 3 completed
+(run_dp_rank pid=4034041) Warmup 3 completed
+(run_dp_rank pid=4034042) Warmup 4 completed
+(run_dp_rank pid=4034042) Running 10 timed iterations...
+(run_dp_rank pid=4034041) Warmup 4 completed
+(run_dp_rank pid=4034041) Running 10 timed iterations...
+(run_dp_rank pid=4034042) rank 1 iter time= 0.158 ± 0.005 s (10 samples)
+(run_dp_rank pid=4034042) rank 1 throughput= 103772.089 tokens/sec (10 samples)
+(run_dp_rank pid=4034041) rank 0 iter time= 0.158 ± 0.005 s (10 samples)
+(run_dp_rank pid=4034041) rank 0 throughput= 103752.690 tokens/sec (10 samples)
+(run_dp_rank pid=4034042) Ray timeline saved to: out/debug-pp2-dp2-interleaved-1f1b.json
+(run_dp_rank pid=4034041) Ray timeline saved to: out/debug-pp2-dp2-interleaved-1f1b.json
diff --git a/logs/piper_zero2_20260311_173556.log b/logs/piper_zero2_20260311_173556.log
new file mode 100644
index 0000000..523765f
--- /dev/null
+++ b/logs/piper_zero2_20260311_173556.log
@@ -0,0 +1,61 @@
+2026-03-11 17:36:08,845	INFO worker.py:2007 -- Started a local Ray instance.
+/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
+  warnings.warn(
+(run_dp_rank pid=4069408) Namespace(model='debug', schedule='interleaved-1f1b', dp=2, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=2)
+(run_dp_rank pid=4069410) Namespace(model='debug', schedule='interleaved-1f1b', dp=2, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=2)
+(run_dp_rank pid=4069408) Schedule:
+(run_dp_rank pid=4069408) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
+(run_dp_rank pid=4069408)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
+(run_dp_rank pid=4069410) Schedule:
+(run_dp_rank pid=4069410) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
+(run_dp_rank pid=4069410)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
+(PiperActor pid=4069412) 2026-03-11 17:36:34,163 - piper_actor - INFO - Initializing Ray actor 2 GPU 1
+(PiperActor pid=4069404) 2026-03-11 17:36:34,186 - piper_actor - INFO - Initializing Ray actor 3 GPU 2
+(PiperActor pid=4069394) 2026-03-11 17:36:34,433 - piper_actor - INFO - Initializing Ray actor 1 GPU 4
+(PiperActor pid=4069409) 2026-03-11 17:36:34,356 - piper_actor - INFO - Initializing Ray actor 0 GPU 3
+(run_dp_rank pid=4069408) 2026-03-11 17:36:39,073 - piper_compile - INFO - DP rank 2 compiling...
+(run_dp_rank pid=4069410) 2026-03-11 17:36:39,292 - piper_compile - INFO - DP rank 1 compiling...
+(run_dp_rank pid=4069408) Stage 0 time measured on controller: 5.43 ms
+(run_dp_rank pid=4069410) Stage 0 time measured on controller: 6.10 ms
+(run_dp_rank pid=4069408) Stage 1 time measured on controller: 4.88 ms
+(run_dp_rank pid=4069410) Stage 1 time measured on controller: 6.16 ms
+(run_dp_rank pid=4069408) Stage 2 time measured on controller: 3.13 ms
+(run_dp_rank pid=4069410) Stage 2 time measured on controller: 3.34 ms
+(run_dp_rank pid=4069408) Stage 3 time measured on controller: 0.09 ms
+(run_dp_rank pid=4069410) Stage 3 time measured on controller: 0.35 ms
+(run_dp_rank pid=4069408) 2026-03-11 17:36:43,655 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=4069408) 2026-03-11 17:36:43,655 - piper_compile - INFO - DP rank 2 done.
+(run_dp_rank pid=4069410) 2026-03-11 17:36:43,939 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
+(run_dp_rank pid=4069410) 2026-03-11 17:36:43,939 - piper_compile - INFO - DP rank 1 done.
+(run_dp_rank pid=4069408) Running 5 warmup iterations...
+(PiperActor pid=4069404) [rank3]:[W311 17:36:43.436084544 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(run_dp_rank pid=4069410) Running 5 warmup iterations...
+(PiperActor pid=4069412) [rank2]:[W311 17:36:44.797272508 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=4069394) [rank1]:[W311 17:36:44.761425137 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=4069409) [rank0]:[W311 17:36:44.134249353 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=4069412) [rank2]:[W311 17:36:46.041164441 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=4069404) [rank3]:[W311 17:36:46.353801336 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=4069412) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
+(PiperActor pid=4069412)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+(PiperActor pid=4069409) [rank0]:[W311 17:36:50.040816664 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=4069394) [rank1]:[W311 17:36:50.416971321 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
+(PiperActor pid=4069409) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
+(PiperActor pid=4069409)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+(run_dp_rank pid=4069410) Warmup 0 completed
+(run_dp_rank pid=4069408) Warmup 0 completed
+(run_dp_rank pid=4069410) Warmup 1 completed
+(run_dp_rank pid=4069408) Warmup 1 completed
+(run_dp_rank pid=4069410) Warmup 2 completed
+(run_dp_rank pid=4069408) Warmup 2 completed
+(run_dp_rank pid=4069410) Warmup 3 completed
+(run_dp_rank pid=4069408) Warmup 3 completed
+(run_dp_rank pid=4069410) Warmup 4 completed
+(run_dp_rank pid=4069410) Running 10 timed iterations...
+(run_dp_rank pid=4069408) Warmup 4 completed
+(run_dp_rank pid=4069408) Running 10 timed iterations...
+(run_dp_rank pid=4069410) rank 0 iter time= 0.150 ± 0.006 s (10 samples)
+(run_dp_rank pid=4069410) rank 0 throughput= 108998.506 tokens/sec (10 samples)
+(run_dp_rank pid=4069408) rank 1 iter time= 0.150 ± 0.005 s (10 samples)
+(run_dp_rank pid=4069408) rank 1 throughput= 109151.695 tokens/sec (10 samples)
+(run_dp_rank pid=4069410) Ray timeline saved to: out/debug-pp2-dp2-interleaved-1f1b.json
+(run_dp_rank pid=4069408) Ray timeline saved to: out/debug-pp2-dp2-interleaved-1f1b.json
diff --git a/run_zero1.sh b/run_zero1.sh
index 3a02940..e7f98bc 100755
--- a/run_zero1.sh
+++ b/run_zero1.sh
@@ -11,7 +11,7 @@ RAY_TMP="${RAY_TMP:-/tmp/ray}"
 mkdir -p "$RAY_TMP"
 
 export RAY_TMPDIR="$RAY_TMP"
-export CUDA_VISIBLE_DEVICES="0,1,2,3,4"
+export CUDA_VISIBLE_DEVICES="6,1,2,3,4"
 export PYTHONUNBUFFERED=1
 export RAY_DEDUP_LOGS=0
 export PIPER_MASTER_PORT="${PIPER_MASTER_PORT:-12388}"
@@ -24,6 +24,6 @@ python3 -m test.test_llama \
   --iters 10 \
   --schedule interleaved-1f1b \
   --mbs 4 \
-  --model 3b \
+  --model debug \
   --zero_stage 1 \
   >"$LOG_FILE" 2>&1
diff --git a/run_zero2.sh b/run_zero2.sh
new file mode 100755
index 0000000..124db26
--- /dev/null
+++ b/run_zero2.sh
@@ -0,0 +1,29 @@
+#!/bin/bash
+set -euo pipefail
+
+LOG_DIR="logs"
+mkdir -p "$LOG_DIR"
+
+TIMESTAMP="$(date +"%Y%m%d_%H%M%S")"
+LOG_FILE="$LOG_DIR/piper_zero2_${TIMESTAMP}.log"
+
+RAY_TMP="${RAY_TMP:-/tmp/ray}"
+mkdir -p "$RAY_TMP"
+
+export RAY_TMPDIR="$RAY_TMP"
+export CUDA_VISIBLE_DEVICES="6,1,2,3,4"
+export PYTHONUNBUFFERED=1
+export RAY_DEDUP_LOGS=0
+export PIPER_MASTER_PORT="${PIPER_MASTER_PORT:-12388}"
+
+echo "Logging to $LOG_FILE"
+python3 -m test.test_llama \
+  --dp 2 \
+  --pp 2 \
+  --warmup 5 \
+  --iters 10 \
+  --schedule interleaved-1f1b \
+  --mbs 4 \
+  --model debug \
+  --zero_stage 2 \
+  >"$LOG_FILE" 2>&1

From b79bf84628a681def802a290c4376236e512aca8 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Thu, 12 Mar 2026 00:08:27 +0000
Subject: [PATCH 079/100] NCCL monitor identifies overlapping NCCL kernels.
 also fix DP allreduce sync bug: wait on an event after completing bwd for all
 microbatches to make sure allreduce isn't launched until bwd compute
 completes

---
 src/piper_actor.py           |  94 ++++++++++++-
 src/piper_coordinator.py     |   2 +-
 src/piper_graph_transform.py |  16 ++-
 src/piper_utils.py           | 264 ++++++++++++++++++++++++++++++++++-
 test/models/mixtral.py       |  26 ++--
 test/test_llama.py           |  12 +-
 test/test_mixtral.py         |  30 +++-
 7 files changed, 407 insertions(+), 37 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index 6c74027..3de4330 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -18,6 +18,7 @@
     create_logger,
     LOG_LEVEL,
     piper_metadata,
+    NcclOverlapDetector,
 )
 from .backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
 
@@ -56,7 +57,7 @@ def _create_actors(
             "stop-on-exit": "true",
         }} if profile else {}
         actor = PiperActor.options(
-            num_gpus=0.8, 
+            num_gpus=1, 
             runtime_env=nsight_env,
             scheduling_strategy=PlacementGroupSchedulingStrategy(
                 placement_group=pg,
@@ -118,6 +119,7 @@ def __init__(
         self.num_stages = num_stages
         self.stage_to_device = stage_to_device or {}
         self.dp_group = None
+        self.ep_group = None  # separate NCCL communicator for expert-parallel (all2all) ops
         # Per-direction communicators: (src_global_rank, dst_global_rank) -> ProcessGroup
         self.pp_groups = {}
         self.device = "cuda"
@@ -139,6 +141,7 @@ def __init__(
         self.p2p_recv_stream = torch.cuda.Stream()
         self.overlapped_comp_stream = torch.cuda.Stream()
         self.overlapped_p2p_stream = torch.cuda.Stream()
+        self.overlap_detector = NcclOverlapDetector()
         if mode == "naive":
             self.per_mb_streams = [torch.cuda.Stream() for _ in range(2)]
         self.n_a2a_ops = dict()
@@ -230,6 +233,47 @@ def _exec_p2p_op(self, target_rank_idx, *deps):
                     self._exec_bwd_send(stage_from, mb_idx)
             self.p2p_cursor += 1
 
+    def enable_nccl_monitoring(self) -> None:
+        """Enable NCCL overlap monitoring for the next training iteration.
+
+        Call this before the iteration you want to profile.  After the iteration
+        completes, call print_nccl_overlaps() to analyse and display results.
+        Monitoring is automatically disabled after print_nccl_overlaps() returns.
+        """
+        self.overlap_detector.enable()
+        self.logger.info("Actor %d: NCCL monitoring enabled", self.global_rank)
+
+    def print_nccl_overlaps(self) -> None:
+        """Synchronise the GPU, analyse recorded NCCL events, and log all overlapping
+        kernel pairs.  Disables monitoring afterwards so subsequent iterations are
+        not affected.
+
+        Call this after the monitored training iteration has completed.
+        """
+        torch.cuda.synchronize()
+        overlaps = self.overlap_detector.find_overlaps(self.logger)
+        n_kernels = len(self.overlap_detector._records)
+        if not overlaps:
+            self.logger.info(
+                "Actor %d: NCCL overlap monitor — no overlaps detected "
+                "(%d kernels recorded)", self.global_rank, n_kernels,
+            )
+        else:
+            self.logger.warning(
+                "Actor %d: NCCL overlap monitor — %d overlapping pair(s) "
+                "across %d kernels:", self.global_rank, len(overlaps), n_kernels,
+            )
+            for a, b, overlap_ms in overlaps:
+                self.logger.warning(
+                    "  '%s' on '%s' [%.3f–%.3f ms]  <->  '%s' on '%s' [%.3f–%.3f ms]"
+                    "  (overlap %.3f ms)",
+                    a.kernel_name, a.stream_label, a.t_start_ms, a.t_end_ms,
+                    b.kernel_name, b.stream_label, b.t_start_ms, b.t_end_ms,
+                    overlap_ms,
+                )
+        self.overlap_detector.build_enforcement(overlaps, self.logger)
+        self.overlap_detector.disable()
+
     def get_trace_data(self) -> dict:
         return self.global_rank, self.trace_data
 
@@ -315,9 +359,14 @@ def _join_dp_process_group(self):
             group_ranks = [
                 (dp_group_id + num_dp_groups * i) for i in range(self.dp_degree)
             ]
+            # Two separate NCCL communicators over the same ranks: one for allreduce,
+            # one for all2all.  Sharing a communicator causes both op types to run on
+            # the same internal NCCL proxy stream, which prevents true overlap.
             process_group = dist.new_group(ranks=group_ranks, backend="nccl")
+            ep_process_group = dist.new_group(ranks=group_ranks, backend="nccl")
             if self.global_rank % num_dp_groups == dp_group_id:
                 self.dp_group = process_group
+                self.ep_group = ep_process_group
                 self.logger.debug(
                     f"Global rank {self.global_rank} joined its dp group {dp_group_id} along with ranks {group_ranks}"
                 )
@@ -352,7 +401,7 @@ def _join_pp_process_group(self):
             self.logger.debug(f"Global rank {self.global_rank} warming up pp communicator {key}")
             dist.all_reduce(dummy, group=pg)
         torch.cuda.synchronize()
-        self.logger.debug(f"Global rank {self.global_rank} warmed up {len(self.pp_groups)} pp communicators")
+        self.logger.info(f"Global rank {self.global_rank} warmed up {len(self.pp_groups)} pp communicators")
 
     def shutdown(self):
         dist.destroy_process_group()
@@ -392,13 +441,21 @@ def _maybe_trigger_grad_allreduce(
         )
 
         if self.comm_op_status[stage_id][tid] == self.num_mbs:
+            # Same ordering fix as in _prepare_dp_comm_ops: the backward_weight call
+            # accumulates into param.grad on comp_stream.  Establish a GPU-side
+            # dependency so comm_stream doesn't start the all-reduce until the
+            # gradient write has completed.  This does not block the CPU.
+            dep_event = torch.cuda.Event()
+            dep_event.record(self.comp_stream)
+            self.comm_stream.wait_event(dep_event)
+            _ov_token = self.overlap_detector.before_kernel(self.comm_stream, "grad_allreduce", "comm_stream")
             with torch.cuda.stream(self.comm_stream):
                 handle = dist.all_reduce(
                     param.grad,
                     op=dist.ReduceOp.AVG,
                     group=self.dp_group,
-                    async_op=True,
                 )
+            self.overlap_detector.after_kernel(self.comm_stream, _ov_token)
             self.logger.debug(
                 f"Split bwd: Allreduce launched on actor {self.global_rank}, tensor={tid}"
             )
@@ -413,13 +470,24 @@ def post_backward_hook(grad):
                     f"Updating status on actor: {self.global_rank}, tensor={tensor_id}, status={self.comm_op_status[stage_id][tensor_id]}/{self.num_mbs}"
                 )
                 if self.comm_op_status[stage_id][tensor_id] == self.num_mbs:
+                    # The AccumulateGrad CUDA kernel that writes the final gradient into
+                    # param.grad is in-flight on comp_stream.  The hook fires on the CPU
+                    # as soon as the kernel is *submitted*, not when it *completes*.
+                    # Record an event on comp_stream now so that comm_stream can wait for
+                    # the gradient to be fully written before starting the all-reduce.
+                    # comm_stream.wait_event() is a GPU-side barrier — it does not block
+                    # the CPU, so backward compute on comp_stream continues to overlap.
+                    dep_event = torch.cuda.Event()
+                    dep_event.record(self.comp_stream)
+                    self.comm_stream.wait_event(dep_event)
+                    _ov_token = self.overlap_detector.before_kernel(self.comm_stream, "grad_allreduce", "comm_stream")
                     with torch.cuda.stream(self.comm_stream):
                         handle = dist.all_reduce(
                             grad,
                             op=dist.ReduceOp.AVG,
                             group=self.dp_group,
-                            async_op=True,
                         )
+                    self.overlap_detector.after_kernel(self.comm_stream, _ov_token)
                     self.logger.debug(
                         f"Allreduce on actor: {self.global_rank}, tensor={tensor_id}"
                     )
@@ -449,7 +517,7 @@ def _wait_for_comm_ops(self):
                         self.logger.debug(
                             f"Waiting for comm op to be finished dp_rank: {self.dp_rank}, tensor={tid}"
                         )
-                        self.comm_op_handles[stage_id][tid].wait()
+                        # self.comm_op_handles[stage_id][tid].wait()
                         done = True
 
     def _load_stage(
@@ -551,6 +619,7 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, com
                 f"Dispatch fwd p2p recv on communicator ({global_src_rank}, {self.global_rank}) to {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
             )
             self._start_timing(p2p_stream, "fwd_p2p_recv")
+            _ov_token = self.overlap_detector.before_kernel(p2p_stream, "fwd_p2p_recv", "p2p_recv_stream")
             with torch.cuda.stream(p2p_stream):
                 for i in self.input_idxs[stage_id]:
                     dist.recv(
@@ -560,6 +629,7 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, com
                     )
             recv_event = torch.cuda.Event()
             recv_event.record(p2p_stream)
+            self.overlap_detector.after_kernel(p2p_stream, _ov_token)
             self.p2p_events[p2p_op] = recv_event
             self._stop_timing(p2p_stream, "fwd_p2p_recv")
             self.logger.debug(
@@ -592,6 +662,7 @@ def _exec_fwd_send(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, com
             )
             self._start_timing(p2p_stream, "fwd_p2p_send")
             p2p_stream.wait_stream(comp_stream)
+            _ov_token = self.overlap_detector.before_kernel(p2p_stream, "fwd_p2p_send", "p2p_send_stream")
             with torch.cuda.stream(p2p_stream):
                 for i in range(len(output)):
                     dist.send(
@@ -599,6 +670,7 @@ def _exec_fwd_send(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, com
                         dst=global_dst_rank,
                         group=self.pp_groups[(self.global_rank, global_dst_rank)],
                     )
+            self.overlap_detector.after_kernel(p2p_stream, _ov_token)
             self._stop_timing(p2p_stream, "fwd_p2p_send")
             self.logger.debug(
                 f"Completed fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
@@ -628,12 +700,14 @@ def _exec_bwd_recv(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, com
                 f"Dispatch bwd p2p recv on communicator ({global_src_rank}, {self.global_rank}) to {self.global_rank} from {global_src_rank}, op: ({stage_id+1} -> {stage_id}, mb {mb_idx})"
             )
             self._start_timing(p2p_stream, "bwd_p2p_recv")
+            _ov_token = self.overlap_detector.before_kernel(p2p_stream, "bwd_p2p_recv", "p2p_recv_stream")
             with torch.cuda.stream(p2p_stream):
                 dist.recv(
                     input_grad, src=global_src_rank, group=self.pp_groups[(global_src_rank, self.global_rank)]
                 )
             recv_event = torch.cuda.Event()
             recv_event.record(p2p_stream)
+            self.overlap_detector.after_kernel(p2p_stream, _ov_token)
             self.p2p_events[p2p_op] = recv_event
             self._stop_timing(p2p_stream, "bwd_p2p_recv")
             self.logger.debug(
@@ -668,10 +742,12 @@ def _exec_bwd_send(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, com
             )
             self._start_timing(p2p_stream, "bwd_p2p_send")
             p2p_stream.wait_stream(comp_stream)
+            _ov_token = self.overlap_detector.before_kernel(p2p_stream, "bwd_p2p_send", "p2p_send_stream")
             with torch.cuda.stream(p2p_stream):
                 dist.send(
                     output_grad, dst=global_src_rank, group=self.pp_groups[(self.global_rank, global_src_rank)]
                 )
+            self.overlap_detector.after_kernel(p2p_stream, _ov_token)
             self._stop_timing(p2p_stream, "bwd_p2p_send")
             self.logger.debug(
                 f"Completed bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
@@ -713,6 +789,7 @@ def _forward_impl(self, stage_id: int, mb_idx: int, *deps):
             recv_event = self.p2p_events.pop(recv_key, None)
             if recv_event is not None:
                 comp_stream.wait_event(recv_event)
+                # self.nccl_monitor.notify_ordered_after(comp_stream, recv_event)
             inputs_from_prev_stage = self.p2p_cache.pop(recv_key)
             self._stop_timing(comp_stream, "fwd_recv_wait")
 
@@ -815,6 +892,7 @@ def _backward_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
             recv_event = self.p2p_events.pop(recv_key, None)
             if recv_event is not None:
                 comp_stream.wait_event(recv_event)
+                # self.nccl_monitor.notify_ordered_after(comp_stream, recv_event)
             input_grad = self.p2p_cache.pop(recv_key)
             self._stop_timing(comp_stream, "bwd_recv_wait")
 
@@ -874,6 +952,7 @@ def _backward_input_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
             recv_event = self.p2p_events.pop(recv_key, None)
             if recv_event is not None:
                 comp_stream.wait_event(recv_event)
+                # self.nccl_monitor.notify_ordered_after(comp_stream, recv_event)
             upstream_grad = self.p2p_cache.pop(recv_key)
             activation_or_loss = out_activation
         else:
@@ -1173,6 +1252,7 @@ def _forward_backward_impl(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_i
             fwd_recv_event = self.p2p_events.pop(fwd_recv_key, None)
             if fwd_recv_event is not None:
                 fwd_comp_stream.wait_event(fwd_recv_event)
+                # self.nccl_monitor.notify_ordered_after(fwd_comp_stream, fwd_recv_event)
             inputs_from_prev_stage = self.p2p_cache.pop(fwd_recv_key)
 
             # Detach to avoid double-backprop if the previous stage is co-located
@@ -1207,6 +1287,7 @@ def _forward_backward_impl(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_i
             bwd_recv_event = self.p2p_events.pop(bwd_recv_key, None)
             if bwd_recv_event is not None:
                 bwd_comp_stream.wait_event(bwd_recv_event)
+                # self.nccl_monitor.notify_ordered_after(bwd_comp_stream, bwd_recv_event)
             input_grad = self.p2p_cache.pop(bwd_recv_key)
         else:
             assert loss_fn is not None
@@ -1311,10 +1392,12 @@ def _synchronize_gradients(self):
         for stage_id, parameters in self.forward_args.items():
             for param in parameters:
                 if param is not None and param.grad is not None:
+                    _ov_token = self.overlap_detector.before_kernel(self.comm_stream, "grad_allreduce_naive", "comm_stream")
                     with torch.cuda.stream(self.comm_stream):
                         dist.all_reduce(
                             param.grad, op=dist.ReduceOp.AVG, group=self.dp_group
                         )
+                    self.overlap_detector.after_kernel(self.comm_stream, _ov_token)
 
     def _update(self, *deps):
         torch.cuda.nvtx.range_push(f"update")
@@ -1350,5 +1433,6 @@ def _update_impl(self, *deps):
         self.comm_stream.synchronize()
 
         self.reset_p2p_states()
+        self.overlap_detector.reset_iteration()
 
         return losses
diff --git a/src/piper_coordinator.py b/src/piper_coordinator.py
index ed71060..7c75fe1 100644
--- a/src/piper_coordinator.py
+++ b/src/piper_coordinator.py
@@ -7,7 +7,7 @@
 from .piper_utils import create_logger, LOG_LEVEL
 
 
-@ray.remote(num_gpus=0.1)
+@ray.remote
 def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, master_addr, master_port, training_func: Callable, *args, **kwargs):
     logger = create_logger("piper_coordinator", LOG_LEVEL)
     logger.debug(f"Running DP rank {dp_rank+1} of {dp_degree}")
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index 0e8195f..47e8b8d 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -58,20 +58,23 @@ def forward(ctx, output, input_tensor):
         # Store group for backward pass
         from .piper_utils import piper_metadata
         ctx.actor_self = piper_metadata.actor_self
-        ctx.group = ctx.actor_self.dp_group
+        ctx.group = ctx.actor_self.ep_group
         ctx.global_rank = ctx.actor_self.global_rank
         ctx.stream = ctx.actor_self.a2a_stream
 
         comp_stream = torch.cuda.current_stream()
         comm_finished_event = torch.cuda.Event()
-        ctx.stream.wait_stream(comp_stream)
 
         ctx.actor_self._start_timing(ctx.stream, "fwd_a2a")
 
+        ctx.stream.wait_stream(comp_stream)
+        _ov_token = ctx.actor_self.overlap_detector.before_kernel(ctx.stream, "fwd_a2a", "a2a_stream")
+
         input_tensor = input_tensor.contiguous()
         with torch.cuda.stream(ctx.stream):
-            dist.all_to_all_single(output, input_tensor, group=ctx.group, async_op=True).wait()
+            dist.all_to_all_single(output, input_tensor, group=ctx.group)
             comm_finished_event.record()
+        ctx.actor_self.overlap_detector.after_kernel(ctx.stream, _ov_token)
 
         ctx.actor_self._stop_timing(ctx.stream, "fwd_a2a")
 
@@ -100,15 +103,18 @@ def backward(ctx, grad_output):
 
         comp_stream = torch.cuda.current_stream()
         comm_finished_event = torch.cuda.Event()
-        ctx.stream.wait_stream(comp_stream)
 
         ctx.actor_self._start_timing(ctx.stream, "bwd_a2a")
 
+        ctx.stream.wait_stream(comp_stream)
+        _ov_token = ctx.actor_self.overlap_detector.before_kernel(ctx.stream, "bwd_a2a", "a2a_stream")
+
         grad_output = grad_output.contiguous()
         grad_input = torch.empty_like(grad_output)
         with torch.cuda.stream(ctx.stream):
-            dist.all_to_all_single(grad_input, grad_output, group=ctx.group, async_op=True).wait()
+            dist.all_to_all_single(grad_input, grad_output, group=ctx.group)
             comm_finished_event.record()
+        ctx.actor_self.overlap_detector.after_kernel(ctx.stream, _ov_token)
 
         ctx.actor_self._stop_timing(ctx.stream, "bwd_a2a")
 
diff --git a/src/piper_utils.py b/src/piper_utils.py
index cdb5f25..a5d342d 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -1,12 +1,12 @@
+import queue
 import ray
 import torch
-import uuid
 import inspect
 import logging
 import json, importlib, operator
 import torch.fx as fx
-import threading
 from collections import defaultdict
+from dataclasses import dataclass
 from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
 from typing import Any, Optional
 
@@ -66,6 +66,266 @@ def create_logger(name: str, log_level: str):
     return logger
 
 
+_CRITICAL_OPS     = frozenset({"fwd_p2p_recv", "fwd_p2p_send", "bwd_p2p_recv", "bwd_p2p_send", "fwd_a2a", "bwd_a2a"})
+_NON_CRITICAL_OPS = frozenset({"grad_allreduce", "grad_allreduce_naive"})
+
+_nccl_log = logging.getLogger("piper_nccl")
+
+
+class _DepGate:
+    """One-shot handoff of a CUDA end-event from a critical op to a non-critical op.
+
+    signal() is called by the critical op's after_kernel.
+    wait()   is called by the non-critical op's before_kernel.
+
+    wait() is intentionally non-blocking: if the critical op hasn't fired yet
+    (e.g. both are dispatched from the same CPU thread and the critical op comes
+    later in the schedule), enforcement is skipped rather than deadlocking.
+    Unmatched signals are drained by NcclOverlapDetector.reset_iteration().
+    """
+    __slots__ = ("_q",)
+
+    def __init__(self):
+        self._q: queue.Queue = queue.Queue()
+
+    def signal(self, end_event: "torch.cuda.Event") -> None:
+        self._q.put(end_event)
+
+    def wait(self, stream: "torch.cuda.Stream") -> None:
+        try:
+            event = self._q.get_nowait()
+            stream.wait_event(event)    # GPU-side dependency
+        except queue.Empty:
+            pass  # critical op not yet dispatched; skip enforcement for this call
+
+    def drain(self) -> None:
+        """Discard any unmatched signals left over from the previous iteration."""
+        while True:
+            try:
+                self._q.get_nowait()
+            except queue.Empty:
+                break
+
+
+@dataclass
+class _NcclKernelRecord:
+    kernel_name: str
+    stream_label: str
+    start_event: "torch.cuda.Event"
+    end_event: Optional["torch.cuda.Event"] = None
+    # GPU-side timestamps (ms from reference event); populated by find_overlaps()
+    t_start_ms: Optional[float] = None
+    t_end_ms: Optional[float] = None
+    # Position of this kernel in dispatch order on its stream; set by build_enforcement
+    stream_instance_idx: Optional[int] = None
+
+
+class NcclOverlapDetector:
+    """
+    Passively records GPU-side start/end events for every NCCL kernel while
+    monitoring is enabled, then performs a single retrospective overlap analysis
+    after the iteration completes.
+
+    No callbacks, no host functions, no GPU stalls.
+
+    Typical usage in a training loop:
+
+        # After warmup, enable monitoring for one iteration:
+        actor.enable_nccl_monitoring.remote()
+        losses = piper.step(batch)
+        actor.print_nccl_overlaps.remote()   # syncs GPU, analyses, prints, then disables
+
+    At each NCCL launch site (AFTER any stream.wait_stream / wait_event):
+        token = detector.before_kernel(cuda_stream, kernel_name, stream_label)
+        # ... launch the NCCL kernel ...
+        detector.after_kernel(cuda_stream, token)
+
+    reset_iteration() is called automatically at the end of every training iteration.
+    It clears records only when monitoring is disabled, so a monitored iteration's
+    records survive until print_nccl_overlaps() consumes them.
+    """
+
+    def __init__(self):
+        self._records: list[_NcclKernelRecord] = []
+        self._ref_event: Optional["torch.cuda.Event"] = None
+        self.enabled: bool = False
+        # Enforcement — keyed by (stream_label, stream_instance_idx)
+        self._signal_gates: dict[tuple[str, int], list[_DepGate]] = {}
+        self._wait_gates:   dict[tuple[str, int], list[_DepGate]] = {}
+        self._per_stream_counter: dict[str, int] = {}  # reset each iteration
+        self._enforcement_active: bool = False
+
+    def enable(self) -> None:
+        """Enable monitoring and reset any previous records."""
+        self._records.clear()
+        self._ref_event = torch.cuda.Event(enable_timing=True)
+        self._ref_event.record()
+        self.enabled = True
+
+    def disable(self) -> None:
+        self.enabled = False
+
+    def before_kernel(
+        self,
+        cuda_stream: "torch.cuda.Stream",
+        kernel_name: str,
+        stream_label: str,
+    ) -> str:
+        """Record start_event on cuda_stream and return an opaque token.
+
+        Must be called AFTER any stream.wait_stream() / stream.wait_event() so the
+        event is enqueued only once GPU-side dependencies have resolved, giving an
+        accurate kernel-start timestamp.
+        """
+        # Enforcement: compute per-stream instance index and wait on any gates for this slot
+        instance_idx = -1
+        if self._enforcement_active:
+            instance_idx = self._per_stream_counter.get(stream_label, 0)
+            for gate in self._wait_gates.get((stream_label, instance_idx), []):
+                gate.wait(cuda_stream)
+            self._per_stream_counter[stream_label] = instance_idx + 1
+
+        if not self.enabled:
+            # Enforcement-only: encode (stream_label, instance_idx) for after_kernel
+            return f"E:{stream_label}:{instance_idx}" if self._enforcement_active else ""
+
+        start_event = torch.cuda.Event(enable_timing=True)
+        start_event.record(cuda_stream)
+        token = str(len(self._records))
+        self._records.append(_NcclKernelRecord(
+            kernel_name=kernel_name,
+            stream_label=stream_label,
+            start_event=start_event,
+            stream_instance_idx=instance_idx if self._enforcement_active else None,
+        ))
+        return token
+
+    def after_kernel(self, cuda_stream: "torch.cuda.Stream", token: str) -> None:
+        """Record end_event on cuda_stream. Call after the NCCL kernel."""
+        if not token:
+            return
+
+        if token.startswith("E:"):
+            # Enforcement-only mode: parse "E:{stream_label}:{instance_idx}"
+            _, stream_label, idx_str = token.split(":")
+            instance_idx = int(idx_str)
+            end_event = torch.cuda.Event(enable_timing=False)
+            end_event.record(cuda_stream)
+            for gate in self._signal_gates.get((stream_label, instance_idx), []):
+                gate.signal(end_event)
+            return
+
+        # Monitoring mode (with or without enforcement): token is a record index
+        record_idx = int(token)
+        record = self._records[record_idx]
+
+        end_event = None
+        if self.enabled:
+            end_event = torch.cuda.Event(enable_timing=True)
+            end_event.record(cuda_stream)
+            record.end_event = end_event
+
+        if self._enforcement_active and record.stream_instance_idx is not None:
+            if end_event is None:
+                end_event = torch.cuda.Event(enable_timing=False)
+                end_event.record(cuda_stream)
+            for gate in self._signal_gates.get(
+                (record.stream_label, record.stream_instance_idx), []
+            ):
+                gate.signal(end_event)
+
+    def build_enforcement(self, overlaps: list, logger) -> None:
+        """Build per-instance gate map from detected overlaps. Call after find_overlaps().
+
+        Assigns a stable stream_instance_idx to every record (= position in dispatch
+        order on that stream), then creates one _DepGate per detected overlap instance.
+        Gates are permanent — they are not rebuilt each iteration; only
+        _per_stream_counter is reset so the same indices are reproduced.
+        """
+        # Assign stream_instance_idx to every record in monitoring order
+        stream_counts: dict[str, int] = {}
+        for r in self._records:
+            r.stream_instance_idx = stream_counts.get(r.stream_label, 0)
+            stream_counts[r.stream_label] = r.stream_instance_idx + 1
+
+        # Build one gate per detected overlap instance
+        signal: dict[tuple[str, int], list[_DepGate]] = {}
+        wait:   dict[tuple[str, int], list[_DepGate]] = {}
+        n_pairs = 0
+        for a, b, _ in overlaps:
+            if a.kernel_name in _CRITICAL_OPS and b.kernel_name in _NON_CRITICAL_OPS:
+                crit, noncrit = a, b
+            elif b.kernel_name in _CRITICAL_OPS and a.kernel_name in _NON_CRITICAL_OPS:
+                crit, noncrit = b, a
+            else:
+                continue
+            logger.debug(f"Enforcing dependency between {crit} -> {noncrit} NCCL kernels")
+            gate = _DepGate()
+            signal.setdefault((crit.stream_label,    crit.stream_instance_idx),    []).append(gate)
+            wait.setdefault(  (noncrit.stream_label, noncrit.stream_instance_idx), []).append(gate)
+            n_pairs += 1
+
+        self._signal_gates = signal
+        self._wait_gates   = wait
+        self._enforcement_active = bool(n_pairs)
+        if self._enforcement_active:
+            _nccl_log.info(
+                "NcclOverlapDetector: enforcement active — %d instance-level gate(s)", n_pairs
+            )
+
+    def find_overlaps(self, logger) -> list[tuple["_NcclKernelRecord", "_NcclKernelRecord", float]]:
+        """Compute GPU-time intervals and return all overlapping cross-stream pairs.
+
+        Must be called after torch.cuda.synchronize() so all events are complete.
+        Returns a list of (record_a, record_b, overlap_ms) tuples.
+        """
+        if not self._records or self._ref_event is None:
+            return []
+
+        ref = self._ref_event
+        for r in self._records:
+            if r.end_event is None or r.t_start_ms is not None:
+                continue
+            try:
+                r.t_start_ms = ref.elapsed_time(r.start_event)
+                r.t_end_ms   = ref.elapsed_time(r.end_event)
+            except Exception:
+                pass
+
+        valid = [r for r in self._records if r.t_start_ms is not None and r.t_end_ms is not None]
+        overlaps = []
+        for i, a in enumerate(valid):
+            # logger.info(f"NCCL kernel '{a.kernel_name}' on stream '{a.stream_label}': {a.t_start_ms:.3f}-{a.t_end_ms:.3f} ms")
+            for b in valid[i + 1:]:
+                if a.stream_label == b.stream_label:
+                    continue
+                if a.t_start_ms < b.t_end_ms and b.t_start_ms < a.t_end_ms:
+                    overlap_ms = (min(a.t_end_ms, b.t_end_ms)
+                                  - max(a.t_start_ms, b.t_start_ms))
+                    overlaps.append((a, b, overlap_ms))
+        return overlaps
+
+    def reset_iteration(self) -> None:
+        """Called at the end of every training iteration (after streams are synced).
+
+        Clears records only when monitoring is disabled.  When monitoring is enabled,
+        records are preserved so that print_nccl_overlaps() can analyse them after
+        the iteration returns.  Gates are permanent and not rebuilt; only
+        _per_stream_counter is reset so each iteration reproduces the same
+        (stream_label, index) assignments as during monitoring.
+        """
+        if not self.enabled:
+            self._records.clear()
+            self._ref_event = None
+        if self._enforcement_active:
+            # Drain any unmatched signals (cases where the critical op fired after
+            # the non-critical op's non-blocking wait already returned empty).
+            for gates in self._signal_gates.values():
+                for gate in gates:
+                    gate.drain()
+            self._per_stream_counter.clear()
+
+
 """
 Piper thread local storage for tracking Piper actors, stages, and microbatches
 """
diff --git a/test/models/mixtral.py b/test/models/mixtral.py
index b1eda19..78891ef 100644
--- a/test/models/mixtral.py
+++ b/test/models/mixtral.py
@@ -56,9 +56,11 @@ def from_name(cls, name: str):
 
 transformer_configs = {
     "tiny": dict(block_size=128, n_layer=4, n_head=2, n_local_heads=1, dim=256, intermediate_size=512, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "small": dict(block_size=512, n_layer=4, n_head=32, n_local_heads=8, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "small": dict(block_size=512, n_layer=2, n_head=32, n_local_heads=8, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
     "medium": dict(block_size=512, n_layer=4, n_head=8, n_local_heads=2, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=4, num_activated_experts=2),
-    "Mixtral-8x7B-v0.1": dict(block_size=32768, n_layer=4, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "Mixtral-8x7B-v0.1": dict(block_size=1024, n_layer=2, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    # "Mixtral-8x22B-v0.1": dict(block_size=2048, n_layer=4, n_head=48, n_local_heads=8, dim=6144, intermediate_size=16384, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "Mixtral-8x22B-v0.1": dict(block_size=1024, n_layer=4, n_head=32, n_local_heads=8, dim=2048, intermediate_size=16384, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
 }
 
 class KVCache(nn.Module):
@@ -85,10 +87,10 @@ def __init__(self, config: ModelArgs) -> None:
         self.config = config
 
         # Initialize layers normally
-        self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim, dtype=torch.bfloat16)
         self.layers = nn.ModuleList(TransformerBlock(config) for _ in range(config.n_layer))
         self.norm = RMSNorm(config.dim, eps=config.norm_eps)
-        self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
+        self.output = nn.Linear(config.dim, config.vocab_size, bias=False, dtype=torch.bfloat16)
 
         self.freqs_cis: Optional[Tensor] = precompute_freqs_cis(
             self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base
@@ -187,8 +189,8 @@ def __init__(self, config: ModelArgs):
         total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
         # key, query, value projections for all heads, but in a batch
         # Initialize layers normally
-        self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
-        self.wo = nn.Linear(config.dim, config.dim, bias=False)
+        self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False, dtype=torch.bfloat16)
+        self.wo = nn.Linear(config.dim, config.dim, bias=False, dtype=torch.bfloat16)
         
         self.kv_cache = None
 
@@ -238,9 +240,9 @@ def __init__(self, config):
         super().__init__()
         self.config = config
 
-        self.w1 = nn.Parameter(torch.randn(config.num_experts, config.intermediate_size, config.dim))
-        self.w2 = nn.Parameter(torch.randn(config.num_experts, config.dim, config.intermediate_size))
-        self.w3 = nn.Parameter(torch.randn(config.num_experts, config.intermediate_size, config.dim))
+        self.w1 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim, dtype=torch.bfloat16))
+        self.w2 = nn.Parameter(torch.empty(config.num_experts, config.dim, config.intermediate_size, dtype=torch.bfloat16))
+        self.w3 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim, dtype=torch.bfloat16))
 
     def forward(self, x: Tensor, dispatch_mask: Tensor, combine_weights: Tensor, capacity: int) -> Tensor:
         """
@@ -270,7 +272,7 @@ class MOEFeedForward(nn.Module):
     def __init__(self, config) -> None:
         super().__init__()
         # Initialize layers normally
-        self.gate = nn.Linear(config.dim, config.num_experts, bias=False)
+        self.gate = nn.Linear(config.dim, config.num_experts, bias=False, dtype=torch.bfloat16)
         self.cond_ffn = ConditionalFeedForward(config)
         
         self.dim = config.dim
@@ -291,7 +293,7 @@ def forward(self, x: Tensor) -> Tensor:
         mask = F.one_hot(expert_indices, num_classes=num_experts).sum(dim=1).bool()  # [T, E]
         capacity = x.shape[0] # // num_experts * 2
         locations = torch.cumsum(mask, dim=0) - 1 # [T, E]
-        locations_sc = F.one_hot(locations * mask, capacity).float() # [T, E, C]
+        locations_sc = F.one_hot(locations * mask, capacity).to(dtype=x.dtype) # [T, E, C]
         combine_weights = torch.einsum("se,sec->sec", gates * mask, locations_sc)
         dispatch_mask = combine_weights.bool()
 
@@ -304,7 +306,7 @@ def __init__(self, dim: int, eps: float = 1e-5):
         super().__init__()
         self.eps = eps
         # Initialize weight normally (ones for RMSNorm)
-        self.weight = nn.Parameter(torch.ones(dim))
+        self.weight = nn.Parameter(torch.ones(dim, dtype=torch.bfloat16))
 
     def _norm(self, x):
         return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
diff --git a/test/test_llama.py b/test/test_llama.py
index 97b09f7..1f0fa64 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -1,5 +1,6 @@
 import ray
 import torch
+import bitsandbytes as bnb
 import time
 import argparse
 import json
@@ -92,13 +93,14 @@ def main(args, pg):
     piper_setup(
         Transformer,
         model_args=(llama_config, args.seq_len),
-        optim_fn=torch.optim.Adam,
+        optim_fn=bnb.optim.Adam8bit,
         example_inputs=[x],
         example_outputs=y,
         schedule=schedule,
         naive_gradient_sync=args.naive_gradient_sync,
         activation_checkpointing=args.activation_checkpointing,
         pg=pg,
+        model_dtype=torch.bfloat16,
     )
 
     # Warmup
@@ -175,11 +177,11 @@ def parse_args():
                         help='Number of data parallel degrees (default: 1)')
     parser.add_argument('--pp', type=int, default=2,
                         help='Number of pipeline parallel degrees (default: 2)')
-    parser.add_argument('--batch_size', type=int, default=16,
+    parser.add_argument('--batch-size', type=int, default=16,
                         help='Batch size (default: 16)')
     parser.add_argument('--mbs', type=int, default=4,
                         help='Number of microbatches (default: 4)')
-    parser.add_argument('--seq_len', type=int, default=256,
+    parser.add_argument('--seq-len', type=int, default=256,
                         help='Sequence length (default: 256)')
     parser.add_argument('--warmup', type=int, default=5,
                         help='Number of warmup iterations (default: 5)')
@@ -187,9 +189,9 @@ def parse_args():
                         help='Number of timing iterations (default: 10)')
     parser.add_argument('--tracing', action='store_true', default=False,
                         help='Enable tracing')
-    parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
+    parser.add_argument('--naive-gradient-sync', action='store_true', default=False,
                         help='Enable naive gradient sync')
-    parser.add_argument('--activation_checkpointing', action='store_true', default=False,
+    parser.add_argument('--activation-checkpointing', action='store_true', default=False,
                         help='Enable activation checkpointing')
     return parser.parse_args()
 
diff --git a/test/test_mixtral.py b/test/test_mixtral.py
index f4b524f..1e90173 100644
--- a/test/test_mixtral.py
+++ b/test/test_mixtral.py
@@ -47,8 +47,10 @@ def main(args, pg):
             config = ModelArgs.from_name("small")
         case 'medium':
             config = ModelArgs.from_name("medium")
-        case 'large':
+        case '7b':
             config = ModelArgs.from_name("Mixtral-8x7B-v0.1")
+        case '22b':
+            config = ModelArgs.from_name("Mixtral-8x22B-v0.1")
 
     schedule = None
     match args.schedule:
@@ -83,12 +85,24 @@ def main(args, pg):
         naive_gradient_sync=args.naive_grad_sync,
         activation_checkpointing=args.activation_checkpointing,
         mode=args.mode,
+        model_dtype=torch.bfloat16,
         pg=pg,
     )
 
+    if args.no_nccl_conflict_handling:
+        ray.get([actor.disable_nccl_monitor.remote() for actor in piper_metadata.actors.values()])
+
     print(f"Running {args.warmup} warmup iterations...")
     for _ in range(args.warmup):
         piper_exec(schedule, loss_fn, args.dp, args.naive_grad_sync)
+        time.sleep(1)
+
+    actors = piper_metadata.actors
+
+    ray.get([actor.enable_nccl_monitoring.remote() for actor in actors.values()])
+    piper_exec(schedule, loss_fn, args.dp, args.naive_grad_sync) 
+    ray.get([actor.print_nccl_overlaps.remote() for actor in actors.values()])
+    time.sleep(1)
 
     print(f"Running {args.iters} timed iterations...")
     iter_times = []
@@ -97,6 +111,7 @@ def main(args, pg):
         piper_exec(schedule, loss_fn, args.dp, args.naive_grad_sync)
         end = time.perf_counter()
         iter_times.append(end - start)
+        time.sleep(1)
     
     dp_rank = int(os.environ['PIPER_DP_RANK'])
     print(
@@ -104,14 +119,13 @@ def main(args, pg):
         f"rank {dp_rank} throughput= {(args.batch_size * args.mbs * config.block_size)/np.mean(iter_times):.3f} tokens/s ({len(iter_times)} samples)"
     )
 
-    actors = piper_metadata.actors
-
     if args.tracing:
         ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
 
         print(f"Running {args.trace_iters} tracing iterations...")
         for _ in range(args.trace_iters):
             piper_exec(schedule, loss_fn, args.dp, args.naive_grad_sync)
+            time.sleep(1)
 
         trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
         for rank, trace_data in trace_data_ret:
@@ -135,15 +149,15 @@ def main(args, pg):
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
-    parser.add_argument('--model', choices=['tiny', 'small', 'medium', 'large'], default='tiny',
-                        help='Model configuration: tiny, small, medium, or large (default: tiny)')
+    parser.add_argument('--model', choices=['tiny', 'small', 'medium', '7b', '22b'], default='tiny',
+                        help='Model configuration: tiny, small, medium, 7b, or 22b (default: tiny)')
     parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'dualpipev', 'dualpipev-nozb', 'no-pp'], default='1f1b',
                         help='Schedule type: gpipe, 1f1b, interleaved-1f1b, dualpipev, dualpipev-nozb, or no-pp (default: 1f1b)')
     parser.add_argument('--dp', type=int, default=2,
                         help='Number of data parallel degrees (default: 2)')
     parser.add_argument('--pp', type=int, default=2,
                         help='Number of pipeline parallel degrees (default: 2)')
-    parser.add_argument('--batch_size', type=int, default=16,
+    parser.add_argument('--batch-size', type=int, default=16,
                         help='Batch size (default: 16)')
     parser.add_argument('--mbs', type=int, default=4,
                         help='Number of microbatches (default: 4)')
@@ -161,6 +175,8 @@ def parse_args():
                         help='Enable activation checkpointing')
     parser.add_argument('--mode', choices=['sequential', 'naive', 'overlapped'], default='overlapped',
                         help='Mode: sequential, naive, or overlapped (default: overlapped)')
+    parser.add_argument('--no-nccl-conflict-handling', action='store_true', default=False,
+                        help='Disable NCCL conflict detection and dependency enforcement')
     return parser.parse_args()
 
 if __name__ == "__main__":
@@ -172,7 +188,7 @@ def parse_args():
         include_dashboard=False,
         _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp",
     )
-    pg = placement_group([{"CPU": 32*args.pp, "GPU": args.pp}] * args.dp)
+    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp)
     ray.get(pg.ready(), timeout=10)  # Wait for the placement group to be ready before proceeding
     print(placement_group_table(pg))
     piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)

From 8119059ba21d55c198bcc12a888b24a856d3f593 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Tue, 24 Mar 2026 21:49:38 +0000
Subject: [PATCH 080/100] new Piper backend based on DAG IR approach. The
 controller builds a unified commuincation/computation task DAG given the
 following inputs: pipeline-parallel schedule represented as a 2D array,
 pytorch model with annotations for EP dispatch/combine and optional
 annotations for pipeline stage boundaries, desired PP and DP/EP degrees,
 bucketing flag. The controller sends to each actor a DAG of tasks to execute
 and the model weights / computation graph for each compute task that the
 actor runs. The actor executes the DAG tasks in topological order and is
 agnostic to any scheduling decisions

---
 .gitignore                   |    9 +-
 Dockerfile                   |   13 +-
 docs/zero1_design.md         |  178 ----
 requirements.txt             |    8 +-
 run.sh                       |    5 -
 run_zero0.sh                 |   29 -
 run_zero1.sh                 |   29 -
 run_zero2.sh                 |   29 -
 src/piper.py                 |  171 +++-
 src/piper_actor.py           | 1293 +++++++++++++++++++++++----
 src/piper_compile.py         |   52 +-
 src/piper_coordinator.py     |    5 +-
 src/piper_exec.py            |   74 +-
 src/piper_graph_transform.py | 1604 +++++++++++++++++++++++++++++++++-
 src/piper_utils.py           |    4 +
 src/piper_zero.py            |  980 ---------------------
 test/all2all_bench.py        |  126 +++
 test/models/mixtral.py       |   64 +-
 test/print_schedules.py      |  102 ++-
 test/schedule_helpers.py     |   22 +-
 test/test_dag.py             |  204 +++++
 test/test_llama.py           |    3 +-
 test/test_mixtral.py         |    2 +-
 23 files changed, 3481 insertions(+), 1525 deletions(-)
 delete mode 100644 docs/zero1_design.md
 delete mode 100755 run.sh
 delete mode 100755 run_zero0.sh
 delete mode 100755 run_zero1.sh
 delete mode 100755 run_zero2.sh
 delete mode 100644 src/piper_zero.py
 create mode 100644 test/all2all_bench.py
 create mode 100644 test/test_dag.py

diff --git a/.gitignore b/.gitignore
index 9235f7a..c66457b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,4 +2,11 @@ __pycache__/
 
 .venv
 piper.egg-info/
-out/
\ No newline at end of file
+out/
+ray_tmp/
+aws/
+docs/
+figs/
+logs/
+.vscode/
+scripts/
diff --git a/Dockerfile b/Dockerfile
index f24c0c1..553f037 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,10 +1,5 @@
-# Use a minimal Python base image
-FROM python:3.10-slim
-
-# Set working directory inside the container
-WORKDIR /piper
-
-RUN pip install numpy ray==2.53.0 cupy-cuda12x
-RUN pip install torch==2.10.0 --index-url https://download.pytorch.org/whl/cu128
-
+FROM rayproject/ray:2.44.1-py310-cu128
 
+COPY requirements.txt /tmp/requirements.txt
+RUN pip install --no-cache-dir -r /tmp/requirements.txt \
+    --extra-index-url https://download.pytorch.org/whl/cu128
diff --git a/docs/zero1_design.md b/docs/zero1_design.md
deleted file mode 100644
index 9f9c065..0000000
--- a/docs/zero1_design.md
+++ /dev/null
@@ -1,178 +0,0 @@
-# ZeRO-1 with Bucketed Reduce-Scatter for Piper
-
-## Overview
-
-ZeRO-1 (Zero Redundancy Optimizer Stage 1) shards optimizer states across data-parallel
-(DP) ranks. This design uses **per-parameter sharding** and **gradient-sync buckets**
-to overlap communication with backward computation, matching PyTorch DDP's bucketing
-approach but adapted for ZeRO's reduce-scatter + all-gather pattern.
-
----
-
-## Memory Savings
-
-For Adam with fp32 states, optimizer state costs 12 bytes/element (fp32 master weight +
-momentum + variance). ZeRO-1 reduces this by `dp_degree`:
-
-| DP degree | Optimizer states per rank |
-|-----------|--------------------------|
-| 1 (DDP)   | 100%                     |
-| 2         | 50%                      |
-| 4         | 25%                      |
-| 8         | 12.5%                    |
-
----
-
-## Per-Parameter Sharding
-
-Each trainable parameter `p` (numel = N) is divided into `dp_degree` contiguous shards
-of size `ceil(N / dp_degree)`. DP rank `r` owns the shard covering flat indices
-`[r * shard_size, (r+1) * shard_size)` (last rank may be padded with zeros).
-
-**Optimizer states** are stored only for the local shard — giving the memory savings.
-The **full parameter** remains on every rank (needed for forward/backward), but the
-optimizer (Adam's `m`, `v`, etc.) operates only on the local slice.
-
----
-
-## Bucketing
-
-Parameters are grouped into fixed-size buckets so that a single collective covers
-multiple parameters. This amortises per-collective latency and improves bandwidth
-utilisation.
-
-### Construction Order
-
-Buckets are built in **reverse forward-use order** to approximate backward gradient
-arrival order:
-
-1. Extract the first-use position of each parameter in the FX graph (exact, no runtime
-   overhead — determined statically from graph node order).
-2. Reverse the list: parameters used last in forward produce gradients first in backward.
-3. Fill buckets by walking this reversed list, starting a new bucket when the running
-   size exceeds the 25 MB threshold (matching PyTorch DDP's default).
-
-Each bucket gets one flat gradient buffer, one flat parameter buffer, and one local
-shard buffer — all allocated once at setup and reused across steps.
-
----
-
-## Communication Pattern per Training Step
-
-```
-Forward pass  ─── all ranks have full params (as in DDP) ───────────────────►
-
-Backward pass:
-  grad(p_k) ready ──► bucket B_i full? ──► async reduce_scatter(B_i)
-                                            ▲ overlaps with backward for B_{i+1},...
-
-_update():
-  for each bucket (in backward order):
-    wait reduce_scatter
-    optimizer step on local shard          ◄── overlaps with all_gather(B_{i-1})
-    async all_gather → flat_param_buf
-
-  for each bucket:
-    wait all_gather
-    copy flat_param_buf → p.data
-```
-
-### Collectives Used
-
-| Operation | API | Direction | Result |
-|-----------|-----|-----------|--------|
-| Gradient averaging | `dist.reduce_scatter_tensor` | N params → local shard | Rank r gets averaged grad for shard r |
-| Parameter restore  | `dist.all_gather_into_tensor` | local shard → full params | Every rank gets complete updated params |
-
-Both are launched with `async_op=True` on a dedicated `comm_stream`, overlapping with
-backward computation on `comp_stream`.
-
----
-
-## Flat Buffer Layout
-
-For a bucket containing params `[p0, p1, p2]`:
-
-```
-flat_grad_buf / flat_param_buf (padded_numel = ceil(T / dp_degree) * dp_degree):
-┌─────────────┬─────────────┬─────────────┬──────────┐
-│  p0 elems   │  p1 elems   │  p2 elems   │ (padding)│
-└─────────────┴─────────────┴─────────────┴──────────┘
-               ◄──── shard for rank r ───►
-                 [r*shard_size, (r+1)*shard_size)
-```
-
-After `reduce_scatter`, rank r holds the averaged gradient for its shard.
-After the optimizer step, rank r holds the updated parameters for its shard.
-After `all_gather`, every rank has `flat_param_buf` fully reconstructed.
-
----
-
-## Implementation Structure
-
-### `src/piper_zero1.py`
-
-| Symbol | Role |
-|--------|------|
-| `BUCKET_SIZE_BYTES_DEFAULT` | 25 MB threshold (same as DDP) |
-| `_get_param_forward_order(gm, param_idxs, params)` | Static FX graph analysis to sort params by first use |
-| `ZeROOneBucket` | One bucket: flat buffers, local shard, per-bucket optimizer, grad-ready counting |
-| `ZeROOneState` | Manages all buckets, registers hooks, runs pipelined `finalize_step` |
-
-### `src/piper_actor.py` changes
-
-| Location | Change |
-|----------|--------|
-| `_create_actors` | Accept `zero_stage: int = 0` |
-| `PiperActor.__init__` | Accept `zero_stage`, store `self.zero1_states: dict` |
-| `_load_stage` | When `zero_stage >= 1`: extract param order from FX graph, build `ZeROOneState` (which registers hooks); skip `_prepare_dp_comm_ops` |
-| `_update` | When `zero_stage >= 1`: call `zero1_state.finalize_step(comm_stream, comp_stream)` |
-
-### `src/piper_compile.py` changes
-
-`piper_setup` accepts and forwards `zero_stage: int = 0`.
-
----
-
-## Hook Counting with Multiple Microbatches
-
-Piper supports `num_mbs` microbatches that accumulate gradients before a single
-optimizer step. `register_post_accumulate_grad_hook` fires once per microbatch per
-parameter. A bucket's reduce_scatter is launched only when every parameter in the
-bucket has accumulated gradients from **all** `num_mbs` microbatches.
-
-Per-step accumulation counts are tracked in `ZeROOneBucket._mb_accumulated` and reset
-in `reset_step_state()` at the end of `finalize_step`.
-
----
-
-## Interaction with BWD_I / BWD_W (Split Backward)
-
-Split backward (`BWD_I` + `BWD_W`) writes gradients to `p.grad` via
-`torch.autograd.grad` and manual accumulation, identical to full backward.
-`register_post_accumulate_grad_hook` fires whenever `p.grad` is updated, so bucket
-counting works transparently across both backward modes.
-
----
-
-## Comparison: Old vs. New Design
-
-| Aspect | Old (round-robin, all-reduce) | New (per-param shard, reduce_scatter + bucket) |
-|--------|-------------------------------|------------------------------------------------|
-| Param ownership | Whole params round-robin | Every rank co-owns every param (as a shard) |
-| Gradient sync | all-reduce (each rank gets full grad) | reduce_scatter (each rank gets its shard's grad) |
-| Gradient memory | Full gradient on every rank | Can free non-local gradient after reduce_scatter |
-| Optimizer memory | Saves 1 − 1/dp for locally-owned params only | Saves 1 − 1/dp for **all** params uniformly |
-| Load balance | Depends on per-param size distribution | Perfect: every rank has exactly `total/dp` elements |
-| Overlap | None | reduce_scatter overlaps with backward; all_gather with optimizer |
-
----
-
-## Future Extensions
-
-- **ZeRO-2**: After reduce_scatter, free `p.grad` for non-owned elements to recover
-  gradient memory. Requires splitting grad buffers.
-- **ZeRO-3**: Shard the parameters themselves; fetch on-demand during forward/backward.
-- **Larger / adaptive buckets**: Tune bucket size based on observed communication costs.
-- **Optimizer overlap with all_gather**: Prefetch parameters for the next microbatch
-  during the all_gather of the current step.
diff --git a/requirements.txt b/requirements.txt
index e521807..ee32a24 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,6 @@
 numpy
-ray
-torch==2.9.1+cu128
-cupy-cuda12x
\ No newline at end of file
+ray[default]==2.44.1
+torch==2.10.0
+cupy-cuda12x
+bitsandbytes
+graphviz
\ No newline at end of file
diff --git a/run.sh b/run.sh
deleted file mode 100755
index 85fdfab..0000000
--- a/run.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-docker run --rm -it \
-  --shm-size=10.24gb \
-  -v "$(pwd)":/piper \
-  piper \
-  bash
\ No newline at end of file
diff --git a/run_zero0.sh b/run_zero0.sh
deleted file mode 100755
index c8cba90..0000000
--- a/run_zero0.sh
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/bin/bash
-set -euo pipefail
-
-LOG_DIR="logs"
-mkdir -p "$LOG_DIR"
-
-TIMESTAMP="$(date +"%Y%m%d_%H%M%S")"
-LOG_FILE="$LOG_DIR/piper_zero1_${TIMESTAMP}.log"
-
-RAY_TMP="${RAY_TMP:-/tmp/ray}"
-mkdir -p "$RAY_TMP"
-
-export RAY_TMPDIR="$RAY_TMP"
-export CUDA_VISIBLE_DEVICES="0,1,2,3,4"
-export PYTHONUNBUFFERED=1
-export RAY_DEDUP_LOGS=0
-export PIPER_MASTER_PORT="${PIPER_MASTER_PORT:-12388}"
-
-echo "Logging to $LOG_FILE"
-python3 -m test.test_llama \
-  --dp 1 \
-  --pp 2 \
-  --warmup 5 \
-  --iters 10 \
-  --schedule interleaved-1f1b \
-  --mbs 4 \
-  --model 3b \
-  --zero_stage 0 \
-  >"$LOG_FILE" 2>&1
diff --git a/run_zero1.sh b/run_zero1.sh
deleted file mode 100755
index e7f98bc..0000000
--- a/run_zero1.sh
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/bin/bash
-set -euo pipefail
-
-LOG_DIR="logs"
-mkdir -p "$LOG_DIR"
-
-TIMESTAMP="$(date +"%Y%m%d_%H%M%S")"
-LOG_FILE="$LOG_DIR/piper_zero1_${TIMESTAMP}.log"
-
-RAY_TMP="${RAY_TMP:-/tmp/ray}"
-mkdir -p "$RAY_TMP"
-
-export RAY_TMPDIR="$RAY_TMP"
-export CUDA_VISIBLE_DEVICES="6,1,2,3,4"
-export PYTHONUNBUFFERED=1
-export RAY_DEDUP_LOGS=0
-export PIPER_MASTER_PORT="${PIPER_MASTER_PORT:-12388}"
-
-echo "Logging to $LOG_FILE"
-python3 -m test.test_llama \
-  --dp 2 \
-  --pp 2 \
-  --warmup 5 \
-  --iters 10 \
-  --schedule interleaved-1f1b \
-  --mbs 4 \
-  --model debug \
-  --zero_stage 1 \
-  >"$LOG_FILE" 2>&1
diff --git a/run_zero2.sh b/run_zero2.sh
deleted file mode 100755
index 124db26..0000000
--- a/run_zero2.sh
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/bin/bash
-set -euo pipefail
-
-LOG_DIR="logs"
-mkdir -p "$LOG_DIR"
-
-TIMESTAMP="$(date +"%Y%m%d_%H%M%S")"
-LOG_FILE="$LOG_DIR/piper_zero2_${TIMESTAMP}.log"
-
-RAY_TMP="${RAY_TMP:-/tmp/ray}"
-mkdir -p "$RAY_TMP"
-
-export RAY_TMPDIR="$RAY_TMP"
-export CUDA_VISIBLE_DEVICES="6,1,2,3,4"
-export PYTHONUNBUFFERED=1
-export RAY_DEDUP_LOGS=0
-export PIPER_MASTER_PORT="${PIPER_MASTER_PORT:-12388}"
-
-echo "Logging to $LOG_FILE"
-python3 -m test.test_llama \
-  --dp 2 \
-  --pp 2 \
-  --warmup 5 \
-  --iters 10 \
-  --schedule interleaved-1f1b \
-  --mbs 4 \
-  --model debug \
-  --zero_stage 2 \
-  >"$LOG_FILE" 2>&1
diff --git a/src/piper.py b/src/piper.py
index 7200840..c60b083 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -4,15 +4,35 @@
 import gc
 from torch._dynamo.backends.registry import register_backend
 from .piper_utils import _serialize_graphmodule, piper_metadata, create_logger, LOG_LEVEL
-from .piper_graph_transform import _get_dp_comm_ops, _split_gm_by_stages, _profile_and_split_gm, _insert_a2a_ops, _insert_p2p_ops
+from .piper_graph_transform import (
+    _get_dp_comm_ops,
+    _split_gm_by_stages,
+    _profile_and_split_gm,
+    _insert_a2a_ops,
+    _insert_p2p_ops,
+    schedule_to_dag,
+    insert_p2p_ops,
+    insert_ar_ops,
+    expand_bucket_tasks,
+    expand_a2a_tasks,
+    visualize_dag,
+    visualize_schedule,
+    print_dag_order,
+    bucket_stage,
+    split_by_a2a,
+)
 from .piper_actor import _get_actor
 
 logger = create_logger("piper_backend", LOG_LEVEL)
 
 
+# ---------------------------------------------------------------------------
+# Piper torch.compile backend
+# ---------------------------------------------------------------------------
+
 @register_backend
 def piper(gm, example_inputs, **kwargs):
-    
+
     # gm.print_readable()
 
     original_gm = gm
@@ -38,24 +58,63 @@ def piper(gm, example_inputs, **kwargs):
 
     refs = []
     actor_stages = []
-    for (stage_id, stage_gm, input_idxs, param_idxs, graphargs, placeholders) in submodules:
-        n_a2a_ops = 0
-        if dp_degree > 1:
-            stage_gm, n_a2a_ops = _insert_a2a_ops(stage_gm)
-
-        stage_gm_data = _serialize_graphmodule(stage_gm)
+    # stage_id -> {boundary_bucket_id -> tensor_idx} collected for DAG expansion
+    stage_a2a_boundaries: dict = {}
 
+    for (stage_id, stage_gm, input_idxs, param_idxs, graphargs, placeholders) in submodules:
         actor_id = piper_metadata.stage_to_device[stage_id]
         actor = _get_actor(actor_id)
         actor_stages.append((actor, stage_id))
 
+        # Split at A2A annotation boundaries (expert-parallel only, requires dp_degree > 1).
+        if dp_degree > 1:
+            a2a_segments, boundary_infos = split_by_a2a(stage_gm, graphargs, input_idxs, param_idxs)
+            if boundary_infos:
+                logger.debug(
+                    f"Stage {stage_id} split into {len(a2a_segments)} A2A segments "
+                    f"with {len(boundary_infos)} boundaries"
+                )
+        else:
+            a2a_segments = [(stage_gm, input_idxs, param_idxs, graphargs)]
+            boundary_infos = []
+
+        # Apply parameter bucketing only to even-indexed segments (segments outside A2A pairs).
+        all_modules: list = []
+        a2a_boundaries: dict = {}  # boundary_bucket_id -> tensor_idx
+
+        for seg_idx, (seg_gm, seg_in, seg_param, seg_args) in enumerate(a2a_segments):
+            if piper_metadata.bucketing and seg_idx % 2 == 0:
+                buckets = bucket_stage(seg_gm, seg_args, seg_in, seg_param)
+                if len(buckets) > 1:
+                    logger.debug(
+                        f"Stage {stage_id} segment {seg_idx} bucketed into {len(buckets)} buckets"
+                    )
+            else:
+                buckets = [(seg_gm, seg_in, seg_param, seg_args)]
+
+            all_modules.extend(buckets)
+            seg_end_bucket = len(all_modules) - 1  # inclusive index of last bucket in this segment
+
+            if seg_idx < len(boundary_infos):
+                binfo = boundary_infos[seg_idx]
+                a2a_boundaries[seg_end_bucket] = binfo["tensor_idx"]
+
+        if a2a_boundaries:
+            stage_a2a_boundaries[stage_id] = a2a_boundaries
+
+        modules_data = [
+            {
+                "gm_data": _serialize_graphmodule(bgm),
+                "graphargs": bargs,
+                "input_idxs": b_in,
+                "param_idxs": b_param,
+            }
+            for bgm, b_in, b_param, bargs in all_modules
+        ]
         refs.append(actor._load_stage.remote(
             stage_id,
-            stage_gm_data,
-            graphargs,
-            input_idxs,
-            param_idxs,
-            n_a2a_ops,
+            modules_data,
+            a2a_boundaries,
             use_activation_checkpointing=piper_metadata.use_activation_checkpointing,
         ))
 
@@ -73,10 +132,66 @@ def piper(gm, example_inputs, **kwargs):
     for stage_id in piper_metadata.stage_to_device.keys():
         if stage_id < len(piper_metadata.stage_to_device.keys()) - 1:
             piper_metadata.dag.add((stage_id, stage_id+1))
-    
+
+    # Build task DAG from the schedule stored by piper_setup.
+    if piper_metadata.schedule is not None:
+        piper_metadata.task_dag = schedule_to_dag(piper_metadata.schedule)
+        logger.info(
+            f"Built task DAG: {len(piper_metadata.task_dag.nodes)} nodes, "
+            f"{sum(len(n.data_succs) for n in piper_metadata.task_dag.nodes)} data edges, "
+            f"{sum(1 for n in piper_metadata.task_dag.nodes if n.temporal_succ is not None)} temporal edges"
+        )
+
+        dag = piper_metadata.task_dag
+
+        # Expand multi-segment stages into per-segment FWD/BWD task chains.
+        # This covers both param-bucketed stages (--bucketing) and A2A-split stages
+        # (--dp > 1 with MoE), both of which load multiple modules per stage.
+        bucket_counts: dict = {}
+        for actor in piper_metadata.actors.values():
+            counts = ray.get(actor.get_bucket_fwd_counts.remote())
+            bucket_counts.update(counts)
+        # Only expand stages that actually have more than one segment.
+        bucket_counts = {sid: n for sid, n in bucket_counts.items() if n > 1}
+        if bucket_counts:
+            dag = expand_bucket_tasks(dag, bucket_counts)
+            logger.info(
+                f"Expanded stages {list(bucket_counts.keys())} into "
+                f"per-segment FWD/BWD nodes"
+            )
+
+        # Insert FWD_A2A / BWD_A2A nodes for expert-parallel all-to-all boundaries.
+        if dp_degree > 1 and stage_a2a_boundaries:
+            dag = expand_a2a_tasks(dag, stage_a2a_boundaries)
+            logger.info(
+                f"Inserted A2A task nodes for stages {list(stage_a2a_boundaries.keys())}"
+            )
+
+        # Split into per-rank DAGs.
+        per_rank_dags = insert_p2p_ops(dag)
+
+        # Insert all-reduce nodes when using data parallelism.
+        if dp_degree > 1:
+            per_rank_dags = insert_ar_ops(per_rank_dags)
+            logger.info("Inserted ALL_REDUCE nodes for DP gradient sync")
+
+        piper_metadata.per_rank_dags = per_rank_dags
+        actors = piper_metadata.actors
+        for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags):
+            visualize_dag(per_rank_dag, output_path=f"figs/rank{pp_rank}_dag")
+            print_dag_order(per_rank_dag, label=f"rank {pp_rank}")
+        ray.get([
+            actors[pp_rank].load_dag.remote(per_rank_dag)
+            for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags)
+        ])
+        logger.info(
+            f"Loaded per-rank DAGs onto {len(piper_metadata.per_rank_dags)} actors"
+        )
+
     example_outputs = original_gm(*example_inputs)
 
     def callback(*args):
+        # assert False
         logger.warning("Should not directly call compiled module, running non-distributed execution")
         return example_outputs
 
@@ -87,4 +202,30 @@ def callback(*args):
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
 
-    return callback
\ No newline at end of file
+    return callback
+
+
+# ---------------------------------------------------------------------------
+# DAG-based execution entry point
+# ---------------------------------------------------------------------------
+
+def piper_exec_dag(loss_fn) -> list:
+    """Execute one training step using the per-rank TaskDAG and :meth:`run_dag`.
+
+    Per-rank DAGs are built and loaded onto actors by the piper backend during
+    compilation.  This function just invokes ``run_dag`` in parallel on all actors.
+
+    Returns:
+        Flat list of per-microbatch losses collected from all actors.
+    """
+    assert piper_metadata.per_rank_dags is not None, (
+        "per_rank_dags is None — ensure the model was compiled with the piper backend "
+        "before calling piper_exec_dag()"
+    )
+    actors = piper_metadata.actors
+    run_refs = [
+        actors[pp_rank].run_dag.remote(loss_fn=loss_fn)
+        for pp_rank in range(len(piper_metadata.per_rank_dags))
+    ]
+    results = ray.get(run_refs)
+    return [item for sublist in results if sublist for item in sublist]
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 5817d90..1164959 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -2,7 +2,7 @@
 import torch
 import logging
 import os
-from typing import Any, Dict, List, Set, Tuple
+from typing import Any, Dict, List, Optional, Set, Tuple
 import gc
 import threading
 from torch._guards import CompileId
@@ -21,7 +21,7 @@
     NcclOverlapDetector,
 )
 from .backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
-from .piper_zero import ZeROOneState, _get_param_forward_order
+from .piper_exec import TaskType, TaskDAG, TaskNode
 
 CLEANUP_MEMORY = False
 
@@ -32,6 +32,14 @@ def _get_rank(pp_rank, dp_rank, pp_degree):
     return pp_rank + dp_rank * pp_degree
 
 
+def find_free_port():
+    import socket
+
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("0.0.0.0", 0))
+        port = s.getsockname()[1]
+    return port
+
 def _create_actors(
     num_actors,
     optim_class,
@@ -42,7 +50,6 @@ def _create_actors(
     mode="sequential",
     stage_to_device=None,
     pg=None,
-    zero_stage: int = 0,
 ):
     dp_rank = int(os.environ["PIPER_DP_RANK"])
     world_size = int(os.environ["PIPER_WORLD_SIZE"])
@@ -58,9 +65,15 @@ def _create_actors(
             "cuda-event-trace": "false",
             "stop-on-exit": "true",
         }} if profile else {}
+        master_env = {
+            "env_vars": {
+                "PIPER_MASTER_ADDR": os.environ.get("PIPER_MASTER_ADDR", "127.0.0.1"),
+                "PIPER_MASTER_PORT": os.environ.get("PIPER_MASTER_PORT", "10000"),
+            }
+        }
         actor = PiperActor.options(
             num_gpus=0.7,
-            runtime_env=nsight_env,
+            runtime_env={**nsight_env, **master_env},
             scheduling_strategy=PlacementGroupSchedulingStrategy(
                 placement_group=pg,
                 placement_group_bundle_index=dp_rank
@@ -77,7 +90,6 @@ def _create_actors(
             pp_degree=pp_degree,
             mode=mode,
             stage_to_device=stage_to_device,
-            zero_stage=zero_stage,
         )
         piper_metadata.actors[pp_rank] = actor
         logger.debug(
@@ -90,7 +102,7 @@ def _get_actor(pp_rank):
 
     return piper_metadata.actors[pp_rank]
 
-@ray.remote(enable_tensor_transport=True)
+@ray.remote
 class PiperActor:
     def __init__(
         self,
@@ -105,7 +117,6 @@ def __init__(
         pp_degree=1,
         mode="sequential",
         stage_to_device=None,
-        zero_stage: int = 0,
     ):
         self.logger = create_logger("piper_actor", LOG_LEVEL)
         self.mode = mode
@@ -113,8 +124,6 @@ def __init__(
         self.pp_rank = pp_rank
         self.optim_class = optim_class
         self.naive_gradient_sync = naive_gradient_sync
-        # ZeRO optimisation stage (0 = disabled, 1 = optimizer state partitioning).
-        self.zero_stage = zero_stage
 
         self.dp_rank = dp_rank
         self.dp_degree = dp_degree
@@ -147,11 +156,19 @@ def __init__(
         self.p2p_recv_stream = torch.cuda.Stream()
         self.overlapped_comp_stream = torch.cuda.Stream()
         self.overlapped_p2p_stream = torch.cuda.Stream()
+        # Aliases for FWD and BWD compute streams.  Setting these to separate
+        # torch.cuda.Stream() instances enables compute-A2A overlap for FWD_BWD
+        # schedule cells; keeping them as aliases of comp_stream is correct but
+        # does not overlap FWD and BWD compute.
+        self.fwd_comp_stream = self.comp_stream
+        self.bwd_comp_stream = self.comp_stream
         self.overlap_detector = NcclOverlapDetector()
         if mode == "naive":
             self.per_mb_streams = [torch.cuda.Stream() for _ in range(2)]
         self.n_a2a_ops = dict()
         self.overlap_a2a_ops = False
+        # A2A boundaries per stage: stage_id -> {boundary_bucket_id -> tensor_idx}
+        self.a2a_boundaries: dict = {}
 
         # map stage id -> compiled fx.Graph function
         self.forward_fns = dict()
@@ -166,15 +183,15 @@ def __init__(
         # map stage id -> indices of the model parameters used by the fx.Graph
         self.param_idxs = dict()
         # map stage id -> indices of the model parameters used by the fx.Graph
-        self.param_idxs = dict()
-        # map stage id -> optimizer for the fx.Graph
+        self.trainable_param_idxs = dict()
+        # map stage id -> optimizer for the fx.Graph (used in legacy non-DAG path)
         self.optims = dict()
-        # map stage id -> ZeROOneState (populated when zero_stage >= 1 and dp_degree > 1)
-        self.zero1_states: dict[int, ZeROOneState] = dict()
         # map stage id -> mb_idx -> previous activation (if this stage is not first)
         self.inp_activation = defaultdict(dict)
         # map stage id -> mb_idx -> current activation
         self.out_activation = defaultdict(dict)
+        # map stage id -> (shape, dtype) of the output activation tensor (for pre-FWD BWD recv)
+        self.output_activation_shape: dict = {}
         # map (src_stage, dst_stage, mb_idx, is_sender) -> tensor for p2p communication
         self.p2p_cache = dict()
         # map recv p2p_op -> CUDA event recorded after the recv completes on p2p_stream
@@ -195,6 +212,32 @@ def __init__(
         self.trace_data = defaultdict(list)
         self.task_labels = dict()
 
+        # DAG execution state
+        self.dag = None
+        self.send_buffer: dict = {}  # ((stage_id, bucket_id_or_None), mb_idx) -> tensor(s); or (None, mb_idx) for BWD
+        self.send_buffer_ready: dict = {}  # same key format as send_buffer -> threading.Event
+        self.recv_buffer: dict = {}  # same key format as send_buffer -> tensor(s)
+        self.recv_events: dict = {}  # same key format as send_buffer -> cuda.Event
+        self.bucket_buffer: dict = {}  # (stage_id, mb_idx, bucket_id) -> (pre_detach_outs, detached_outs)
+        # A2A boundary state: (stage_id, mb_idx, boundary_bucket_id) -> (x_detached, x_a2a)
+        self.a2a_buffer: dict = {}
+        # CUDA events recorded on a2a_stream after each FWD_A2A / BWD_A2A op
+        self.a2a_events: dict = {}  # (stage_id, mb_idx, type, bucket_id) -> cuda.Event
+        # CUDA events recorded on comm_stream after each all-reduce launches
+        self.ar_events: dict = {}  # (stage_id, bucket_id) -> cuda.Event
+
+        # Per-bucket stage data (populated by _load_stage)
+        self.bucket_fwd_fns: dict[int, list] = {}   # stage_id -> [fwd_fn per bucket]
+        self.bucket_fwd_args: dict[int, list] = {}  # stage_id -> [args_list per bucket]
+        self.bucket_param_idxs: dict[int, list] = {}  # stage_id -> [param_idxs per bucket]
+        self.bucket_optims: dict[int, list] = {}      # stage_id -> [optimizer per bucket]
+        # Contiguous flat tensors for param data and gradients, keyed (stage_id, bucket_id)
+        self.bucket_flat_params: dict = {}   # (stage_id, bucket_id) -> flat param tensor
+        self.bucket_flat_grads: dict = {}    # (stage_id, bucket_id) -> flat grad tensor
+        self.bucket_trainable_param_idxs: dict = {}  # (stage_id, bucket_id) -> list of indices into bucket_fwd_args
+        # stage_id -> mb_idx -> list of (pre_detach_out, detached_input_to_next) per boundary
+        self.bucket_boundaries: dict = defaultdict(dict)
+
         # map stage id -> mb_idx -> parameter groups for backward pass
         self.bw_param_groups = defaultdict(dict)
         # map stage id -> mb_idx -> gradient cache for backward pass
@@ -340,11 +383,19 @@ def _stop_timing(self, stream, label):
                 stop.synchronize()
                 self.trace_data[label].append(start.elapsed_time(stop))
 
-    def _join_process_groups(self):
-        master_addr = os.environ.get("PIPER_MASTER_ADDR", "127.0.0.1")
-        master_port = os.environ.get("PIPER_MASTER_PORT", "10000")
+    def get_node_ip_and_free_port(self):
+        import socket
+
+        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+            s.bind(("0.0.0.0", 0))
+            port = s.getsockname()[1]
+        return ray.util.get_node_ip_address(), port
+
+    def _join_process_groups(self, master_addr, master_port):
         init_method = f"tcp://{master_addr}:{master_port}"
 
+        self.logger.info(f"Actor {self.global_rank} initializing process groups with master address {master_addr}:{master_port}")
+
         self.device = f"cuda:{self.global_rank % torch.cuda.device_count()}"
         torch.cuda.set_device(self.device)
 
@@ -396,14 +447,15 @@ def _join_pp_process_group(self):
                     rank_lo, rank_hi = group_ranks[i], group_ranks[j]
                     pg_lo_to_hi = dist.new_group(ranks=[rank_lo, rank_hi], backend="nccl")
                     pg_hi_to_lo = dist.new_group(ranks=[rank_lo, rank_hi], backend="nccl")
-                    if pp_group_id == my_pp_group_id:
+                    if self.global_rank in (rank_lo, rank_hi):
+                        self.logger.debug(f"Global rank {self.global_rank} saving pp communicators for ranks {[rank_lo, rank_hi]}")
                         self.pp_groups[(rank_lo, rank_hi)] = pg_lo_to_hi
                         self.pp_groups[(rank_hi, rank_lo)] = pg_hi_to_lo
-            if pp_group_id == my_pp_group_id:
-                self.logger.debug(
-                    f"Global rank {self.global_rank} joined pp group {pp_group_id} "
-                    f"with communicators for ranks {group_ranks}"
-                )
+            # if pp_group_id == my_pp_group_id:
+            #     self.logger.debug(
+            #         f"Global rank {self.global_rank} joined pp group {pp_group_id} "
+            #         f"with communicators for ranks {group_ranks}"
+            #     )
 
         # Warm up all communicators to force eager NCCL initialization.
         dummy = torch.zeros(1, device=self.device)
@@ -531,114 +583,182 @@ def _wait_for_comm_ops(self):
                         done = True
 
     def _load_stage(
-        self, stage_id: int, gm_data, forward_args, input_idxs, param_idxs, n_a2a_ops,
+        self,
+        stage_id: int,
+        modules_data: list,
+        a2a_boundaries: dict = None,
         use_activation_checkpointing: bool = False,
-    ):
-        self.logger.debug(f"Loading stage {stage_id} graph on actor {self.global_rank}")
-
-        gm = _deserialize_graphmodule(gm_data)
-
-        self.graph_modules[stage_id] = gm
+    ) -> None:
+        """Load a (possibly bucketed) stage.
 
-        if use_activation_checkpointing:
-            forward_fn = gm.forward
-            self.forward_fns[stage_id] = lambda *args, _fn=forward_fn: torch.utils.checkpoint.checkpoint(_fn, *args, use_reentrant=False)
-            self.logger.debug(f"Applied activation checkpointing to stage {stage_id}")
-        else:
-            self.forward_fns[stage_id] = gm.forward
+        *modules_data* is a list of dicts, one per module/bucket, each with keys:
+        ``gm_data``, ``graphargs``, ``input_idxs``, ``param_idxs``.
 
-        # initialize A2A states
-        self.n_a2a_ops[stage_id] = n_a2a_ops
+        A non-bucketed stage is represented as a single-element list.
+        Bucket 0 handles the activation input from the previous stage (or
+        ``self.inputs`` for stage 0).  Subsequent modules receive the
+        cross-segment activation output of the previous module.
+        """
+        self.logger.debug(
+            f"Loading stage {stage_id} ({len(modules_data)} module(s)) on actor {self.global_rank}"
+        )
 
-        self.stage_id = stage_id
-        self.input_idxs[stage_id] = input_idxs
-        self.param_idxs[stage_id] = param_idxs
-
-        # Save input meta and punch holes
-        for i in self.input_idxs[stage_id]:
-            self.forward_input_meta[stage_id][i] = (
-                forward_args[i].shape,
-                forward_args[i].dtype,
-                forward_args[i].requires_grad,
-            )
-            forward_args[i] = None
+        self.bucket_fwd_fns[stage_id] = []
+        self.bucket_fwd_args[stage_id] = []
+        self.bucket_param_idxs[stage_id] = []
+        self.bucket_optims[stage_id] = []
+        self.a2a_boundaries[stage_id] = a2a_boundaries or {}
 
-        realized = [None] * len(forward_args)
         g = torch.Generator(device=self.device)
         g.manual_seed(1000 * self.global_rank + stage_id)
 
-        for i, arg in enumerate(forward_args):
-            if arg is None:
-                continue
+        first_gm = None
+        last_gm = None
+
+        for b_idx, bd in enumerate(modules_data):
+            gm = _deserialize_graphmodule(bd["gm_data"])
+            if b_idx == 0:
+                first_gm = gm
+            last_gm = gm
 
-            t = torch.empty(arg.shape, dtype=arg.dtype, device=self.device)
+            forward_args = list(bd["graphargs"])
+            b_input_idxs = list(bd["input_idxs"])
+            b_param_idxs = list(bd["param_idxs"])
 
-            if arg.requires_grad:
-                t.requires_grad_(True)
-                torch.nn.init.normal_(t, mean=0.0, std=0.02, generator=g)
+            self.logger.debug(
+                f"Stage {stage_id} module {b_idx} input indices: {b_input_idxs}"
+            )
+
+            # Module 0: save activation-input metadata for the stage interface
+            # (used by _exec_recv to pre-allocate recv buffers).
+            if b_idx == 0:
+                self.input_idxs[(stage_id, None)] = b_input_idxs  # legacy path alias
+                for i in b_input_idxs:
+                    meta = forward_args[i]
+                    if meta is not None:
+                        self.forward_input_meta[stage_id][i] = (
+                            tuple(meta.shape),
+                            meta.dtype,
+                            bool(getattr(meta, "requires_grad", False)),
+                        )
+                    forward_args[i] = None
             else:
-                t.zero_()
-
-            realized[i] = t
-
-        self.forward_args[stage_id] = realized
-
-        # Collect trainable parameters (input slots are already None at this point).
-        trainable_param_idxs = [
-            i for i in param_idxs
-            if realized[i] is not None and realized[i].requires_grad
-        ]
-        params = [realized[i] for i in trainable_param_idxs]
-
-        if self.zero_stage >= 1 and self.dp_degree > 1:
-            # ZeRO path (stage 1, 2, or 3): gradient hooks and per-bucket
-            # optimizers are managed inside ZeROOneState.
-            if use_activation_checkpointing and self.zero_stage >= 3:
-                raise ValueError(
-                    "ZeRO-3 (parameter sharding) is incompatible with "
-                    "activation checkpointing."
-                )
-            params_in_fwd_order = _get_param_forward_order(gm, trainable_param_idxs, params)
-            if stage_id not in self.zero1_states:
-                self.zero1_states[stage_id] = ZeROOneState(
-                    all_params=params,
-                    params_in_forward_order=params_in_fwd_order,
-                    dp_rank=self.dp_rank,
-                    dp_degree=self.dp_degree,
-                    dp_group=self.dp_group,
-                    device=self.device,
-                    num_mbs=self.num_mbs,
-                    optim_class=self.optim_class,
-                    comm_stream=self.comm_stream,
-                    comp_stream=self.comp_stream,
-                    zero_stage=self.zero_stage,
-                    gm=gm if self.zero_stage == 3 else None,
-                    param_idxs=trainable_param_idxs if self.zero_stage == 3 else None,
+                for i in b_input_idxs:
+                    forward_args[i] = None
+
+            self.input_idxs[(stage_id, b_idx)] = b_input_idxs
+
+            # Realize parameter tensors.
+            realized = [None] * len(forward_args)
+            for i, arg in enumerate(forward_args):
+                if arg is None:
+                    continue
+                t = torch.empty(arg.shape, dtype=arg.dtype, device=self.device)
+                if arg.requires_grad:
+                    t.requires_grad_(True)
+                    torch.nn.init.normal_(t, mean=0.0, std=0.02, generator=g)
+                else:
+                    t.zero_()
+                realized[i] = t
+
+            # Forward function (with optional activation checkpointing).
+            if use_activation_checkpointing:
+                fwd = gm.forward
+                forward_fn = lambda *args, _fn=fwd: torch.utils.checkpoint.checkpoint(
+                    _fn, *args, use_reentrant=False
                 )
-                # ZeRO-3: use the split GraphModule for forward so that
-                # per-bucket module hooks drive all-gather pipelining.
-                if self.zero_stage == 3:
-                    self.forward_fns[stage_id] = (
-                        self.zero1_states[stage_id].split_gm.forward
-                    )
             else:
-                # A second graph loaded onto the same stage (shouldn't normally
-                # happen, but guard against it gracefully).
-                self.logger.warning(
-                    f"ZeROOneState already exists for stage {stage_id}; "
-                    "skipping re-initialisation."
-                )
-        else:
-            # Standard DDP path: per-param post-accumulate hooks for gradient sync.
-            if not self.naive_gradient_sync and self.dp_degree > 1:
-                self._prepare_dp_comm_ops(stage_id)
+                forward_fn = gm.forward
+
+            self.bucket_fwd_fns[stage_id].append(forward_fn)
+            self.bucket_fwd_args[stage_id].append(realized)
+            self.bucket_param_idxs[stage_id].append(b_param_idxs)
 
-            if stage_id not in self.optims:
-                self.optims[stage_id] = self.optim_class(params)
+            # Collect trainable parameters and build a contiguous flat tensor so
+            # a single all-reduce call can sync the entire module's gradients.
+            trainable_idxs = [
+                i for i in b_param_idxs
+                if realized[i] is not None and realized[i].requires_grad
+            ]
+            trainable = [realized[i] for i in trainable_idxs]
+            self.bucket_trainable_param_idxs[(stage_id, b_idx)] = trainable_idxs
+
+            if trainable:
+                flat_params = torch.cat([p.detach().view(-1) for p in trainable]).contiguous()
+                flat_params.requires_grad_(False)
+                flat_grads = torch.zeros_like(flat_params)
+                offset = 0
+                for idx, p in zip(trainable_idxs, trainable):
+                    numel = p.numel()
+                    realized[idx] = realized[idx].detach()
+                    realized[idx].data = flat_params[offset:offset + numel].view(p.shape)
+                    realized[idx].requires_grad_(True)
+                    offset += numel
+                self.bucket_flat_params[(stage_id, b_idx)] = flat_params
+                self.bucket_flat_grads[(stage_id, b_idx)] = flat_grads
             else:
-                self.optims[stage_id].add_param_group({"params": params})
+                self.bucket_flat_params[(stage_id, b_idx)] = None
+                self.bucket_flat_grads[(stage_id, b_idx)] = None
+
+            # Optimizer for this module's trainable parameters.
+            trainable_for_optim = [realized[i] for i in trainable_idxs]
+            optim = self.optim_class(trainable_for_optim) if trainable_for_optim else None
+            self.bucket_optims[stage_id].append(optim)
+
+            # Legacy non-DAG DDP: register per-param allreduce hooks for module 0.
+            # if b_idx == 0 and self.dp_degree > 1 and not self.naive_gradient_sync:
+            #     self.forward_args[stage_id] = realized
+                # self._prepare_dp_comm_ops(stage_id)
+
+        # Set legacy fields from first module for ZeroBubble BWD_I/BWD_W compatibility.
+        self.forward_args[stage_id] = self.bucket_fwd_args[stage_id][0]
+        self.param_idxs[stage_id] = self.bucket_param_idxs[stage_id][0]
+        self.graph_modules[stage_id] = first_gm
+        self.stage_id = stage_id
 
-        del gm_data
+        # Compute output activation shape from the last module for BWD recv buffer
+        # pre-allocation (e.g. interleaved 1F1B recv before forward has run).
+        last_realized = self.bucket_fwd_args[stage_id][-1]
+        last_b_input_idxs = self.input_idxs[(stage_id, len(modules_data) - 1)]
+        output_shapes = []
+
+        # Try FX graph node metadata first.
+        for node in last_gm.graph.nodes:
+            if node.op == "output":
+                out_args = node.args[0]
+                if not isinstance(out_args, (tuple, list)):
+                    out_args = [out_args]
+                for out_node in out_args:
+                    if isinstance(out_node, torch.fx.Node):
+                        meta_val = out_node.meta.get("val", None)
+                        if meta_val is not None and isinstance(meta_val, torch.Tensor) and meta_val.requires_grad:
+                            output_shapes.append((tuple(meta_val.shape), meta_val.dtype))
+                break
+
+        if not output_shapes:
+            # Fallback: probe forward pass with dummy inputs.
+            probe_args = list(last_realized)
+            g_probe = torch.Generator(device=self.device)
+            g_probe.manual_seed(42 + stage_id)
+            for i in last_b_input_idxs:
+                meta = modules_data[-1]["graphargs"][i]
+                if meta is not None:
+                    t = torch.zeros(meta.shape, dtype=meta.dtype, device=self.device)
+                    t.requires_grad_(bool(getattr(meta, "requires_grad", True)))
+                    probe_args[i] = t
+            with torch.no_grad():
+                probe_out = last_gm.forward(*probe_args)
+            for out in (probe_out if isinstance(probe_out, (tuple, list)) else [probe_out]):
+                if isinstance(out, torch.Tensor) and out.requires_grad:
+                    output_shapes.append((tuple(out.shape), out.dtype))
+
+        if output_shapes:
+            self.output_activation_shape[stage_id] = output_shapes
+            self.logger.debug(f"Stage {stage_id} output activation shapes: {output_shapes}")
+        else:
+            self.logger.warning(
+                f"Could not determine output activation shape for stage {stage_id}"
+            )
 
     def _exec_fwd_recv(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, comp_stream=None):
         if stage_id == 0:
@@ -653,7 +773,7 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, com
         # assert p2p_op not in self.p2p_cache
 
         inputs_to_recv = []
-        for i in self.input_idxs[stage_id]:
+        for i in self.input_idxs[(stage_id, None)]:
             shape, dtype, requires_grad = self.forward_input_meta[stage_id][i]
             inputs_to_recv.append(
                 torch.empty(
@@ -662,11 +782,11 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, com
             )
 
         # For non-first stages, receive input tensors from the previous stage
-        pp_rank = piper_metadata.stage_to_device[stage_id - 1]
+        pp_rank = self.stage_to_device[stage_id - 1]
         global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
         if self.global_rank == global_src_rank:
-            for i in self.input_idxs[stage_id]:
+            for i in self.input_idxs[(stage_id, None)]:
                 inputs_to_recv[i] = self.p2p_cache.pop((stage_id-1, stage_id, mb_idx, True))
         else:
             self.logger.debug(
@@ -675,7 +795,7 @@ def _exec_fwd_recv(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, com
             self._start_timing(p2p_stream, "fwd_p2p_recv")
             _ov_token = self.overlap_detector.before_kernel(p2p_stream, "fwd_p2p_recv", "p2p_recv_stream")
             with torch.cuda.stream(p2p_stream):
-                for i in self.input_idxs[stage_id]:
+                for i in self.input_idxs[(stage_id, None)]:
                     dist.recv(
                         inputs_to_recv[i],
                         src=global_src_rank,
@@ -704,7 +824,7 @@ def _exec_fwd_send(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, com
         p2p_op = (stage_id, stage_id + 1, mb_idx, True)
         output = self.p2p_cache.pop(p2p_op)
         # For non-final stages, send output tensors to the next stage
-        pp_rank = piper_metadata.stage_to_device[stage_id + 1]
+        pp_rank = self.stage_to_device[stage_id + 1]
         global_dst_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
         if self.global_rank == global_dst_rank:
@@ -742,7 +862,7 @@ def _exec_bwd_recv(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, com
         out_activation = self.out_activation[stage_id][mb_idx]
         # For non-final stages, recieve input gradients from the subsequent backward pass
         input_grad = torch.empty_like(out_activation)
-        pp_rank = piper_metadata.stage_to_device[stage_id + 1]
+        pp_rank = self.stage_to_device[stage_id + 1]
         global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
         p2p_op = (stage_id + 1, stage_id, mb_idx, False)
@@ -784,7 +904,7 @@ def _exec_bwd_send(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, com
         if output_grad is None:
             self.logger.warning(f"No output gradient found for stage {stage_id} mb {mb_idx} on actor {self.global_rank}")
             assert False
-        pp_rank = piper_metadata.stage_to_device[stage_id - 1]
+        pp_rank = self.stage_to_device[stage_id - 1]
         global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
 
         if self.global_rank == global_src_rank:
@@ -834,7 +954,7 @@ def _forward_impl(self, stage_id: int, mb_idx: int, *deps):
 
         if stage_id == 0:
             # For the first stage, load input tensors from self.inputs
-            for i, inp in zip(self.input_idxs[stage_id], self.inputs):
+            for i, inp in zip(self.input_idxs[(stage_id, None)], self.inputs):
                 self.forward_args[stage_id][i] = inp
         else:
             # Wait for recv to complete if this stage receives from the previous stage
@@ -848,18 +968,18 @@ def _forward_impl(self, stage_id: int, mb_idx: int, *deps):
             self._stop_timing(comp_stream, "fwd_recv_wait")
 
             # Detach to avoid double-backprop if the previous stage is co-located
-            for i, tensor in zip(self.input_idxs[stage_id], inputs_from_prev_stage):
+            for i, tensor in zip(self.input_idxs[(stage_id, None)], inputs_from_prev_stage):
                 if isinstance(tensor, (tuple, list)):
                     assert len(tensor) == 1
                     tensor = tensor[0]
-                if stage_id > 0 and piper_metadata.stage_to_device[stage_id] == piper_metadata.stage_to_device[stage_id - 1] and tensor.requires_grad:
+                if stage_id > 0 and self.stage_to_device[stage_id] == self.stage_to_device[stage_id - 1] and tensor.requires_grad:
                     tensor = tensor.detach().requires_grad_(True)
                 self.forward_args[stage_id][i] = tensor
 
             # save first input that requires grad as input activation
             inp_with_grad = [
                 self.forward_args[stage_id][i]
-                for i in self.input_idxs[stage_id]
+                for i in self.input_idxs[(stage_id, None)]
                 if self.forward_args[stage_id][i].requires_grad
             ]
             assert (
@@ -867,10 +987,6 @@ def _forward_impl(self, stage_id: int, mb_idx: int, *deps):
             ), "Exactly one input per stage should require a gradient"
             self.inp_activation[stage_id][mb_idx] = inp_with_grad[0]
 
-        # ZeRO-3: seed all-gather pipeline for this microbatch's forward pass.
-        if stage_id in self.zero1_states:
-            self.zero1_states[stage_id].before_microbatch_forward()
-
         # Run the forward pass
         self._start_timing(comp_stream, "forward_comp")
         with torch.cuda.stream(comp_stream):
@@ -890,7 +1006,7 @@ def _forward_impl(self, stage_id: int, mb_idx: int, *deps):
         self.out_activation[stage_id][mb_idx] = out_with_grad[0]
 
         # clear the input tensors
-        for i in self.input_idxs[stage_id]:
+        for i in self.input_idxs[(stage_id, None)]:
             self.forward_args[stage_id][i] = None
 
         if stage_id < self.num_stages - 1:
@@ -932,10 +1048,6 @@ def _backward_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
 
         out_activation = self.out_activation[stage_id][mb_idx]
 
-        # ZeRO-3: seed all-gather pipeline for this microbatch's backward pass.
-        if stage_id in self.zero1_states:
-            self.zero1_states[stage_id].before_microbatch_backward()
-
         # torch.cuda.nvtx.range_push(f"backward_stage_{stage_id}_mb_{mb_idx}")
         if stage_id < self.num_stages - 1:
             # Wait for recv to complete if this stage receives from the next stage
@@ -949,8 +1061,11 @@ def _backward_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
             self._stop_timing(comp_stream, "bwd_recv_wait")
 
             self._start_timing(comp_stream, "backward_comp")
-            with torch.cuda.stream(comp_stream):
-                out_activation.backward(gradient=input_grad)
+            try:
+                with torch.cuda.stream(comp_stream):
+                    out_activation.backward(gradient=input_grad)
+            except RuntimeError:
+                raise
             self._stop_timing(comp_stream, "backward_comp")
         else:
             assert loss_fn is not None
@@ -958,17 +1073,16 @@ def _backward_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
             assert out_activation.shape == labels.shape
 
             self._start_timing(comp_stream, "backward_comp")
-            with torch.cuda.stream(comp_stream):
-                loss = loss_fn(out_activation, labels)
-                loss.backward()
+            try:
+                with torch.cuda.stream(comp_stream):
+                    loss = loss_fn(out_activation, labels)
+                    loss.backward()
+            except RuntimeError:
+                raise
             self._stop_timing(comp_stream, "backward_comp")
 
             # self.loss.append(loss.item())
 
-        # ZeRO-3: restore all params to shard storage now that backward is done.
-        # if stage_id in self.zero1_states:
-        #     self.zero1_states[stage_id].after_microbatch_backward()
-
         # Clear output activation after backward pass
         self.out_activation[stage_id][mb_idx] = None
         del out_activation
@@ -1825,7 +1939,7 @@ def _forward_backward_impl(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_i
 
         if fwd_stage_id == 0:
             # For the first stage, load input tensors from self.inputs
-            for i, inp in zip(self.input_idxs[fwd_stage_id], self.inputs):
+            for i, inp in zip(self.input_idxs[(fwd_stage_id, None)], self.inputs):
                 self.forward_args[fwd_stage_id][i] = inp
         else:
             fwd_recv_key = (fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False)
@@ -1838,18 +1952,18 @@ def _forward_backward_impl(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_i
             inputs_from_prev_stage = self.p2p_cache.pop(fwd_recv_key)
 
             # Detach to avoid double-backprop if the previous stage is co-located
-            for i, tensor in zip(self.input_idxs[fwd_stage_id], inputs_from_prev_stage):
+            for i, tensor in zip(self.input_idxs[(fwd_stage_id, None)], inputs_from_prev_stage):
                 if isinstance(tensor, (tuple, list)):
                     assert len(tensor) == 1
                     tensor = tensor[0]
-                if fwd_stage_id > 0 and piper_metadata.stage_to_device[fwd_stage_id] == piper_metadata.stage_to_device[fwd_stage_id - 1] and tensor.requires_grad:
+                if fwd_stage_id > 0 and self.stage_to_device[fwd_stage_id] == self.stage_to_device[fwd_stage_id - 1] and tensor.requires_grad:
                     tensor = tensor.detach().requires_grad_(True)
                 self.forward_args[fwd_stage_id][i] = tensor
 
             # save first input that requires grad as input activation
             inp_with_grad = [
                 self.forward_args[fwd_stage_id][i]
-                for i in self.input_idxs[fwd_stage_id]
+                for i in self.input_idxs[(fwd_stage_id, None)]
                 if self.forward_args[fwd_stage_id][i].requires_grad
             ]
             assert (
@@ -1934,9 +2048,9 @@ def run_bwd():
         self.out_activation[fwd_stage_id][fwd_mb_idx] = out_with_grad[0]
 
         # clear the input tensors
-        for i in self.input_idxs[fwd_stage_id]:
+        for i in self.input_idxs[(fwd_stage_id, None)]:
             self.forward_args[fwd_stage_id][i] = None
-        
+
         if fwd_stage_id < self.num_stages - 1:
             send_p2p_op = (fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True)
             assert send_p2p_op not in self.p2p_cache
@@ -1989,48 +2103,879 @@ def _synchronize_gradients(self):
                     self.overlap_detector.after_kernel(self.comm_stream, _ov_token)
 
     def _update(self, *deps):
-        torch.cuda.nvtx.range_push(f"update")
         ret = self._update_impl(*deps)
-        torch.cuda.nvtx.range_pop()
         return ret
 
     def _update_impl(self, *deps):
         self._label_task("update")
-        self.logger.debug(f"Actor {self.global_rank} waiting for backward sync events")
 
-        if self.zero_stage >= 1 and self.dp_degree > 1:
-            # ZeRO path: hooks launch bucketed async gradient-sync collectives
-            # during backward; finalize_step completes grad-sync → optimizer →
-            # all_gather sequence for every bucket on every stage.
+        if self.ar_events:
+            # DAG execution path: wait for in-flight all-reduces then step optimizers.
+            self._start_timing(self.comp_stream, "optim_step")
+            for ar_evt in self.ar_events.values():
+                self.comp_stream.wait_event(ar_evt)
+            for s_id, optim_list in self.bucket_optims.items():
+                for optim in optim_list:
+                    if optim is not None:
+                        optim.step()
+                        optim.zero_grad(set_to_none=False)
+            self._stop_timing(self.comp_stream, "optim_step")
+        elif self.dp_degree > 1:
+            # Legacy non-DAG DDP path (no ALL_REDUCE nodes in the DAG).
             self._start_timing(self.comm_stream, "backward_sync")
-            for zero1_state in self.zero1_states.values():
-                zero1_state.finalize_step(self.comm_stream, self.comp_stream)
+            if self.naive_gradient_sync:
+                self._synchronize_gradients()
+            else:
+                self._wait_for_comm_ops()
             self._stop_timing(self.comm_stream, "backward_sync")
-        else:
-            # Standard DDP path.
-            if self.dp_degree > 1:
-                # if dp degree > 1, make sure all gradients are synchronized before optimizer step
-                # TODO: this does not allow overlapping with the optimizer step
-                self._start_timing(self.comm_stream, "backward_sync")
-                if self.naive_gradient_sync:
-                    self._synchronize_gradients()
-                else:
-                    self._wait_for_comm_ops()
-                self._stop_timing(self.comm_stream, "backward_sync")
 
-            # step the optimizer for each stage
             self._start_timing(self.comp_stream, "optim_step")
-            for _, optim in self.optims.items():
-                optim.step()
-                optim.zero_grad()
+            for s_id, optim_list in self.bucket_optims.items():
+                for optim in optim_list:
+                    if optim is not None:
+                        optim.step()
+                        optim.zero_grad(set_to_none=False)
+            self._stop_timing(self.comp_stream, "optim_step")
+        else:
+            # Single-device path.
+            self._start_timing(self.comp_stream, "optim_step")
+            for s_id, optim_list in self.bucket_optims.items():
+                for optim in optim_list:
+                    if optim is not None:
+                        optim.step()
+                        optim.zero_grad(set_to_none=False)
             self._stop_timing(self.comp_stream, "optim_step")
 
         torch.cuda.synchronize()
 
         losses = self.loss
         self.loss.clear()
-        
+
         self.reset_p2p_states()
         self.overlap_detector.reset_iteration()
 
         return losses
+
+    # -----------------------------------------------------------------------
+    # DAG-based execution
+    # -----------------------------------------------------------------------
+
+    def load_dag(self, dag: TaskDAG) -> None:
+        """Store the per-rank TaskDAG for subsequent run_dag() calls."""
+        self.dag = dag
+
+    def get_bucket_fwd_counts(self) -> dict:
+        """Return the number of forward buckets for each bucketed stage on this actor."""
+        return {stage_id: len(fns) for stage_id, fns in self.bucket_fwd_fns.items()}
+
+    def get_a2a_boundaries(self) -> dict:
+        """Return A2A boundary info: stage_id -> {boundary_bucket_id -> tensor_idx}."""
+        return dict(self.a2a_boundaries)
+
+    def run_dag(self, loss_fn=None):
+        """Execute the loaded TaskDAG in topological order.
+
+        Synchronisation contract:
+        - Before a SEND node: p2p_send_stream waits on the compute event so the
+          send buffer is fully written before the transfer begins.
+        - Before a compute node that has a RECV predecessor: comp_stream waits
+          on the recv_event so the recv buffer is populated before compute reads it.
+        - RECV records a recv_event after the dist.recv completes.
+        """
+        from collections import deque
+
+        assert self.dag is not None, "load_dag() must be called before run_dag()"
+        dag = self.dag
+
+        # Reset per-iteration DAG buffers
+        self.send_buffer = {}
+        self.bucket_buffer = {}
+        self.a2a_buffer = {}
+        self.a2a_events = {}
+        self.ar_events = {}
+
+        # Point each trainable param's .grad at the appropriate slice of its
+        # bucket's flat_grads tensor so backward accumulates contiguously.
+        for (s_id, b_idx), trainable_idxs in self.bucket_trainable_param_idxs.items():
+            flat_grads = self.bucket_flat_grads.get((s_id, b_idx))
+            if flat_grads is None:
+                continue
+            flat_grads.zero_()
+            args = self.bucket_fwd_args[s_id][b_idx]
+            offset = 0
+            for i in trainable_idxs:
+                p = args[i]
+                if p is not None:
+                    numel = p.numel()
+                    p.grad = flat_grads[offset:offset + numel].view(p.shape)
+                    offset += numel
+        # Pre-create a threading.Event for every expected send key so that
+        # _exec_send can wait on it if the compute function hasn't written the
+        # buffer yet (guards against out-of-order CPU dispatch).
+        self.send_buffer_ready = {}
+        for _node in dag.nodes:
+            if _node.task.type == TaskType.SEND:
+                _compute = _node.data_preds[0]
+                _b = _compute.task.batches[0]
+                _sid, _mid, _ct = _b.stage_id, _b.mb_idx, _compute.task.type
+                if _ct == TaskType.FWD:
+                    _k = (_sid, _compute.task.bucket_id)
+                else:
+                    _k = None
+                self.send_buffer_ready[(_k, _mid)] = threading.Event()
+        self.recv_buffer = {}
+        self.recv_events = {}
+        comp_events: dict = {}  # (stage_id, mb_idx, task_type, bucket_id) -> cuda.Event
+
+        # Kahn's algorithm: combined data + temporal predecessor counts
+        in_degree = {
+            id(n): len(n.data_preds) + (1 if n.temporal_pred is not None else 0)
+            for n in dag.nodes
+        }
+        ready: deque = deque(n for n in dag.nodes if in_degree[id(n)] == 0)
+
+        while ready:
+            node = ready.popleft()
+            task = node.task
+            # batches always has exactly one entry for single-stage tasks
+            batch = task.batches[0]
+            stage_id = batch.stage_id
+            mb_idx = batch.mb_idx
+
+            self.logger.debug(
+                f"run_dag dispatch: {task.type.value} s{stage_id} mb{mb_idx} bkt={task.bucket_id}"
+            )
+
+            match task.type:
+
+                case TaskType.SEND:
+                    compute_node = node.data_preds[0]
+                    _cs = compute_node.task.batches[0].stage_id
+                    _cm = compute_node.task.batches[0].mb_idx
+                    _ct = compute_node.task.type
+                    send_key = (_cs, compute_node.task.bucket_id) if _ct == TaskType.FWD else None
+                    comp_key = (_cs, _cm, _ct, compute_node.task.bucket_id)
+                    self.p2p_send_stream.wait_event(comp_events[comp_key])
+                    self._exec_send(stage_id, mb_idx, send_key, node.peer_pp_rank)
+
+                case TaskType.RECV:
+                    compute_node = node.data_succs[0]
+                    recv_key = (stage_id, compute_node.task.bucket_id) \
+                        if compute_node.task.type == TaskType.FWD else None
+                    self._exec_recv(stage_id, mb_idx, recv_key, node.peer_pp_rank)
+
+                case TaskType.FWD:
+                    bucket_id = task.bucket_id
+                    recv_key = ((stage_id, bucket_id), mb_idx)
+                    if recv_key in self.recv_events:
+                        self.fwd_comp_stream.wait_event(self.recv_events.pop(recv_key))
+                    torch.cuda.nvtx.range_push(f"forward_s{stage_id}_b{bucket_id}_mb{mb_idx}")
+                    self._forward_dag(stage_id, bucket_id, mb_idx)
+                    torch.cuda.nvtx.range_pop()
+                    evt = torch.cuda.Event()
+                    evt.record(self.fwd_comp_stream)
+                    comp_events[(stage_id, mb_idx, TaskType.FWD, bucket_id)] = evt
+
+                case TaskType.BWD:
+                    bucket_id = task.bucket_id
+                    recv_key = (None, mb_idx)
+                    if recv_key in self.recv_events:
+                        self.bwd_comp_stream.wait_event(self.recv_events.pop(recv_key))
+                    torch.cuda.nvtx.range_push(f"backward_s{stage_id}_b{bucket_id}_mb{mb_idx}")
+                    self._backward_dag(stage_id, bucket_id, mb_idx, loss_fn=loss_fn)
+                    torch.cuda.nvtx.range_pop()
+                    evt = torch.cuda.Event()
+                    evt.record(self.bwd_comp_stream)
+                    comp_events[(stage_id, mb_idx, TaskType.BWD, bucket_id)] = evt
+
+                case TaskType.BWD_I:
+                    bucket_id = task.bucket_id
+                    recv_key = (None, mb_idx)
+                    if recv_key in self.recv_events:
+                        self.bwd_comp_stream.wait_event(self.recv_events.pop(recv_key))
+                    torch.cuda.nvtx.range_push(f"backward_input_stage_{stage_id}_b{bucket_id}_mb_{mb_idx}")
+                    if len(self.bucket_fwd_fns.get(stage_id, [])) > 1:
+                        self._backward_input_dag_bucket(stage_id, bucket_id, mb_idx, loss_fn=loss_fn)
+                    else:
+                        self._backward_input_dag(stage_id, mb_idx, loss_fn=loss_fn)
+                    torch.cuda.nvtx.range_pop()
+                    evt = torch.cuda.Event()
+                    evt.record(self.bwd_comp_stream)
+                    comp_events[(stage_id, mb_idx, TaskType.BWD_I, bucket_id)] = evt
+
+                case TaskType.BWD_W:
+                    torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
+                    self._backward_weight_dag(stage_id, mb_idx, loss_fn=loss_fn)
+                    torch.cuda.nvtx.range_pop()
+
+                case TaskType.FWD_A2A:
+                    bucket_id = task.bucket_id
+                    torch.cuda.nvtx.range_push(f"fwd_a2a_s{stage_id}_b{bucket_id}_mb{mb_idx}")
+                    self.a2a_stream.wait_event(
+                        comp_events[(stage_id, mb_idx, TaskType.FWD, bucket_id)]
+                    )
+                    self._exec_fwd_a2a(stage_id, bucket_id, mb_idx)
+                    torch.cuda.nvtx.range_pop()
+                    a2a_evt = torch.cuda.Event()
+                    a2a_evt.record(self.a2a_stream)
+                    self.a2a_events[(stage_id, mb_idx, TaskType.FWD_A2A, bucket_id)] = a2a_evt
+                    self.fwd_comp_stream.wait_event(a2a_evt)
+
+                case TaskType.BWD_A2A:
+                    bucket_id = task.bucket_id
+                    torch.cuda.nvtx.range_push(f"bwd_a2a_s{stage_id}_b{bucket_id}_mb{mb_idx}")
+                    bwd_evt = (
+                        comp_events.get((stage_id, mb_idx, TaskType.BWD, bucket_id + 1))
+                        or comp_events.get((stage_id, mb_idx, TaskType.BWD_I, bucket_id + 1))
+                    )
+                    self.a2a_stream.wait_event(bwd_evt)
+                    self._exec_bwd_a2a(stage_id, bucket_id, mb_idx)
+                    torch.cuda.nvtx.range_pop()
+                    a2a_evt = torch.cuda.Event()
+                    a2a_evt.record(self.a2a_stream)
+                    self.a2a_events[(stage_id, mb_idx, TaskType.BWD_A2A, bucket_id)] = a2a_evt
+                    self.bwd_comp_stream.wait_event(a2a_evt)
+
+                case TaskType.ALL_REDUCE:
+                    bucket_id = task.bucket_id
+                    bwd_node = node.data_preds[0]
+                    bwd_key = (
+                        bwd_node.task.batches[0].stage_id,
+                        bwd_node.task.batches[0].mb_idx,
+                        bwd_node.task.type,
+                        bwd_node.task.bucket_id,
+                    )
+                    torch.cuda.nvtx.range_push(f"all_reduce_s{stage_id}_b{bucket_id}")
+                    self._exec_all_reduce(stage_id, bucket_id, comp_events[bwd_key])
+                    torch.cuda.nvtx.range_pop()
+
+                case TaskType.UPD:
+                    torch.cuda.nvtx.range_push("update")
+                    self._update_impl()
+                    torch.cuda.nvtx.range_pop()
+
+            # Decrement in-degree for all successors; execute in topological order.
+            all_succs = list(node.data_succs)
+            if node.temporal_succ is not None:
+                all_succs.append(node.temporal_succ)
+            for succ in all_succs:
+                in_degree[id(succ)] -= 1
+                if in_degree[id(succ)] == 0:
+                    ready.append(succ)
+
+    def _exec_send(
+        self, stage_id: int, mb_idx: int, key, peer_pp_rank: int
+    ) -> None:
+        """Send the contents of send_buffer[(key, mb_idx)] to peer_pp_rank.
+
+        key is (stage_id, None) for non-bucket compute dependencies and
+        (stage_id, bucket_id) for bucket compute dependencies; None for BWD/BWD_I.
+
+        The caller (run_dag) must have already made p2p_send_stream wait on the
+        compute event before calling this method.
+        """
+        self.logger.debug(f"exec_send key {(key, mb_idx)} to peer pp rank {peer_pp_rank}")
+
+        buf = self.send_buffer.pop((key, mb_idx))
+        global_dst_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
+
+        if self.global_rank == global_dst_rank:
+            # Co-located stages: write to recv_buffer under the peer's expected key.
+            if key is not None:  # FWD send: peer receives into its stage's input slot
+                recv_stage_id = stage_id + 1
+                peer_recv_key = (recv_stage_id, 0)
+            else:  # BWD send
+                peer_recv_key = None
+            self.recv_buffer[(peer_recv_key, mb_idx)] = buf
+            return
+
+        with torch.cuda.stream(self.p2p_send_stream):
+            tensors = buf if isinstance(buf, (list, tuple)) else [buf]
+            for tensor in tensors:
+                dist.send(
+                    tensor,
+                    dst=global_dst_rank,
+                    group=self.pp_groups[(self.global_rank, global_dst_rank)],
+                )
+
+    def _exec_recv(
+        self, stage_id: int, mb_idx: int, key, peer_pp_rank: int
+    ) -> None:
+        """Receive data from peer_pp_rank into recv_buffer[(key, mb_idx)].
+
+        key is (stage_id, None) for non-bucket compute dependencies and
+        (stage_id, bucket_id) for bucket compute dependencies; None for BWD/BWD_I.
+
+        Records a cuda.Event in recv_events[(key, mb_idx)] once the recv
+        completes on p2p_recv_stream.
+        """
+        self.logger.debug(f"exec_recv key {(key, mb_idx)} from peer pp rank {peer_pp_rank}")
+
+        buf_key = (key, mb_idx)
+        global_src_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
+
+        if self.global_rank == global_src_rank:
+            # Co-located: _exec_send already wrote to recv_buffer[(key, mb_idx)].
+            return
+
+        # Allocate receive buffer
+        if key is not None:  # FWD recv
+            buf = []
+            for i in self.input_idxs[key]:
+                shape, dtype, requires_grad = self.forward_input_meta[stage_id][i]
+                buf.append(
+                    torch.empty(shape, dtype=dtype, requires_grad=requires_grad, device=self.device)
+                )
+        else:
+            # BWD / BWD_I: gradient shaped like the saved output activation.
+            # In interleaved schedules the BWD recv may be issued before the
+            # corresponding FWD has run, so fall back to the pre-computed shape.
+            act_list = self.out_activation[stage_id].get(mb_idx)
+            if act_list is not None:
+                buf = [torch.empty_like(a) for a in act_list]
+            else:
+                shapes = self.output_activation_shape.get(stage_id, [])
+                buf = [torch.empty(shape, dtype=dtype, device=self.device) for shape, dtype in shapes]
+
+        with torch.cuda.stream(self.p2p_recv_stream):
+            tensors = buf if isinstance(buf, list) else [buf]
+            for tensor in tensors:
+                dist.recv(
+                    tensor,
+                    src=global_src_rank,
+                    group=self.pp_groups[(global_src_rank, self.global_rank)],
+                )
+
+        recv_event = torch.cuda.Event()
+        recv_event.record(self.p2p_recv_stream)
+        self.recv_events[buf_key] = recv_event
+        self.recv_buffer[buf_key] = buf
+
+    def _forward_dag(self, stage_id: int, bucket_id: int, mb_idx: int) -> None:
+        """Forward step for DAG execution (unified for bucketed and non-bucketed stages).
+
+        Non-bucketed stages are loaded as single-bucket stages (bucket_id=0 only).
+        Bucketed stages have multiple bucket_ids dispatched individually by run_dag.
+        """
+        comp_stream = self.comp_stream
+        bucket_fns = self.bucket_fwd_fns[stage_id]
+        bucket_args = self.bucket_fwd_args[stage_id]
+        n_buckets = len(bucket_fns)
+
+        # --- First bucket of stage: load activation inputs ---
+        if bucket_id == 0:
+            if stage_id == 0:
+                for i, inp in zip(self.input_idxs[(stage_id, 0)], self.inputs):
+                    bucket_args[0][i] = inp
+            else:
+                recv_key = ((stage_id, 0), mb_idx)
+                inputs_from_prev = self.recv_buffer.pop(recv_key)
+                if not isinstance(inputs_from_prev, (list, tuple)):
+                    inputs_from_prev = [inputs_from_prev]
+                for i, tensor in zip(self.input_idxs[(stage_id, 0)], inputs_from_prev):
+                    if isinstance(tensor, (list, tuple)):
+                        tensor = tensor[0]
+                    if (
+                        self.stage_to_device[stage_id] == self.stage_to_device[stage_id - 1]
+                        and tensor.requires_grad
+                    ):
+                        tensor = tensor.detach().requires_grad_(True)
+                    bucket_args[0][i] = tensor
+
+                inp_with_grad = [
+                    bucket_args[0][i] for i in self.input_idxs[(stage_id, 0)]
+                    if bucket_args[0][i] is not None and bucket_args[0][i].requires_grad
+                ]
+                self.inp_activation[stage_id][mb_idx] = inp_with_grad  # list
+
+        # --- Run this bucket ---
+        with torch.cuda.stream(comp_stream):
+            output = bucket_fns[bucket_id](*bucket_args[bucket_id])
+
+        # Clear activation input slots
+        for i in self.input_idxs[(stage_id, bucket_id)]:
+            bucket_args[bucket_id][i] = None
+
+        out_list = list(output) if isinstance(output, tuple) else [output]
+
+        # --- Store boundary or finalize ---
+        if bucket_id < n_buckets - 1:
+            possibly_detached = [
+                t.detach().requires_grad_(True) if t.requires_grad else t
+                for t in out_list
+            ]
+            self.bucket_buffer[(stage_id, mb_idx, bucket_id)] = (out_list, possibly_detached)
+            # Skip direct feed at A2A boundaries — _exec_fwd_a2a will feed after applying A2A.
+            stage_a2a = self.a2a_boundaries.get(stage_id, {})
+            if bucket_id not in stage_a2a:
+                for i, t in zip(self.input_idxs[(stage_id, bucket_id + 1)], possibly_detached):
+                    bucket_args[bucket_id + 1][i] = t
+        else:
+            # Last bucket: save requires_grad outputs for backward
+            out_with_grad = [t for t in out_list if isinstance(t, torch.Tensor) and t.requires_grad]
+            self.out_activation[stage_id][mb_idx] = out_with_grad  # list
+            if stage_id < self.num_stages - 1:
+                self.send_buffer[((stage_id, bucket_id), mb_idx)] = output
+                # Co-located next stage: no SEND/RECV DAG nodes are created by insert_p2p_ops
+                # for same-rank edges, so populate recv_buffer directly.
+                next_stage = stage_id + 1
+                if (next_stage in self.stage_to_device and
+                        self.stage_to_device[next_stage] == self.stage_to_device[stage_id]):
+                    self.recv_buffer[((next_stage, 0), mb_idx)] = output
+
+    def _backward_dag(self, stage_id: int, bucket_id: int, mb_idx: int, *, loss_fn=None) -> None:
+        """Backward step for DAG execution (unified for bucketed and non-bucketed stages).
+
+        The backward chain runs in reverse bucket order: the highest bucket_id
+        runs first (receives upstream gradient or computes loss) and bucket 0
+        runs last (sends input gradient to the previous stage).
+        """
+        comp_stream = self.comp_stream
+        n_buckets = len(self.bucket_fwd_fns[stage_id])
+
+        if bucket_id == n_buckets - 1:
+            # First to backward: receive upstream gradient or compute loss
+            out_with_grad = self.out_activation[stage_id][mb_idx]  # list
+            if stage_id < self.num_stages - 1:
+                recv_key = (None, mb_idx)
+                upstream_grads = self.recv_buffer.pop(recv_key)
+                if not isinstance(upstream_grads, (list, tuple)):
+                    upstream_grads = [upstream_grads]
+                with torch.cuda.stream(comp_stream):
+                    torch.autograd.backward(out_with_grad, upstream_grads)
+            else:
+                assert loss_fn is not None
+                with torch.cuda.stream(comp_stream):
+                    loss = loss_fn(out_with_grad[0], self.labels)
+                    loss.backward()
+            self.out_activation[stage_id][mb_idx] = None
+        else:
+            # Middle / earlier bucket: propagate backward through the boundary
+            pre_detach_outs, detached_outs = self.bucket_buffer.pop((stage_id, mb_idx, bucket_id))
+            outputs_bwd = [p for p, d in zip(pre_detach_outs, detached_outs) if d.requires_grad]
+            grads_bwd = [d.grad for p, d in zip(pre_detach_outs, detached_outs) if d.requires_grad]
+            assert all(g is not None for g in grads_bwd), (
+                f"Stage {stage_id} bucket {bucket_id}: detached boundary has no .grad"
+            )
+            with torch.cuda.stream(comp_stream):
+                torch.autograd.backward(outputs_bwd, grads_bwd)
+
+        # Bucket 0 is last to run: send input gradients to previous stage
+        if bucket_id == 0 and stage_id > 0:
+            inp_list = self.inp_activation[stage_id][mb_idx]  # list
+            output_grads = [t.grad for t in inp_list if t.grad is not None]
+            self.send_buffer[(None, mb_idx)] = output_grads
+            # Co-located prev stage: no BWD SEND/RECV DAG nodes for same-rank edges.
+            prev_stage = stage_id - 1
+            if (prev_stage in self.stage_to_device and
+                    self.stage_to_device[prev_stage] == self.stage_to_device[stage_id]):
+                self.recv_buffer[(None, mb_idx)] = output_grads
+            self.inp_activation[stage_id][mb_idx] = None
+
+    def _exec_fwd_a2a(self, stage_id: int, boundary_bucket_id: int, mb_idx: int) -> None:
+        """Apply a forward all-to-all at the given A2A boundary.
+
+        Called after FWD(bucket_id=boundary_bucket_id) completes.  Reads the
+        boundary tensor from bucket_buffer, applies dist.all_to_all_single
+        on the a2a_stream, replaces the entry in detached_outs with the
+        communicated tensor, then feeds all detached_outs to the next bucket.
+        """
+        tensor_idx = self.a2a_boundaries[stage_id][boundary_bucket_id]
+        pre_detach_outs, detached_outs = self.bucket_buffer[(stage_id, mb_idx, boundary_bucket_id)]
+
+        x_detached = detached_outs[tensor_idx]
+        output_buf = torch.empty_like(x_detached)
+        with torch.cuda.stream(self.a2a_stream):
+            dist.all_to_all_single(output_buf, x_detached, group=self.ep_group)
+        x_a2a = output_buf.requires_grad_(True)
+
+        # Store for BWD_A2A to reverse
+        self.a2a_buffer[(stage_id, mb_idx, boundary_bucket_id)] = (x_detached, x_a2a)
+
+        # Replace entry so that _backward_dag will read x_a2a.grad (set by seg+1 backward)
+        detached_outs[tensor_idx] = x_a2a
+
+        # Feed all detached_outs to next bucket's input slots
+        next_bucket_id = boundary_bucket_id + 1
+        bucket_args = self.bucket_fwd_args[stage_id]
+        for i, t in zip(self.input_idxs[(stage_id, next_bucket_id)], detached_outs):
+            bucket_args[next_bucket_id][i] = t
+
+    def _exec_bwd_a2a(self, stage_id: int, boundary_bucket_id: int, mb_idx: int) -> None:
+        """Apply the reverse all-to-all for the backward pass at an A2A boundary.
+
+        Called after BWD(bucket_id=boundary_bucket_id+1) completes (which has set
+        x_a2a.grad).  Applies the reverse A2A to obtain the gradient in the
+        pre-A2A space, sets it on x_detached.grad, and restores x_detached in
+        detached_outs so that _backward_dag for boundary_bucket_id uses the
+        correct gradient.
+        """
+        tensor_idx = self.a2a_boundaries[stage_id][boundary_bucket_id]
+        x_detached, x_a2a = self.a2a_buffer.pop((stage_id, mb_idx, boundary_bucket_id))
+
+        grad_a2a_out = x_a2a.grad
+        assert grad_a2a_out is not None, (
+            f"Stage {stage_id} A2A boundary {boundary_bucket_id} mb {mb_idx}: "
+            f"x_a2a.grad is None after backward through next segment"
+        )
+        if not grad_a2a_out.is_contiguous():
+            grad_a2a_out = grad_a2a_out.contiguous()
+        reversed_grad = torch.empty_like(grad_a2a_out)
+        with torch.cuda.stream(self.a2a_stream):
+            dist.all_to_all_single(reversed_grad, grad_a2a_out, group=self.ep_group)
+        x_detached.grad = reversed_grad
+
+        # Restore x_detached in bucket_buffer so _backward_dag reads x_detached.grad
+        _, detached_outs = self.bucket_buffer[(stage_id, mb_idx, boundary_bucket_id)]
+        detached_outs[tensor_idx] = x_detached
+
+    def _backward_input_dag(self, stage_id: int, mb_idx: int, *, loss_fn=None) -> None:
+        """Backward-input pass for DAG execution (ZeroBubble split backward).
+
+        Reads upstream gradient from recv_buffer and writes input gradient to
+        send_buffer.  Does NOT call comp_stream.synchronize().
+        """
+        comp_stream = self.comp_stream
+        _out = self.out_activation[stage_id][mb_idx]
+        out_activation = _out[0] if isinstance(_out, list) else _out
+
+        activation_or_loss = None
+        upstream_grad = None
+        if stage_id < self.num_stages - 1:
+            recv_key = (None, mb_idx)
+            upstream_grad = self.recv_buffer.pop(recv_key)
+            activation_or_loss = out_activation
+        else:
+            assert loss_fn is not None
+            labels = self.labels
+            with torch.cuda.stream(comp_stream):
+                loss = loss_fn(out_activation, labels)
+            activation_or_loss = loss
+            upstream_grad = torch.ones_like(loss)
+
+        if stage_id == 0:
+            self.upstream_grad_cache[stage_id][mb_idx] = upstream_grad
+            return
+
+        _inp = self.inp_activation[stage_id][mb_idx]
+        stage_input = _inp[0] if isinstance(_inp, list) else _inp
+        stage_params = [self.forward_args[stage_id][i] for i in self.param_idxs[stage_id]]
+
+        output_nodes = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [activation_or_loss]) if n is not None]
+        input_nodes  = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [stage_input]) if n is not None]
+        param_nodes  = [n for n in (_get_grad_fn_or_grad_acc(p) for p in stage_params) if n is not None]
+
+        reverse_edges = construct_reverse_graph(output_nodes)
+        param_groups = get_param_groups(input_nodes, param_nodes, reverse_edges)
+
+        handles = []
+        for pg in param_groups:
+            intermediates = pg["intermediates"]
+            if not intermediates:
+                continue
+            pg["grads"] = [None] * len(intermediates)
+            for i, intermediate_node in enumerate(intermediates):
+                def make_hook(group: Dict[str, Any], idx: int):
+                    def hook(grad_inputs):
+                        group["grads"][idx] = grad_inputs
+                    return hook
+                handles.append(intermediate_node.register_prehook(make_hook(pg, i)))
+
+        with torch.cuda.stream(comp_stream):
+            gx = torch.autograd.grad(
+                outputs=activation_or_loss,
+                inputs=stage_input,
+                grad_outputs=upstream_grad,
+                retain_graph=True,
+                allow_unused=True,
+            )
+
+        gx = gx[0]
+        if gx is not None and stage_input.requires_grad:
+            if stage_input.grad is None:
+                stage_input.grad = gx
+            else:
+                stage_input.grad.add_(gx)
+
+        if not isinstance(activation_or_loss, list):
+            activation_or_loss = [activation_or_loss]
+        for t in activation_or_loss:
+            t.detach_()
+
+        for h in handles:
+            h.remove()
+
+        # Write input gradient to send_buffer for the upstream stage to recv
+        # stage_input is the unwrapped tensor set above; read .grad before deleting.
+        output_grad = stage_input.grad
+        if output_grad is not None:
+            self.send_buffer[(None, mb_idx)] = output_grad
+            # Co-located prev stage: no BWD_I SEND/RECV DAG nodes for same-rank edges.
+            prev_stage = stage_id - 1
+            if (prev_stage in self.stage_to_device and
+                    self.stage_to_device[prev_stage] == self.stage_to_device[stage_id]):
+                self.recv_buffer[(None, mb_idx)] = output_grad
+        else:
+            self.inp_activation[stage_id][mb_idx] = None
+
+        self.bw_param_groups[stage_id][mb_idx] = param_groups
+        del activation_or_loss
+        del stage_input
+
+    def _backward_input_dag_bucket(self, stage_id: int, bucket_id: int, mb_idx: int, *, loss_fn=None) -> None:
+        """Per-bucket backward-input pass for ZeroBubble with bucketed / A2A stages.
+
+        Mirrors _backward_dag but uses autograd.grad(..., retain_graph=True) instead of
+        backward(), so weight gradients are NOT accumulated during BWD_I (they are
+        deferred to BWD_W via bw_param_groups).
+
+        Must be called in reverse bucket order: n_buckets-1, …, 1, 0.
+        BWD_A2A must run between consecutive BWD_I calls at A2A boundaries.
+        """
+        comp_stream = self.comp_stream
+        n_buckets = len(self.bucket_fwd_fns[stage_id])
+        a2a_bnd = self.a2a_boundaries.get(stage_id, {})
+
+        # ---- Step 1: outputs and upstream grads for this bucket ----
+        if bucket_id == n_buckets - 1:
+            _out = self.out_activation[stage_id][mb_idx]
+            out_list = _out if isinstance(_out, list) else [_out]
+            if stage_id < self.num_stages - 1:
+                upstream_grads_raw = self.recv_buffer.pop((None, mb_idx))
+                if not isinstance(upstream_grads_raw, (list, tuple)):
+                    upstream_grads_raw = [upstream_grads_raw]
+                outputs = out_list
+                upstream_grads = list(upstream_grads_raw)
+            else:
+                assert loss_fn is not None
+                with torch.cuda.stream(comp_stream):
+                    loss = loss_fn(out_list[0], self.labels)
+                outputs = [loss]
+                upstream_grads = [torch.ones_like(loss)]
+        else:
+            # BWD_A2A has restored x_detached (with .grad set) in detached_outs
+            pre_detach_outs, detached_outs = self.bucket_buffer[(stage_id, mb_idx, bucket_id)]
+            outputs = [p for p, d in zip(pre_detach_outs, detached_outs) if d.requires_grad]
+            upstream_grads = [d.grad for p, d in zip(pre_detach_outs, detached_outs) if d.requires_grad]
+            assert all(g is not None for g in upstream_grads), (
+                f"Stage {stage_id} BWD_I bucket {bucket_id} mb {mb_idx}: boundary .grad is None"
+            )
+
+        # Stage 0 special case: no prev stage to send grad to.  Cache upstream_grad for
+        # BWD_W and return early (BWD_W will use out_activation directly for stage 0).
+        if stage_id == 0:
+            if bucket_id == n_buckets - 1:
+                ug = upstream_grads[0] if len(upstream_grads) == 1 else upstream_grads
+                self.upstream_grad_cache[stage_id][mb_idx] = ug
+            return
+
+        # ---- Step 2: activation inputs (what we compute grad w.r.t.) ----
+        if bucket_id > 0:
+            prev_bkt = bucket_id - 1
+            _, detached_outs_prev = self.bucket_buffer[(stage_id, mb_idx, prev_bkt)]
+            if prev_bkt in a2a_bnd:
+                tensor_idx = a2a_bnd[prev_bkt]
+                activation_inputs = [detached_outs_prev[tensor_idx]]  # x_a2a leaf
+            else:
+                activation_inputs = [d for d in detached_outs_prev if d.requires_grad]
+        else:
+            # Bucket 0: grad to stage_input
+            _inp = self.inp_activation[stage_id][mb_idx]
+            stage_input = _inp[0] if isinstance(_inp, list) else _inp
+            activation_inputs = [stage_input] if (stage_input is not None and stage_input.requires_grad) else []
+
+        # ---- Step 3: ZeroBubble hooks so BWD_W can compute weight grads ----
+        stage_params = [self.forward_args[stage_id][i] for i in self.param_idxs[stage_id]]
+        output_nodes = [n for n in (_get_grad_fn_or_grad_acc(t) for t in outputs) if n is not None]
+        input_nodes = [n for n in (_get_grad_fn_or_grad_acc(t) for t in activation_inputs) if n is not None]
+        param_nodes = [n for n in (_get_grad_fn_or_grad_acc(p) for p in stage_params) if n is not None]
+        reverse_edges = construct_reverse_graph(output_nodes)
+        param_groups = get_param_groups(input_nodes, param_nodes, reverse_edges)
+
+        handles = []
+        for pg in param_groups:
+            intermediates = pg["intermediates"]
+            if not intermediates:
+                continue
+            pg["grads"] = [None] * len(intermediates)
+            for i, intermediate_node in enumerate(intermediates):
+                def make_hook(group, idx):
+                    def hook(grad_inputs):
+                        group["grads"][idx] = grad_inputs
+                    return hook
+                handles.append(intermediate_node.register_prehook(make_hook(pg, i)))
+
+        # ---- Step 4: compute grad w.r.t. activation inputs only ----
+        if activation_inputs:
+            with torch.cuda.stream(comp_stream):
+                grads = torch.autograd.grad(
+                    outputs=outputs,
+                    inputs=activation_inputs,
+                    grad_outputs=upstream_grads,
+                    retain_graph=True,
+                    allow_unused=True,
+                )
+            for inp, g in zip(activation_inputs, grads):
+                if g is not None:
+                    if inp.grad is None:
+                        inp.grad = g
+                    else:
+                        inp.grad.add_(g)
+
+        for h in handles:
+            h.remove()
+
+        # ---- Step 5: accumulate bw_param_groups for BWD_W ----
+        existing = self.bw_param_groups[stage_id].get(mb_idx)
+        if existing is None:
+            self.bw_param_groups[stage_id][mb_idx] = param_groups
+        else:
+            existing.extend(param_groups)
+
+        # ---- Step 6: bucket 0 sends input grad to prev stage ----
+        if bucket_id == 0:
+            _inp = self.inp_activation[stage_id][mb_idx]
+            stage_input = _inp[0] if isinstance(_inp, list) else _inp
+            output_grad = stage_input.grad if stage_input is not None else None
+            if output_grad is not None and stage_id > 0:
+                self.send_buffer[(None, mb_idx)] = output_grad
+                prev_stage = stage_id - 1
+                if (prev_stage in self.stage_to_device and
+                        self.stage_to_device[prev_stage] == self.stage_to_device[stage_id]):
+                    self.recv_buffer[(None, mb_idx)] = output_grad
+            self.inp_activation[stage_id][mb_idx] = None
+
+        # Free output reference after processing the first-to-bwd bucket
+        if bucket_id == n_buckets - 1:
+            for t in outputs:
+                t.detach_()
+            self.out_activation[stage_id][mb_idx] = None
+
+    def _exec_all_reduce(self, stage_id: int, bucket_id: int, bwd_event: torch.cuda.Event) -> None:
+        """Launch an all-reduce for a stage/bucket's gradients.
+
+        All-reduces the pre-allocated flat_grads tensor for the given
+        (stage_id, bucket_id).
+
+        comm_stream waits on *bwd_event* first (ensuring all gradient
+        accumulation is complete) then records an event so _update_impl can
+        wait on it before stepping the optimizer.
+        """
+        lookup_key = (stage_id, bucket_id)
+        with torch.cuda.stream(self.comm_stream):
+            self.comm_stream.wait_event(bwd_event)
+            flat_grads = self.bucket_flat_grads.get(lookup_key)
+            if flat_grads is not None:
+                dist.all_reduce(flat_grads, group=self.dp_group)
+            evt = torch.cuda.Event()
+            evt.record(self.comm_stream)
+        self.ar_events[(stage_id, bucket_id)] = evt
+
+    def _backward_weight_dag(self, stage_id: int, mb_idx: int, *, loss_fn=None) -> None:
+        """Backward-weight pass for DAG execution (ZeroBubble split backward).
+
+        Does NOT call comp_stream.synchronize().
+        """
+        comp_stream = self.comp_stream
+        stage_params = [self.forward_args[stage_id][i] for i in self.param_idxs[stage_id]]
+        updated_params: dict[int, torch.nn.Parameter] = {}
+
+        if stage_id == 0:
+            upstream_grad = self.upstream_grad_cache[stage_id][mb_idx]
+            _out = self.out_activation[stage_id][mb_idx]
+            out_activation = _out[0] if isinstance(_out, list) else _out
+            if stage_id < self.num_stages - 1:
+                with torch.cuda.stream(comp_stream):
+                    gparams = torch.autograd.grad(
+                        outputs=out_activation,
+                        inputs=stage_params,
+                        grad_outputs=upstream_grad,
+                        retain_graph=False,
+                    )
+            else:
+                assert loss_fn is not None
+                labels = self.labels
+                with torch.cuda.stream(comp_stream):
+                    loss = loss_fn(out_activation, labels)
+                    gparams = torch.autograd.grad(
+                        outputs=loss,
+                        inputs=stage_params,
+                        retain_graph=False,
+                    )
+
+            assert len(gparams) == len(stage_params)
+            for p, pg in zip(stage_params, gparams):
+                if pg is None:
+                    continue
+                if p.grad is None:
+                    p.grad = pg.clone()
+                else:
+                    p.grad.add_(pg)
+                updated_params[id(p)] = p
+        else:
+            grad_acc_to_weight: Dict[Node, Tuple[Parameter, int]] = {}
+            for param in stage_params:
+                node = _get_grad_fn_or_grad_acc(param)
+                grad_acc_to_weight[node] = param
+
+            param_groups = self.bw_param_groups[stage_id][mb_idx]
+
+            for pg in param_groups:
+                intermediates: List[Node] = pg.get("intermediates", [])
+                intermediate_grads = pg.get("grads", None)
+
+                if not intermediates or intermediate_grads is None:
+                    continue
+
+                intermediate_edges: List[GradientEdge] = []
+                intermediate_edge_grads: List[torch.Tensor] = []
+
+                for intermediate_node, grad_inputs in zip(intermediates, intermediate_grads):
+                    if grad_inputs is None:
+                        continue
+                    gs = [x for x in grad_inputs if x is not None]
+                    if not gs:
+                        continue
+                    summed = sum(gs)
+                    intermediate_edges.append(GradientEdge(intermediate_node, 0))
+                    intermediate_edge_grads.append(summed)
+
+                del pg["intermediates"]
+
+                if not intermediate_edges:
+                    continue
+
+                mapped_param_nodes = [p for p in pg["params"] if p in grad_acc_to_weight]
+                if not mapped_param_nodes:
+                    continue
+
+                weight_edges = tuple(GradientEdge(p, 0) for p in mapped_param_nodes)
+
+                with torch.cuda.stream(comp_stream):
+                    gparams = torch.autograd.grad(
+                        outputs=intermediate_edges,
+                        inputs=weight_edges,
+                        grad_outputs=intermediate_edge_grads,
+                        retain_graph=False,
+                    )
+
+                del pg["grads"]
+
+                assert len(gparams) == len(mapped_param_nodes)
+                for param_node, dw in zip(mapped_param_nodes, gparams):
+                    if dw is None:
+                        continue
+                    weight = grad_acc_to_weight[param_node]
+                    if weight.grad is None:
+                        weight.grad = dw
+                    else:
+                        weight.grad.add_(dw)
+                    updated_params[id(weight)] = weight
+
+        for p in updated_params.values():
+            self._maybe_trigger_grad_allreduce(stage_id, p)
+
+        self.bw_grad_cache[stage_id][mb_idx] = None
+        self.upstream_grad_cache[stage_id][mb_idx] = None
+        self.bw_param_groups[stage_id][mb_idx] = None
+        self.out_activation[stage_id][mb_idx] = None
diff --git a/src/piper_compile.py b/src/piper_compile.py
index d43ddc6..3d4869c 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -19,6 +19,17 @@
 
 logger = create_logger("piper_compile", LOG_LEVEL)
 
+_RANK0_ADDR_ACTOR = "piper_rank0_addr"
+
+@ray.remote
+class _Rank0AddrStore:
+    """Named actor used to share global rank-0's IP+port across independent dp_rank workers."""
+    def __init__(self, addr: str, port: int):
+        self._addr = addr
+        self._port = port
+    def get(self):
+        return self._addr, self._port
+
 def piper_setup(
     model_class,
     model_args=(),
@@ -29,10 +40,11 @@ def piper_setup(
     schedule: Schedule2D=None,
     naive_gradient_sync=False,
     activation_checkpointing=False,
+    bucketing=False,
     mode="sequential",
     pg=None,
     model_dtype=None,
-    zero_stage: int = 0,
+    nsight=False,
 ):
     """
     Compile a model with the piper backend.
@@ -44,17 +56,16 @@ def piper_setup(
         example_outputs: Example outputs (labels) for tracing.
         schedule: 2D schedule grid (rank x time_step).
         naive_gradient_sync: Use a simple blocking all-reduce instead of
-            pipelined per-param hooks. Ignored when ``zero_stage >= 1``.
-        zero_stage: ZeRO optimisation stage.
-            0 = disabled (standard DDP).
-            1 = strict ZeRO-1 (optimizer sharding + bucketed all-reduce grads).
-            2 = strict ZeRO-2 (optimizer sharding + bucketed reduce-scatter grads).
+            pipelined per-param hooks.
     """
 
     stage_to_device = schedule.stage_to_device()
+    print("Stage to device mapping:", stage_to_device)
     assert len(stage_to_device) > 0
     piper_metadata.stage_to_device = stage_to_device
     piper_metadata.use_activation_checkpointing = activation_checkpointing
+    piper_metadata.bucketing = bucketing
+    piper_metadata.schedule = schedule
 
     num_mbs = schedule.num_mbs()
     num_stages = schedule.num_stages()
@@ -62,15 +73,40 @@ def piper_setup(
 
     _create_actors(
         num_devices, optim_fn, num_mbs, num_stages,
-        naive_gradient_sync, profile=True, mode=mode, stage_to_device=stage_to_device, pg=pg, zero_stage=zero_stage,
+        naive_gradient_sync, profile=nsight, mode=mode, stage_to_device=stage_to_device, pg=pg,
     )
 
+    # All dp_ranks must agree on a single master_addr: the IP of the actor with
+    # global_rank=0 (pp_rank=0, dp_rank=0).  dp_rank=0 publishes it via a named
+    # Ray actor; other dp_ranks wait until it's available.
+    import time
+    dp_rank = int(os.environ["PIPER_DP_RANK"])
+    if dp_rank == 0:
+        # Get IP and a free port from actor 0's node — it will be the TCPStore server.
+        master_addr, master_port = ray.get(piper_metadata.actors[0].get_node_ip_and_free_port.remote())
+        _Rank0AddrStore.options(
+            name=_RANK0_ADDR_ACTOR, lifetime="detached", get_if_exists=True
+        ).remote(master_addr, master_port)
+    else:
+        master_addr = master_port = None
+        while master_addr is None:
+            try:
+                store = ray.get_actor(_RANK0_ADDR_ACTOR)
+                master_addr, master_port = ray.get(store.get.remote())
+            except Exception:
+                time.sleep(0.05)
+    logger.debug(f"Master address for process groups: {master_addr}:{master_port}")
     ray.get(
         [
-            actor._join_process_groups.remote()
+            actor._join_process_groups.remote(master_addr, master_port)
             for actor in piper_metadata.actors.values()
         ]
     )
+    if dp_rank == 0:
+        try:
+            ray.kill(ray.get_actor(_RANK0_ADDR_ACTOR))
+        except Exception:
+            pass
 
     # Build the model directly on meta device
     with torch.device("meta"):
diff --git a/src/piper_coordinator.py b/src/piper_coordinator.py
index 933ead7..6d0a1d4 100644
--- a/src/piper_coordinator.py
+++ b/src/piper_coordinator.py
@@ -10,7 +10,7 @@
 @ray.remote(num_gpus=0.2)
 def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, master_addr, master_port, training_func: Callable, *args, **kwargs):
     logger = create_logger("piper_coordinator", LOG_LEVEL)
-    logger.debug(f"Running DP rank {dp_rank+1} of {dp_degree}")
+    logger.debug(f"Running DP rank {dp_rank+1} of {dp_degree} with {master_addr}:{master_port} as master address")
 
     os.environ["PIPER_DP_RANK"] = str(dp_rank)
     os.environ["PIPER_DP_DEGREE"] = str(dp_degree)
@@ -42,6 +42,7 @@ def __init__(self, dp_degree, pp_degree):
         self.master_port = find_free_port()
 
     def run_program(self, training_func: Callable, *args, **kwargs):
+        master_addr = ray.util.get_node_ip_address()
         return ray.get(
             [
                 run_dp_rank.remote(
@@ -49,7 +50,7 @@ def run_program(self, training_func: Callable, *args, **kwargs):
                     self.dp_degree,
                     self.pp_degree,
                     self.world_size,
-                    "127.0.0.1",
+                    master_addr,
                     self.master_port,
                     training_func,
                     *args,
diff --git a/src/piper_exec.py b/src/piper_exec.py
index d37a759..c70a4fb 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -4,7 +4,7 @@
 import time
 import ray
 import torch.distributed as dist
-from typing import NamedTuple
+from typing import NamedTuple, Optional
 from enum import Enum
 from dataclasses import dataclass, field
 import threading
@@ -16,13 +16,21 @@
 
 logger = create_logger("piper_exec", LOG_LEVEL)
 
-class CompType(Enum):
+class TaskType(Enum):
     FWD = "forward"
     BWD = "backward"
     UPD = "update"
     BWD_I = "backward_input"
     BWD_W = "backward_weight"
     FWD_BWD = "forward_backward"
+    SEND = "send"
+    RECV = "recv"
+    ALL_REDUCE = "all_reduce"
+    FWD_A2A = "forward_a2a"
+    BWD_A2A = "backward_a2a"
+
+# Backwards-compatible alias
+CompType = TaskType
 
 
 class BatchMeta(NamedTuple):
@@ -34,10 +42,14 @@ class BatchMeta(NamedTuple):
 class Task(NamedTuple):
     pp_rank: int
     batches: list[BatchMeta]
-    type: CompType
+    type: TaskType
+    bucket_id: int = 0
+    a2a_tensor_idx: Optional[int] = None
 
     def __repr__(self) -> str:
-        return f"Task(pp_rank={self.pp_rank}, batches={[(batch.stage_id, batch.mb_idx) for batch in self.batches]}, type={self.type})"
+        bucket_str = f", bucket_id={self.bucket_id}" if self.bucket_id else ""
+        a2a_str = f", a2a_tensor_idx={self.a2a_tensor_idx}" if self.a2a_tensor_idx is not None else ""
+        return f"Task(pp_rank={self.pp_rank}, batches={[(batch.stage_id, batch.mb_idx) for batch in self.batches]}, type={self.type}{bucket_str}{a2a_str})"
 
 
 @dataclass
@@ -75,6 +87,56 @@ def num_stages(self) -> int:
     def num_ranks(self) -> int:
         return len(self.grid)
 
+@dataclass
+class TaskNode:
+    """A single node in the task DAG, corresponding to one non-None cell of a
+    Schedule2D grid.
+
+    Edges are of two kinds:
+
+    * **Data-dependency** (``data_preds`` / ``data_succs``): drawn between tasks
+      that share a ``mb_idx`` and are *adjacent* in time – i.e. no other task with
+      the same microbatch index sits between them in the time-step ordering.  These
+      edges may cross rows (actors).
+
+    * **Temporal** (``temporal_pred`` / ``temporal_succ``): drawn between
+      consecutive non-None tasks within the same row, but **only** when those
+      tasks do not already share a data-dependency edge.  Each task has at most
+      one temporal predecessor and one successor.
+    """
+    task: Task
+    pp_rank: int    # row index in Schedule2D.grid
+    time_step: int  # column index in Schedule2D.grid
+
+    # Filled in by schedule_to_dag()
+    data_preds: list["TaskNode"] = field(default_factory=list)
+    data_succs: list["TaskNode"] = field(default_factory=list)
+    temporal_pred: Optional["TaskNode"] = None
+    temporal_succ: Optional["TaskNode"] = None
+
+    # For SEND/RECV nodes: the peer pipeline rank
+    peer_pp_rank: Optional[int] = None
+
+    def node_id(self) -> str:
+        """Unique string identifier for use as a graph node key."""
+        ttype = self.task.type.value if self.task.type is not None else "none"
+        mb = self.task.batches[0].mb_idx if self.task.batches else "x"
+        return f"r{self.pp_rank}_t{self.time_step}_{ttype}_mb{mb}"
+
+
+@dataclass
+class TaskDAG:
+    """DAG of :class:`TaskNode` objects for one training iteration.
+
+    Produced by :func:`~src.piper.schedule_to_dag`.
+    """
+    nodes: list[TaskNode]
+
+    def roots(self) -> list[TaskNode]:
+        """Return nodes that have no predecessors of any kind."""
+        return [n for n in self.nodes if not n.data_preds and n.temporal_pred is None]
+
+
 class DAGEdge(NamedTuple):
     from_stage: int
     to_stage: int
@@ -256,7 +318,7 @@ def _build_p2p_schedule(schedule, num_stages, num_devices, num_steps, stage_to_d
             task = schedule[j][i]
             if task is None:
                 continue
-            pp_rank, batches, task_type = task
+            pp_rank, batches, task_type, *_ = task
 
             # Forward send (added after forward compute produces output)
             if task_type in (CompType.FWD, CompType.FWD_BWD):
@@ -363,7 +425,7 @@ def _p2p_send(actor, pp_rank, stage_from, stage_to, mb_idx, is_fwd, dep):
                 continue
             task = schedule[j][i]
             if task:
-                pp_rank, batches, task_type = task
+                pp_rank, batches, task_type, *_ = task
                 actor = actors[j]
                 match task_type:
                     case CompType.UPD:
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index 6850989..cf6993e 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -13,6 +13,7 @@
 from torch.autograd import Function
 
 from .piper_utils import create_logger, LOG_LEVEL, piper_metadata
+from .piper_exec import TaskType, Schedule2D, Task, TaskNode, TaskDAG
 
 logger = create_logger("piper_graph_transform", LOG_LEVEL)
 
@@ -1380,4 +1381,1605 @@ def _wrap_with_send(arg):
 
     new_gm = fx.GraphModule(root_module, new_graph)
     new_gm.recompile()
-    return new_gm
\ No newline at end of file
+    return new_gm
+
+
+# ---------------------------------------------------------------------------
+# Schedule → Task DAG
+# ---------------------------------------------------------------------------
+
+def schedule_to_dag(schedule: Schedule2D) -> TaskDAG:
+    """Convert a :class:`Schedule2D` into a :class:`TaskDAG`.
+
+    Two kinds of edges are built:
+
+    **Data-dependency edges** connect tasks that share a ``mb_idx`` and are
+    *adjacent* in time – i.e. no other task carrying the same microbatch index
+    exists at any time step between them.  These edges may go between rows
+    (actors), representing the point-to-point activation / gradient tensors
+    that cross pipeline stage boundaries.
+
+    **Temporal edges** connect every pair of consecutive non-None tasks within
+    the same row (actor).  They model the serialisation constraint that a single
+    actor executes one cell at a time.
+    """
+    nodes: list[TaskNode] = []
+    grid_nodes: list[list[Optional[TaskNode]]] = []
+
+    for pp_rank, row in enumerate(schedule.grid):
+        row_nodes: list[Optional[TaskNode]] = []
+        for time_step, task in enumerate(row):
+            if task is None:
+                row_nodes.append(None)
+            elif task.type == TaskType.FWD_BWD:
+                # Expand inline into a FWD node followed by a BWD node.
+                # Placing both consecutively in row_nodes causes the temporal-edge
+                # pass to chain: prev_cell → fwd_node → bwd_node → next_cell.
+                fwd_batch, bwd_batch = task.batches[0], task.batches[1]
+                fwd_node = TaskNode(
+                    task=Task(pp_rank=pp_rank, batches=[fwd_batch], type=TaskType.FWD),
+                    pp_rank=pp_rank,
+                    time_step=time_step,
+                )
+                bwd_node = TaskNode(
+                    task=Task(pp_rank=pp_rank, batches=[bwd_batch], type=TaskType.BWD),
+                    pp_rank=pp_rank,
+                    time_step=time_step,
+                )
+                nodes.extend([fwd_node, bwd_node])
+                row_nodes.extend([fwd_node, bwd_node])
+            else:
+                node = TaskNode(task=task, pp_rank=pp_rank, time_step=time_step)
+                nodes.append(node)
+                row_nodes.append(node)
+        grid_nodes.append(row_nodes)
+
+    mb_to_nodes: dict[int, list[TaskNode]] = defaultdict(list)
+    seen_per_mb: dict[int, set[int]] = defaultdict(set)
+
+    for node in nodes:
+        # UPD and BWD_W are purely local: UPD has no cross-stage dependency;
+        # BWD_W only depends on its paired BWD_I (same rank/stage/mb) and must
+        # not appear in the cross-rank data chain so that insert_p2p_ops does
+        # not add spurious SEND/RECV nodes for BWD_W.
+        if node.task.type in (TaskType.UPD, TaskType.BWD_W):
+            continue
+        for batch in node.task.batches:
+            mb_idx = batch.mb_idx
+            node_ident = id(node)
+            if node_ident not in seen_per_mb[mb_idx]:
+                seen_per_mb[mb_idx].add(node_ident)
+                mb_to_nodes[mb_idx].append(node)
+
+    for mb_nodes in mb_to_nodes.values():
+        mb_nodes.sort(key=lambda n: (n.time_step, n.pp_rank))
+        for i in range(len(mb_nodes) - 1):
+            pred, succ = mb_nodes[i], mb_nodes[i + 1]
+            pred.data_succs.append(succ)
+            succ.data_preds.append(pred)
+
+    # Add explicit BWD_I -> BWD_W data edges for each (rank, stage_id, mb_idx)
+    # pair.  BWD_W is excluded from the mb chain above, but it still needs a
+    # data dependency on its upstream BWD_I so the actor knows to wait for it.
+    bwdi_index: dict = {}  # (pp_rank, stage_id, mb_idx) -> BWD_I node
+    for node in nodes:
+        if node.task.type == TaskType.BWD_I:
+            b = node.task.batches[0]
+            bwdi_index[(node.pp_rank, b.stage_id, b.mb_idx)] = node
+    for node in nodes:
+        if node.task.type == TaskType.BWD_W:
+            b = node.task.batches[0]
+            bwdi = bwdi_index.get((node.pp_rank, b.stage_id, b.mb_idx))
+            if bwdi is not None:
+                bwdi.data_succs.append(node)
+                node.data_preds.append(bwdi)
+
+    data_edge_pairs: set[tuple[int, int]] = set()
+    for node in nodes:
+        for succ in node.data_succs:
+            data_edge_pairs.add((id(node), id(succ)))
+            data_edge_pairs.add((id(succ), id(node)))
+
+    for row_nodes in grid_nodes:
+        non_null = [n for n in row_nodes if n is not None]
+        for i in range(len(non_null) - 1):
+            pred, succ = non_null[i], non_null[i + 1]
+            if (id(pred), id(succ)) not in data_edge_pairs:
+                pred.temporal_succ = succ
+                succ.temporal_pred = pred
+
+    return TaskDAG(nodes=nodes)
+
+
+
+# ---------------------------------------------------------------------------
+# Expand bucketed FWD/BWD tasks into per-bucket task chains
+# ---------------------------------------------------------------------------
+
+def expand_bucket_tasks(dag: TaskDAG, bucket_counts: dict) -> TaskDAG:
+    """Replace each FWD/BWD TaskNode for a bucketed stage with a chain of
+    FWD / BWD TaskNodes with explicit bucket_id, one per bucket.
+
+    For FWD stages the chain executes in bucket order 0, 1, …, K-1.
+    For BWD stages the chain executes in reverse order K-1, K-2, …, 0
+    (highest bucket_id first receives the upstream gradient; bucket_id=0 is
+    last to execute and sends the input gradient to the previous stage).
+
+    Incoming data / temporal edges are attached to the first node in the
+    execution chain; outgoing edges are attached to the last node.
+
+    Args:
+        dag: Global TaskDAG produced by schedule_to_dag().
+        bucket_counts: Mapping stage_id -> number of buckets. Only stages
+            present in this dict are expanded.
+
+    Returns:
+        A new TaskDAG with expanded nodes.
+    """
+    TIME_SCALE = 1000
+
+    # ---- Step 2a: create bucket chains for all expandable nodes ----
+    expandable = [
+        n for n in dag.nodes
+        if n.task.type in (TaskType.FWD, TaskType.BWD, TaskType.BWD_I)
+        and n.task.batches[0].stage_id in bucket_counts
+    ]
+
+    # id(orig) -> (first_bkt, last_bkt, chain_list)
+    node_to_first_last: dict = {}
+
+    for orig in expandable:
+        stage_id = orig.task.batches[0].stage_id
+        K = bucket_counts[stage_id]
+
+        if orig.task.type == TaskType.FWD:
+            # Execution order: bucket 0, 1, …, K-1
+            chain = [
+                TaskNode(
+                    task=Task(
+                        pp_rank=orig.pp_rank,
+                        batches=list(orig.task.batches),
+                        type=TaskType.FWD,
+                        bucket_id=b,
+                    ),
+                    pp_rank=orig.pp_rank,
+                    time_step=orig.time_step * TIME_SCALE + i,
+                )
+                for i, b in enumerate(range(K))
+            ]
+        else:  # BWD / BWD_I: execution order K-1, K-2, …, 0
+            chain = [
+                TaskNode(
+                    task=Task(
+                        pp_rank=orig.pp_rank,
+                        batches=list(orig.task.batches),
+                        type=orig.task.type,   # preserve BWD or BWD_I
+                        bucket_id=K - 1 - i,
+                    ),
+                    pp_rank=orig.pp_rank,
+                    time_step=orig.time_step * TIME_SCALE + i,
+                )
+                for i in range(K)
+            ]
+
+        # Data-dep chain between consecutive bucket nodes
+        for i in range(K - 1):
+            chain[i].data_succs.append(chain[i + 1])
+            chain[i + 1].data_preds.append(chain[i])
+
+        first_bkt = chain[0]
+        last_bkt = chain[-1]
+        node_to_first_last[id(orig)] = (first_bkt, last_bkt, chain)
+
+    # ---- Step 2b: redirect all edges ----
+    for orig in expandable:
+        first_bkt, last_bkt, _ = node_to_first_last[id(orig)]
+
+        # Incoming data edges: pred -> orig  =>  new_src -> first_bkt
+        for pred in list(orig.data_preds):
+            if orig in pred.data_succs:
+                pred.data_succs.remove(orig)
+            new_src = node_to_first_last[id(pred)][1] if id(pred) in node_to_first_last else pred
+            if first_bkt not in new_src.data_succs:
+                new_src.data_succs.append(first_bkt)
+                first_bkt.data_preds.append(new_src)
+        orig.data_preds.clear()
+
+        # Outgoing data edges: orig -> succ  =>  last_bkt -> new_dst
+        for succ in list(orig.data_succs):
+            if orig in succ.data_preds:
+                succ.data_preds.remove(orig)
+            new_dst = node_to_first_last[id(succ)][0] if id(succ) in node_to_first_last else succ
+            if new_dst not in last_bkt.data_succs:
+                last_bkt.data_succs.append(new_dst)
+                new_dst.data_preds.append(last_bkt)
+        orig.data_succs.clear()
+
+        # Incoming temporal edge: tp -> orig  =>  new_tp -> first_bkt
+        if orig.temporal_pred is not None:
+            tp = orig.temporal_pred
+            tp.temporal_succ = None  # disconnect old link
+            new_tp = node_to_first_last[id(tp)][1] if id(tp) in node_to_first_last else tp
+            new_tp.temporal_succ = first_bkt
+            first_bkt.temporal_pred = new_tp
+            orig.temporal_pred = None
+
+        # Outgoing temporal edge: orig -> ts  =>  last_bkt -> new_ts
+        if orig.temporal_succ is not None:
+            ts = orig.temporal_succ
+            ts.temporal_pred = None  # disconnect old link
+            new_ts = node_to_first_last[id(ts)][0] if id(ts) in node_to_first_last else ts
+            last_bkt.temporal_succ = new_ts
+            new_ts.temporal_pred = last_bkt
+            orig.temporal_succ = None
+
+    # ---- Step 2c: build new node list ----
+    orig_ids = set(id(n) for n in expandable)
+    expanded_nodes = []
+    for n in dag.nodes:
+        if id(n) in orig_ids:
+            expanded_nodes.extend(node_to_first_last[id(n)][2])
+        else:
+            expanded_nodes.append(n)
+
+    return TaskDAG(nodes=expanded_nodes)
+
+
+# ---------------------------------------------------------------------------
+# Insert SEND / RECV nodes for cross-rank data dependencies
+# ---------------------------------------------------------------------------
+
+def insert_p2p_ops(dag: TaskDAG) -> list[TaskDAG]:
+    """Transform a :class:`TaskDAG` into one :class:`TaskDAG` per PP rank.
+
+    For every cross-rank data-dependency edge ``task1 (rank A) → task2 (rank B)``:
+
+    1. Remove the data-dep edge.
+    2. Create a SEND node on rank A with a data-dep edge ``task1 → SEND``.
+    3. Create a RECV node on rank B with a data-dep edge ``RECV → task2``.
+    4. If ``task1`` had a temporal successor, re-attach it from SEND instead.
+    5. If ``task2`` had a temporal predecessor, re-attach it to RECV instead.
+
+    After the transformation no cross-rank edges should remain.  The function
+    asserts this, then splits the node list by ``pp_rank`` and returns a list of
+    :class:`TaskDAG` objects ordered by ascending PP rank.
+    """
+    all_nodes: list[TaskNode] = list(dag.nodes)
+
+    cross_edges: list[tuple[TaskNode, TaskNode]] = [
+        (src, dst)
+        for src in all_nodes
+        for dst in src.data_succs
+        if src.pp_rank != dst.pp_rank
+    ]
+
+    synthetic_ts: dict[int, int] = {}
+
+    def _next_ts(pp_rank: int) -> int:
+        if pp_rank not in synthetic_ts:
+            synthetic_ts[pp_rank] = -1
+        ts = synthetic_ts[pp_rank]
+        synthetic_ts[pp_rank] -= 1
+        return ts
+
+    for src, dst in cross_edges:
+        src.data_succs.remove(dst)
+        dst.data_preds.remove(src)
+
+        send_node = TaskNode(
+            task=Task(pp_rank=src.pp_rank, batches=list(src.task.batches), type=TaskType.SEND),
+            pp_rank=src.pp_rank,
+            time_step=_next_ts(src.pp_rank),
+            peer_pp_rank=dst.pp_rank,
+        )
+        src.data_succs.append(send_node)
+        send_node.data_preds.append(src)
+
+        if src.temporal_succ is not None:
+            ts_next = src.temporal_succ
+            ts_next.temporal_pred = send_node
+            send_node.temporal_succ = ts_next
+            src.temporal_succ = None
+
+        recv_node = TaskNode(
+            task=Task(pp_rank=dst.pp_rank, batches=list(dst.task.batches), type=TaskType.RECV),
+            pp_rank=dst.pp_rank,
+            time_step=_next_ts(dst.pp_rank),
+            peer_pp_rank=src.pp_rank,
+        )
+        recv_node.data_succs.append(dst)
+        dst.data_preds.append(recv_node)
+
+        if dst.temporal_pred is not None:
+            tp_prev = dst.temporal_pred
+            tp_prev.temporal_succ = recv_node
+            recv_node.temporal_pred = tp_prev
+            dst.temporal_pred = None
+
+        all_nodes.append(send_node)
+        all_nodes.append(recv_node)
+
+    for node in all_nodes:
+        for succ in node.data_succs:
+            assert node.pp_rank == succ.pp_rank, (
+                f"Cross-rank data edge still present: "
+                f"{node.node_id()} (rank {node.pp_rank}) → "
+                f"{succ.node_id()} (rank {succ.pp_rank})"
+            )
+        if node.temporal_succ is not None:
+            assert node.pp_rank == node.temporal_succ.pp_rank, (
+                f"Cross-rank temporal edge present: "
+                f"{node.node_id()} (rank {node.pp_rank}) → "
+                f"{node.temporal_succ.node_id()} (rank {node.temporal_succ.pp_rank})"
+            )
+
+    rank_nodes: dict[int, list[TaskNode]] = defaultdict(list)
+    for node in all_nodes:
+        rank_nodes[node.pp_rank].append(node)
+
+    # Task types that represent real backward compute — UPD must be temporally
+    # ordered after the last of these, not after communication nodes (A2A, AR,
+    # SEND, RECV) whose time_steps may be synthetic and higher than the last BWD.
+    _BWD_COMPUTE_TYPES = {
+        TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W,
+    }
+    upd_ts_counter = -(max(abs(v) for v in synthetic_ts.values()) + 1) if synthetic_ts else -1
+    for rank, nodes in rank_nodes.items():
+        bwd_nodes = [n for n in nodes if n.task.type in _BWD_COMPUTE_TYPES]
+        last = max(
+            bwd_nodes if bwd_nodes else (n for n in nodes if n.time_step >= 0),
+            key=lambda n: n.time_step,
+        )
+        upd_task = Task(pp_rank=rank, batches=list(last.task.batches), type=TaskType.UPD)
+        upd_node = TaskNode(task=upd_task, pp_rank=rank, time_step=upd_ts_counter)
+        upd_ts_counter -= 1
+        last.temporal_succ = upd_node
+        upd_node.temporal_pred = last
+        nodes.append(upd_node)
+
+    return [TaskDAG(nodes=rank_nodes[r]) for r in sorted(rank_nodes)]
+
+
+def insert_ar_ops(per_rank_dags: list) -> list:
+    """Insert ALL_REDUCE nodes into each per-rank DAG for DP gradient sync.
+
+    Two cases are handled:
+
+    For each (stage_id, bucket_id) group of BWD/BWD_I nodes, add one ALL_REDUCE
+    after the final microbatch's node.  The actor all-reduces the pre-allocated
+    flat_grads tensor for that (stage, bucket) pair.
+
+    The ALL_REDUCE node has exactly one data edge: from the final microbatch
+    BWD/BWD_I node.  Synchronisation with UPD is handled at the CUDA level via
+    ar_events in _update_impl.
+    """
+    for rank_dag in per_rank_dags:
+        new_nodes = []
+
+        # Group BWD/BWD_I nodes by (stage_id, bucket_id).  One ALL_REDUCE is
+        # inserted per group, triggered by the final microbatch's node in that group.
+        bwd_groups: dict = defaultdict(list)
+        for node in rank_dag.nodes:
+            if node.task.type in (TaskType.BWD, TaskType.BWD_I):
+                s = node.task.batches[0].stage_id
+                bwd_groups[(s, node.task.bucket_id)].append(node)
+
+        for (stage_id, bucket_id), group in bwd_groups.items():
+            trigger_node = max(group, key=lambda n: n.time_step)
+            ar_task = Task(
+                pp_rank=trigger_node.pp_rank,
+                batches=list(trigger_node.task.batches),
+                type=TaskType.ALL_REDUCE,
+                bucket_id=bucket_id,
+            )
+            ar_node = TaskNode(
+                task=ar_task,
+                pp_rank=trigger_node.pp_rank,
+                time_step=-10000 - len(new_nodes),
+            )
+            trigger_node.data_succs.append(ar_node)
+            ar_node.data_preds.append(trigger_node)
+            new_nodes.append(ar_node)
+
+        rank_dag.nodes.extend(new_nodes)
+
+    return per_rank_dags
+
+
+# ---------------------------------------------------------------------------
+# Graphviz visualisation
+# ---------------------------------------------------------------------------
+
+def _task_node_label(node: TaskNode) -> str:
+    """Short human-readable label for a TaskNode."""
+    task = node.task
+
+    if task.type in (TaskType.SEND, TaskType.RECV):
+        op = "SEND" if task.type == TaskType.SEND else "RECV"
+        return op
+
+    type_abbrev = {
+        TaskType.FWD:     "F",
+        TaskType.BWD:     "B",
+        TaskType.UPD:     "U",
+        TaskType.BWD_I:   "BI",
+        TaskType.BWD_W:   "Bw",
+        TaskType.FWD_BWD: "FB",
+        TaskType.ALL_REDUCE: "AR",
+        TaskType.FWD_A2A: "F_A2A",
+        TaskType.BWD_A2A: "B_A2A",
+    }.get(task.type, "?")
+
+    if task.type == TaskType.UPD:
+        return type_abbrev
+
+    if task.type == TaskType.ALL_REDUCE:
+        stage_id = task.batches[0].stage_id
+        return "AR"
+    
+    if task.type == TaskType.FWD_A2A or task.type == TaskType.BWD_A2A:
+        return "A2A"
+
+    parts = " + ".join(f"S{b.stage_id} M{b.mb_idx}" for b in task.batches)
+    return f"{type_abbrev} {parts}"
+
+
+def visualize_dag(
+    dag: TaskDAG,
+    output_path: str = "dag",
+    fmt: str = "png",
+) -> None:
+    """Render a :class:`TaskDAG` as a labelled image using *graphviz*.
+
+    Nodes are coloured by pipeline rank.  Two edge styles:
+
+    * **Dashed grey** – temporal edges (serialisation within one actor).
+    * **Solid black** – data-dependency edges.
+
+    The image is saved to ``{output_path}.{fmt}``.
+    If *graphviz* is not installed the function logs a warning and returns.
+    """
+    try:
+        import graphviz
+    except ImportError:
+        logger.warning("graphviz Python package not installed; skipping DAG visualisation")
+        return
+
+    if not dag.nodes:
+        logger.warning("Empty task DAG; nothing to visualise")
+        return
+
+    # Build a mapping from pp_rank -> sorted list of stage_ids on that rank.
+    pp_rank_stages: dict[int, list[int]] = {}
+    for n in dag.nodes:
+        if n.task.batches:
+            rank = n.task.pp_rank
+            sid = n.task.batches[0].stage_id
+            if rank not in pp_rank_stages:
+                pp_rank_stages[rank] = []
+            if sid not in pp_rank_stages[rank]:
+                pp_rank_stages[rank].append(sid)
+    for rank in pp_rank_stages:
+        pp_rank_stages[rank].sort()
+
+    _COMPUTE_TYPES = (
+        TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W, TaskType.FWD_BWD,
+    )
+
+    def _node_fill(node: TaskNode) -> str:
+        t = node.task.type
+        if t in (TaskType.SEND, TaskType.RECV, TaskType.FWD_A2A, TaskType.BWD_A2A, TaskType.ALL_REDUCE):
+            return "#FFFFFF"
+        if t == TaskType.FWD:
+            return "#FFA500"  # orange
+        if t in (TaskType.BWD, TaskType.BWD_I):
+            return "#27AE60"  # green
+        if t == TaskType.BWD_W:
+            return "#2E86C1"  # blue
+        return "#D5D8DC"
+
+    def _node_fontcolor(node: TaskNode) -> str:
+        t = node.task.type
+        if t not in _COMPUTE_TYPES or not node.task.batches:
+            return "black"
+        rank = node.task.pp_rank
+        stages = pp_rank_stages.get(rank, [])
+        if len(stages) < 2:
+            return "black"
+        sid = node.task.batches[0].stage_id
+        # First stage -> black text, second (and beyond) stage -> white text
+        return "black" if sid == stages[0] else "white"
+
+    dot = graphviz.Digraph("PiperDAG", comment="Piper Task DAG")
+    dot.attr(rankdir="LR", splines="ortho", nodesep="0.4", ranksep="0.6", fontname="Helvetica")
+    dot.attr("node", shape="box", style="filled", fontsize="9", fontname="Helvetica")
+    dot.attr("edge", fontsize="8", fontname="Helvetica")
+
+    for node in dag.nodes:
+        dot.node(
+            node.node_id(),
+            label=_task_node_label(node),
+            fillcolor=_node_fill(node),
+            fontcolor=_node_fontcolor(node),
+            tooltip=repr(node.task),
+        )
+
+    step_to_nodes: dict[int, list[TaskNode]] = defaultdict(list)
+    for node in dag.nodes:
+        if node.time_step >= 0:
+            step_to_nodes[node.time_step].append(node)
+
+    for col_nodes in step_to_nodes.values():
+        if len(col_nodes) > 1:
+            with dot.subgraph() as sub:
+                sub.attr(rank="same")
+                for node in col_nodes:
+                    sub.node(node.node_id())
+
+    data_pairs: set[tuple[str, str]] = set()
+    for node in dag.nodes:
+        for succ in node.data_succs:
+            data_pairs.add((node.node_id(), succ.node_id()))
+
+    for node in dag.nodes:
+        if node.temporal_succ is not None:
+            key = (node.node_id(), node.temporal_succ.node_id())
+            if key not in data_pairs:
+                dot.edge(
+                    key[0], key[1],
+                    style="dashed", color="grey60",
+                    penwidth="1.0", arrowsize="0.6", constraint="true",
+                )
+
+    seen_edges: set[tuple[str, str]] = set()
+    for node in dag.nodes:
+        for succ in node.data_succs:
+            key = (node.node_id(), succ.node_id())
+            if key in seen_edges:
+                continue
+            seen_edges.add(key)
+            cross_rank = node.pp_rank != succ.pp_rank
+            dot.edge(
+                key[0], key[1],
+                color="black",
+                penwidth="2.0", arrowsize="0.8",
+                constraint="false" if cross_rank else "true",
+            )
+
+    out = dot.render(output_path, format=fmt, cleanup=False)
+    logger.info(f"DAG visualisation saved to {out}")
+
+
+def visualize_schedule(
+    schedule: Schedule2D,
+    output_path: str = "schedule",
+    fmt: str = "png",
+) -> None:
+    """Render a :class:`Schedule2D` as a labelled DAG image using *graphviz*.
+
+    Converts the schedule to a :class:`TaskDAG` via :func:`schedule_to_dag`
+    then delegates to :func:`visualize_dag`.
+    """
+    dag = schedule_to_dag(schedule)
+    visualize_dag(dag, output_path=output_path, fmt=fmt)
+
+
+def print_dag_order(dag: TaskDAG, label: str = "") -> None:
+    """Print the topological execution order of nodes in a :class:`TaskDAG`.
+
+    Uses Kahn's algorithm (same order as ``run_dag``) so the output reflects
+    exactly what the actor will execute.  Useful for debugging schedule issues.
+    """
+    from collections import deque as _deque
+
+    in_degree = {
+        id(n): len(n.data_preds) + (1 if n.temporal_pred is not None else 0)
+        for n in dag.nodes
+    }
+    queue: _deque = _deque(n for n in dag.nodes if in_degree[id(n)] == 0)
+
+    header = f"--- DAG execution order{': ' + label if label else ''} ---"
+    print(header)
+    step = 0
+    while queue:
+        node = queue.popleft()
+        task = node.task
+        ttype = task.type.value if task.type is not None else "?"
+        batches_str = ", ".join(
+            f"s{b.stage_id} mb{b.mb_idx}" for b in task.batches
+        ) if task.batches else ""
+        bkt = f" bkt={task.bucket_id}" if task.bucket_id is not None else ""
+        print(f"  {step:3d}  rank={node.pp_rank}  {ttype:<14s}  {batches_str}{bkt}")
+        step += 1
+        all_succs = list(node.data_succs)
+        if node.temporal_succ is not None:
+            all_succs.append(node.temporal_succ)
+        for succ in all_succs:
+            in_degree[id(succ)] -= 1
+            if in_degree[id(succ)] == 0:
+                queue.append(succ)
+    print("-" * len(header))
+
+
+# ---------------------------------------------------------------------------
+# bucket_parameters – FX graph transformation
+# ---------------------------------------------------------------------------
+
+def _iter_node_args(node: fx.Node):
+    """Yield every :class:`fx.Node` appearing in *node*'s args and kwargs."""
+    def _collect(obj):
+        if isinstance(obj, fx.Node):
+            yield obj
+        elif isinstance(obj, (list, tuple)):
+            for item in obj:
+                yield from _collect(item)
+        elif isinstance(obj, dict):
+            for v in obj.values():
+                yield from _collect(v)
+    yield from _collect(node.args)
+    yield from _collect(node.kwargs)
+
+
+def bucket_parameters(
+    gm: fx.GraphModule,
+    bucket_size_bytes: int = 25 * 1024 * 1024,
+) -> list[fx.GraphModule]:
+    """Split an FX GraphModule into sequential sub-modules based on parameter buckets.
+
+    Adjacent parameters (in graph order) are greedily grouped into buckets of
+    approximately *bucket_size_bytes* bytes.  If a parameter is used by compute
+    nodes that fall into more than one bucket's node-range, it is promoted to
+    its own singleton bucket so the graph can be cut cleanly.
+
+    The graph is then cut at bucket boundaries and each segment is wrapped in a
+    new :class:`fx.GraphModule`.  All original model inputs (``placeholder``
+    nodes) are re-created in every sub-module.  Intermediate activation tensors
+    that flow between segments become additional placeholder inputs on the
+    receiving module.
+
+    The modules are ordered so that the *i*-th module's outputs are the
+    additional inputs of the *(i+1)*-th module (appended after the original
+    model inputs), producing a pipeline that replicates the original graph.
+
+    Args:
+        gm: Source ``fx.GraphModule``.  ``node.meta["example_value"]`` is used
+            for parameter size if available; falls back to direct attribute
+            inspection.
+        bucket_size_bytes: Target bucket size in bytes (default 25 MB).
+
+    Returns:
+        A list of ``fx.GraphModule`` objects in execution order.  If there is
+        only one bucket (or no parameters), the list contains *gm* unchanged.
+    """
+    nodes = list(gm.graph.nodes)
+    node_idx: dict[fx.Node, int] = {nd: i for i, nd in enumerate(nodes)}
+
+    # ------------------------------------------------------------------ #
+    # Helpers                                                             #
+    # ------------------------------------------------------------------ #
+
+    def _is_param(nd: fx.Node) -> bool:
+        if nd.op != "get_attr":
+            return False
+        obj = gm
+        for part in nd.target.split("."):
+            obj = getattr(obj, part, None)
+            if obj is None:
+                return False
+        return isinstance(obj, torch.nn.Parameter)
+
+    def _attr_size(nd: fx.Node) -> int:
+        ev = nd.meta.get("example_value")
+        if ev is not None and hasattr(ev, "numel"):
+            return int(ev.numel() * ev.element_size())
+        obj = gm
+        for part in nd.target.split("."):
+            obj = getattr(obj, part, None)
+            if obj is None:
+                return 0
+        return int(obj.numel() * obj.element_size()) if isinstance(obj, torch.Tensor) else 0
+
+    # ------------------------------------------------------------------ #
+    # Step 1 – Collect parameter nodes                                   #
+    # ------------------------------------------------------------------ #
+    param_nodes: list[fx.Node] = [nd for nd in nodes if _is_param(nd)]
+    if not param_nodes:
+        return [gm]
+
+    # ------------------------------------------------------------------ #
+    # Step 2 – Per-parameter compute-node use range                      #
+    # ------------------------------------------------------------------ #
+    compute_set: frozenset[int] = frozenset(
+        i for i, nd in enumerate(nodes)
+        if nd.op not in ("placeholder", "get_attr", "output")
+    )
+
+    def _use_range(pnd: fx.Node) -> tuple[int, int]:
+        idxs = [node_idx[u] for u in pnd.users if node_idx[u] in compute_set]
+        if not idxs:
+            return (node_idx[pnd], node_idx[pnd])
+        return (min(idxs), max(idxs))
+
+    param_ranges: dict[fx.Node, tuple[int, int]] = {pn: _use_range(pn) for pn in param_nodes}
+
+    # ------------------------------------------------------------------ #
+    # Step 3 – Greedy bucket assignment                                  #
+    # ------------------------------------------------------------------ #
+    bucket_id: dict[fx.Node, int] = {}
+    cur_bucket = 0
+    cur_size = 0
+    for pn in param_nodes:
+        sz = _attr_size(pn)
+        if cur_size + sz > bucket_size_bytes and cur_size > 0:
+            cur_bucket += 1
+            cur_size = 0
+        bucket_id[pn] = cur_bucket
+        cur_size += sz
+    n_initial = cur_bucket + 1
+
+    # ------------------------------------------------------------------ #
+    # Step 4 – Singleton promotion for cross-bucket parameters           #
+    # ------------------------------------------------------------------ #
+    def _initial_bucket_range(bid: int) -> tuple[int, int]:
+        members = [pn for pn in param_nodes if bucket_id[pn] == bid]
+        if not members:
+            return (0, 0)
+        return (
+            min(param_ranges[pn][0] for pn in members),
+            max(param_ranges[pn][1] for pn in members),
+        )
+
+    init_ranges = [_initial_bucket_range(b) for b in range(n_initial)]
+
+    def _initial_bucket_at(idx: int) -> int:
+        for bid, (lo, hi) in enumerate(init_ranges):
+            if lo <= idx <= hi:
+                return bid
+        return 0
+
+    singleton_next = n_initial
+    for pn in param_nodes:
+        first, last = param_ranges[pn]
+        if _initial_bucket_at(first) != _initial_bucket_at(last):
+            bucket_id[pn] = singleton_next
+            singleton_next += 1
+
+    # ------------------------------------------------------------------ #
+    # Step 5 – Build final bucket list, sort, merge overlapping ranges  #
+    # ------------------------------------------------------------------ #
+    bucket_members: dict[int, list[fx.Node]] = defaultdict(list)
+    for pn in param_nodes:
+        bucket_members[bucket_id[pn]].append(pn)
+
+    def _bucket_use_range(members: list[fx.Node]) -> tuple[int, int]:
+        return (
+            min(param_ranges[pn][0] for pn in members),
+            max(param_ranges[pn][1] for pn in members),
+        )
+
+    bucket_list: list[tuple[int, int, list[fx.Node]]] = []
+    for bid, members in bucket_members.items():
+        f, l = _bucket_use_range(members)
+        bucket_list.append((f, l, members))
+    bucket_list.sort(key=lambda t: t[0])
+
+    merged: list[tuple[int, int, list[fx.Node]]] = []
+    for f, l, members in bucket_list:
+        if merged and f <= merged[-1][1]:
+            pf, pl, pm = merged[-1]
+            merged[-1] = (pf, max(pl, l), pm + members)
+        else:
+            merged.append((f, l, members))
+
+    n_segs = len(merged)
+    if n_segs == 1:
+        return [gm]
+
+    # ------------------------------------------------------------------ #
+    # Step 6 – Cut points (max last-use per bucket except the final one) #
+    # ------------------------------------------------------------------ #
+    # cut_after[i] is the node index after which we cut between seg i and i+1.
+    cut_after: list[int] = [merged[i][1] for i in range(n_segs - 1)]
+
+    # ------------------------------------------------------------------ #
+    # Step 7 – Assign each node to a segment                            #
+    # ------------------------------------------------------------------ #
+    def _seg_of(idx: int) -> int:
+        return sum(1 for c in cut_after if c < idx)
+
+    node_seg: dict[fx.Node, int] = {}
+    for nd in nodes:
+        if nd.op == "placeholder":
+            node_seg[nd] = 0
+        elif nd.op == "output":
+            node_seg[nd] = n_segs - 1
+        elif nd.op == "get_attr":
+            user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
+            node_seg[nd] = _seg_of(min(user_idxs)) if user_idxs else 0
+        else:
+            node_seg[nd] = _seg_of(node_idx[nd])
+
+    # ------------------------------------------------------------------ #
+    # Step 8 – Compute cross-segment activation sets                    #
+    # ------------------------------------------------------------------ #
+    # seg_cross_in[seg] = ordered list of compute nodes produced in segment
+    # < seg that are consumed by any segment >= seg.  Placeholders and
+    # get_attr nodes are excluded: placeholder (model input) tensors are
+    # assumed to be available in every segment directly; parameters never
+    # cross segments after singleton promotion and can be re-issued.
+    node_max_user_seg: dict[fx.Node, int] = {}
+    for nd in nodes:
+        if nd.op == "output":
+            continue
+        node_max_user_seg[nd] = (
+            max(node_seg[u] for u in nd.users) if nd.users else node_seg[nd]
+        )
+
+    seg_cross_in: list[list[fx.Node]] = [[] for _ in range(n_segs)]
+    for nd in nodes:
+        if nd.op in ("placeholder", "get_attr", "output"):
+            continue
+        s = node_seg[nd]
+        mu = node_max_user_seg[nd]
+        for seg in range(s + 1, mu + 1):
+            seg_cross_in[seg].append(nd)
+    # Lists are already in graph order since we iterate nodes in graph order.
+
+    # For each segment, determine which original placeholder nodes it uses
+    # directly (so we only add the placeholders each segment actually needs).
+    seg_placeholders: list[list[fx.Node]] = [[] for _ in range(n_segs)]
+    for nd in nodes:
+        if nd.op != "placeholder":
+            continue
+        for seg in range(n_segs):
+            if any(node_seg[u] == seg for u in nd.users):
+                seg_placeholders[seg].append(nd)
+
+    # ------------------------------------------------------------------ #
+    # Step 9 – Build sub-graphs                                          #
+    # ------------------------------------------------------------------ #
+    sub_graphs: list[fx.GraphModule] = []
+
+    for seg in range(n_segs):
+        sub_g = fx.Graph()
+        remap: dict[fx.Node, fx.Node] = {}
+
+        # Original model inputs used directly by this segment.
+        for nd in seg_placeholders[seg]:
+            new_ph = sub_g.placeholder(nd.name)
+            new_ph.type = nd.type
+            remap[nd] = new_ph
+
+        # Cross-segment activation inputs from earlier segments.
+        for orig in seg_cross_in[seg]:
+            new_ph = sub_g.placeholder(f"_xseg_{orig.name}")
+            new_ph.type = orig.type
+            remap[orig] = new_ph
+
+        # get_attr nodes whose attributes belong to this segment.
+        for nd in nodes:
+            if nd.op == "get_attr" and node_seg[nd] == seg:
+                new_ga = sub_g.get_attr(nd.target)
+                new_ga.type = nd.type
+                remap[nd] = new_ga
+
+        # Compute nodes for this segment (graph order preserved).
+        for nd in nodes:
+            if nd.op in ("placeholder", "get_attr", "output"):
+                continue
+            if node_seg[nd] != seg:
+                continue
+            new_nd = sub_g.node_copy(nd, arg_transform=lambda x, r=remap: r[x])
+            remap[nd] = new_nd
+
+        # Output node.
+        if seg == n_segs - 1:
+            orig_out = next(nd for nd in nodes if nd.op == "output")
+            # orig_out.args[0] is the actual return value (node, tuple of nodes, etc.)
+            sub_g.output(fx.map_arg(orig_out.args[0], lambda x: remap[x]))
+        else:
+            out_nodes = [remap[orig] for orig in seg_cross_in[seg + 1]]
+            sub_g.output(tuple(out_nodes) if len(out_nodes) != 1 else out_nodes[0])
+
+        sub_g.lint()
+        sub_graphs.append(fx.GraphModule(gm, sub_g))
+
+    return sub_graphs
+
+
+# ---------------------------------------------------------------------------
+# bucket_stage – stage-level parameter bucketing (placeholder params)
+# ---------------------------------------------------------------------------
+
+def bucket_stage(
+    stage_gm: fx.GraphModule,
+    graphargs: list,
+    input_idxs: list[int],
+    param_idxs: list[int],
+    bucket_size_bytes: int = 25 * 1024 * 1024,
+) -> list[tuple[fx.GraphModule, list[int], list[int], list]]:
+    """Split a stage GraphModule into per-parameter-bucket sub-modules.
+
+    Unlike :func:`bucket_parameters`, which handles ``get_attr`` parameter
+    nodes, this function handles stages produced by :func:`_split_gm_by_stages`
+    where trainable parameters are passed as **placeholder inputs** identified
+    by *param_idxs*.
+
+    Args:
+        stage_gm: The stage ``fx.GraphModule`` to split.
+        graphargs: Flat arg list for ``stage_gm.forward``; entries at
+            *param_idxs* are meta tensors, entries at *input_idxs* are None.
+        input_idxs: Positions of activation-input placeholders.
+        param_idxs: Positions of parameter placeholders.
+        bucket_size_bytes: Target bucket size (default 25 MB).
+
+    Returns:
+        A list of ``(bucket_gm, bucket_input_idxs, bucket_param_idxs,
+        bucket_graphargs)`` tuples in execution order.  Returns a
+        single-element list containing the original stage when no split is
+        needed.
+    """
+    nodes = list(stage_gm.graph.nodes)
+    node_idx: dict[fx.Node, int] = {nd: i for i, nd in enumerate(nodes)}
+    ph_nodes = [nd for nd in nodes if nd.op == "placeholder"]
+
+    param_ph_set = {ph_nodes[i] for i in param_idxs if i < len(ph_nodes)}
+    param_ph_list = [ph_nodes[i] for i in param_idxs if i < len(ph_nodes)]
+    input_ph_set  = {ph_nodes[i] for i in input_idxs  if i < len(ph_nodes)}
+
+    if not param_ph_list:
+        return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))]
+
+    compute_set: frozenset[int] = frozenset(
+        i for i, nd in enumerate(nodes)
+        if nd.op not in ("placeholder", "get_attr", "output")
+    )
+
+    def _use_range(pnd: fx.Node) -> tuple[int, int]:
+        idxs = [node_idx[u] for u in pnd.users if node_idx[u] in compute_set]
+        return (min(idxs), max(idxs)) if idxs else (node_idx[pnd], node_idx[pnd])
+
+    def _size(pnd: fx.Node) -> int:
+        i = ph_nodes.index(pnd)
+        if i < len(graphargs) and graphargs[i] is not None and hasattr(graphargs[i], "numel"):
+            return int(graphargs[i].numel() * graphargs[i].element_size())
+        ev = pnd.meta.get("example_value")
+        if ev is not None and hasattr(ev, "numel"):
+            return int(ev.numel() * ev.element_size())
+        return 0
+
+    param_ranges = {pn: _use_range(pn) for pn in param_ph_list}
+
+    # Greedy bucket assignment
+    bucket_id: dict[fx.Node, int] = {}
+    cur_b, cur_sz = 0, 0
+    for pn in param_ph_list:
+        sz = _size(pn)
+        if cur_sz + sz > bucket_size_bytes and cur_sz > 0:
+            cur_b += 1
+            cur_sz = 0
+        bucket_id[pn] = cur_b
+        cur_sz += sz
+    n_init = cur_b + 1
+
+    # Singleton promotion
+    def _init_range(b: int) -> tuple[int, int]:
+        ms = [pn for pn in param_ph_list if bucket_id[pn] == b]
+        if not ms:
+            return (0, 0)
+        return (min(param_ranges[pn][0] for pn in ms), max(param_ranges[pn][1] for pn in ms))
+
+    init_ranges = [_init_range(b) for b in range(n_init)]
+
+    def _bucket_at(idx: int) -> int:
+        for b, (lo, hi) in enumerate(init_ranges):
+            if lo <= idx <= hi:
+                return b
+        return 0
+
+    sn = n_init
+    for pn in param_ph_list:
+        f, l = param_ranges[pn]
+        if _bucket_at(f) != _bucket_at(l):
+            bucket_id[pn] = sn
+            sn += 1
+
+    # Build bucket list, sort, merge overlapping ranges
+    bm: dict[int, list[fx.Node]] = defaultdict(list)
+    for pn in param_ph_list:
+        bm[bucket_id[pn]].append(pn)
+
+    blist: list[tuple[int, int, list[fx.Node]]] = []
+    for members in bm.values():
+        f = min(param_ranges[pn][0] for pn in members)
+        l = max(param_ranges[pn][1] for pn in members)
+        blist.append((f, l, members))
+    blist.sort(key=lambda t: t[0])
+
+    merged: list[tuple[int, int, list[fx.Node]]] = []
+    for f, l, ms in blist:
+        if merged and f <= merged[-1][1]:
+            pf, pl, pm = merged[-1]
+            merged[-1] = (pf, max(pl, l), pm + ms)
+        else:
+            merged.append((f, l, ms))
+
+    n_segs = len(merged)
+    if n_segs == 1:
+        return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))]
+
+    cut_after = [merged[i][1] for i in range(n_segs - 1)]
+
+    def _seg_of(idx: int) -> int:
+        return sum(1 for c in cut_after if c < idx)
+
+    # Assign nodes to segments
+    node_seg: dict[fx.Node, int] = {}
+    for nd in nodes:
+        if nd.op == "output":
+            node_seg[nd] = n_segs - 1
+        elif nd.op == "placeholder":
+            if nd in param_ph_set:
+                user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
+                node_seg[nd] = _seg_of(min(user_idxs)) if user_idxs else 0
+            else:
+                node_seg[nd] = 0
+        elif nd.op == "get_attr":
+            user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
+            node_seg[nd] = _seg_of(min(user_idxs)) if user_idxs else 0
+        else:
+            node_seg[nd] = _seg_of(node_idx[nd])
+
+    # Cross-segment inputs: compute outputs needed in later segments, plus
+    # non-param placeholders (e.g. freqs_cis, attention_mask) that live in
+    # segment 0 but are consumed by compute nodes in later segments.
+    node_max_user_seg: dict[fx.Node, int] = {}
+    for nd in nodes:
+        if nd.op == "output":
+            continue
+        node_max_user_seg[nd] = (
+            max(node_seg[u] for u in nd.users) if nd.users else node_seg[nd]
+        )
+
+    seg_cross_in: list[list[fx.Node]] = [[] for _ in range(n_segs)]
+    for nd in nodes:
+        if nd.op in ("get_attr", "output"):
+            continue
+        # Param placeholders are assigned to exactly one segment — not cross-segment.
+        if nd.op == "placeholder" and nd in param_ph_set:
+            continue
+        s = node_seg[nd]
+        mu = node_max_user_seg[nd]
+        for seg in range(s + 1, mu + 1):
+            seg_cross_in[seg].append(nd)
+
+    # Build sub-graphs
+    results: list[tuple[fx.GraphModule, list[int], list[int], list]] = []
+
+    for seg in range(n_segs):
+        sub_g = fx.Graph()
+        remap: dict[fx.Node, fx.Node] = {}
+        new_input_idxs: list[int] = []
+        new_param_idxs: list[int] = []
+        new_graphargs: list = []
+        pos = 0
+
+        def _meta_tensor_for(nd: fx.Node):
+            """Return a meta tensor matching nd's shape/dtype, or None if unavailable."""
+            ev = nd.meta.get("example_value")
+            if ev is None:
+                ev = nd.meta.get("val")
+            if ev is not None and hasattr(ev, "shape"):
+                return torch.empty(ev.shape, dtype=ev.dtype, device="meta", requires_grad=ev.requires_grad)
+            return None
+
+        def _add_ph(nd: fx.Node, name: str, is_input: bool) -> None:
+            nonlocal pos
+            new_ph = sub_g.placeholder(name)
+            new_ph.type = nd.type
+            remap[nd] = new_ph
+            if is_input:
+                new_input_idxs.append(pos)
+                meta = _meta_tensor_for(nd)
+                if meta is None:
+                    # Fallback: use the corresponding entry from the incoming graphargs.
+                    # This handles sub-graphs produced by split_by_a2a whose placeholder
+                    # nodes are freshly created and lack example_value metadata.
+                    i_orig = ph_nodes.index(nd) if nd in ph_nodes else -1
+                    if 0 <= i_orig < len(graphargs):
+                        meta = graphargs[i_orig]
+                new_graphargs.append(meta)
+            else:
+                new_param_idxs.append(pos)
+                i_orig = ph_nodes.index(nd) if nd in ph_nodes else -1
+                new_graphargs.append(graphargs[i_orig] if 0 <= i_orig < len(graphargs) else None)
+            pos += 1
+
+        if seg == 0:
+            # Non-param placeholders (activation inputs + other non-param inputs).
+            for nd in nodes:
+                if nd.op == "placeholder" and nd not in param_ph_set:
+                    _add_ph(nd, nd.name, is_input=(nd in input_ph_set))
+        else:
+            # Cross-segment activation inputs from the previous segment.
+            for orig in seg_cross_in[seg]:
+                _add_ph(orig, f"_xseg_{orig.name}", is_input=True)
+
+        # Parameter placeholders belonging to this segment.
+        for nd in nodes:
+            if nd.op == "placeholder" and nd in param_ph_set and node_seg[nd] == seg:
+                _add_ph(nd, nd.name, is_input=False)
+
+        # get_attr nodes for this segment.
+        for nd in nodes:
+            if nd.op == "get_attr" and node_seg[nd] == seg:
+                new_ga = sub_g.get_attr(nd.target)
+                new_ga.type = nd.type
+                remap[nd] = new_ga
+
+        # Compute nodes for this segment (graph order preserved).
+        for nd in nodes:
+            if nd.op in ("placeholder", "get_attr", "output"):
+                continue
+            if node_seg[nd] != seg:
+                continue
+            remap[nd] = sub_g.node_copy(nd, arg_transform=lambda x, r=remap: r[x])
+
+        # Output node.
+        if seg == n_segs - 1:
+            orig_out = next(nd for nd in nodes if nd.op == "output")
+            sub_g.output(fx.map_arg(orig_out.args[0], lambda x: remap[x]))
+        else:
+            out_nodes = [remap[orig] for orig in seg_cross_in[seg + 1]]
+            sub_g.output(tuple(out_nodes) if len(out_nodes) != 1 else out_nodes[0])
+
+        sub_g.lint()
+        results.append((fx.GraphModule(stage_gm, sub_g), new_input_idxs, new_param_idxs, new_graphargs))
+
+    return results
+
+
+# ---------------------------------------------------------------------------
+# split_by_a2a: split an FX GraphModule at all-to-all annotation boundaries
+# ---------------------------------------------------------------------------
+
+def split_by_a2a(
+    stage_gm: fx.GraphModule,
+    graphargs: list,
+    input_idxs: list[int],
+    param_idxs: list[int],
+) -> tuple[list[tuple], list[dict]]:
+    """Split a stage GraphModule at all-to-all annotation boundaries.
+
+    Each all-to-all annotation (``node.meta['custom']['collective'] ==
+    'all_to_all_single'``) marks a tensor that will be communicated via an
+    expert-parallel all-to-all operation.  The annotations must come in pairs;
+    the graph is cut at each boundary so that the A2A operation itself is
+    handled at the DAG level (FWD_A2A / BWD_A2A task types) rather than being
+    embedded in the FX graph.
+
+    Args:
+        stage_gm: The stage ``fx.GraphModule`` to split.
+        graphargs: Flat arg list for ``stage_gm.forward``.
+        input_idxs: Positions of activation-input placeholders.
+        param_idxs: Positions of parameter placeholders.
+
+    Returns:
+        ``(segments, boundary_infos)`` where:
+
+        * *segments* is a list of ``(gm, input_idxs, param_idxs, graphargs)``
+          tuples in execution order.  If no A2A annotations are found the
+          original stage is returned as a single-element list with an empty
+          *boundary_infos*.
+
+        * *boundary_infos* is a list of dicts, one per A2A boundary (length ==
+          number of annotation blocks).  Each dict has keys:
+
+          - ``"tensor_idx"`` (int): index in the output tuple of the preceding
+            segment of the tensor to communicate.
+          - ``"reshape_input"`` (tuple | None): reshape the tensor to this
+            shape before the A2A send.
+          - ``"reshape_output"`` (tuple | None): reshape the A2A result to
+            this shape before feeding the next segment.
+
+    Raises:
+        ValueError: If the number of A2A annotation blocks is not even
+            (annotations must come in pairs).
+    """
+    nodes = list(stage_gm.graph.nodes)
+    node_idx: dict[fx.Node, int] = {nd: i for i, nd in enumerate(nodes)}
+    ph_nodes = [nd for nd in nodes if nd.op == "placeholder"]
+    param_ph_set = {ph_nodes[i] for i in param_idxs if i < len(ph_nodes)}
+    input_ph_set = {ph_nodes[i] for i in input_idxs if i < len(ph_nodes)}
+
+    # ------------------------------------------------------------------
+    # 1. Find all A2A annotation blocks (same logic as _insert_a2a_ops)
+    # ------------------------------------------------------------------
+    annotated_nodes = []
+    for idx, node in enumerate(nodes):
+        # Skip non-compute nodes: get_attr (parameters) and placeholders may
+        # inherit annotations from the enclosing `with annotate(...)` block
+        # but are not A2A compute boundaries.
+        if node.op in ("placeholder", "get_attr", "output"):
+            continue
+        custom = node.meta.get("custom")
+        if isinstance(custom, dict) and custom.get("collective") == "all_to_all_single":
+            reshape = custom.get("reshape")
+            annotation_key = tuple(sorted(custom.items()))
+            annotated_nodes.append((idx, node, annotation_key, reshape))
+
+    if not annotated_nodes:
+        return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))], []
+
+    logger.debug(
+        f"split_by_a2a: found {len(annotated_nodes)} annotated nodes:\n"
+        + "\n".join(
+            f"  [{i}] pos={idx} name={nd.name!r} "
+            f"key={'...' + str(ak)[-60:]}"
+            for i, (idx, nd, ak, _) in enumerate(annotated_nodes)
+        )
+    )
+
+    # Group CONSECUTIVE annotated nodes with the same annotation_key into blocks.
+    # This handles multiple layers: each contiguous run of same-key nodes becomes
+    # its own block, so a 2-layer model produces 4 blocks (dispatch0, combine0,
+    # dispatch1, combine1) rather than 2 (all-dispatches, all-combines).
+    # annotated_nodes is already in graph order.
+    blocks: list = []
+    for idx, node, annotation_key, reshape in annotated_nodes:
+        if blocks and blocks[-1]["annotation_key"] == annotation_key:
+            blocks[-1]["nodes"].append((idx, node))
+            blocks[-1]["last_idx"] = idx
+        else:
+            blocks.append({
+                "nodes": [(idx, node)],
+                "last_idx": idx,
+                "reshape": reshape,
+                "annotation_key": annotation_key,
+            })
+    n_boundaries = len(blocks)
+
+    logger.debug(
+        f"split_by_a2a: grouped into {n_boundaries} consecutive blocks:\n"
+        + "\n".join(
+            f"  block[{i}]: {len(b['nodes'])} nodes "
+            f"[{', '.join(nd.name for _, nd in b['nodes'])}] "
+            f"reshape={b['reshape']}"
+            for i, b in enumerate(blocks)
+        )
+    )
+
+    if n_boundaries % 2 != 0:
+        raise ValueError(
+            f"split_by_a2a: expected an even number of A2A annotation blocks "
+            f"(got {n_boundaries}).  Annotations must come in pairs.\n"
+            f"Blocks: " + ", ".join(
+                f"[{' '.join(nd.name for _, nd in b['nodes'])}]" for b in blocks
+            )
+        )
+
+    # ------------------------------------------------------------------
+    # 2. For each block, find the "boundary node" — the block node that
+    #    has users outside the block.  This becomes the A2A output tensor.
+    # ------------------------------------------------------------------
+    boundary_nodes: list[tuple[fx.Node, tuple | None, tuple | None]] = []
+    for block in blocks:
+        block_node_set = {nd for _, nd in block["nodes"]}
+        reshape_hint = block["reshape"]
+        reshape_input = None
+        reshape_output = None
+        if isinstance(reshape_hint, (list, tuple)) and len(reshape_hint) == 2:
+            kind, shape = reshape_hint
+            if kind == "input":
+                reshape_input = tuple(shape)
+            elif kind == "output":
+                reshape_output = tuple(shape)
+
+        # Find output node of the block
+        output_node = None
+        for _, nd in block["nodes"]:
+            for user in nd.users:
+                if user not in block_node_set:
+                    output_node = nd
+                    break
+            if output_node is not None:
+                break
+        if output_node is None:
+            # Fallback: last node in the block by graph position
+            output_node = max((nd for _, nd in block["nodes"]),
+                              key=lambda n: node_idx[n])
+        boundary_nodes.append((output_node, reshape_input, reshape_output))
+
+    # ------------------------------------------------------------------
+    # 3. Assign each node to a segment.
+    #    Segments are separated by boundary_nodes.  Node `n` goes into
+    #    segment `s` where `s` is the number of boundary nodes that appear
+    #    strictly before `n` in graph order (i.e. boundary node itself is in
+    #    the segment BEFORE the cut).
+    # ------------------------------------------------------------------
+    boundary_positions = [node_idx[bn] for bn, _, _ in boundary_nodes]
+    # boundary_positions[i] is the graph index of the i-th boundary node.
+    # Segment i contains nodes with graph index in [prev_cut+1, boundary_pos_i].
+    # Segment n_boundaries contains all nodes after the last boundary.
+    n_segs = n_boundaries + 1
+
+    compute_set: frozenset[int] = frozenset(
+        i for i, nd in enumerate(nodes)
+        if nd.op not in ("placeholder", "get_attr", "output")
+    )
+
+    def _seg_of_idx(idx: int) -> int:
+        """Return the segment index for a graph-node at position idx."""
+        s = 0
+        for bp in boundary_positions:
+            if idx > bp:
+                s += 1
+            else:
+                break
+        return s
+
+    node_seg: dict[fx.Node, int] = {}
+    for nd in nodes:
+        if nd.op == "output":
+            node_seg[nd] = n_segs - 1
+        elif nd.op == "placeholder":
+            if nd in param_ph_set:
+                user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
+                node_seg[nd] = _seg_of_idx(min(user_idxs)) if user_idxs else 0
+            else:
+                node_seg[nd] = 0
+        elif nd.op == "get_attr":
+            user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
+            node_seg[nd] = _seg_of_idx(min(user_idxs)) if user_idxs else 0
+        else:
+            node_seg[nd] = _seg_of_idx(node_idx[nd])
+
+    # ------------------------------------------------------------------
+    # 4. Compute cross-segment inputs (same pattern as bucket_stage)
+    # ------------------------------------------------------------------
+    node_max_user_seg: dict[fx.Node, int] = {}
+    for nd in nodes:
+        if nd.op == "output":
+            continue
+        node_max_user_seg[nd] = (
+            max(node_seg[u] for u in nd.users) if nd.users else node_seg[nd]
+        )
+
+    seg_cross_in: list[list[fx.Node]] = [[] for _ in range(n_segs)]
+    for nd in nodes:
+        if nd.op in ("get_attr", "output"):
+            continue
+        if nd.op == "placeholder" and nd in param_ph_set:
+            continue
+        s = node_seg[nd]
+        mu = node_max_user_seg[nd]
+        for seg in range(s + 1, mu + 1):
+            seg_cross_in[seg].append(nd)
+
+    # ------------------------------------------------------------------
+    # 5. Build per-segment sub-graphs (same pattern as bucket_stage)
+    # ------------------------------------------------------------------
+    results: list[tuple[fx.GraphModule, list[int], list[int], list]] = []
+    boundary_infos: list[dict] = []
+
+    for seg in range(n_segs):
+        sub_g = fx.Graph()
+        remap: dict[fx.Node, fx.Node] = {}
+        new_input_idxs: list[int] = []
+        new_param_idxs: list[int] = []
+        new_graphargs: list = []
+        pos = 0
+
+        def _meta_tensor_for(nd: fx.Node):
+            ev = nd.meta.get("example_value")
+            if ev is None:
+                ev = nd.meta.get("val")
+            if ev is not None and hasattr(ev, "shape"):
+                return torch.empty(ev.shape, dtype=ev.dtype, device="meta",
+                                   requires_grad=getattr(ev, "requires_grad", False))
+            return None
+
+        def _add_ph(nd: fx.Node, name: str, is_input: bool) -> None:
+            nonlocal pos
+            new_ph = sub_g.placeholder(name)
+            new_ph.type = nd.type
+            remap[nd] = new_ph
+            if is_input:
+                new_input_idxs.append(pos)
+                new_graphargs.append(_meta_tensor_for(nd))
+            else:
+                new_param_idxs.append(pos)
+                i_orig = ph_nodes.index(nd) if nd in ph_nodes else -1
+                new_graphargs.append(graphargs[i_orig] if 0 <= i_orig < len(graphargs) else None)
+            pos += 1
+
+        if seg == 0:
+            for nd in nodes:
+                if nd.op == "placeholder" and nd not in param_ph_set:
+                    _add_ph(nd, nd.name, is_input=(nd in input_ph_set))
+        else:
+            for orig in seg_cross_in[seg]:
+                _add_ph(orig, f"_xseg_{orig.name}", is_input=True)
+
+        for nd in nodes:
+            if nd.op == "placeholder" and nd in param_ph_set and node_seg[nd] == seg:
+                _add_ph(nd, nd.name, is_input=False)
+
+        for nd in nodes:
+            if nd.op == "get_attr" and node_seg[nd] == seg:
+                new_ga = sub_g.get_attr(nd.target)
+                new_ga.type = nd.type
+                remap[nd] = new_ga
+
+        for nd in nodes:
+            if nd.op in ("placeholder", "get_attr", "output"):
+                continue
+            if node_seg[nd] != seg:
+                continue
+            remap[nd] = sub_g.node_copy(nd, arg_transform=lambda x, r=remap: r[x])
+
+        if seg == n_segs - 1:
+            orig_out = next(nd for nd in nodes if nd.op == "output")
+            sub_g.output(fx.map_arg(orig_out.args[0], lambda x: remap[x]))
+        else:
+            # Output is all cross-segment values for the next segment.
+            out_nodes = [remap[orig] for orig in seg_cross_in[seg + 1]]
+            sub_g.output(tuple(out_nodes) if len(out_nodes) != 1 else out_nodes[0])
+
+            # Record which output position holds the A2A boundary tensor.
+            # boundary_nodes[seg] is the A2A output node for the boundary AFTER seg.
+            boundary_nd, reshape_input, reshape_output = boundary_nodes[seg]
+            cross_out_list = seg_cross_in[seg + 1]
+            if boundary_nd in cross_out_list:
+                tensor_idx = cross_out_list.index(boundary_nd)
+            else:
+                raise ValueError(
+                    f"split_by_a2a: A2A boundary node '{boundary_nd.name}' "
+                    f"(segment {seg}) is not in seg_cross_in[{seg + 1}]; "
+                    f"seg_cross_in = {[n.name for n in cross_out_list]}"
+                )
+            boundary_infos.append({
+                "tensor_idx": tensor_idx,
+                "reshape_input": reshape_input,
+                "reshape_output": reshape_output,
+            })
+
+        sub_g.lint()
+        results.append((fx.GraphModule(stage_gm, sub_g), new_input_idxs, new_param_idxs, new_graphargs))
+
+    return results, boundary_infos
+
+
+# ---------------------------------------------------------------------------
+# expand_a2a_tasks: insert FWD_A2A / BWD_A2A nodes into the task DAG
+# ---------------------------------------------------------------------------
+
+def expand_a2a_tasks(dag: TaskDAG, a2a_info: dict) -> TaskDAG:
+    """Insert FWD_A2A / BWD_A2A task nodes into the DAG between adjacent bucket
+    nodes at all-to-all boundaries.
+
+    Must be called *after* :func:`expand_bucket_tasks`.
+
+    For the forward pass, between FWD(bucket_id=i) and
+    FWD(bucket_id=i+1) a FWD_A2A node is inserted.
+
+    For the backward pass (which runs in reverse bucket order), between
+    BWD(bucket_id=i+1) and BWD(bucket_id=i) a BWD_A2A node is
+    inserted.
+
+    Args:
+        dag: TaskDAG produced by :func:`expand_bucket_tasks`.
+        a2a_info: Mapping ``stage_id -> {boundary_bucket_id -> tensor_idx}``.
+            Only stages present here are modified.
+
+    Returns:
+        A new TaskDAG with inserted FWD_A2A / BWD_A2A nodes.
+    """
+    if not a2a_info:
+        return dag
+
+    TIME_SCALE = 1000  # same scale used by expand_bucket_tasks
+
+    # Index FWD/BWD/BWD_I nodes by (stage_id, mb_idx, type, bucket_id)
+    bucket_node_map: dict = {}
+    for node in dag.nodes:
+        t = node.task
+        if t.type in (TaskType.FWD, TaskType.BWD, TaskType.BWD_I):
+            sid = t.batches[0].stage_id
+            mid = t.batches[0].mb_idx
+            bucket_node_map[(sid, mid, t.type, t.bucket_id)] = node
+
+    new_nodes: list[TaskNode] = []
+
+    for node in dag.nodes:
+        t = node.task
+        if t.type not in (TaskType.FWD, TaskType.BWD, TaskType.BWD_I):
+            continue
+        sid = t.batches[0].stage_id
+        if sid not in a2a_info:
+            continue
+        boundaries = a2a_info[sid]
+
+        if t.type == TaskType.FWD:
+            # Insert FWD_A2A after FWD(bucket_id) if it's a boundary
+            bkt_id = t.bucket_id
+            if bkt_id not in boundaries:
+                continue
+            tensor_idx = boundaries[bkt_id]
+            mb_idx = t.batches[0].mb_idx
+
+            # Find the successor FWD(bucket_id+1)
+            next_bkt_key = (sid, mb_idx, TaskType.FWD, bkt_id + 1)
+            if next_bkt_key not in bucket_node_map:
+                continue
+            next_node = bucket_node_map[next_bkt_key]
+
+            a2a_node = TaskNode(
+                task=Task(
+                    pp_rank=node.pp_rank,
+                    batches=list(t.batches),
+                    type=TaskType.FWD_A2A,
+                    bucket_id=bkt_id,
+                    a2a_tensor_idx=tensor_idx,
+                ),
+                pp_rank=node.pp_rank,
+                time_step=node.time_step + TIME_SCALE // 2,
+            )
+
+            # Rewire: node -> a2a_node -> next_node  (replacing node -> next_node)
+            if next_node in node.data_succs:
+                node.data_succs.remove(next_node)
+            if node in next_node.data_preds:
+                next_node.data_preds.remove(node)
+
+            node.data_succs.append(a2a_node)
+            a2a_node.data_preds.append(node)
+            a2a_node.data_succs.append(next_node)
+            next_node.data_preds.append(a2a_node)
+
+            new_nodes.append(a2a_node)
+
+        else:  # BWD / BWD_I with bucket_id
+            # Insert BWD_A2A after BWD/BWD_I(bucket_id+1) if there's a boundary there
+            # (i.e. between bkt_id+1 and bkt_id in backward execution order)
+            bkt_id = t.bucket_id
+            if bkt_id not in boundaries:
+                continue
+            # This BWD/BWD_I(bkt_id) is the later node in the backward chain.
+            # We need to insert BWD_A2A between BWD/BWD_I(bkt_id+1) and BWD/BWD_I(bkt_id).
+            # The predecessor in the data chain is BWD/BWD_I(bkt_id+1).
+            tensor_idx = boundaries[bkt_id]
+            mb_idx = t.batches[0].mb_idx
+
+            prev_bkt_key = (sid, mb_idx, t.type, bkt_id + 1)
+            if prev_bkt_key not in bucket_node_map:
+                continue
+            prev_node = bucket_node_map[prev_bkt_key]
+
+            a2a_node = TaskNode(
+                task=Task(
+                    pp_rank=node.pp_rank,
+                    batches=list(t.batches),
+                    type=TaskType.BWD_A2A,
+                    bucket_id=bkt_id,
+                    a2a_tensor_idx=tensor_idx,
+                ),
+                pp_rank=node.pp_rank,
+                time_step=prev_node.time_step + TIME_SCALE // 2,
+            )
+
+            # Rewire: prev_node -> a2a_node -> node  (replacing prev_node -> node)
+            if node in prev_node.data_succs:
+                prev_node.data_succs.remove(node)
+            if prev_node in node.data_preds:
+                node.data_preds.remove(prev_node)
+
+            prev_node.data_succs.append(a2a_node)
+            a2a_node.data_preds.append(prev_node)
+            a2a_node.data_succs.append(node)
+            node.data_preds.append(a2a_node)
+
+            new_nodes.append(a2a_node)
+
+    all_nodes = list(dag.nodes) + new_nodes
+    return TaskDAG(nodes=all_nodes)
\ No newline at end of file
diff --git a/src/piper_utils.py b/src/piper_utils.py
index 2faa04a..3aa8941 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -336,6 +336,10 @@ class PiperMetadata:
     stage_to_device = dict()
     naive_gradient_sync = False
     use_activation_checkpointing = False
+    bucketing = False  # Whether to split stages into per-param-bucket sub-modules
+    schedule = None   # Schedule2D set by piper_setup; used by the piper backend
+    task_dag = None   # TaskDAG built from schedule by the piper backend
+    per_rank_dags = None  # Per-rank TaskDAGs built by the piper backend
 
 piper_metadata = PiperMetadata()
 
diff --git a/src/piper_zero.py b/src/piper_zero.py
deleted file mode 100644
index 2a3f5ac..0000000
--- a/src/piper_zero.py
+++ /dev/null
@@ -1,980 +0,0 @@
-"""
-Bucketed ZeRO optimizer stages for Piper.
-
-Each trainable parameter is divided into dp_degree contiguous shards; each DP rank
-owns and optimises one shard. Parameters are grouped into 25 MB gradient-sync buckets
-ordered by reverse forward-use (approximating backward arrival order), enabling
-overlap between communication and backward computation.
-
-Per-step communication pipeline (per bucket, pipelined across buckets):
-  1. stage-1: all_reduce flat gradients (async, on comm_stream), or
-     stage-2: reduce_scatter gradient flat buffer → local grad shard (async)
-  2. Optimizer step on local param shard                      (comp_stream)
-  3. all_gather local param shard → flat param buffer         (async, on comm_stream)
-  4. Scatter flat param buffer back to p.data
-
-See docs/zero1_design.md for the full design rationale.
-"""
-
-from __future__ import annotations
-
-import math
-from typing import Callable, Optional, Type
-
-import torch
-import torch.distributed as dist
-import torch.fx
-from torch.fx.passes.split_module import split_module
-from torch.nn import Parameter
-from torch.optim import Optimizer
-
-from .piper_utils import create_logger, LOG_LEVEL
-
-logger = create_logger("piper_zero", LOG_LEVEL)
-
-# Default bucket size in bytes — matches PyTorch DDP's default of 25 MB.
-BUCKET_SIZE_BYTES_DEFAULT: int = 25 * 1024 * 1024
-
-
-# ---------------------------------------------------------------------------
-# FX graph analysis
-# ---------------------------------------------------------------------------
-
-def _get_param_forward_order(
-    gm: torch.fx.GraphModule,
-    param_idxs: list[int],
-    params: list[Parameter],
-) -> list[Parameter]:
-    """
-    Return ``params`` sorted by the first position in ``gm``'s FX graph where
-    each parameter placeholder is used as an input to a computation node.
-
-    Parameters used earlier in the forward graph (lower node index) come first.
-    Parameters never referenced as inputs are appended last in original order.
-
-    Args:
-        gm: The FX GraphModule for this pipeline stage.
-        param_idxs: Indices of parameter placeholders within the graph's full
-            placeholder list (matches the positional order of ``forward_args``).
-        params: Trainable parameter tensors, one per entry in ``param_idxs``.
-
-    Returns:
-        ``params`` in forward-use order.
-    """
-    placeholder_nodes = [n for n in gm.graph.nodes if n.op == "placeholder"]
-    param_idx_set = set(param_idxs)
-
-    # Build a set of placeholder nodes that correspond to parameters.
-    param_ph_nodes: set[torch.fx.Node] = {
-        placeholder_nodes[i]
-        for i in param_idxs
-        if i < len(placeholder_nodes)
-    }
-
-    # Walk graph nodes to find the first computation node that uses each param.
-    first_use_pos: dict[torch.fx.Node, int] = {}
-    for node_pos, node in enumerate(gm.graph.nodes):
-        if node.op == "placeholder":
-            continue
-        for inp in node.all_input_nodes:
-            if inp in param_ph_nodes and inp not in first_use_pos:
-                first_use_pos[inp] = node_pos
-
-    # Sort params by first-use position.
-    param_ph_by_idx = {
-        i: placeholder_nodes[i]
-        for i in param_idxs
-        if i < len(placeholder_nodes)
-    }
-    pairs: list[tuple[Parameter, float]] = []
-    for idx, p in zip(param_idxs, params):
-        ph = param_ph_by_idx.get(idx)
-        pos = first_use_pos.get(ph, math.inf) if ph is not None else math.inf
-        pairs.append((p, pos))
-
-    pairs.sort(key=lambda x: x[1])
-    return [p for p, _ in pairs]
-
-
-def _build_bucket_segments(
-    gm: torch.fx.GraphModule,
-    param_idxs: list[int],
-    params: list[Parameter],
-    param_to_bucket: dict[int, "ZeROOneBucket"],
-) -> list[tuple[int, "ZeROOneBucket"]]:
-    """
-    For each bucket, find the last graph position at which any of its parameters
-    are used as an input to a computation node.
-
-    Returns a list of ``(last_use_pos, bucket)`` tuples sorted in ascending
-    order of ``last_use_pos`` — i.e. in *forward* order.
-
-    Args:
-        gm: The FX GraphModule for this pipeline stage.
-        param_idxs: Indices of parameter placeholder nodes in the graph.
-        params: Trainable parameter tensors (one per entry in ``param_idxs``).
-        param_to_bucket: Mapping from ``id(param)`` to owning bucket.
-    """
-    placeholder_nodes = [n for n in gm.graph.nodes if n.op == "placeholder"]
-
-    # Map placeholder node → bucket.
-    ph_to_bucket: dict[torch.fx.Node, "ZeROOneBucket"] = {}
-    for i, p in zip(param_idxs, params):
-        if i < len(placeholder_nodes):
-            bucket = param_to_bucket.get(id(p))
-            if bucket is not None:
-                ph_to_bucket[placeholder_nodes[i]] = bucket
-
-    # Walk graph to find last-use position per bucket.
-    last_use_pos: dict[int, int] = {}  # bucket_id → node position
-    for node_pos, node in enumerate(gm.graph.nodes):
-        if node.op == "placeholder":
-            continue
-        for inp in node.all_input_nodes:
-            if inp in ph_to_bucket:
-                bid = ph_to_bucket[inp].bucket_id
-                if bid not in last_use_pos or node_pos > last_use_pos[bid]:
-                    last_use_pos[bid] = node_pos
-
-    # Collect unique buckets and sort by last-use position.
-    seen: dict[int, "ZeROOneBucket"] = {b.bucket_id: b for b in ph_to_bucket.values()}
-    segments = [(last_use_pos.get(bid, 0), bucket) for bid, bucket in seen.items()]
-    segments.sort(key=lambda x: x[0])
-    return segments
-
-
-def _make_zero3_split_gm(
-    gm: torch.fx.GraphModule,
-    fwd_segments: list[tuple[int, "ZeROOneBucket"]],
-) -> torch.fx.GraphModule:
-    """
-    Split *gm* into ``len(fwd_segments)`` sub-modules using
-    ``torch.fx.passes.split_module``.
-
-    Partition k contains all computation nodes whose topological position falls
-    at or before ``fwd_segments[k][0]`` (the last-use position of bucket k).
-    Placeholder nodes are all assigned to partition 0; ``split_module``
-    automatically threads them through as inputs to later partitions when needed.
-
-    Returns the new wrapper ``GraphModule`` whose sub-modules are named
-    ``submod_0``, ``submod_1``, …
-    """
-    topo_idx: dict[torch.fx.Node, int] = {
-        node: i for i, node in enumerate(gm.graph.nodes)
-    }
-    num_partitions = len(fwd_segments)
-
-    def split_callback(node: torch.fx.Node) -> int:
-        if node.op == "placeholder":
-            return 0
-        pos = topo_idx[node]
-        for k, (last_pos, _) in enumerate(fwd_segments):
-            if pos <= last_pos:
-                return k
-        return num_partitions - 1
-
-    return split_module(gm, gm, split_callback)
-
-
-# ---------------------------------------------------------------------------
-# Bucket
-# ---------------------------------------------------------------------------
-
-class ZeROOneBucket:
-    """
-    One gradient-synchronisation bucket for ZeRO-1.
-
-    Holds a flat parameter buffer and a flat gradient buffer covering all params
-    in the bucket. The local shard (owned by this DP rank) is a contiguous slice
-    of those flat buffers. One optimizer instance manages the local param shard.
-
-    Lifecycle per training step:
-      ``on_grad_accumulated`` → (when bucket full) ``launch_reduce_scatter``
-      → (in finalize_step) ``optimizer_step`` → ``launch_all_gather``
-      → ``copy_flat_to_params`` → ``reset_step_state``
-    """
-
-    def __init__(
-        self,
-        bucket_id: int,
-        params: list[Parameter],
-        dp_rank: int,
-        dp_degree: int,
-        dp_group: dist.ProcessGroup,
-        device: str,
-        dtype: torch.dtype,
-        optim_class: Callable[[list[Parameter]], Optimizer],
-        num_mbs: int,
-        zero_stage: int,
-    ) -> None:
-        self.bucket_id = bucket_id
-        self.params = params
-        self.dp_rank = dp_rank
-        self.dp_degree = dp_degree
-        self.dp_group = dp_group
-        self.num_mbs = num_mbs
-        self.zero_stage = zero_stage
-
-        # ------------------------------------------------------------------
-        # Flat buffer layout: [p0 elems | p1 elems | ... | padding]
-        # ------------------------------------------------------------------
-        # Capture original shapes and numels before any p.data reassignment.
-        self.param_original_shapes: list[torch.Size] = [p.shape for p in params]
-        self.param_offsets: list[int] = []
-        self.param_numels: list[int] = []
-        total_numel = 0
-        for p in params:
-            self.param_offsets.append(total_numel)
-            self.param_numels.append(p.numel())
-            total_numel += p.numel()
-
-        # Pad so that padded_numel is divisible by dp_degree (for equal shards).
-        self.shard_numel: int = math.ceil(total_numel / dp_degree)
-        self.padded_numel: int = self.shard_numel * dp_degree
-        self.shard_start: int = dp_rank * self.shard_numel
-        self.shard_end: int = self.shard_start + self.shard_numel
-
-        # Persistent flat buffers (allocated once, reused each step).
-        self.flat_param_buf: torch.Tensor = torch.empty(
-            self.padded_numel, device=device, dtype=dtype
-        )
-        # ZeRO-1: persistent full gradient buffer (all_reduce writes into it).
-        # ZeRO-2: None — a transient buffer is allocated per-step inside
-        #         launch_grad_sync and released once reduce_scatter returns.
-        self.flat_grad_buf: Optional[torch.Tensor] = (
-            torch.zeros(self.padded_numel, device=device, dtype=dtype)
-            if zero_stage == 1
-            else None
-        )
-
-        # Initialise flat_param_buf from current parameter values.
-        for p, off, numel in zip(params, self.param_offsets, self.param_numels):
-            self.flat_param_buf[off : off + numel].copy_(p.data.view(-1))
-
-        # Local shard: a separate tensor (not a view of flat_param_buf) to
-        # avoid aliasing when all_gather writes into flat_param_buf.
-        self.local_param_shard: Parameter = Parameter(
-            self.flat_param_buf[self.shard_start : self.shard_end].clone(),
-            requires_grad=True,
-        )
-        # Receives the reduce-scattered gradient for the local shard.
-        self.local_grad_shard: torch.Tensor = torch.zeros(
-            self.shard_numel, device=device, dtype=dtype
-        )
-
-        # One optimizer per bucket, managing only the local param shard.
-        self.optimizer: Optimizer = optim_class([self.local_param_shard])
-
-        # ------------------------------------------------------------------
-        # Per-step state (reset by reset_step_state)
-        # ------------------------------------------------------------------
-        # Counts how many microbatch backward passes have touched each param.
-        self._mb_accumulated: dict[int, int] = {id(p): 0 for p in params}
-        # Number of params that have fully accumulated all num_mbs microbatches.
-        self._ready_param_count: int = 0
-
-        # Async collective handles (None until launched).
-        self.grad_sync_handle: Optional[dist.Work] = None
-        self.all_gather_handle: Optional[dist.Work] = None
-
-        # ------------------------------------------------------------------
-        # ZeRO-3: CUDA events for all-gather pipelining.
-        # ``_ag_fwd_event`` fires when the forward all-gather into
-        # ``flat_param_buf`` completes; ``_ag_bwd_event`` for backward.
-        # ------------------------------------------------------------------
-        if zero_stage == 3:
-            self._ag_fwd_event: Optional[torch.cuda.Event] = torch.cuda.Event()
-            self._ag_bwd_event: Optional[torch.cuda.Event] = torch.cuda.Event()
-            # Redirect each param's .data to only its owned shard slice inside
-            # local_param_shard.  Full-shape views are installed temporarily by
-            # swap_to_full_params() in the per-bucket pre-hooks and torn down by
-            # restore_to_shard_params() in the corresponding post-hooks.
-            for p, off, numel in zip(self.params, self.param_offsets, self.param_numels):
-                flat_lo = max(off, self.shard_start)
-                flat_hi = min(off + numel, self.shard_end)
-                if flat_lo < flat_hi:
-                    p.data = self.local_param_shard[
-                        flat_lo - self.shard_start : flat_hi - self.shard_start
-                    ]
-                    logger.debug(f"Bucket {bucket_id} param shard: flat[{flat_lo}:{flat_hi}] → {p.shape}")
-                else:
-                    # This param has no owned elements on this rank.
-                    p.data = self.local_param_shard.new_empty(0)
-        else:
-            self._ag_fwd_event = None
-            self._ag_bwd_event = None
-
-        logger.debug(
-            f"ZeROOneBucket {bucket_id}: {len(params)} params, "
-            f"{total_numel} elems, shard [{self.shard_start}:{self.shard_end}]"
-        )
-
-    # ------------------------------------------------------------------
-    # Per-step trigger (called from grad hook)
-    # ------------------------------------------------------------------
-
-    def on_grad_accumulated(self, param: Parameter) -> bool:
-        """
-        Record one microbatch gradient accumulation for *param*.
-
-        Returns ``True`` when every parameter in this bucket has accumulated
-        gradients from all ``num_mbs`` microbatches, signalling that the bucket
-        is ready for reduce_scatter.
-        """
-        pid = id(param)
-        self._mb_accumulated[pid] += 1
-        if self._mb_accumulated[pid] == self.num_mbs:
-            self._ready_param_count += 1
-        return self._ready_param_count == len(self.params)
-
-    # ------------------------------------------------------------------
-    # Communication and computation
-    # ------------------------------------------------------------------
-
-    def launch_grad_sync(
-        self,
-        comm_stream: torch.cuda.Stream,
-        # producer_stream: torch.cuda.Stream,
-    ) -> dist.Work:
-        """
-        Copy accumulated per-param gradients into the flat gradient buffer, then
-        launch stage-specific async gradient synchronisation on *comm_stream*.
-
-        - stage 1: ``all_reduce`` on ``flat_grad_buf`` (full grads preserved)
-        - stage 2: ``reduce_scatter_tensor`` into ``local_grad_shard``
-
-        Returns the ``dist.Work`` handle for the caller to wait on.
-        """
-        # comm_stream.wait_stream(producer_stream)
-        with torch.cuda.stream(comm_stream):
-            if self.zero_stage == 1:
-                for p, off in zip(self.params, self.param_offsets):
-                    if p.grad is not None:
-                        self.flat_grad_buf[off : off + p.numel()].copy_(p.grad.view(-1))
-                    else:
-                        self.flat_grad_buf[off : off + p.numel()].zero_()
-                handle = dist.all_reduce(
-                    self.flat_grad_buf,
-                    op=dist.ReduceOp.AVG,
-                    group=self.dp_group,
-                    # async_op=True,
-                )
-            else:
-                # Transient buffer: exists only for the duration of this call.
-                # Because the collective is currently synchronous (async_op
-                # commented out), the buffer is safe to release on return.
-                # NOTE: if async_op=True is re-enabled, store this tensor as
-                # self._tmp_flat_grad_buf and clear it in prepare_local_grad_shard
-                # after grad_sync_handle.wait() so it stays alive long enough.
-                tmp_flat = torch.empty(
-                    self.padded_numel,
-                    device=self.local_grad_shard.device,
-                    dtype=self.local_grad_shard.dtype,
-                )
-                for p, off in zip(self.params, self.param_offsets):
-                    if p.grad is not None:
-                        tmp_flat[off : off + p.numel()].copy_(p.grad.view(-1))
-                    else:
-                        tmp_flat[off : off + p.numel()].zero_()
-                handle = dist.reduce_scatter_tensor(
-                    self.local_grad_shard,
-                    tmp_flat,
-                    op=dist.ReduceOp.AVG,
-                    group=self.dp_group,
-                    # async_op=True,
-                )
-
-        # Free per-param gradients; the flat buffer (persistent for ZeRO-1,
-        # transient for ZeRO-2) has already captured or scattered them.
-        for p in self.params:
-            p.grad = None
-
-        return handle
-
-    def prepare_local_grad_shard(self) -> None:
-        """Prepare local shard gradient after grad sync."""
-        if self.zero_stage == 1:
-            self.local_grad_shard.copy_(
-                self.flat_grad_buf[self.shard_start : self.shard_end]
-            )
-            self.flat_grad_buf.zero_()
-        # In stage-2, local_grad_shard is already the reduce_scatter output.
-        # The transient flat buffer was released when launch_grad_sync returned.
-
-    def optimizer_step(self) -> None:
-        """
-        Attach the reduce-scattered gradient to ``local_param_shard`` and run
-        the optimizer.
-
-        Must be called after ``reduce_scatter_handle.wait()``.
-        """
-        if self.local_param_shard.grad is None:
-            self.local_param_shard.grad = self.local_grad_shard.clone()
-        else:
-            self.local_param_shard.grad.copy_(self.local_grad_shard)
-        self.optimizer.step()
-        self.optimizer.zero_grad(set_to_none=True)
-
-    def launch_all_gather(
-        self,
-        comm_stream: torch.cuda.Stream,
-        comp_stream: torch.cuda.Stream,
-    ) -> dist.Work:
-        """
-        Launch an async ``all_gather_into_tensor`` that reconstructs
-        ``flat_param_buf`` from all ranks' updated ``local_param_shard``.
-
-        Must be called after ``optimizer_step()``.
-        Returns the ``dist.Work`` handle.
-        """
-        comm_stream.wait_stream(comp_stream)
-        with torch.cuda.stream(comm_stream):
-            handle = dist.all_gather_into_tensor(
-                self.flat_param_buf,
-                self.local_param_shard.data,
-                group=self.dp_group,
-                # async_op=True,
-            )
-        return handle
-
-    def copy_flat_to_params(self) -> None:
-        """
-        Scatter ``flat_param_buf`` back into each parameter's ``.data``.
-
-        Must be called after ``all_gather_handle.wait()``.
-        Used by ZeRO-1/2 finalize_step; ZeRO-3 uses ``swap_to_full_params``
-        instead (a zero-copy view rather than a copy).
-        """
-        for p, off, numel, shape in zip(
-            self.params, self.param_offsets, self.param_numels, self.param_original_shapes
-        ):
-            p.data.copy_(self.flat_param_buf[off : off + numel].view(shape))
-
-    def reset_step_state(self) -> None:
-        """Reset per-step counters and handles for the next training step."""
-        for pid in self._mb_accumulated:
-            self._mb_accumulated[pid] = 0
-        self._ready_param_count = 0
-        self.grad_sync_handle = None
-        self.all_gather_handle = None
-
-    # ------------------------------------------------------------------
-    # ZeRO-3 helpers
-    # ------------------------------------------------------------------
-
-    def swap_to_full_params(self) -> None:
-        """
-        Redirect each parameter's ``.data`` to a view of ``flat_param_buf``
-        with the parameter's original shape.
-
-        ``flat_param_buf`` must already be populated by ``launch_all_gather_params``
-        before this is called.  This is a zero-copy operation — ``flat_param_buf``
-        IS the temporary buffer; no extra allocation is performed.
-
-        Called in the forward/backward pre-hook for ZeRO-3 to give the FX
-        sub-module access to fully-gathered parameters.
-        """
-        logger.debug(f"Bucket {self.bucket_id} swapping to full param views")
-        for p, off, numel, shape in zip(
-            self.params, self.param_offsets, self.param_numels, self.param_original_shapes
-        ):
-            p.data = self.flat_param_buf[off : off + numel].view(shape)
-
-    def restore_to_shard_params(self) -> None:
-        """
-        Restore each parameter's ``.data`` to its owned shard slice inside
-        ``local_param_shard``, releasing the reference to the full-shape view
-        of ``flat_param_buf`` set by ``swap_to_full_params``.
-
-        Called in the forward/backward post-hook for ZeRO-3 after sub-module
-        execution.  The released view has no remaining strong references so the
-        GPU memory occupied by the non-owned regions becomes reclaimable once the
-        autograd graph releases its own references (after backward completes).
-        """
-        for p, off, numel in zip(self.params, self.param_offsets, self.param_numels):
-            flat_lo = max(off, self.shard_start)
-            flat_hi = min(off + numel, self.shard_end)
-            if flat_lo < flat_hi:
-                p.data = self.local_param_shard[
-                    flat_lo - self.shard_start : flat_hi - self.shard_start
-                ]
-            else:
-                p.data = self.local_param_shard.new_empty(0)
-
-    def launch_all_gather_params(
-        self,
-        comm_stream: torch.cuda.Stream,
-        is_bwd: bool = False,
-    ) -> None:
-        """
-        Launch an all-gather of ``local_param_shard`` → ``flat_param_buf`` on
-        *comm_stream*, then record the corresponding CUDA event.
-
-        ``is_bwd=True`` records ``_ag_bwd_event`` (used by backward pre-hooks);
-        ``is_bwd=False`` records ``_ag_fwd_event`` (used by forward pre-hooks).
-
-        Call ``swap_to_full_params()`` inside ``comp_stream.wait_event(ag_event)``
-        to install gathered parameters into each ``p.data`` as a zero-copy view.
-        """
-        logger.debug(f"Bucket {self.bucket_id} launching all-gather for {'backward' if is_bwd else 'forward'}")
-        ag_event = self._ag_bwd_event if is_bwd else self._ag_fwd_event
-        with torch.cuda.stream(comm_stream):
-            dist.all_gather_into_tensor(
-                self.flat_param_buf,
-                self.local_param_shard.data,
-                group=self.dp_group,
-            )
-            ag_event.record()  # records on comm_stream (current stream)
-
-    @property
-    def size_bytes(self) -> int:
-        """Total byte size of parameters in this bucket (based on original shapes)."""
-        return sum(n * p.element_size() for n, p in zip(self.param_numels, self.params))
-
-
-# ---------------------------------------------------------------------------
-# State manager
-# ---------------------------------------------------------------------------
-
-class ZeROOneState:
-    """
-    Manages ZeRO-1 optimizer state partitioning for one pipeline stage.
-
-    At construction time, parameters are grouped into gradient-sync buckets in
-    reverse forward-use order. Post-accumulate-grad hooks are registered on every
-    parameter; each hook decrements a per-bucket counter and launches an async
-    reduce_scatter when the bucket's counter reaches zero.
-
-    At the end of each training step, call ``finalize_step`` to complete the
-    pipelined reduce_scatter → optimizer step → all_gather sequence.
-
-    Args:
-        all_params: All trainable parameters for this stage.
-        params_in_forward_order: Same parameters sorted by first use in the
-            FX graph forward pass (from ``_get_param_forward_order``).
-        dp_rank: Data-parallel rank of this actor.
-        dp_degree: Total number of data-parallel replicas.
-        dp_group: ``torch.distributed`` process group for this DP replica set.
-        device: CUDA device string for the actor.
-        num_mbs: Number of microbatches per training step.
-        optim_class: Callable ``(params) -> Optimizer`` used per bucket.
-        comm_stream: CUDA stream for collective communication ops.
-        comp_stream: CUDA stream for computation (backward / optimizer).
-        bucket_size_bytes: Maximum byte size per bucket (default 25 MB).
-    """
-
-    def __init__(
-        self,
-        all_params: list[Parameter],
-        params_in_forward_order: list[Parameter],
-        dp_rank: int,
-        dp_degree: int,
-        dp_group: dist.ProcessGroup,
-        device: str,
-        num_mbs: int,
-        optim_class: Callable[[list[Parameter]], Optimizer],
-        comm_stream: torch.cuda.Stream,
-        comp_stream: torch.cuda.Stream,
-        bucket_size_bytes: int = BUCKET_SIZE_BYTES_DEFAULT,
-        zero_stage: int = 1,
-        # ZeRO-3 only: FX graph and parameter index list for graph splitting.
-        gm: Optional[torch.fx.GraphModule] = None,
-        param_idxs: Optional[list[int]] = None,
-    ) -> None:
-        if zero_stage not in (1, 2, 3):
-            raise ValueError(f"ZeRO state must be 1, 2, or 3, got {zero_stage}")
-
-        self.all_params = all_params
-        self.zero_stage = zero_stage
-        self.dp_rank = dp_rank
-        self.dp_degree = dp_degree
-        self.dp_group = dp_group
-        self._comm_stream = comm_stream
-        self._comp_stream = comp_stream
-        self._num_mbs = num_mbs
-
-        # Build buckets in reverse forward order ≈ backward arrival order.
-        self._buckets: list[ZeROOneBucket] = _build_buckets(
-            params_in_bwd_order=list(reversed(params_in_forward_order)),
-            dp_rank=dp_rank,
-            dp_degree=dp_degree,
-            dp_group=dp_group,
-            device=device,
-            num_mbs=num_mbs,
-            optim_class=optim_class,
-            bucket_size_bytes=bucket_size_bytes,
-            zero_stage=zero_stage,
-        )
-
-        # Fast lookup: parameter id → owning bucket.
-        self._param_to_bucket: dict[int, ZeROOneBucket] = {
-            id(p): bucket
-            for bucket in self._buckets
-            for p in bucket.params
-        }
-
-        if zero_stage == 3:
-            if gm is None or param_idxs is None:
-                raise ValueError("gm and param_idxs are required for ZeRO-3")
-            # Build per-bucket FX sub-modules for pipelined all-gather.
-            fwd_segments = _build_bucket_segments(
-                gm, param_idxs, all_params, self._param_to_bucket
-            )
-            # Buckets in forward order (sub-module k ↔ fwd_ordered_buckets[k]).
-            self._fwd_ordered_buckets: list[ZeROOneBucket] = [
-                b for _, b in fwd_segments
-            ]
-            self.split_gm: Optional[torch.fx.GraphModule] = _make_zero3_split_gm(
-                gm, fwd_segments
-            )
-            self._setup_zero3_module_hooks()
-        else:
-            self._fwd_ordered_buckets = []
-            self.split_gm = None
-            # Grad hooks drive reduce-scatter for ZeRO-1/2 (same mechanism).
-            self._setup_grad_hooks()
-
-        logger.debug(
-            f"ZeROOneState: {len(self._buckets)} buckets, "
-            f"{len(all_params)} params, dp_rank={dp_rank}/{dp_degree}, "
-            f"zero_stage={zero_stage}"
-        )
-
-    # ------------------------------------------------------------------
-    # Hook registration
-    # ------------------------------------------------------------------
-
-    def _setup_grad_hooks(self) -> None:
-        """Register a post-accumulate-grad hook on every tracked parameter."""
-        for p in self.all_params:
-            if id(p) in self._param_to_bucket:
-                p.register_post_accumulate_grad_hook(self._make_hook(p))
-
-    def _make_hook(self, param: Parameter) -> Callable[[Parameter], None]:
-        """Return a closure that fires when *param*'s gradient is accumulated."""
-        bucket = self._param_to_bucket[id(param)]
-
-        def _hook(_param: Parameter) -> None:
-            if bucket.on_grad_accumulated(_param):
-                # Wait for backward on comp stream to complete
-                dep_event = torch.cuda.Event()
-                dep_event.record(self._comp_stream)
-                self._comm_stream.wait_event(dep_event)
-                # All params in bucket have full gradients: launch reduce_scatter.
-                bucket.grad_sync_handle = bucket.launch_grad_sync(
-                    self._comm_stream,
-                    # torch.cuda.current_stream(),
-                )
-                logger.debug(
-                    f"Launched grad sync for bucket {bucket.bucket_id}"
-                )
-
-        return _hook
-
-    def _setup_zero3_module_hooks(self) -> None:
-        """
-        Register forward hooks on each sub-module of ``split_gm`` that also
-        install tensor-level backward hooks to pipeline all-gather with
-        computation during the backward pass.
-
-        Forward hooks (per sub-module k):
-          - ``forward_pre_hook``: wait for fwd AG event, swap params to full
-            shape; also register a post-backward tensor hook on the first
-            requires-grad input (fires after backward through this sub-module)
-            to restore params to shard and seed backward AG lookahead.
-          - ``forward_hook``: restore params to shard after sub-module's forward;
-            seed fwd AG for bucket k+2; register a pre-backward tensor hook on
-            the output (fires when grad arrives at this output, i.e., before
-            backward through this sub-module) to wait for bwd AG and swap params
-            to full shape.
-
-        ``register_full_backward_pre_hook`` is intentionally avoided because it
-        does not fire reliably on ``GraphModule`` sub-modules created by
-        ``split_module`` where parameters arrive as positional placeholders
-        rather than registered ``nn.Parameter`` children.
-        """
-        fwd_buckets = self._fwd_ordered_buckets
-        N = len(fwd_buckets)
-        comm_stream = self._comm_stream
-        comp_stream = self._comp_stream
-
-        for k in range(N):
-            bucket = fwd_buckets[k]
-            submod = getattr(self.split_gm, f"submod_{k}")
-
-            # ---- forward pre-hook ----------------------------------------
-            def _make_fwd_pre(bkt: ZeROOneBucket, k_: int):
-                def _fwd_pre(module, args):
-                    # GPU-side wait: computation doesn't start until the
-                    # all-gather into flat_param_buf has completed.
-                    logger.debug(f"Bucket {bkt.bucket_id} pre-forward hook waiting for fwd AG event")
-                    comp_stream.wait_event(bkt._ag_fwd_event)
-                    bkt.swap_to_full_params()
-
-                    # Register a post-backward hook on the first requires-grad
-                    # input tensor.  Its gradient is computed AFTER backward has
-                    # finished processing all ops inside this sub-module, making
-                    # it the right signal to restore params to shard storage and
-                    # seed the lookahead all-gather for backward.
-                    lookahead_k = k_ - 2  # 2 positions back = 2 ahead in bwd
-                    for inp in (args if isinstance(args, (list, tuple)) else [args]):
-                        if isinstance(inp, torch.Tensor) and inp.requires_grad:
-                            def _post_bwd(grad, _bkt=bkt, _lk=lookahead_k):
-                                logger.debug(f"Bucket {bkt.bucket_id} post-backward hook releasing params")
-                                _bkt.restore_to_shard_params()
-                                if _lk >= 0:
-                                    fwd_buckets[_lk].launch_all_gather_params(
-                                        comm_stream, is_bwd=True
-                                    )
-                            inp.register_hook(_post_bwd)
-                            break
-
-                return _fwd_pre
-
-            # ---- forward post-hook ----------------------------------------
-            def _make_fwd_post(bkt: ZeROOneBucket, k_: int):
-                def _fwd_post(module, args, output):
-                    # Restore p.data to the owned shard slice after forward.
-                    logger.debug(f"Bucket {bkt.bucket_id} post-forward hook releasing params")
-                    bkt.restore_to_shard_params()
-                    # Seed forward all-gather lookahead for bucket k+2.
-                    if k_ + 2 < N:
-                        fwd_buckets[k_ + 2].launch_all_gather_params(
-                            comm_stream, is_bwd=False
-                        )
-
-                    # Register a pre-backward hook on the output tensor.
-                    # Its gradient is computed AFTER backward finishes all ops
-                    # that consume this output but BEFORE backward processes
-                    # this sub-module's own ops — i.e., exactly when we need
-                    # to swap params back to full shape for this sub-module's
-                    # backward pass.
-                    out: Optional[torch.Tensor] = None
-                    if isinstance(output, torch.Tensor):
-                        out = output
-                    elif isinstance(output, (list, tuple)):
-                        for t in output:
-                            if isinstance(t, torch.Tensor) and t.requires_grad:
-                                out = t
-                                break
-                    if out is not None and out.requires_grad:
-                        def _pre_bwd(grad, _bkt=bkt):
-                            logger.debug(f"Bucket {bkt.bucket_id} pre-backward hook waiting for bwd AG event")
-                            comp_stream.wait_event(_bkt._ag_bwd_event)
-                            _bkt.swap_to_full_params()
-                            return grad
-                        out.register_hook(_pre_bwd)
-
-                return _fwd_post
-
-            submod.register_forward_pre_hook(_make_fwd_pre(bucket, k))
-            submod.register_forward_hook(_make_fwd_post(bucket, k))
-
-    # ------------------------------------------------------------------
-    # ZeRO-3 per-microbatch seeding
-    # ------------------------------------------------------------------
-
-    def before_microbatch_forward(self) -> None:
-        """
-        Seed the forward all-gather pipeline for one microbatch.
-
-        Launches AGs for the first (and second) buckets in forward order so
-        that at least two buckets are in-flight before the first sub-module's
-        forward pre-hook fires.  Must be called immediately before the
-        microbatch's forward pass (outside ``torch.cuda.stream(comp_stream)``).
-        """
-        if self.zero_stage != 3:
-            return
-        fwd_buckets = self._fwd_ordered_buckets
-        N = len(fwd_buckets)
-        if N > 0:
-            fwd_buckets[0].launch_all_gather_params(self._comm_stream, is_bwd=False)
-        if N > 1:
-            fwd_buckets[1].launch_all_gather_params(self._comm_stream, is_bwd=False)
-
-    def before_microbatch_backward(self) -> None:
-        """
-        Seed the backward all-gather pipeline for one microbatch.
-
-        Launches AGs for the last two buckets in forward order (which are the
-        first two encountered during backward, since backward runs in reverse).
-        The tensor-level hooks installed during forward take over from there,
-        pipelining subsequent all-gathers two buckets ahead as backward proceeds.
-
-        Must be called immediately before ``backward()``/``loss.backward()``.
-        """
-        if self.zero_stage != 3:
-            return
-        fwd_buckets = self._fwd_ordered_buckets
-        N = len(fwd_buckets)
-        if N > 0:
-            fwd_buckets[N - 1].launch_all_gather_params(self._comm_stream, is_bwd=True)
-        if N > 1:
-            fwd_buckets[N - 2].launch_all_gather_params(self._comm_stream, is_bwd=True)
-
-    def after_microbatch_backward(self) -> None:
-        """
-        Restore all parameters to their shard-only storage after the microbatch
-        backward pass completes.
-
-        Must be called after ``backward()``/``loss.backward()`` returns.
-        """
-        if self.zero_stage != 3:
-            return
-        for bucket in self._buckets:
-            bucket.restore_to_shard_params()
-
-    # ------------------------------------------------------------------
-    # End-of-step pipeline
-    # ------------------------------------------------------------------
-
-    def finalize_step(
-        self,
-        comm_stream: torch.cuda.Stream,
-        comp_stream: torch.cuda.Stream,
-    ) -> None:
-        """
-        Complete the pipelined reduce_scatter → optimizer → all_gather sequence
-        for all buckets, then reset state for the next step.
-
-        Should be called in ``_update`` after all backward passes are complete.
-
-        Pipeline structure:
-          For bucket k: wait rs_k → optim_k → launch ag_k
-          The all_gather of bucket k-1 overlaps with optim_k and launch_ag_k.
-        """
-        # Ensure every bucket has gradient sync in flight (handles buckets
-        # whose hooks were never triggered, e.g. params with no gradient).
-        # for bucket in self._buckets:
-        #     if bucket.grad_sync_handle is None:
-        #         bucket.grad_sync_handle = bucket.launch_grad_sync(
-        #             comm_stream,
-        #             torch.cuda.current_stream(),
-        #         )
-
-        if self.zero_stage == 3:
-            # ZeRO-3: optimizer step on local shard only.  No all-gather and no
-            # copy back needed: each p.data is a live view of its owned slice of
-            # local_param_shard (set in __init__ and restored by
-            # restore_to_shard_params after each sub-module), so optimizer
-            # updates to local_param_shard are instantly visible through p.data.
-            for bucket in self._buckets:
-                bucket.prepare_local_grad_shard()
-                with torch.cuda.stream(comp_stream):
-                    bucket.optimizer_step()
-
-            # Wait for all comp_stream work to finish.
-            event = torch.cuda.Event()
-            with torch.cuda.stream(comp_stream):
-                event.record()
-            event.synchronize()
-
-            for p in self.all_params:
-                p.grad = None
-            for bucket in self._buckets:
-                bucket.reset_step_state()
-            return
-
-        # ZeRO-1 / ZeRO-2: pipeline reduce_scatter → optimizer → all_gather.
-        for bucket in self._buckets:
-            # bucket.grad_sync_handle.wait()
-            bucket.prepare_local_grad_shard()
-            with torch.cuda.stream(comp_stream):
-                bucket.optimizer_step()
-            bucket.all_gather_handle = bucket.launch_all_gather(
-                comm_stream, comp_stream
-            )
-
-        event = torch.cuda.Event()
-        with torch.cuda.stream(comm_stream):
-            event.record()
-        event.synchronize() # Ensure all_gathers complete before copying params back.
-
-        # Wait for all all_gathers and write results back into p.data.
-        for bucket in self._buckets:
-            # bucket.all_gather_handle.wait()
-            bucket.copy_flat_to_params()
-
-        # Ensure no stale parameter gradients remain.
-        for p in self.all_params:
-            p.grad = None
-        for bucket in self._buckets:
-            bucket.reset_step_state()
-
-    # ------------------------------------------------------------------
-    # Diagnostics
-    # ------------------------------------------------------------------
-
-    def num_buckets(self) -> int:
-        return len(self._buckets)
-
-    def bucket_sizes(self) -> list[int]:
-        return [b.size_bytes for b in self._buckets]
-
-    def __repr__(self) -> str:
-        return (
-            f"ZeROOneState(dp_rank={self.dp_rank}/{self.dp_degree}, "
-            f"zero_stage={self.zero_stage}, "
-            f"buckets={self.num_buckets()}, "
-            f"params={len(self.all_params)})"
-        )
-
-
-# ---------------------------------------------------------------------------
-# Internal helpers
-# ---------------------------------------------------------------------------
-
-def _build_buckets(
-    params_in_bwd_order: list[Parameter],
-    dp_rank: int,
-    dp_degree: int,
-    dp_group: dist.ProcessGroup,
-    device: str,
-    num_mbs: int,
-    optim_class: Callable[[list[Parameter]], Optimizer],
-    bucket_size_bytes: int,
-    zero_stage: int,
-) -> list[ZeROOneBucket]:
-    """
-    Fill buckets greedily: add parameters in *params_in_bwd_order* until a
-    bucket would exceed *bucket_size_bytes*, then start a new bucket.
-
-    The first parameter always starts a new bucket regardless of size (no
-    parameter is skipped).
-    """
-    buckets: list[ZeROOneBucket] = []
-    current: list[Parameter] = []
-    current_bytes = 0
-
-    for p in params_in_bwd_order:
-        p_bytes = p.numel() * p.element_size()
-        if current and current_bytes + p_bytes > bucket_size_bytes:
-            dtype = _bucket_dtype(current)
-            buckets.append(
-                ZeROOneBucket(
-                    len(buckets), current,
-                    dp_rank, dp_degree, dp_group,
-                    device, dtype, optim_class, num_mbs, zero_stage,
-                )
-            )
-            current = []
-            current_bytes = 0
-        current.append(p)
-        current_bytes += p_bytes
-
-    if current:
-        dtype = _bucket_dtype(current)
-        buckets.append(
-            ZeROOneBucket(
-                len(buckets), current,
-                dp_rank, dp_degree, dp_group,
-                device, dtype, optim_class, num_mbs, zero_stage,
-            )
-        )
-
-    return buckets
-
-
-def _bucket_dtype(params: list[Parameter]) -> torch.dtype:
-    """Return the dtype of the first parameter (all params in a bucket share dtype)."""
-    return params[0].dtype
diff --git a/test/all2all_bench.py b/test/all2all_bench.py
new file mode 100644
index 0000000..076bf71
--- /dev/null
+++ b/test/all2all_bench.py
@@ -0,0 +1,126 @@
+#!/usr/bin/env python
+import argparse
+import time
+from typing import List
+
+import torch
+import torch.distributed as dist
+import numpy as np
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser("all_to_all_single benchmark")
+    parser.add_argument(
+        "--backend", type=str, default="nccl", choices=["nccl", "gloo"],
+        help="Process group backend (default: nccl)",
+    )
+    parser.add_argument(
+        "--dtype", type=str, default="float16",
+        choices=["float16", "float32", "bfloat16"],
+        help="Tensor dtype (default: float16)",
+    )
+    parser.add_argument(
+        "--warmup", type=int, default=10,
+        help="Warmup iterations (default: 10)",
+    )
+    parser.add_argument(
+        "--iters", type=int, default=10,
+        help="Timed iterations (default: 50)",
+    )
+    return parser.parse_args()
+
+
+def get_dtype(name: str) -> torch.dtype:
+    if name == "float16":
+        return torch.float16
+    if name == "float32":
+        return torch.float32
+    if name == "bfloat16":
+        return torch.bfloat16
+    raise ValueError(f"Unsupported dtype: {name}")
+
+
+def main() -> None:
+    args = parse_args()
+
+    dist.init_process_group(backend=args.backend)
+    world_size = dist.get_world_size()
+    rank = dist.get_rank()
+
+    torch.cuda.set_device(rank % torch.cuda.device_count())
+    device = torch.device("cuda", rank % torch.cuda.device_count())
+
+    dtype = get_dtype(args.dtype)
+    bytes_per_elem = torch.tensor([], dtype=dtype).element_size()
+
+    # Tensor sizes per rank (bytes). Adjust as desired.
+    tensor_shapes = [
+        (2, 4, 16, 2048),
+        (2, 4, 16, 4096),
+        (2, 4, 64, 14336),
+        (4, 8, 64, 2048),
+        (4, 8, 64, 4096),
+        (4, 8, 64, 14336),
+        (8, 8, 64, 2048),
+        (8, 8, 64, 4096),
+        (8, 8, 64, 14336),
+    ]
+
+    if rank == 0:
+        print(
+            f"Running all_to_all_single benchmark with world_size={world_size}, "
+            f"dtype={dtype}, warmup={args.warmup}, iters={args.iters}"
+        )
+
+    for shape in tensor_shapes:
+        # Number of elements per rank
+        numel = np.prod(shape)
+
+        # all_to_all_single with equal splits requires numel divisible by world_size
+        if numel % world_size != 0:
+            if rank == 0:
+                print(
+                    f"Skipping shape {shape}: "
+                    f"numel={numel} not divisible by world_size={world_size}"
+                )
+            continue
+
+        in_tensor = torch.empty(shape, device=device, dtype=dtype)
+        out_tensor = torch.empty_like(in_tensor)
+
+        # Warmup
+        dist.barrier()
+        for _ in range(args.warmup):
+            dist.all_to_all_single(out_tensor, in_tensor)
+        dist.barrier()
+
+        # Timed iterations
+        dist.barrier()
+        start_event = torch.cuda.Event(enable_timing=True)
+        end_event = torch.cuda.Event(enable_timing=True)
+
+        torch.cuda.synchronize(device)
+        start_event.record()
+        for _ in range(args.iters):
+            dist.all_to_all_single(out_tensor, in_tensor)
+        end_event.record()
+        torch.cuda.synchronize(device)
+
+        total_ms = start_event.elapsed_time(end_event)  # milliseconds
+        avg_ms = total_ms / args.iters
+        avg_sec = avg_ms / 1e3
+        gb = np.prod(shape) * bytes_per_elem / (1 << 30)
+        gbps = gb / avg_sec
+
+        if rank == 0:
+            print(
+                f"shape={shape}, "
+                f"size={gb:.3f} GB per rank, "
+                f"time={avg_ms:.3f} ms, "
+                f"eff_bw={gbps:.2f} GB/s (per rank, one direction)"
+            )
+
+    dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/test/models/mixtral.py b/test/models/mixtral.py
index e0a6283..6760436 100644
--- a/test/models/mixtral.py
+++ b/test/models/mixtral.py
@@ -56,8 +56,8 @@ def from_name(cls, name: str):
 
 transformer_configs = {
     "tiny": dict(block_size=128, n_layer=4, n_head=2, n_local_heads=1, dim=256, intermediate_size=512, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "small": dict(block_size=512, n_layer=2, n_head=32, n_local_heads=8, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "medium": dict(block_size=512, n_layer=4, n_head=8, n_local_heads=2, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=4, num_activated_experts=2),
+    "small": dict(block_size=512, n_layer=4, n_head=4, n_local_heads=2, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+    "medium": dict(block_size=512, n_layer=4, n_head=8, n_local_heads=4, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
     "Mixtral-8x7B-v0.1": dict(block_size=1024, n_layer=2, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
     # "Mixtral-8x22B-v0.1": dict(block_size=2048, n_layer=4, n_head=48, n_local_heads=8, dim=6144, intermediate_size=16384, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
     "Mixtral-8x22B-v0.1": dict(block_size=1024, n_layer=4, n_head=32, n_local_heads=8, dim=2048, intermediate_size=16384, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
@@ -117,52 +117,52 @@ def setup_caches(self, max_batch_size, max_seq_length):
         self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base).to(torch.bfloat16)
         self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
 
-    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
-        """
-        2 Stages
-        """
-        self.first_arg = idx.shape
-        with torch.fx.traceback.annotate({"stage": 0}):
-            mask = self.causal_mask[None, None, input_pos]
-            freqs_cis = self.freqs_cis[input_pos]
-            x = self.tok_embeddings(idx)
-            for layer in self.layers[:len(self.layers)//2]:
-                x = layer(x, input_pos, freqs_cis, mask)
-        
-        with torch.fx.traceback.annotate({"stage": 1}):
-            for layer in self.layers[len(self.layers)//2:]:
-                x = layer(x, input_pos, freqs_cis, mask)
-            x = self.norm(x)
-            logits = self.output(x)
-        return logits
-
     # def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
     #     """
-    #     4 Stages
+    #     2 Stages
     #     """
     #     self.first_arg = idx.shape
     #     with torch.fx.traceback.annotate({"stage": 0}):
     #         mask = self.causal_mask[None, None, input_pos]
     #         freqs_cis = self.freqs_cis[input_pos]
     #         x = self.tok_embeddings(idx)
-    #         for layer in self.layers[:len(self.layers)//4]:
+    #         for layer in self.layers[:len(self.layers)//2]:
     #             x = layer(x, input_pos, freqs_cis, mask)
         
     #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[len(self.layers)//4:len(self.layers)//2]:
-    #             x = layer(x, input_pos, freqs_cis, mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 2}):
-    #         for layer in self.layers[len(self.layers)//2:3*len(self.layers)//4]:
-    #             x = layer(x, input_pos, freqs_cis, mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 3}):
-    #         for layer in self.layers[3*len(self.layers)//4:]:
+    #         for layer in self.layers[len(self.layers)//2:]:
     #             x = layer(x, input_pos, freqs_cis, mask)
     #         x = self.norm(x)
     #         logits = self.output(x)
     #     return logits
 
+    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        """
+        4 Stages
+        """
+        self.first_arg = idx.shape
+        with torch.fx.traceback.annotate({"stage": 0}):
+            mask = self.causal_mask[None, None, input_pos]
+            freqs_cis = self.freqs_cis[input_pos]
+            x = self.tok_embeddings(idx)
+            for layer in self.layers[:len(self.layers)//4]:
+                x = layer(x, input_pos, freqs_cis, mask)
+        
+        with torch.fx.traceback.annotate({"stage": 1}):
+            for layer in self.layers[len(self.layers)//4:len(self.layers)//2]:
+                x = layer(x, input_pos, freqs_cis, mask)
+
+        with torch.fx.traceback.annotate({"stage": 2}):
+            for layer in self.layers[len(self.layers)//2:3*len(self.layers)//4]:
+                x = layer(x, input_pos, freqs_cis, mask)
+
+        with torch.fx.traceback.annotate({"stage": 3}):
+            for layer in self.layers[3*len(self.layers)//4:]:
+                x = layer(x, input_pos, freqs_cis, mask)
+            x = self.norm(x)
+            logits = self.output(x)
+        return logits
+
     @classmethod
     def from_name(cls, name: str):
         return cls(ModelArgs.from_name(name))
diff --git a/test/print_schedules.py b/test/print_schedules.py
index b488d9c..cbccf55 100644
--- a/test/print_schedules.py
+++ b/test/print_schedules.py
@@ -1,8 +1,94 @@
-from .schedule_helpers import print_schedule, build_1f1b_schedule, build_gpipe_schedule
-from .schedule_helpers import DUALPIPEV_SCHEDULE, DUALPIPEV_NO_ZB_SCHEDULE
-from src.piper_exec import _validate_schedule, DAGEdge
-
-schedule = DUALPIPEV_SCHEDULE
-dag_edges = [DAGEdge(from_stage=0, to_stage=1), DAGEdge(from_stage=1, to_stage=2), DAGEdge(from_stage=2, to_stage=3)]
-num_mbs = 6
-_validate_schedule(schedule.grid, dag_edges, num_mbs)
+"""
+Step-by-step test of the Schedule2D → TaskDAG → per-rank DAG pipeline.
+
+Run with:
+    python3 -m test.print_schedules
+"""
+
+from .schedule_helpers import INTERLEAVED_1F1B_PP2_MB4_SCHEDULE, build_1f1b_schedule, print_schedule
+from src.piper_exec import TaskType
+from src.piper_graph_transform import schedule_to_dag, insert_p2p_ops, visualize_schedule, visualize_dag
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _type_abbrev(tt: TaskType) -> str:
+    return {
+        TaskType.FWD:     "F",
+        TaskType.BWD:     "B",
+        TaskType.UPD:     "U",
+        TaskType.BWD_I:   "Bi",
+        TaskType.BWD_W:   "Bw",
+        TaskType.FWD_BWD: "FB",
+        TaskType.SEND:    "Snd",
+        TaskType.RECV:    "Rcv",
+    }.get(tt, "?")
+
+
+def _node_str(node) -> str:
+    batches = "+".join(f"s{b.stage_id}m{b.mb_idx}" for b in node.task.batches)
+    peer = f" peer=r{node.peer_pp_rank}" if node.peer_pp_rank is not None else ""
+    return f"[r{node.pp_rank} t={node.time_step:+d} {_type_abbrev(node.task.type)} {batches}{peer}]"
+
+
+def print_dag(dag, title: str) -> None:
+    print(f"\n{'='*60}")
+    print(f"  {title}")
+    print(f"{'='*60}")
+    for node in sorted(dag.nodes, key=lambda n: (n.pp_rank, n.time_step)):
+        data_preds  = [_node_str(p) for p in node.data_preds]
+        data_succs  = [_node_str(s) for s in node.data_succs]
+        temp_pred   = _node_str(node.temporal_pred)  if node.temporal_pred  else "—"
+        temp_succ   = _node_str(node.temporal_succ)  if node.temporal_succ  else "—"
+        print(f"  {_node_str(node)}")
+        print(f"    data_preds : {data_preds or '—'}")
+        print(f"    data_succs : {data_succs or '—'}")
+        print(f"    temp_pred  : {temp_pred}")
+        print(f"    temp_succ  : {temp_succ}")
+
+
+# ---------------------------------------------------------------------------
+# Step 1: Build Schedule2D
+# ---------------------------------------------------------------------------
+
+print("\n" + "="*60)
+print("  STEP 1 — 1F1B Schedule2D  (PP=2, MB=2)")
+print("="*60)
+schedule = build_1f1b_schedule(n_mbs=2, n_stages=2)
+schedule = INTERLEAVED_1F1B_PP2_MB4_SCHEDULE
+print_schedule(schedule)
+
+# ---------------------------------------------------------------------------
+# Step 2: Convert to TaskDAG (with cross-rank data edges)
+# ---------------------------------------------------------------------------
+
+dag = schedule_to_dag(schedule)
+print_dag(dag, "STEP 2 — TaskDAG (cross-rank data edges present)")
+
+# Summarise edge counts
+n_data  = sum(len(n.data_succs) for n in dag.nodes)
+n_temp  = sum(1 for n in dag.nodes if n.temporal_succ is not None)
+n_cross = sum(
+    1 for n in dag.nodes for s in n.data_succs if n.pp_rank != s.pp_rank
+)
+print(f"\n  nodes={len(dag.nodes)}  data_edges={n_data}  "
+      f"temporal_edges={n_temp}  cross_rank_data_edges={n_cross}")
+
+# ---------------------------------------------------------------------------
+# Step 3: Insert SEND / RECV nodes (insert_p2p_ops)
+# ---------------------------------------------------------------------------
+
+rank_dags = insert_p2p_ops(dag)
+
+visualize_schedule(schedule, "schedule_1f1b")
+
+for rank, rank_dag in enumerate(rank_dags):
+    visualize_dag(rank_dag, f"rank{rank}_dag")
+    print_dag(rank_dag, f"STEP 3 — Per-rank DAG for PP rank {rank}")
+    n_data = sum(len(n.data_succs) for n in rank_dag.nodes)
+    n_temp = sum(1 for n in rank_dag.nodes if n.temporal_succ is not None)
+    print(f"\n  nodes={len(rank_dag.nodes)}  data_edges={n_data}  temporal_edges={n_temp}")
+
+print()
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index 4bfe3c2..b0f4cd4 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -2,9 +2,13 @@
 from src.piper_exec import (
     Task,
     BatchMeta,
-    CompType,
+    TaskType,
+    CompType,  # backwards-compatible alias for TaskType
     Schedule2D,
+    TaskNode,
+    TaskDAG,
 )
+from src.piper_graph_transform import schedule_to_dag, visualize_schedule, visualize_dag, insert_p2p_ops
 
 
 def _task_label(task: Task) -> str:
@@ -75,7 +79,7 @@ def build_gpipe_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
 
 def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
     steps = n_mbs + n_stages - 1
-    schedule = [[None] * (steps * 2 + 1) for _ in range(n_stages)]
+    schedule = [[None] * (steps * 2) for _ in range(n_stages)]
     stage_mb = [[0, 0] for _ in range(n_stages)]
     for step in range(n_stages):
         for stage_id in range(n_stages):
@@ -103,12 +107,6 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
                         type=task_type,
                     )
                     stage_mb[stage_id][fwd_or_bwd] += 1
-    for i, stage in enumerate(range(n_stages)):
-        schedule[stage][-i-1] = Task(
-            pp_rank=stage,
-            batches=[BatchMeta(stage_id=stage, mb_idx=n_mbs - 1)],
-            type=CompType.UPD,
-        )
     return Schedule2D(grid=schedule)
 
 
@@ -128,7 +126,6 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD_W),        # t10: W2
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD_I),        # t11: I3
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD_W),        # t12: W3
-            Task(pp_rank=0, batches=[], type=CompType.UPD),                                         # t13: U0
         ],
         [
             None,                                                                                   # t0:  -
@@ -144,7 +141,6 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
             Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD_I),        # t10: I3
             Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD_W),        # t11: W2
             Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD_W),        # t12: W3
-            Task(pp_rank=1, batches=[], type=CompType.UPD),                                         # t13: U0
         ],
     ])
 
@@ -158,6 +154,7 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
     ],
 )
 
+
 DUALPIPEV_MB6_SCHEDULE = Schedule2D(
     grid=[
         [
@@ -184,7 +181,6 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_W),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_W),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[], type=CompType.UPD),
             None,
         ],
         [
@@ -212,10 +208,10 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
             Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_I),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_W),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_W),
-            Task(pp_rank=1, batches=[], type=CompType.UPD),
         ]
     ])
 
+
 DUALPIPEV_NOZB_MB6_SCHEDULE = Schedule2D(
     grid=[
         [
@@ -347,7 +343,6 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
             Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
             Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.UPD),
         ],
         [
             None,
@@ -367,7 +362,6 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
             Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
             Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.UPD),
             None,
         ],
     ])
diff --git a/test/test_dag.py b/test/test_dag.py
new file mode 100644
index 0000000..5df6342
--- /dev/null
+++ b/test/test_dag.py
@@ -0,0 +1,204 @@
+"""
+test_dag.py – end-to-end test for the DAG-based execution path (piper_exec_dag).
+
+Run exactly like test_mixtral.py:
+
+    python -m test.test_dag [--model tiny] [--pp 2] [--dp 1] ...
+
+The main difference is that each training step is dispatched through
+piper_exec_dag() rather than the legacy piper_exec() scheduler.
+"""
+import logging
+import ray
+import torch
+import argparse
+import json
+import time
+import os
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+from ray.util.placement_group import (
+    placement_group,
+    placement_group_table,
+    remove_placement_group,
+)
+
+from src.piper_coordinator import PiperProgramCoordinator
+from src.piper_compile import piper_setup
+from src.piper import piper_exec_dag
+from src.piper_utils import piper_metadata
+
+from .models.mixtral import Transformer, ModelArgs
+from .schedule_helpers import (
+    build_1f1b_schedule,
+    build_gpipe_schedule,
+    print_schedule,
+    NO_PP_SCHEDULE,
+    INTERLEAVED_1F1B_PP2_MB4_SCHEDULE,
+    DUALPIPEV_MB6_SCHEDULE,
+    ZEROBUBBLE_MB4_SCHEDULE,
+)
+
+
+def main(args, pg):
+    world_size = args.dp * args.pp
+    mbs = args.mbs
+    batch_size = args.batch_size
+
+    match args.model:
+        case "tiny":
+            config = ModelArgs.from_name("tiny")
+        case "small":
+            config = ModelArgs.from_name("small")
+        case "medium":
+            config = ModelArgs.from_name("medium")
+        case "7b":
+            config = ModelArgs.from_name("Mixtral-8x7B-v0.1")
+        case "22b":
+            config = ModelArgs.from_name("Mixtral-8x22B-v0.1")
+
+    match args.schedule:
+        case "no-pp":
+            schedule = NO_PP_SCHEDULE
+        case "1f1b":
+            schedule = build_1f1b_schedule(args.mbs, args.pp)
+        case "gpipe":
+            schedule = build_gpipe_schedule(args.mbs, args.pp)
+        case "interleaved-1f1b":
+            schedule = INTERLEAVED_1F1B_PP2_MB4_SCHEDULE
+        case "dualpipev":
+            schedule = DUALPIPEV_MB6_SCHEDULE
+        case "zerobubble":
+            schedule = ZEROBUBBLE_MB4_SCHEDULE
+
+    print("Schedule:")
+    print_schedule(schedule)
+
+    x = torch.randint(0, config.vocab_size, (batch_size, config.block_size))
+    input_pos = torch.arange(config.block_size)
+    y = torch.randn(batch_size, config.block_size, config.vocab_size)
+
+    loss_fn = torch.nn.CrossEntropyLoss()
+
+    piper_setup(
+        Transformer,
+        model_args=(config,),
+        optim_fn=torch.optim.Adam,
+        example_inputs=[x, input_pos],
+        example_outputs=y,
+        schedule=schedule,
+        naive_gradient_sync=args.naive_grad_sync,
+        activation_checkpointing=False,
+        bucketing=args.bucketing,
+        mode=args.mode,
+        model_dtype=torch.bfloat16,
+        pg=pg,
+        nsight=args.nsight,
+    )
+
+    print(f"Running {args.warmup} warmup iterations (dag path)...")
+    for _ in range(args.warmup):
+        piper_exec_dag(loss_fn)
+        time.sleep(1)
+
+    actors = piper_metadata.actors
+
+    print(f"Running {args.iters} timed iterations (dag path)...")
+    iter_times = []
+    for _ in range(args.iters):
+        start = time.perf_counter()
+        losses = piper_exec_dag(loss_fn)
+        end = time.perf_counter()
+        iter_times.append(end - start)
+        time.sleep(1)
+
+    dp_rank = int(os.environ["PIPER_DP_RANK"])
+    print(
+        f"rank {dp_rank} iter time= {np.mean(iter_times):.5f} ± {np.std(iter_times):.5f} s "
+        f"({len(iter_times)} samples)\n"
+        f"rank {dp_rank} throughput= "
+        f"{(args.batch_size * args.mbs * config.block_size) / np.mean(iter_times):.3f} tokens/s"
+    )
+
+    if args.tracing:
+        ray.get([actor.set_tracing.remote(True) for actor in actors.values()])
+        print(f"Running {args.trace_iters} tracing iterations...")
+        for _ in range(args.trace_iters):
+            piper_exec_dag(loss_fn)
+            time.sleep(1)
+        trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
+        for rank, trace_data in trace_data_ret:
+            for key in trace_data:
+                all_times = trace_data[key]
+                print(
+                    f"rank {rank} {key} time= {np.mean(all_times):.3f} ± "
+                    f"{np.std(all_times):.3f} ms ({len(all_times)} samples)"
+                )
+
+    all_task_labels = {}
+    for labels in ray.get([actor.get_task_labels.remote() for actor in actors.values()]):
+        all_task_labels.update(labels)
+
+    os.makedirs("out", exist_ok=True)
+    timeline_filename = f"out/mixtral-dag-pp{args.pp}-dp{args.dp}-{args.schedule}-{args.mode}"
+    ray.timeline(timeline_filename)
+    labels_filename = timeline_filename + "-labels.json"
+    with open(labels_filename, "w") as f:
+        json.dump(all_task_labels, f)
+    print(f"Ray timeline saved to: {timeline_filename}")
+    print(f"Task labels saved to: {labels_filename}")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Test DAG-based piper execution with the Mixtral model"
+    )
+    parser.add_argument(
+        "--model",
+        choices=["tiny", "small", "medium", "7b", "22b"],
+        default="tiny",
+    )
+    parser.add_argument(
+        "--schedule",
+        choices=["gpipe", "1f1b", "no-pp", "interleaved-1f1b", "dualpipev", "zerobubble"],
+        default="1f1b",
+    )
+    parser.add_argument("--dp", type=int, default=1)
+    parser.add_argument("--pp", type=int, default=2)
+    parser.add_argument("--batch-size", type=int, default=4)
+    parser.add_argument("--mbs", type=int, default=4)
+    parser.add_argument("--warmup", type=int, default=3)
+    parser.add_argument("--iters", type=int, default=5)
+    parser.add_argument("--trace-iters", type=int, default=3)
+    parser.add_argument("--tracing", action="store_true", default=False)
+    parser.add_argument("--naive-grad-sync", action="store_true", default=False)
+    parser.add_argument(
+        "--mode",
+        choices=["sequential", "naive", "overlapped"],
+        default="sequential",
+    )
+    parser.add_argument("--bucketing", action="store_true", default=False,
+                        help="Split stages into per-param-bucket sub-modules for overlapped all-reduce")
+    parser.add_argument("--nsight", action="store_true", default=False,
+                        help="Whether to use Nsight Systems for tracing")
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    print(args)
+    ray.init(
+        log_to_driver=True,
+        namespace="mixtral_dag",
+        include_dashboard=False,
+        # _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp",
+    )
+    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="STRICT_SPREAD")
+    ray.get(pg.ready(), timeout=600)
+    print(placement_group_table(pg))
+    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
+    handles = piper_coordinator.run_program.remote(main, args, pg)
+    ray.get(handles)
+    ray.shutdown()
diff --git a/test/test_llama.py b/test/test_llama.py
index 7347f53..9669d77 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -201,7 +201,8 @@ def parse_args():
 
 if __name__ == "__main__":
     args = parse_args()
-    ray.init(log_to_driver=True, namespace="llama", include_dashboard=False, _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp")
+    ray.init(log_to_driver=True, namespace="llama", include_dashboard=False)
+    # ray.init(log_to_driver=True, namespace="llama", include_dashboard=False, _temp_dir="/m-coriander/coriander/mfris/ray_tmp")
     pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp)
     ray.get(pg.ready(), timeout=10)  # Wait for the placement group to be ready before proceeding
     print(placement_group_table(pg))
diff --git a/test/test_mixtral.py b/test/test_mixtral.py
index 5ef38f2..84b2124 100644
--- a/test/test_mixtral.py
+++ b/test/test_mixtral.py
@@ -184,7 +184,7 @@ def parse_args():
         log_to_driver=True,
         namespace="mixtral",
         include_dashboard=False,
-        _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp",
+        _temp_dir="/m-coriander/coriander/mfris/ray_tmp",
     )
     pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp)
     ray.get(pg.ready(), timeout=10)  # Wait for the placement group to be ready before proceeding

From f06d50f0b8cd9200f3ab04d90421a2cae6b8f1b2 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Tue, 24 Mar 2026 23:23:40 +0000
Subject: [PATCH 081/100] remove old actor compute method impls.

---
 src/piper_actor.py       | 1831 ++++----------------------------------
 src/piper_coordinator.py |    2 +-
 test/models/mixtral.py   |   60 +-
 test/test_dag.py         |    8 +-
 4 files changed, 194 insertions(+), 1707 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index 1164959..bba8a41 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -72,7 +72,7 @@ def _create_actors(
             }
         }
         actor = PiperActor.options(
-            num_gpus=0.7,
+            num_gpus=1,
             runtime_env={**nsight_env, **master_env},
             scheduling_strategy=PlacementGroupSchedulingStrategy(
                 placement_group=pg,
@@ -262,71 +262,6 @@ def reset_p2p_states(self):
         self.p2p_events = dict()
         self.p2p_cursor = 0
 
-    def set_p2p_schedule(self, schedule):
-        """Set the per-rank p2p schedule. Each entry: (op_type, stage_from, stage_to, mb_idx, is_fwd)."""
-        self.p2p_rank_schedule = schedule
-        self.p2p_cursor = 0
-        self.logger.debug(
-            f"Global rank {self.global_rank} set p2p schedule with {len(schedule)} ops"
-        )
-
-    def _exec_p2p_op(self, target_rank_idx, *deps):
-        """Execute all p2p ops from cursor up to and including target_rank_idx."""
-        while self.p2p_cursor <= target_rank_idx:
-            op_type, stage_from, stage_to, mb_idx, is_fwd = self.p2p_rank_schedule[self.p2p_cursor]
-            if is_fwd:
-                if op_type == "recv":
-                    self._exec_fwd_recv(stage_to, mb_idx)
-                else:
-                    self._exec_fwd_send(stage_from, mb_idx)
-            else:
-                if op_type == "recv":
-                    self._exec_bwd_recv(stage_to, mb_idx)
-                else:
-                    self._exec_bwd_send(stage_from, mb_idx)
-            self.p2p_cursor += 1
-
-    def enable_nccl_monitoring(self) -> None:
-        """Enable NCCL overlap monitoring for the next training iteration.
-
-        Call this before the iteration you want to profile.  After the iteration
-        completes, call print_nccl_overlaps() to analyse and display results.
-        Monitoring is automatically disabled after print_nccl_overlaps() returns.
-        """
-        self.overlap_detector.enable()
-        self.logger.info("Actor %d: NCCL monitoring enabled", self.global_rank)
-
-    def print_nccl_overlaps(self) -> None:
-        """Synchronise the GPU, analyse recorded NCCL events, and log all overlapping
-        kernel pairs.  Disables monitoring afterwards so subsequent iterations are
-        not affected.
-
-        Call this after the monitored training iteration has completed.
-        """
-        torch.cuda.synchronize()
-        overlaps = self.overlap_detector.find_overlaps(self.logger)
-        n_kernels = len(self.overlap_detector._records)
-        if not overlaps:
-            self.logger.info(
-                "Actor %d: NCCL overlap monitor — no overlaps detected "
-                "(%d kernels recorded)", self.global_rank, n_kernels,
-            )
-        else:
-            self.logger.warning(
-                "Actor %d: NCCL overlap monitor — %d overlapping pair(s) "
-                "across %d kernels:", self.global_rank, len(overlaps), n_kernels,
-            )
-            for a, b, overlap_ms in overlaps:
-                self.logger.warning(
-                    "  '%s' on '%s' [%.3f–%.3f ms]  <->  '%s' on '%s' [%.3f–%.3f ms]"
-                    "  (overlap %.3f ms)",
-                    a.kernel_name, a.stream_label, a.t_start_ms, a.t_end_ms,
-                    b.kernel_name, b.stream_label, b.t_start_ms, b.t_end_ms,
-                    overlap_ms,
-                )
-        self.overlap_detector.build_enforcement(overlaps, self.logger)
-        self.overlap_detector.disable()
-
     def get_trace_data(self) -> dict:
         return self.global_rank, self.trace_data
 
@@ -454,1640 +389,199 @@ def _join_pp_process_group(self):
             # if pp_group_id == my_pp_group_id:
             #     self.logger.debug(
             #         f"Global rank {self.global_rank} joined pp group {pp_group_id} "
-            #         f"with communicators for ranks {group_ranks}"
-            #     )
-
-        # Warm up all communicators to force eager NCCL initialization.
-        dummy = torch.zeros(1, device=self.device)
-        for key, pg in self.pp_groups.items():
-            self.logger.debug(f"Global rank {self.global_rank} warming up pp communicator {key}")
-            dist.all_reduce(dummy, group=pg)
-        torch.cuda.synchronize()
-        self.logger.info(f"Global rank {self.global_rank} warmed up {len(self.pp_groups)} pp communicators")
-
-    def shutdown(self):
-        dist.destroy_process_group()
-
-    def _maybe_trigger_grad_allreduce(
-        self, stage_id: int, param: torch.nn.Parameter
-    ) -> None:
-        """
-        Preserve `_prepare_dp_comm_ops` semantics for split backward (BWD_I/BWD_W).
-
-        `_prepare_dp_comm_ops` attaches `register_post_accumulate_grad_hook` to params.
-        Those hooks fire only when autograd's AccumulateGrad runs. In ZeroBubble's
-        split backward, we compute grads via `torch.autograd.grad` and manually
-        accumulate into `param.grad`, so AccumulateGrad (and thus the hooks) never run.
-
-        This helper mirrors the hook behavior: once a parameter has had gradients
-        accumulated for all microbatches (`num_mbs`), launch an async all-reduce on
-        the accumulated `param.grad` and store the handle so `_wait_for_comm_ops()`
-        can wait on it before the optimizer step.
-        """
-        if self.naive_gradient_sync or self.dp_degree <= 1:
-            return
-        if param.grad is None:
-            return
-
-        tid = id(param)
-        self.logger.debug(f"Split bwd: Updating status on actor {self.global_rank}, tensor={tid}")
-        if stage_id not in self.comm_op_tensor_ids:
-            return
-        if tid not in self.comm_op_tensor_ids[stage_id]:
-            return
-
-        self.comm_op_status[stage_id][tid] += 1
-        self.logger.debug(
-            f"Split bwd: Updating status on actor {self.global_rank}, tensor={tid}, "
-            f"status={self.comm_op_status[stage_id][tid]}/{self.num_mbs}"
-        )
-
-        if self.comm_op_status[stage_id][tid] == self.num_mbs:
-            # Same ordering fix as in _prepare_dp_comm_ops: the backward_weight call
-            # accumulates into param.grad on comp_stream.  Establish a GPU-side
-            # dependency so comm_stream doesn't start the all-reduce until the
-            # gradient write has completed.  This does not block the CPU.
-            dep_event = torch.cuda.Event()
-            dep_event.record(self.comp_stream)
-            self.comm_stream.wait_event(dep_event)
-            _ov_token = self.overlap_detector.before_kernel(self.comm_stream, "grad_allreduce", "comm_stream")
-            with torch.cuda.stream(self.comm_stream):
-                handle = dist.all_reduce(
-                    param.grad,
-                    op=dist.ReduceOp.AVG,
-                    group=self.dp_group,
-                )
-            self.overlap_detector.after_kernel(self.comm_stream, _ov_token)
-            self.logger.debug(
-                f"Split bwd: Allreduce launched on actor {self.global_rank}, tensor={tid}"
-            )
-            self.comm_op_status[stage_id][tid] = 0
-            self.comm_op_handles[stage_id][tid] = handle
-
-    def _prepare_dp_comm_ops(self, stage_id):
-        def hook_maker(tensor_id):
-            def post_backward_hook(grad):
-                self.comm_op_status[stage_id][tensor_id] += 1
-                self.logger.debug(
-                    f"Updating status on actor: {self.global_rank}, tensor={tensor_id}, status={self.comm_op_status[stage_id][tensor_id]}/{self.num_mbs}"
-                )
-                if self.comm_op_status[stage_id][tensor_id] == self.num_mbs:
-                    # The AccumulateGrad CUDA kernel that writes the final gradient into
-                    # param.grad is in-flight on comp_stream.  The hook fires on the CPU
-                    # as soon as the kernel is *submitted*, not when it *completes*.
-                    # Record an event on comp_stream now so that comm_stream can wait for
-                    # the gradient to be fully written before starting the all-reduce.
-                    # comm_stream.wait_event() is a GPU-side barrier — it does not block
-                    # the CPU, so backward compute on comp_stream continues to overlap.
-                    dep_event = torch.cuda.Event()
-                    dep_event.record(self.comp_stream)
-                    self.comm_stream.wait_event(dep_event)
-                    _ov_token = self.overlap_detector.before_kernel(self.comm_stream, "grad_allreduce", "comm_stream")
-                    with torch.cuda.stream(self.comm_stream):
-                        handle = dist.all_reduce(
-                            grad,
-                            op=dist.ReduceOp.AVG,
-                            group=self.dp_group,
-                        )
-                    self.overlap_detector.after_kernel(self.comm_stream, _ov_token)
-                    self.logger.debug(
-                        f"Allreduce on actor: {self.global_rank}, tensor={tensor_id}"
-                    )
-                    self.comm_op_status[stage_id][tensor_id] = 0
-                    self.comm_op_handles[stage_id][tensor_id] = handle
-                return grad
-            return post_backward_hook
-
-        ids = []
-        for t in self.forward_args[stage_id]:
-            if t is not None and t.requires_grad:
-                tid = id(t)
-                ids.append(tid)
-                t.register_post_accumulate_grad_hook(hook_maker(tid))
-        self.comm_op_tensor_ids[stage_id] = ids
-
-    def _wait_for_comm_ops(self):
-        self.logger.debug(f"Actor {self.global_rank} waiting for comm ops")
-        for stage_id, tids in self.comm_op_tensor_ids.items():
-            for tid in tids:
-                self.logger.debug(
-                    f"Waiting for comm op to be launched dp_rank: {self.dp_rank}, tensor={tid}"
-                )
-                done = False
-                while not done:
-                    if tid in self.comm_op_handles[stage_id]:
-                        self.logger.debug(
-                            f"Waiting for comm op to be finished dp_rank: {self.dp_rank}, tensor={tid}"
-                        )
-                        # self.comm_op_handles[stage_id][tid].wait()
-                        done = True
-
-    def _load_stage(
-        self,
-        stage_id: int,
-        modules_data: list,
-        a2a_boundaries: dict = None,
-        use_activation_checkpointing: bool = False,
-    ) -> None:
-        """Load a (possibly bucketed) stage.
-
-        *modules_data* is a list of dicts, one per module/bucket, each with keys:
-        ``gm_data``, ``graphargs``, ``input_idxs``, ``param_idxs``.
-
-        A non-bucketed stage is represented as a single-element list.
-        Bucket 0 handles the activation input from the previous stage (or
-        ``self.inputs`` for stage 0).  Subsequent modules receive the
-        cross-segment activation output of the previous module.
-        """
-        self.logger.debug(
-            f"Loading stage {stage_id} ({len(modules_data)} module(s)) on actor {self.global_rank}"
-        )
-
-        self.bucket_fwd_fns[stage_id] = []
-        self.bucket_fwd_args[stage_id] = []
-        self.bucket_param_idxs[stage_id] = []
-        self.bucket_optims[stage_id] = []
-        self.a2a_boundaries[stage_id] = a2a_boundaries or {}
-
-        g = torch.Generator(device=self.device)
-        g.manual_seed(1000 * self.global_rank + stage_id)
-
-        first_gm = None
-        last_gm = None
-
-        for b_idx, bd in enumerate(modules_data):
-            gm = _deserialize_graphmodule(bd["gm_data"])
-            if b_idx == 0:
-                first_gm = gm
-            last_gm = gm
-
-            forward_args = list(bd["graphargs"])
-            b_input_idxs = list(bd["input_idxs"])
-            b_param_idxs = list(bd["param_idxs"])
-
-            self.logger.debug(
-                f"Stage {stage_id} module {b_idx} input indices: {b_input_idxs}"
-            )
-
-            # Module 0: save activation-input metadata for the stage interface
-            # (used by _exec_recv to pre-allocate recv buffers).
-            if b_idx == 0:
-                self.input_idxs[(stage_id, None)] = b_input_idxs  # legacy path alias
-                for i in b_input_idxs:
-                    meta = forward_args[i]
-                    if meta is not None:
-                        self.forward_input_meta[stage_id][i] = (
-                            tuple(meta.shape),
-                            meta.dtype,
-                            bool(getattr(meta, "requires_grad", False)),
-                        )
-                    forward_args[i] = None
-            else:
-                for i in b_input_idxs:
-                    forward_args[i] = None
-
-            self.input_idxs[(stage_id, b_idx)] = b_input_idxs
-
-            # Realize parameter tensors.
-            realized = [None] * len(forward_args)
-            for i, arg in enumerate(forward_args):
-                if arg is None:
-                    continue
-                t = torch.empty(arg.shape, dtype=arg.dtype, device=self.device)
-                if arg.requires_grad:
-                    t.requires_grad_(True)
-                    torch.nn.init.normal_(t, mean=0.0, std=0.02, generator=g)
-                else:
-                    t.zero_()
-                realized[i] = t
-
-            # Forward function (with optional activation checkpointing).
-            if use_activation_checkpointing:
-                fwd = gm.forward
-                forward_fn = lambda *args, _fn=fwd: torch.utils.checkpoint.checkpoint(
-                    _fn, *args, use_reentrant=False
-                )
-            else:
-                forward_fn = gm.forward
-
-            self.bucket_fwd_fns[stage_id].append(forward_fn)
-            self.bucket_fwd_args[stage_id].append(realized)
-            self.bucket_param_idxs[stage_id].append(b_param_idxs)
-
-            # Collect trainable parameters and build a contiguous flat tensor so
-            # a single all-reduce call can sync the entire module's gradients.
-            trainable_idxs = [
-                i for i in b_param_idxs
-                if realized[i] is not None and realized[i].requires_grad
-            ]
-            trainable = [realized[i] for i in trainable_idxs]
-            self.bucket_trainable_param_idxs[(stage_id, b_idx)] = trainable_idxs
-
-            if trainable:
-                flat_params = torch.cat([p.detach().view(-1) for p in trainable]).contiguous()
-                flat_params.requires_grad_(False)
-                flat_grads = torch.zeros_like(flat_params)
-                offset = 0
-                for idx, p in zip(trainable_idxs, trainable):
-                    numel = p.numel()
-                    realized[idx] = realized[idx].detach()
-                    realized[idx].data = flat_params[offset:offset + numel].view(p.shape)
-                    realized[idx].requires_grad_(True)
-                    offset += numel
-                self.bucket_flat_params[(stage_id, b_idx)] = flat_params
-                self.bucket_flat_grads[(stage_id, b_idx)] = flat_grads
-            else:
-                self.bucket_flat_params[(stage_id, b_idx)] = None
-                self.bucket_flat_grads[(stage_id, b_idx)] = None
-
-            # Optimizer for this module's trainable parameters.
-            trainable_for_optim = [realized[i] for i in trainable_idxs]
-            optim = self.optim_class(trainable_for_optim) if trainable_for_optim else None
-            self.bucket_optims[stage_id].append(optim)
-
-            # Legacy non-DAG DDP: register per-param allreduce hooks for module 0.
-            # if b_idx == 0 and self.dp_degree > 1 and not self.naive_gradient_sync:
-            #     self.forward_args[stage_id] = realized
-                # self._prepare_dp_comm_ops(stage_id)
-
-        # Set legacy fields from first module for ZeroBubble BWD_I/BWD_W compatibility.
-        self.forward_args[stage_id] = self.bucket_fwd_args[stage_id][0]
-        self.param_idxs[stage_id] = self.bucket_param_idxs[stage_id][0]
-        self.graph_modules[stage_id] = first_gm
-        self.stage_id = stage_id
-
-        # Compute output activation shape from the last module for BWD recv buffer
-        # pre-allocation (e.g. interleaved 1F1B recv before forward has run).
-        last_realized = self.bucket_fwd_args[stage_id][-1]
-        last_b_input_idxs = self.input_idxs[(stage_id, len(modules_data) - 1)]
-        output_shapes = []
-
-        # Try FX graph node metadata first.
-        for node in last_gm.graph.nodes:
-            if node.op == "output":
-                out_args = node.args[0]
-                if not isinstance(out_args, (tuple, list)):
-                    out_args = [out_args]
-                for out_node in out_args:
-                    if isinstance(out_node, torch.fx.Node):
-                        meta_val = out_node.meta.get("val", None)
-                        if meta_val is not None and isinstance(meta_val, torch.Tensor) and meta_val.requires_grad:
-                            output_shapes.append((tuple(meta_val.shape), meta_val.dtype))
-                break
-
-        if not output_shapes:
-            # Fallback: probe forward pass with dummy inputs.
-            probe_args = list(last_realized)
-            g_probe = torch.Generator(device=self.device)
-            g_probe.manual_seed(42 + stage_id)
-            for i in last_b_input_idxs:
-                meta = modules_data[-1]["graphargs"][i]
-                if meta is not None:
-                    t = torch.zeros(meta.shape, dtype=meta.dtype, device=self.device)
-                    t.requires_grad_(bool(getattr(meta, "requires_grad", True)))
-                    probe_args[i] = t
-            with torch.no_grad():
-                probe_out = last_gm.forward(*probe_args)
-            for out in (probe_out if isinstance(probe_out, (tuple, list)) else [probe_out]):
-                if isinstance(out, torch.Tensor) and out.requires_grad:
-                    output_shapes.append((tuple(out.shape), out.dtype))
-
-        if output_shapes:
-            self.output_activation_shape[stage_id] = output_shapes
-            self.logger.debug(f"Stage {stage_id} output activation shapes: {output_shapes}")
-        else:
-            self.logger.warning(
-                f"Could not determine output activation shape for stage {stage_id}"
-            )
-
-    def _exec_fwd_recv(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, comp_stream=None):
-        if stage_id == 0:
-            return
-
-        if p2p_stream is None:
-            p2p_stream = self.p2p_recv_stream
-        if comp_stream is None:
-            comp_stream = self.comp_stream
-
-        p2p_op = (stage_id - 1, stage_id, mb_idx, False)
-        # assert p2p_op not in self.p2p_cache
-
-        inputs_to_recv = []
-        for i in self.input_idxs[(stage_id, None)]:
-            shape, dtype, requires_grad = self.forward_input_meta[stage_id][i]
-            inputs_to_recv.append(
-                torch.empty(
-                    shape, dtype=dtype, requires_grad=requires_grad, device=self.device
-                )
-            )
-
-        # For non-first stages, receive input tensors from the previous stage
-        pp_rank = self.stage_to_device[stage_id - 1]
-        global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
-
-        if self.global_rank == global_src_rank:
-            for i in self.input_idxs[(stage_id, None)]:
-                inputs_to_recv[i] = self.p2p_cache.pop((stage_id-1, stage_id, mb_idx, True))
-        else:
-            self.logger.debug(
-                f"Dispatch fwd p2p recv on communicator ({global_src_rank}, {self.global_rank}) to {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
-            )
-            self._start_timing(p2p_stream, "fwd_p2p_recv")
-            _ov_token = self.overlap_detector.before_kernel(p2p_stream, "fwd_p2p_recv", "p2p_recv_stream")
-            with torch.cuda.stream(p2p_stream):
-                for i in self.input_idxs[(stage_id, None)]:
-                    dist.recv(
-                        inputs_to_recv[i],
-                        src=global_src_rank,
-                        group=self.pp_groups[(global_src_rank, self.global_rank)],
-                    )
-            recv_event = torch.cuda.Event()
-            recv_event.record(p2p_stream)
-            self.overlap_detector.after_kernel(p2p_stream, _ov_token)
-            self.p2p_events[p2p_op] = recv_event
-            self._stop_timing(p2p_stream, "fwd_p2p_recv")
-            self.logger.debug(
-                f"Completed fwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id-1} -> {stage_id}, mb {mb_idx})"
-            )
-
-        self.p2p_cache[p2p_op] = inputs_to_recv
-
-    def _exec_fwd_send(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, comp_stream=None):
-        if stage_id == self.num_stages - 1:
-            return
-
-        if p2p_stream is None:
-            p2p_stream = self.p2p_send_stream
-        if comp_stream is None:
-            comp_stream = self.comp_stream
-
-        p2p_op = (stage_id, stage_id + 1, mb_idx, True)
-        output = self.p2p_cache.pop(p2p_op)
-        # For non-final stages, send output tensors to the next stage
-        pp_rank = self.stage_to_device[stage_id + 1]
-        global_dst_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
-
-        if self.global_rank == global_dst_rank:
-            self.p2p_cache[p2p_op] = output
-        else:
-            # Ensure send sees the latest writes from the default stream.
-            self.logger.debug(
-                f"Dispatch fwd p2p send on communicator ({self.global_rank}, {global_dst_rank}) from {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
-            )
-            self._start_timing(p2p_stream, "fwd_p2p_send")
-            p2p_stream.wait_stream(comp_stream)
-            _ov_token = self.overlap_detector.before_kernel(p2p_stream, "fwd_p2p_send", "p2p_send_stream")
-            with torch.cuda.stream(p2p_stream):
-                for i in range(len(output)):
-                    dist.send(
-                        output[i],
-                        dst=global_dst_rank,
-                        group=self.pp_groups[(self.global_rank, global_dst_rank)],
-                    )
-            self.overlap_detector.after_kernel(p2p_stream, _ov_token)
-            self._stop_timing(p2p_stream, "fwd_p2p_send")
-            self.logger.debug(
-                f"Completed fwd p2p send on {self.global_rank} to {global_dst_rank}, op: ({stage_id} -> {stage_id+1}, mb {mb_idx})"
-            )
-
-    def _exec_bwd_recv(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, comp_stream=None):
-        if stage_id >= self.num_stages - 1:
-            return
-
-        if p2p_stream is None:
-            p2p_stream = self.p2p_recv_stream
-        if comp_stream is None:
-            comp_stream = self.comp_stream
-
-        out_activation = self.out_activation[stage_id][mb_idx]
-        # For non-final stages, recieve input gradients from the subsequent backward pass
-        input_grad = torch.empty_like(out_activation)
-        pp_rank = self.stage_to_device[stage_id + 1]
-        global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
-
-        p2p_op = (stage_id + 1, stage_id, mb_idx, False)
-
-        if self.global_rank == global_src_rank:
-            input_grad = self.p2p_cache.pop((stage_id + 1, stage_id, mb_idx, True))
-        else:
-            self.logger.debug(
-                f"Dispatch bwd p2p recv on communicator ({global_src_rank}, {self.global_rank}) to {self.global_rank} from {global_src_rank}, op: ({stage_id+1} -> {stage_id}, mb {mb_idx})"
-            )
-            self._start_timing(p2p_stream, "bwd_p2p_recv")
-            _ov_token = self.overlap_detector.before_kernel(p2p_stream, "bwd_p2p_recv", "p2p_recv_stream")
-            with torch.cuda.stream(p2p_stream):
-                dist.recv(
-                    input_grad, src=global_src_rank, group=self.pp_groups[(global_src_rank, self.global_rank)]
-                )
-            recv_event = torch.cuda.Event()
-            recv_event.record(p2p_stream)
-            self.overlap_detector.after_kernel(p2p_stream, _ov_token)
-            self.p2p_events[p2p_op] = recv_event
-            self._stop_timing(p2p_stream, "bwd_p2p_recv")
-            self.logger.debug(
-                f"Completed bwd p2p recv on {self.global_rank} from {global_src_rank}, op: ({stage_id+1} -> {stage_id}, mb {mb_idx})"
-            )
-        
-        self.p2p_cache[p2p_op] = input_grad
-
-    def _exec_bwd_send(self, stage_id: int, mb_idx: int, *deps, p2p_stream=None, comp_stream=None):
-        if stage_id <= 0:
-            return
-
-        if p2p_stream is None:
-            p2p_stream = self.p2p_send_stream
-        if comp_stream is None:
-            comp_stream = self.comp_stream
-
-        # For non-first stages, send output gradients to the previous backward stage
-        output_grad = self.inp_activation[stage_id][mb_idx].grad
-        if output_grad is None:
-            self.logger.warning(f"No output gradient found for stage {stage_id} mb {mb_idx} on actor {self.global_rank}")
-            assert False
-        pp_rank = self.stage_to_device[stage_id - 1]
-        global_src_rank = _get_rank(pp_rank, self.dp_rank, self.pp_degree)
-
-        if self.global_rank == global_src_rank:
-            p2p_op = (stage_id, stage_id - 1, mb_idx, True)
-            self.p2p_cache[p2p_op] = output_grad
-        else:
-            self.logger.debug(
-                f"Dispatch bwd p2p send on communicator ({self.global_rank}, {global_src_rank}) from {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
-            )
-            self._start_timing(p2p_stream, "bwd_p2p_send")
-            p2p_stream.wait_stream(comp_stream)
-            _ov_token = self.overlap_detector.before_kernel(p2p_stream, "bwd_p2p_send", "p2p_send_stream")
-            with torch.cuda.stream(p2p_stream):
-                dist.send(
-                    output_grad, dst=global_src_rank, group=self.pp_groups[(self.global_rank, global_src_rank)]
-                )
-            self.overlap_detector.after_kernel(p2p_stream, _ov_token)
-            self._stop_timing(p2p_stream, "bwd_p2p_send")
-            self.logger.debug(
-                f"Completed bwd p2p send on {self.global_rank} to {global_src_rank}, op: ({stage_id} -> {stage_id-1}, mb {mb_idx})"
-            )
-
-        self.inp_activation[stage_id][mb_idx] = None
-    
-    def _forward(self, stage_id: int, mb_idx: int, *deps):
-        torch.cuda.nvtx.range_push(f"forward_stage_{stage_id}_mb_{mb_idx}")
-        ret = self._forward_impl(stage_id, mb_idx, *deps)
-        torch.cuda.nvtx.range_pop()
-        return ret
-
-    def _forward_impl(self, stage_id: int, mb_idx: int, *deps):
-        self._label_task(f"{stage_id}:{mb_idx}")
-        if self.mode == "sequential":
-            comp_stream = self.comp_stream
-        elif self.mode == "naive":
-            comp_stream = self.per_mb_streams[(stage_id + mb_idx) % 2]
-        elif self.mode == "overlapped":
-            comp_stream = self.comp_stream
-        else:
-            raise ValueError(f"Invalid mode: {self.mode}")
-
-        self.overlap_a2a_ops = False
-
-        self.logger.debug(
-            f"Calling forward {stage_id} mb {mb_idx} on actor {self.global_rank}"
-        )
-
-        if stage_id == 0:
-            # For the first stage, load input tensors from self.inputs
-            for i, inp in zip(self.input_idxs[(stage_id, None)], self.inputs):
-                self.forward_args[stage_id][i] = inp
-        else:
-            # Wait for recv to complete if this stage receives from the previous stage
-            self._start_timing(comp_stream, "fwd_recv_wait")
-            recv_key = (stage_id - 1, stage_id, mb_idx, False)
-            recv_event = self.p2p_events.pop(recv_key, None)
-            if recv_event is not None:
-                comp_stream.wait_event(recv_event)
-                # self.nccl_monitor.notify_ordered_after(comp_stream, recv_event)
-            inputs_from_prev_stage = self.p2p_cache.pop(recv_key)
-            self._stop_timing(comp_stream, "fwd_recv_wait")
-
-            # Detach to avoid double-backprop if the previous stage is co-located
-            for i, tensor in zip(self.input_idxs[(stage_id, None)], inputs_from_prev_stage):
-                if isinstance(tensor, (tuple, list)):
-                    assert len(tensor) == 1
-                    tensor = tensor[0]
-                if stage_id > 0 and self.stage_to_device[stage_id] == self.stage_to_device[stage_id - 1] and tensor.requires_grad:
-                    tensor = tensor.detach().requires_grad_(True)
-                self.forward_args[stage_id][i] = tensor
-
-            # save first input that requires grad as input activation
-            inp_with_grad = [
-                self.forward_args[stage_id][i]
-                for i in self.input_idxs[(stage_id, None)]
-                if self.forward_args[stage_id][i].requires_grad
-            ]
-            assert (
-                len(inp_with_grad) == 1
-            ), "Exactly one input per stage should require a gradient"
-            self.inp_activation[stage_id][mb_idx] = inp_with_grad[0]
-
-        # Run the forward pass
-        self._start_timing(comp_stream, "forward_comp")
-        with torch.cuda.stream(comp_stream):
-            # torch.cuda.nvtx.range_push(f"forward_stage_{stage_id}_mb_{mb_idx}")
-            output = self.forward_fns[stage_id](*self.forward_args[stage_id])
-            # torch.cuda.nvtx.range_pop()
-        self._stop_timing(comp_stream, "forward_comp")
-
-        # Save first output that requires grad as output activation
-        # TODO: support multiple outputs
-        out_with_grad = [out for out in output if out.requires_grad]
-        if len(out_with_grad) != 1:
-            self.logger.warning(
-                f"Expected exactly one output with requires_grad in stage {stage_id} mb {mb_idx} on actor {self.global_rank}, but found {len(out_with_grad)}. Output shapes: {[out.shape for out in output]}"
-            )
-            assert False
-        self.out_activation[stage_id][mb_idx] = out_with_grad[0]
-
-        # clear the input tensors
-        for i in self.input_idxs[(stage_id, None)]:
-            self.forward_args[stage_id][i] = None
-
-        if stage_id < self.num_stages - 1:
-            send_p2p_op = (stage_id, stage_id + 1, mb_idx, True)
-            assert send_p2p_op not in self.p2p_cache
-            self.p2p_cache[send_p2p_op] = output
-
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-        comp_stream.synchronize()
-
-        return 0
-
-    def _backward(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        torch.cuda.nvtx.range_push(f"backward_stage_{stage_id}_mb_{mb_idx}")
-        ret = self._backward_impl(stage_id, mb_idx, *deps, loss_fn=loss_fn)
-        torch.cuda.nvtx.range_pop()
-        return ret
-
-    def _backward_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        self._label_task(f"{stage_id}:{mb_idx}")
-        if self.mode == "sequential":
-            comp_stream = self.comp_stream
-        elif self.mode == "naive":
-            comp_stream = self.per_mb_streams[(stage_id + mb_idx) % 2]
-        elif self.mode == "overlapped":
-            comp_stream = self.comp_stream
-        else:
-            raise ValueError(f"Invalid mode: {self.mode}")
-
-        self.overlap_a2a_ops = False
-
-        self.logger.debug(
-            f"Calling backward {stage_id} mb {mb_idx} on actor {self.global_rank}"
-        )
-
-        out_activation = self.out_activation[stage_id][mb_idx]
-
-        # torch.cuda.nvtx.range_push(f"backward_stage_{stage_id}_mb_{mb_idx}")
-        if stage_id < self.num_stages - 1:
-            # Wait for recv to complete if this stage receives from the next stage
-            self._start_timing(comp_stream, "bwd_recv_wait")
-            recv_key = (stage_id + 1, stage_id, mb_idx, False)
-            recv_event = self.p2p_events.pop(recv_key, None)
-            if recv_event is not None:
-                comp_stream.wait_event(recv_event)
-                # self.nccl_monitor.notify_ordered_after(comp_stream, recv_event)
-            input_grad = self.p2p_cache.pop(recv_key)
-            self._stop_timing(comp_stream, "bwd_recv_wait")
-
-            self._start_timing(comp_stream, "backward_comp")
-            try:
-                with torch.cuda.stream(comp_stream):
-                    out_activation.backward(gradient=input_grad)
-            except RuntimeError:
-                raise
-            self._stop_timing(comp_stream, "backward_comp")
-        else:
-            assert loss_fn is not None
-            labels = self.labels
-            assert out_activation.shape == labels.shape
-
-            self._start_timing(comp_stream, "backward_comp")
-            try:
-                with torch.cuda.stream(comp_stream):
-                    loss = loss_fn(out_activation, labels)
-                    loss.backward()
-            except RuntimeError:
-                raise
-            self._stop_timing(comp_stream, "backward_comp")
-
-            # self.loss.append(loss.item())
-
-        # Clear output activation after backward pass
-        self.out_activation[stage_id][mb_idx] = None
-        del out_activation
-
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-        comp_stream.synchronize()
-
-        return 0
-
-
-    def _backward_input(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        torch.cuda.nvtx.range_push(f"backward_input_stage_{stage_id}_mb_{mb_idx}")
-        ret = self._backward_input_impl(stage_id, mb_idx, *deps, loss_fn=loss_fn)
-        torch.cuda.nvtx.range_pop()
-        return ret
-
-    def _backward_input_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        self._label_task(f"{stage_id}:{mb_idx}")
-        comp_stream = self.comp_stream
-
-        self.logger.debug(
-            f"Calling backward I {stage_id} mb {mb_idx} on actor {self.global_rank}"
-        )
-
-        out_activation = self.out_activation[stage_id][mb_idx]
-
-        activation_or_loss = None
-        upstream_grad = None
-        if stage_id < self.num_stages - 1:
-            # Wait for recv to complete if this stage receives from the next stage
-            recv_key = (stage_id + 1, stage_id, mb_idx, False)
-            recv_event = self.p2p_events.pop(recv_key, None)
-            if recv_event is not None:
-                comp_stream.wait_event(recv_event)
-                # self.nccl_monitor.notify_ordered_after(comp_stream, recv_event)
-            upstream_grad = self.p2p_cache.pop(recv_key)
-            activation_or_loss = out_activation
-        else:
-            assert loss_fn is not None
-            labels = self.labels
-            assert out_activation.shape == labels.shape
-            with torch.cuda.stream(comp_stream):
-                # torch.cuda.nvtx.range_push(f"backward_loss_stage_{stage_id}_mb_{mb_idx}")
-                loss = loss_fn(out_activation, labels)
-                # self.loss.append(loss.item())
-                # torch.cuda.nvtx.range_pop()
-            activation_or_loss = loss
-            upstream_grad = torch.ones_like(loss)
-
-        # no-op for the first stage
-        if stage_id == 0:
-            self.upstream_grad_cache[stage_id][mb_idx] = upstream_grad
-            self.logger.debug(
-                f"Saving upstream gradient {upstream_grad.shape} for stage {stage_id} mb {mb_idx}"
-            )
-            return 0
-
-        stage_input = self.inp_activation[stage_id][mb_idx]
-        stage_params = [self.forward_args[stage_id][i] for i in self.param_idxs[stage_id]]
-
-        output_nodes = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [activation_or_loss]) if n is not None]
-        input_nodes  = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [stage_input]) if n is not None]
-        param_nodes   = [n for n in (_get_grad_fn_or_grad_acc(p) for p in stage_params) if n is not None]
-
-        # Use the autograd graph with edges reversed to compute parameter groups, which are groups 
-        # of parameters that share the same intermediate nodes. Intermediate nodes are the nodes that
-        # lie on both (1) a backward path from the output node(s) to the stage input nodes and 
-        # (2) in a path from the output node(s) a parameter node/gradient accumulator
-        reverse_edges = construct_reverse_graph(output_nodes)
-        param_groups = get_param_groups(input_nodes, param_nodes, reverse_edges)
-
-        # Hooks to capture grads at intermediate nodes. In backward_weight,
-        # we'll backprop from these intermediate values
-        handles = []
-        for pg in param_groups:
-            intermediates = pg["intermediates"]
-            if not intermediates:
-                continue
-
-            pg["grads"] = [None] * len(intermediates)
-
-            for i, intermediate_node in enumerate(intermediates):
-                def make_hook(group: Dict[str, Any], idx: int):
-                    def hook(grad_inputs):
-                        group["grads"][idx] = grad_inputs
-                    return hook
-                handles.append(intermediate_node.register_prehook(make_hook(pg, i)))
-
-        self._start_timing(comp_stream, "backward_input")
-        with torch.cuda.stream(comp_stream):
-            # torch.cuda.nvtx.range_push(f"backward_input_stage_{stage_id}_mb_{mb_idx}")
-            gx = torch.autograd.grad(
-                outputs=activation_or_loss,
-                inputs=stage_input,
-                grad_outputs=upstream_grad,
-                retain_graph=True,
-                allow_unused=True,
-            )
-            # torch.cuda.nvtx.range_pop()
-        self._stop_timing(comp_stream, "backward_input")
-
-        gx = gx[0] # Take the gx gradient out of the tuple returned by autograd.grad
-        if gx is not None and stage_input.requires_grad:
-            if stage_input.grad is None:
-                stage_input.grad = gx
-            else:
-                stage_input.grad.add_(gx)
-
-        # Free output tensors between the output nodes and the intermediate nodes
-        if not isinstance(activation_or_loss, list):
-            activation_or_loss = [activation_or_loss]
-
-        for t in activation_or_loss:
-            t.detach_()
-
-        for h in handles:
-            h.remove()
-
-        del activation_or_loss
-        del stage_input
-
-        # Save parameter groups for use in backward_weight
-        self.bw_param_groups[stage_id][mb_idx] = param_groups
-
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-        comp_stream.synchronize()
-
-        return 0
-
-    def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
-        ret = self._backward_weight_impl(stage_id, mb_idx, *deps, loss_fn=loss_fn)
-        torch.cuda.nvtx.range_pop()
-        return ret
-
-    def _backward_weight_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        self._label_task(f"{stage_id}:{mb_idx}")
-        comp_stream = self.comp_stream
-
-        self.logger.debug(
-            f"Calling backward W {stage_id} mb {mb_idx} on actor {self.global_rank}"
-        )
-
-        stage_params = [self.forward_args[stage_id][i] for i in self.param_idxs[stage_id]]
-        updated_params: dict[int, torch.nn.Parameter] = {}
-
-        # Special case to handle stage 0 since backward_input is a NOOP, 
-        # meaning no parameter groups are created
-        if stage_id == 0:
-            upstream_grad = self.upstream_grad_cache[stage_id][mb_idx]
-            out_activation = self.out_activation[stage_id][mb_idx]
-            self._start_timing(comp_stream, "backward_weight")
-            if stage_id < self.num_stages - 1:
-                with torch.cuda.stream(comp_stream):
-                    # torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
-                    gparams = torch.autograd.grad(
-                        outputs=out_activation,
-                        inputs=stage_params,
-                        grad_outputs=upstream_grad,
-                        retain_graph=False,
-                    )
-                    # torch.cuda.nvtx.range_pop()
-            else:
-                assert loss_fn is not None
-                labels = self.labels
-                assert out_activation.shape == labels.shape
-                with torch.cuda.stream(comp_stream):
-                    # torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
-                    loss = loss_fn(out_activation, labels)
-                    gparams = torch.autograd.grad(
-                        outputs=loss,
-                        inputs=stage_params,
-                        retain_graph=False,
-                    )
-                    # torch.cuda.nvtx.range_pop()
-            self._stop_timing(comp_stream, "backward_weight")
-
-            assert len(gparams) == len(stage_params), (
-                f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(stage_params)}"
-            )
-            
-            for p, pg in zip(stage_params, gparams):
-                if pg is None:
-                    continue
-                if p.grad is None:
-                    p.grad = pg.clone()
-                else:
-                    p.grad.add_(pg)
-                updated_params[id(p)] = p
-        else:
-            # Create mapping from autograd nodes -> parameters
-            grad_acc_to_weight: Dict[Node, Tuple[Parameter, int]] = {}
-            for param in stage_params:
-                node = _get_grad_fn_or_grad_acc(param)
-                grad_acc_to_weight[node] = param
-
-            param_groups = self.bw_param_groups[stage_id][mb_idx]
-
-            # Perform the weight updates separately for each param_group, beginning
-            # backprop from each the intermediate node(s) of each group
-            for pg in param_groups:
-                intermediates: List[Node] = pg.get("intermediates", [])
-                intermediate_grads = pg.get("grads", None) # List of intermediate node gradients, captured by the hooks
-
-                # Skip groups without intermediate nodes (could happen in weird cases
-                # where one node is disconnected from the rest of the autograd graph for some reason)
-                if not intermediates or intermediate_grads is None:
-                    continue
-
-                intermediate_edges: List[GradientEdge] = []
-                intermediate_edge_grads: List[torch.Tensor] = []
-
-                for intermediate_node, grad_inputs in zip(intermediates, intermediate_grads):
-                    if grad_inputs is None:
-                        continue
-
-                    gs = [x for x in grad_inputs if x is not None]
-                    if not gs:
-                        continue
-                    
-                    # Sum all gradients arriving at the current intermediate node
-                    # in case the node has multiple source of gradients
-                    summed = sum(gs)
-
-                    # Create a GradientEdge for each intermediate node (we can backprop with respect to these)
-                    # and store the summed gradient for that node
-                    intermediate_edges.append(GradientEdge(intermediate_node, 0))
-                    intermediate_edge_grads.append(summed)
-
-                del pg["intermediates"]
-
-                if not intermediate_edges:
-                    continue
-
-                # Grab params for the param_nodes in this param group using our grad_acc_to_weight map from earlier
-                mapped_param_nodes = [p for p in pg["params"] if p in grad_acc_to_weight]
-                if not mapped_param_nodes:
-                    continue
-
-                # Use these parameters to create a GradientEdge that we'll use as our input to autograd.grad
-                weight_edges = tuple(GradientEdge(p, 0) for p in mapped_param_nodes)
-
-                self._start_timing(comp_stream, "backward_weight")
-                with torch.cuda.stream(comp_stream):
-                    # torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
-                    gparams = torch.autograd.grad(
-                        outputs=intermediate_edges,
-                        inputs=weight_edges,
-                        grad_outputs=intermediate_edge_grads,
-                        retain_graph=False,
-                    )
-                    # torch.cuda.nvtx.range_pop()
-                self._stop_timing(comp_stream, "backward_weight")
-
-                del pg["grads"]
-
-                assert len(gparams) == len(mapped_param_nodes), (
-                    f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(mapped_param_nodes)}"
-                )
-                
-                # Finally, update gradients for the params in this param_group
-                for param_node, dw in zip(mapped_param_nodes, gparams):
-                    if dw is None:
-                        continue
-
-                    weight = grad_acc_to_weight[param_node]
-
-                    if weight.grad is None:
-                        weight.grad = dw
-                    else:
-                        weight.grad.add_(dw)
-                    updated_params[id(weight)] = weight
-
-        # Mirror post-accumulate hook behavior for split backward.
-        for p in updated_params.values():
-            self._maybe_trigger_grad_allreduce(stage_id, p)
-
-        self.bw_grad_cache[stage_id][mb_idx] = None
-        self.upstream_grad_cache[stage_id][mb_idx] = None
-        self.bw_param_groups[stage_id][mb_idx] = None
-        self.out_activation[stage_id][mb_idx] = None
-
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-        comp_stream.synchronize()
-
-        return 0
-
-
-    def _backward_input(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        comp_stream = self.comp_stream
-
-        self.logger.debug(
-            f"Calling backward I {stage_id} mb {mb_idx} on actor {self.global_rank}"
-        )
-
-        out_activation = self.out_activation[stage_id][mb_idx]
-
-        activation_or_loss = None
-        upstream_grad = None
-        if stage_id < self.num_stages - 1:
-            activation_or_loss = out_activation
-            upstream_grad = self.p2p_cache.pop(
-                (stage_id + 1, stage_id, mb_idx, False)
-            )
-        else:
-            assert loss_fn is not None
-            labels = self.labels
-            assert out_activation.shape == labels.shape
-            with torch.cuda.stream(comp_stream):
-                loss = loss_fn(out_activation, labels)
-                self.loss.append(loss.item())
-            activation_or_loss = loss
-            upstream_grad = torch.ones_like(loss)
-
-        # no-op for the first stage
-        if stage_id == 0:
-            self.upstream_grad_cache[stage_id][mb_idx] = upstream_grad
-            self.logger.debug(
-                f"Saving upstream gradient {upstream_grad.shape} for stage {stage_id} mb {mb_idx}"
-            )
-            return 1
-
-        stage_input = self.inp_activation[stage_id][mb_idx]
-        stage_params = [self.forward_args[stage_id][i] for i in self.param_idxs[stage_id]]
-
-        output_nodes = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [activation_or_loss]) if n is not None]
-        input_nodes  = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [stage_input]) if n is not None]
-        param_nodes   = [n for n in (_get_grad_fn_or_grad_acc(p) for p in stage_params) if n is not None]
-
-        # Use the autograd graph with edges reversed to compute parameter groups, which are groups 
-        # of parameters that share the same intermediate nodes. Intermediate nodes are the nodes that
-        # lie on both (1) a backward path from the output node(s) to the stage input nodes and 
-        # (2) in a path from the output node(s) a parameter node/gradient accumulator
-        reverse_edges = construct_reverse_graph(output_nodes)
-        param_groups = get_param_groups(input_nodes, param_nodes, reverse_edges)
-
-        # Hooks to capture grads at intermediate nodes. In backward_weight,
-        # we'll backprop from these intermediate values
-        handles = []
-        for pg in param_groups:
-            intermediates = pg["intermediates"]
-            if not intermediates:
-                continue
-
-            pg["grads"] = [None] * len(intermediates)
-
-            for i, intermediate_node in enumerate(intermediates):
-                def make_hook(group: Dict[str, Any], idx: int):
-                    def hook(grad_inputs):
-                        group["grads"][idx] = grad_inputs
-                    return hook
-                handles.append(intermediate_node.register_prehook(make_hook(pg, i)))
-
-        gx = torch.autograd.grad(
-            outputs=activation_or_loss,
-            inputs=stage_input,
-            grad_outputs=upstream_grad,
-            retain_graph=True,
-            allow_unused=True,
-        )
-
-        gx = gx[0] # Take the gx gradient out of the tuple returned by autograd.grad
-        if gx is not None and stage_input.requires_grad:
-            if stage_input.grad is None:
-                stage_input.grad = gx
-            else:
-                stage_input.grad.add_(gx)
-
-        # Free output tensors between the output nodes and the intermediate nodes
-        if not isinstance(activation_or_loss, list):
-            activation_or_loss = [activation_or_loss]
-
-        for t in activation_or_loss:
-            t.detach_()
-
-        for h in handles:
-            h.remove()
-
-        del activation_or_loss
-        del stage_input
-
-        # Save parameter groups for use in backward_weight
-        self.bw_param_groups[stage_id][mb_idx] = param_groups
-
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-        comp_stream.synchronize()
-
-        return 0
-
-
-    def _backward_input(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        torch.cuda.nvtx.range_push(f"backward_input_stage_{stage_id}_mb_{mb_idx}")
-        ret = self._backward_input_impl(stage_id, mb_idx, *deps, loss_fn=loss_fn)
-        torch.cuda.nvtx.range_pop()
-        return ret
-
-    def _backward_input_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        self._label_task(f"{stage_id}:{mb_idx}")
-        comp_stream = self.comp_stream
-
-        self.logger.debug(
-            f"Calling backward I {stage_id} mb {mb_idx} on actor {self.global_rank}"
-        )
-
-        out_activation = self.out_activation[stage_id][mb_idx]
-
-        activation_or_loss = None
-        upstream_grad = None
-        if stage_id < self.num_stages - 1:
-            # Wait for recv to complete if this stage receives from the next stage
-            recv_key = (stage_id + 1, stage_id, mb_idx, False)
-            recv_event = self.p2p_events.pop(recv_key, None)
-            if recv_event is not None:
-                comp_stream.wait_event(recv_event)
-                # self.nccl_monitor.notify_ordered_after(comp_stream, recv_event)
-            upstream_grad = self.p2p_cache.pop(recv_key)
-            activation_or_loss = out_activation
-        else:
-            assert loss_fn is not None
-            labels = self.labels
-            assert out_activation.shape == labels.shape
-            with torch.cuda.stream(comp_stream):
-                # torch.cuda.nvtx.range_push(f"backward_loss_stage_{stage_id}_mb_{mb_idx}")
-                loss = loss_fn(out_activation, labels)
-                # self.loss.append(loss.item())
-                # torch.cuda.nvtx.range_pop()
-            activation_or_loss = loss
-            upstream_grad = torch.ones_like(loss)
-
-        # no-op for the first stage
-        if stage_id == 0:
-            self.upstream_grad_cache[stage_id][mb_idx] = upstream_grad
-            self.logger.debug(
-                f"Saving upstream gradient {upstream_grad.shape} for stage {stage_id} mb {mb_idx}"
-            )
-            return 0
-
-        stage_input = self.inp_activation[stage_id][mb_idx]
-        stage_params = [self.forward_args[stage_id][i] for i in self.param_idxs[stage_id]]
-
-        output_nodes = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [activation_or_loss]) if n is not None]
-        input_nodes  = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [stage_input]) if n is not None]
-        param_nodes   = [n for n in (_get_grad_fn_or_grad_acc(p) for p in stage_params) if n is not None]
-
-        # Use the autograd graph with edges reversed to compute parameter groups, which are groups 
-        # of parameters that share the same intermediate nodes. Intermediate nodes are the nodes that
-        # lie on both (1) a backward path from the output node(s) to the stage input nodes and 
-        # (2) in a path from the output node(s) a parameter node/gradient accumulator
-        reverse_edges = construct_reverse_graph(output_nodes)
-        param_groups = get_param_groups(input_nodes, param_nodes, reverse_edges)
-
-        # Hooks to capture grads at intermediate nodes. In backward_weight,
-        # we'll backprop from these intermediate values
-        handles = []
-        for pg in param_groups:
-            intermediates = pg["intermediates"]
-            if not intermediates:
-                continue
-
-            pg["grads"] = [None] * len(intermediates)
-
-            for i, intermediate_node in enumerate(intermediates):
-                def make_hook(group: Dict[str, Any], idx: int):
-                    def hook(grad_inputs):
-                        group["grads"][idx] = grad_inputs
-                    return hook
-                handles.append(intermediate_node.register_prehook(make_hook(pg, i)))
-
-        self._start_timing(comp_stream, "backward_input")
-        with torch.cuda.stream(comp_stream):
-            # torch.cuda.nvtx.range_push(f"backward_input_stage_{stage_id}_mb_{mb_idx}")
-            gx = torch.autograd.grad(
-                outputs=activation_or_loss,
-                inputs=stage_input,
-                grad_outputs=upstream_grad,
-                retain_graph=True,
-                allow_unused=True,
-            )
-            # torch.cuda.nvtx.range_pop()
-        self._stop_timing(comp_stream, "backward_input")
-
-        gx = gx[0] # Take the gx gradient out of the tuple returned by autograd.grad
-        if gx is not None and stage_input.requires_grad:
-            if stage_input.grad is None:
-                stage_input.grad = gx
-            else:
-                stage_input.grad.add_(gx)
-
-        # Free output tensors between the output nodes and the intermediate nodes
-        if not isinstance(activation_or_loss, list):
-            activation_or_loss = [activation_or_loss]
-
-        for t in activation_or_loss:
-            t.detach_()
-
-        for h in handles:
-            h.remove()
-
-        del activation_or_loss
-        del stage_input
-
-        # Save parameter groups for use in backward_weight
-        self.bw_param_groups[stage_id][mb_idx] = param_groups
-
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-        comp_stream.synchronize()
-
-        return 0
-
-    def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
-        ret = self._backward_weight_impl(stage_id, mb_idx, *deps, loss_fn=loss_fn)
-        torch.cuda.nvtx.range_pop()
-        return ret
-
-    def _backward_weight_impl(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        self._label_task(f"{stage_id}:{mb_idx}")
-        comp_stream = self.comp_stream
-
-        self.logger.debug(
-            f"Calling backward W {stage_id} mb {mb_idx} on actor {self.global_rank}"
-        )
-
-        stage_params = [self.forward_args[stage_id][i] for i in self.param_idxs[stage_id]]
-        updated_params: dict[int, torch.nn.Parameter] = {}
-
-        # Special case to handle stage 0 since backward_input is a NOOP, 
-        # meaning no parameter groups are created
-        if stage_id == 0:
-            upstream_grad = self.upstream_grad_cache[stage_id][mb_idx]
-            out_activation = self.out_activation[stage_id][mb_idx]
-            self._start_timing(comp_stream, "backward_weight")
-            if stage_id < self.num_stages - 1:
-                with torch.cuda.stream(comp_stream):
-                    # torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
-                    gparams = torch.autograd.grad(
-                        outputs=out_activation,
-                        inputs=stage_params,
-                        grad_outputs=upstream_grad,
-                        retain_graph=False,
-                    )
-                    # torch.cuda.nvtx.range_pop()
-            else:
-                assert loss_fn is not None
-                labels = self.labels
-                assert out_activation.shape == labels.shape
-                with torch.cuda.stream(comp_stream):
-                    # torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
-                    loss = loss_fn(out_activation, labels)
-                    gparams = torch.autograd.grad(
-                        outputs=loss,
-                        inputs=stage_params,
-                        retain_graph=False,
-                    )
-                    # torch.cuda.nvtx.range_pop()
-            self._stop_timing(comp_stream, "backward_weight")
-
-            assert len(gparams) == len(stage_params), (
-                f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(stage_params)}"
-            )
-            
-            for p, pg in zip(stage_params, gparams):
-                if pg is None:
-                    continue
-                if p.grad is None:
-                    p.grad = pg.clone()
-                else:
-                    p.grad.add_(pg)
-                updated_params[id(p)] = p
-        else:
-            # Create mapping from autograd nodes -> parameters
-            grad_acc_to_weight: Dict[Node, Tuple[Parameter, int]] = {}
-            for param in stage_params:
-                node = _get_grad_fn_or_grad_acc(param)
-                grad_acc_to_weight[node] = param
-
-            param_groups = self.bw_param_groups[stage_id][mb_idx]
-
-            # Perform the weight updates separately for each param_group, beginning
-            # backprop from each the intermediate node(s) of each group
-            for pg in param_groups:
-                intermediates: List[Node] = pg.get("intermediates", [])
-                intermediate_grads = pg.get("grads", None) # List of intermediate node gradients, captured by the hooks
-
-                # Skip groups without intermediate nodes (could happen in weird cases
-                # where one node is disconnected from the rest of the autograd graph for some reason)
-                if not intermediates or intermediate_grads is None:
-                    continue
-
-                intermediate_edges: List[GradientEdge] = []
-                intermediate_edge_grads: List[torch.Tensor] = []
-
-                for intermediate_node, grad_inputs in zip(intermediates, intermediate_grads):
-                    if grad_inputs is None:
-                        continue
-
-                    gs = [x for x in grad_inputs if x is not None]
-                    if not gs:
-                        continue
-                    
-                    # Sum all gradients arriving at the current intermediate node
-                    # in case the node has multiple source of gradients
-                    summed = sum(gs)
-
-                    # Create a GradientEdge for each intermediate node (we can backprop with respect to these)
-                    # and store the summed gradient for that node
-                    intermediate_edges.append(GradientEdge(intermediate_node, 0))
-                    intermediate_edge_grads.append(summed)
-
-                del pg["intermediates"]
-
-                if not intermediate_edges:
-                    continue
-
-                # Grab params for the param_nodes in this param group using our grad_acc_to_weight map from earlier
-                mapped_param_nodes = [p for p in pg["params"] if p in grad_acc_to_weight]
-                if not mapped_param_nodes:
-                    continue
-
-                # Use these parameters to create a GradientEdge that we'll use as our input to autograd.grad
-                weight_edges = tuple(GradientEdge(p, 0) for p in mapped_param_nodes)
-
-                self._start_timing(comp_stream, "backward_weight")
-                with torch.cuda.stream(comp_stream):
-                    # torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
-                    gparams = torch.autograd.grad(
-                        outputs=intermediate_edges,
-                        inputs=weight_edges,
-                        grad_outputs=intermediate_edge_grads,
-                        retain_graph=False,
-                    )
-                    # torch.cuda.nvtx.range_pop()
-                self._stop_timing(comp_stream, "backward_weight")
-
-                del pg["grads"]
-
-                assert len(gparams) == len(mapped_param_nodes), (
-                    f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(mapped_param_nodes)}"
-                )
-                
-                # Finally, update gradients for the params in this param_group
-                for param_node, dw in zip(mapped_param_nodes, gparams):
-                    if dw is None:
-                        continue
-
-                    weight = grad_acc_to_weight[param_node]
-
-                    if weight.grad is None:
-                        weight.grad = dw
-                    else:
-                        weight.grad.add_(dw)
-                    updated_params[id(weight)] = weight
-
-        # Mirror post-accumulate hook behavior for split backward.
-        for p in updated_params.values():
-            self._maybe_trigger_grad_allreduce(stage_id, p)
+            #         f"with communicators for ranks {group_ranks}"
+            #     )
 
-        self.bw_grad_cache[stage_id][mb_idx] = None
-        self.upstream_grad_cache[stage_id][mb_idx] = None
-        self.bw_param_groups[stage_id][mb_idx] = None
-        self.out_activation[stage_id][mb_idx] = None
+        # Warm up all communicators to force eager NCCL initialization.
+        dummy = torch.zeros(1, device=self.device)
+        for key, pg in self.pp_groups.items():
+            self.logger.debug(f"Global rank {self.global_rank} warming up pp communicator {key}")
+            dist.all_reduce(dummy, group=pg)
+        torch.cuda.synchronize()
+        self.logger.info(f"Global rank {self.global_rank} warmed up {len(self.pp_groups)} pp communicators")
 
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
+    def shutdown(self):
+        dist.destroy_process_group()
 
-        comp_stream.synchronize()
 
-        return 0
+    def _load_stage(
+        self,
+        stage_id: int,
+        modules_data: list,
+        a2a_boundaries: dict = None,
+        use_activation_checkpointing: bool = False,
+    ) -> None:
+        """Load a (possibly bucketed) stage.
 
-    def _backward_weight(self, stage_id: int, mb_idx: int, *deps, loss_fn=None):
-        comp_stream = self.comp_stream
+        *modules_data* is a list of dicts, one per module/bucket, each with keys:
+        ``gm_data``, ``graphargs``, ``input_idxs``, ``param_idxs``.
 
+        A non-bucketed stage is represented as a single-element list.
+        Bucket 0 handles the activation input from the previous stage (or
+        ``self.inputs`` for stage 0).  Subsequent modules receive the
+        cross-segment activation output of the previous module.
+        """
         self.logger.debug(
-            f"Calling backward W {stage_id} mb {mb_idx} on actor {self.global_rank}"
+            f"Loading stage {stage_id} ({len(modules_data)} module(s)) on actor {self.global_rank}"
         )
 
-        stage_params = [self.forward_args[stage_id][i] for i in self.param_idxs[stage_id]]
-
-        # Special case to handle stage 0 since backward_input is a NOOP, 
-        # meaning no parameter groups are created
-        if stage_id == 0:
-            upstream_grad = self.upstream_grad_cache[stage_id][mb_idx]
-            out_activation = self.out_activation[stage_id][mb_idx]
-            if stage_id < self.num_stages - 1:
-                gparams = torch.autograd.grad(
-                    outputs=out_activation,
-                    inputs=stage_params,
-                    grad_outputs=upstream_grad,
-                    retain_graph=False,
-                )
-            else:
-                assert loss_fn is not None
-                labels = self.labels
-                assert out_activation.shape == labels.shape
-                with torch.cuda.stream(comp_stream):
-                    loss = loss_fn(out_activation, labels)
-                    gparams = torch.autograd.grad(
-                        outputs=loss,
-                        inputs=stage_params,
-                        retain_graph=False,
-                    )
-
-            assert len(gparams) == len(stage_params), (
-                f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(stage_params)}"
-            )
-            
-            for p, pg in zip(stage_params, gparams):
-                if p.grad is None:
-                    p.grad = pg.clone()
-                else:
-                    p.grad.add_(pg)
-        else:
-            # Create mapping from autograd nodes -> parameters
-            grad_acc_to_weight: Dict[Node, Tuple[Parameter, int]] = {}
-            for param in stage_params:
-                node = _get_grad_fn_or_grad_acc(param)
-                grad_acc_to_weight[node] = param
-
-            param_groups = self.bw_param_groups[stage_id][mb_idx]
-
-            # Perform the weight updates separately for each param_group, beginning
-            # backprop from each the intermediate node(s) of each group
-            for pg in param_groups:
-                intermediates: List[Node] = pg.get("intermediates", [])
-                intermediate_grads = pg.get("grads", None) # List of intermediate node gradients, captured by the hooks
+        self.bucket_fwd_fns[stage_id] = []
+        self.bucket_fwd_args[stage_id] = []
+        self.bucket_param_idxs[stage_id] = []
+        self.bucket_optims[stage_id] = []
+        self.a2a_boundaries[stage_id] = a2a_boundaries or {}
 
-                # Skip groups without intermediate nodes (could happen in weird cases
-                # where one node is disconnected from the rest of the autograd graph for some reason)
-                if not intermediates or intermediate_grads is None:
-                    continue
+        g = torch.Generator(device=self.device)
+        g.manual_seed(1000 * self.global_rank + stage_id)
 
-                intermediate_edges: List[GradientEdge] = []
-                intermediate_edge_grads: List[torch.Tensor] = []
+        first_gm = None
+        last_gm = None
 
-                for intermediate_node, grad_inputs in zip(intermediates, intermediate_grads):
-                    if grad_inputs is None:
-                        continue
+        for b_idx, bd in enumerate(modules_data):
+            gm = _deserialize_graphmodule(bd["gm_data"])
+            if b_idx == 0:
+                first_gm = gm
+            last_gm = gm
 
-                    gs = [x for x in grad_inputs if x is not None]
-                    if not gs:
-                        continue
-                    
-                    # Sum all gradients arriving at the current intermediate node
-                    # in case the node has multiple source of gradients
-                    summed = sum(gs)
+            forward_args = list(bd["graphargs"])
+            b_input_idxs = list(bd["input_idxs"])
+            b_param_idxs = list(bd["param_idxs"])
 
-                    # Create a GradientEdge for each intermediate node (we can backprop with respect to these)
-                    # and store the summed gradient for that node
-                    intermediate_edges.append(GradientEdge(intermediate_node, 0))
-                    intermediate_edge_grads.append(summed)
+            self.logger.debug(
+                f"Stage {stage_id} module {b_idx} input indices: {b_input_idxs}"
+            )
 
-                del pg["intermediates"]
+            # Module 0: save activation-input metadata for the stage interface
+            # (used by _exec_recv to pre-allocate recv buffers).
+            if b_idx == 0:
+                self.input_idxs[(stage_id, None)] = b_input_idxs  # legacy path alias
+                for i in b_input_idxs:
+                    meta = forward_args[i]
+                    if meta is not None:
+                        self.forward_input_meta[stage_id][i] = (
+                            tuple(meta.shape),
+                            meta.dtype,
+                            bool(getattr(meta, "requires_grad", False)),
+                        )
+                    forward_args[i] = None
+            else:
+                for i in b_input_idxs:
+                    forward_args[i] = None
 
-                if not intermediate_edges:
-                    continue
+            self.input_idxs[(stage_id, b_idx)] = b_input_idxs
 
-                # Grab params for the param_nodes in this param group using our grad_acc_to_weight map from earlier
-                mapped_param_nodes = [p for p in pg["params"] if p in grad_acc_to_weight]
-                if not mapped_param_nodes:
+            # Realize parameter tensors.
+            realized = [None] * len(forward_args)
+            for i, arg in enumerate(forward_args):
+                if arg is None:
                     continue
+                t = torch.empty(arg.shape, dtype=arg.dtype, device=self.device)
+                if arg.requires_grad:
+                    t.requires_grad_(True)
+                    torch.nn.init.normal_(t, mean=0.0, std=0.02, generator=g)
+                else:
+                    t.zero_()
+                realized[i] = t
 
-                # Use these parameters to create a GradientEdge that we'll use as our input to autograd.grad
-                weight_edges = tuple(GradientEdge(p, 0) for p in mapped_param_nodes)
-
-                gparams = torch.autograd.grad(
-                    outputs=intermediate_edges,
-                    inputs=weight_edges,
-                    grad_outputs=intermediate_edge_grads,
-                    retain_graph=False,
-                )
-
-                del pg["grads"]
-
-                assert len(gparams) == len(mapped_param_nodes), (
-                    f"Stage {stage_id}: mismatch #param grads {len(gparams)} vs params {len(mapped_param_nodes)}"
+            # Forward function (with optional activation checkpointing).
+            if use_activation_checkpointing:
+                fwd = gm.forward
+                forward_fn = lambda *args, _fn=fwd: torch.utils.checkpoint.checkpoint(
+                    _fn, *args, use_reentrant=False
                 )
-                
-                # Finally, update gradients for the params in this param_group
-                for param_node, dw in zip(pg["params"], gparams):
-                    if dw is None:
-                        continue
+            else:
+                forward_fn = gm.forward
 
-                    weight = grad_acc_to_weight[param_node]
+            self.bucket_fwd_fns[stage_id].append(forward_fn)
+            self.bucket_fwd_args[stage_id].append(realized)
+            self.bucket_param_idxs[stage_id].append(b_param_idxs)
 
-                    if weight.grad is None:
-                        weight.grad = dw
-                    else:
-                        weight.grad.add_(dw)
+            # Collect trainable parameters and build a contiguous flat tensor so
+            # a single all-reduce call can sync the entire module's gradients.
+            trainable_idxs = [
+                i for i in b_param_idxs
+                if realized[i] is not None and realized[i].requires_grad
+            ]
+            trainable = [realized[i] for i in trainable_idxs]
+            self.bucket_trainable_param_idxs[(stage_id, b_idx)] = trainable_idxs
 
-        self.bw_grad_cache[stage_id][mb_idx] = None
-        self.upstream_grad_cache[stage_id][mb_idx] = None
-        self.bw_param_groups[stage_id][mb_idx] = None
-        self.out_activation[stage_id][mb_idx] = None
+            if trainable:
+                flat_params = torch.cat([p.detach().view(-1) for p in trainable]).contiguous()
+                flat_params.requires_grad_(False)
+                flat_grads = torch.zeros_like(flat_params)
+                offset = 0
+                for idx, p in zip(trainable_idxs, trainable):
+                    numel = p.numel()
+                    realized[idx] = realized[idx].detach()
+                    realized[idx].data = flat_params[offset:offset + numel].view(p.shape)
+                    realized[idx].requires_grad_(True)
+                    offset += numel
+                self.bucket_flat_params[(stage_id, b_idx)] = flat_params
+                self.bucket_flat_grads[(stage_id, b_idx)] = flat_grads
+            else:
+                self.bucket_flat_params[(stage_id, b_idx)] = None
+                self.bucket_flat_grads[(stage_id, b_idx)] = None
 
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
+            # Optimizer for this module's trainable parameters.
+            trainable_for_optim = [realized[i] for i in trainable_idxs]
+            optim = self.optim_class(trainable_for_optim) if trainable_for_optim else None
+            self.bucket_optims[stage_id].append(optim)
 
-        torch.cuda.synchronize()
+            # Legacy non-DAG DDP: register per-param allreduce hooks for module 0.
+            # if b_idx == 0 and self.dp_degree > 1 and not self.naive_gradient_sync:
+            #     self.forward_args[stage_id] = realized
+                # self._prepare_dp_comm_ops(stage_id)
 
-        return 1
+        # Set legacy fields from first module for ZeroBubble BWD_I/BWD_W compatibility.
+        self.forward_args[stage_id] = self.bucket_fwd_args[stage_id][0]
+        self.param_idxs[stage_id] = self.bucket_param_idxs[stage_id][0]
+        self.graph_modules[stage_id] = first_gm
+        self.stage_id = stage_id
 
-    def _forward_backward(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: int, bwd_mb_idx: int, *deps, loss_fn=None):
-        torch.cuda.nvtx.range_push(f"forward_stage_{fwd_stage_id}_mb_{fwd_mb_idx}_backward_stage_{bwd_stage_id}_mb_{bwd_mb_idx}")
-        ret = self._forward_backward_impl(fwd_stage_id, fwd_mb_idx, bwd_stage_id, bwd_mb_idx, *deps, loss_fn=loss_fn)
-        torch.cuda.nvtx.range_pop()
-        return ret
+        # Compute output activation shape from the last module for BWD recv buffer
+        # pre-allocation (e.g. interleaved 1F1B recv before forward has run).
+        last_realized = self.bucket_fwd_args[stage_id][-1]
+        last_b_input_idxs = self.input_idxs[(stage_id, len(modules_data) - 1)]
+        output_shapes = []
 
-    def _forward_backward_impl(self, fwd_stage_id: int, fwd_mb_idx: int, bwd_stage_id: int, bwd_mb_idx: int, *deps, loss_fn=None):
-        self._label_task(f"{fwd_stage_id}:{fwd_mb_idx}|{bwd_stage_id}:{bwd_mb_idx}")
-        if self.mode == "sequential":
-            fwd_comp_stream = self.comp_stream
-            bwd_comp_stream = self.comp_stream
-            self.overlap_a2a_ops = False
-        elif self.mode == "naive":
-            fwd_comp_stream = self.per_mb_streams[(fwd_stage_id + fwd_mb_idx) % 2]
-            bwd_comp_stream = self.per_mb_streams[(bwd_stage_id + bwd_mb_idx) % 2]
-            self.overlap_a2a_ops = False
-        elif self.mode == "overlapped":
-            fwd_comp_stream = self.comp_stream
-            bwd_comp_stream = self.comp_stream
-            self.overlap_a2a_ops = True
-        else:
-            raise ValueError(f"Invalid mode: {self.mode}")
+        # Try FX graph node metadata first.
+        for node in last_gm.graph.nodes:
+            if node.op == "output":
+                out_args = node.args[0]
+                if not isinstance(out_args, (tuple, list)):
+                    out_args = [out_args]
+                for out_node in out_args:
+                    if isinstance(out_node, torch.fx.Node):
+                        meta_val = out_node.meta.get("val", None)
+                        if meta_val is not None and isinstance(meta_val, torch.Tensor) and meta_val.requires_grad:
+                            output_shapes.append((tuple(meta_val.shape), meta_val.dtype))
+                break
 
-        self.logger.debug(
-            f"Calling forward {fwd_stage_id} mb {fwd_mb_idx} backward {bwd_stage_id} mb {bwd_mb_idx} on actor {self.global_rank}"
-        )
-        
-        # PREPARE FORWARD PASS
+        if not output_shapes:
+            # Fallback: probe forward pass with dummy inputs.
+            probe_args = list(last_realized)
+            g_probe = torch.Generator(device=self.device)
+            g_probe.manual_seed(42 + stage_id)
+            for i in last_b_input_idxs:
+                meta = modules_data[-1]["graphargs"][i]
+                if meta is not None:
+                    t = torch.zeros(meta.shape, dtype=meta.dtype, device=self.device)
+                    t.requires_grad_(bool(getattr(meta, "requires_grad", True)))
+                    probe_args[i] = t
+            with torch.no_grad():
+                probe_out = last_gm.forward(*probe_args)
+            for out in (probe_out if isinstance(probe_out, (tuple, list)) else [probe_out]):
+                if isinstance(out, torch.Tensor) and out.requires_grad:
+                    output_shapes.append((tuple(out.shape), out.dtype))
 
-        if fwd_stage_id == 0:
-            # For the first stage, load input tensors from self.inputs
-            for i, inp in zip(self.input_idxs[(fwd_stage_id, None)], self.inputs):
-                self.forward_args[fwd_stage_id][i] = inp
+        if output_shapes:
+            self.output_activation_shape[stage_id] = output_shapes
+            self.logger.debug(f"Stage {stage_id} output activation shapes: {output_shapes}")
         else:
-            fwd_recv_key = (fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, False)
-
-            # Wait for recv to complete if this stage receives from the previous stage
-            fwd_recv_event = self.p2p_events.pop(fwd_recv_key, None)
-            if fwd_recv_event is not None:
-                fwd_comp_stream.wait_event(fwd_recv_event)
-                # self.nccl_monitor.notify_ordered_after(fwd_comp_stream, fwd_recv_event)
-            inputs_from_prev_stage = self.p2p_cache.pop(fwd_recv_key)
-
-            # Detach to avoid double-backprop if the previous stage is co-located
-            for i, tensor in zip(self.input_idxs[(fwd_stage_id, None)], inputs_from_prev_stage):
-                if isinstance(tensor, (tuple, list)):
-                    assert len(tensor) == 1
-                    tensor = tensor[0]
-                if fwd_stage_id > 0 and self.stage_to_device[fwd_stage_id] == self.stage_to_device[fwd_stage_id - 1] and tensor.requires_grad:
-                    tensor = tensor.detach().requires_grad_(True)
-                self.forward_args[fwd_stage_id][i] = tensor
-
-            # save first input that requires grad as input activation
-            inp_with_grad = [
-                self.forward_args[fwd_stage_id][i]
-                for i in self.input_idxs[(fwd_stage_id, None)]
-                if self.forward_args[fwd_stage_id][i].requires_grad
-            ]
-            assert (
-                len(inp_with_grad) == 1
-            ), "Exactly one input per stage should require a gradient"
-            self.logger.debug(
-                f"Saving input activation {inp_with_grad[0].shape} for stage {fwd_stage_id} mb {fwd_mb_idx}"
+            self.logger.warning(
+                f"Could not determine output activation shape for stage {stage_id}"
             )
-            self.inp_activation[fwd_stage_id][fwd_mb_idx] = inp_with_grad[0]
 
-        # PREPARE BACKWARD PASS
-
-        out_activation = self.out_activation[bwd_stage_id][bwd_mb_idx]
-        if bwd_stage_id < self.num_stages - 1:
-            bwd_recv_key = (bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False)
-
-            # Wait for recv to complete if this stage receives from the next stage
-            bwd_recv_event = self.p2p_events.pop(bwd_recv_key, None)
-            if bwd_recv_event is not None:
-                bwd_comp_stream.wait_event(bwd_recv_event)
-                # self.nccl_monitor.notify_ordered_after(bwd_comp_stream, bwd_recv_event)
-            input_grad = self.p2p_cache.pop(bwd_recv_key)
-        else:
-            assert loss_fn is not None
-            labels = self.labels
-            assert out_activation.shape == labels.shape
-            
-        # RUN FORWARD AND BACKWARD PASSES
-        if self.overlap_a2a_ops:
-            output = None
-            loss = None
-            # threading.Events for CPU-side thread coordination
-            n_a2a = self.n_a2a_ops[fwd_stage_id]
-            self.fwd_a2a_submitted = [threading.Event() for _ in range(n_a2a)]
-            self.bwd_a2a_submitted = [threading.Event() for _ in range(n_a2a)]
-            self.fwd_a2a_counter = 0
-            self.bwd_a2a_counter = 0
-
-            def run_fwd():
-                nonlocal output
-                with torch.cuda.stream(fwd_comp_stream):
-                    output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
-
-            def run_bwd():
-                nonlocal loss
-                self.fwd_a2a_submitted[0].wait()
-                if bwd_stage_id < self.num_stages - 1:
-                    with torch.cuda.stream(bwd_comp_stream):
-                        out_activation.backward(gradient=input_grad)
-                else:
-                    with torch.cuda.stream(bwd_comp_stream):
-                        loss = loss_fn(out_activation, labels)
-                        loss.backward()
-
-            fwd_thread = threading.Thread(target=run_fwd)
-            bwd_thread = threading.Thread(target=run_bwd)
-            fwd_thread.start()
-            bwd_thread.start()
-            fwd_thread.join()
-            bwd_thread.join()
-        else:
-            with torch.cuda.stream(fwd_comp_stream):
-                output = self.forward_fns[fwd_stage_id](*self.forward_args[fwd_stage_id])
-
-            if bwd_stage_id < self.num_stages - 1:
-                with torch.cuda.stream(bwd_comp_stream):
-                    out_activation.backward(gradient=input_grad)
-            else:
-                with torch.cuda.stream(bwd_comp_stream):
-                    loss = loss_fn(out_activation, labels)
-                    loss.backward()
-
-        # POST PROCESS FORWARD PASS
-
-        out_with_grad = [out for out in output if out.requires_grad]
-        assert (
-            len(out_with_grad) == 1
-        ), "Piper only supports one output per subgraph with requires_grad"
-        self.logger.debug(
-            f"Saving output activation {out_with_grad[0].shape} for stage {fwd_stage_id} mb {fwd_mb_idx}"
-        )
-        self.out_activation[fwd_stage_id][fwd_mb_idx] = out_with_grad[0]
-
-        # clear the input tensors
-        for i in self.input_idxs[(fwd_stage_id, None)]:
-            self.forward_args[fwd_stage_id][i] = None
-
-        if fwd_stage_id < self.num_stages - 1:
-            send_p2p_op = (fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True)
-            assert send_p2p_op not in self.p2p_cache
-            self.p2p_cache[send_p2p_op] = output
-
-        # POST PROCESS BACKWARD PASS
-        # if bwd_stage_id == self.num_stages - 1:
-        #     self.loss.append(loss.item())
-
-        # Clear output activation after backward pass
-        self.out_activation[bwd_stage_id][bwd_mb_idx] = None
-        del out_activation
-
-        # POST FORWARD P2P OPERATIONS
-        if fwd_stage_id < self.num_stages - 1:
-            send_p2p_op = (fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True)
-            assert send_p2p_op not in self.p2p_cache
-            self.p2p_cache[send_p2p_op] = output
-
-        if CLEANUP_MEMORY:
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-        fwd_comp_stream.synchronize()
-        if bwd_comp_stream is not fwd_comp_stream:
-            bwd_comp_stream.synchronize()
-
-        # clear A2A states
-        if self.overlap_a2a_ops:
-            self.overlap_a2a_ops = False
-            self.fwd_a2a_submitted.clear()
-            self.bwd_a2a_submitted.clear()
-            self.fwd_a2a_counter = 0
-            self.bwd_a2a_counter = 0
-
-        return 0
 
     def _synchronize_gradients(self):
         self.logger.info(f"Actor {self.global_rank} synchronizing gradients")
@@ -2972,9 +1466,6 @@ def _backward_weight_dag(self, stage_id: int, mb_idx: int, *, loss_fn=None) -> N
                         weight.grad.add_(dw)
                     updated_params[id(weight)] = weight
 
-        for p in updated_params.values():
-            self._maybe_trigger_grad_allreduce(stage_id, p)
-
         self.bw_grad_cache[stage_id][mb_idx] = None
         self.upstream_grad_cache[stage_id][mb_idx] = None
         self.bw_param_groups[stage_id][mb_idx] = None
diff --git a/src/piper_coordinator.py b/src/piper_coordinator.py
index 6d0a1d4..e4689c4 100644
--- a/src/piper_coordinator.py
+++ b/src/piper_coordinator.py
@@ -7,7 +7,7 @@
 from .piper_utils import create_logger, LOG_LEVEL
 
 
-@ray.remote(num_gpus=0.2)
+@ray.remote
 def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, master_addr, master_port, training_func: Callable, *args, **kwargs):
     logger = create_logger("piper_coordinator", LOG_LEVEL)
     logger.debug(f"Running DP rank {dp_rank+1} of {dp_degree} with {master_addr}:{master_port} as master address")
diff --git a/test/models/mixtral.py b/test/models/mixtral.py
index 6760436..193c5a5 100644
--- a/test/models/mixtral.py
+++ b/test/models/mixtral.py
@@ -117,52 +117,52 @@ def setup_caches(self, max_batch_size, max_seq_length):
         self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base).to(torch.bfloat16)
         self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
 
-    # def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
-    #     """
-    #     2 Stages
-    #     """
-    #     self.first_arg = idx.shape
-    #     with torch.fx.traceback.annotate({"stage": 0}):
-    #         mask = self.causal_mask[None, None, input_pos]
-    #         freqs_cis = self.freqs_cis[input_pos]
-    #         x = self.tok_embeddings(idx)
-    #         for layer in self.layers[:len(self.layers)//2]:
-    #             x = layer(x, input_pos, freqs_cis, mask)
-        
-    #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[len(self.layers)//2:]:
-    #             x = layer(x, input_pos, freqs_cis, mask)
-    #         x = self.norm(x)
-    #         logits = self.output(x)
-    #     return logits
-
     def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
         """
-        4 Stages
+        2 Stages
         """
         self.first_arg = idx.shape
         with torch.fx.traceback.annotate({"stage": 0}):
             mask = self.causal_mask[None, None, input_pos]
             freqs_cis = self.freqs_cis[input_pos]
             x = self.tok_embeddings(idx)
-            for layer in self.layers[:len(self.layers)//4]:
+            for layer in self.layers[:len(self.layers)//2]:
                 x = layer(x, input_pos, freqs_cis, mask)
         
         with torch.fx.traceback.annotate({"stage": 1}):
-            for layer in self.layers[len(self.layers)//4:len(self.layers)//2]:
-                x = layer(x, input_pos, freqs_cis, mask)
-
-        with torch.fx.traceback.annotate({"stage": 2}):
-            for layer in self.layers[len(self.layers)//2:3*len(self.layers)//4]:
-                x = layer(x, input_pos, freqs_cis, mask)
-
-        with torch.fx.traceback.annotate({"stage": 3}):
-            for layer in self.layers[3*len(self.layers)//4:]:
+            for layer in self.layers[len(self.layers)//2:]:
                 x = layer(x, input_pos, freqs_cis, mask)
             x = self.norm(x)
             logits = self.output(x)
         return logits
 
+    # def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+    #     """
+    #     4 Stages
+    #     """
+    #     self.first_arg = idx.shape
+    #     with torch.fx.traceback.annotate({"stage": 0}):
+    #         mask = self.causal_mask[None, None, input_pos]
+    #         freqs_cis = self.freqs_cis[input_pos]
+    #         x = self.tok_embeddings(idx)
+    #         for layer in self.layers[:len(self.layers)//4]:
+    #             x = layer(x, input_pos, freqs_cis, mask)
+        
+    #     with torch.fx.traceback.annotate({"stage": 1}):
+    #         for layer in self.layers[len(self.layers)//4:len(self.layers)//2]:
+    #             x = layer(x, input_pos, freqs_cis, mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 2}):
+    #         for layer in self.layers[len(self.layers)//2:3*len(self.layers)//4]:
+    #             x = layer(x, input_pos, freqs_cis, mask)
+
+    #     with torch.fx.traceback.annotate({"stage": 3}):
+    #         for layer in self.layers[3*len(self.layers)//4:]:
+    #             x = layer(x, input_pos, freqs_cis, mask)
+    #         x = self.norm(x)
+    #         logits = self.output(x)
+    #     return logits
+
     @classmethod
     def from_name(cls, name: str):
         return cls(ModelArgs.from_name(name))
diff --git a/test/test_dag.py b/test/test_dag.py
index 5df6342..c2d4529 100644
--- a/test/test_dag.py
+++ b/test/test_dag.py
@@ -174,11 +174,7 @@ def parse_args():
     parser.add_argument("--trace-iters", type=int, default=3)
     parser.add_argument("--tracing", action="store_true", default=False)
     parser.add_argument("--naive-grad-sync", action="store_true", default=False)
-    parser.add_argument(
-        "--mode",
-        choices=["sequential", "naive", "overlapped"],
-        default="sequential",
-    )
+    parser.add_argument("--mode", choices=["sequential", "naive", "overlapped"], default="sequential")
     parser.add_argument("--bucketing", action="store_true", default=False,
                         help="Split stages into per-param-bucket sub-modules for overlapped all-reduce")
     parser.add_argument("--nsight", action="store_true", default=False,
@@ -195,7 +191,7 @@ def parse_args():
         include_dashboard=False,
         # _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp",
     )
-    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="STRICT_SPREAD")
+    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="SPREAD")
     ray.get(pg.ready(), timeout=600)
     print(placement_group_table(pg))
     piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)

From ff37fa6e59320c22a3bd599c38e8629ec09e653a Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Wed, 25 Mar 2026 18:41:01 +0000
Subject: [PATCH 082/100] update llama test file with new DAG API. known bug:
 unexpected stage 0 forward/backward compute times

---
 src/piper.py                 |   7 +-
 src/piper_actor.py           | 611 +++++++++++------------------------
 src/piper_compile.py         |  14 +-
 src/piper_coordinator.py     |   4 +
 src/piper_exec.py            |  33 ++
 src/piper_graph_transform.py |  21 +-
 src/piper_utils.py           |   2 +-
 src/test_graph_transform.py  | 257 +++++++++++++++
 test/models/llama.py         |  50 +--
 test/models/mixtral.py       |  14 +
 test/models/qwen3.py         |  49 +--
 test/test_llama.py           | 164 +++++-----
 test/test_mixtral.py         |  19 +-
 test/test_qwen.py            |  28 +-
 14 files changed, 668 insertions(+), 605 deletions(-)
 create mode 100644 src/test_graph_transform.py

diff --git a/src/piper.py b/src/piper.py
index 85cbca4..d729eed 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -40,6 +40,8 @@ def piper(gm, example_inputs, **kwargs):
         for node in gm.graph.nodes
     )
 
+    logger.info(f"Graph has stage annotations: {has_annotations}")
+
     if has_annotations:
         top_level_gm, submodules = _split_gm_by_stages(gm)
     else:
@@ -168,12 +170,15 @@ def piper(gm, example_inputs, **kwargs):
         if dp_degree > 1:
             per_rank_dags = insert_ar_ops(per_rank_dags)
             logger.info("Inserted ALL_REDUCE nodes for DP gradient sync")
+            # ZeRO-1/2/3: insert AG/RS into the task DAG before/after the appropriate FWD/BWD tasks.
+
 
         piper_metadata.per_rank_dags = per_rank_dags
         actors = piper_metadata.actors
         for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags):
             visualize_dag(per_rank_dag, output_path=f"figs/rank{pp_rank}_dag")
-            print_dag_order(per_rank_dag, label=f"rank {pp_rank}")
+            # uncomment for debugging DAG construction:
+            # print_dag_order(per_rank_dag, label=f"rank {pp_rank}")
         ray.get([
             actors[pp_rank].load_dag.remote(per_rank_dag)
             for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags)
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 3fec109..5ce322e 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -16,7 +16,6 @@
     _deserialize_graphmodule,
     create_logger,
     LOG_LEVEL,
-    piper_metadata,
     NcclOverlapDetector,
 )
 from .backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
@@ -47,7 +46,6 @@ def _create_actors(
     num_stages,
     naive_gradient_sync=False,
     profile=False,
-    mode="sequential",
     stage_to_device=None,
     pg=None,
 ):
@@ -72,7 +70,7 @@ def _create_actors(
             }
         }
         actor = PiperActor.options(
-            num_gpus=1,
+            num_gpus=0.8,
             runtime_env={**nsight_env, **master_env},
             scheduling_strategy=PlacementGroupSchedulingStrategy(
                 placement_group=pg,
@@ -88,7 +86,6 @@ def _create_actors(
             dp_rank=dp_rank,
             dp_degree=dp_degree,
             pp_degree=pp_degree,
-            mode=mode,
             stage_to_device=stage_to_device,
         )
         piper_metadata.actors[pp_rank] = actor
@@ -115,11 +112,9 @@ def __init__(
         dp_rank=0,
         dp_degree=1,
         pp_degree=1,
-        mode="sequential",
         stage_to_device=None,
     ):
         self.logger = create_logger("piper_actor", LOG_LEVEL)
-        self.mode = mode
 
         self.pp_rank = pp_rank
         self.optim_class = optim_class
@@ -151,27 +146,9 @@ def __init__(
         self.comp_stream = torch.cuda.Stream()
         self.comm_stream = torch.cuda.Stream()
         self.a2a_stream = torch.cuda.Stream()
-        self.p2p_stream = torch.cuda.Stream()
         self.p2p_send_stream = torch.cuda.Stream()
         self.p2p_recv_stream = torch.cuda.Stream()
-        self.overlapped_comp_stream = torch.cuda.Stream()
-        self.overlapped_p2p_stream = torch.cuda.Stream()
-        # Aliases for FWD and BWD compute streams.  Setting these to separate
-        # torch.cuda.Stream() instances enables compute-A2A overlap for FWD_BWD
-        # schedule cells; keeping them as aliases of comp_stream is correct but
-        # does not overlap FWD and BWD compute.
-        self.fwd_comp_stream = self.comp_stream
-        self.bwd_comp_stream = self.comp_stream
-        self.overlap_detector = NcclOverlapDetector()
-        if mode == "naive":
-            self.per_mb_streams = [torch.cuda.Stream() for _ in range(2)]
-        self.n_a2a_ops = dict()
-        self.overlap_a2a_ops = False
-        # A2A boundaries per stage: stage_id -> {boundary_bucket_id -> tensor_idx}
-        self.a2a_boundaries: dict = {}
 
-        # map stage id -> compiled fx.Graph function
-        self.forward_fns = dict()
         # map stage id -> original GraphModule (for hook registration)
         self.graph_modules = dict()
         # map stage id -> model parameters used by the fx.Graph with holes (None values) for input tensors
@@ -192,34 +169,22 @@ def __init__(
         self.out_activation = defaultdict(dict)
         # map stage id -> (shape, dtype) of the output activation tensor (for pre-FWD BWD recv)
         self.output_activation_shape: dict = {}
-        # map (src_stage, dst_stage, mb_idx, is_sender) -> tensor for p2p communication
-        self.p2p_cache = dict()
-        # map recv p2p_op -> CUDA event recorded after the recv completes on p2p_stream
-        self.p2p_events = dict()
         # accumuate loss for each microbatch
         self.loss = []
-        # map stage id -> data parallel communication operations
-        self.comm_ops = dict()
-        # map stage id -> tensor id -> comm op status
-        self.comm_op_status = defaultdict(lambda: defaultdict(int))
-        # map stage id -> tensor id -> comm op handle
-        self.comm_op_handles = defaultdict(dict)
-        # map stage id -> list of tensor ids that require communciation
-        self.comm_op_tensor_ids = dict()
 
         self.tracing = False
-        self.trace_events = dict()
+        self._pending_timing_events: list = []  # (label, start_event, stop_event)
         self.trace_data = defaultdict(list)
         self.memory_tracing_enabled = False
-        self.task_labels = dict()
 
         # DAG execution state
         self.dag = None
         self.send_buffer: dict = {}  # ((stage_id, bucket_id_or_None), mb_idx) -> tensor(s); or (None, mb_idx) for BWD
-        self.send_buffer_ready: dict = {}  # same key format as send_buffer -> threading.Event
         self.recv_buffer: dict = {}  # same key format as send_buffer -> tensor(s)
         self.recv_events: dict = {}  # same key format as send_buffer -> cuda.Event
         self.bucket_buffer: dict = {}  # (stage_id, mb_idx, bucket_id) -> (pre_detach_outs, detached_outs)
+        # A2A boundaries per stage: stage_id -> {boundary_bucket_id -> tensor_idx}
+        self.a2a_boundaries: dict = {}
         # A2A boundary state: (stage_id, mb_idx, boundary_bucket_id) -> (x_detached, x_a2a)
         self.a2a_buffer: dict = {}
         # CUDA events recorded on a2a_stream after each FWD_A2A / BWD_A2A op
@@ -240,131 +205,14 @@ def __init__(
         self.bucket_boundaries: dict = defaultdict(dict)
 
         from .piper_utils import piper_metadata
-
         piper_metadata.actor_self = self
 
-        self.profile = False
-
-        # In PiperActor.__init__, add:
-        self.pytorch_profiler = None
-        self.pytorch_profiler_output_dir = None
-        self.pytorch_profiler_iter_idx = 0
-        self.pytorch_profiler_running = False
-
-    def enable_pytorch_profiler(
-        self, 
-        enabled: bool = True, 
-        output_dir: str = "piper_profiling/chrome_traces/",
-        iter_idx: int = 0
-    ) -> None:
-        """Enable/disable PyTorch profiler on this actor."""
-        if enabled:
-            self.pytorch_profiler = torch.profiler.profile(
-                activities=[
-                    torch.profiler.ProfilerActivity.CPU,
-                    torch.profiler.ProfilerActivity.CUDA,
-                ],
-                record_shapes=True,
-                with_stack=True,
-            )
-            # Use context manager entry - this initializes the profiler
-            self.pytorch_profiler.__enter__()
-
-            # Force Kineto to initialize by doing a dummy GPU operation
-            if torch.cuda.is_available():
-                try:
-                    dummy = torch.zeros(1, device=self.device)
-                    _ = dummy + 1
-                    torch.cuda.synchronize()
-                    self.logger.debug(f"Actor {self.global_rank}: Initialized Kineto with dummy GPU op")
-                except Exception as e:
-                    self.logger.warning(f"Actor {self.global_rank}: Failed to initialize Kineto: {e}")
-
-            self.pytorch_profiler_output_dir = output_dir
-            self.pytorch_profiler_iter_idx = iter_idx
-            self.pytorch_profiler_running = True
-            self.logger.info(f"Actor {self.global_rank}: PyTorch profiler enabled, output directory: {self.pytorch_profiler_output_dir}, iteration index: {self.pytorch_profiler_iter_idx}")
-        else:
-            if hasattr(self, 'pytorch_profiler') and hasattr(self, 'pytorch_profiler_running') and self.pytorch_profiler_running:
-                try:
-                    self.pytorch_profiler.__exit__(None, None, None)
-                except RuntimeError:
-                    pass
-                self.pytorch_profiler_running = False
-            if hasattr(self, 'pytorch_profiler'):
-                del self.pytorch_profiler
-            self.logger.info(f"Actor {self.global_rank}: PyTorch profiler disabled")
-
-
-    def stop_pytorch_profiler(self) -> str:
-        """Stop PyTorch profiler and export trace. Returns path to trace file."""
-        if not hasattr(self, 'pytorch_profiler') or self.pytorch_profiler is None:
-            self.logger.warning(f"Actor {self.global_rank}: No profiler to stop")
-            return None
-        
-        if not hasattr(self, 'pytorch_profiler_running') or not self.pytorch_profiler_running:
-            self.logger.warning(f"Actor {self.global_rank}: Profiler not in running state")
-            return None
-        
-        # Stop using context manager exit - this properly stops Kineto
-        try:
-            self.pytorch_profiler.__exit__(None, None, None)
-            self.pytorch_profiler_running = False
-        except RuntimeError as e:
-            error_msg = str(e).lower()
-            if "not running" in error_msg or "not enabled" in error_msg or "can't disable" in error_msg:
-                self.logger.warning(f"Actor {self.global_rank}: Profiler was not running: {e}")
-                self.pytorch_profiler_running = False
-                if hasattr(self, 'pytorch_profiler'):
-                    del self.pytorch_profiler
-                return None
-            else:
-                raise
-        
-        # Export trace
-        if not hasattr(self, 'pytorch_profiler_output_dir') or self.pytorch_profiler_output_dir is None:
-            self.logger.warning(f"Actor {self.global_rank}: No profiler output dir set")
-            if hasattr(self, 'pytorch_profiler'):
-                del self.pytorch_profiler
-            return None
-        
-        try:
-            os.makedirs(self.pytorch_profiler_output_dir, exist_ok=True)
-            trace_path = os.path.join(
-                self.pytorch_profiler_output_dir,
-                f"actor_{self.global_rank}_iter_{self.pytorch_profiler_iter_idx}.pt.trace.json"
-            )
-            self.pytorch_profiler.export_chrome_trace(trace_path)
-            
-            self.logger.info(f"Actor {self.global_rank}: PyTorch profiler trace saved to {trace_path}")
-        except Exception as e:
-            self.logger.error(f"Actor {self.global_rank}: Failed to export profiler trace: {e}")
-            trace_path = None
-        finally:
-            # Cleanup
-            if hasattr(self, 'pytorch_profiler'):
-                del self.pytorch_profiler
-        
-        return trace_path
-
-    def _label_task(self, label: str):
-        task_id = ray.get_runtime_context().get_task_id()
-        if task_id:
-            self.task_labels[task_id] = label
-
-    def get_task_labels(self) -> dict:
-        return self.task_labels
-
-    def reset_p2p_states(self):
-        self.p2p_cache = dict()
-        self.p2p_events = dict()
-        self.p2p_cursor = 0
-
     def get_trace_data(self) -> dict:
         return self.global_rank, self.trace_data
 
     def clear_trace_data(self) -> None:
         self.trace_data.clear()
+        self._pending_timing_events.clear()
 
     def set_tracing(self, enabled: bool) -> None:
         self.tracing = enabled
@@ -384,13 +232,6 @@ def stop_mem_tracing(self) -> None:
         )
         torch.cuda.memory._record_memory_history(enabled=None)
     
-    def enable_profiling(self, enabled: bool = True) -> None:
-        """Enable/disable per-task profiling (Chrome traces)."""
-        self.profile = enabled
-        self.logger.info(
-            f"Actor {self.global_rank}: Profiling {'enabled' if enabled else 'disabled'}"
-        )
-
     def enable_memory_tracing(self, enabled: bool = True) -> None:
         """Enable/disable memory tracing."""
         self.memory_tracing_enabled = enabled
@@ -438,21 +279,29 @@ def load_labels(self, labels):
 
     def _start_timing(self, stream, label):
         if self.tracing:
-            if label not in self.trace_events:
-                self.trace_events[label] = (
-                    torch.cuda.Event(enable_timing=True),
-                    torch.cuda.Event(enable_timing=True),
-                )
-            start, _ = self.trace_events[label]
+            start = torch.cuda.Event(enable_timing=True)
+            stop = torch.cuda.Event(enable_timing=True)
             start.record(stream)
+            self._pending_timing_events.append((label, start, stop))
 
     def _stop_timing(self, stream, label):
         if self.tracing:
-            if label in self.trace_events:
-                start, stop = self.trace_events[label]
-                stop.record(stream)
-                stop.synchronize()
-                self.trace_data[label].append(start.elapsed_time(stop))
+            for i in range(len(self._pending_timing_events) - 1, -1, -1):
+                lbl, _start, stop = self._pending_timing_events[i]
+                if lbl == label:
+                    stop.record(stream)
+                    break
+
+    def flush_timing_events(self) -> None:
+        """Synchronize all pending timing events and accumulate elapsed times.
+        Call this once per iteration after GPU work completes, instead of
+        synchronizing in _stop_timing which would serialize GPU execution."""
+        if not self.tracing or not self._pending_timing_events:
+            return
+        torch.cuda.synchronize()
+        for label, start, stop in self._pending_timing_events:
+            self.trace_data[label].append(start.elapsed_time(stop))
+        self._pending_timing_events.clear()
 
     def get_node_ip_and_free_port(self):
         import socket
@@ -505,7 +354,6 @@ def _join_dp_process_group(self):
 
     def _join_pp_process_group(self):
         num_pp_groups = self.world_size // self.pp_degree
-        my_pp_group_id = self.global_rank // self.pp_degree
 
         # One communicator per direction per rank pair. The global p2p schedule
         # (built in piper_exec) ensures both ranks issue ops in the same FIFO order.
@@ -579,7 +427,7 @@ def replace_in_module(module, module_name=""):
             for name, param in module.named_parameters(recurse=False):
                 if is_meta_or_fake(param):
                     full_name = f"{module_name}.{name}" if module_name else name
-                    logger.info(f"Replacing parameter '{full_name}' from meta/fake to {device} (shape={param.shape}, dtype={param.dtype}, requires_grad={param.requires_grad})")
+                    logger.debug(f"Replacing parameter '{full_name}' from meta/fake to {device} (shape={param.shape}, dtype={param.dtype}, requires_grad={param.requires_grad})")
                     new_param = torch.empty_like(param, device=device)
                     new_param.requires_grad_(param.requires_grad)
                     setattr(module, name, torch.nn.Parameter(new_param, requires_grad=param.requires_grad))
@@ -590,7 +438,7 @@ def replace_in_module(module, module_name=""):
             for name, buffer in module.named_buffers(recurse=False):
                 if is_meta_or_fake(buffer):
                     full_name = f"{module_name}.{name}" if module_name else name
-                    logger.info(f"Replacing buffer '{full_name}' from meta/fake to {device} (shape={buffer.shape}, dtype={buffer.dtype})")
+                    logger.debug(f"Replacing buffer '{full_name}' from meta/fake to {device} (shape={buffer.shape}, dtype={buffer.dtype})")
                     new_buffer = torch.empty_like(buffer, device=device)
                     setattr(module, name, new_buffer)
                     buffers_count += 1
@@ -605,7 +453,7 @@ def replace_in_module(module, module_name=""):
                             attr_value = getattr(module, node.target)
                             if is_meta_or_fake(attr_value):
                                 full_name = f"{module_name}.{node.target}" if module_name else node.target
-                                logger.info(f"Replacing get_attr '{full_name}' from meta/fake to {device} (shape={attr_value.shape}, dtype={attr_value.dtype})")
+                                logger.debug(f"Replacing get_attr '{full_name}' from meta/fake to {device} (shape={attr_value.shape}, dtype={attr_value.dtype})")
                                 new_tensor = torch.empty_like(attr_value, device=device)
                                 setattr(module, node.target, new_tensor)
                                 get_attr_count += 1
@@ -618,7 +466,7 @@ def replace_in_module(module, module_name=""):
                         for key, value in node.meta.items():
                             if is_meta_or_fake(value):
                                 full_name = f"{module_name}.{node.name}.meta['{key}']" if module_name else f"{node.name}.meta['{key}']"
-                                logger.info(f"Replacing node.meta['{key}'] for node '{node.name}' from meta/fake to {device} (shape={value.shape}, dtype={value.dtype})")
+                                logger.debug(f"Replacing node.meta['{key}'] for node '{node.name}' from meta/fake to {device} (shape={value.shape}, dtype={value.dtype})")
                                 node.meta[key] = torch.empty_like(value, device=device)
                                 node_meta_count += 1
                                 replaced_count += 1
@@ -628,7 +476,7 @@ def replace_in_module(module, module_name=""):
                     for key, value in module._constants.items():
                         if is_meta_or_fake(value):
                             full_name = f"{module_name}._constants['{key}']" if module_name else f"_constants['{key}']"
-                            logger.info(f"Replacing constant '{full_name}' from meta/fake to {device} (shape={value.shape}, dtype={value.dtype})")
+                            logger.debug(f"Replacing constant '{full_name}' from meta/fake to {device} (shape={value.shape}, dtype={value.dtype})")
                             module._constants[key] = torch.empty_like(value, device=device)
                             constants_count += 1
                             replaced_count += 1
@@ -640,14 +488,14 @@ def replace_in_module(module, module_name=""):
                     for arg in node.args:
                         if is_meta_or_fake(arg):
                             full_name = f"{module_name}.{node.name}" if module_name else node.name
-                            logger.info(f"Replacing meta/fake tensor in node '{full_name}' args (shape={arg.shape}, dtype={arg.dtype})")
+                            logger.debug(f"Replacing meta/fake tensor in node '{full_name}' args (shape={arg.shape}, dtype={arg.dtype})")
                             new_args.append(torch.empty_like(arg, device=device))
                             node_args_count += 1
                             replaced_count += 1
                         elif isinstance(arg, torch.device) and arg.type == 'meta':
                             # replace meta device objects with the actual device
                             full_name = f"{module_name}.{node.name}" if module_name else node.name
-                            logger.info(f"Replacing meta device object in node '{full_name}' args with {device}")
+                            logger.debug(f"Replacing meta device object in node '{full_name}' args with {device}")
                             new_args.append(torch.device(device))
                             node_args_count += 1
                             replaced_count += 1
@@ -662,14 +510,14 @@ def replace_in_module(module, module_name=""):
                     for key, value in node.kwargs.items():
                         if is_meta_or_fake(value):
                             full_name = f"{module_name}.{node.name}" if module_name else node.name
-                            logger.info(f"Replacing meta/fake tensor in node '{full_name}' kwargs['{key}'] (shape={value.shape}, dtype={value.dtype})")
+                            logger.debug(f"Replacing meta/fake tensor in node '{full_name}' kwargs['{key}'] (shape={value.shape}, dtype={value.dtype})")
                             new_kwargs[key] = torch.empty_like(value, device=device)
                             node_args_count += 1
                             replaced_count += 1
                         elif isinstance(value, torch.device) and value.type == 'meta':
                             # replace meta device objects with the actual device
                             full_name = f"{module_name}.{node.name}" if module_name else node.name
-                            logger.info(f"Replacing meta device object in node '{full_name}' kwargs['{key}'] with {device}")
+                            logger.debug(f"Replacing meta device object in node '{full_name}' kwargs['{key}'] with {device}")
                             new_kwargs[key] = torch.device(device)
                             node_args_count += 1
                             replaced_count += 1
@@ -691,27 +539,31 @@ def replace_in_module(module, module_name=""):
         replace_in_module(gm)
         
         if replaced_count > 0:
-            logger.info(
+            logger.debug(
                 f"_replace_meta_constants: Replaced {replaced_count} meta device tensors "
                 f"(params: {params_count}, buffers: {buffers_count}, get_attr: {get_attr_count}, "
                 f"constants: {constants_count}, attrs: {attrs_count}, node_args: {node_args_count}, "
                 f"node_meta: {node_meta_count}, checked {submodule_count} submodules)"
             )
         else:
-            logger.warning(f"_replace_meta_constants: No meta device tensors found to replace! (checked {submodule_count} submodules)")
+            logger.debug(f"_replace_meta_constants: No meta device tensors found to replace! (checked {submodule_count} submodules)")
         
         return gm
 
     def _load_stage_kernels(self):
         try:
-            import torchtitan.models.common.moe.kernels
-            import torchtitan.models.common.moe.utils
+            try:
+                import torchtitan.models.common.moe.kernels
+                import torchtitan.models.common.moe.utils
+                from torchtitan.models.common.moe.kernels import generate_permute_indices
+                from torchtitan.models.common.moe.kernels import fill_indices_wrapper
+            except ModuleNotFoundError:
+                import torchtitan.models.moe.kernels
+                import torchtitan.models.moe.utils
+                from torchtitan.models.moe.kernels import generate_permute_indices
+                from torchtitan.models.moe.kernels import fill_indices_wrapper
             import torch._higher_order_ops.triton_kernel_wrap
-
-            # Trigger module initialization by accessing a function
-            from torchtitan.models.common.moe.kernels import generate_permute_indices
-            from torchtitan.models.common.moe.kernels import fill_indices_wrapper
-            self.logger.info(f"Actor {self.global_rank} imported and initialized Triton kernel modules")
+            self.logger.debug(f"Actor {self.global_rank} imported and initialized Triton kernel modules")
         except ImportError as e:
             self.logger.warning(
                 f"Actor {self.global_rank} failed to import Triton kernel modules: {e}. "
@@ -720,11 +572,14 @@ def _load_stage_kernels(self):
 
         # HACK: manually register Triton kernel in the side table
         # the graph expects kernel_idx=0, so we need to register it at that index
-        self.logger.info(f"Manually registering Triton kernel at index 0 on actor {self.global_rank}...")
+        self.logger.debug(f"Manually registering Triton kernel at index 0 on actor {self.global_rank}...")
         try:
             # Import the kernel wrapper
-            from torchtitan.models.common.moe.kernels import fill_indices_wrapper
-            
+            try:
+                from torchtitan.models.common.moe.kernels import fill_indices_wrapper
+            except ModuleNotFoundError:
+                from torchtitan.models.moe.kernels import fill_indices_wrapper
+
             # create a dummy function that uses the Triton kernel
             # This needs to be compiled with torch.compile to register the kernel
             def dummy_kernel_user(tokens, starts, offsets):
@@ -766,7 +621,7 @@ def dummy_kernel_user(tokens, starts, offsets):
                         max_const_idx, const_args = max(registered_constants, key=lambda x: x[0])
                         for idx in range(20):
                             kernel_side_table.constant_args[idx] = const_args
-                        self.logger.info(
+                        self.logger.debug(
                             f"Actor {self.global_rank} manually registered kernel at index 0 "
                             f"(copied from index {max_idx}, total kernels: {len(registered_kernels)}) "
                             f"and constant_args at index 1 (copied from index {max_const_idx}, total constants: {len(registered_constants)})"
@@ -965,6 +820,33 @@ def _load_stage(
                     t = torch.zeros(meta.shape, dtype=meta.dtype, device=self.device)
                     t.requires_grad_(bool(getattr(meta, "requires_grad", True)))
                     probe_args[i] = t
+            # Ensure any Triton kernel_idx / constant_args_idx values baked into
+            # this graph are registered in the side table.  During compilation on
+            # the coordinator the side table may have accumulated many entries
+            # before our kernel was registered, giving it a high index (e.g. 20).
+            # The actor's dummy compilation registers the same data at a low index;
+            # we just alias the graph's expected index to that existing entry.
+            try:
+                from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
+                for _node in last_gm.graph.nodes:
+                    if _node.op != "call_function":
+                        continue
+                    if "triton_kernel_wrapper" not in str(getattr(_node, "target", "")):
+                        continue
+                    _ca_idx = _node.kwargs.get("constant_args_idx")
+                    _k_idx = _node.kwargs.get("kernel_idx")
+                    if _ca_idx is not None and _ca_idx not in kernel_side_table.constant_args:
+                        if kernel_side_table.constant_args:
+                            kernel_side_table.constant_args[_ca_idx] = next(
+                                iter(kernel_side_table.constant_args.values())
+                            )
+                    if _k_idx is not None and _k_idx not in kernel_side_table.id_to_kernel:
+                        if kernel_side_table.id_to_kernel:
+                            kernel_side_table.id_to_kernel[_k_idx] = next(
+                                iter(kernel_side_table.id_to_kernel.values())
+                            )
+            except Exception:
+                pass  # best-effort; probe below will surface the real error if needed
             with torch.no_grad():
                 probe_out = last_gm.forward(*probe_args)
             for out in (probe_out if isinstance(probe_out, (tuple, list)) else [probe_out]):
@@ -979,74 +861,6 @@ def _load_stage(
                 f"Could not determine output activation shape for stage {stage_id}"
             )
 
-
-    def _synchronize_gradients(self):
-        self.logger.info(f"Actor {self.global_rank} synchronizing gradients")
-        # Iterate over all stages on this actor and synchronize their parameters
-        for stage_id, parameters in self.forward_args.items():
-            for param in parameters:
-                if param is not None and param.grad is not None:
-                    _ov_token = self.overlap_detector.before_kernel(self.comm_stream, "grad_allreduce_naive", "comm_stream")
-                    with torch.cuda.stream(self.comm_stream):
-                        dist.all_reduce(
-                            param.grad, op=dist.ReduceOp.AVG, group=self.dp_group
-                        )
-                    self.overlap_detector.after_kernel(self.comm_stream, _ov_token)
-
-    def _update(self, *deps):
-        ret = self._update_impl(*deps)
-        return ret
-
-    def _update_impl(self, *deps):
-        self._label_task("update")
-
-        if self.ar_events:
-            # DAG execution path: wait for in-flight all-reduces then step optimizers.
-            self._start_timing(self.comp_stream, "optim_step")
-            for ar_evt in self.ar_events.values():
-                self.comp_stream.wait_event(ar_evt)
-            for s_id, optim_list in self.bucket_optims.items():
-                for optim in optim_list:
-                    if optim is not None:
-                        optim.step()
-                        optim.zero_grad(set_to_none=False)
-            self._stop_timing(self.comp_stream, "optim_step")
-        elif self.dp_degree > 1:
-            # Legacy non-DAG DDP path (no ALL_REDUCE nodes in the DAG).
-            self._start_timing(self.comm_stream, "backward_sync")
-            if self.naive_gradient_sync:
-                self._synchronize_gradients()
-            else:
-                self._wait_for_comm_ops()
-            self._stop_timing(self.comm_stream, "backward_sync")
-
-            self._start_timing(self.comp_stream, "optim_step")
-            for s_id, optim_list in self.bucket_optims.items():
-                for optim in optim_list:
-                    if optim is not None:
-                        optim.step()
-                        optim.zero_grad(set_to_none=False)
-            self._stop_timing(self.comp_stream, "optim_step")
-        else:
-            # Single-device path.
-            self._start_timing(self.comp_stream, "optim_step")
-            for s_id, optim_list in self.bucket_optims.items():
-                for optim in optim_list:
-                    if optim is not None:
-                        optim.step()
-                        optim.zero_grad(set_to_none=False)
-            self._stop_timing(self.comp_stream, "optim_step")
-
-        torch.cuda.synchronize()
-
-        losses = self.loss
-        self.loss.clear()
-
-        self.reset_p2p_states()
-        self.overlap_detector.reset_iteration()
-
-        return losses
-
     # -----------------------------------------------------------------------
     # DAG-based execution
     # -----------------------------------------------------------------------
@@ -1100,20 +914,7 @@ def run_dag(self, loss_fn=None):
                     numel = p.numel()
                     p.grad = flat_grads[offset:offset + numel].view(p.shape)
                     offset += numel
-        # Pre-create a threading.Event for every expected send key so that
-        # _exec_send can wait on it if the compute function hasn't written the
-        # buffer yet (guards against out-of-order CPU dispatch).
-        self.send_buffer_ready = {}
-        for _node in dag.nodes:
-            if _node.task.type == TaskType.SEND:
-                _compute = _node.data_preds[0]
-                _b = _compute.task.batches[0]
-                _sid, _mid, _ct = _b.stage_id, _b.mb_idx, _compute.task.type
-                if _ct == TaskType.FWD:
-                    _k = (_sid, _compute.task.bucket_id)
-                else:
-                    _k = None
-                self.send_buffer_ready[(_k, _mid)] = threading.Event()
+
         self.recv_buffer = {}
         self.recv_events = {}
         comp_events: dict = {}  # (stage_id, mb_idx, task_type, bucket_id) -> cuda.Event
@@ -1155,50 +956,6 @@ def run_dag(self, loss_fn=None):
                         if compute_node.task.type == TaskType.FWD else None
                     self._exec_recv(stage_id, mb_idx, recv_key, node.peer_pp_rank)
 
-                case TaskType.FWD:
-                    bucket_id = task.bucket_id
-                    recv_key = ((stage_id, bucket_id), mb_idx)
-                    if recv_key in self.recv_events:
-                        self.fwd_comp_stream.wait_event(self.recv_events.pop(recv_key))
-                    torch.cuda.nvtx.range_push(f"forward_s{stage_id}_b{bucket_id}_mb{mb_idx}")
-                    self._forward_dag(stage_id, bucket_id, mb_idx)
-                    torch.cuda.nvtx.range_pop()
-                    evt = torch.cuda.Event()
-                    evt.record(self.fwd_comp_stream)
-                    comp_events[(stage_id, mb_idx, TaskType.FWD, bucket_id)] = evt
-
-                case TaskType.BWD:
-                    bucket_id = task.bucket_id
-                    recv_key = (None, mb_idx)
-                    if recv_key in self.recv_events:
-                        self.bwd_comp_stream.wait_event(self.recv_events.pop(recv_key))
-                    torch.cuda.nvtx.range_push(f"backward_s{stage_id}_b{bucket_id}_mb{mb_idx}")
-                    self._backward_dag(stage_id, bucket_id, mb_idx, loss_fn=loss_fn)
-                    torch.cuda.nvtx.range_pop()
-                    evt = torch.cuda.Event()
-                    evt.record(self.bwd_comp_stream)
-                    comp_events[(stage_id, mb_idx, TaskType.BWD, bucket_id)] = evt
-
-                case TaskType.BWD_I:
-                    bucket_id = task.bucket_id
-                    recv_key = (None, mb_idx)
-                    if recv_key in self.recv_events:
-                        self.bwd_comp_stream.wait_event(self.recv_events.pop(recv_key))
-                    torch.cuda.nvtx.range_push(f"backward_input_stage_{stage_id}_b{bucket_id}_mb_{mb_idx}")
-                    if len(self.bucket_fwd_fns.get(stage_id, [])) > 1:
-                        self._backward_input_dag_bucket(stage_id, bucket_id, mb_idx, loss_fn=loss_fn)
-                    else:
-                        self._backward_input_dag(stage_id, mb_idx, loss_fn=loss_fn)
-                    torch.cuda.nvtx.range_pop()
-                    evt = torch.cuda.Event()
-                    evt.record(self.bwd_comp_stream)
-                    comp_events[(stage_id, mb_idx, TaskType.BWD_I, bucket_id)] = evt
-
-                case TaskType.BWD_W:
-                    torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
-                    self._backward_weight_dag(stage_id, mb_idx, loss_fn=loss_fn)
-                    torch.cuda.nvtx.range_pop()
-
                 case TaskType.FWD_A2A:
                     bucket_id = task.bucket_id
                     torch.cuda.nvtx.range_push(f"fwd_a2a_s{stage_id}_b{bucket_id}_mb{mb_idx}")
@@ -1210,7 +967,7 @@ def run_dag(self, loss_fn=None):
                     a2a_evt = torch.cuda.Event()
                     a2a_evt.record(self.a2a_stream)
                     self.a2a_events[(stage_id, mb_idx, TaskType.FWD_A2A, bucket_id)] = a2a_evt
-                    self.fwd_comp_stream.wait_event(a2a_evt)
+                    self.comp_stream.wait_event(a2a_evt)
 
                 case TaskType.BWD_A2A:
                     bucket_id = task.bucket_id
@@ -1225,7 +982,7 @@ def run_dag(self, loss_fn=None):
                     a2a_evt = torch.cuda.Event()
                     a2a_evt.record(self.a2a_stream)
                     self.a2a_events[(stage_id, mb_idx, TaskType.BWD_A2A, bucket_id)] = a2a_evt
-                    self.bwd_comp_stream.wait_event(a2a_evt)
+                    self.comp_stream.wait_event(a2a_evt)
 
                 case TaskType.ALL_REDUCE:
                     bucket_id = task.bucket_id
@@ -1239,10 +996,59 @@ def run_dag(self, loss_fn=None):
                     torch.cuda.nvtx.range_push(f"all_reduce_s{stage_id}_b{bucket_id}")
                     self._exec_all_reduce(stage_id, bucket_id, comp_events[bwd_key])
                     torch.cuda.nvtx.range_pop()
+            
+                case TaskType.FWD:
+                    bucket_id = task.bucket_id
+                    recv_key = ((stage_id, bucket_id), mb_idx)
+                    if recv_key in self.recv_events:
+                        self.comp_stream.wait_event(self.recv_events.pop(recv_key))
+                    torch.cuda.nvtx.range_push(f"forward_s{stage_id}_b{bucket_id}_mb{mb_idx}")
+                    self._start_timing(self.comp_stream, f"forward")
+                    self._forward_dag(stage_id, bucket_id, mb_idx)
+                    self._stop_timing(self.comp_stream, f"forward")
+                    torch.cuda.nvtx.range_pop()
+                    evt = torch.cuda.Event()
+                    evt.record(self.comp_stream)
+                    comp_events[(stage_id, mb_idx, TaskType.FWD, bucket_id)] = evt
+
+                case TaskType.BWD:
+                    bucket_id = task.bucket_id
+                    recv_key = (None, mb_idx)
+                    if recv_key in self.recv_events:
+                        self.comp_stream.wait_event(self.recv_events.pop(recv_key))
+                    torch.cuda.nvtx.range_push(f"backward_s{stage_id}_b{bucket_id}_mb{mb_idx}")
+                    self._start_timing(self.comp_stream, f"backward")
+                    self._backward_dag(stage_id, bucket_id, mb_idx, loss_fn=loss_fn)
+                    self._stop_timing(self.comp_stream, f"backward")
+                    torch.cuda.nvtx.range_pop()
+                    evt = torch.cuda.Event()
+                    evt.record(self.comp_stream)
+                    comp_events[(stage_id, mb_idx, TaskType.BWD, bucket_id)] = evt
+
+                case TaskType.BWD_I:
+                    bucket_id = task.bucket_id
+                    recv_key = (None, mb_idx)
+                    if recv_key in self.recv_events:
+                        self.comp_stream.wait_event(self.recv_events.pop(recv_key))
+                    torch.cuda.nvtx.range_push(f"backward_input_stage_{stage_id}_b{bucket_id}_mb_{mb_idx}")
+                    self._start_timing(self.comp_stream, f"backward_input")
+                    self._backward_input_dag(stage_id, bucket_id, mb_idx, loss_fn=loss_fn)
+                    self._stop_timing(self.comp_stream, f"backward_input")
+                    torch.cuda.nvtx.range_pop()
+                    evt = torch.cuda.Event()
+                    evt.record(self.comp_stream)
+                    comp_events[(stage_id, mb_idx, TaskType.BWD_I, bucket_id)] = evt
+
+                case TaskType.BWD_W:
+                    torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
+                    self._start_timing(self.comp_stream, f"backward_weight")
+                    self._backward_weight_dag(stage_id, mb_idx, loss_fn=loss_fn)
+                    self._stop_timing(self.comp_stream, f"backward_weight")
+                    torch.cuda.nvtx.range_pop()
 
                 case TaskType.UPD:
                     torch.cuda.nvtx.range_push("update")
-                    self._update_impl()
+                    self._update()
                     torch.cuda.nvtx.range_pop()
 
             # Decrement in-degree for all successors; execute in topological order.
@@ -1282,12 +1088,14 @@ def _exec_send(
 
         with torch.cuda.stream(self.p2p_send_stream):
             tensors = buf if isinstance(buf, (list, tuple)) else [buf]
+            self._start_timing(self.p2p_send_stream, "p2p_send")
             for tensor in tensors:
                 dist.send(
                     tensor,
                     dst=global_dst_rank,
                     group=self.pp_groups[(self.global_rank, global_dst_rank)],
                 )
+            self._stop_timing(self.p2p_send_stream, "p2p_send")
 
     def _exec_recv(
         self, stage_id: int, mb_idx: int, key, peer_pp_rank: int
@@ -1330,12 +1138,14 @@ def _exec_recv(
 
         with torch.cuda.stream(self.p2p_recv_stream):
             tensors = buf if isinstance(buf, list) else [buf]
+            self._start_timing(self.p2p_recv_stream, "p2p_recv")
             for tensor in tensors:
                 dist.recv(
                     tensor,
                     src=global_src_rank,
                     group=self.pp_groups[(global_src_rank, self.global_rank)],
                 )
+            self._stop_timing(self.p2p_recv_stream, "p2p_recv")
 
         recv_event = torch.cuda.Event()
         recv_event.record(self.p2p_recv_stream)
@@ -1477,7 +1287,9 @@ def _exec_fwd_a2a(self, stage_id: int, boundary_bucket_id: int, mb_idx: int) ->
         x_detached = detached_outs[tensor_idx]
         output_buf = torch.empty_like(x_detached)
         with torch.cuda.stream(self.a2a_stream):
+            self._start_timing(self.a2a_stream, "fwd_a2a")
             dist.all_to_all_single(output_buf, x_detached, group=self.ep_group)
+            self._stop_timing(self.a2a_stream, "fwd_a2a")
         x_a2a = output_buf.requires_grad_(True)
 
         # Store for BWD_A2A to reverse
@@ -1513,115 +1325,25 @@ def _exec_bwd_a2a(self, stage_id: int, boundary_bucket_id: int, mb_idx: int) ->
             grad_a2a_out = grad_a2a_out.contiguous()
         reversed_grad = torch.empty_like(grad_a2a_out)
         with torch.cuda.stream(self.a2a_stream):
+            self._start_timing(self.a2a_stream, "bwd_a2a")
             dist.all_to_all_single(reversed_grad, grad_a2a_out, group=self.ep_group)
+            self._stop_timing(self.a2a_stream, "bwd_a2a")
         x_detached.grad = reversed_grad
 
         # Restore x_detached in bucket_buffer so _backward_dag reads x_detached.grad
         _, detached_outs = self.bucket_buffer[(stage_id, mb_idx, boundary_bucket_id)]
         detached_outs[tensor_idx] = x_detached
 
-    def _backward_input_dag(self, stage_id: int, mb_idx: int, *, loss_fn=None) -> None:
+    def _backward_input_dag(self, stage_id: int, bucket_id: int, mb_idx: int, *, loss_fn=None) -> None:
         """Backward-input pass for DAG execution (ZeroBubble split backward).
 
-        Reads upstream gradient from recv_buffer and writes input gradient to
-        send_buffer.  Does NOT call comp_stream.synchronize().
-        """
-        comp_stream = self.comp_stream
-        _out = self.out_activation[stage_id][mb_idx]
-        out_activation = _out[0] if isinstance(_out, list) else _out
-
-        activation_or_loss = None
-        upstream_grad = None
-        if stage_id < self.num_stages - 1:
-            recv_key = (None, mb_idx)
-            upstream_grad = self.recv_buffer.pop(recv_key)
-            activation_or_loss = out_activation
-        else:
-            assert loss_fn is not None
-            labels = self.labels
-            with torch.cuda.stream(comp_stream):
-                loss = loss_fn(out_activation, labels)
-            activation_or_loss = loss
-            upstream_grad = torch.ones_like(loss)
-
-        if stage_id == 0:
-            self.upstream_grad_cache[stage_id][mb_idx] = upstream_grad
-            return
-
-        _inp = self.inp_activation[stage_id][mb_idx]
-        stage_input = _inp[0] if isinstance(_inp, list) else _inp
-        stage_params = [self.forward_args[stage_id][i] for i in self.param_idxs[stage_id]]
-
-        output_nodes = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [activation_or_loss]) if n is not None]
-        input_nodes  = [n for n in (_get_grad_fn_or_grad_acc(t) for t in [stage_input]) if n is not None]
-        param_nodes  = [n for n in (_get_grad_fn_or_grad_acc(p) for p in stage_params) if n is not None]
-
-        reverse_edges = construct_reverse_graph(output_nodes)
-        param_groups = get_param_groups(input_nodes, param_nodes, reverse_edges)
-
-        handles = []
-        for pg in param_groups:
-            intermediates = pg["intermediates"]
-            if not intermediates:
-                continue
-            pg["grads"] = [None] * len(intermediates)
-            for i, intermediate_node in enumerate(intermediates):
-                def make_hook(group: Dict[str, Any], idx: int):
-                    def hook(grad_inputs):
-                        group["grads"][idx] = grad_inputs
-                    return hook
-                handles.append(intermediate_node.register_prehook(make_hook(pg, i)))
-
-        with torch.cuda.stream(comp_stream):
-            gx = torch.autograd.grad(
-                outputs=activation_or_loss,
-                inputs=stage_input,
-                grad_outputs=upstream_grad,
-                retain_graph=True,
-                allow_unused=True,
-            )
-
-        gx = gx[0]
-        if gx is not None and stage_input.requires_grad:
-            if stage_input.grad is None:
-                stage_input.grad = gx
-            else:
-                stage_input.grad.add_(gx)
-
-        if not isinstance(activation_or_loss, list):
-            activation_or_loss = [activation_or_loss]
-        for t in activation_or_loss:
-            t.detach_()
-
-        for h in handles:
-            h.remove()
-
-        # Write input gradient to send_buffer for the upstream stage to recv
-        # stage_input is the unwrapped tensor set above; read .grad before deleting.
-        output_grad = stage_input.grad
-        if output_grad is not None:
-            self.send_buffer[(None, mb_idx)] = output_grad
-            # Co-located prev stage: no BWD_I SEND/RECV DAG nodes for same-rank edges.
-            prev_stage = stage_id - 1
-            if (prev_stage in self.stage_to_device and
-                    self.stage_to_device[prev_stage] == self.stage_to_device[stage_id]):
-                self.recv_buffer[(None, mb_idx)] = output_grad
-        else:
-            self.inp_activation[stage_id][mb_idx] = None
-
-        self.bw_param_groups[stage_id][mb_idx] = param_groups
-        del activation_or_loss
-        del stage_input
-
-    def _backward_input_dag_bucket(self, stage_id: int, bucket_id: int, mb_idx: int, *, loss_fn=None) -> None:
-        """Per-bucket backward-input pass for ZeroBubble with bucketed / A2A stages.
-
-        Mirrors _backward_dag but uses autograd.grad(..., retain_graph=True) instead of
-        backward(), so weight gradients are NOT accumulated during BWD_I (they are
-        deferred to BWD_W via bw_param_groups).
+        Unified for both bucketed (A2A) and non-bucketed stages.  Non-bucketed
+        stages use bucket_id=0 with n_buckets=1, so the first-bucket and
+        last-bucket conditions both fire in the same call — identical to the
+        single-pass behaviour of the old non-bucketed path.
 
         Must be called in reverse bucket order: n_buckets-1, …, 1, 0.
-        BWD_A2A must run between consecutive BWD_I calls at A2A boundaries.
+        BWD_A2A must run between consecutive calls at A2A boundaries.
         """
         comp_stream = self.comp_stream
         n_buckets = len(self.bucket_fwd_fns[stage_id])
@@ -1670,7 +1392,7 @@ def _backward_input_dag_bucket(self, stage_id: int, bucket_id: int, mb_idx: int,
             else:
                 activation_inputs = [d for d in detached_outs_prev if d.requires_grad]
         else:
-            # Bucket 0: grad to stage_input
+            # Bucket 0: grad w.r.t. stage input
             _inp = self.inp_activation[stage_id][mb_idx]
             stage_input = _inp[0] if isinstance(_inp, list) else _inp
             activation_inputs = [stage_input] if (stage_input is not None and stage_input.requires_grad) else []
@@ -1756,8 +1478,9 @@ def _exec_all_reduce(self, stage_id: int, bucket_id: int, bwd_event: torch.cuda.
         with torch.cuda.stream(self.comm_stream):
             self.comm_stream.wait_event(bwd_event)
             flat_grads = self.bucket_flat_grads.get(lookup_key)
-            if flat_grads is not None:
-                dist.all_reduce(flat_grads, group=self.dp_group)
+            self._start_timing(self.comm_stream, f"all_reduce")
+            dist.all_reduce(flat_grads, group=self.dp_group)
+            self._stop_timing(self.comm_stream, f"all_reduce")
             evt = torch.cuda.Event()
             evt.record(self.comm_stream)
         self.ar_events[(stage_id, bucket_id)] = evt
@@ -1867,3 +1590,35 @@ def _backward_weight_dag(self, stage_id: int, mb_idx: int, *, loss_fn=None) -> N
         self.upstream_grad_cache[stage_id][mb_idx] = None
         self.bw_param_groups[stage_id][mb_idx] = None
         self.out_activation[stage_id][mb_idx] = None
+
+    def _update(self, *deps):
+        if self.ar_events:
+            # DAG execution path: wait for in-flight all-reduces then step optimizers.
+            self._start_timing(self.comp_stream, "backward_sync")
+            for ar_evt in self.ar_events.values():
+                self.comp_stream.wait_event(ar_evt)
+            self._stop_timing(self.comp_stream, "backward_sync")
+            self._start_timing(self.comp_stream, "optim_step")
+            for s_id, optim_list in self.bucket_optims.items():
+                for optim in optim_list:
+                    if optim is not None:
+                        optim.step()
+                        optim.zero_grad(set_to_none=False)
+            self._stop_timing(self.comp_stream, "optim_step")
+        else:
+            # Single-device path.
+            self._start_timing(self.comp_stream, "optim_step")
+            for s_id, optim_list in self.bucket_optims.items():
+                for optim in optim_list:
+                    if optim is not None:
+                        optim.step()
+                        optim.zero_grad(set_to_none=False)
+            self._stop_timing(self.comp_stream, "optim_step")
+
+        torch.cuda.synchronize()
+
+        losses = self.loss
+        self.loss.clear()
+
+        return losses
+    
\ No newline at end of file
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 230b6b0..92d2a00 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -42,7 +42,6 @@ def piper_setup(
     naive_gradient_sync=False,
     activation_checkpointing=False,
     bucketing=False,
-    mode="sequential",
     pg=None,
     nsight=False,
 ):
@@ -73,7 +72,7 @@ def piper_setup(
 
     _create_actors(
         num_devices, optim_fn, num_mbs, num_stages,
-        naive_gradient_sync, profile=nsight, mode=mode, stage_to_device=stage_to_device, pg=pg,
+        naive_gradient_sync, profile=nsight, stage_to_device=stage_to_device, pg=pg,
     )
 
     # All dp_ranks must agree on a single master_addr: the IP of the actor with
@@ -110,9 +109,9 @@ def piper_setup(
 
     # Build the model directly on meta device
     with torch.device("meta"):
-        model = model_class(*model_args, **model_kwargs)
-    if model_dtype is not None:
-        model = model.to(model_dtype)
+        model = model_class(*model_args, **model_kwargs).to(model_dtype)
+        # if model_dtype is not None:
+        #     model = model.to(model_dtype)
 
     num_params = sum(p.numel() for p in model.parameters())
 
@@ -124,14 +123,15 @@ def piper_setup(
         raise ValueError(f"Unsupported model dtype: {model_dtype}")
     print(f"Model size: {num_params/(1e6):.0f} M parameters ({param_size_mb:.2f} GB), dtype: {model_dtype}")
     
-    compiled = torch.compile(model, backend=piper)
+
+    compiled = torch.compile(model, backend=piper, fullgraph=True)
 
     dp_rank = int(os.environ["PIPER_DP_RANK"])
     logger.info(f"DP rank {dp_rank+1} compiling (meta)...")
 
     # Create meta tensors from our example_inputs to pass to the compiled graph
+    
     meta_inputs = [x.to(device="meta") for x in example_inputs]
-
     _ = compiled(*meta_inputs)
 
     logger.info(f"DP rank {dp_rank+1} stage graphs loaded onto actors.")
diff --git a/src/piper_coordinator.py b/src/piper_coordinator.py
index 0619e09..0563845 100644
--- a/src/piper_coordinator.py
+++ b/src/piper_coordinator.py
@@ -5,6 +5,10 @@
 from .piper_utils import create_logger, LOG_LEVEL
 
 
+# Coordinator needs GPUs when using profiling to infer stage boundaries
+# @ray.remote(num_gpus=0.1)
+
+# Use manual stage annotations- more stable
 @ray.remote
 def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, master_addr, master_port, training_func: Callable, *args, **kwargs):
     logger = create_logger("piper_coordinator", LOG_LEVEL)
diff --git a/src/piper_exec.py b/src/piper_exec.py
index c70a4fb..f39a771 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -124,6 +124,22 @@ def node_id(self) -> str:
         return f"r{self.pp_rank}_t{self.time_step}_{ttype}_mb{mb}"
 
 
+def _rebuild_task_dag(node_data):
+    """Reconstruct a TaskDAG from the flat index-based representation produced
+    by TaskDAG.__reduce__.  Must be a module-level function so pickle can find
+    it by name."""
+    nodes = [
+        TaskNode(task=d[0], pp_rank=d[1], time_step=d[2], peer_pp_rank=d[3])
+        for d in node_data
+    ]
+    for node, d in zip(nodes, node_data):
+        node.data_preds    = [nodes[j] for j in d[4]]
+        node.data_succs    = [nodes[j] for j in d[5]]
+        node.temporal_pred = nodes[d[6]] if d[6] is not None else None
+        node.temporal_succ = nodes[d[7]] if d[7] is not None else None
+    return TaskDAG(nodes=nodes)
+
+
 @dataclass
 class TaskDAG:
     """DAG of :class:`TaskNode` objects for one training iteration.
@@ -136,6 +152,23 @@ def roots(self) -> list[TaskNode]:
         """Return nodes that have no predecessors of any kind."""
         return [n for n in self.nodes if not n.data_preds and n.temporal_pred is None]
 
+    def __reduce__(self):
+        """Serialize as a flat list of index-referenced tuples to avoid the
+        deep recursion that cloudpickle would need when following temporal_succ
+        chains through the full node graph."""
+        idx = {id(n): i for i, n in enumerate(self.nodes)}
+        node_data = [
+            (
+                n.task, n.pp_rank, n.time_step, n.peer_pp_rank,
+                [idx[id(p)] for p in n.data_preds],
+                [idx[id(s)] for s in n.data_succs],
+                idx[id(n.temporal_pred)] if n.temporal_pred is not None else None,
+                idx[id(n.temporal_succ)] if n.temporal_succ is not None else None,
+            )
+            for n in self.nodes
+        ]
+        return (_rebuild_task_dag, (node_data,))
+
 
 class DAGEdge(NamedTuple):
     from_stage: int
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index 7490b00..e6d4960 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -251,15 +251,34 @@ def _ensure_in_env(node):
             else:
                 env[node.name] = attr
 
+    def _ensure_real(val):
+        """Recursively replace any meta tensors with real tensors on device."""
+        if isinstance(val, torch.Tensor) and val.device.type == "meta":
+            return _materialize_tensor(val, device)
+        elif isinstance(val, (list, tuple)):
+            return type(val)(_ensure_real(v) for v in val)
+        elif isinstance(val, dict):
+            return {k: _ensure_real(v) for k, v in val.items()}
+        return val
+
     def _fetch_arg(arg):
         if isinstance(arg, fx.Node):
             _ensure_in_env(arg)
-            return env.get(arg.name)
+            val = env.get(arg.name)
+            real_val = _ensure_real(val)
+            if real_val is not val:
+                env[arg.name] = real_val  # cache so future accesses also get real tensor
+            return real_val
         elif isinstance(arg, (list, tuple)):
             vals = [_fetch_arg(a) for a in arg]
             return type(arg)(vals)
         elif isinstance(arg, dict):
             return {k: _fetch_arg(v) for k, v in arg.items()}
+        elif isinstance(arg, torch.Tensor) and arg.device.type == "meta":
+            # Constants baked into node.args at trace time (e.g. RoPE frequency
+            # matrices) are meta tensors when the model was traced on meta device.
+            # Materialize them on the profiling device so call_function nodes don't fail.
+            return _materialize_tensor(arg, device)
         return arg
 
     def _dec_ref(node):
diff --git a/src/piper_utils.py b/src/piper_utils.py
index 0218ace..fb7e8c4 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -10,7 +10,7 @@
 from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
 from typing import Any, Optional
 
-LOG_LEVEL = "DEBUG"
+LOG_LEVEL = "INFO"
 
 """ 
 Print the backward graph of a tensor
diff --git a/src/test_graph_transform.py b/src/test_graph_transform.py
new file mode 100644
index 0000000..e6f4ec3
--- /dev/null
+++ b/src/test_graph_transform.py
@@ -0,0 +1,257 @@
+"""
+Correctness test for _split_gm_by_stages and _profile_and_split_gm.
+
+Verifies that when the stage GraphModules produced by each split function are
+composed (run sequentially, feeding each stage's outputs into the next), the
+final output matches the original model's output on the same input.
+
+Both split functions are designed to operate on FX graphs captured by
+torch.compile (AOT autograd), where model parameters are lifted to placeholder
+inputs rather than represented as get_attr nodes.  This test captures graphs
+in that same way via a custom torch.compile backend.
+
+Usage:
+    python -m src.test_graph_transform
+"""
+import argparse
+import copy
+import sys
+import torch
+import torch.nn as nn
+import torch.fx as fx
+
+from .piper_graph_transform import _split_gm_by_stages, _profile_and_split_gm
+
+
+# ---------------------------------------------------------------------------
+# Simple test model
+# ---------------------------------------------------------------------------
+
+class _Net(nn.Module):
+    """Three-layer MLP that is easily splittable into two stages."""
+
+    def __init__(self, dim: int = 32):
+        super().__init__()
+        self.fc1 = nn.Linear(dim, dim, bias=False)
+        self.fc2 = nn.Linear(dim, dim, bias=False)
+        self.fc3 = nn.Linear(dim, dim, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+
+# ---------------------------------------------------------------------------
+# FX graph capture via torch.compile
+# ---------------------------------------------------------------------------
+
+def _capture_gm(model: nn.Module, x: torch.Tensor):
+    """
+    Capture the FX GraphModule that torch.compile produces for *model*(*x*).
+
+    Returns (gm, example_inputs, ref_output) where:
+      - gm            : the FX GraphModule with proper example_value metadata
+      - example_inputs: real tensors corresponding to gm's placeholder nodes
+                        (lifted parameters first, then the data input x)
+      - ref_output    : model(x) evaluated once with real weights
+    """
+    captured: dict = {}
+
+    def _backend(gm, example_inputs):
+        captured["gm"] = gm
+        captured["inputs"] = list(example_inputs)
+        return gm.forward
+
+    with torch.no_grad():
+        ref_output = model(x).detach().clone()
+
+    compiled = torch.compile(model, backend=_backend, fullgraph=True)
+    with torch.no_grad():
+        compiled(x)
+
+    return captured["gm"], captured["inputs"], ref_output
+
+
+# ---------------------------------------------------------------------------
+# Stage annotation
+# ---------------------------------------------------------------------------
+
+def _annotate_stages(gm: fx.GraphModule, num_stages: int) -> None:
+    """
+    Divide compute nodes (everything except placeholder / get_attr / output)
+    into *num_stages* equal-ish chunks and stamp ``node.meta['custom']['stage']``.
+    """
+    compute = [n for n in gm.graph.nodes if n.op not in ("placeholder", "get_attr", "output")]
+    chunk = max(1, (len(compute) + num_stages - 1) // num_stages)
+    for i, node in enumerate(compute):
+        stage = min(i // chunk, num_stages - 1)
+        node.meta.setdefault("custom", {})["stage"] = stage
+
+
+# ---------------------------------------------------------------------------
+# Running the split stages
+# ---------------------------------------------------------------------------
+
+def _build_ph_map(gm: fx.GraphModule, example_inputs: list) -> dict:
+    """
+    Return a dict mapping each placeholder node's *name* to its corresponding
+    real tensor from *example_inputs*.  Used to fill parameter slots in stage
+    GraphModules by name matching.
+    """
+    ph_nodes = [n for n in gm.graph.nodes if n.op == "placeholder"]
+    return {node.name: val for node, val in zip(ph_nodes, example_inputs)}
+
+
+def _run_stage_chain(
+    submodules,
+    orig_ph_map: dict,
+) -> torch.Tensor:
+    """
+    Execute stage GraphModules sequentially, feeding each stage's outputs into
+    the next stage's inter-stage placeholder inputs.
+
+    For each stage_gm:
+      - Placeholders whose names appear in *orig_ph_map* (parameters / original
+        data inputs) are filled from that map.
+      - Remaining placeholders (inter-stage activations from the previous stage)
+        are filled from the previous stage's output in order.
+
+    Returns the final stage's output tensor.
+    """
+    prev_output = None
+
+    for stage_id, stage_gm, _input_idxs, _param_idxs, _graphargs, _placeholders in sorted(
+        submodules, key=lambda t: t[0]
+    ):
+        ph_nodes = list(stage_gm.graph.find_nodes(op="placeholder"))
+        args: list = [None] * len(ph_nodes)
+
+        # 1. Fill placeholders by name from the original graph's inputs.
+        for i, node in enumerate(ph_nodes):
+            if node.name in orig_ph_map:
+                args[i] = orig_ph_map[node.name]
+
+        # 2. Fill remaining (inter-stage) placeholders from the previous stage's
+        #    output, in left-to-right placeholder order.
+        if prev_output is not None:
+            inter = (
+                (prev_output,)
+                if isinstance(prev_output, torch.Tensor)
+                else tuple(prev_output)
+            )
+            unfilled = [i for i, v in enumerate(args) if v is None]
+            for pos, idx in enumerate(unfilled):
+                if pos < len(inter):
+                    args[idx] = inter[pos]
+
+        with torch.no_grad():
+            prev_output = stage_gm(*args)
+
+    # Unwrap single-element tuples that some output nodes produce.
+    if isinstance(prev_output, (list, tuple)) and len(prev_output) == 1:
+        prev_output = prev_output[0]
+    return prev_output
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+
+def test_split_gm_by_stages(verbose: bool = False) -> None:
+    """
+    _split_gm_by_stages: stage GMs composed sequentially reproduce the
+    original model's output.
+    """
+    torch.manual_seed(0)
+    dim = 32
+    model = _Net(dim)
+    x = torch.randn(4, dim)
+
+    gm, example_inputs, ref = _capture_gm(model, x)
+    orig_ph_map = _build_ph_map(gm, example_inputs)
+
+    # Annotate the captured graph so _split_gm_by_stages knows where to split.
+    _annotate_stages(gm, num_stages=2)
+
+    _, submodules = _split_gm_by_stages(gm)
+
+    assert len(submodules) == 2, (
+        f"_split_gm_by_stages: expected 2 stages, got {len(submodules)}"
+    )
+
+    result = _run_stage_chain(submodules, orig_ph_map)
+
+    max_diff = (ref - result.detach()).abs().max().item()
+    assert torch.allclose(ref, result.detach(), atol=1e-5), (
+        f"_split_gm_by_stages output mismatch: max abs diff = {max_diff:.2e}"
+    )
+
+    if verbose:
+        print(f"  max_diff={max_diff:.2e}")
+
+
+def test_profile_and_split_gm(verbose: bool = False) -> None:
+    """
+    _profile_and_split_gm: auto-profiled stages composed sequentially
+    reproduce the original model's output.
+
+    _profile_and_split_gm may move call_module submodules to meta device
+    during profiling.  Because torch.compile's AOT-traced graphs lift
+    parameters to placeholder inputs (no call_module / get_attr nodes for
+    weights), this is not an issue for simple models.  A deepcopy of the
+    model is used for tracing to avoid any side-effects on the original.
+    """
+    torch.manual_seed(0)
+    dim = 32
+    model = _Net(dim)
+    x = torch.randn(4, dim)
+
+    gm, example_inputs, ref = _capture_gm(model, x)
+    orig_ph_map = _build_ph_map(gm, example_inputs)
+
+    # Use a fresh capture on a deep copy so profiling doesn't mutate `gm`.
+    gm_for_profile, _, _ = _capture_gm(copy.deepcopy(model), x)
+
+    # No stage annotations; _profile_and_split_gm determines the split.
+    _, submodules = _profile_and_split_gm(gm_for_profile, num_stages=2)
+
+    assert len(submodules) == 2, (
+        f"_profile_and_split_gm: expected 2 stages, got {len(submodules)}"
+    )
+
+    # Run stage chain with the *original* example_inputs (real weights).
+    result = _run_stage_chain(submodules, orig_ph_map)
+
+    max_diff = (ref - result.detach()).abs().max().item()
+    assert torch.allclose(ref, result.detach(), atol=1e-5), (
+        f"_profile_and_split_gm output mismatch: max abs diff = {max_diff:.2e}"
+    )
+
+    if verbose:
+        print(f"  max_diff={max_diff:.2e}")
+
+
+# ---------------------------------------------------------------------------
+# Entry point
+# ---------------------------------------------------------------------------
+
+def main() -> None:
+    p = argparse.ArgumentParser(description="Graph-transform correctness tests")
+    p.add_argument("--verbose", action="store_true")
+    args = p.parse_args()
+
+    print("Running test_split_gm_by_stages ...", flush=True)
+    test_split_gm_by_stages(verbose=args.verbose)
+    print("  PASSED")
+
+    print("Running test_profile_and_split_gm ...", flush=True)
+    test_profile_and_split_gm(verbose=args.verbose)
+    print("  PASSED")
+
+    print("\nAll tests passed.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/test/models/llama.py b/test/models/llama.py
index ce40cc1..d099e7b 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -480,31 +480,31 @@ def log_size(layer, indent=0):
 
     #     return output
 
-    # """
-    # 2 STAGES
-    # """
-    # def forward(self, tokens: torch.Tensor):
-    #     with torch.fx.traceback.annotate({"stage": 0}):
-    #         h = self.tok_embeddings(tokens)
-    #         start_pos = 0
-    #         for layer in self.layers[:self.n_layers//2+2]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-    #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[self.n_layers//2+2:]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-    #         h = self.norm(h)
-    #         output = self.output(h).float()
-
-    #     return output
-
     """
-    No annotations: auto infer stages
+    2 STAGES
     """
     def forward(self, tokens: torch.Tensor):
-        h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-        start_pos = 0
-        for layer in self.layers:
-            h = layer(h, start_pos, self.freqs_cis, self.mask)
-        h = self.norm(h) if self.norm else h
-        output = self.output(h).float() if self.output else h
-        return output
\ No newline at end of file
+        with torch.fx.traceback.annotate({"stage": 0}):
+            h = self.tok_embeddings(tokens)
+            start_pos = 0
+            for layer in self.layers[:self.n_layers//2+2]:
+                h = layer(h, start_pos, self.freqs_cis, self.mask)
+        with torch.fx.traceback.annotate({"stage": 1}):
+            for layer in self.layers[self.n_layers//2+2:]:
+                h = layer(h, start_pos, self.freqs_cis, self.mask)
+            h = self.norm(h)
+            output = self.output(h).float()
+
+        return output
+
+    # """
+    # No annotations: auto infer stages
+    # """
+    # def forward(self, tokens: torch.Tensor):
+    #     h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
+    #     start_pos = 0
+    #     for layer in self.layers:
+    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
+    #     h = self.norm(h) if self.norm else h
+    #     output = self.output(h).float() if self.output else h
+    #     return output
\ No newline at end of file
diff --git a/test/models/mixtral.py b/test/models/mixtral.py
index bea7f95..c63e913 100644
--- a/test/models/mixtral.py
+++ b/test/models/mixtral.py
@@ -117,6 +117,20 @@ def setup_caches(self, max_batch_size, max_seq_length):
         self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base).to(torch.bfloat16)
         self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
 
+    # def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+    #     """
+    #     2 Stages
+    #     """
+    #     self.first_arg = idx.shape
+    #     mask = self.causal_mask[None, None, input_pos]
+    #     freqs_cis = self.freqs_cis[input_pos]
+    #     x = self.tok_embeddings(idx)
+    #     for layer in self.layers:
+    #         x = layer(x, input_pos, freqs_cis, mask)
+    #     x = self.norm(x)
+    #     logits = self.output(x)
+    #     return logits
+    
     def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
         """
         2 Stages
diff --git a/test/models/qwen3.py b/test/models/qwen3.py
index 4a2e086..f4ff6f7 100644
--- a/test/models/qwen3.py
+++ b/test/models/qwen3.py
@@ -306,24 +306,35 @@ def forward(
         attention_masks: Optional[AttentionMasksType] = None,
         positions: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
-        num_layers = len(self.layers)
-        layers_per_stage = num_layers // self.num_stages
-
-        # stage 0: embedding + first N layers
-        with torch.fx.traceback.annotate({"stage": 0}):
-            h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
-
-            for i in range(layers_per_stage * (self.num_stages - 1)):
-                layer = self.layers[str(i)]
-                h = layer(h, self.rope_cache, attention_masks, positions)
-
-        # stage 1: remaining layers + norm + output
-        with torch.fx.traceback.annotate({"stage": 1}):
-            for i in range(layers_per_stage * (self.num_stages - 1), num_layers):
-                layer = self.layers[str(i)]
-                h = layer(h, self.rope_cache, attention_masks, positions)
-
-            h = self.norm(h) if self.norm is not None else h
-            output = self.output(h) if self.output is not None else h
+        
+        h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
+        for layer in self.layers.values():
+            h = layer(h, self.rope_cache, attention_masks, positions)
+        h = self.norm(h) if self.norm is not None else h
+        output = self.output(h) if self.output is not None else h
 
         return output
+    
+    # def forward(
+    #     self,
+    #     tokens: torch.Tensor,
+    #     attention_masks: Optional[AttentionMasksType] = None,
+    #     positions: Optional[torch.Tensor] = None,
+    # ) -> torch.Tensor:
+    #     num_layers = len(self.layers)
+    #     layers_per_stage = num_layers // self.num_stages
+
+    #     for stage_id in range(self.num_stages):
+    #         with torch.fx.traceback.annotate({"stage": stage_id}):
+    #             if stage_id == 0:
+    #                 h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
+
+    #             for i in range(stage_id * layers_per_stage, (stage_id + 1) * layers_per_stage):
+    #                 layer = self.layers[str(i)]
+    #                 h = layer(h, self.rope_cache, attention_masks, positions)
+
+    #             if stage_id == self.num_stages - 1:
+    #                 h = self.norm(h) if self.norm is not None else h
+    #                 output = self.output(h) if self.output is not None else h
+
+    #     return output
diff --git a/test/test_llama.py b/test/test_llama.py
index 9669d77..cb707c5 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -1,27 +1,27 @@
+import logging
 import ray
 import torch
-import bitsandbytes as bnb
 import time
 import argparse
-import json
 import os
 import numpy as np
-import gc
+
+logger = logging.getLogger(__name__)
 
 from ray.util.placement_group import (
     placement_group,
     placement_group_table,
-    remove_placement_group,
 )
 
-from src.piper_exec import piper_exec
-from src.piper_compile import piper_setup, piper_shutdown
 from src.piper_coordinator import PiperProgramCoordinator
+from src.piper_compile import piper_setup
+from src.piper import piper_exec_dag
 from src.piper_utils import piper_metadata
+
 from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B, LLAMA_70B
 from .schedule_helpers import (
-    build_1f1b_schedule, 
-    build_gpipe_schedule, 
+    build_1f1b_schedule,
+    build_gpipe_schedule,
     print_schedule,
     INTERLEAVED_1F1B_PP2_MB4_SCHEDULE,
     INTERLEAVED_1F1B_PP2_MB6_SCHEDULE,
@@ -35,8 +35,6 @@
 
 
 def main(args, pg):
-    
-    # Set model configuration based on argument
     match args.model:
         case 'debug':
             llama_config = LLAMA_DEBUG
@@ -48,15 +46,13 @@ def main(args, pg):
             llama_config = LLAMA_8B
         case '70b':
             llama_config = LLAMA_70B
-    print(args) 
+    print(args)
 
     loss_fn = torch.nn.CrossEntropyLoss()
 
     x = torch.randint(0, llama_config.vocab_size, (args.batch_size, args.seq_len))
     y = torch.randn((args.batch_size, args.seq_len, llama_config.vocab_size))
 
-    schedule = None
-
     match args.schedule:
         case "no-pp":
             schedule = NO_PP_SCHEDULE
@@ -67,7 +63,7 @@ def main(args, pg):
                 elif args.mbs == 4:
                     schedule = INTERLEAVED_1F1B_PP2_MB4_SCHEDULE
                 else:
-                    raise ValueError(f"Unsupported number of microbatches: for interleaved-1f1b with PP={args.pp}: {args.mbs}")
+                    raise ValueError(f"Unsupported number of microbatches for interleaved-1f1b with PP={args.pp}: {args.mbs}")
             elif args.pp == 4:
                 assert args.mbs == 8
                 schedule = INTERLEAVED_1F1B_PP4_MB8_SCHEDULE
@@ -87,126 +83,110 @@ def main(args, pg):
         case "zerobubble":
             assert args.pp == 2 and args.mbs == 4
             schedule = ZEROBUBBLE_MB4_SCHEDULE
+
     print("Schedule:")
     print_schedule(schedule)
 
     piper_setup(
         Transformer,
         model_args=(llama_config, args.seq_len),
-        optim_fn=bnb.optim.Adam8bit,
+        optim_fn=torch.optim.Adam,
         example_inputs=[x],
         example_outputs=y,
         schedule=schedule,
-        naive_gradient_sync=args.naive_gradient_sync,
-        zero_stage=args.zero_stage,
+        naive_gradient_sync=args.naive_grad_sync,
         activation_checkpointing=args.activation_checkpointing,
+        bucketing=args.bucketing,
         model_dtype=torch.bfloat16,
         pg=pg,
+        nsight=args.nsight,
     )
 
-    # Warmup
     print(f"Running {args.warmup} warmup iterations...")
-    for i in range(args.warmup):
-        piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
-        print(f"Warmup {i} completed")
+    for _ in range(args.warmup):
+        piper_exec_dag(loss_fn)
+        time.sleep(1)
+
+    actors = piper_metadata.actors
 
-    # Time training steps
     print(f"Running {args.iters} timed iterations...")
     iter_times = []
-    for i in range(args.iters):
+    for _ in range(args.iters):
         start = time.perf_counter()
-        piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
+        piper_exec_dag(loss_fn)
         end = time.perf_counter()
         iter_times.append(end - start)
-        print(f"Iter {i} completed")
-        time.sleep(1) 
-    
+        time.sleep(1)
+
     dp_rank = int(os.environ['PIPER_DP_RANK'])
     print(
-        f"rank {dp_rank} iter time= {np.mean(iter_times):.3f} ± {np.std(iter_times):.3f} s ({len(iter_times)} samples)\n"
-        f"rank {dp_rank} throughput= {(args.batch_size * args.mbs * args.seq_len)/np.mean(iter_times):.3f} tokens/sec ({len(iter_times)} samples)"
+        f"rank {dp_rank} iter time= {np.mean(iter_times):.5f} ± {np.std(iter_times):.5f} s "
+        f"({len(iter_times)} samples)\n"
+        f"rank {dp_rank} throughput= "
+        f"{(args.batch_size * args.mbs * args.seq_len) / np.mean(iter_times):.3f} tokens/s"
     )
 
     if args.tracing:
-        actors = piper_metadata.actors
-        ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
-        ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
-        ray.get([actor.start_mem_tracing.remote() for actor in actors.values()])
-
-        print(f"Running {args.warmup} tracing iterations...")
-        for _ in range(args.warmup):
-            piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
-
-        mem_data_ret = ray.get([actor.get_peak_memory.remote() for actor in actors.values()])
-        for rank, peak_mem in mem_data_ret:
-            print(f"rank {rank} peak memory= {peak_mem:.3f} GB")
-
+        ray.get([actor.set_tracing.remote(True) for actor in actors.values()])
+        print(f"Running {args.trace_iters} tracing iterations...")
+        for _ in range(args.trace_iters):
+            piper_exec_dag(loss_fn)
+            ray.get([actor.flush_timing_events.remote() for actor in actors.values()])
+            time.sleep(1)
         trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
         for rank, trace_data in trace_data_ret:
             for key in trace_data:
                 all_times = trace_data[key]
-                print(f"rank {rank} {key} time= {np.mean(all_times):.3f} ± {np.std(all_times):.3f} ms ({len(all_times)} samples)")
-
-    if args.naive_gradient_sync:
-        suffix = "-naive-sync"
-    else:
-        suffix = ""
-    # Collect task_id -> label mappings from all actors
-    all_task_labels = {}
-    actors = piper_metadata.actors
-    for labels in ray.get([actor.get_task_labels.remote() for actor in actors.values()]):
-        all_task_labels.update(labels)
+                print(
+                    f"rank {rank} {key} time= {np.mean(all_times):.3f} ± "
+                    f"{np.std(all_times):.3f} ms ({len(all_times)} samples)"
+                )
 
     os.makedirs("out", exist_ok=True)
-    timeline_filename = f"out/{args.model}-pp{args.pp}-dp{args.dp}-{args.schedule}{suffix}.json"
+    timeline_filename = f"out/llama-dag-pp{args.pp}-dp{args.dp}-{args.schedule}"
     ray.timeline(timeline_filename)
-    labels_filename = timeline_filename.replace(".json", "-labels.json")
-    with open(labels_filename, "w") as f:
-        json.dump(all_task_labels, f)
     print(f"Ray timeline saved to: {timeline_filename}")
-    print(f"Task labels saved to: {labels_filename}")
 
-    piper_shutdown()
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
-    parser.add_argument('--model', choices=['debug', '1b', '3b', '8b', '70b'], default='debug',
-                        help='Model configuration: debug, 1b, 3b, or 8b (default: debug)')
-    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'interleaved-gpipe', 'dualpipev-nozb', 'dualpipev', 'zerobubble', 'no-pp'], default='1f1b',
-                        help='Schedule type: gpipe, 1f1b, interleaved-1f1b, dualpipev, zerobubble, or no-pp (default: 1f1b)')
-    parser.add_argument('--dp', type=int, default=1,
-                        help='Number of data parallel degrees (default: 1)')
-    parser.add_argument('--pp', type=int, default=2,
-                        help='Number of pipeline parallel degrees (default: 2)')
-    parser.add_argument('--batch-size', type=int, default=16,
-                        help='Batch size (default: 16)')
-    parser.add_argument('--mbs', type=int, default=4,
-                        help='Number of microbatches (default: 4)')
-    parser.add_argument('--seq-len', type=int, default=256,
-                        help='Sequence length (default: 256)')
-    parser.add_argument('--warmup', type=int, default=5,
-                        help='Number of warmup iterations (default: 5)')
-    parser.add_argument('--iters', type=int, default=10,
-                        help='Number of timing iterations (default: 10)')
-    parser.add_argument('--tracing', action='store_true', default=False,
-                        help='Enable tracing')
-    parser.add_argument('--naive-gradient-sync', action='store_true', default=False,
-                        help='Enable naive gradient sync')
-    parser.add_argument('--activation-checkpointing', action='store_true', default=False,
-                        help='Enable activation checkpointing')
-    parser.add_argument('--zero-stage', type=int, choices=[0, 1, 2, 3], default=0,
-                        help='ZeRO optimization stage (0: disabled, 1: strict ZeRO-1, 2: strict ZeRO-2, 3: strict ZeRO-3)')
+    parser.add_argument('--model', choices=['debug', '1b', '3b', '8b', '70b'], default='debug')
+    parser.add_argument(
+        '--schedule',
+        choices=['gpipe', '1f1b', 'interleaved-1f1b', 'interleaved-gpipe',
+                 'dualpipev-nozb', 'dualpipev', 'zerobubble', 'no-pp'],
+        default='1f1b',
+    )
+    parser.add_argument('--dp', type=int, default=1)
+    parser.add_argument('--pp', type=int, default=2)
+    parser.add_argument('--batch-size', type=int, default=16)
+    parser.add_argument('--mbs', type=int, default=4)
+    parser.add_argument('--seq-len', type=int, default=256)
+    parser.add_argument('--warmup', type=int, default=3)
+    parser.add_argument('--iters', type=int, default=5)
+    parser.add_argument('--trace-iters', type=int, default=3)
+    parser.add_argument('--tracing', action='store_true', default=False)
+    parser.add_argument('--naive-grad-sync', action='store_true', default=False)
+    parser.add_argument('--activation-checkpointing', action='store_true', default=False)
+    parser.add_argument('--bucketing', action='store_true', default=False,
+                        help='Split stages into per-param-bucket sub-modules for overlapped all-reduce')
+    parser.add_argument('--nsight', action='store_true', default=False,
+                        help='Whether to use Nsight Systems for tracing')
     return parser.parse_args()
 
 
 if __name__ == "__main__":
     args = parse_args()
-    ray.init(log_to_driver=True, namespace="llama", include_dashboard=False)
-    # ray.init(log_to_driver=True, namespace="llama", include_dashboard=False, _temp_dir="/m-coriander/coriander/mfris/ray_tmp")
-    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp)
-    ray.get(pg.ready(), timeout=10)  # Wait for the placement group to be ready before proceeding
+    print(args)
+    ray.init(
+        namespace="llama",
+        log_to_driver=True,
+        include_dashboard=False,
+    )
+    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="PACK")
+    ray.get(pg.ready(), timeout=600)
     print(placement_group_table(pg))
-    piper_coordinator = PiperProgramCoordinator.remote(dp_degree=args.dp, pp_degree=args.pp)
-    ray.get(piper_coordinator.run_program.remote(main, args, pg))
-    time.sleep(3)
+    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
+    handles = piper_coordinator.run_program.remote(main, args, pg)
+    ray.get(handles)
     ray.shutdown()
diff --git a/test/test_mixtral.py b/test/test_mixtral.py
index b3f14a0..b55d37d 100644
--- a/test/test_mixtral.py
+++ b/test/test_mixtral.py
@@ -92,7 +92,6 @@ def main(args, pg):
         naive_gradient_sync=args.naive_grad_sync,
         activation_checkpointing=False,
         bucketing=args.bucketing,
-        mode=args.mode,
         model_dtype=torch.bfloat16,
         pg=pg,
         nsight=args.nsight,
@@ -127,6 +126,7 @@ def main(args, pg):
         print(f"Running {args.trace_iters} tracing iterations...")
         for _ in range(args.trace_iters):
             piper_exec_dag(loss_fn)
+            ray.get([actor.flush_timing_events.remote() for actor in actors.values()])
             time.sleep(1)
         trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
         for rank, trace_data in trace_data_ret:
@@ -137,18 +137,10 @@ def main(args, pg):
                     f"{np.std(all_times):.3f} ms ({len(all_times)} samples)"
                 )
 
-    all_task_labels = {}
-    for labels in ray.get([actor.get_task_labels.remote() for actor in actors.values()]):
-        all_task_labels.update(labels)
-
     os.makedirs("out", exist_ok=True)
-    timeline_filename = f"out/mixtral-dag-pp{args.pp}-dp{args.dp}-{args.schedule}-{args.mode}"
+    timeline_filename = f"out/mixtral-dag-pp{args.pp}-dp{args.dp}-{args.schedule}"
     ray.timeline(timeline_filename)
-    labels_filename = timeline_filename + "-labels.json"
-    with open(labels_filename, "w") as f:
-        json.dump(all_task_labels, f)
     print(f"Ray timeline saved to: {timeline_filename}")
-    print(f"Task labels saved to: {labels_filename}")
 
 
 def parse_args():
@@ -174,7 +166,6 @@ def parse_args():
     parser.add_argument("--trace-iters", type=int, default=3)
     parser.add_argument("--tracing", action="store_true", default=False)
     parser.add_argument("--naive-grad-sync", action="store_true", default=False)
-    parser.add_argument("--mode", choices=["sequential", "naive", "overlapped"], default="sequential")
     parser.add_argument("--bucketing", action="store_true", default=False,
                         help="Split stages into per-param-bucket sub-modules for overlapped all-reduce")
     parser.add_argument("--nsight", action="store_true", default=False,
@@ -186,12 +177,12 @@ def parse_args():
     args = parse_args()
     print(args)
     ray.init(
+        namespace="mixtral",
         log_to_driver=True,
-        namespace="mixtral_dag",
         include_dashboard=False,
-        # _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp",
+        _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp",
     )
-    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="SPREAD")
+    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="PACK")
     ray.get(pg.ready(), timeout=600)
     print(placement_group_table(pg))
     piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
diff --git a/test/test_qwen.py b/test/test_qwen.py
index 8c9f01d..df33749 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -41,6 +41,9 @@
 )
 
 
+# from triton.backends.amd.driver import HIPDriver
+# HIPDriver.is_active = staticmethod(lambda: False)
+
 def main(args, pg):
     batch_size = args.batch_size
 
@@ -78,20 +81,19 @@ def main(args, pg):
         naive_gradient_sync=args.naive_grad_sync,
         activation_checkpointing=False,
         bucketing=args.bucketing,
-        mode=args.mode,
         model_dtype=torch.bfloat16,
         pg=pg,
         nsight=args.nsight,
     )
 
-    print(f"Running {args.warmup} warmup iterations (dag path)...")
+    print(f"Running {args.warmup} warmup iterations")
     for _ in range(args.warmup):
         piper_exec_dag(loss_fn)
         time.sleep(1)
 
     actors = piper_metadata.actors
 
-    print(f"Running {args.iters} timed iterations (dag path)...")
+    print(f"Running {args.iters} timed iterations")
     iter_times = []
     for _ in range(args.iters):
         start = time.perf_counter()
@@ -105,7 +107,7 @@ def main(args, pg):
         f"rank {dp_rank} iter time= {np.mean(iter_times):.5f} ± {np.std(iter_times):.5f} s "
         f"({len(iter_times)} samples)\n"
         f"rank {dp_rank} throughput= "
-        f"{(args.batch_size * args.mbs * config.block_size) / np.mean(iter_times):.3f} tokens/s"
+        f"{(args.batch_size * args.mbs * args.seq_len) / np.mean(iter_times):.3f} tokens/s"
     )
 
     if args.tracing:
@@ -113,6 +115,7 @@ def main(args, pg):
         print(f"Running {args.trace_iters} tracing iterations...")
         for _ in range(args.trace_iters):
             piper_exec_dag(loss_fn)
+            ray.get([actor.flush_timing_events.remote() for actor in actors.values()])
             time.sleep(1)
         trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
         for rank, trace_data in trace_data_ret:
@@ -123,18 +126,10 @@ def main(args, pg):
                     f"{np.std(all_times):.3f} ms ({len(all_times)} samples)"
                 )
 
-    all_task_labels = {}
-    for labels in ray.get([actor.get_task_labels.remote() for actor in actors.values()]):
-        all_task_labels.update(labels)
-
     os.makedirs("out", exist_ok=True)
-    timeline_filename = f"out/mixtral-dag-pp{args.pp}-dp{args.dp}-{args.schedule}-{args.mode}"
+    timeline_filename = f"out/qwen-dag-pp{args.pp}-dp{args.dp}-{args.schedule}"
     ray.timeline(timeline_filename)
-    labels_filename = timeline_filename + "-labels.json"
-    with open(labels_filename, "w") as f:
-        json.dump(all_task_labels, f)
     print(f"Ray timeline saved to: {timeline_filename}")
-    print(f"Task labels saved to: {labels_filename}")
 
 
 def parse_args():
@@ -154,7 +149,6 @@ def parse_args():
     parser.add_argument("--trace-iters", type=int, default=3)
     parser.add_argument("--tracing", action="store_true", default=False)
     parser.add_argument("--naive-grad-sync", action="store_true", default=False)
-    parser.add_argument("--mode", choices=["sequential", "naive", "overlapped"], default="sequential")
     parser.add_argument("--bucketing", action="store_true", default=False,
                         help="Split stages into per-param-bucket sub-modules for overlapped all-reduce")
     parser.add_argument("--nsight", action="store_true", default=False,
@@ -166,12 +160,12 @@ def parse_args():
     args = parse_args()
     print(args)
     ray.init(
+        namespace="qwen",
         log_to_driver=True,
-        namespace="mixtral_dag",
         include_dashboard=False,
-        # _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp",
+        _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp",
     )
-    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="SPREAD")
+    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="PACK")
     ray.get(pg.ready(), timeout=600)
     print(placement_group_table(pg))
     piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)

From 4460cb46589bdfab297230d51301df18e3db95c4 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Tue, 31 Mar 2026 01:15:03 +0000
Subject: [PATCH 083/100] slow startup time fix: use 2 pp communicators per pp
 group rather than 2 per pp rank pair. also limit model compilation to a
 single dp rank in the case DP > 1. other changes reflect deepseek overlapping
 implementation: currently assumes comp-comm pattern matches for overlapping
 task chains

---
 src/piper.py                 | 122 +++++++--
 src/piper_actor.py           | 457 +++++++++++++++++---------------
 src/piper_compile.py         | 283 ++++++++++++++++++--
 src/piper_coordinator.py     |   2 +-
 src/piper_exec.py            |  41 +--
 src/piper_graph_transform.py | 500 ++++++++++++++++++++++++-----------
 src/piper_utils.py           |   8 +-
 test/models/llama.py         |   4 +-
 test/models/qwen3.py         |  85 ++++--
 test/print_schedules.py      |  23 +-
 test/schedule_helpers.py     |  71 +++++
 test/test_llama.py           |   1 +
 test/test_qwen.py            |  18 +-
 13 files changed, 1127 insertions(+), 488 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index d729eed..b79de95 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -1,22 +1,51 @@
 import ray
 import torch
+import torch.fx as fx
 import os
 import gc
 from torch._dynamo.backends.registry import register_backend
 from .piper_utils import _serialize_graphmodule, piper_metadata, create_logger, LOG_LEVEL
+
+
+def _collect_triton_constant_args(gm):
+    """Return {constant_args_idx: args_dict} for every triton_kernel_wrapper node in gm."""
+    try:
+        from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
+    except ImportError:
+        return {}
+
+    result = {}
+
+    def scan(module):
+        if isinstance(module, fx.GraphModule):
+            for node in module.graph.nodes:
+                if node.op == "call_function":
+                    target_str = getattr(node.target, "__name__", "") or str(node.target)
+                    if "triton_kernel_wrapper" in target_str:
+                        idx = node.kwargs.get("constant_args_idx")
+                        if idx is not None and idx in kernel_side_table.constant_args:
+                            result[idx] = kernel_side_table.constant_args[idx]
+        for child in module.children():
+            scan(child)
+
+    scan(gm)
+    return result
 from .piper_graph_transform import (
     _split_gm_by_stages,
     _profile_and_split_gm,
     schedule_to_dag,
+    assign_time_steps,
     insert_p2p_ops,
     insert_ar_ops,
     expand_bucket_tasks,
     expand_a2a_tasks,
+    overlap_a2a_tasks,
     visualize_dag,
     print_dag_order,
     bucket_stage,
     split_by_a2a,
 )
+from .piper_exec import Task, TaskType, BatchMeta
 from .piper_actor import _get_actor
 
 logger = create_logger("piper_backend", LOG_LEVEL)
@@ -40,7 +69,7 @@ def piper(gm, example_inputs, **kwargs):
         for node in gm.graph.nodes
     )
 
-    logger.info(f"Graph has stage annotations: {has_annotations}")
+    logger.debug(f"Graph has stage annotations: {has_annotations}")
 
     if has_annotations:
         top_level_gm, submodules = _split_gm_by_stages(gm)
@@ -56,6 +85,10 @@ def piper(gm, example_inputs, **kwargs):
     actor_stages = []
     # stage_id -> {boundary_bucket_id -> tensor_idx} collected for DAG expansion
     stage_a2a_boundaries: dict = {}
+    stage_bucket_counts: dict = {}
+    trainable_bucket_keys: set = set()
+    # Collected for DP broadcasting: stage_id -> (modules_data, a2a_boundaries)
+    all_stage_compiled: dict = {}
 
     for (stage_id, stage_gm, input_idxs, param_idxs, graphargs, placeholders) in submodules:
         actor_id = piper_metadata.stage_to_device[stage_id]
@@ -98,12 +131,26 @@ def piper(gm, example_inputs, **kwargs):
         if a2a_boundaries:
             stage_a2a_boundaries[stage_id] = a2a_boundaries
 
+        stage_bucket_counts[stage_id] = len(all_modules)
+        for b_idx, (bgm, b_in, b_param, bargs) in enumerate(all_modules):
+            has_trainable = any(
+                bargs[i] is not None and getattr(bargs[i], "requires_grad", False)
+                for i in b_param
+            )
+            if has_trainable:
+                trainable_bucket_keys.add((stage_id, b_idx))
+            else:
+                logger.warning(
+                    f"Stage {stage_id} bucket {b_idx} has no trainable parameters."
+                )
+
         modules_data = [
             {
                 "gm_data": _serialize_graphmodule(bgm),
                 "graphargs": bargs,
                 "input_idxs": b_in,
                 "param_idxs": b_param,
+                "triton_constant_args": _collect_triton_constant_args(bgm),
             }
             for bgm, b_in, b_param, bargs in all_modules
         ]
@@ -114,6 +161,8 @@ def piper(gm, example_inputs, **kwargs):
             use_activation_checkpointing=piper_metadata.use_activation_checkpointing,
         ))
 
+        all_stage_compiled[stage_id] = (modules_data, a2a_boundaries)
+
         del stage_gm
         del graphargs
         del placeholders
@@ -123,19 +172,16 @@ def piper(gm, example_inputs, **kwargs):
 
     ray.get(refs)
 
-    # TODO: build dag by analyzing stage dependencies
-    # this only supports sequential pipelines
-    for stage_id in piper_metadata.stage_to_device.keys():
-        if stage_id < len(piper_metadata.stage_to_device.keys()) - 1:
-            piper_metadata.dag.add((stage_id, stage_id+1))
+    piper_metadata.stage_bucket_counts = stage_bucket_counts
+    piper_metadata.trainable_bucket_keys = trainable_bucket_keys
 
     # Build task DAG from the schedule stored by piper_setup.
     if piper_metadata.schedule is not None:
         piper_metadata.task_dag = schedule_to_dag(piper_metadata.schedule)
-        logger.info(
+        logger.debug(
             f"Built task DAG: {len(piper_metadata.task_dag.nodes)} nodes, "
             f"{sum(len(n.data_succs) for n in piper_metadata.task_dag.nodes)} data edges, "
-            f"{sum(1 for n in piper_metadata.task_dag.nodes if n.temporal_succ is not None)} temporal edges"
+            f"{sum(len(n.temporal_succs) for n in piper_metadata.task_dag.nodes)} temporal edges"
         )
 
         dag = piper_metadata.task_dag
@@ -143,15 +189,13 @@ def piper(gm, example_inputs, **kwargs):
         # Expand multi-segment stages into per-segment FWD/BWD task chains.
         # This covers both param-bucketed stages (--bucketing) and A2A-split stages
         # (--dp > 1 with MoE), both of which load multiple modules per stage.
-        bucket_counts: dict = {}
-        for actor in piper_metadata.actors.values():
-            counts = ray.get(actor.get_bucket_fwd_counts.remote())
-            bucket_counts.update(counts)
         # Only expand stages that actually have more than one segment.
-        bucket_counts = {sid: n for sid, n in bucket_counts.items() if n > 1}
+        bucket_counts = {
+            sid: n for sid, n in piper_metadata.stage_bucket_counts.items() if n > 1
+        }
         if bucket_counts:
             dag = expand_bucket_tasks(dag, bucket_counts)
-            logger.info(
+            logger.debug(
                 f"Expanded stages {list(bucket_counts.keys())} into "
                 f"per-segment FWD/BWD nodes"
             )
@@ -159,34 +203,74 @@ def piper(gm, example_inputs, **kwargs):
         # Insert FWD_A2A / BWD_A2A nodes for expert-parallel all-to-all boundaries.
         if dp_degree > 1 and stage_a2a_boundaries:
             dag = expand_a2a_tasks(dag, stage_a2a_boundaries)
-            logger.info(
+            logger.debug(
                 f"Inserted A2A task nodes for stages {list(stage_a2a_boundaries.keys())}"
             )
 
+            # Overlap compute and A2A tasks across FWD_BWD task pairs so that
+            # one task's A2A communication runs concurrently with the other's compute.
+            fwdbwd_pairs: list[tuple[Task, Task]] = []
+            for row in piper_metadata.schedule.grid:
+                for cell in row:
+                    if cell is not None and cell.type == TaskType.FWD_BWD:
+                        fwd_task = Task(
+                            pp_rank=cell.pp_rank,
+                            batches=[cell.batches[0]],
+                            type=TaskType.FWD,
+                        )
+                        bwd_task = Task(
+                            pp_rank=cell.pp_rank,
+                            batches=[cell.batches[1]],
+                            type=TaskType.BWD,
+                        )
+                        fwdbwd_pairs.append((fwd_task, bwd_task))
+            # fwdbwd_pairs.append((
+            #     Task(pp_rank=0, batches=[BatchMeta(0, 1)], type=TaskType.FWD), 
+            #     Task(pp_rank=0, batches=[BatchMeta(0, 2)], type=TaskType.FWD)))
+            if fwdbwd_pairs:
+                dag = overlap_a2a_tasks(dag, fwdbwd_pairs)
+                logger.debug(
+                    f"Overlapped A2A and compute tasks for {len(fwdbwd_pairs)} FWD_BWD pairs"
+                )
+
+        # Assign final time_step values from the temporal dependency graph.
+        assign_time_steps(dag)
+
         # Split into per-rank DAGs.
         per_rank_dags = insert_p2p_ops(dag)
 
         # Insert all-reduce nodes when using data parallelism.
         if dp_degree > 1:
-            per_rank_dags = insert_ar_ops(per_rank_dags)
-            logger.info("Inserted ALL_REDUCE nodes for DP gradient sync")
+            per_rank_dags = insert_ar_ops(per_rank_dags, piper_metadata.trainable_bucket_keys)
+            logger.debug("Inserted ALL_REDUCE nodes for DP gradient sync")
             # ZeRO-1/2/3: insert AG/RS into the task DAG before/after the appropriate FWD/BWD tasks.
 
 
         piper_metadata.per_rank_dags = per_rank_dags
         actors = piper_metadata.actors
         for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags):
-            visualize_dag(per_rank_dag, output_path=f"figs/rank{pp_rank}_dag")
+            try:
+                visualize_dag(per_rank_dag, output_path=f"figs/rank{pp_rank}_dag")
+            except Exception as e:
+                logger.warning(f"DAG visualization failed for rank {pp_rank} (DAG may be too large for dot): {e}")
             # uncomment for debugging DAG construction:
             # print_dag_order(per_rank_dag, label=f"rank {pp_rank}")
         ray.get([
             actors[pp_rank].load_dag.remote(per_rank_dag)
             for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags)
         ])
-        logger.info(
+        logger.debug(
             f"Loaded per-rank DAGs onto {len(piper_metadata.per_rank_dags)} actors"
         )
 
+        # Publish compiled data so dp_rank > 0 can skip torch.compile.
+        piper_metadata.compiled_stage_data = {
+            "stages": all_stage_compiled,           # {stage_id: (modules_data, a2a_boundaries)}
+            "stage_bucket_counts": stage_bucket_counts,
+            "trainable_bucket_keys": trainable_bucket_keys,
+            "per_rank_dags": per_rank_dags,
+        }
+
     example_outputs = original_gm(*example_inputs)
 
     def callback(*args):
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 5ce322e..741466e 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -6,7 +6,7 @@
 import gc
 import threading
 from torch.nn import Parameter
-from torch.autograd.graph import GradientEdge, Node
+from torch.autograd.graph import GradientEdge, Node, set_warn_on_accumulate_grad_stream_mismatch
 import torch.distributed as dist
 from collections import defaultdict
 
@@ -25,8 +25,6 @@
 
 logger = create_logger("piper_actor", LOG_LEVEL)
 
-torch.autograd.set_detect_anomaly(True, check_nan=False)
-
 def _get_rank(pp_rank, dp_rank, pp_degree):
     return pp_rank + dp_rank * pp_degree
 
@@ -116,6 +114,11 @@ def __init__(
     ):
         self.logger = create_logger("piper_actor", LOG_LEVEL)
 
+        # BWD_I uses torch.autograd.grad (not .backward), so AccumulateGrad nodes are
+        # traversed for stream-sync bookkeeping but never accumulate to p.grad.
+        # Suppress the spurious stream-mismatch warning.
+        set_warn_on_accumulate_grad_stream_mismatch(False)
+
         self.pp_rank = pp_rank
         self.optim_class = optim_class
         self.naive_gradient_sync = naive_gradient_sync
@@ -132,11 +135,13 @@ def __init__(
         self.ep_group = None  # separate NCCL communicator for expert-parallel (all2all) ops
         # Per-direction communicators: (src_global_rank, dst_global_rank) -> ProcessGroup
         self.pp_groups = {}
+        self.pp_lo_hi = None
+        self.pp_hi_lo = None
         self.device = "cuda"
 
         self.global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
 
-        self.logger.info(
+        self.logger.debug(
             f"Initializing Ray actor {self.global_rank} GPU {os.environ['CUDA_VISIBLE_DEVICES']}"
         )
 
@@ -167,8 +172,6 @@ def __init__(
         self.inp_activation = defaultdict(dict)
         # map stage id -> mb_idx -> current activation
         self.out_activation = defaultdict(dict)
-        # map stage id -> (shape, dtype) of the output activation tensor (for pre-FWD BWD recv)
-        self.output_activation_shape: dict = {}
         # accumuate loss for each microbatch
         self.loss = []
 
@@ -204,6 +207,11 @@ def __init__(
         # stage_id -> mb_idx -> list of (pre_detach_out, detached_input_to_next) per boundary
         self.bucket_boundaries: dict = defaultdict(dict)
 
+        # Split backward 
+        self.bw_param_groups = defaultdict(dict)  # stage_id -> mb_idx -> param group for split backward pass
+        self.bw_grad_cache = defaultdict(dict)  # stage_id -> mb_idx -> cached activations between BWD_I and BWD_W passes
+        self.upstream_grad_cache = defaultdict(dict)  # stage_id -> mb_idx -> cached upstream grads between BWD_I and BWD_W passes
+
         from .piper_utils import piper_metadata
         piper_metadata.actor_self = self
 
@@ -314,7 +322,7 @@ def get_node_ip_and_free_port(self):
     def _join_process_groups(self, master_addr, master_port):
         init_method = f"tcp://{master_addr}:{master_port}"
 
-        self.logger.info(f"Actor {self.global_rank} initializing process groups with master address {master_addr}:{master_port}")
+        self.logger.info(f"Actor {self.global_rank} using GPU {os.environ['CUDA_VISIBLE_DEVICES']}, master addr {master_addr}:{master_port}")
 
         self.device = f"cuda:{self.global_rank % torch.cuda.device_count()}"
         torch.cuda.set_device(self.device)
@@ -334,6 +342,13 @@ def _join_process_groups(self, master_addr, master_port):
         if self.pp_degree > 1:
             self._join_pp_process_group()
 
+        # Force cuBLAS context initialization on comp_stream so the first
+        # backward pass does not encounter a "no current CUDA context" warning.
+        with torch.cuda.stream(self.comp_stream):
+            _w = torch.zeros(4, 4, device=self.device)
+            torch.mm(_w, _w)
+        torch.cuda.synchronize()
+
     def _join_dp_process_group(self):
         num_dp_groups = self.world_size // self.dp_degree
         for dp_group_id in range(num_dp_groups):
@@ -355,34 +370,28 @@ def _join_dp_process_group(self):
     def _join_pp_process_group(self):
         num_pp_groups = self.world_size // self.pp_degree
 
-        # One communicator per direction per rank pair. The global p2p schedule
-        # (built in piper_exec) ensures both ranks issue ops in the same FIFO order.
         for pp_group_id in range(num_pp_groups):
             group_ranks = [
                 (pp_group_id * self.pp_degree + i) for i in range(self.pp_degree)
             ]
-            for i in range(len(group_ranks)):
-                for j in range(i + 1, len(group_ranks)):
-                    rank_lo, rank_hi = group_ranks[i], group_ranks[j]
-                    pg_lo_to_hi = dist.new_group(ranks=[rank_lo, rank_hi], backend="nccl")
-                    pg_hi_to_lo = dist.new_group(ranks=[rank_lo, rank_hi], backend="nccl")
-                    if self.global_rank in (rank_lo, rank_hi):
-                        self.logger.debug(f"Global rank {self.global_rank} saving pp communicators for ranks {[rank_lo, rank_hi]}")
-                        self.pp_groups[(rank_lo, rank_hi)] = pg_lo_to_hi
-                        self.pp_groups[(rank_hi, rank_lo)] = pg_hi_to_lo
-            # if pp_group_id == my_pp_group_id:
-            #     self.logger.debug(
-            #         f"Global rank {self.global_rank} joined pp group {pp_group_id} "
-            #         f"with communicators for ranks {group_ranks}"
-            #     )
-
-        # Warm up all communicators to force eager NCCL initialization.
-        dummy = torch.zeros(1, device=self.device)
-        for key, pg in self.pp_groups.items():
-            self.logger.debug(f"Global rank {self.global_rank} warming up pp communicator {key}")
-            dist.all_reduce(dummy, group=pg)
-        torch.cuda.synchronize()
-        self.logger.info(f"Global rank {self.global_rank} warmed up {len(self.pp_groups)} pp communicators")
+            lo_hi_group = dist.new_group(ranks=group_ranks, backend="nccl")
+            hi_lo_group = dist.new_group(ranks=group_ranks, backend="nccl")
+            
+            if self.global_rank in group_ranks:
+                self.pp_lo_hi = lo_hi_group
+                self.pp_hi_lo = hi_lo_group
+            
+            # for i in range(len(group_ranks)):
+            #     for j in range(i + 1, len(group_ranks)):
+            #         rank_lo, rank_hi = group_ranks[i], group_ranks[j]
+            #         pg_lo_to_hi = dist.new_group(ranks=[rank_lo, rank_hi], backend="nccl")
+            #         pg_hi_to_lo = dist.new_group(ranks=[rank_lo, rank_hi], backend="nccl")
+            #         if self.global_rank in (rank_lo, rank_hi):
+            #             self.logger.debug(f"Global rank {self.global_rank} saving pp communicators for ranks {[rank_lo, rank_hi]}")
+            #             self.pp_groups[(rank_lo, rank_hi)] = pg_lo_to_hi
+            #             self.pp_groups[(rank_hi, rank_lo)] = pg_hi_to_lo
+
+        self.logger.info(f"Global rank {self.global_rank} warmed up pp communicators")
 
     def shutdown(self):
         dist.destroy_process_group()
@@ -600,51 +609,22 @@ def dummy_kernel_user(tokens, starts, offsets):
                 compiled_dummy = torch.compile(dummy_kernel_user, mode="reduce-overhead")
                 _ = compiled_dummy(dummy_tokens, dummy_start, dummy_offsets)
             
-            # now try to access the kernel side table and register at index 0
+            # Register the kernel at index 0 in id_to_kernel (kernel_idx=0 is baked into
+            # compiled graphs; constant_args are restored separately in _load_stage).
             from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
-            
-            # move the kernel to index 0
-            if hasattr(kernel_side_table, 'id_to_kernel') and hasattr(kernel_side_table, 'constant_args'):
+            if hasattr(kernel_side_table, 'id_to_kernel'):
                 registered_kernels = list(kernel_side_table.id_to_kernel.items())
-                registered_constants = list(kernel_side_table.constant_args.items())
-                
-                if len(registered_kernels) > 0:
-                    # get the most recently registered kernel (highest index)
+                if registered_kernels:
                     max_idx, kernel = max(registered_kernels, key=lambda x: x[0])
-                    # manually register it at index 0
                     kernel_side_table.id_to_kernel[0] = kernel
-                    
-                    # register constant args if available
-                    # the graph might expect constant_args_idx=1, so we need to check what was registered
-                    if len(registered_constants) > 0:
-                        # get the constant args that correspond to our kernel
-                        max_const_idx, const_args = max(registered_constants, key=lambda x: x[0])
-                        for idx in range(20):
-                            kernel_side_table.constant_args[idx] = const_args
-                        self.logger.debug(
-                            f"Actor {self.global_rank} manually registered kernel at index 0 "
-                            f"(copied from index {max_idx}, total kernels: {len(registered_kernels)}) "
-                            f"and constant_args at index 1 (copied from index {max_const_idx}, total constants: {len(registered_constants)})"
-                        )
-                    else:
-                        # If no constant args were registered, we might need to create empty ones
-                        # But let's see if the graph actually needs them
-                        self.logger.info(
-                            f"Actor {self.global_rank} manually registered kernel at index 0 "
-                            f"(copied from index {max_idx}, total kernels: {len(registered_kernels)}) "
-                            f"but no constant_args found"
-                        )
+                    self.logger.debug(
+                        f"Actor {self.global_rank} registered kernel at id_to_kernel[0] "
+                        f"(copied from index {max_idx})"
+                    )
                 else:
                     self.logger.warning(
-                        f"Actor {self.global_rank} no kernels found in side table after compilation"
+                        f"Actor {self.global_rank} no kernels found in side table after dummy compile"
                     )
-            else:
-                # try accessing via different attribute names
-                attrs = [attr for attr in dir(kernel_side_table) if not attr.startswith('_')]
-                self.logger.warning(
-                    f"Actor {self.global_rank} kernel_side_table doesn't have expected attributes. "
-                    f"Available attributes: {attrs}"
-                )
         except Exception as e:
             self.logger.warning(
                 f"Actor {self.global_rank} failed to manually register kernel: {e}. "
@@ -685,6 +665,17 @@ def _load_stage(
 
         self._load_stage_kernels()
 
+        # Restore triton kernel constant_args that were captured at compile time.
+        # Each bucket may reference specific constant_args_idx values baked into the
+        # compiled graph; we restore them at the exact indices so the graph runs correctly.
+        try:
+            from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
+            for bd in modules_data:
+                for idx, args in bd.get("triton_constant_args", {}).items():
+                    kernel_side_table.constant_args[int(idx)] = args
+        except Exception as e:
+            self.logger.warning(f"Actor {self.global_rank} failed to restore triton constant_args: {e}")
+
         first_gm = None
         last_gm = None
 
@@ -790,77 +781,6 @@ def _load_stage(
         self.graph_modules[stage_id] = first_gm
         self.stage_id = stage_id
 
-        # Compute output activation shape from the last module for BWD recv buffer
-        # pre-allocation (e.g. interleaved 1F1B recv before forward has run).
-        last_realized = self.bucket_fwd_args[stage_id][-1]
-        last_b_input_idxs = self.input_idxs[(stage_id, len(modules_data) - 1)]
-        output_shapes = []
-
-        # Try FX graph node metadata first.
-        for node in last_gm.graph.nodes:
-            if node.op == "output":
-                out_args = node.args[0]
-                if not isinstance(out_args, (tuple, list)):
-                    out_args = [out_args]
-                for out_node in out_args:
-                    if isinstance(out_node, torch.fx.Node):
-                        meta_val = out_node.meta.get("val", None)
-                        if meta_val is not None and isinstance(meta_val, torch.Tensor) and meta_val.requires_grad:
-                            output_shapes.append((tuple(meta_val.shape), meta_val.dtype))
-                break
-
-        if not output_shapes:
-            # Fallback: probe forward pass with dummy inputs.
-            probe_args = list(last_realized)
-            g_probe = torch.Generator(device=self.device)
-            g_probe.manual_seed(42 + stage_id)
-            for i in last_b_input_idxs:
-                meta = modules_data[-1]["graphargs"][i]
-                if meta is not None:
-                    t = torch.zeros(meta.shape, dtype=meta.dtype, device=self.device)
-                    t.requires_grad_(bool(getattr(meta, "requires_grad", True)))
-                    probe_args[i] = t
-            # Ensure any Triton kernel_idx / constant_args_idx values baked into
-            # this graph are registered in the side table.  During compilation on
-            # the coordinator the side table may have accumulated many entries
-            # before our kernel was registered, giving it a high index (e.g. 20).
-            # The actor's dummy compilation registers the same data at a low index;
-            # we just alias the graph's expected index to that existing entry.
-            try:
-                from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
-                for _node in last_gm.graph.nodes:
-                    if _node.op != "call_function":
-                        continue
-                    if "triton_kernel_wrapper" not in str(getattr(_node, "target", "")):
-                        continue
-                    _ca_idx = _node.kwargs.get("constant_args_idx")
-                    _k_idx = _node.kwargs.get("kernel_idx")
-                    if _ca_idx is not None and _ca_idx not in kernel_side_table.constant_args:
-                        if kernel_side_table.constant_args:
-                            kernel_side_table.constant_args[_ca_idx] = next(
-                                iter(kernel_side_table.constant_args.values())
-                            )
-                    if _k_idx is not None and _k_idx not in kernel_side_table.id_to_kernel:
-                        if kernel_side_table.id_to_kernel:
-                            kernel_side_table.id_to_kernel[_k_idx] = next(
-                                iter(kernel_side_table.id_to_kernel.values())
-                            )
-            except Exception:
-                pass  # best-effort; probe below will surface the real error if needed
-            with torch.no_grad():
-                probe_out = last_gm.forward(*probe_args)
-            for out in (probe_out if isinstance(probe_out, (tuple, list)) else [probe_out]):
-                if isinstance(out, torch.Tensor) and out.requires_grad:
-                    output_shapes.append((tuple(out.shape), out.dtype))
-
-        if output_shapes:
-            self.output_activation_shape[stage_id] = output_shapes
-            self.logger.debug(f"Stage {stage_id} output activation shapes: {output_shapes}")
-        else:
-            self.logger.warning(
-                f"Could not determine output activation shape for stage {stage_id}"
-            )
-
     # -----------------------------------------------------------------------
     # DAG-based execution
     # -----------------------------------------------------------------------
@@ -869,6 +789,14 @@ def load_dag(self, dag: TaskDAG) -> None:
         """Store the per-rank TaskDAG for subsequent run_dag() calls."""
         self.dag = dag
 
+    def get_all_params(self) -> dict:
+        """Return {(stage_id, bucket_id): flat_cpu_tensor} for every trainable bucket."""
+        return {
+            key: tensor.detach().cpu().clone()
+            for key, tensor in self.bucket_flat_params.items()
+            if tensor is not None
+        }
+
     def get_bucket_fwd_counts(self) -> dict:
         """Return the number of forward buckets for each bucketed stage on this actor."""
         return {stage_id: len(fns) for stage_id, fns in self.bucket_fwd_fns.items()}
@@ -887,8 +815,6 @@ def run_dag(self, loss_fn=None):
           on the recv_event so the recv buffer is populated before compute reads it.
         - RECV records a recv_event after the dist.recv completes.
         """
-        from collections import deque
-
         assert self.dag is not None, "load_dag() must be called before run_dag()"
         dag = self.dag
 
@@ -919,15 +845,20 @@ def run_dag(self, loss_fn=None):
         self.recv_events = {}
         comp_events: dict = {}  # (stage_id, mb_idx, task_type, bucket_id) -> cuda.Event
 
-        # Kahn's algorithm: combined data + temporal predecessor counts
-        in_degree = {
-            id(n): len(n.data_preds) + (1 if n.temporal_pred is not None else 0)
-            for n in dag.nodes
-        }
-        ready: deque = deque(n for n in dag.nodes if in_degree[id(n)] == 0)
+        # Sort nodes by time_step.  Nodes that share a time_step run on different
+        # CUDA streams (e.g. a RECV alongside the preceding compute task), so
+        # having two nodes of the same type at the same time_step would indicate
+        # a scheduling error — assert against it before dispatching.
+        sorted_nodes = sorted(dag.nodes, key=lambda n: n.time_step)
+        ts_types: dict[int, set] = {}
+        for node in sorted_nodes:
+            ts, ttype = node.time_step, node.task.type
+            assert ttype not in ts_types.get(ts, set()), (
+                f"run_dag: time_step={ts} has two nodes of type {ttype}"
+            )
+            ts_types.setdefault(ts, set()).add(ttype)
 
-        while ready:
-            node = ready.popleft()
+        for node in sorted_nodes:
             task = node.task
             # batches always has exactly one entry for single-stage tasks
             batch = task.batches[0]
@@ -935,7 +866,7 @@ def run_dag(self, loss_fn=None):
             mb_idx = batch.mb_idx
 
             self.logger.debug(
-                f"run_dag dispatch: {task.type.value} s{stage_id} mb{mb_idx} bkt={task.bucket_id}"
+                f"run_dag dispatch (time {node.time_step}): {task.type.value} s{stage_id} mb{mb_idx} bkt={node.bucket_id}"
             )
 
             match task.type:
@@ -945,19 +876,19 @@ def run_dag(self, loss_fn=None):
                     _cs = compute_node.task.batches[0].stage_id
                     _cm = compute_node.task.batches[0].mb_idx
                     _ct = compute_node.task.type
-                    send_key = (_cs, compute_node.task.bucket_id) if _ct == TaskType.FWD else None
-                    comp_key = (_cs, _cm, _ct, compute_node.task.bucket_id)
+                    send_key = (_cs, compute_node.bucket_id) if _ct == TaskType.FWD else None
+                    comp_key = (_cs, _cm, _ct, compute_node.bucket_id)
                     self.p2p_send_stream.wait_event(comp_events[comp_key])
                     self._exec_send(stage_id, mb_idx, send_key, node.peer_pp_rank)
 
                 case TaskType.RECV:
                     compute_node = node.data_succs[0]
-                    recv_key = (stage_id, compute_node.task.bucket_id) \
+                    recv_key = (stage_id, compute_node.bucket_id) \
                         if compute_node.task.type == TaskType.FWD else None
                     self._exec_recv(stage_id, mb_idx, recv_key, node.peer_pp_rank)
 
                 case TaskType.FWD_A2A:
-                    bucket_id = task.bucket_id
+                    bucket_id = node.bucket_id
                     torch.cuda.nvtx.range_push(f"fwd_a2a_s{stage_id}_b{bucket_id}_mb{mb_idx}")
                     self.a2a_stream.wait_event(
                         comp_events[(stage_id, mb_idx, TaskType.FWD, bucket_id)]
@@ -970,7 +901,7 @@ def run_dag(self, loss_fn=None):
                     self.comp_stream.wait_event(a2a_evt)
 
                 case TaskType.BWD_A2A:
-                    bucket_id = task.bucket_id
+                    bucket_id = node.bucket_id
                     torch.cuda.nvtx.range_push(f"bwd_a2a_s{stage_id}_b{bucket_id}_mb{mb_idx}")
                     bwd_evt = (
                         comp_events.get((stage_id, mb_idx, TaskType.BWD, bucket_id + 1))
@@ -985,20 +916,20 @@ def run_dag(self, loss_fn=None):
                     self.comp_stream.wait_event(a2a_evt)
 
                 case TaskType.ALL_REDUCE:
-                    bucket_id = task.bucket_id
+                    bucket_id = node.bucket_id
                     bwd_node = node.data_preds[0]
                     bwd_key = (
                         bwd_node.task.batches[0].stage_id,
                         bwd_node.task.batches[0].mb_idx,
                         bwd_node.task.type,
-                        bwd_node.task.bucket_id,
+                        bwd_node.bucket_id,
                     )
                     torch.cuda.nvtx.range_push(f"all_reduce_s{stage_id}_b{bucket_id}")
                     self._exec_all_reduce(stage_id, bucket_id, comp_events[bwd_key])
                     torch.cuda.nvtx.range_pop()
             
                 case TaskType.FWD:
-                    bucket_id = task.bucket_id
+                    bucket_id = node.bucket_id
                     recv_key = ((stage_id, bucket_id), mb_idx)
                     if recv_key in self.recv_events:
                         self.comp_stream.wait_event(self.recv_events.pop(recv_key))
@@ -1012,7 +943,7 @@ def run_dag(self, loss_fn=None):
                     comp_events[(stage_id, mb_idx, TaskType.FWD, bucket_id)] = evt
 
                 case TaskType.BWD:
-                    bucket_id = task.bucket_id
+                    bucket_id = node.bucket_id
                     recv_key = (None, mb_idx)
                     if recv_key in self.recv_events:
                         self.comp_stream.wait_event(self.recv_events.pop(recv_key))
@@ -1026,7 +957,7 @@ def run_dag(self, loss_fn=None):
                     comp_events[(stage_id, mb_idx, TaskType.BWD, bucket_id)] = evt
 
                 case TaskType.BWD_I:
-                    bucket_id = task.bucket_id
+                    bucket_id = node.bucket_id
                     recv_key = (None, mb_idx)
                     if recv_key in self.recv_events:
                         self.comp_stream.wait_event(self.recv_events.pop(recv_key))
@@ -1051,15 +982,6 @@ def run_dag(self, loss_fn=None):
                     self._update()
                     torch.cuda.nvtx.range_pop()
 
-            # Decrement in-degree for all successors; execute in topological order.
-            all_succs = list(node.data_succs)
-            if node.temporal_succ is not None:
-                all_succs.append(node.temporal_succ)
-            for succ in all_succs:
-                in_degree[id(succ)] -= 1
-                if in_degree[id(succ)] == 0:
-                    ready.append(succ)
-
     def _exec_send(
         self, stage_id: int, mb_idx: int, key, peer_pp_rank: int
     ) -> None:
@@ -1089,11 +1011,13 @@ def _exec_send(
         with torch.cuda.stream(self.p2p_send_stream):
             tensors = buf if isinstance(buf, (list, tuple)) else [buf]
             self._start_timing(self.p2p_send_stream, "p2p_send")
+            pp_group = self.pp_lo_hi if global_dst_rank > self.global_rank else self.pp_hi_lo
             for tensor in tensors:
                 dist.send(
                     tensor,
                     dst=global_dst_rank,
-                    group=self.pp_groups[(self.global_rank, global_dst_rank)],
+                    # group=self.pp_groups[(self.global_rank, global_dst_rank)],
+                    group=pp_group,
                 )
             self._stop_timing(self.p2p_send_stream, "p2p_send")
 
@@ -1130,20 +1054,21 @@ def _exec_recv(
             # In interleaved schedules the BWD recv may be issued before the
             # corresponding FWD has run, so fall back to the pre-computed shape.
             act_list = self.out_activation[stage_id].get(mb_idx)
-            if act_list is not None:
-                buf = [torch.empty_like(a) for a in act_list]
-            else:
-                shapes = self.output_activation_shape.get(stage_id, [])
-                buf = [torch.empty(shape, dtype=dtype, device=self.device) for shape, dtype in shapes]
+            assert act_list is not None, (
+                f"BWD recv for stage {stage_id} mb {mb_idx} before FWD has run"
+            )
+            buf = [torch.empty_like(a) for a in act_list]
 
         with torch.cuda.stream(self.p2p_recv_stream):
             tensors = buf if isinstance(buf, list) else [buf]
             self._start_timing(self.p2p_recv_stream, "p2p_recv")
+            pp_group = self.pp_hi_lo if global_src_rank > self.global_rank else self.pp_lo_hi
             for tensor in tensors:
                 dist.recv(
                     tensor,
                     src=global_src_rank,
-                    group=self.pp_groups[(global_src_rank, self.global_rank)],
+                    # group=self.pp_groups[(global_src_rank, self.global_rank)],
+                    group=pp_group,
                 )
             self._stop_timing(self.p2p_recv_stream, "p2p_recv")
 
@@ -1374,23 +1299,27 @@ def _backward_input_dag(self, stage_id: int, bucket_id: int, mb_idx: int, *, los
                 f"Stage {stage_id} BWD_I bucket {bucket_id} mb {mb_idx}: boundary .grad is None"
             )
 
-        # Stage 0 special case: no prev stage to send grad to.  Cache upstream_grad for
-        # BWD_W and return early (BWD_W will use out_activation directly for stage 0).
+        # Stage 0: no prev stage, so no input-grad send.  Cache upstream_grad for BWD_W
+        # which runs the full backward for stage 0.
+        # When the stage has A2A boundaries we still need to propagate through each
+        # intermediate bucket to set x_a2a.grad for the BWD_A2A nodes.  Fall through
+        # in that case; bucket 0 always returns early (nothing further to propagate).
         if stage_id == 0:
             if bucket_id == n_buckets - 1:
                 ug = upstream_grads[0] if len(upstream_grads) == 1 else upstream_grads
                 self.upstream_grad_cache[stage_id][mb_idx] = ug
-            return
+            stage_a2a = self.a2a_boundaries.get(stage_id, {})
+            if not stage_a2a or bucket_id == 0:
+                return
 
         # ---- Step 2: activation inputs (what we compute grad w.r.t.) ----
         if bucket_id > 0:
             prev_bkt = bucket_id - 1
             _, detached_outs_prev = self.bucket_buffer[(stage_id, mb_idx, prev_bkt)]
-            if prev_bkt in a2a_bnd:
-                tensor_idx = a2a_bnd[prev_bkt]
-                activation_inputs = [detached_outs_prev[tensor_idx]]  # x_a2a leaf
-            else:
-                activation_inputs = [d for d in detached_outs_prev if d.requires_grad]
+            # Include all requires_grad outputs of prev_bkt, not just the A2A tensor.
+            # _exec_fwd_a2a feeds all detached_outs to the next bucket, so other cross-segment
+            # tensors (e.g. top_scores, token_indices) are also real inputs and need gradients.
+            activation_inputs = [d for d in detached_outs_prev if d.requires_grad]
         else:
             # Bucket 0: grad w.r.t. stage input
             _inp = self.inp_activation[stage_id][mb_idx]
@@ -1458,8 +1387,9 @@ def hook(grad_inputs):
                     self.recv_buffer[(None, mb_idx)] = output_grad
             self.inp_activation[stage_id][mb_idx] = None
 
-        # Free output reference after processing the first-to-bwd bucket
-        if bucket_id == n_buckets - 1:
+        # Free output reference after processing the first-to-bwd bucket.
+        # Stage 0 skips this: BWD_W reads out_activation directly for stage 0.
+        if bucket_id == n_buckets - 1 and stage_id != 0:
             for t in outputs:
                 t.detach_()
             self.out_activation[stage_id][mb_idx] = None
@@ -1475,9 +1405,13 @@ def _exec_all_reduce(self, stage_id: int, bucket_id: int, bwd_event: torch.cuda.
         wait on it before stepping the optimizer.
         """
         lookup_key = (stage_id, bucket_id)
+        flat_grads = self.bucket_flat_grads.get(lookup_key)
+        assert flat_grads is not None, (
+            f"ALL_REDUCE dispatched for stage {stage_id} bucket {bucket_id} "
+            f"but no flat_grads tensor was allocated — this bucket has no trainable parameters."
+        )
         with torch.cuda.stream(self.comm_stream):
             self.comm_stream.wait_event(bwd_event)
-            flat_grads = self.bucket_flat_grads.get(lookup_key)
             self._start_timing(self.comm_stream, f"all_reduce")
             dist.all_reduce(flat_grads, group=self.dp_group)
             self._stop_timing(self.comm_stream, f"all_reduce")
@@ -1495,37 +1429,126 @@ def _backward_weight_dag(self, stage_id: int, mb_idx: int, *, loss_fn=None) -> N
         updated_params: dict[int, torch.nn.Parameter] = {}
 
         if stage_id == 0:
-            upstream_grad = self.upstream_grad_cache[stage_id][mb_idx]
-            _out = self.out_activation[stage_id][mb_idx]
-            out_activation = _out[0] if isinstance(_out, list) else _out
-            if stage_id < self.num_stages - 1:
-                with torch.cuda.stream(comp_stream):
-                    gparams = torch.autograd.grad(
-                        outputs=out_activation,
-                        inputs=stage_params,
-                        grad_outputs=upstream_grad,
-                        retain_graph=False,
-                    )
-            else:
-                assert loss_fn is not None
-                labels = self.labels
-                with torch.cuda.stream(comp_stream):
-                    loss = loss_fn(out_activation, labels)
-                    gparams = torch.autograd.grad(
-                        outputs=loss,
-                        inputs=stage_params,
-                        retain_graph=False,
-                    )
+            stage_a2a = self.a2a_boundaries.get(stage_id, {})
+            if stage_a2a:
+                # Bucketed stage 0 with A2A: out_activation (bkt=last) has no
+                # differentiable path to pre-dispatch params because all
+                # intermediate bucket inputs are detached leaves.
+                # Two-step approach:
+                #   Step 1 — ZeroBubble mechanism for intermediate-bucket params
+                #             (e.g. expert params) captured by BWD_I hooks.
+                #   Step 2 — full backward through bkt=0 using .grad values set
+                #             on its detached_outs by BWD_I(bkt=1) and BWD_A2A(bkt=0).
+
+                # Step 1: expert / intermediate-bucket params via GradientEdge
+                grad_acc_to_weight_s0: Dict[Node, Parameter] = {}
+                for param in stage_params:
+                    node = _get_grad_fn_or_grad_acc(param)
+                    grad_acc_to_weight_s0[node] = param
+
+                param_groups_s0 = self.bw_param_groups[stage_id].get(mb_idx) or []
+                for pg in param_groups_s0:
+                    intermediates: List[Node] = pg.get("intermediates", [])
+                    intermediate_grads = pg.get("grads", None)
+
+                    if not intermediates or intermediate_grads is None:
+                        continue
 
-            assert len(gparams) == len(stage_params)
-            for p, pg in zip(stage_params, gparams):
-                if pg is None:
-                    continue
-                if p.grad is None:
-                    p.grad = pg.clone()
+                    intermediate_edges: List[GradientEdge] = []
+                    intermediate_edge_grads: List[torch.Tensor] = []
+
+                    for intermediate_node, grad_inputs in zip(intermediates, intermediate_grads):
+                        if grad_inputs is None:
+                            continue
+                        gs = [x for x in grad_inputs if x is not None]
+                        if not gs:
+                            continue
+                        summed = sum(gs)
+                        intermediate_edges.append(GradientEdge(intermediate_node, 0))
+                        intermediate_edge_grads.append(summed)
+
+                    del pg["intermediates"]
+
+                    if not intermediate_edges:
+                        continue
+
+                    mapped_param_nodes = [p for p in pg["params"] if p in grad_acc_to_weight_s0]
+                    if not mapped_param_nodes:
+                        continue
+
+                    weight_edges = tuple(GradientEdge(p, 0) for p in mapped_param_nodes)
+
+                    with torch.cuda.stream(comp_stream):
+                        gparams_s0 = torch.autograd.grad(
+                            outputs=intermediate_edges,
+                            inputs=weight_edges,
+                            grad_outputs=intermediate_edge_grads,
+                            retain_graph=False,
+                        )
+
+                    del pg["grads"]
+
+                    assert len(gparams_s0) == len(mapped_param_nodes)
+                    for param_node, dw in zip(mapped_param_nodes, gparams_s0):
+                        if dw is None:
+                            continue
+                        weight = grad_acc_to_weight_s0[param_node]
+                        if weight.grad is None:
+                            weight.grad = dw
+                        else:
+                            weight.grad.add_(dw)
+                        updated_params[id(weight)] = weight
+
+                # Step 2: pre-dispatch params (embedding, router, attention) via
+                #         .grad values set on bkt=0 detached_outs by BWD_A2A(bkt=0)
+                #         and BWD_I(bkt=1).
+                buf0 = self.bucket_buffer.pop((stage_id, mb_idx, 0), None)
+                if buf0 is not None:
+                    pre_detach_outs0, detached_outs0 = buf0
+                    outputs_bwd = [
+                        p for p, d in zip(pre_detach_outs0, detached_outs0)
+                        if d.requires_grad and d.grad is not None
+                    ]
+                    grads_bwd = [
+                        d.grad for p, d in zip(pre_detach_outs0, detached_outs0)
+                        if d.requires_grad and d.grad is not None
+                    ]
+                    if outputs_bwd:
+                        with torch.cuda.stream(comp_stream):
+                            torch.autograd.backward(outputs_bwd, grads_bwd)
+            else:
+                # Non-bucketed stage 0: single-segment full backward from out_activation.
+                upstream_grad = self.upstream_grad_cache[stage_id][mb_idx]
+                _out = self.out_activation[stage_id][mb_idx]
+                out_activation = _out[0] if isinstance(_out, list) else _out
+                if stage_id < self.num_stages - 1:
+                    with torch.cuda.stream(comp_stream):
+                        gparams = torch.autograd.grad(
+                            outputs=out_activation,
+                            inputs=stage_params,
+                            grad_outputs=upstream_grad,
+                            retain_graph=False,
+                        )
                 else:
-                    p.grad.add_(pg)
-                updated_params[id(p)] = p
+                    assert loss_fn is not None
+                    labels = self.labels
+                    with torch.cuda.stream(comp_stream):
+                        loss = loss_fn(out_activation, labels)
+                        gparams = torch.autograd.grad(
+                            outputs=loss,
+                            inputs=stage_params,
+                            retain_graph=False,
+                        )
+
+                assert len(gparams) == len(stage_params)
+                for p, pg in zip(stage_params, gparams):
+                    if pg is None:
+                        continue
+                    if p.grad is None:
+                        p.grad = pg.clone()
+                    else:
+                        p.grad.add_(pg)
+                    updated_params[id(p)] = p
         else:
             grad_acc_to_weight: Dict[Node, Tuple[Parameter, int]] = {}
             for param in stage_params:
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 92d2a00..f90931f 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -14,12 +14,13 @@
 
 from .piper_actor import _create_actors
 from .piper_utils import piper_metadata, create_logger, LOG_LEVEL
-from .piper_exec import Schedule2D
+from .piper_exec import DAGEdge, Schedule2D, CompType, Task, BatchMeta
 from .piper import piper
 
 logger = create_logger("piper_compile", LOG_LEVEL)
 
 _RANK0_ADDR_ACTOR = "piper_rank0_addr"
+_COMPILED_DATA_ACTOR = "piper_compiled_data"
 
 @ray.remote
 class _Rank0AddrStore:
@@ -30,6 +31,185 @@ def __init__(self, addr: str, port: int):
     def get(self):
         return self._addr, self._port
 
+
+@ray.remote
+class _CompiledDataStore:
+    """Named actor used to share compiled stage/DAG data from dp_rank=0 to dp_rank>0.
+
+    dp_rank=0 calls publish() once after compilation.  All other dp_ranks poll
+    is_ready() and then call get() to retrieve the data, avoiding a redundant
+    torch.compile call for large models.
+    """
+    def __init__(self):
+        self._data = None
+        self._ready = False
+
+    def publish(self, data: dict):
+        self._data = data
+        self._ready = True
+
+    def is_ready(self) -> bool:
+        return self._ready
+
+    def get(self) -> dict:
+        return self._data
+
+
+
+def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdge], num_mbs: int) -> None:
+    """
+    Validate that the schedule respects well-formedness rules and DAG dependencies.
+    
+    Args:
+        schedule: 2D array with one row per device and one column per time step
+        dag_edges: List of DAG edges defining stage dependencies
+        num_mbs: Number of microbatches in the schedule
+        
+    Raises:
+        ValueError: If the schedule violates any validation rules
+    """
+    num_devices, num_steps = len(schedule), len(schedule[0]) if schedule else 0
+    
+    for row in schedule:
+        assert len(row) == num_steps, "Each row must have the same number of time steps"
+    
+    # Check well-formedness: no duplicates, pp_rank matches row, and all stages present
+    all_tasks = set()
+    microbatch_tasks = {}  # mb_idx -> set of (stage_id, type)
+    
+    for pp_rank in range(num_devices):
+        for time_step in range(num_steps):
+            task = schedule[pp_rank][time_step]
+            if task is not None:
+                # Check pp_rank matches row
+                if task.pp_rank != pp_rank:
+                    raise ValueError(
+                        f"Task pp_rank {task.pp_rank} does not match row {pp_rank} "
+                        f"at time step {time_step}"
+                    )
+                
+                # Check for duplicates and track by (stage_id, mb_idx, type) per batch
+                for batch in task.batches:
+                    comp_type = (
+                        task.type
+                        if task.type != CompType.FWD_BWD
+                        else (CompType.FWD if batch is task.batches[0] else CompType.BWD)
+                    )
+                    task_key = (batch.stage_id, batch.mb_idx, comp_type)
+                    if task_key in all_tasks:
+                        raise ValueError(
+                            f"Duplicate task found: stage_id={batch.stage_id}, "
+                            f"mb_idx={batch.mb_idx}, type={comp_type}"
+                        )
+                    all_tasks.add(task_key)
+                    if batch.mb_idx not in microbatch_tasks:
+                        microbatch_tasks[batch.mb_idx] = set()
+                    microbatch_tasks[batch.mb_idx].add((batch.stage_id, comp_type))
+    
+    # Get all required stages from DAG edges
+    all_required_stages = set()
+    for edge in dag_edges:
+        all_required_stages.add(edge.from_stage)
+        all_required_stages.add(edge.to_stage)
+    
+    # Check that each microbatch has all required forward and backward stages
+    for mb_idx, tasks in microbatch_tasks.items():
+        # Find all stages that have forward/backward tasks for this microbatch
+        fwd_stages = {stage_id for stage_id, task_type in tasks if task_type == CompType.FWD}
+        bwd_stages = {
+            stage_id
+            for stage_id, task_type in tasks
+            if task_type in (CompType.BWD, CompType.BWD_I)
+        }
+        
+        # Check that all required stages have forward tasks
+        missing_fwd = all_required_stages - fwd_stages
+        if missing_fwd:
+            raise ValueError(f"Microbatch {mb_idx} missing forward stages: {missing_fwd}")
+
+        # Check that all required stages have backward tasks (BWD or BWD_I)
+        missing_bwd = all_required_stages - bwd_stages
+        if missing_bwd:
+            raise ValueError(
+                f"Microbatch {mb_idx} missing backward stages: {missing_bwd} "
+                f"(need BWD, BWD_I, or FWD_BWD backward for each stage)"
+            )
+    
+    # Check pipeline stage dependencies
+    for mb_idx in range(num_mbs):
+        # Find all tasks for this microbatch
+        fwd_times: dict[int, int] = {}  # stage_id -> time_step
+        bwd_times: dict[int, int] = {}  # stage_id -> time_step (BWD or BWD_I)
+        bwd_w_times: dict[int, int] = {}  # stage_id -> time_step (BWD_W)
+        
+        for pp_rank in range(num_devices):
+            for time_step in range(num_steps):
+                task = schedule[pp_rank][time_step]
+                if task is None:
+                    continue
+                for batch in task.batches:
+                    if batch.mb_idx != mb_idx:
+                        continue
+                    comp_type = (
+                        task.type
+                        if task.type != CompType.FWD_BWD
+                        else (CompType.FWD if batch is task.batches[0] else CompType.BWD)
+                    )
+                    if comp_type == CompType.FWD:
+                        fwd_times[batch.stage_id] = time_step
+                    elif comp_type in (CompType.BWD, CompType.BWD_I):
+                        bwd_times[batch.stage_id] = time_step
+                    elif comp_type == CompType.BWD_W:
+                        bwd_w_times[batch.stage_id] = time_step
+        
+        # Check forward stage ordering: if A -> B, then fwd(A) < fwd(B)
+        for edge in dag_edges:
+            from_stage, to_stage = edge.from_stage, edge.to_stage
+            if from_stage in fwd_times and to_stage in fwd_times:
+                if fwd_times[from_stage] >= fwd_times[to_stage]:
+                    raise ValueError(
+                        f"Forward stage ordering violation for microbatch {mb_idx}: "
+                        f"forward stage {from_stage} (time {fwd_times[from_stage]}) must come "
+                        f"before forward stage {to_stage} (time {fwd_times[to_stage]})"
+                    )
+        
+        # Check forward-backward ordering: fwd(A) < bwd(A)
+        for stage_id in fwd_times:
+            if stage_id in bwd_times:
+                if fwd_times[stage_id] >= bwd_times[stage_id]:
+                    raise ValueError(
+                        f"Forward-backward ordering violation for microbatch {mb_idx}, "
+                        f"stage {stage_id}: forward (time {fwd_times[stage_id]}) must come "
+                        f"before backward (time {bwd_times[stage_id]})"
+                    )
+        
+        # Check backward stage ordering: if A -> B, then bwd(B) < bwd(A)
+        for edge in dag_edges:
+            from_stage, to_stage = edge.from_stage, edge.to_stage
+            if from_stage in bwd_times and to_stage in bwd_times:
+                if bwd_times[to_stage] >= bwd_times[from_stage]:
+                    raise ValueError(
+                        f"Backward stage ordering violation for microbatch {mb_idx}: "
+                        f"backward stage {to_stage} (time {bwd_times[to_stage]}) must come "
+                        f"before backward stage {from_stage} (time {bwd_times[from_stage]})"
+                    )
+        
+        # Check BWD_W must come after BWD_I for same (stage_id, mb_idx)
+        for stage_id in bwd_w_times:
+            if stage_id not in bwd_times:
+                raise ValueError(
+                    f"BWD_W for microbatch {mb_idx} stage {stage_id} has no corresponding "
+                    f"BWD_I or BWD task"
+                )
+            if bwd_w_times[stage_id] <= bwd_times[stage_id]:
+                raise ValueError(
+                    f"BWD_W ordering violation for microbatch {mb_idx}, stage {stage_id}: "
+                    f"BWD_W (time {bwd_w_times[stage_id]}) must come after "
+                    f"BWD_I/BWD (time {bwd_times[stage_id]})"
+                )
+            
+
+
 def piper_setup(
     model_class,
     model_args=(),
@@ -59,17 +239,23 @@ def piper_setup(
     """
 
     stage_to_device = schedule.stage_to_device()
-    print("Stage to device mapping:", stage_to_device)
     assert len(stage_to_device) > 0
     piper_metadata.stage_to_device = stage_to_device
     piper_metadata.use_activation_checkpointing = activation_checkpointing
     piper_metadata.bucketing = bucketing
     piper_metadata.schedule = schedule
 
+    dag_edges = []
+    # TODO: build dag by analyzing stage dependencies rather than assuming a linear chain
+    for stage_id in piper_metadata.stage_to_device.keys():
+        if stage_id < len(piper_metadata.stage_to_device.keys()) - 1:
+            dag_edges.append(DAGEdge(stage_id, stage_id+1))
+    _validate_schedule(schedule.grid, dag_edges=dag_edges, num_mbs=schedule.num_mbs())
+
     num_mbs = schedule.num_mbs()
     num_stages = schedule.num_stages()
     num_devices = schedule.num_ranks()
-
+    
     _create_actors(
         num_devices, optim_fn, num_mbs, num_stages,
         naive_gradient_sync, profile=nsight, stage_to_device=stage_to_device, pg=pg,
@@ -81,6 +267,11 @@ def piper_setup(
     import time
     dp_rank = int(os.environ["PIPER_DP_RANK"])
     if dp_rank == 0:
+        # Create the compiled-data store early so dp_rank>0 can poll for it.
+        _CompiledDataStore.options(
+            name=_COMPILED_DATA_ACTOR, lifetime="detached", get_if_exists=True
+        ).remote()
+
         # Get IP and a free port from actor 0's node — it will be the TCPStore server.
         master_addr, master_port = ray.get(piper_metadata.actors[0].get_node_ip_and_free_port.remote())
         _Rank0AddrStore.options(
@@ -107,42 +298,80 @@ def piper_setup(
         except Exception:
             pass
 
-    # Build the model directly on meta device
-    with torch.device("meta"):
-        model = model_class(*model_args, **model_kwargs).to(model_dtype)
-        # if model_dtype is not None:
-        #     model = model.to(model_dtype)
+    if dp_rank == 0:
+        # --- dp_rank=0: run torch.compile, build DAGs, publish compiled data ---
 
-    num_params = sum(p.numel() for p in model.parameters())
+        # Build the model directly on meta device
+        with torch.device("meta"):
+            model = model_class(*model_args, **model_kwargs)
+            if model_dtype is not None:
+                model = model.to(model_dtype)
 
-    if model_dtype == torch.bfloat16:
-        param_size_mb = num_params * 2 / (1024**3)  # bfloat16
-    elif model_dtype == torch.float32:
-        param_size_mb = num_params * 4 / (1024**3)  # float32
-    else:
-        raise ValueError(f"Unsupported model dtype: {model_dtype}")
-    print(f"Model size: {num_params/(1e6):.0f} M parameters ({param_size_mb:.2f} GB), dtype: {model_dtype}")
-    
+        num_params = sum(p.numel() for p in model.parameters())
+        if model_dtype == torch.bfloat16:
+            param_size_gb = num_params * 2 / (1024**3)
+        elif model_dtype == torch.float32:
+            param_size_gb = num_params * 4 / (1024**3)
+        else:
+            raise ValueError(f"Unsupported model dtype: {model_dtype}")
+        print(f"Model size: {num_params/(1e6):.0f} M parameters ({param_size_gb:.2f} GB), dtype: {model_dtype}")
 
-    compiled = torch.compile(model, backend=piper, fullgraph=True)
+        compiled = torch.compile(model, backend=piper, fullgraph=True)
+        meta_inputs = [x.to(device="meta") for x in example_inputs]
+        _ = compiled(*meta_inputs)
+        logger.info(f"DP rank 0 stage graphs loaded onto actors.")
 
-    dp_rank = int(os.environ["PIPER_DP_RANK"])
-    logger.info(f"DP rank {dp_rank+1} compiling (meta)...")
+        # Publish compiled stage data for dp_rank > 0.
+        compiled_store = ray.get_actor(_COMPILED_DATA_ACTOR)
+        ray.get(compiled_store.publish.remote(piper_metadata.compiled_stage_data))
+        logger.info(f"DP rank 0 published compiled stage data.")
 
-    # Create meta tensors from our example_inputs to pass to the compiled graph
-    
-    meta_inputs = [x.to(device="meta") for x in example_inputs]
-    _ = compiled(*meta_inputs)
+    else:
+        # --- dp_rank>0: wait for dp_rank=0 to finish, then load directly ---
+        logger.info(f"DP rank {dp_rank} waiting for dp_rank=0 to finish compiling...")
+        compiled_data = None
+        while compiled_data is None:
+            try:
+                store = ray.get_actor(_COMPILED_DATA_ACTOR)
+                if ray.get(store.is_ready.remote()):
+                    compiled_data = ray.get(store.get.remote())
+            except Exception:
+                pass
+            if compiled_data is None:
+                time.sleep(0.2)
+        logger.info(f"DP rank {dp_rank} received compiled stage data, loading onto actors...")
+
+        # Load stages onto this dp_rank's actors (same serialized graphs, different actor refs).
+        refs = []
+        for stage_id, (modules_data, a2a_boundaries) in compiled_data["stages"].items():
+            actor_id = piper_metadata.stage_to_device[stage_id]
+            actor = piper_metadata.actors[actor_id]
+            refs.append(actor._load_stage.remote(
+                stage_id,
+                modules_data,
+                a2a_boundaries,
+                use_activation_checkpointing=activation_checkpointing,
+            ))
+        ray.get(refs)
 
-    logger.info(f"DP rank {dp_rank+1} stage graphs loaded onto actors.")
+        # Restore piper_metadata fields that the piper backend sets on dp_rank=0.
+        piper_metadata.stage_bucket_counts = compiled_data["stage_bucket_counts"]
+        piper_metadata.trainable_bucket_keys = compiled_data["trainable_bucket_keys"]
+        piper_metadata.per_rank_dags = compiled_data["per_rank_dags"]
 
+        # Load the same per-rank DAGs onto this dp_rank's actors.
+        ray.get([
+            piper_metadata.actors[pp_rank].load_dag.remote(per_rank_dag)
+            for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags)
+        ])
+        logger.info(f"DP rank {dp_rank} stage graphs and DAGs loaded onto actors.")
 
     last_stage_rank = stage_to_device[num_stages - 1]
     ray.get(piper_metadata.actors[0].load_input.remote(example_inputs))
     ray.get(piper_metadata.actors[last_stage_rank].load_labels.remote(example_outputs))
-    logger.info(f"DP rank {dp_rank+1} real inputs/labels loaded onto actors.")
+    logger.info(f"DP rank {dp_rank} real inputs/labels loaded onto actors.")
 
-    logger.info(f"DP rank {dp_rank+1} done.")
+    logger.info(f"DP rank {dp_rank} done.")
     
 
 def piper_shutdown():
diff --git a/src/piper_coordinator.py b/src/piper_coordinator.py
index 0563845..f632eee 100644
--- a/src/piper_coordinator.py
+++ b/src/piper_coordinator.py
@@ -9,7 +9,7 @@
 # @ray.remote(num_gpus=0.1)
 
 # Use manual stage annotations- more stable
-@ray.remote
+@ray.remote(num_gpus=0.1)
 def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, master_addr, master_port, training_func: Callable, *args, **kwargs):
     logger = create_logger("piper_coordinator", LOG_LEVEL)
     logger.debug(f"Running DP rank {dp_rank+1} of {dp_degree} with {master_addr}:{master_port} as master address")
diff --git a/src/piper_exec.py b/src/piper_exec.py
index f39a771..4d5a6d1 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -43,13 +43,9 @@ class Task(NamedTuple):
     pp_rank: int
     batches: list[BatchMeta]
     type: TaskType
-    bucket_id: int = 0
-    a2a_tensor_idx: Optional[int] = None
 
     def __repr__(self) -> str:
-        bucket_str = f", bucket_id={self.bucket_id}" if self.bucket_id else ""
-        a2a_str = f", a2a_tensor_idx={self.a2a_tensor_idx}" if self.a2a_tensor_idx is not None else ""
-        return f"Task(pp_rank={self.pp_rank}, batches={[(batch.stage_id, batch.mb_idx) for batch in self.batches]}, type={self.type}{bucket_str}{a2a_str})"
+        return f"Task(pp_rank={self.pp_rank}, batches={[(batch.stage_id, batch.mb_idx) for batch in self.batches]}, type={self.type})"
 
 
 @dataclass
@@ -99,10 +95,11 @@ class TaskNode:
       the same microbatch index sits between them in the time-step ordering.  These
       edges may cross rows (actors).
 
-    * **Temporal** (``temporal_pred`` / ``temporal_succ``): drawn between
-      consecutive non-None tasks within the same row, but **only** when those
-      tasks do not already share a data-dependency edge.  Each task has at most
-      one temporal predecessor and one successor.
+    * **Temporal** (``temporal_preds`` / ``temporal_succs``): drawn between
+      consecutive non-None tasks within the same row.  Normally each task has
+      at most one temporal predecessor and one successor (a linear chain), but
+      :func:`overlap_a2a_tasks` can introduce forks and joins so the fields
+      are lists, mirroring ``data_preds`` / ``data_succs``.
     """
     task: Task
     pp_rank: int    # row index in Schedule2D.grid
@@ -111,12 +108,16 @@ class TaskNode:
     # Filled in by schedule_to_dag()
     data_preds: list["TaskNode"] = field(default_factory=list)
     data_succs: list["TaskNode"] = field(default_factory=list)
-    temporal_pred: Optional["TaskNode"] = None
-    temporal_succ: Optional["TaskNode"] = None
+    temporal_preds: list["TaskNode"] = field(default_factory=list)
+    temporal_succs: list["TaskNode"] = field(default_factory=list)
 
     # For SEND/RECV nodes: the peer pipeline rank
     peer_pp_rank: Optional[int] = None
 
+    # Set by graph-transform passes (not meaningful for raw schedule tasks)
+    bucket_id: int = 0
+    a2a_tensor_idx: Optional[int] = None
+
     def node_id(self) -> str:
         """Unique string identifier for use as a graph node key."""
         ttype = self.task.type.value if self.task.type is not None else "none"
@@ -129,14 +130,15 @@ def _rebuild_task_dag(node_data):
     by TaskDAG.__reduce__.  Must be a module-level function so pickle can find
     it by name."""
     nodes = [
-        TaskNode(task=d[0], pp_rank=d[1], time_step=d[2], peer_pp_rank=d[3])
+        TaskNode(task=d[0], pp_rank=d[1], time_step=d[2], peer_pp_rank=d[3],
+                 bucket_id=d[8], a2a_tensor_idx=d[9])
         for d in node_data
     ]
     for node, d in zip(nodes, node_data):
-        node.data_preds    = [nodes[j] for j in d[4]]
-        node.data_succs    = [nodes[j] for j in d[5]]
-        node.temporal_pred = nodes[d[6]] if d[6] is not None else None
-        node.temporal_succ = nodes[d[7]] if d[7] is not None else None
+        node.data_preds     = [nodes[j] for j in d[4]]
+        node.data_succs     = [nodes[j] for j in d[5]]
+        node.temporal_preds = [nodes[j] for j in d[6]]
+        node.temporal_succs = [nodes[j] for j in d[7]]
     return TaskDAG(nodes=nodes)
 
 
@@ -150,7 +152,7 @@ class TaskDAG:
 
     def roots(self) -> list[TaskNode]:
         """Return nodes that have no predecessors of any kind."""
-        return [n for n in self.nodes if not n.data_preds and n.temporal_pred is None]
+        return [n for n in self.nodes if not n.data_preds and not n.temporal_preds]
 
     def __reduce__(self):
         """Serialize as a flat list of index-referenced tuples to avoid the
@@ -162,8 +164,9 @@ def __reduce__(self):
                 n.task, n.pp_rank, n.time_step, n.peer_pp_rank,
                 [idx[id(p)] for p in n.data_preds],
                 [idx[id(s)] for s in n.data_succs],
-                idx[id(n.temporal_pred)] if n.temporal_pred is not None else None,
-                idx[id(n.temporal_succ)] if n.temporal_succ is not None else None,
+                [idx[id(p)] for p in n.temporal_preds],
+                [idx[id(s)] for s in n.temporal_succs],
+                n.bucket_id, n.a2a_tensor_idx,
             )
             for n in self.nodes
         ]
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index e6d4960..35bcf4f 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -4,7 +4,7 @@
 import torch
 import torch.fx as fx
 import torch.distributed as dist
-from collections import defaultdict
+from collections import defaultdict, deque
 from pathlib import Path
 from typing import Optional
 import operator
@@ -96,7 +96,7 @@ def backward(ctx, grad_output):
         The backward of all_to_all_single is another all_to_all_single operation
         that reverses the communication pattern.
         """
-        logger.info(f"Dispatch AllToAllSingleFunction backward rank={ctx.global_rank}, shape={grad_output.shape}")
+        logger.debug(f"Dispatch AllToAllSingleFunction backward rank={ctx.global_rank}, shape={grad_output.shape}")
 
         if grad_output is None:
             return None, None, None
@@ -127,11 +127,7 @@ def backward(ctx, grad_output):
             ctx.actor_self.bwd_a2a_counter += 1
         comp_stream.wait_event(comm_finished_event)
 
-        # logger.info(f"Completed AllToAllSingleFunction backward rank={ctx.global_rank} time={ctx.actor_self.trace_data['bwd_a2a'][-1]:.2f} ms")
-        if ctx.actor_self.trace_data.get('bwd_a2a') and len(ctx.actor_self.trace_data['bwd_a2a']) > 0:
-            logger.info(f"Completed AllToAllSingleFunction backward rank={ctx.global_rank} time={ctx.actor_self.trace_data['bwd_a2a'][-1]:.2f} ms")
-        else:
-            logger.info(f"Completed AllToAllSingleFunction backward rank={ctx.global_rank}")
+        logger.debug(f"Completed AllToAllSingleFunction backward rank={ctx.global_rank}")
         
         # Return gradients: grad_output flows to grad_input, None for group
         return grad_input, grad_input, None
@@ -549,10 +545,10 @@ def _split_gm_by_stages(gm) -> tuple[fx.GraphModule, list[tuple[int, fx.GraphMod
                     nodes_with_metadata.append((node, stage_annotation_id))
     
     if not nodes_with_metadata:
-        logger.info("No stage nodes found in graph")
+        logger.debug("No stage nodes found in graph")
         return gm, []
     else:
-        logger.info(f"Found stage nodes in graph")
+        logger.debug(f"Found stage nodes in graph")
     
     # Group nodes by stage ID for creating stage modules
     stage_code = defaultdict(list)  # stage_id -> list of all nodes for this stage
@@ -1132,7 +1128,7 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> tuple[fx.GraphModule, int]:
     annotation_blocks = list(blocks_by_key.values())
     
     n_a2a_ops = len(annotation_blocks)
-    logger.info(f"Found {len(annotation_blocks)} contiguous annotation blocks")
+    logger.debug(f"Found {len(annotation_blocks)} contiguous annotation blocks")
     
     # For each contiguous block, find the output node (the one used outside the annotation)
     # This is the node that should have communication applied to it
@@ -1165,7 +1161,7 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> tuple[fx.GraphModule, int]:
                 last_node = max(nodes_in_block, key=lambda x: node_list.index(x[0]))
                 annotated_output_nodes.append(last_node)
     
-    logger.info(f"Found {len(annotated_output_nodes)} annotation blocks requiring communication operations")
+    logger.debug(f"Found {len(annotated_output_nodes)} annotation blocks requiring communication operations")
     
     # Create a new graph to build the transformed version
     new_graph = fx.Graph()
@@ -1221,7 +1217,7 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> tuple[fx.GraphModule, int]:
                 reshape_type, reshape_shape = reshape_info
                 if reshape_type == "input":
                     # DEBUG: Log reshape information
-                    logger.info(f"Applying input reshape {reshape_shape} to node {node.name}")
+                    logger.debug(f"Applying input reshape {reshape_shape} to node {node.name}")
                     if isinstance(reshape_shape, (list, tuple)) and -1 in reshape_shape:
                         logger.warning(f"Input reshape shape contains -1: {reshape_shape}, this may cause shape inference issues")
                     # Reshape before communication
@@ -1229,7 +1225,6 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> tuple[fx.GraphModule, int]:
                         torch.reshape,
                         (new_node, reshape_shape)
                     )
-                    logger.debug(f"Applied input reshape {reshape_shape} for node {node.name}")
             
             # Insert communication operations after this node
             # Following the exact pattern from the comments in mixtral.py:
@@ -1260,7 +1255,7 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> tuple[fx.GraphModule, int]:
                 if reshape_type == "output":
                     # Reshape after communication
                     # DEBUG: Log reshape information
-                    logger.info(f"Applying output reshape {reshape_shape} to node {node.name}, buf_node shape inference needed")
+                    logger.debug(f"Applying output reshape {reshape_shape} to node {node.name}, buf_node shape inference needed")
                     # Log the reshape shape for debugging
                     if isinstance(reshape_shape, (list, tuple)) and -1 in reshape_shape:
                         logger.warning(f"Reshape shape contains -1: {reshape_shape}, this may cause shape inference issues")
@@ -1441,14 +1436,21 @@ def schedule_to_dag(schedule: Schedule2D) -> TaskDAG:
 
     **Temporal edges** connect every pair of consecutive non-None tasks within
     the same row (actor).  They model the serialisation constraint that a single
-    actor executes one cell at a time.
+    actor executes one cell at a time.  A task pair may carry both a temporal
+    edge and a data edge simultaneously.
     """
     nodes: list[TaskNode] = []
     grid_nodes: list[list[Optional[TaskNode]]] = []
+    # col_meta records the original 2D schedule position of each node so that
+    # data edges can be constructed from (col_idx, sub_idx) order rather than
+    # time_step.  sub_idx distinguishes the FWD and BWD sub-nodes that expand
+    # from a single FWD_BWD cell (same col_idx, different sub_idx).  This dict
+    # is used only inside schedule_to_dag and is not stored on the nodes.
+    col_meta: dict[int, tuple[int, int]] = {}
 
     for pp_rank, row in enumerate(schedule.grid):
         row_nodes: list[Optional[TaskNode]] = []
-        for time_step, task in enumerate(row):
+        for col_idx, task in enumerate(row):
             if task is None:
                 row_nodes.append(None)
             elif task.type == TaskType.FWD_BWD:
@@ -1459,17 +1461,20 @@ def schedule_to_dag(schedule: Schedule2D) -> TaskDAG:
                 fwd_node = TaskNode(
                     task=Task(pp_rank=pp_rank, batches=[fwd_batch], type=TaskType.FWD),
                     pp_rank=pp_rank,
-                    time_step=time_step,
+                    time_step=col_idx,
                 )
                 bwd_node = TaskNode(
                     task=Task(pp_rank=pp_rank, batches=[bwd_batch], type=TaskType.BWD),
                     pp_rank=pp_rank,
-                    time_step=time_step,
+                    time_step=col_idx,
                 )
+                col_meta[id(fwd_node)] = (col_idx, 0)
+                col_meta[id(bwd_node)] = (col_idx, 1)
                 nodes.extend([fwd_node, bwd_node])
                 row_nodes.extend([fwd_node, bwd_node])
             else:
-                node = TaskNode(task=task, pp_rank=pp_rank, time_step=time_step)
+                node = TaskNode(task=task, pp_rank=pp_rank, time_step=col_idx)
+                col_meta[id(node)] = (col_idx, 0)
                 nodes.append(node)
                 row_nodes.append(node)
         grid_nodes.append(row_nodes)
@@ -1492,7 +1497,7 @@ def schedule_to_dag(schedule: Schedule2D) -> TaskDAG:
                 mb_to_nodes[mb_idx].append(node)
 
     for mb_nodes in mb_to_nodes.values():
-        mb_nodes.sort(key=lambda n: (n.time_step, n.pp_rank))
+        mb_nodes.sort(key=lambda n: (*col_meta[id(n)], n.pp_rank))
         for i in range(len(mb_nodes) - 1):
             pred, succ = mb_nodes[i], mb_nodes[i + 1]
             pred.data_succs.append(succ)
@@ -1514,19 +1519,26 @@ def schedule_to_dag(schedule: Schedule2D) -> TaskDAG:
                 bwdi.data_succs.append(node)
                 node.data_preds.append(bwdi)
 
-    data_edge_pairs: set[tuple[int, int]] = set()
-    for node in nodes:
-        for succ in node.data_succs:
-            data_edge_pairs.add((id(node), id(succ)))
-            data_edge_pairs.add((id(succ), id(node)))
-
     for row_nodes in grid_nodes:
         non_null = [n for n in row_nodes if n is not None]
+        if not non_null:
+            continue
         for i in range(len(non_null) - 1):
             pred, succ = non_null[i], non_null[i + 1]
-            if (id(pred), id(succ)) not in data_edge_pairs:
-                pred.temporal_succ = succ
-                succ.temporal_pred = pred
+            pred.temporal_succs.append(succ)
+            succ.temporal_preds.append(pred)
+        # Synthesize a UPD node as the terminal task for this rank's temporal
+        # chain.  time_step is a placeholder; assign_time_steps sets the real
+        # value after all three expansion passes complete.
+        last = non_null[-1]
+        upd_node = TaskNode(
+            task=Task(pp_rank=last.pp_rank, batches=list(last.task.batches), type=TaskType.UPD),
+            pp_rank=last.pp_rank,
+            time_step=0,
+        )
+        last.temporal_succs.append(upd_node)
+        upd_node.temporal_preds.append(last)
+        nodes.append(upd_node)
 
     return TaskDAG(nodes=nodes)
 
@@ -1580,10 +1592,10 @@ def expand_bucket_tasks(dag: TaskDAG, bucket_counts: dict) -> TaskDAG:
                         pp_rank=orig.pp_rank,
                         batches=list(orig.task.batches),
                         type=TaskType.FWD,
-                        bucket_id=b,
                     ),
                     pp_rank=orig.pp_rank,
                     time_step=orig.time_step * TIME_SCALE + i,
+                    bucket_id=b,
                 )
                 for i, b in enumerate(range(K))
             ]
@@ -1594,18 +1606,20 @@ def expand_bucket_tasks(dag: TaskDAG, bucket_counts: dict) -> TaskDAG:
                         pp_rank=orig.pp_rank,
                         batches=list(orig.task.batches),
                         type=orig.task.type,   # preserve BWD or BWD_I
-                        bucket_id=K - 1 - i,
                     ),
                     pp_rank=orig.pp_rank,
                     time_step=orig.time_step * TIME_SCALE + i,
+                    bucket_id=K - 1 - i,
                 )
                 for i in range(K)
             ]
 
-        # Data-dep chain between consecutive bucket nodes
+        # Data-dep and temporal chain between consecutive bucket nodes
         for i in range(K - 1):
             chain[i].data_succs.append(chain[i + 1])
             chain[i + 1].data_preds.append(chain[i])
+            chain[i].temporal_succs.append(chain[i + 1])
+            chain[i + 1].temporal_preds.append(chain[i])
 
         first_bkt = chain[0]
         last_bkt = chain[-1]
@@ -1635,23 +1649,21 @@ def expand_bucket_tasks(dag: TaskDAG, bucket_counts: dict) -> TaskDAG:
                 new_dst.data_preds.append(last_bkt)
         orig.data_succs.clear()
 
-        # Incoming temporal edge: tp -> orig  =>  new_tp -> first_bkt
-        if orig.temporal_pred is not None:
-            tp = orig.temporal_pred
-            tp.temporal_succ = None  # disconnect old link
+        # Incoming temporal edges: tp -> orig  =>  new_tp -> first_bkt
+        for tp in list(orig.temporal_preds):
+            tp.temporal_succs.remove(orig)
             new_tp = node_to_first_last[id(tp)][1] if id(tp) in node_to_first_last else tp
-            new_tp.temporal_succ = first_bkt
-            first_bkt.temporal_pred = new_tp
-            orig.temporal_pred = None
-
-        # Outgoing temporal edge: orig -> ts  =>  last_bkt -> new_ts
-        if orig.temporal_succ is not None:
-            ts = orig.temporal_succ
-            ts.temporal_pred = None  # disconnect old link
+            new_tp.temporal_succs.append(first_bkt)
+            first_bkt.temporal_preds.append(new_tp)
+        orig.temporal_preds.clear()
+
+        # Outgoing temporal edges: orig -> ts  =>  last_bkt -> new_ts
+        for ts in list(orig.temporal_succs):
+            ts.temporal_preds.remove(orig)
             new_ts = node_to_first_last[id(ts)][0] if id(ts) in node_to_first_last else ts
-            last_bkt.temporal_succ = new_ts
-            new_ts.temporal_pred = last_bkt
-            orig.temporal_succ = None
+            last_bkt.temporal_succs.append(new_ts)
+            new_ts.temporal_preds.append(last_bkt)
+        orig.temporal_succs.clear()
 
     # ---- Step 2c: build new node list ----
     orig_ids = set(id(n) for n in expandable)
@@ -1677,8 +1689,13 @@ def insert_p2p_ops(dag: TaskDAG) -> list[TaskDAG]:
     1. Remove the data-dep edge.
     2. Create a SEND node on rank A with a data-dep edge ``task1 → SEND``.
     3. Create a RECV node on rank B with a data-dep edge ``RECV → task2``.
-    4. If ``task1`` had a temporal successor, re-attach it from SEND instead.
-    5. If ``task2`` had a temporal predecessor, re-attach it to RECV instead.
+
+    SEND / RECV nodes are not inserted into the temporal chain.  Their
+    ``time_step`` is set relative to their compute neighbour: SEND gets
+    ``src.time_step + 1``; RECV gets ``dst.time_step - 1``.
+
+    :func:`assign_time_steps` must have been called on *dag* before this
+    function so that the compute nodes carry their final ``time_step`` values.
 
     After the transformation no cross-rank edges should remain.  The function
     asserts this, then splits the node list by ``pp_rank`` and returns a list of
@@ -1693,15 +1710,6 @@ def insert_p2p_ops(dag: TaskDAG) -> list[TaskDAG]:
         if src.pp_rank != dst.pp_rank
     ]
 
-    synthetic_ts: dict[int, int] = {}
-
-    def _next_ts(pp_rank: int) -> int:
-        if pp_rank not in synthetic_ts:
-            synthetic_ts[pp_rank] = -1
-        ts = synthetic_ts[pp_rank]
-        synthetic_ts[pp_rank] -= 1
-        return ts
-
     for src, dst in cross_edges:
         src.data_succs.remove(dst)
         dst.data_preds.remove(src)
@@ -1709,33 +1717,21 @@ def _next_ts(pp_rank: int) -> int:
         send_node = TaskNode(
             task=Task(pp_rank=src.pp_rank, batches=list(src.task.batches), type=TaskType.SEND),
             pp_rank=src.pp_rank,
-            time_step=_next_ts(src.pp_rank),
+            time_step=src.time_step + 1,
             peer_pp_rank=dst.pp_rank,
         )
         src.data_succs.append(send_node)
         send_node.data_preds.append(src)
 
-        if src.temporal_succ is not None:
-            ts_next = src.temporal_succ
-            ts_next.temporal_pred = send_node
-            send_node.temporal_succ = ts_next
-            src.temporal_succ = None
-
         recv_node = TaskNode(
             task=Task(pp_rank=dst.pp_rank, batches=list(dst.task.batches), type=TaskType.RECV),
             pp_rank=dst.pp_rank,
-            time_step=_next_ts(dst.pp_rank),
+            time_step=dst.time_step - 1,
             peer_pp_rank=src.pp_rank,
         )
         recv_node.data_succs.append(dst)
         dst.data_preds.append(recv_node)
 
-        if dst.temporal_pred is not None:
-            tp_prev = dst.temporal_pred
-            tp_prev.temporal_succ = recv_node
-            recv_node.temporal_pred = tp_prev
-            dst.temporal_pred = None
-
         all_nodes.append(send_node)
         all_nodes.append(recv_node)
 
@@ -1746,45 +1742,23 @@ def _next_ts(pp_rank: int) -> int:
                 f"{node.node_id()} (rank {node.pp_rank}) → "
                 f"{succ.node_id()} (rank {succ.pp_rank})"
             )
-        if node.temporal_succ is not None:
-            assert node.pp_rank == node.temporal_succ.pp_rank, (
+        for ts in node.temporal_succs:
+            assert node.pp_rank == ts.pp_rank, (
                 f"Cross-rank temporal edge present: "
                 f"{node.node_id()} (rank {node.pp_rank}) → "
-                f"{node.temporal_succ.node_id()} (rank {node.temporal_succ.pp_rank})"
+                f"{ts.node_id()} (rank {ts.pp_rank})"
             )
 
     rank_nodes: dict[int, list[TaskNode]] = defaultdict(list)
     for node in all_nodes:
         rank_nodes[node.pp_rank].append(node)
 
-    # Task types that represent real backward compute — UPD must be temporally
-    # ordered after the last of these, not after communication nodes (A2A, AR,
-    # SEND, RECV) whose time_steps may be synthetic and higher than the last BWD.
-    _BWD_COMPUTE_TYPES = {
-        TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W,
-    }
-    upd_ts_counter = -(max(abs(v) for v in synthetic_ts.values()) + 1) if synthetic_ts else -1
-    for rank, nodes in rank_nodes.items():
-        bwd_nodes = [n for n in nodes if n.task.type in _BWD_COMPUTE_TYPES]
-        last = max(
-            bwd_nodes if bwd_nodes else (n for n in nodes if n.time_step >= 0),
-            key=lambda n: n.time_step,
-        )
-        upd_task = Task(pp_rank=rank, batches=list(last.task.batches), type=TaskType.UPD)
-        upd_node = TaskNode(task=upd_task, pp_rank=rank, time_step=upd_ts_counter)
-        upd_ts_counter -= 1
-        last.temporal_succ = upd_node
-        upd_node.temporal_pred = last
-        nodes.append(upd_node)
-
     return [TaskDAG(nodes=rank_nodes[r]) for r in sorted(rank_nodes)]
 
 
-def insert_ar_ops(per_rank_dags: list) -> list:
+def insert_ar_ops(per_rank_dags: list, trainable_bucket_keys: set | None = None) -> list:
     """Insert ALL_REDUCE nodes into each per-rank DAG for DP gradient sync.
 
-    Two cases are handled:
-
     For each (stage_id, bucket_id) group of BWD/BWD_I nodes, add one ALL_REDUCE
     after the final microbatch's node.  The actor all-reduces the pre-allocated
     flat_grads tensor for that (stage, bucket) pair.
@@ -1792,6 +1766,13 @@ def insert_ar_ops(per_rank_dags: list) -> list:
     The ALL_REDUCE node has exactly one data edge: from the final microbatch
     BWD/BWD_I node.  Synchronisation with UPD is handled at the CUDA level via
     ar_events in _update_impl.
+
+    Args:
+        per_rank_dags: Per-rank TaskDAG list to mutate in-place.
+        trainable_bucket_keys: If provided, only insert ALL_REDUCE for
+            (stage_id, bucket_id) pairs present in this set.  Parameterless
+            buckets (e.g. A2A dispatch/combine segments in MoE models) must be
+            excluded or the actor will crash with a None flat_grads tensor.
     """
     for rank_dag in per_rank_dags:
         new_nodes = []
@@ -1802,7 +1783,10 @@ def insert_ar_ops(per_rank_dags: list) -> list:
         for node in rank_dag.nodes:
             if node.task.type in (TaskType.BWD, TaskType.BWD_I):
                 s = node.task.batches[0].stage_id
-                bwd_groups[(s, node.task.bucket_id)].append(node)
+                key = (s, node.bucket_id)
+                if trainable_bucket_keys is not None and key not in trainable_bucket_keys:
+                    continue
+                bwd_groups[key].append(node)
 
         for (stage_id, bucket_id), group in bwd_groups.items():
             trigger_node = max(group, key=lambda n: n.time_step)
@@ -1810,12 +1794,12 @@ def insert_ar_ops(per_rank_dags: list) -> list:
                 pp_rank=trigger_node.pp_rank,
                 batches=list(trigger_node.task.batches),
                 type=TaskType.ALL_REDUCE,
-                bucket_id=bucket_id,
             )
             ar_node = TaskNode(
                 task=ar_task,
                 pp_rank=trigger_node.pp_rank,
-                time_step=-10000 - len(new_nodes),
+                time_step=trigger_node.time_step + 1,
+                bucket_id=bucket_id,
             )
             trigger_node.data_succs.append(ar_node)
             ar_node.data_preds.append(trigger_node)
@@ -1930,46 +1914,77 @@ def _node_fontcolor(node: TaskNode) -> str:
         # First stage -> black text, second (and beyond) stage -> white text
         return "black" if sid == stages[0] else "white"
 
+    # Compute topological order using Kahn's algorithm (data + temporal edges).
+    from collections import deque as _deque
+    _in_degree: dict[str, int] = {
+        n.node_id(): len(n.data_preds) + len(n.temporal_preds)
+        for n in dag.nodes
+    }
+    _topo_order: dict[str, int] = {}
+    _queue: _deque = _deque(n for n in dag.nodes if _in_degree[n.node_id()] == 0)
+    _step = 0
+    while _queue:
+        _n = _queue.popleft()
+        _topo_order[_n.node_id()] = _step
+        _step += 1
+        for _s in list(_n.data_succs) + list(_n.temporal_succs):
+            _in_degree[_s.node_id()] -= 1
+            if _in_degree[_s.node_id()] == 0:
+                _queue.append(_s)
+
     dot = graphviz.Digraph("PiperDAG", comment="Piper Task DAG")
     dot.attr(rankdir="LR", splines="ortho", nodesep="0.4", ranksep="0.6", fontname="Helvetica")
     dot.attr("node", shape="box", style="filled", fontsize="9", fontname="Helvetica")
     dot.attr("edge", fontsize="8", fontname="Helvetica")
 
     for node in dag.nodes:
+        label = f"{node.time_step}\n{_task_node_label(node)}"
         dot.node(
             node.node_id(),
-            label=_task_node_label(node),
+            label=label,
             fillcolor=_node_fill(node),
             fontcolor=_node_fontcolor(node),
             tooltip=repr(node.task),
         )
 
-    step_to_nodes: dict[int, list[TaskNode]] = defaultdict(list)
+    # Assign column positions so that every temporal edge spans exactly one column.
+    # Assign column positions via BFS from temporal roots.
+    # When the temporal graph has forks/joins (after overlap_a2a_tasks), use
+    # max-predecessor depth to place join nodes correctly.
+    # SEND/RECV/AR nodes have no temporal edges and are left unranked.
+    _tdepth: dict[str, int] = {}
+    _tq: _deque = _deque()
+    for _n in dag.nodes:
+        if not _n.temporal_preds and _n.temporal_succs:
+            _tdepth[_n.node_id()] = 0
+            _tq.append(_n)
+    while _tq:
+        _n = _tq.popleft()
+        for _s in _n.temporal_succs:
+            new_d = _tdepth[_n.node_id()] + 1
+            if _s.node_id() not in _tdepth or _tdepth[_s.node_id()] < new_d:
+                _tdepth[_s.node_id()] = new_d
+                _tq.append(_s)
+
+    depth_to_nodes: dict[int, list[TaskNode]] = defaultdict(list)
     for node in dag.nodes:
-        if node.time_step >= 0:
-            step_to_nodes[node.time_step].append(node)
+        if node.node_id() in _tdepth:
+            depth_to_nodes[_tdepth[node.node_id()]].append(node)
 
-    for col_nodes in step_to_nodes.values():
-        if len(col_nodes) > 1:
-            with dot.subgraph() as sub:
-                sub.attr(rank="same")
-                for node in col_nodes:
-                    sub.node(node.node_id())
+    for col_nodes in depth_to_nodes.values():
+        with dot.subgraph() as sub:
+            sub.attr(rank="same")
+            for node in col_nodes:
+                sub.node(node.node_id())
 
-    data_pairs: set[tuple[str, str]] = set()
     for node in dag.nodes:
-        for succ in node.data_succs:
-            data_pairs.add((node.node_id(), succ.node_id()))
-
-    for node in dag.nodes:
-        if node.temporal_succ is not None:
-            key = (node.node_id(), node.temporal_succ.node_id())
-            if key not in data_pairs:
-                dot.edge(
-                    key[0], key[1],
-                    style="dashed", color="grey60",
-                    penwidth="1.0", arrowsize="0.6", constraint="true",
-                )
+        for ts in node.temporal_succs:
+            key = (node.node_id(), ts.node_id())
+            dot.edge(
+                key[0], key[1],
+                style="dashed", color="grey60",
+                penwidth="1.0", arrowsize="0.6", constraint="true",
+            )
 
     seen_edges: set[tuple[str, str]] = set()
     for node in dag.nodes:
@@ -2013,7 +2028,7 @@ def print_dag_order(dag: TaskDAG, label: str = "") -> None:
     from collections import deque as _deque
 
     in_degree = {
-        id(n): len(n.data_preds) + (1 if n.temporal_pred is not None else 0)
+        id(n): len(n.data_preds) + len(n.temporal_preds)
         for n in dag.nodes
     }
     queue: _deque = _deque(n for n in dag.nodes if in_degree[id(n)] == 0)
@@ -2028,13 +2043,10 @@ def print_dag_order(dag: TaskDAG, label: str = "") -> None:
         batches_str = ", ".join(
             f"s{b.stage_id} mb{b.mb_idx}" for b in task.batches
         ) if task.batches else ""
-        bkt = f" bkt={task.bucket_id}" if task.bucket_id is not None else ""
+        bkt = f" bkt={node.bucket_id}" if node.bucket_id else ""
         print(f"  {step:3d}  rank={node.pp_rank}  {ttype:<14s}  {batches_str}{bkt}")
         step += 1
-        all_succs = list(node.data_succs)
-        if node.temporal_succ is not None:
-            all_succs.append(node.temporal_succ)
-        for succ in all_succs:
+        for succ in list(node.data_succs) + list(node.temporal_succs):
             in_degree[id(succ)] -= 1
             if in_degree[id(succ)] == 0:
                 queue.append(succ)
@@ -2660,23 +2672,35 @@ def split_by_a2a(
         )
     )
 
-    # Group CONSECUTIVE annotated nodes with the same annotation_key into blocks.
-    # This handles multiple layers: each contiguous run of same-key nodes becomes
-    # its own block, so a 2-layer model produces 4 blocks (dispatch0, combine0,
-    # dispatch1, combine1) rather than 2 (all-dispatches, all-combines).
-    # annotated_nodes is already in graph order.
+    # Group CONSECUTIVE annotated nodes with the same annotation_key into blocks,
+    # but only if they are graph-adjacent (no other compute nodes between them).
+    # Adjacency is required to handle models where all A2A annotations share the
+    # same key (e.g. dispatch and gather both tagged "all_to_all_single"): without
+    # the adjacency check they would all merge into one block.
+    # The grouping still handles the case where a single A2A boundary spans
+    # multiple truly-adjacent nodes (e.g. reshape + contiguous inside one
+    # `with annotate(...)` block).
+    annotated_idx_set = {idx for idx, _, _, _ in annotated_nodes}
     blocks: list = []
     for idx, node, annotation_key, reshape in annotated_nodes:
         if blocks and blocks[-1]["annotation_key"] == annotation_key:
-            blocks[-1]["nodes"].append((idx, node))
-            blocks[-1]["last_idx"] = idx
-        else:
-            blocks.append({
-                "nodes": [(idx, node)],
-                "last_idx": idx,
-                "reshape": reshape,
-                "annotation_key": annotation_key,
-            })
+            prev_last_idx = blocks[-1]["last_idx"]
+            # Only merge if no non-annotated compute nodes exist in the gap.
+            gap_has_compute = any(
+                nodes[j].op not in ("placeholder", "get_attr", "output")
+                and j not in annotated_idx_set
+                for j in range(prev_last_idx + 1, idx)
+            )
+            if not gap_has_compute:
+                blocks[-1]["nodes"].append((idx, node))
+                blocks[-1]["last_idx"] = idx
+                continue
+        blocks.append({
+            "nodes": [(idx, node)],
+            "last_idx": idx,
+            "reshape": reshape,
+            "annotation_key": annotation_key,
+        })
     n_boundaries = len(blocks)
 
     logger.debug(
@@ -2918,8 +2942,6 @@ def expand_a2a_tasks(dag: TaskDAG, a2a_info: dict) -> TaskDAG:
     if not a2a_info:
         return dag
 
-    TIME_SCALE = 1000  # same scale used by expand_bucket_tasks
-
     # Index FWD/BWD/BWD_I nodes by (stage_id, mb_idx, type, bucket_id)
     bucket_node_map: dict = {}
     for node in dag.nodes:
@@ -2927,7 +2949,7 @@ def expand_a2a_tasks(dag: TaskDAG, a2a_info: dict) -> TaskDAG:
         if t.type in (TaskType.FWD, TaskType.BWD, TaskType.BWD_I):
             sid = t.batches[0].stage_id
             mid = t.batches[0].mb_idx
-            bucket_node_map[(sid, mid, t.type, t.bucket_id)] = node
+            bucket_node_map[(sid, mid, t.type, node.bucket_id)] = node
 
     new_nodes: list[TaskNode] = []
 
@@ -2942,7 +2964,7 @@ def expand_a2a_tasks(dag: TaskDAG, a2a_info: dict) -> TaskDAG:
 
         if t.type == TaskType.FWD:
             # Insert FWD_A2A after FWD(bucket_id) if it's a boundary
-            bkt_id = t.bucket_id
+            bkt_id = node.bucket_id
             if bkt_id not in boundaries:
                 continue
             tensor_idx = boundaries[bkt_id]
@@ -2959,14 +2981,14 @@ def expand_a2a_tasks(dag: TaskDAG, a2a_info: dict) -> TaskDAG:
                     pp_rank=node.pp_rank,
                     batches=list(t.batches),
                     type=TaskType.FWD_A2A,
-                    bucket_id=bkt_id,
-                    a2a_tensor_idx=tensor_idx,
                 ),
                 pp_rank=node.pp_rank,
-                time_step=node.time_step + TIME_SCALE // 2,
+                time_step=0,  # assigned later by assign_time_steps
+                bucket_id=bkt_id,
+                a2a_tensor_idx=tensor_idx,
             )
 
-            # Rewire: node -> a2a_node -> next_node  (replacing node -> next_node)
+            # Rewire data: node -> a2a_node -> next_node  (replacing node -> next_node)
             if next_node in node.data_succs:
                 node.data_succs.remove(next_node)
             if node in next_node.data_preds:
@@ -2977,12 +2999,20 @@ def expand_a2a_tasks(dag: TaskDAG, a2a_info: dict) -> TaskDAG:
             a2a_node.data_succs.append(next_node)
             next_node.data_preds.append(a2a_node)
 
+            # Splice into temporal chain: node → a2a_node → next_node
+            node.temporal_succs.remove(next_node)
+            next_node.temporal_preds.remove(node)
+            node.temporal_succs.append(a2a_node)
+            a2a_node.temporal_preds.append(node)
+            a2a_node.temporal_succs.append(next_node)
+            next_node.temporal_preds.append(a2a_node)
+
             new_nodes.append(a2a_node)
 
         else:  # BWD / BWD_I with bucket_id
             # Insert BWD_A2A after BWD/BWD_I(bucket_id+1) if there's a boundary there
             # (i.e. between bkt_id+1 and bkt_id in backward execution order)
-            bkt_id = t.bucket_id
+            bkt_id = node.bucket_id
             if bkt_id not in boundaries:
                 continue
             # This BWD/BWD_I(bkt_id) is the later node in the backward chain.
@@ -3001,14 +3031,14 @@ def expand_a2a_tasks(dag: TaskDAG, a2a_info: dict) -> TaskDAG:
                     pp_rank=node.pp_rank,
                     batches=list(t.batches),
                     type=TaskType.BWD_A2A,
-                    bucket_id=bkt_id,
-                    a2a_tensor_idx=tensor_idx,
                 ),
                 pp_rank=node.pp_rank,
-                time_step=prev_node.time_step + TIME_SCALE // 2,
+                time_step=0,  # assigned later by assign_time_steps
+                bucket_id=bkt_id,
+                a2a_tensor_idx=tensor_idx,
             )
 
-            # Rewire: prev_node -> a2a_node -> node  (replacing prev_node -> node)
+            # Rewire data: prev_node -> a2a_node -> node  (replacing prev_node -> node)
             if node in prev_node.data_succs:
                 prev_node.data_succs.remove(node)
             if prev_node in node.data_preds:
@@ -3019,7 +3049,161 @@ def expand_a2a_tasks(dag: TaskDAG, a2a_info: dict) -> TaskDAG:
             a2a_node.data_succs.append(node)
             node.data_preds.append(a2a_node)
 
+            # Splice into temporal chain: prev_node → a2a_node → node
+            prev_node.temporal_succs.remove(node)
+            node.temporal_preds.remove(prev_node)
+            prev_node.temporal_succs.append(a2a_node)
+            a2a_node.temporal_preds.append(prev_node)
+            a2a_node.temporal_succs.append(node)
+            node.temporal_preds.append(a2a_node)
+
             new_nodes.append(a2a_node)
 
     all_nodes = list(dag.nodes) + new_nodes
-    return TaskDAG(nodes=all_nodes)
\ No newline at end of file
+    return TaskDAG(nodes=all_nodes)
+
+# ---------------------------------------------------------------------------
+# Overlap compute and A2A tasks across consecutive task pairs
+# ---------------------------------------------------------------------------
+
+def _find_task_chain_endpoints(dag: TaskDAG, task: Task) -> tuple[TaskNode, TaskNode]:
+    """Return (first_node, last_node) in the temporal chain for the given task.
+
+    "First" is the node in the group none of whose temporal_preds are also in
+    the group; "last" is the node none of whose temporal_succs are in the group.
+
+    The "group" for a task is all DAG nodes sharing the same pp_rank,
+    stage_id, mb_idx, and either the task's compute type or its paired A2A type.
+    """
+    stage_id = task.batches[0].stage_id
+    mb_idx   = task.batches[0].mb_idx
+    compute_type = task.type
+    a2a_type = TaskType.FWD_A2A if compute_type == TaskType.FWD else TaskType.BWD_A2A
+
+    group = [
+        n for n in dag.nodes
+        if n.pp_rank == task.pp_rank
+        and n.task.batches
+        and n.task.batches[0].stage_id == stage_id
+        and n.task.batches[0].mb_idx   == mb_idx
+        and n.task.type in (compute_type, a2a_type)
+    ]
+    assert group, (
+        f"No DAG nodes found for task pp_rank={task.pp_rank} "
+        f"stage={stage_id} mb={mb_idx} type={compute_type}"
+    )
+
+    group_ids = {id(n) for n in group}
+    first = next(
+        n for n in group
+        if not any(id(p) in group_ids for p in n.temporal_preds)
+    )
+    last = next(
+        n for n in group
+        if not any(id(s) in group_ids for s in n.temporal_succs)
+    )
+    return first, last
+
+
+def overlap_a2a_tasks(dag: TaskDAG, task_pairs: list[tuple[Task, Task]]) -> TaskDAG:
+    """Overlap compute and A2A tasks across each consecutive task pair.
+
+    For each pair ``(t1, t2)`` the pre-transform temporal chain contains:
+
+        T1_first → … → T1_last → T2_first → … → T2_last
+
+    This function rewires the temporal dependencies so that T2 starts as soon
+    as T1 starts (not after T1 finishes), while still requiring T2_last to
+    complete after T1_last:
+
+      1. Remove  T1_last  → T2_first  (from temporal_succs / temporal_preds)
+      2. Add     T1_first → T2_first  (appended to temporal_succs / temporal_preds)
+      3. Add     T1_last  → T2_last   (appended to temporal_succs / temporal_preds)
+
+    After :func:`assign_time_steps` (which now does a topological sort), nodes
+    in T1 and T2 that have no other dependency between them receive the *same*
+    ``time_step``, allowing the actor to dispatch them to their respective CUDA
+    streams concurrently — e.g. T1's A2A overlaps with T2's compute and vice
+    versa.
+
+    Args:
+        dag: TaskDAG produced by :func:`expand_a2a_tasks`.
+        task_pairs: List of ``(t1, t2)`` pairs where each ``Task`` identifies
+            a compute-task group in the DAG by its pp_rank, stage_id, mb_idx,
+            and type.  The two tasks in each pair must be on the same pp_rank
+            and T2 must immediately follow T1 in the temporal chain.
+
+    Returns:
+        The same ``dag`` object with temporal edges mutated in-place.
+    """
+    for t1, t2 in task_pairs:
+        t1_first, t1_last = _find_task_chain_endpoints(dag, t1)
+        t2_first, t2_last = _find_task_chain_endpoints(dag, t2)
+
+        # Assert T2 directly follows T1 in the pre-transform temporal chain.
+        assert t2_first in t1_last.temporal_succs, (
+            f"overlap_a2a_tasks: expected T2_first ({t2_first.node_id()}) to be "
+            f"an immediate temporal successor of T1_last ({t1_last.node_id()})"
+        )
+        assert t1_last in t2_first.temporal_preds, (
+            f"overlap_a2a_tasks: expected T1_last ({t1_last.node_id()}) to be "
+            f"a temporal predecessor of T2_first ({t2_first.node_id()})"
+        )
+
+        # 1. Remove T1_last → T2_first
+        t1_last.temporal_succs.remove(t2_first)
+        t2_first.temporal_preds.remove(t1_last)
+
+        # 2. Add T1_first → T2_first
+        t1_first.temporal_succs.append(t2_first)
+        t2_first.temporal_preds.append(t1_first)
+
+        # 3. Add T1_last → T2_last (merged join point)
+        t1_last.temporal_succs.append(t2_last)
+        t2_last.temporal_preds.append(t1_last)
+
+    return dag
+
+
+# ---------------------------------------------------------------------------
+# Assign time_step from temporal chain position
+# ---------------------------------------------------------------------------
+
+def assign_time_steps(dag: TaskDAG) -> None:
+    """Assign ``time_step`` to every node based on its position in the temporal
+    dependency graph.
+
+    Must be called after :func:`expand_a2a_tasks` (and
+    :func:`overlap_a2a_tasks` if used) and before :func:`insert_p2p_ops`.
+
+    Each node's ``time_step`` is ``max(pred.time_step for all temporal preds) + 1``,
+    with roots (no temporal predecessors) receiving ``time_step = 0``.  When the
+    temporal graph is a simple chain this matches the previous sequential
+    assignment; when :func:`overlap_a2a_tasks` introduces forks and joins the
+    topological sort correctly assigns shared time steps to concurrent nodes
+    and deferred time steps to join points.
+
+    SEND / RECV / ALL_REDUCE nodes are added later by :func:`insert_p2p_ops`
+    and :func:`insert_ar_ops`; they receive ``time_step = adjacent_node ± 1``
+    at insertion time.
+    """
+    # Kahn's toposort over the temporal graph (temporal_preds / temporal_succs).
+    # time_step = max(pred.time_step for all temporal preds) + 1.
+    # When the graph is a simple chain this matches the previous sequential
+    # assignment; forks and joins from overlap_a2a_tasks are handled correctly.
+    in_degree: dict[int, int] = {id(n): len(n.temporal_preds) for n in dag.nodes}
+    ts_map: dict[int, int] = {}
+    queue: deque[TaskNode] = deque()
+    for node in dag.nodes:
+        if in_degree[id(node)] == 0:
+            ts_map[id(node)] = 0
+            queue.append(node)
+
+    while queue:
+        node = queue.popleft()
+        node.time_step = ts_map[id(node)]
+        for succ in node.temporal_succs:
+            in_degree[id(succ)] -= 1
+            ts_map[id(succ)] = max(ts_map.get(id(succ), 0), node.time_step + 1)
+            if in_degree[id(succ)] == 0:
+                queue.append(succ)
diff --git a/src/piper_utils.py b/src/piper_utils.py
index fb7e8c4..6576999 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -10,7 +10,7 @@
 from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
 from typing import Any, Optional
 
-LOG_LEVEL = "INFO"
+LOG_LEVEL = "DEBUG"
 
 """ 
 Print the backward graph of a tensor
@@ -340,6 +340,12 @@ class PiperMetadata:
     schedule = None   # Schedule2D set by piper_setup; used by the piper backend
     task_dag = None   # TaskDAG built from schedule by the piper backend
     per_rank_dags = None  # Per-rank TaskDAGs built by the piper backend
+    stage_bucket_counts: dict = {}   # stage_id -> number of buckets (set by piper backend)
+    trainable_bucket_keys: set = set()  # (stage_id, bucket_id) pairs with trainable params
+    # Populated by the piper backend on dp_rank=0; broadcast to dp_rank>0 by piper_setup
+    # so they can skip torch.compile entirely.  Contains serialized graph + param metadata
+    # (no actual weight values) for all stages, plus the built per-rank TaskDAGs.
+    compiled_stage_data: dict = None
 
 piper_metadata = PiperMetadata()
 
diff --git a/test/models/llama.py b/test/models/llama.py
index d099e7b..58cbe2a 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -487,10 +487,10 @@ def forward(self, tokens: torch.Tensor):
         with torch.fx.traceback.annotate({"stage": 0}):
             h = self.tok_embeddings(tokens)
             start_pos = 0
-            for layer in self.layers[:self.n_layers//2+2]:
+            for layer in self.layers[:self.n_layers//2]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
         with torch.fx.traceback.annotate({"stage": 1}):
-            for layer in self.layers[self.n_layers//2+2:]:
+            for layer in self.layers[self.n_layers//2:]:
                 h = layer(h, start_pos, self.freqs_cis, self.mask)
             h = self.norm(h)
             output = self.output(h).float()
diff --git a/test/models/qwen3.py b/test/models/qwen3.py
index f4ff6f7..30e2005 100644
--- a/test/models/qwen3.py
+++ b/test/models/qwen3.py
@@ -14,7 +14,7 @@ def create_qwen3_config(name: str) -> Qwen3ModelArgs:
             return Qwen3ModelArgs(
                 vocab_size=2048,
                 dim=256,
-                n_layers=8,
+                n_layers=4,
                 n_heads=8,
                 n_kv_heads=4,
                 head_dim=32,
@@ -169,6 +169,33 @@ def create_qwen3_config(name: str) -> Qwen3ModelArgs:
                     load_balance_coeff=None,
                 ),
             )
+        case '30B-A3B-half':
+            return Qwen3ModelArgs(
+                vocab_size=151936,
+                dim=2048,
+                n_layers=24,
+                n_heads=32,
+                n_kv_heads=4,
+                head_dim=128,
+                hidden_dim=6144,
+                norm_eps=1e-6,
+                qk_norm=True,
+                max_seq_len=262144,
+                rope_theta=1000000.0,
+                enable_weight_tying=False,
+                moe_enabled=True,
+                moe_inter_dim=768,
+                moe_args=MoEArgs(
+                    num_experts=64,
+                    num_shared_experts=0,
+                    top_k=8,
+                    score_func="softmax",
+                    route_norm=True,
+                    route_scale=1.0,
+                    score_before_experts=False,
+                    load_balance_coeff=None,
+                ),
+            )
         case '72B':
             return Qwen3ModelArgs(
                 vocab_size=152064,
@@ -300,41 +327,41 @@ def __init__(self, config: Qwen3ModelArgs, num_stages: int = 2):
         for layer_id in range(config.n_layers):
             self.layers[str(layer_id)] = AnnotatedQwen3TransformerBlock(layer_id, config)
 
-    def forward(
-        self,
-        tokens: torch.Tensor,
-        attention_masks: Optional[AttentionMasksType] = None,
-        positions: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        
-        h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
-        for layer in self.layers.values():
-            h = layer(h, self.rope_cache, attention_masks, positions)
-        h = self.norm(h) if self.norm is not None else h
-        output = self.output(h) if self.output is not None else h
-
-        return output
-    
     # def forward(
     #     self,
     #     tokens: torch.Tensor,
     #     attention_masks: Optional[AttentionMasksType] = None,
     #     positions: Optional[torch.Tensor] = None,
     # ) -> torch.Tensor:
-    #     num_layers = len(self.layers)
-    #     layers_per_stage = num_layers // self.num_stages
+        
+    #     h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
+    #     for layer in self.layers.values():
+    #         h = layer(h, self.rope_cache, attention_masks, positions)
+    #     h = self.norm(h) if self.norm is not None else h
+    #     output = self.output(h) if self.output is not None else h
+
+    #     return output
+    
+    def forward(
+        self,
+        tokens: torch.Tensor,
+        attention_masks: Optional[AttentionMasksType] = None,
+        positions: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        num_layers = len(self.layers)
+        layers_per_stage = num_layers // self.num_stages
 
-    #     for stage_id in range(self.num_stages):
-    #         with torch.fx.traceback.annotate({"stage": stage_id}):
-    #             if stage_id == 0:
-    #                 h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
+        for stage_id in range(self.num_stages):
+            with torch.fx.traceback.annotate({"stage": stage_id}):
+                if stage_id == 0:
+                    h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
 
-    #             for i in range(stage_id * layers_per_stage, (stage_id + 1) * layers_per_stage):
-    #                 layer = self.layers[str(i)]
-    #                 h = layer(h, self.rope_cache, attention_masks, positions)
+                for i in range(stage_id * layers_per_stage, (stage_id + 1) * layers_per_stage):
+                    layer = self.layers[str(i)]
+                    h = layer(h, self.rope_cache, attention_masks, positions)
 
-    #             if stage_id == self.num_stages - 1:
-    #                 h = self.norm(h) if self.norm is not None else h
-    #                 output = self.output(h) if self.output is not None else h
+                if stage_id == self.num_stages - 1:
+                    h = self.norm(h) if self.norm is not None else h
+                    output = self.output(h) if self.output is not None else h
 
-    #     return output
+        return output
diff --git a/test/print_schedules.py b/test/print_schedules.py
index cbccf55..9dbff23 100644
--- a/test/print_schedules.py
+++ b/test/print_schedules.py
@@ -5,8 +5,8 @@
     python3 -m test.print_schedules
 """
 
-from .schedule_helpers import INTERLEAVED_1F1B_PP2_MB4_SCHEDULE, build_1f1b_schedule, print_schedule
-from src.piper_exec import TaskType
+from .schedule_helpers import DUALPIPEV_SEQUENTIAL_MB6_SCHEDULE, INTERLEAVED_1F1B_PP2_MB4_SCHEDULE, build_1f1b_schedule, print_schedule
+from src.piper_exec import DAGEdge, TaskType, _validate_schedule
 from src.piper_graph_transform import schedule_to_dag, insert_p2p_ops, visualize_schedule, visualize_dag
 
 
@@ -40,13 +40,13 @@ def print_dag(dag, title: str) -> None:
     for node in sorted(dag.nodes, key=lambda n: (n.pp_rank, n.time_step)):
         data_preds  = [_node_str(p) for p in node.data_preds]
         data_succs  = [_node_str(s) for s in node.data_succs]
-        temp_pred   = _node_str(node.temporal_pred)  if node.temporal_pred  else "—"
-        temp_succ   = _node_str(node.temporal_succ)  if node.temporal_succ  else "—"
+        temp_preds  = [_node_str(p) for p in node.temporal_preds]
+        temp_succs  = [_node_str(s) for s in node.temporal_succs]
         print(f"  {_node_str(node)}")
-        print(f"    data_preds : {data_preds or '—'}")
-        print(f"    data_succs : {data_succs or '—'}")
-        print(f"    temp_pred  : {temp_pred}")
-        print(f"    temp_succ  : {temp_succ}")
+        print(f"    data_preds  : {data_preds or '—'}")
+        print(f"    data_succs  : {data_succs or '—'}")
+        print(f"    temp_preds  : {temp_preds or '—'}")
+        print(f"    temp_succs  : {temp_succs or '—'}")
 
 
 # ---------------------------------------------------------------------------
@@ -57,8 +57,9 @@ def print_dag(dag, title: str) -> None:
 print("  STEP 1 — 1F1B Schedule2D  (PP=2, MB=2)")
 print("="*60)
 schedule = build_1f1b_schedule(n_mbs=2, n_stages=2)
-schedule = INTERLEAVED_1F1B_PP2_MB4_SCHEDULE
+schedule = DUALPIPEV_SEQUENTIAL_MB6_SCHEDULE
 print_schedule(schedule)
+_validate_schedule(schedule.grid, dag_edges=(DAGEdge(0, 1), DAGEdge(1, 2), DAGEdge(2, 3)), num_mbs=6)
 
 # ---------------------------------------------------------------------------
 # Step 2: Convert to TaskDAG (with cross-rank data edges)
@@ -69,7 +70,7 @@ def print_dag(dag, title: str) -> None:
 
 # Summarise edge counts
 n_data  = sum(len(n.data_succs) for n in dag.nodes)
-n_temp  = sum(1 for n in dag.nodes if n.temporal_succ is not None)
+n_temp  = sum(len(n.temporal_succs) for n in dag.nodes)
 n_cross = sum(
     1 for n in dag.nodes for s in n.data_succs if n.pp_rank != s.pp_rank
 )
@@ -88,7 +89,7 @@ def print_dag(dag, title: str) -> None:
     visualize_dag(rank_dag, f"rank{rank}_dag")
     print_dag(rank_dag, f"STEP 3 — Per-rank DAG for PP rank {rank}")
     n_data = sum(len(n.data_succs) for n in rank_dag.nodes)
-    n_temp = sum(1 for n in rank_dag.nodes if n.temporal_succ is not None)
+    n_temp = sum(len(n.temporal_succs) for n in rank_dag.nodes)
     print(f"\n  nodes={len(rank_dag.nodes)}  data_edges={n_data}  temporal_edges={n_temp}")
 
 print()
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index b0f4cd4..0cd303c 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -211,6 +211,77 @@ def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
         ]
     ])
 
+DUALPIPEV_SEQUENTIAL_MB6_SCHEDULE = Schedule2D(
+    grid=[
+        [
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_I),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_W),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_I),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_W),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
+            None,
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_I),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_I),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_W),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_W),
+            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
+            None,
+        ],
+        [
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
+            None,
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_I),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_W),
+            None,
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_I),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_I),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_W),
+            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_W),
+        ]
+    ])
 
 DUALPIPEV_NOZB_MB6_SCHEDULE = Schedule2D(
     grid=[
diff --git a/test/test_llama.py b/test/test_llama.py
index cb707c5..579e5d8 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -182,6 +182,7 @@ def parse_args():
         namespace="llama",
         log_to_driver=True,
         include_dashboard=False,
+        _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp",
     )
     pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="PACK")
     ray.get(pg.ready(), timeout=600)
diff --git a/test/test_qwen.py b/test/test_qwen.py
index df33749..c5fee44 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -37,6 +37,7 @@
     NO_PP_SCHEDULE,
     INTERLEAVED_1F1B_PP2_MB4_SCHEDULE,
     DUALPIPEV_MB6_SCHEDULE,
+    DUALPIPEV_SEQUENTIAL_MB6_SCHEDULE,
     ZEROBUBBLE_MB4_SCHEDULE,
 )
 
@@ -52,16 +53,25 @@ def main(args, pg):
     match args.schedule:
         case "no-pp":
             schedule = NO_PP_SCHEDULE
+            num_stages = 1
         case "1f1b":
             schedule = build_1f1b_schedule(args.mbs, args.pp)
+            num_stages = args.pp
         case "gpipe":
             schedule = build_gpipe_schedule(args.mbs, args.pp)
+            num_stages = args.pp
         case "interleaved-1f1b":
             schedule = INTERLEAVED_1F1B_PP2_MB4_SCHEDULE
+            num_stages = args.pp * 2
         case "dualpipev":
             schedule = DUALPIPEV_MB6_SCHEDULE
+            num_stages = args.pp * 2
+        case "dualpipev-seq":
+            schedule = DUALPIPEV_SEQUENTIAL_MB6_SCHEDULE
+            num_stages = args.pp * 2    
         case "zerobubble":
             schedule = ZEROBUBBLE_MB4_SCHEDULE
+            num_stages = args.pp
 
     print("Schedule:")
     print_schedule(schedule)
@@ -73,7 +83,7 @@ def main(args, pg):
 
     piper_setup(
         PiperQwen3Model,
-        model_args=(config, args.pp),
+        model_args=(config, num_stages),
         optim_fn=torch.optim.Adam,
         example_inputs=[x],
         example_outputs=y,
@@ -136,9 +146,9 @@ def parse_args():
     parser = argparse.ArgumentParser(
         description="Test DAG-based piper execution with the Mixtral model"
     )
-    parser.add_argument('--model', choices=['9M', '1B', '9B', '48B', '30B-A3B', '72B'], default='1B',
-                        help='Model configuration: 9M, 1B, 9B, 48B, 30B-A3B, or 72B (default: 1B)')
-    parser.add_argument("--schedule", choices=["gpipe", "1f1b", "no-pp", "interleaved-1f1b", "dualpipev", "zerobubble"], default="1f1b",)
+    parser.add_argument('--model', choices=['9M', '1B', '9B', '48B', '30B-A3B', '30-A3B-half', '72B'], default='1B',
+                        help='Model configuration: 9M, 1B, 9B, 48B, 30B-A3B, 30-A3B-half, or 72B (default: 1B)')
+    parser.add_argument("--schedule", choices=["gpipe", "1f1b", "no-pp", "interleaved-1f1b", "dualpipev", "dualpipev-seq", "zerobubble"], default="1f1b",)
     parser.add_argument("--dp", type=int, default=1)
     parser.add_argument("--pp", type=int, default=2)
     parser.add_argument("--batch-size", type=int, default=4)

From 7c7a9fd974763483497349b5843053d772c8d4bb Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Thu, 9 Apr 2026 15:19:12 +0000
Subject: [PATCH 084/100] Change pipeline schedule interface (no None entries
 required, use logical time steps instead. refactor PiperActor so that all
 intermediate storage is managed by run_dag, and the actual
 compute/communication methods operate on data passes as inputs

---
 src/piper.py                 |  202 +--
 src/piper_actor.py           | 1761 +++++++++++++------------
 src/piper_compile.py         |  257 ++--
 src/piper_coordinator.py     |   42 +-
 src/piper_exec.py            |  556 +++++---
 src/piper_graph_transform.py | 1388 +++++++++++--------
 src/piper_utils.py           |   78 +-
 test/models/llama.py         |   66 +-
 test/models/qwen3.py         |   22 +-
 test/print_schedules.py      |  103 +-
 test/schedule_helpers.py     | 2416 ++++++++++++++++++++++++++--------
 test/test_llama.py           |   96 +-
 test/test_qwen.py            |   82 +-
 13 files changed, 4436 insertions(+), 2633 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index b79de95..de6363a 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -3,8 +3,10 @@
 import torch.fx as fx
 import os
 import gc
+import pickle
+import time
 from torch._dynamo.backends.registry import register_backend
-from .piper_utils import _serialize_graphmodule, piper_metadata, create_logger, LOG_LEVEL
+from .piper_utils import _serialize_graphmodule, piper_metadata, create_logger, LOG_LEVEL, get_gpu_peak_flops_bf16
 
 
 def _collect_triton_constant_args(gm):
@@ -33,19 +35,19 @@ def scan(module):
 from .piper_graph_transform import (
     _split_gm_by_stages,
     _profile_and_split_gm,
-    schedule_to_dag,
+    expand_chunks_to_dags,
+    add_temporal_dependencies,
+    split_dag_by_rank,
     assign_time_steps,
-    insert_p2p_ops,
-    insert_ar_ops,
-    expand_bucket_tasks,
-    expand_a2a_tasks,
+    find_overlappable_tasks,
     overlap_a2a_tasks,
     visualize_dag,
+    compute_critical_path,
     print_dag_order,
     bucket_stage,
     split_by_a2a,
 )
-from .piper_exec import Task, TaskType, BatchMeta
+from .piper_exec import Chunk, TaskType, BatchMeta
 from .piper_actor import _get_actor
 
 logger = create_logger("piper_backend", LOG_LEVEL)
@@ -89,6 +91,10 @@ def piper(gm, example_inputs, **kwargs):
     trainable_bucket_keys: set = set()
     # Collected for DP broadcasting: stage_id -> (modules_data, a2a_boundaries)
     all_stage_compiled: dict = {}
+    # Running counter for assigning globally unique bucket IDs (same ordering as
+    # expand_chunks_to_dags Pass 1.5, which sorts by stage_id).  Populated after
+    # all stages are compiled so we can compute offsets in a second pass.
+    _stage_ubid_offsets: dict = {}  # stage_id -> first unique_bucket_id for that stage
 
     for (stage_id, stage_gm, input_idxs, param_idxs, graphargs, placeholders) in submodules:
         actor_id = piper_metadata.stage_to_device[stage_id]
@@ -154,14 +160,8 @@ def piper(gm, example_inputs, **kwargs):
             }
             for bgm, b_in, b_param, bargs in all_modules
         ]
-        refs.append(actor._load_stage.remote(
-            stage_id,
-            modules_data,
-            a2a_boundaries,
-            use_activation_checkpointing=piper_metadata.use_activation_checkpointing,
-        ))
 
-        all_stage_compiled[stage_id] = (modules_data, a2a_boundaries)
+        all_stage_compiled[stage_id] = (modules_data, a2a_boundaries, actor)
 
         del stage_gm
         del graphargs
@@ -170,6 +170,30 @@ def piper(gm, example_inputs, **kwargs):
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
 
+    # Compute globally unique bucket IDs for each (stage_id, stage_bucket_id) pair.
+    # Stages are sorted by stage_id to match the ordering in expand_chunks_to_dags
+    # Pass 1.5, ensuring DAG task.unique_bucket_id matches actor data structure keys.
+    _ubid = 0
+    for s in sorted(stage_bucket_counts.keys()):
+        _stage_ubid_offsets[s] = _ubid
+        _ubid += stage_bucket_counts[s]
+
+    # Now dispatch _load_stage with unique_bucket_ids embedded in modules_data.
+    refs = []
+    for stage_id, (modules_data, a2a_boundaries, actor) in all_stage_compiled.items():
+        K = stage_bucket_counts[stage_id]
+        ubid_offset = _stage_ubid_offsets[stage_id]
+        for b_idx, md in enumerate(modules_data):
+            md["unique_bucket_id"] = ubid_offset + b_idx
+        refs.append(actor._load_stage.remote(
+            stage_id,
+            modules_data,
+            a2a_boundaries,
+            use_activation_checkpointing=piper_metadata.use_activation_checkpointing,
+        ))
+        # Remove actor from stored compiled data (not needed downstream)
+        all_stage_compiled[stage_id] = (modules_data, a2a_boundaries)
+
     ray.get(refs)
 
     piper_metadata.stage_bucket_counts = stage_bucket_counts
@@ -177,82 +201,53 @@ def piper(gm, example_inputs, **kwargs):
 
     # Build task DAG from the schedule stored by piper_setup.
     if piper_metadata.schedule is not None:
-        piper_metadata.task_dag = schedule_to_dag(piper_metadata.schedule)
+        dag = expand_chunks_to_dags(
+            piper_metadata.schedule,
+            piper_metadata.stage_bucket_counts,
+            stage_a2a_boundaries,
+            dp_degree,
+        )
         logger.debug(
-            f"Built task DAG: {len(piper_metadata.task_dag.nodes)} nodes, "
-            f"{sum(len(n.data_succs) for n in piper_metadata.task_dag.nodes)} data edges, "
-            f"{sum(len(n.temporal_succs) for n in piper_metadata.task_dag.nodes)} temporal edges"
+            f"Built task DAG: {len(dag.nodes)} nodes, "
+            f"{sum(len(n.data_succs) for n in dag.nodes)} data edges"
         )
 
-        dag = piper_metadata.task_dag
-
-        # Expand multi-segment stages into per-segment FWD/BWD task chains.
-        # This covers both param-bucketed stages (--bucketing) and A2A-split stages
-        # (--dp > 1 with MoE), both of which load multiple modules per stage.
-        # Only expand stages that actually have more than one segment.
-        bucket_counts = {
-            sid: n for sid, n in piper_metadata.stage_bucket_counts.items() if n > 1
-        }
-        if bucket_counts:
-            dag = expand_bucket_tasks(dag, bucket_counts)
-            logger.debug(
-                f"Expanded stages {list(bucket_counts.keys())} into "
-                f"per-segment FWD/BWD nodes"
-            )
+        add_temporal_dependencies(dag, piper_metadata.schedule)
+        logger.debug(
+            f"Added temporal dependencies: "
+            f"{sum(len(n.temporal_succs) for n in dag.nodes)} temporal edges"
+        )
 
-        # Insert FWD_A2A / BWD_A2A nodes for expert-parallel all-to-all boundaries.
         if dp_degree > 1 and stage_a2a_boundaries:
-            dag = expand_a2a_tasks(dag, stage_a2a_boundaries)
-            logger.debug(
-                f"Inserted A2A task nodes for stages {list(stage_a2a_boundaries.keys())}"
-            )
-
-            # Overlap compute and A2A tasks across FWD_BWD task pairs so that
-            # one task's A2A communication runs concurrently with the other's compute.
-            fwdbwd_pairs: list[tuple[Task, Task]] = []
-            for row in piper_metadata.schedule.grid:
-                for cell in row:
-                    if cell is not None and cell.type == TaskType.FWD_BWD:
-                        fwd_task = Task(
-                            pp_rank=cell.pp_rank,
-                            batches=[cell.batches[0]],
-                            type=TaskType.FWD,
-                        )
-                        bwd_task = Task(
-                            pp_rank=cell.pp_rank,
-                            batches=[cell.batches[1]],
-                            type=TaskType.BWD,
-                        )
-                        fwdbwd_pairs.append((fwd_task, bwd_task))
-            # fwdbwd_pairs.append((
-            #     Task(pp_rank=0, batches=[BatchMeta(0, 1)], type=TaskType.FWD), 
-            #     Task(pp_rank=0, batches=[BatchMeta(0, 2)], type=TaskType.FWD)))
-            if fwdbwd_pairs:
-                dag = overlap_a2a_tasks(dag, fwdbwd_pairs)
-                logger.debug(
-                    f"Overlapped A2A and compute tasks for {len(fwdbwd_pairs)} FWD_BWD pairs"
+            overlappable = find_overlappable_tasks(piper_metadata.schedule)
+            for t1, t2 in overlappable:
+                logger.info(
+                    f"Found adjacent task pair for A2A/compute overlap: "
+                    f"{t1} -> {t2}"
                 )
+            # if overlappable:
+            #     dag = overlap_a2a_tasks(dag, overlappable)
+            #     logger.debug(
+            #         f"Overlapped {len(overlappable)} adjacent task pair(s) for A2A/compute overlap"
+            #     )
 
-        # Assign final time_step values from the temporal dependency graph.
         assign_time_steps(dag)
 
-        # Split into per-rank DAGs.
-        per_rank_dags = insert_p2p_ops(dag)
-
-        # Insert all-reduce nodes when using data parallelism.
-        if dp_degree > 1:
-            per_rank_dags = insert_ar_ops(per_rank_dags, piper_metadata.trainable_bucket_keys)
-            logger.debug("Inserted ALL_REDUCE nodes for DP gradient sync")
-            # ZeRO-1/2/3: insert AG/RS into the task DAG before/after the appropriate FWD/BWD tasks.
+        # Save a deep copy of the full DAG before split_dag_by_rank removes
+        # cross-rank data edges.  Used later for profiling and critical-path analysis.
+        piper_metadata.full_dag_no_overlap = pickle.loads(pickle.dumps(dag))
 
+        # Split into per-rank DAGs.
+        per_rank_dags = split_dag_by_rank(dag)
 
         piper_metadata.per_rank_dags = per_rank_dags
         actors = piper_metadata.actors
-        for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags):
-            try:
-                visualize_dag(per_rank_dag, output_path=f"figs/rank{pp_rank}_dag")
-            except Exception as e:
-                logger.warning(f"DAG visualization failed for rank {pp_rank} (DAG may be too large for dot): {e}")
+        if piper_metadata.visualize_dag:
+            for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags):
+                try:
+                    visualize_dag(per_rank_dag, output_path=f"out/rank{pp_rank}_dag")
+                except Exception as e:
+                    logger.warning(f"DAG visualization failed for rank {pp_rank} (DAG may be too large for dot): {e}")
             # uncomment for debugging DAG construction:
             # print_dag_order(per_rank_dag, label=f"rank {pp_rank}")
         ray.get([
@@ -269,6 +264,7 @@ def piper(gm, example_inputs, **kwargs):
             "stage_bucket_counts": stage_bucket_counts,
             "trainable_bucket_keys": trainable_bucket_keys,
             "per_rank_dags": per_rank_dags,
+            "full_dag_no_overlap": piper_metadata.full_dag_no_overlap,
         }
 
     example_outputs = original_gm(*example_inputs)
@@ -292,12 +288,20 @@ def callback(*args):
 # DAG-based execution entry point
 # ---------------------------------------------------------------------------
 
-def piper_exec_dag(loss_fn) -> list:
+def piper_exec_dag(loss_fn, profiling: bool = False, log_stats: bool = False) -> list:
     """Execute one training step using the per-rank TaskDAG and :meth:`run_dag`.
 
     Per-rank DAGs are built and loaded onto actors by the piper backend during
     compilation.  This function just invokes ``run_dag`` in parallel on all actors.
 
+    Args:
+        loss_fn: Loss function passed through to each actor's run_dag.
+        profiling: If True, each actor times every node and accumulates the
+            measurement into ``node.profiling_measurements``.
+        log_stats: If True, log step time, throughput, MFU (when
+            ``model_flops_per_token`` was set in :func:`piper_setup`), and
+            peak GPU memory for each rank.
+
     Returns:
         Flat list of per-microbatch losses collected from all actors.
     """
@@ -307,8 +311,48 @@ def piper_exec_dag(loss_fn) -> list:
     )
     actors = piper_metadata.actors
     run_refs = [
-        actors[pp_rank].run_dag.remote(loss_fn=loss_fn)
+        actors[pp_rank].run_dag.remote(loss_fn=loss_fn, profiling=profiling)
         for pp_rank in range(len(piper_metadata.per_rank_dags))
     ]
+    t0 = time.perf_counter()
     results = ray.get(run_refs)
+    step_time = time.perf_counter() - t0
+
+    if log_stats or piper_metadata.model_flops_per_token is not None:
+        _log_step_stats(step_time, log_stats, actors)
+
     return [item for sublist in results if sublist for item in sublist]
+
+
+def _log_step_stats(step_time: float, log_memory: bool, actors: dict) -> None:
+    """Log throughput, MFU, and optionally per-rank peak GPU memory."""
+    stats = [f"step_time={step_time:.3f}s"]
+
+    tokens = piper_metadata.tokens_per_step
+    if tokens is not None:
+        stats.append(f"throughput={tokens / step_time:.1f} tok/s")
+
+    flops_per_token = piper_metadata.model_flops_per_token
+    if tokens is not None and flops_per_token is not None:
+        peak_flops = 1979e12
+        if peak_flops is not None:
+            dp_degree = int(os.environ.get("PIPER_DP_DEGREE", "1"))
+            total_gpus = len(actors) * dp_degree
+            achieved_flops = flops_per_token * tokens / step_time
+            mfu = achieved_flops / (peak_flops * total_gpus)
+            stats.append(f"MFU={mfu:.2%}")
+        else:
+            logger.warning(
+                "MFU not computed: GPU peak FLOPs unknown for this device. "
+                "Add it to _GPU_PEAK_FLOPS_BF16 in piper_utils.py."
+            )
+
+    if log_memory:
+        mem_refs = [
+            actors[pp_rank].get_and_reset_peak_memory_stats.remote()
+            for pp_rank in range(len(piper_metadata.per_rank_dags))
+        ]
+        for global_rank, max_alloc_bytes in ray.get(mem_refs):
+            stats.append(f"rank{global_rank}_peak_mem={max_alloc_bytes / 1e9:.2f}GB")
+
+    logger.info("  ".join(stats))
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 741466e..b76b169 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -2,7 +2,8 @@
 import torch
 import logging
 import os
-from typing import Any, Dict, List, Optional, Set, Tuple
+import re
+from typing import Any, Dict, List, Optional, Set
 import gc
 import threading
 from torch.nn import Parameter
@@ -19,9 +20,18 @@
     NcclOverlapDetector,
 )
 from .backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
-from .piper_exec import TaskType, TaskDAG, TaskNode
+from .piper_exec import TaskType, TaskDAG, Task
 
-CLEANUP_MEMORY = True
+CLEANUP_MEMORY = False
+
+# Disable AOT autograd's donated-buffer optimization so that retain_graph=True
+# works during the split BWD_I pass (which must keep the graph alive for BWD_W).
+# try:
+#     import torch._functorch.config as _tmp_fc
+#     _tmp_fc.donated_buffer = False
+#     del _tmp_fc  # remove from module globals so Ray/cloudpickle never sees it
+# except (ImportError, AttributeError):
+#     pass
 
 logger = create_logger("piper_actor", LOG_LEVEL)
 
@@ -29,14 +39,6 @@ def _get_rank(pp_rank, dp_rank, pp_degree):
     return pp_rank + dp_rank * pp_degree
 
 
-def find_free_port():
-    import socket
-
-    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-        s.bind(("0.0.0.0", 0))
-        port = s.getsockname()[1]
-    return port
-
 def _create_actors(
     num_actors,
     optim_class,
@@ -61,18 +63,18 @@ def _create_actors(
             "cuda-event-trace": "false",
             "stop-on-exit": "true",
         }} if profile else {}
-        master_env = {
+        nccl_env = {
             "env_vars": {
-                "PIPER_MASTER_ADDR": os.environ.get("PIPER_MASTER_ADDR", "127.0.0.1"),
-                "PIPER_MASTER_PORT": os.environ.get("PIPER_MASTER_PORT", "10000"),
+                "NCCL_SOCKET_IFNAME": "^lo,docker",
+                "NCCL_DEBUG": "WARN",
             }
         }
         actor = PiperActor.options(
-            num_gpus=0.8,
-            runtime_env={**nsight_env, **master_env},
+            num_gpus=0.6,
+            runtime_env={**nsight_env, **nccl_env},
             scheduling_strategy=PlacementGroupSchedulingStrategy(
                 placement_group=pg,
-                placement_group_bundle_index=dp_rank
+                placement_group_bundle_index=dp_rank,
             ),
         ).remote(
             pp_rank,
@@ -160,18 +162,6 @@ def __init__(
         self.forward_args = dict()
         # map stage id -> input idx -> input tensor metadata
         self.forward_input_meta = defaultdict(dict)
-        # map stage id -> indices of the input tensors (as opposed to model parameters) used by the fx.Graph
-        self.input_idxs = dict()
-        # map stage id -> indices of the model parameters used by the fx.Graph
-        self.param_idxs = dict()
-        # map stage id -> indices of the model parameters used by the fx.Graph
-        self.trainable_param_idxs = dict()
-        # map stage id -> optimizer for the fx.Graph (used in legacy non-DAG path)
-        self.optims = dict()
-        # map stage id -> mb_idx -> previous activation (if this stage is not first)
-        self.inp_activation = defaultdict(dict)
-        # map stage id -> mb_idx -> current activation
-        self.out_activation = defaultdict(dict)
         # accumuate loss for each microbatch
         self.loss = []
 
@@ -182,35 +172,36 @@ def __init__(
 
         # DAG execution state
         self.dag = None
-        self.send_buffer: dict = {}  # ((stage_id, bucket_id_or_None), mb_idx) -> tensor(s); or (None, mb_idx) for BWD
-        self.recv_buffer: dict = {}  # same key format as send_buffer -> tensor(s)
-        self.recv_events: dict = {}  # same key format as send_buffer -> cuda.Event
-        self.bucket_buffer: dict = {}  # (stage_id, mb_idx, bucket_id) -> (pre_detach_outs, detached_outs)
-        # A2A boundaries per stage: stage_id -> {boundary_bucket_id -> tensor_idx}
-        self.a2a_boundaries: dict = {}
-        # A2A boundary state: (stage_id, mb_idx, boundary_bucket_id) -> (x_detached, x_a2a)
-        self.a2a_buffer: dict = {}
-        # CUDA events recorded on a2a_stream after each FWD_A2A / BWD_A2A op
-        self.a2a_events: dict = {}  # (stage_id, mb_idx, type, bucket_id) -> cuda.Event
-        # CUDA events recorded on comm_stream after each all-reduce launches
-        self.ar_events: dict = {}  # (stage_id, bucket_id) -> cuda.Event
-
-        # Per-bucket stage data (populated by _load_stage)
-        self.bucket_fwd_fns: dict[int, list] = {}   # stage_id -> [fwd_fn per bucket]
-        self.bucket_fwd_args: dict[int, list] = {}  # stage_id -> [args_list per bucket]
-        self.bucket_param_idxs: dict[int, list] = {}  # stage_id -> [param_idxs per bucket]
-        self.bucket_optims: dict[int, list] = {}      # stage_id -> [optimizer per bucket]
-        # Contiguous flat tensors for param data and gradients, keyed (stage_id, bucket_id)
-        self.bucket_flat_params: dict = {}   # (stage_id, bucket_id) -> flat param tensor
-        self.bucket_flat_grads: dict = {}    # (stage_id, bucket_id) -> flat grad tensor
-        self.bucket_trainable_param_idxs: dict = {}  # (stage_id, bucket_id) -> list of indices into bucket_fwd_args
-        # stage_id -> mb_idx -> list of (pre_detach_out, detached_input_to_next) per boundary
-        self.bucket_boundaries: dict = defaultdict(dict)
-
-        # Split backward 
-        self.bw_param_groups = defaultdict(dict)  # stage_id -> mb_idx -> param group for split backward pass
-        self.bw_grad_cache = defaultdict(dict)  # stage_id -> mb_idx -> cached activations between BWD_I and BWD_W passes
-        self.upstream_grad_cache = defaultdict(dict)  # stage_id -> mb_idx -> cached upstream grads between BWD_I and BWD_W passes
+
+        # Unified inter-task buffer.  Keyed by task.uid (int), storing whatever
+        # data the task produced for downstream consumers.
+        # Cleared at the start of each run_dag() call.
+        self.task_buffer: dict = {}
+
+        # CUDA events for synchronisation between streams
+        self.recv_events: dict = {}   # task_uid -> cuda.Event (for RECV tasks)
+        self.a2a_events: dict = {}    # task_uid -> cuda.Event (for FWD_A2A/BWD_A2A tasks)
+        self.ar_events: dict = {}     # unique_bucket_id -> cuda.Event (for ALL_REDUCE tasks)
+        self.bwd_events: dict = {}    # unique_bucket_id -> cuda.Event (for BWD/BWD_I/BWD_W tasks)
+
+        # Per-bucket data keyed by unique_bucket_id (populated by _load_stage).
+        # "Bucket" here is a globally unique unit of computation; each stage may
+        # contribute one or more buckets.  The actor is agnostic to which stage
+        # owns a bucket.
+        self.bucket_fwd_fns: dict = {}          # ubid -> fwd function
+        self.bucket_fwd_args: dict = {}         # ubid -> args list (with None holes for inputs)
+        self.bucket_input_idxs: dict = {}       # ubid -> list of input slot indices
+        self.bucket_param_idxs: dict = {}       # ubid -> list of param slot indices
+        self.bucket_param_names: dict = {}      # ubid -> [FX placeholder name per param]
+        self.bucket_optims: dict = {}           # ubid -> optimizer (or None)
+        self.bucket_flat_params: dict = {}      # ubid -> flat param tensor (or None)
+        self.bucket_flat_grads: dict = {}       # ubid -> flat grad tensor (or None)
+        self.bucket_trainable_param_idxs: dict = {}  # ubid -> list of trainable param slot indices
+
+        # Non-trainable constant tensor attributes (e.g. freqs_cis, mask) pushed
+        # from the coordinator before compilation so _load_stage can fill them in
+        # instead of zero-initializing.  Keyed by bare attribute name (e.g. "freqs_cis").
+        self.model_const_attrs: dict = {}
 
         from .piper_utils import piper_metadata
         piper_metadata.actor_self = self
@@ -218,6 +209,12 @@ def __init__(
     def get_trace_data(self) -> dict:
         return self.global_rank, self.trace_data
 
+    def get_and_reset_peak_memory_stats(self) -> tuple:
+        """Return (global_rank, max_memory_allocated_bytes) and reset peak stats."""
+        max_alloc = torch.cuda.max_memory_allocated()
+        torch.cuda.reset_peak_memory_stats()
+        return self.global_rank, max_alloc
+
     def clear_trace_data(self) -> None:
         self.trace_data.clear()
         self._pending_timing_events.clear()
@@ -228,49 +225,6 @@ def set_tracing(self, enabled: bool) -> None:
             f"Actor {self.global_rank}: Tracing {'enabled' if enabled else 'disabled'}"
         )
 
-    def start_mem_tracing(self) -> None:
-        torch.cuda.memory._record_memory_history()
-
-    def stop_mem_tracing(self) -> None:
-        torch.cuda.memory._dump_snapshot(
-            f"/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/actor{self.global_rank}_memory_snapshot_mb4_gpipe.pickle"
-        )
-        self.logger.info(
-            f"Saved memory snapshot to actor{self.global_rank}_memory_snapshot_mb4_gpipe.pickle"
-        )
-        torch.cuda.memory._record_memory_history(enabled=None)
-    
-    def enable_memory_tracing(self, enabled: bool = True) -> None:
-        """Enable/disable memory tracing."""
-        self.memory_tracing_enabled = enabled
-        if enabled:
-            torch.cuda.memory._record_memory_history()
-            self.logger.info(f"Actor {self.global_rank}: Memory tracing enabled")
-        else:
-            if hasattr(self, 'memory_tracing_enabled') and self.memory_tracing_enabled:
-                # Dump snapshot before disabling
-                snapshot_path = f"actor{self.global_rank}_iter_memory.pickle"
-                os.makedirs(os.path.dirname(snapshot_path), exist_ok=True)
-                torch.cuda.memory._dump_snapshot(snapshot_path)
-                self.logger.info(f"Saved memory snapshot to {snapshot_path}")
-            torch.cuda.memory._record_memory_history(enabled=None)
-            self.memory_tracing_enabled = False
-            self.logger.info(f"Actor {self.global_rank}: Memory tracing disabled")
-
-    def dump_memory_snapshot(self, filename: str = None) -> str:
-        """Dump current memory snapshot. Returns snapshot path."""
-        if not (hasattr(self, 'memory_tracing_enabled') and self.memory_tracing_enabled):
-            self.logger.warning("Memory tracing not enabled, cannot dump snapshot")
-            return None
-        
-        if filename is None:
-            filename = f"actor{self.global_rank}_iter_memory.pickle"
-        
-        os.makedirs(os.path.dirname(filename), exist_ok=True)
-        torch.cuda.memory._dump_snapshot(filename)
-        self.logger.info(f"Saved memory snapshot to {filename}")
-        return filename
-
     def reset_peak_memory(self):
         torch.cuda.reset_peak_memory_stats()
 
@@ -285,6 +239,18 @@ def load_labels(self, labels):
         self.labels = labels.to(self.device)
         self.logger.debug(f"Actor {self.global_rank} loaded labels {self.labels.shape}")
 
+    def load_const_attrs(self, const_attrs: dict) -> None:
+        """Store non-trainable constant tensor attributes (e.g. freqs_cis, mask).
+
+        *const_attrs* maps bare attribute name → CPU tensor.  Values are moved to
+        the actor's device so ``_load_stage`` can copy them directly.
+        """
+        self.model_const_attrs = {k: v.to(self.device) for k, v in const_attrs.items()}
+        self.logger.debug(
+            f"Actor {self.global_rank} loaded {len(self.model_const_attrs)} "
+            f"constant attrs: {list(self.model_const_attrs.keys())}"
+        )
+
     def _start_timing(self, stream, label):
         if self.tracing:
             start = torch.cuda.Event(enable_timing=True)
@@ -327,27 +293,28 @@ def _join_process_groups(self, master_addr, master_port):
         self.device = f"cuda:{self.global_rank % torch.cuda.device_count()}"
         torch.cuda.set_device(self.device)
 
-        dist.init_process_group(
-            "nccl",
-            init_method=init_method,
-            rank=self.global_rank,
-            world_size=self.world_size,
-        )
-        self.logger.debug(
-            f"Actor {self.global_rank} has GPU {os.environ['CUDA_VISIBLE_DEVICES']}, joined the global process group"
-        )
+        if self.pp_degree > 1 or self.dp_degree > 1:
+            dist.init_process_group(
+                "nccl",
+                init_method=init_method,
+                rank=self.global_rank,
+                world_size=self.world_size,
+            )
+            self.logger.debug(
+                f"Actor {self.global_rank} has GPU {os.environ['CUDA_VISIBLE_DEVICES']}, joined the global process group"
+            )
 
-        if self.dp_degree > 1:
-            self._join_dp_process_group()
-        if self.pp_degree > 1:
-            self._join_pp_process_group()
+            if self.dp_degree > 1:
+                self._join_dp_process_group()
+            if self.pp_degree > 1:
+                self._join_pp_process_group()
 
-        # Force cuBLAS context initialization on comp_stream so the first
-        # backward pass does not encounter a "no current CUDA context" warning.
-        with torch.cuda.stream(self.comp_stream):
-            _w = torch.zeros(4, 4, device=self.device)
-            torch.mm(_w, _w)
-        torch.cuda.synchronize()
+            # Force cuBLAS context initialization on comp_stream so the first
+            # backward pass does not encounter a "no current CUDA context" warning.
+            with torch.cuda.stream(self.comp_stream):
+                _w = torch.zeros(4, 4, device=self.device)
+                torch.mm(_w, _w)
+            torch.cuda.synchronize()
 
     def _join_dp_process_group(self):
         num_dp_groups = self.world_size // self.dp_degree
@@ -391,6 +358,13 @@ def _join_pp_process_group(self):
             #             self.pp_groups[(rank_lo, rank_hi)] = pg_lo_to_hi
             #             self.pp_groups[(rank_hi, rank_lo)] = pg_hi_to_lo
 
+        # Warm up both communicators to force eager NCCL initialization.
+        # Without this, NCCL defers init to first use and the entire first
+        # training iteration pays the (very expensive) init cost.
+        dummy = torch.zeros(1, device=self.device)
+        dist.all_reduce(dummy, group=self.pp_lo_hi)
+        dist.all_reduce(dummy, group=self.pp_hi_lo)
+        torch.cuda.synchronize()
         self.logger.info(f"Global rank {self.global_rank} warmed up pp communicators")
 
     def shutdown(self):
@@ -643,31 +617,23 @@ def _load_stage(
         """Load a (possibly bucketed) stage.
 
         *modules_data* is a list of dicts, one per module/bucket, each with keys:
-        ``gm_data``, ``graphargs``, ``input_idxs``, ``param_idxs``.
+        ``gm_data``, ``graphargs``, ``input_idxs``, ``param_idxs``, ``unique_bucket_id``.
 
         A non-bucketed stage is represented as a single-element list.
-        Bucket 0 handles the activation input from the previous stage (or
-        ``self.inputs`` for stage 0).  Subsequent modules receive the
-        cross-segment activation output of the previous module.
+        All per-bucket data structures are keyed by ``unique_bucket_id`` — the
+        globally unique bucket index assigned by piper.py — so that run_dag can
+        look up bucket data without knowing which stage owns a bucket.
         """
         self.logger.debug(
             f"Loading stage {stage_id} ({len(modules_data)} module(s)) on actor {self.global_rank}"
         )
 
-        self.bucket_fwd_fns[stage_id] = []
-        self.bucket_fwd_args[stage_id] = []
-        self.bucket_param_idxs[stage_id] = []
-        self.bucket_optims[stage_id] = []
-        self.a2a_boundaries[stage_id] = a2a_boundaries or {}
-
         g = torch.Generator(device=self.device)
         g.manual_seed(1000 * self.global_rank + stage_id)
 
         self._load_stage_kernels()
 
         # Restore triton kernel constant_args that were captured at compile time.
-        # Each bucket may reference specific constant_args_idx values baked into the
-        # compiled graph; we restore them at the exact indices so the graph runs correctly.
         try:
             from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
             for bd in modules_data:
@@ -677,41 +643,41 @@ def _load_stage(
             self.logger.warning(f"Actor {self.global_rank} failed to restore triton constant_args: {e}")
 
         first_gm = None
-        last_gm = None
 
         for b_idx, bd in enumerate(modules_data):
+            ubid: int = bd["unique_bucket_id"]
             gm = _deserialize_graphmodule(bd["gm_data"])
             gm = self._replace_meta_constants(gm, self.device)
             if b_idx == 0:
                 first_gm = gm
-            last_gm = gm
 
             forward_args = list(bd["graphargs"])
             b_input_idxs = list(bd["input_idxs"])
             b_param_idxs = list(bd["param_idxs"])
 
+            # Extract FX placeholder names for each param index.
+            gm_placeholders = [n for n in gm.graph.nodes if n.op == 'placeholder']
+            self.bucket_param_names[ubid] = [
+                gm_placeholders[i].name if i < len(gm_placeholders) else f"ubid{ubid}_p{i}"
+                for i in b_param_idxs
+            ]
+
             self.logger.debug(
-                f"Stage {stage_id} module {b_idx} input indices: {b_input_idxs}"
+                f"Stage {stage_id} bucket {b_idx} (ubid={ubid}) input indices: {b_input_idxs}"
             )
 
-            # Module 0: save activation-input metadata for the stage interface
-            # (used by _exec_recv to pre-allocate recv buffers).
-            if b_idx == 0:
-                self.input_idxs[(stage_id, None)] = b_input_idxs  # legacy path alias
-                for i in b_input_idxs:
-                    meta = forward_args[i]
-                    if meta is not None:
-                        self.forward_input_meta[stage_id][i] = (
-                            tuple(meta.shape),
-                            meta.dtype,
-                            bool(getattr(meta, "requires_grad", False)),
-                        )
-                    forward_args[i] = None
-            else:
-                for i in b_input_idxs:
-                    forward_args[i] = None
+            # Save input tensor metadata for pre-allocating FWD recv buffers.
+            # Stored as a list of (shape, dtype, requires_grad) in input-slot order.
+            recv_meta = []
+            for i in b_input_idxs:
+                meta = forward_args[i]
+                if meta is not None:
+                    recv_meta.append((tuple(meta.shape), meta.dtype,
+                                      bool(getattr(meta, "requires_grad", False))))
+                forward_args[i] = None  # clear slot; run_dag will fill it at execution time
+            self.forward_input_meta[ubid] = recv_meta
 
-            self.input_idxs[(stage_id, b_idx)] = b_input_idxs
+            self.bucket_input_idxs[ubid] = b_input_idxs
 
             # Realize parameter tensors.
             realized = [None] * len(forward_args)
@@ -723,7 +689,20 @@ def _load_stage(
                     t.requires_grad_(True)
                     torch.nn.init.normal_(t, mean=0.0, std=0.02, generator=g)
                 else:
-                    t.zero_()
+                    # Non-trainable slot: try to fill from const attrs (freqs_cis, mask, …)
+                    # before falling back to zeros.  Dynamo names direct model attrs as
+                    # "l_self_<attr_name>", so strip that prefix to get the bare name.
+                    ph_name = gm_placeholders[i].name if i < len(gm_placeholders) else ""
+                    attr_name = re.sub(r'^l_self_', '', ph_name)
+                    const_val = self.model_const_attrs.get(attr_name)
+                    if (
+                        const_val is not None
+                        and tuple(const_val.shape) == tuple(arg.shape)
+                        and const_val.dtype == arg.dtype
+                    ):
+                        t.copy_(const_val)
+                    else:
+                        t.zero_()
                 realized[i] = t
 
             # Forward function (with optional activation checkpointing).
@@ -735,9 +714,9 @@ def _load_stage(
             else:
                 forward_fn = gm.forward
 
-            self.bucket_fwd_fns[stage_id].append(forward_fn)
-            self.bucket_fwd_args[stage_id].append(realized)
-            self.bucket_param_idxs[stage_id].append(b_param_idxs)
+            self.bucket_fwd_fns[ubid] = forward_fn
+            self.bucket_fwd_args[ubid] = realized
+            self.bucket_param_idxs[ubid] = b_param_idxs
 
             # Collect trainable parameters and build a contiguous flat tensor so
             # a single all-reduce call can sync the entire module's gradients.
@@ -746,40 +725,33 @@ def _load_stage(
                 if realized[i] is not None and realized[i].requires_grad
             ]
             trainable = [realized[i] for i in trainable_idxs]
-            self.bucket_trainable_param_idxs[(stage_id, b_idx)] = trainable_idxs
+            self.bucket_trainable_param_idxs[ubid] = trainable_idxs
 
             if trainable:
-                flat_params = torch.cat([p.detach().view(-1) for p in trainable]).contiguous()
+                flat_params = torch.cat([t.detach().view(-1) for t in trainable]).contiguous()
                 flat_params.requires_grad_(False)
                 flat_grads = torch.zeros_like(flat_params)
                 offset = 0
-                for idx, p in zip(trainable_idxs, trainable):
-                    numel = p.numel()
-                    realized[idx] = realized[idx].detach()
-                    realized[idx].data = flat_params[offset:offset + numel].view(p.shape)
-                    realized[idx].requires_grad_(True)
+                for tidx, t in zip(trainable_idxs, trainable):
+                    numel = t.numel()
+                    realized[tidx] = realized[tidx].detach()
+                    realized[tidx].data = flat_params[offset:offset + numel].view(t.shape)
+                    realized[tidx].requires_grad_(True)
                     offset += numel
-                self.bucket_flat_params[(stage_id, b_idx)] = flat_params
-                self.bucket_flat_grads[(stage_id, b_idx)] = flat_grads
+                self.bucket_flat_params[ubid] = flat_params
+                self.bucket_flat_grads[ubid] = flat_grads
             else:
-                self.bucket_flat_params[(stage_id, b_idx)] = None
-                self.bucket_flat_grads[(stage_id, b_idx)] = None
+                self.bucket_flat_params[ubid] = None
+                self.bucket_flat_grads[ubid] = None
 
             # Optimizer for this module's trainable parameters.
             trainable_for_optim = [realized[i] for i in trainable_idxs]
-            optim = self.optim_class(trainable_for_optim) if trainable_for_optim else None
-            self.bucket_optims[stage_id].append(optim)
+            optim = self.optim_class(trainable_for_optim, fused=True) if trainable_for_optim else None
+            self.bucket_optims[ubid] = optim
 
-            # Legacy non-DAG DDP: register per-param allreduce hooks for module 0.
-            # if b_idx == 0 and self.dp_degree > 1 and not self.naive_gradient_sync:
-            #     self.forward_args[stage_id] = realized
-                # self._prepare_dp_comm_ops(stage_id)
 
-        # Set legacy fields from first module for ZeroBubble BWD_I/BWD_W compatibility.
-        self.forward_args[stage_id] = self.bucket_fwd_args[stage_id][0]
-        self.param_idxs[stage_id] = self.bucket_param_idxs[stage_id][0]
+        # Keep first GraphModule for compatibility with external inspection tools.
         self.graph_modules[stage_id] = first_gm
-        self.stage_id = stage_id
 
     # -----------------------------------------------------------------------
     # DAG-based execution
@@ -790,27 +762,78 @@ def load_dag(self, dag: TaskDAG) -> None:
         self.dag = dag
 
     def get_all_params(self) -> dict:
-        """Return {(stage_id, bucket_id): flat_cpu_tensor} for every trainable bucket."""
+        """Return {unique_bucket_id: flat_cpu_tensor} for every trainable bucket."""
         return {
-            key: tensor.detach().cpu().clone()
-            for key, tensor in self.bucket_flat_params.items()
+            ubid: tensor.detach().cpu().clone()
+            for ubid, tensor in self.bucket_flat_params.items()
             if tensor is not None
         }
 
-    def get_bucket_fwd_counts(self) -> dict:
-        """Return the number of forward buckets for each bucketed stage on this actor."""
-        return {stage_id: len(fns) for stage_id, fns in self.bucket_fwd_fns.items()}
+    def get_named_params(self) -> dict:
+        """Return {fx_placeholder_name: cpu_float32_tensor} for all trainable params."""
+        result: dict = {}
+        for ubid, b_fwd_args in self.bucket_fwd_args.items():
+            b_param_idxs = self.bucket_param_idxs[ubid]
+            names = self.bucket_param_names.get(ubid, [])
+            for i, idx in enumerate(b_param_idxs):
+                p = b_fwd_args[idx]
+                if p is not None and p.requires_grad:
+                    name = names[i] if i < len(names) else f"ubid{ubid}_p{i}"
+                    result[name] = p.detach().cpu().clone().float()
+        return result
+
+    def set_named_params(self, named_params: dict) -> None:
+        """Load parameter values from ``{fx_placeholder_name: tensor}`` dict."""
+        for ubid, b_fwd_args in self.bucket_fwd_args.items():
+            b_param_idxs = self.bucket_param_idxs[ubid]
+            names = self.bucket_param_names.get(ubid, [])
+            for i, idx in enumerate(b_param_idxs):
+                p = b_fwd_args[idx]
+                if p is not None and p.requires_grad:
+                    name = names[i] if i < len(names) else None
+                    if name and name in named_params:
+                        with torch.no_grad():
+                            p.data.copy_(named_params[name].to(p.device, p.dtype))
 
-    def get_a2a_boundaries(self) -> dict:
-        """Return A2A boundary info: stage_id -> {boundary_bucket_id -> tensor_idx}."""
-        return dict(self.a2a_boundaries)
+    def get_bucket_fwd_counts(self) -> dict:
+        """Return the set of unique_bucket_ids loaded on this actor."""
+        return list(self.bucket_fwd_fns.keys())
+
+    def _timing_stream(self, task_type) -> "torch.cuda.Stream":
+        """Return the CUDA stream that drives work for the given task type."""
+        if task_type == TaskType.SEND:
+            return self.p2p_send_stream
+        if task_type == TaskType.RECV:
+            return self.p2p_recv_stream
+        if task_type in (TaskType.FWD_A2A, TaskType.BWD_A2A):
+            return self.a2a_stream
+        if task_type == TaskType.ALL_REDUCE:
+            return self.comm_stream
+        return self.comp_stream
+
+    def get_node_runtimes(self) -> dict:
+        """Return ``{uid: profiling_measurements}`` for every profiled DAG node.
+
+        Only nodes that have at least one measurement are included.  Call this
+        after one or more ``run_dag(profiling=True)`` iterations.
+        """
+        return {
+            node.uid: list(node.profiling_measurements)
+            for node in self.dag.nodes
+            if node.profiling_measurements
+        }
 
-    def run_dag(self, loss_fn=None):
+    def run_dag(self, loss_fn=None, profiling: bool = False):
         """Execute the loaded TaskDAG in topological order.
 
+        All inter-task data is routed through ``task_buffer``, keyed by the
+        producing task's ``uid``.  Each task method receives its inputs as
+        arguments and returns its outputs; ``run_dag`` is the only place that
+        reads from and writes to ``task_buffer``.
+
         Synchronisation contract:
         - Before a SEND node: p2p_send_stream waits on the compute event so the
-          send buffer is fully written before the transfer begins.
+          activation / gradient tensor is fully computed before the send begins.
         - Before a compute node that has a RECV predecessor: comp_stream waits
           on the recv_event so the recv buffer is populated before compute reads it.
         - RECV records a recv_event after the dist.recv completes.
@@ -818,830 +841,824 @@ def run_dag(self, loss_fn=None):
         assert self.dag is not None, "load_dag() must be called before run_dag()"
         dag = self.dag
 
-        # Reset per-iteration DAG buffers
-        self.send_buffer = {}
-        self.bucket_buffer = {}
-        self.a2a_buffer = {}
+        # Reset per-iteration state
+        self.task_buffer = {}
+        self.recv_events = {}
         self.a2a_events = {}
         self.ar_events = {}
+        self.bwd_events = {}
 
         # Point each trainable param's .grad at the appropriate slice of its
         # bucket's flat_grads tensor so backward accumulates contiguously.
-        for (s_id, b_idx), trainable_idxs in self.bucket_trainable_param_idxs.items():
-            flat_grads = self.bucket_flat_grads.get((s_id, b_idx))
+        for ubid, trainable_idxs in self.bucket_trainable_param_idxs.items():
+            flat_grads = self.bucket_flat_grads.get(ubid)
             if flat_grads is None:
                 continue
             flat_grads.zero_()
-            args = self.bucket_fwd_args[s_id][b_idx]
+            args = self.bucket_fwd_args[ubid]
             offset = 0
             for i in trainable_idxs:
-                p = args[i]
-                if p is not None:
-                    numel = p.numel()
-                    p.grad = flat_grads[offset:offset + numel].view(p.shape)
+                t = args[i]
+                if t is not None:
+                    numel = t.numel()
+                    t.grad = flat_grads[offset:offset + numel].view(t.shape)
                     offset += numel
 
-        self.recv_buffer = {}
-        self.recv_events = {}
-        comp_events: dict = {}  # (stage_id, mb_idx, task_type, bucket_id) -> cuda.Event
-
-        # Sort nodes by time_step.  Nodes that share a time_step run on different
-        # CUDA streams (e.g. a RECV alongside the preceding compute task), so
-        # having two nodes of the same type at the same time_step would indicate
-        # a scheduling error — assert against it before dispatching.
-        sorted_nodes = sorted(dag.nodes, key=lambda n: n.time_step)
-        ts_types: dict[int, set] = {}
-        for node in sorted_nodes:
-            ts, ttype = node.time_step, node.task.type
-            assert ttype not in ts_types.get(ts, set()), (
-                f"run_dag: time_step={ts} has two nodes of type {ttype}"
-            )
-            ts_types.setdefault(ts, set()).add(ttype)
+        comp_events: dict = {}  # task_uid -> cuda.Event for compute tasks
+
+        # profiling: list of (node, start_evt, end_evt, mem_before_bytes)
+        # populated during the loop; elapsed_time() is only called after the
+        # final synchronize() so we never block the CPU mid-dispatch.
+        self._prof_records: list = []
+
+        # Sort nodes by time_step with tie-breaking: SEND/AR/A2A first (0),
+        # compute second (1), RECV last (2).  RECV is deferred so that when a
+        # RECV overlaps with compute at the same time_step the recv kernel only
+        # launches after compute finishes (p2p_recv_stream waits on the compute
+        # event), avoiding SM contention from an idle-spinning recv kernel.
+        _SEND_PRIORITY_TYPES = {TaskType.SEND, TaskType.ALL_REDUCE, TaskType.FWD_A2A, TaskType.BWD_A2A}
+
+        def _ts_key(n):
+            t = n.chunk.type
+            if t in _SEND_PRIORITY_TYPES:
+                sub = 0
+            elif t == TaskType.RECV:
+                sub = 2
+            else:
+                sub = 1
+            return (n.time_step, sub)
+
+        sorted_nodes = sorted(dag.nodes, key=_ts_key)
+        ts_to_comp_event: dict[int, torch.cuda.Event] = {}
 
         for node in sorted_nodes:
-            task = node.task
-            # batches always has exactly one entry for single-stage tasks
-            batch = task.batches[0]
-            stage_id = batch.stage_id
+
+            chunk = node.chunk
+            batch = chunk.batches[0]
             mb_idx = batch.mb_idx
+            ubid = node.unique_bucket_id  # None for non-compute nodes
 
-            self.logger.debug(
-                f"run_dag dispatch (time {node.time_step}): {task.type.value} s{stage_id} mb{mb_idx} bkt={node.bucket_id}"
+            self.logger.info(
+                f"run_dag dispatch (time {node.time_step}): {chunk.type.value} "
+                f"mb{mb_idx} ubid={ubid}"
             )
 
-            match task.type:
+            if profiling:
+                _stream = self._timing_stream(chunk.type)
+                _start_evt = torch.cuda.Event(enable_timing=True)
+                _start_evt.record(_stream)
+                _mem_before = torch.cuda.memory_allocated()
+
+            match chunk.type:
 
                 case TaskType.SEND:
+                    # data_preds[0] is the compute task whose output we send.
                     compute_node = node.data_preds[0]
-                    _cs = compute_node.task.batches[0].stage_id
-                    _cm = compute_node.task.batches[0].mb_idx
-                    _ct = compute_node.task.type
-                    send_key = (_cs, compute_node.bucket_id) if _ct == TaskType.FWD else None
-                    comp_key = (_cs, _cm, _ct, compute_node.bucket_id)
-                    self.p2p_send_stream.wait_event(comp_events[comp_key])
-                    self._exec_send(stage_id, mb_idx, send_key, node.peer_pp_rank)
+                    self.p2p_send_stream.wait_event(comp_events[compute_node.uid])
+                    send_data = self.task_buffer[compute_node.uid]["send_output"]
+                    self._exec_send(send_data, node.peer_pp_rank)
+                    # Free the send buffer.  For FWD/FWD_A2A the whole entry is stale
+                    # after the send (task_buffer[(ubid,mb)] is the BWD consumer).
+                    # For BWD/BWD_I/BWD_A2A keep the dict but drop the send tensor —
+                    # BWD_W still needs param_groups (BWD_I), co-located BWD still
+                    # needs inp_grads (BWD/BWD_A2A).
+                    _send_buf = self.task_buffer.get(compute_node.uid)
+                    if compute_node.chunk.type in (TaskType.FWD, TaskType.FWD_A2A):
+                        del self.task_buffer[compute_node.uid]
+                    elif isinstance(_send_buf, dict):
+                        _send_buf["send_output"] = None
 
                 case TaskType.RECV:
+                    # data_succs[0] is the downstream compute task.
                     compute_node = node.data_succs[0]
-                    recv_key = (stage_id, compute_node.bucket_id) \
-                        if compute_node.task.type == TaskType.FWD else None
-                    self._exec_recv(stage_id, mb_idx, recv_key, node.peer_pp_rank)
+                    comp_evt = ts_to_comp_event.get(node.time_step)
+                    if comp_evt is not None:
+                        self.p2p_recv_stream.wait_event(comp_evt)
+                    if compute_node.chunk.type == TaskType.FWD:
+                        # FWD recv: pre-allocate based on target bucket's input metadata.
+                        recv_ubid = compute_node.unique_bucket_id
+                        recv_tensors = self._exec_recv_fwd(recv_ubid, node.peer_pp_rank)
+                    else:
+                        # BWD recv: pre-allocate based on FWD output shapes.
+                        fwd_ubid = node.custom_metadata["fwd_ubid"]
+                        out_with_grad = self.task_buffer[("shape_ref", fwd_ubid)]
+                        recv_tensors = self._exec_recv_bwd(out_with_grad, node.peer_pp_rank)
+                    self.task_buffer[node.uid] = recv_tensors
+                    recv_evt = torch.cuda.Event()
+                    recv_evt.record(self.p2p_recv_stream)
+                    self.recv_events[node.uid] = recv_evt
 
                 case TaskType.FWD_A2A:
-                    bucket_id = node.bucket_id
-                    torch.cuda.nvtx.range_push(f"fwd_a2a_s{stage_id}_b{bucket_id}_mb{mb_idx}")
-                    self.a2a_stream.wait_event(
-                        comp_events[(stage_id, mb_idx, TaskType.FWD, bucket_id)]
-                    )
-                    self._exec_fwd_a2a(stage_id, bucket_id, mb_idx)
+                    fwd_pred = next(p for p in node.data_preds if p.chunk.type == TaskType.FWD)
+                    self.a2a_stream.wait_event(comp_events[fwd_pred.uid])
+                    tensor_idx = node.custom_metadata["a2a_tensor_idx"]
+                    # Pass-through: copy upstream FWD task_buffer, apply A2A only at tensor_idx.
+                    fwd_buf = dict(self.task_buffer[fwd_pred.uid])
+                    # FWD_A2A now owns the copy; original FWD entry is no longer needed.
+                    del self.task_buffer[fwd_pred.uid]
+                    detached_outs = list(fwd_buf["detached_outs"])
+                    torch.cuda.nvtx.range_push(f"fwd_a2a_ubid{fwd_pred.unique_bucket_id}_mb{mb_idx}")
+                    self._start_timing(self.a2a_stream, "fwd_a2a")
+                    detached_outs[tensor_idx] = self._exec_a2a(detached_outs[tensor_idx]).requires_grad_(True)
+                    self._stop_timing(self.a2a_stream, "fwd_a2a")
                     torch.cuda.nvtx.range_pop()
+                    fwd_buf["detached_outs"] = detached_outs
+                    self.task_buffer[node.uid] = fwd_buf
                     a2a_evt = torch.cuda.Event()
                     a2a_evt.record(self.a2a_stream)
-                    self.a2a_events[(stage_id, mb_idx, TaskType.FWD_A2A, bucket_id)] = a2a_evt
+                    self.a2a_events[node.uid] = a2a_evt
                     self.comp_stream.wait_event(a2a_evt)
 
                 case TaskType.BWD_A2A:
-                    bucket_id = node.bucket_id
-                    torch.cuda.nvtx.range_push(f"bwd_a2a_s{stage_id}_b{bucket_id}_mb{mb_idx}")
-                    bwd_evt = (
-                        comp_events.get((stage_id, mb_idx, TaskType.BWD, bucket_id + 1))
-                        or comp_events.get((stage_id, mb_idx, TaskType.BWD_I, bucket_id + 1))
+                    bwd_pred = next(
+                        p for p in node.data_preds
+                        if p.chunk.type in (TaskType.BWD, TaskType.BWD_I)
                     )
-                    self.a2a_stream.wait_event(bwd_evt)
-                    self._exec_bwd_a2a(stage_id, bucket_id, mb_idx)
+                    self.a2a_stream.wait_event(comp_events[bwd_pred.uid])
+                    tensor_idx = node.custom_metadata["a2a_tensor_idx"]
+                    # Pass-through: copy upstream BWD/BWD_I task_buffer, apply reversed A2A only at tensor_idx.
+                    bwd_buf = dict(self.task_buffer[bwd_pred.uid])
+                    # Analog of FWD_A2A change 3: free predecessor buffer after copying.
+                    # Skip for BWD_I — its param_groups must survive until BWD_W.
+                    if bwd_pred.chunk.type == TaskType.BWD:
+                        del self.task_buffer[bwd_pred.uid]
+                    inp_grads = list(bwd_buf["inp_grads"])
+                    grad_a2a_out = inp_grads[tensor_idx]
+                    assert grad_a2a_out is not None, (
+                        f"BWD_A2A mb={mb_idx}: grad at a2a_tensor_idx={tensor_idx} is None"
+                    )
+                    if not grad_a2a_out.is_contiguous():
+                        grad_a2a_out = grad_a2a_out.contiguous()
+                    torch.cuda.nvtx.range_push(f"bwd_a2a_ubid{bwd_pred.unique_bucket_id}_mb{mb_idx}")
+                    self._start_timing(self.a2a_stream, "bwd_a2a")
+                    inp_grads[tensor_idx] = self._exec_a2a(grad_a2a_out)
+                    self._stop_timing(self.a2a_stream, "bwd_a2a")
                     torch.cuda.nvtx.range_pop()
+                    bwd_buf["inp_grads"] = inp_grads
+                    self.task_buffer[node.uid] = bwd_buf
                     a2a_evt = torch.cuda.Event()
                     a2a_evt.record(self.a2a_stream)
-                    self.a2a_events[(stage_id, mb_idx, TaskType.BWD_A2A, bucket_id)] = a2a_evt
+                    self.a2a_events[node.uid] = a2a_evt
                     self.comp_stream.wait_event(a2a_evt)
 
                 case TaskType.ALL_REDUCE:
-                    bucket_id = node.bucket_id
                     bwd_node = node.data_preds[0]
-                    bwd_key = (
-                        bwd_node.task.batches[0].stage_id,
-                        bwd_node.task.batches[0].mb_idx,
-                        bwd_node.task.type,
-                        bwd_node.bucket_id,
-                    )
-                    torch.cuda.nvtx.range_push(f"all_reduce_s{stage_id}_b{bucket_id}")
-                    self._exec_all_reduce(stage_id, bucket_id, comp_events[bwd_key])
-                    torch.cuda.nvtx.range_pop()
-            
+                    flat_grads = self.task_buffer[bwd_node.uid].get("flat_grads")
+                    if flat_grads is not None:
+                        torch.cuda.nvtx.range_push(f"all_reduce_ubid{bwd_node.unique_bucket_id}")
+                        self._exec_all_reduce(flat_grads, comp_events[bwd_node.uid], bwd_node.unique_bucket_id)
+                        torch.cuda.nvtx.range_pop()
+                    else:
+                        self.logger.debug(
+                            f"ALL_REDUCE skipped: no trainable params at time_step={node.time_step}"
+                        )
+
                 case TaskType.FWD:
-                    bucket_id = node.bucket_id
-                    recv_key = ((stage_id, bucket_id), mb_idx)
-                    if recv_key in self.recv_events:
-                        self.comp_stream.wait_event(self.recv_events.pop(recv_key))
-                    torch.cuda.nvtx.range_push(f"forward_s{stage_id}_b{bucket_id}_mb{mb_idx}")
-                    self._start_timing(self.comp_stream, f"forward")
-                    self._forward_dag(stage_id, bucket_id, mb_idx)
-                    self._stop_timing(self.comp_stream, f"forward")
+                    # Wait on any RECV predecessor.
+                    recv_pred = next(
+                        (p for p in node.data_preds if p.chunk.type == TaskType.RECV), None
+                    )
+                    if recv_pred is not None and recv_pred.uid in self.recv_events:
+                        self.comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
+
+                    # Gather input tensors from task_buffer.
+                    # FWD(bkt=0 of first stage): inputs come from self.inputs.
+                    # FWD(bkt=0 of non-first stage): inputs come from RECV or prev FWD (co-located).
+                    # FWD(bkt>0): inputs come from prev FWD or FWD_A2A output.
+                    fwd_data_pred = next(
+                        (p for p in node.data_preds
+                         if p.chunk.type in (TaskType.FWD, TaskType.FWD_A2A)), None
+                    )
+                    if fwd_data_pred is not None:
+                        # Both FWD and FWD_A2A task_buffer entries carry "detached_outs".
+                        input_tensors = self.task_buffer[fwd_data_pred.uid]["detached_outs"]
+                        # Upstream bucket consumed; free its buffer (no further reader).
+                        del self.task_buffer[fwd_data_pred.uid]
+                    elif recv_pred is not None:
+                        input_tensors = self.task_buffer[recv_pred.uid]
+                    else:
+                        input_tensors = self.inputs  # first stage, first bucket
+
+                    torch.cuda.nvtx.range_push(f"forward_ubid{ubid}_mb{mb_idx}")
+                    self._start_timing(self.comp_stream, "forward")
+                    fwd_out = self._forward_dag(ubid, mb_idx, input_tensors)
+                    self._stop_timing(self.comp_stream, "forward")
                     torch.cuda.nvtx.range_pop()
+                    self.task_buffer[node.uid] = fwd_out
+                    # Shape ref for BWD RECV: store empty placeholder tensors under a
+                    # tuple key so it never aliases task_buffer[node.uid] (both are ints).
+                    # Allows fwd_out to be freed after BWD_I consumes (ubid, mb_idx).
+                    self.task_buffer[("shape_ref", ubid)] = [torch.empty_like(t) for t in fwd_out["out_with_grad"]]
+                    self.task_buffer[(ubid, mb_idx)] = fwd_out  # per-mb FWD data for BWD
                     evt = torch.cuda.Event()
                     evt.record(self.comp_stream)
-                    comp_events[(stage_id, mb_idx, TaskType.FWD, bucket_id)] = evt
+                    comp_events[node.uid] = evt
+                    ts_to_comp_event[node.time_step] = evt
 
                 case TaskType.BWD:
-                    bucket_id = node.bucket_id
-                    recv_key = (None, mb_idx)
-                    if recv_key in self.recv_events:
-                        self.comp_stream.wait_event(self.recv_events.pop(recv_key))
-                    torch.cuda.nvtx.range_push(f"backward_s{stage_id}_b{bucket_id}_mb{mb_idx}")
-                    self._start_timing(self.comp_stream, f"backward")
-                    self._backward_dag(stage_id, bucket_id, mb_idx, loss_fn=loss_fn)
-                    self._stop_timing(self.comp_stream, f"backward")
+                    # Wait on upstream grad RECV if present.
+                    recv_pred = next(
+                        (p for p in node.data_preds if p.chunk.type == TaskType.RECV), None
+                    )
+                    if recv_pred is not None and recv_pred.uid in self.recv_events:
+                        self.comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
+
+                    # Resolve outputs and upstream grads via per-mb ubid lookup.
+                    fwd_out = self.task_buffer[(ubid, mb_idx)]
+
+                    if node.compute_loss:
+                        assert loss_fn is not None
+                        outputs_or_loss = [loss_fn(fwd_out["out_with_grad"][0], self.labels)]
+                        upstream_grads = None
+                    elif recv_pred is not None:
+                        upstream_grads = self.task_buffer[recv_pred.uid]
+                        if not isinstance(upstream_grads, (list, tuple)):
+                            upstream_grads = [upstream_grads]
+                        outputs_or_loss = fwd_out["out_with_grad"]
+                        upstream_grads = list(upstream_grads)
+                    else:
+                        outputs_or_loss = fwd_out["out_with_grad"]
+                        upstream_grads = None  # last stage, no recv
+
+                    bwd_a2a_pred = next(
+                        (p for p in node.data_preds if p.chunk.type == TaskType.BWD_A2A), None
+                    )
+                    if bwd_a2a_pred is not None:
+                        a2a_buf = self.task_buffer[bwd_a2a_pred.uid]
+                        pre_detach_outs = fwd_out["pre_detach_outs"]
+                        detached_outs = fwd_out["detached_outs"]
+                        # Set .grad for ALL requires_grad detached boundary tensors from
+                        # BWD_A2A's inp_grads.  inp_grads[tensor_idx] is the reversed A2A
+                        # gradient; other positions carry pass-through grads (residuals, etc.)
+                        # computed by the preceding BWD bucket.  The assertion in _backward_dag
+                        # requires every requires_grad entry to have a non-None .grad.
+                        for i, (d, g) in enumerate(zip(detached_outs, a2a_buf["inp_grads"])):
+                            if d is not None and isinstance(d, torch.Tensor) and d.requires_grad and g is not None:
+                                d.grad = g
+                    else:
+                        bwd_pred = next(
+                            (p for p in node.data_preds
+                             if p.chunk.type in (TaskType.BWD, TaskType.BWD_I)), None
+                        )
+                        if bwd_pred is not None and recv_pred is None:
+                            # Co-located boundary-bridge: the downstream BWD stored
+                            # inp_grads for D' (its re-detached inputs). D' is parallel
+                            # to this stage's detached_outs (D), so copy grad explicitly
+                            # — same pattern as BWD_A2A.
+                            # recv_pred is None ensures this is a true co-located pair,
+                            # not a scheduling mb-chain edge from a cross-rank predecessor.
+                            prev_buf = self.task_buffer[bwd_pred.uid]
+                            pre_detach_outs = fwd_out.get("pre_detach_outs")
+                            detached_outs = fwd_out.get("detached_outs")
+                            for d, g in zip(detached_outs, prev_buf["inp_grads"]):
+                                if (d is not None and isinstance(d, torch.Tensor)
+                                        and d.requires_grad and g is not None):
+                                    d.grad = g
+                            # Analog of FWD change 2: free predecessor buffer after consuming
+                            # inp_grads.  Only for plain BWD — BWD_I must survive until BWD_W.
+                            if bwd_pred.chunk.type == TaskType.BWD:
+                                del self.task_buffer[bwd_pred.uid]
+                        else:
+                            # Last (or only) stage in backward order, or has a RECV for
+                            # upstream grads (cross-rank predecessor): standard backward.
+                            pre_detach_outs = None
+                            detached_outs = None
+
+                    inp_with_grad = fwd_out.get("inp_with_grad")
+
+                    torch.cuda.nvtx.range_push(f"backward_ubid{ubid}_mb{mb_idx}")
+                    self._start_timing(self.comp_stream, "backward")
+                    bwd_out = self._backward_dag(
+                        ubid, mb_idx, outputs_or_loss, upstream_grads,
+                        pre_detach_outs, detached_outs, inp_with_grad,
+                        fwd_out.get("out_with_grad"),
+                    )
+                    self._stop_timing(self.comp_stream, "backward")
                     torch.cuda.nvtx.range_pop()
+                    buf = bwd_out if bwd_out is not None else {}
+                    buf["flat_grads"] = self.bucket_flat_grads.get(ubid)
+                    # Full-length inp_grads parallel to fwd_inputs / detached_outs so
+                    # BWD_A2A can index with a2a_tensor_idx without filtering offset.
+                    fwd_inputs_full = fwd_out.get("fwd_inputs")
+                    buf["inp_grads"] = (
+                        [t.grad if (t is not None and t.requires_grad) else None
+                         for t in fwd_inputs_full]
+                        if fwd_inputs_full is not None
+                        else [t.grad for t in (inp_with_grad or [])]
+                    )
+                    self.task_buffer[node.uid] = buf
                     evt = torch.cuda.Event()
                     evt.record(self.comp_stream)
-                    comp_events[(stage_id, mb_idx, TaskType.BWD, bucket_id)] = evt
+                    comp_events[node.uid] = evt
+                    self.bwd_events[ubid] = evt
+                    ts_to_comp_event[node.time_step] = evt
 
                 case TaskType.BWD_I:
-                    bucket_id = node.bucket_id
-                    recv_key = (None, mb_idx)
-                    if recv_key in self.recv_events:
-                        self.comp_stream.wait_event(self.recv_events.pop(recv_key))
-                    torch.cuda.nvtx.range_push(f"backward_input_stage_{stage_id}_b{bucket_id}_mb_{mb_idx}")
-                    self._start_timing(self.comp_stream, f"backward_input")
-                    self._backward_input_dag(stage_id, bucket_id, mb_idx, loss_fn=loss_fn)
-                    self._stop_timing(self.comp_stream, f"backward_input")
+                    # Wait on upstream grad RECV if present.
+                    recv_pred = next(
+                        (p for p in node.data_preds if p.chunk.type == TaskType.RECV), None
+                    )
+                    if recv_pred is not None and recv_pred.uid in self.recv_events:
+                        self.comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
+
+                    # Resolve stage_outputs_or_loss and output_grads.
+                    fwd_out = self.task_buffer[(ubid, mb_idx)]
+
+                    if node.compute_loss:
+                        assert loss_fn is not None
+                        stage_outputs_or_loss = [loss_fn(fwd_out["out_with_grad"][0], self.labels)]
+                        output_grads = None
+                    elif recv_pred is not None:
+                        upstream_raw = self.task_buffer[recv_pred.uid]
+                        if not isinstance(upstream_raw, (list, tuple)):
+                            upstream_raw = [upstream_raw]
+                        stage_outputs_or_loss = fwd_out["out_with_grad"]
+                        output_grads = list(upstream_raw)
+                    else:
+                        stage_outputs_or_loss = fwd_out["out_with_grad"]
+                        output_grads = None
+
+                    # If a BWD_A2A precedes this bucket, use the reversed grad as
+                    # output_grads so backward flows correctly through the A2A boundary.
+                    bwd_a2a_pred = next(
+                        (p for p in node.data_preds if p.chunk.type == TaskType.BWD_A2A), None
+                    )
+                    if bwd_a2a_pred is not None and not node.compute_loss and recv_pred is None:
+                        a2a_buf = self.task_buffer[bwd_a2a_pred.uid]
+                        # BWD_A2A passes through BWD_I(bkt+1)'s task_buffer with
+                        # inp_grads[tensor_idx] replaced by the reversed A2A gradient.
+                        # Build output_grads aligned with out_with_grad (requires_grad
+                        # positions of detached_outs) then filter to non-None entries.
+                        detached_outs = fwd_out["detached_outs"]
+                        output_grads_full = [
+                            a2a_buf["inp_grads"][i]
+                            for i, t in enumerate(detached_outs)
+                            if t is not None and getattr(t, "requires_grad", False)
+                        ]
+                        pairs = [
+                            (o, g) for o, g in zip(stage_outputs_or_loss, output_grads_full)
+                            if g is not None
+                        ]
+                        stage_outputs_or_loss = [p[0] for p in pairs]
+                        output_grads = [p[1] for p in pairs]
+
+                    input_values = fwd_out.get("inp_with_grad") or []
+
+                    # Weights for this bucket.
+                    b_fwd_args = self.bucket_fwd_args[ubid]
+                    weights = [b_fwd_args[i] for i in self.bucket_param_idxs[ubid]
+                               if b_fwd_args[i] is not None]
+
+                    torch.cuda.nvtx.range_push(f"backward_input_ubid{ubid}_mb{mb_idx}")
+                    self._start_timing(self.comp_stream, "backward_input")
+                    with torch.cuda.stream(self.comp_stream):
+                        dinputs, param_groups = self._bucket_backward_input(
+                            stage_outputs_or_loss, output_grads, input_values, iter(weights)
+                        )
+                    self._stop_timing(self.comp_stream, "backward_input")
                     torch.cuda.nvtx.range_pop()
+                    # Full-length inp_grads parallel to fwd_inputs / detached_outs.
+                    fwd_inputs_full = fwd_out.get("fwd_inputs")
+                    if fwd_inputs_full is not None:
+                        inp_grads_full = [
+                            t.grad if (t is not None and t.requires_grad) else None
+                            for t in fwd_inputs_full
+                        ]
+                    else:
+                        inp_grads_full = list(dinputs)
+                    # Store param_groups for BWD_W (accessed via data_succs edge).
+                    self.task_buffer[node.uid] = {
+                        "dinputs": dinputs,
+                        "inp_grads": inp_grads_full,
+                        "param_groups": param_groups,
+                        "flat_grads": self.bucket_flat_grads.get(ubid),
+                    }
+                    # Propagate input grad to SEND (if this is the first bucket and has predecessors).
+                    if dinputs:
+                        self.task_buffer[node.uid]["send_output"] = list(dinputs)
+                    # Free per-microbatch FWD data: all tensors from it have been consumed
+                    # by _bucket_backward_input.  This removes the last Python reference to
+                    # fwd_out (task_buffer[ubid] now holds only shape placeholders after
+                    # the FWD task change; task_buffer[node_fwd.uid] was freed by SEND),
+                    # allowing the stage-boundary activation tensors to be GC'd.
+                    del self.task_buffer[(ubid, mb_idx)]
                     evt = torch.cuda.Event()
                     evt.record(self.comp_stream)
-                    comp_events[(stage_id, mb_idx, TaskType.BWD_I, bucket_id)] = evt
+                    comp_events[node.uid] = evt
+                    self.bwd_events[ubid] = evt
+                    ts_to_comp_event[node.time_step] = evt
 
                 case TaskType.BWD_W:
-                    torch.cuda.nvtx.range_push(f"backward_weight_stage_{stage_id}_mb_{mb_idx}")
-                    self._start_timing(self.comp_stream, f"backward_weight")
-                    self._backward_weight_dag(stage_id, mb_idx, loss_fn=loss_fn)
-                    self._stop_timing(self.comp_stream, f"backward_weight")
+                    # data_preds[0] is the BWD_I task for bkt=K-1 (head of chain).
+                    # For bkt<K-1 the chain-link edge (BWD_W predecessor) is prepended
+                    # before Pass 2 appends the BWD_I edge, so data_preds[0] would be
+                    # the wrong task.  Find BWD_I explicitly by type to be safe.
+                    bwdi_node = next(p for p in node.data_preds if p.chunk.type == TaskType.BWD_I)
+                    param_groups = self.task_buffer[bwdi_node.uid]["param_groups"]
+                    b_fwd_args = self.bucket_fwd_args[ubid]
+                    weights = [b_fwd_args[i] for i in self.bucket_param_idxs[ubid]
+                               if b_fwd_args[i] is not None]
+                    torch.cuda.nvtx.range_push(f"backward_weight_ubid{ubid}_mb{mb_idx}")
+                    self._start_timing(self.comp_stream, "backward_weight")
+                    with torch.cuda.stream(self.comp_stream):
+                        self._bucket_backward_weight(iter(weights), param_groups)
+                    self._stop_timing(self.comp_stream, "backward_weight")
                     torch.cuda.nvtx.range_pop()
+                    # Store flat_grads so ALL_REDUCE can find them when BWD_W is the trigger.
+                    # bucket_flat_grads.get(ubid) is None for non-trainable buckets; the
+                    # ALL_REDUCE handler's "if flat_grads is not None" guard skips those.
+                    self.task_buffer[node.uid] = {"flat_grads": self.bucket_flat_grads.get(ubid)}
+                    # BWD_I buffer is no longer needed: _bucket_backward_weight already
+                    # deleted param_groups["intermediates"] and param_groups["grads"] and
+                    # the autograd graph was freed by retain_graph=False.  Drop the shell.
+                    del self.task_buffer[bwdi_node.uid]
+                    evt = torch.cuda.Event()
+                    evt.record(self.comp_stream)
+                    comp_events[node.uid] = evt
+                    self.bwd_events[ubid] = evt
+                    ts_to_comp_event[node.time_step] = evt
 
                 case TaskType.UPD:
                     torch.cuda.nvtx.range_push("update")
                     self._update()
                     torch.cuda.nvtx.range_pop()
 
-    def _exec_send(
-        self, stage_id: int, mb_idx: int, key, peer_pp_rank: int
-    ) -> None:
-        """Send the contents of send_buffer[(key, mb_idx)] to peer_pp_rank.
+            if profiling:
+                _end_evt = torch.cuda.Event(enable_timing=True)
+                _end_evt.record(_stream)
+                _mem_after = torch.cuda.memory_allocated()
+                self._prof_records.append((node, mb_idx, ubid, _start_evt, _end_evt, _mem_before))
+                self.logger.info(f"Rank {self.global_rank} task {chunk.type.value} (time {node.time_step}) mem before {_mem_before / 1024 ** 3:.2f} GB after {_mem_after / 1024 ** 3:.2f} GB")
 
-        key is (stage_id, None) for non-bucket compute dependencies and
-        (stage_id, bucket_id) for bucket compute dependencies; None for BWD/BWD_I.
+    def _exec_send(self, send_data, peer_pp_rank: int) -> None:
+        """Send tensors to peer_pp_rank on p2p_send_stream.
 
         The caller (run_dag) must have already made p2p_send_stream wait on the
         compute event before calling this method.
         """
-        self.logger.debug(f"exec_send key {(key, mb_idx)} to peer pp rank {peer_pp_rank}")
-
-        buf = self.send_buffer.pop((key, mb_idx))
         global_dst_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
-
-        if self.global_rank == global_dst_rank:
-            # Co-located stages: write to recv_buffer under the peer's expected key.
-            if key is not None:  # FWD send: peer receives into its stage's input slot
-                recv_stage_id = stage_id + 1
-                peer_recv_key = (recv_stage_id, 0)
-            else:  # BWD send
-                peer_recv_key = None
-            self.recv_buffer[(peer_recv_key, mb_idx)] = buf
-            return
+        self.logger.debug(f"exec_send to global rank {global_dst_rank}")
 
         with torch.cuda.stream(self.p2p_send_stream):
-            tensors = buf if isinstance(buf, (list, tuple)) else [buf]
+            tensors = send_data if isinstance(send_data, (list, tuple)) else [send_data]
             self._start_timing(self.p2p_send_stream, "p2p_send")
             pp_group = self.pp_lo_hi if global_dst_rank > self.global_rank else self.pp_hi_lo
             for tensor in tensors:
-                dist.send(
-                    tensor,
-                    dst=global_dst_rank,
-                    # group=self.pp_groups[(self.global_rank, global_dst_rank)],
-                    group=pp_group,
-                )
+                dist.send(tensor, dst=global_dst_rank, group=pp_group)
             self._stop_timing(self.p2p_send_stream, "p2p_send")
 
-    def _exec_recv(
-        self, stage_id: int, mb_idx: int, key, peer_pp_rank: int
-    ) -> None:
-        """Receive data from peer_pp_rank into recv_buffer[(key, mb_idx)].
-
-        key is (stage_id, None) for non-bucket compute dependencies and
-        (stage_id, bucket_id) for bucket compute dependencies; None for BWD/BWD_I.
+    def _exec_recv_fwd(self, recv_ubid: int, peer_pp_rank: int) -> list:
+        """Receive FWD activations from peer_pp_rank into pre-allocated buffers.
 
-        Records a cuda.Event in recv_events[(key, mb_idx)] once the recv
-        completes on p2p_recv_stream.
+        Buffer shapes are derived from the target bucket's stored input metadata.
+        Returns the received tensor list (stored in task_buffer by run_dag).
         """
-        self.logger.debug(f"exec_recv key {(key, mb_idx)} from peer pp rank {peer_pp_rank}")
-
-        buf_key = (key, mb_idx)
         global_src_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
+        self.logger.debug(f"exec_recv_fwd ubid={recv_ubid} from global rank {global_src_rank}")
 
-        if self.global_rank == global_src_rank:
-            # Co-located: _exec_send already wrote to recv_buffer[(key, mb_idx)].
-            return
+        buf = [
+            torch.empty(shape, dtype=dtype, requires_grad=requires_grad, device=self.device)
+            for shape, dtype, requires_grad in self.forward_input_meta[recv_ubid]
+        ]
+        with torch.cuda.stream(self.p2p_recv_stream):
+            self._start_timing(self.p2p_recv_stream, "p2p_recv")
+            pp_group = self.pp_hi_lo if global_src_rank > self.global_rank else self.pp_lo_hi
+            for tensor in buf:
+                dist.recv(tensor, src=global_src_rank, group=pp_group)
+            self._stop_timing(self.p2p_recv_stream, "p2p_recv")
+        return buf
 
-        # Allocate receive buffer
-        if key is not None:  # FWD recv
-            buf = []
-            for i in self.input_idxs[key]:
-                shape, dtype, requires_grad = self.forward_input_meta[stage_id][i]
-                buf.append(
-                    torch.empty(shape, dtype=dtype, requires_grad=requires_grad, device=self.device)
-                )
-        else:
-            # BWD / BWD_I: gradient shaped like the saved output activation.
-            # In interleaved schedules the BWD recv may be issued before the
-            # corresponding FWD has run, so fall back to the pre-computed shape.
-            act_list = self.out_activation[stage_id].get(mb_idx)
-            assert act_list is not None, (
-                f"BWD recv for stage {stage_id} mb {mb_idx} before FWD has run"
-            )
-            buf = [torch.empty_like(a) for a in act_list]
+    def _exec_recv_bwd(self, out_with_grad: list, peer_pp_rank: int) -> list:
+        """Receive BWD upstream gradients from peer_pp_rank.
+
+        Buffer shapes are derived from out_with_grad (FWD outputs saved in task_buffer).
+        Returns the received gradient list (stored in task_buffer by run_dag).
+        """
+        global_src_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
+        self.logger.debug(f"exec_recv_bwd from global rank {global_src_rank}")
 
+        buf = [torch.empty_like(a) for a in out_with_grad]
         with torch.cuda.stream(self.p2p_recv_stream):
-            tensors = buf if isinstance(buf, list) else [buf]
             self._start_timing(self.p2p_recv_stream, "p2p_recv")
             pp_group = self.pp_hi_lo if global_src_rank > self.global_rank else self.pp_lo_hi
-            for tensor in tensors:
-                dist.recv(
-                    tensor,
-                    src=global_src_rank,
-                    # group=self.pp_groups[(global_src_rank, self.global_rank)],
-                    group=pp_group,
-                )
+            for tensor in buf:
+                dist.recv(tensor, src=global_src_rank, group=pp_group)
             self._stop_timing(self.p2p_recv_stream, "p2p_recv")
+        return buf
 
-        recv_event = torch.cuda.Event()
-        recv_event.record(self.p2p_recv_stream)
-        self.recv_events[buf_key] = recv_event
-        self.recv_buffer[buf_key] = buf
-
-    def _forward_dag(self, stage_id: int, bucket_id: int, mb_idx: int) -> None:
-        """Forward step for DAG execution (unified for bucketed and non-bucketed stages).
+    def _exec_a2a(self, input_tensor: torch.Tensor) -> torch.Tensor:
+        """Apply dist.all_to_all_single on a2a_stream and return the output tensor.
 
-        Non-bucketed stages are loaded as single-bucket stages (bucket_id=0 only).
-        Bucketed stages have multiple bucket_ids dispatched individually by run_dag.
+        Used for both FWD_A2A and BWD_A2A — the operation is symmetric.
+        Stream waits and event recording are handled by run_dag.
         """
-        comp_stream = self.comp_stream
-        bucket_fns = self.bucket_fwd_fns[stage_id]
-        bucket_args = self.bucket_fwd_args[stage_id]
-        n_buckets = len(bucket_fns)
-
-        # --- First bucket of stage: load activation inputs ---
-        if bucket_id == 0:
-            if stage_id == 0:
-                for i, inp in zip(self.input_idxs[(stage_id, 0)], self.inputs):
-                    bucket_args[0][i] = inp
-            else:
-                recv_key = ((stage_id, 0), mb_idx)
-                inputs_from_prev = self.recv_buffer.pop(recv_key)
-                if not isinstance(inputs_from_prev, (list, tuple)):
-                    inputs_from_prev = [inputs_from_prev]
-                for i, tensor in zip(self.input_idxs[(stage_id, 0)], inputs_from_prev):
-                    if isinstance(tensor, (list, tuple)):
-                        tensor = tensor[0]
-                    if (
-                        self.stage_to_device[stage_id] == self.stage_to_device[stage_id - 1]
-                        and tensor.requires_grad
-                    ):
-                        tensor = tensor.detach().requires_grad_(True)
-                    bucket_args[0][i] = tensor
+        output_buf = torch.empty_like(input_tensor)
+        with torch.cuda.stream(self.a2a_stream):
+            dist.all_to_all_single(output_buf, input_tensor, group=self.ep_group)
+        return output_buf
+
+    def _forward_dag(self, ubid: int, mb_idx: int, input_tensors) -> dict:
+        """Run the forward function for bucket *ubid* and return a result dict.
 
-                inp_with_grad = [
-                    bucket_args[0][i] for i in self.input_idxs[(stage_id, 0)]
-                    if bucket_args[0][i] is not None and bucket_args[0][i].requires_grad
-                ]
-                self.inp_activation[stage_id][mb_idx] = inp_with_grad  # list
+        The result dict always contains:
+        - ``"send_output"``: raw output tuple/tensor for SEND (last bucket only)
+        - ``"out_with_grad"``: list of output tensors that require grad (last bucket only)
+        - ``"pre_detach_outs"`` / ``"detached_outs"``: boundary tensors (non-last bucket only)
+        - ``"inp_with_grad"``: input tensors that require grad (always; empty list if none)
 
-        # --- Run this bucket ---
-        with torch.cuda.stream(comp_stream):
-            output = bucket_fns[bucket_id](*bucket_args[bucket_id])
+        run_dag stores the result in task_buffer[node.uid] and passes fields to
+        downstream tasks as appropriate.
+        """
+        fwd_fn = self.bucket_fwd_fns[ubid]
+        fwd_args = self.bucket_fwd_args[ubid]
+        input_idxs = self.bucket_input_idxs[ubid]
+
+        # Place input tensors into the args array.
+        # For co-located stages, input_tensors may be the raw output tuple from
+        # the previous stage's FWD (a tuple or a single tensor).
+        if not isinstance(input_tensors, (list, tuple)):
+            input_tensors = [input_tensors]
+
+        for i, tensor in zip(input_idxs, input_tensors):
+            if isinstance(tensor, (list, tuple)):
+                tensor = tensor[0]
+            # Detach activations arriving from a co-located stage so backward
+            # treats them as leaves (same device, no cross-stage grad flow).
+            if tensor.requires_grad:
+                tensor = tensor.detach().requires_grad_(True)
+            fwd_args[i] = tensor
+
+        # Full input list (after double-detach), parallel to detached_outs of the
+        # predecessor FWD/FWD_A2A.  Stored so that BWD can build a full-length
+        # inp_grads list (None for non-grad entries) that BWD_A2A can index with
+        # a2a_tensor_idx without any requires_grad filtering offset.
+        fwd_inputs = [fwd_args[i] for i in input_idxs]
+
+        inp_with_grad = [t for t in fwd_inputs if t is not None and t.requires_grad]
+
+        # Run the forward function.
+        with torch.cuda.stream(self.comp_stream):
+            output = fwd_fn(*fwd_args)
 
-        # Clear activation input slots
-        for i in self.input_idxs[(stage_id, bucket_id)]:
-            bucket_args[bucket_id][i] = None
+        # Clear input slots so we don't hold stale tensor references.
+        for i in input_idxs:
+            fwd_args[i] = None
 
         out_list = list(output) if isinstance(output, tuple) else [output]
 
-        # --- Store boundary or finalize ---
-        if bucket_id < n_buckets - 1:
-            possibly_detached = [
-                t.detach().requires_grad_(True) if t.requires_grad else t
-                for t in out_list
-            ]
-            self.bucket_buffer[(stage_id, mb_idx, bucket_id)] = (out_list, possibly_detached)
-            # Skip direct feed at A2A boundaries — _exec_fwd_a2a will feed after applying A2A.
-            stage_a2a = self.a2a_boundaries.get(stage_id, {})
-            if bucket_id not in stage_a2a:
-                for i, t in zip(self.input_idxs[(stage_id, bucket_id + 1)], possibly_detached):
-                    bucket_args[bucket_id + 1][i] = t
-        else:
-            # Last bucket: save requires_grad outputs for backward
-            out_with_grad = [t for t in out_list if isinstance(t, torch.Tensor) and t.requires_grad]
-            self.out_activation[stage_id][mb_idx] = out_with_grad  # list
-            if stage_id < self.num_stages - 1:
-                self.send_buffer[((stage_id, bucket_id), mb_idx)] = output
-                # Co-located next stage: no SEND/RECV DAG nodes are created by insert_p2p_ops
-                # for same-rank edges, so populate recv_buffer directly.
-                next_stage = stage_id + 1
-                if (next_stage in self.stage_to_device and
-                        self.stage_to_device[next_stage] == self.stage_to_device[stage_id]):
-                    self.recv_buffer[((next_stage, 0), mb_idx)] = output
-
-    def _backward_dag(self, stage_id: int, bucket_id: int, mb_idx: int, *, loss_fn=None) -> None:
-        """Backward step for DAG execution (unified for bucketed and non-bucketed stages).
-
-        The backward chain runs in reverse bucket order: the highest bucket_id
-        runs first (receives upstream gradient or computes loss) and bucket 0
-        runs last (sends input gradient to the previous stage).
+        # Is this the last bucket of its stage?  Determined from DAG structure in
+        # run_dag, but here we use a simpler proxy: if the bucket has an A2A
+        # boundary entry it is NOT the last bucket.  More precisely, run_dag
+        # calls us with correct inputs; we always produce both boundary and
+        # last-bucket fields and let run_dag consume what's relevant.
+        possibly_detached = [
+            t.detach().requires_grad_(True) if isinstance(t, torch.Tensor) and t.requires_grad else t
+            for t in out_list
+        ]
+        out_with_grad = [t for t in out_list if isinstance(t, torch.Tensor) and t.requires_grad]
+
+        return {
+            "pre_detach_outs": out_list,
+            "detached_outs": possibly_detached,
+            "out_with_grad": out_with_grad,
+            "send_output": output,    # raw output for SEND node
+            "inp_with_grad": inp_with_grad,
+            "fwd_inputs": fwd_inputs,
+        }
+
+    def _backward_dag(
+        self,
+        ubid: int,
+        mb_idx: int,
+        outputs_or_loss: list,
+        upstream_grads,
+        pre_detach_outs,
+        detached_outs,
+        inp_with_grad,
+        out_with_grad,
+    ) -> dict | None:
+        """Fused backward pass (BWD) for a single bucket.
+
+        Handles both:
+        - Last-bucket-of-stage case: backward from out_with_grad / loss.
+        - Non-last-bucket case: backward through the (pre_detach, detached) boundary.
+
+        Returns a dict with ``"send_output"`` (input grad list) when this is the
+        first-bucket-of-stage and the stage has a predecessor, or None otherwise.
         """
         comp_stream = self.comp_stream
-        n_buckets = len(self.bucket_fwd_fns[stage_id])
-
-        if bucket_id == n_buckets - 1:
-            # First to backward: receive upstream gradient or compute loss
-            out_with_grad = self.out_activation[stage_id][mb_idx]  # list
-            if stage_id < self.num_stages - 1:
-                recv_key = (None, mb_idx)
-                upstream_grads = self.recv_buffer.pop(recv_key)
-                if not isinstance(upstream_grads, (list, tuple)):
-                    upstream_grads = [upstream_grads]
+
+        if pre_detach_outs is None:
+            # Last bucket (or only bucket): backward from stage outputs or loss.
+            if upstream_grads is not None:
                 with torch.cuda.stream(comp_stream):
-                    torch.autograd.backward(out_with_grad, upstream_grads)
+                    torch.autograd.backward(outputs_or_loss, upstream_grads)
             else:
-                assert loss_fn is not None
                 with torch.cuda.stream(comp_stream):
-                    loss = loss_fn(out_with_grad[0], self.labels)
-                    loss.backward()
-            self.out_activation[stage_id][mb_idx] = None
+                    outputs_or_loss[0].backward()
         else:
-            # Middle / earlier bucket: propagate backward through the boundary
-            pre_detach_outs, detached_outs = self.bucket_buffer.pop((stage_id, mb_idx, bucket_id))
+            # Non-last bucket: propagate through the bucket boundary.
             outputs_bwd = [p for p, d in zip(pre_detach_outs, detached_outs) if d.requires_grad]
             grads_bwd = [d.grad for p, d in zip(pre_detach_outs, detached_outs) if d.requires_grad]
             assert all(g is not None for g in grads_bwd), (
-                f"Stage {stage_id} bucket {bucket_id}: detached boundary has no .grad"
+                f"BWD ubid={ubid} mb={mb_idx}: detached boundary has no .grad"
             )
             with torch.cuda.stream(comp_stream):
                 torch.autograd.backward(outputs_bwd, grads_bwd)
 
-        # Bucket 0 is last to run: send input gradients to previous stage
-        if bucket_id == 0 and stage_id > 0:
-            inp_list = self.inp_activation[stage_id][mb_idx]  # list
-            output_grads = [t.grad for t in inp_list if t.grad is not None]
-            self.send_buffer[(None, mb_idx)] = output_grads
-            # Co-located prev stage: no BWD SEND/RECV DAG nodes for same-rank edges.
-            prev_stage = stage_id - 1
-            if (prev_stage in self.stage_to_device and
-                    self.stage_to_device[prev_stage] == self.stage_to_device[stage_id]):
-                self.recv_buffer[(None, mb_idx)] = output_grads
-            self.inp_activation[stage_id][mb_idx] = None
-
-    def _exec_fwd_a2a(self, stage_id: int, boundary_bucket_id: int, mb_idx: int) -> None:
-        """Apply a forward all-to-all at the given A2A boundary.
-
-        Called after FWD(bucket_id=boundary_bucket_id) completes.  Reads the
-        boundary tensor from bucket_buffer, applies dist.all_to_all_single
-        on the a2a_stream, replaces the entry in detached_outs with the
-        communicated tensor, then feeds all detached_outs to the next bucket.
-        """
-        tensor_idx = self.a2a_boundaries[stage_id][boundary_bucket_id]
-        pre_detach_outs, detached_outs = self.bucket_buffer[(stage_id, mb_idx, boundary_bucket_id)]
-
-        x_detached = detached_outs[tensor_idx]
-        output_buf = torch.empty_like(x_detached)
-        with torch.cuda.stream(self.a2a_stream):
-            self._start_timing(self.a2a_stream, "fwd_a2a")
-            dist.all_to_all_single(output_buf, x_detached, group=self.ep_group)
-            self._stop_timing(self.a2a_stream, "fwd_a2a")
-        x_a2a = output_buf.requires_grad_(True)
-
-        # Store for BWD_A2A to reverse
-        self.a2a_buffer[(stage_id, mb_idx, boundary_bucket_id)] = (x_detached, x_a2a)
-
-        # Replace entry so that _backward_dag will read x_a2a.grad (set by seg+1 backward)
-        detached_outs[tensor_idx] = x_a2a
-
-        # Feed all detached_outs to next bucket's input slots
-        next_bucket_id = boundary_bucket_id + 1
-        bucket_args = self.bucket_fwd_args[stage_id]
-        for i, t in zip(self.input_idxs[(stage_id, next_bucket_id)], detached_outs):
-            bucket_args[next_bucket_id][i] = t
-
-    def _exec_bwd_a2a(self, stage_id: int, boundary_bucket_id: int, mb_idx: int) -> None:
-        """Apply the reverse all-to-all for the backward pass at an A2A boundary.
-
-        Called after BWD(bucket_id=boundary_bucket_id+1) completes (which has set
-        x_a2a.grad).  Applies the reverse A2A to obtain the gradient in the
-        pre-A2A space, sets it on x_detached.grad, and restores x_detached in
-        detached_outs so that _backward_dag for boundary_bucket_id uses the
-        correct gradient.
-        """
-        tensor_idx = self.a2a_boundaries[stage_id][boundary_bucket_id]
-        x_detached, x_a2a = self.a2a_buffer.pop((stage_id, mb_idx, boundary_bucket_id))
-
-        grad_a2a_out = x_a2a.grad
-        assert grad_a2a_out is not None, (
-            f"Stage {stage_id} A2A boundary {boundary_bucket_id} mb {mb_idx}: "
-            f"x_a2a.grad is None after backward through next segment"
-        )
-        if not grad_a2a_out.is_contiguous():
-            grad_a2a_out = grad_a2a_out.contiguous()
-        reversed_grad = torch.empty_like(grad_a2a_out)
-        with torch.cuda.stream(self.a2a_stream):
-            self._start_timing(self.a2a_stream, "bwd_a2a")
-            dist.all_to_all_single(reversed_grad, grad_a2a_out, group=self.ep_group)
-            self._stop_timing(self.a2a_stream, "bwd_a2a")
-        x_detached.grad = reversed_grad
-
-        # Restore x_detached in bucket_buffer so _backward_dag reads x_detached.grad
-        _, detached_outs = self.bucket_buffer[(stage_id, mb_idx, boundary_bucket_id)]
-        detached_outs[tensor_idx] = x_detached
-
-    def _backward_input_dag(self, stage_id: int, bucket_id: int, mb_idx: int, *, loss_fn=None) -> None:
-        """Backward-input pass for DAG execution (ZeroBubble split backward).
+        # Collect stage-input grads for SEND — applies to both last-bucket and
+        # non-last-bucket paths (e.g. when downstream of BWD_A2A).
+        if inp_with_grad:
+            output_grads = [t.grad for t in inp_with_grad if t.grad is not None]
+            if output_grads:
+                return {"send_output": output_grads}
+        return None
 
-        Unified for both bucketed (A2A) and non-bucketed stages.  Non-bucketed
-        stages use bucket_id=0 with n_buckets=1, so the first-bucket and
-        last-bucket conditions both fire in the same call — identical to the
-        single-pass behaviour of the old non-bucketed path.
+    def _bucket_backward_input(
+        self,
+        stage_outputs_or_loss: list,
+        output_grads,
+        input_values: list,
+        weights,
+    ):
+        """Compute input gradients for a single bucket (BWD_I).
 
-        Must be called in reverse bucket order: n_buckets-1, …, 1, 0.
-        BWD_A2A must run between consecutive calls at A2A boundaries.
+        Matches ``torch.distributed.pipelining.stage_backward_input`` exactly,
+        with "stage" replaced by "bucket".  Returns ``(dinputs, param_groups)``.
         """
-        comp_stream = self.comp_stream
-        n_buckets = len(self.bucket_fwd_fns[stage_id])
-        a2a_bnd = self.a2a_boundaries.get(stage_id, {})
-
-        # ---- Step 1: outputs and upstream grads for this bucket ----
-        if bucket_id == n_buckets - 1:
-            _out = self.out_activation[stage_id][mb_idx]
-            out_list = _out if isinstance(_out, list) else [_out]
-            if stage_id < self.num_stages - 1:
-                upstream_grads_raw = self.recv_buffer.pop((None, mb_idx))
-                if not isinstance(upstream_grads_raw, (list, tuple)):
-                    upstream_grads_raw = [upstream_grads_raw]
-                outputs = out_list
-                upstream_grads = list(upstream_grads_raw)
-            else:
-                assert loss_fn is not None
-                with torch.cuda.stream(comp_stream):
-                    loss = loss_fn(out_list[0], self.labels)
-                outputs = [loss]
-                upstream_grads = [torch.ones_like(loss)]
-        else:
-            # BWD_A2A has restored x_detached (with .grad set) in detached_outs
-            pre_detach_outs, detached_outs = self.bucket_buffer[(stage_id, mb_idx, bucket_id)]
-            outputs = [p for p, d in zip(pre_detach_outs, detached_outs) if d.requires_grad]
-            upstream_grads = [d.grad for p, d in zip(pre_detach_outs, detached_outs) if d.requires_grad]
-            assert all(g is not None for g in upstream_grads), (
-                f"Stage {stage_id} BWD_I bucket {bucket_id} mb {mb_idx}: boundary .grad is None"
-            )
+        from .backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
 
-        # Stage 0: no prev stage, so no input-grad send.  Cache upstream_grad for BWD_W
-        # which runs the full backward for stage 0.
-        # When the stage has A2A boundaries we still need to propagate through each
-        # intermediate bucket to set x_a2a.grad for the BWD_A2A nodes.  Fall through
-        # in that case; bucket 0 always returns early (nothing further to propagate).
-        if stage_id == 0:
-            if bucket_id == n_buckets - 1:
-                ug = upstream_grads[0] if len(upstream_grads) == 1 else upstream_grads
-                self.upstream_grad_cache[stage_id][mb_idx] = ug
-            stage_a2a = self.a2a_boundaries.get(stage_id, {})
-            if not stage_a2a or bucket_id == 0:
-                return
-
-        # ---- Step 2: activation inputs (what we compute grad w.r.t.) ----
-        if bucket_id > 0:
-            prev_bkt = bucket_id - 1
-            _, detached_outs_prev = self.bucket_buffer[(stage_id, mb_idx, prev_bkt)]
-            # Include all requires_grad outputs of prev_bkt, not just the A2A tensor.
-            # _exec_fwd_a2a feeds all detached_outs to the next bucket, so other cross-segment
-            # tensors (e.g. top_scores, token_indices) are also real inputs and need gradients.
-            activation_inputs = [d for d in detached_outs_prev if d.requires_grad]
-        else:
-            # Bucket 0: grad w.r.t. stage input
-            _inp = self.inp_activation[stage_id][mb_idx]
-            stage_input = _inp[0] if isinstance(_inp, list) else _inp
-            activation_inputs = [stage_input] if (stage_input is not None and stage_input.requires_grad) else []
-
-        # ---- Step 3: ZeroBubble hooks so BWD_W can compute weight grads ----
-        stage_params = [self.forward_args[stage_id][i] for i in self.param_idxs[stage_id]]
-        output_nodes = [n for n in (_get_grad_fn_or_grad_acc(t) for t in outputs) if n is not None]
-        input_nodes = [n for n in (_get_grad_fn_or_grad_acc(t) for t in activation_inputs) if n is not None]
-        param_nodes = [n for n in (_get_grad_fn_or_grad_acc(p) for p in stage_params) if n is not None]
-        reverse_edges = construct_reverse_graph(output_nodes)
-        param_groups = get_param_groups(input_nodes, param_nodes, reverse_edges)
+        stage_output_grad_fns = list(filter(None, map(_get_grad_fn_or_grad_acc, stage_outputs_or_loss)))
+        stage_input_grad_fns = list(filter(None, map(_get_grad_fn_or_grad_acc, input_values)))
+        weight_grad_fns = list(filter(None, map(_get_grad_fn_or_grad_acc, weights)))
+
+        reverse_edges_dict = construct_reverse_graph(stage_output_grad_fns)
+        param_groups = get_param_groups(stage_input_grad_fns, weight_grad_fns, reverse_edges_dict)
 
         handles = []
-        for pg in param_groups:
-            intermediates = pg["intermediates"]
-            if not intermediates:
-                continue
-            pg["grads"] = [None] * len(intermediates)
-            for i, intermediate_node in enumerate(intermediates):
-                def make_hook(group, idx):
+        for param_group in param_groups:
+            for i, intermediate in enumerate(param_group["intermediates"]):
+                def get_hook(pg, idx):
                     def hook(grad_inputs):
-                        group["grads"][idx] = grad_inputs
+                        if pg.get("grads") is None:
+                            pg["grads"] = [None] * len(pg["intermediates"])
+                        pg["grads"][idx] = tuple(grad_inputs)
                     return hook
-                handles.append(intermediate_node.register_prehook(make_hook(pg, i)))
+                handles.append(intermediate.register_prehook(get_hook(param_group, i)))
+
+        if output_grads is None:
+            output_grads = [torch.ones_like(o) for o in stage_outputs_or_loss]
+
+        input_values = [inp for inp in input_values if inp.requires_grad]
+        if input_values:
+            dinputs = torch.autograd.grad(
+                stage_outputs_or_loss,
+                inputs=input_values,
+                grad_outputs=output_grads,
+                retain_graph=True,
+                allow_unused=True,
+            )
+            for inp, dinput in zip(input_values, dinputs):
+                if inp.grad is None:
+                    inp.grad = dinput
+                else:
+                    inp.grad += dinput
+        else:
+            dinputs = ()
 
-        # ---- Step 4: compute grad w.r.t. activation inputs only ----
-        if activation_inputs:
-            with torch.cuda.stream(comp_stream):
-                grads = torch.autograd.grad(
-                    outputs=outputs,
-                    inputs=activation_inputs,
-                    grad_outputs=upstream_grads,
-                    retain_graph=True,
-                    allow_unused=True,
-                )
-            for inp, g in zip(activation_inputs, grads):
-                if g is not None:
-                    if inp.grad is None:
-                        inp.grad = g
-                    else:
-                        inp.grad.add_(g)
+        for t in stage_outputs_or_loss:
+            t.detach_()
+        # stage_outputs_or_loss = [t.detach() for t in stage_outputs_or_loss]
 
-        for h in handles:
-            h.remove()
+        for handle in handles:
+            handle.remove()
 
-        # ---- Step 5: accumulate bw_param_groups for BWD_W ----
-        existing = self.bw_param_groups[stage_id].get(mb_idx)
-        if existing is None:
-            self.bw_param_groups[stage_id][mb_idx] = param_groups
-        else:
-            existing.extend(param_groups)
-
-        # ---- Step 6: bucket 0 sends input grad to prev stage ----
-        if bucket_id == 0:
-            _inp = self.inp_activation[stage_id][mb_idx]
-            stage_input = _inp[0] if isinstance(_inp, list) else _inp
-            output_grad = stage_input.grad if stage_input is not None else None
-            if output_grad is not None and stage_id > 0:
-                self.send_buffer[(None, mb_idx)] = output_grad
-                prev_stage = stage_id - 1
-                if (prev_stage in self.stage_to_device and
-                        self.stage_to_device[prev_stage] == self.stage_to_device[stage_id]):
-                    self.recv_buffer[(None, mb_idx)] = output_grad
-            self.inp_activation[stage_id][mb_idx] = None
-
-        # Free output reference after processing the first-to-bwd bucket.
-        # Stage 0 skips this: BWD_W reads out_activation directly for stage 0.
-        if bucket_id == n_buckets - 1 and stage_id != 0:
-            for t in outputs:
-                t.detach_()
-            self.out_activation[stage_id][mb_idx] = None
-
-    def _exec_all_reduce(self, stage_id: int, bucket_id: int, bwd_event: torch.cuda.Event) -> None:
-        """Launch an all-reduce for a stage/bucket's gradients.
-
-        All-reduces the pre-allocated flat_grads tensor for the given
-        (stage_id, bucket_id).
-
-        comm_stream waits on *bwd_event* first (ensuring all gradient
-        accumulation is complete) then records an event so _update_impl can
-        wait on it before stepping the optimizer.
-        """
-        lookup_key = (stage_id, bucket_id)
-        flat_grads = self.bucket_flat_grads.get(lookup_key)
-        assert flat_grads is not None, (
-            f"ALL_REDUCE dispatched for stage {stage_id} bucket {bucket_id} "
-            f"but no flat_grads tensor was allocated — this bucket has no trainable parameters."
-        )
-        with torch.cuda.stream(self.comm_stream):
-            self.comm_stream.wait_event(bwd_event)
-            self._start_timing(self.comm_stream, f"all_reduce")
-            dist.all_reduce(flat_grads, group=self.dp_group)
-            self._stop_timing(self.comm_stream, f"all_reduce")
-            evt = torch.cuda.Event()
-            evt.record(self.comm_stream)
-        self.ar_events[(stage_id, bucket_id)] = evt
+        return dinputs, param_groups
 
-    def _backward_weight_dag(self, stage_id: int, mb_idx: int, *, loss_fn=None) -> None:
-        """Backward-weight pass for DAG execution (ZeroBubble split backward).
+    def _bucket_backward_weight(self, weights, param_groups: list) -> None:
+        """Compute weight gradients for a single bucket (BWD_W).
 
-        Does NOT call comp_stream.synchronize().
+        Matches ``torch.distributed.pipelining.stage_backward_weight`` exactly,
+        with "stage" replaced by "bucket".  Accumulates gradients into param.grad.
         """
-        comp_stream = self.comp_stream
-        stage_params = [self.forward_args[stage_id][i] for i in self.param_idxs[stage_id]]
-        updated_params: dict[int, torch.nn.Parameter] = {}
-
-        if stage_id == 0:
-            stage_a2a = self.a2a_boundaries.get(stage_id, {})
-            if stage_a2a:
-                # Bucketed stage 0 with A2A: out_activation (bkt=last) has no
-                # differentiable path to pre-dispatch params because all
-                # intermediate bucket inputs are detached leaves.
-                # Two-step approach:
-                #   Step 1 — ZeroBubble mechanism for intermediate-bucket params
-                #             (e.g. expert params) captured by BWD_I hooks.
-                #   Step 2 — full backward through bkt=0 using .grad values set
-                #             on its detached_outs by BWD_I(bkt=1) and BWD_A2A(bkt=0).
-
-                # Step 1: expert / intermediate-bucket params via GradientEdge
-                grad_acc_to_weight_s0: Dict[Node, Parameter] = {}
-                for param in stage_params:
-                    node = _get_grad_fn_or_grad_acc(param)
-                    grad_acc_to_weight_s0[node] = param
-
-                param_groups_s0 = self.bw_param_groups[stage_id].get(mb_idx) or []
-                for pg in param_groups_s0:
-                    intermediates: List[Node] = pg.get("intermediates", [])
-                    intermediate_grads = pg.get("grads", None)
-
-                    if not intermediates or intermediate_grads is None:
-                        continue
-
-                    intermediate_edges: List[GradientEdge] = []
-                    intermediate_edge_grads: List[torch.Tensor] = []
-
-                    for intermediate_node, grad_inputs in zip(intermediates, intermediate_grads):
-                        if grad_inputs is None:
-                            continue
-                        gs = [x for x in grad_inputs if x is not None]
-                        if not gs:
-                            continue
-                        summed = sum(gs)
-                        intermediate_edges.append(GradientEdge(intermediate_node, 0))
-                        intermediate_edge_grads.append(summed)
-
-                    del pg["intermediates"]
-
-                    if not intermediate_edges:
-                        continue
-
-                    mapped_param_nodes = [p for p in pg["params"] if p in grad_acc_to_weight_s0]
-                    if not mapped_param_nodes:
-                        continue
-
-                    weight_edges = tuple(GradientEdge(p, 0) for p in mapped_param_nodes)
-
-                    with torch.cuda.stream(comp_stream):
-                        gparams_s0 = torch.autograd.grad(
-                            outputs=intermediate_edges,
-                            inputs=weight_edges,
-                            grad_outputs=intermediate_edge_grads,
-                            retain_graph=False,
-                        )
+        grad_acc_to_weight: Dict[Node, Parameter] = {}
+        for weight in weights:
+            grad_acc = _get_grad_fn_or_grad_acc(weight)
+            grad_acc_to_weight[grad_acc] = weight
+
+        for param_group in param_groups:
+            valid_edges: List[GradientEdge] = []
+            valid_grad_outputs: List[torch.Tensor] = []
+
+            for grads_tuple, intermediate in zip(
+                param_group.get("grads", []), param_group["intermediates"]
+            ):
+                if grads_tuple is None:
+                    continue
+                for i, grad in enumerate(grads_tuple):
+                    if grad is not None:
+                        valid_edges.append(GradientEdge(intermediate, i))
+                        valid_grad_outputs.append(grad)
 
-                    del pg["grads"]
+            del param_group["intermediates"]
 
-                    assert len(gparams_s0) == len(mapped_param_nodes)
-                    for param_node, dw in zip(mapped_param_nodes, gparams_s0):
-                        if dw is None:
-                            continue
-                        weight = grad_acc_to_weight_s0[param_node]
-                        if weight.grad is None:
-                            weight.grad = dw
-                        else:
-                            weight.grad.add_(dw)
-                        updated_params[id(weight)] = weight
-
-                # Step 2: pre-dispatch params (embedding, router, attention) via
-                #         .grad values set on bkt=0 detached_outs by BWD_A2A(bkt=0)
-                #         and BWD_I(bkt=1).
-                buf0 = self.bucket_buffer.pop((stage_id, mb_idx, 0), None)
-                if buf0 is not None:
-                    pre_detach_outs0, detached_outs0 = buf0
-                    outputs_bwd = [
-                        p for p, d in zip(pre_detach_outs0, detached_outs0)
-                        if d.requires_grad and d.grad is not None
-                    ]
-                    grads_bwd = [
-                        d.grad for p, d in zip(pre_detach_outs0, detached_outs0)
-                        if d.requires_grad and d.grad is not None
-                    ]
-                    if outputs_bwd:
-                        with torch.cuda.stream(comp_stream):
-                            torch.autograd.backward(outputs_bwd, grads_bwd)
-            else:
-                # Non-bucketed stage 0: single-segment full backward from out_activation.
-                upstream_grad = self.upstream_grad_cache[stage_id][mb_idx]
-                _out = self.out_activation[stage_id][mb_idx]
-                out_activation = _out[0] if isinstance(_out, list) else _out
-                if stage_id < self.num_stages - 1:
-                    with torch.cuda.stream(comp_stream):
-                        gparams = torch.autograd.grad(
-                            outputs=out_activation,
-                            inputs=stage_params,
-                            grad_outputs=upstream_grad,
-                            retain_graph=False,
-                        )
-                else:
-                    assert loss_fn is not None
-                    labels = self.labels
-                    with torch.cuda.stream(comp_stream):
-                        loss = loss_fn(out_activation, labels)
-                        gparams = torch.autograd.grad(
-                            outputs=loss,
-                            inputs=stage_params,
-                            retain_graph=False,
-                        )
+            if not valid_edges:
+                continue
 
-                assert len(gparams) == len(stage_params)
-                for p, pg in zip(stage_params, gparams):
-                    if pg is None:
-                        continue
-                    if p.grad is None:
-                        p.grad = pg.clone()
-                    else:
-                        p.grad.add_(pg)
-                    updated_params[id(p)] = p
-        else:
-            grad_acc_to_weight: Dict[Node, Tuple[Parameter, int]] = {}
-            for param in stage_params:
-                node = _get_grad_fn_or_grad_acc(param)
-                grad_acc_to_weight[node] = param
+            weight_edges = tuple(GradientEdge(w, 0) for w in param_group["params"])
+            if not weight_edges:
+                continue
 
-            param_groups = self.bw_param_groups[stage_id][mb_idx]
+            dweights = torch.autograd.grad(
+                valid_edges,
+                weight_edges,
+                grad_outputs=valid_grad_outputs,
+                retain_graph=False,
+            )
 
-            for pg in param_groups:
-                intermediates: List[Node] = pg.get("intermediates", [])
-                intermediate_grads = pg.get("grads", None)
+            del param_group["grads"]
 
-                if not intermediates or intermediate_grads is None:
+            for grad_acc, dw in zip(param_group["params"], dweights):
+                if dw is None or grad_acc not in grad_acc_to_weight:
                     continue
+                weight = grad_acc_to_weight[grad_acc]
+                if weight.grad is None:
+                    weight.grad = dw
+                else:
+                    weight.grad += dw
 
-                intermediate_edges: List[GradientEdge] = []
-                intermediate_edge_grads: List[torch.Tensor] = []
-
-                for intermediate_node, grad_inputs in zip(intermediates, intermediate_grads):
-                    if grad_inputs is None:
-                        continue
-                    gs = [x for x in grad_inputs if x is not None]
-                    if not gs:
-                        continue
-                    summed = sum(gs)
-                    intermediate_edges.append(GradientEdge(intermediate_node, 0))
-                    intermediate_edge_grads.append(summed)
-
-                del pg["intermediates"]
-
-                if not intermediate_edges:
-                    continue
+    def _exec_all_reduce(self, flat_grads: torch.Tensor, bwd_event: torch.cuda.Event, ubid: int) -> None:
+        """Launch an all-reduce for *flat_grads* on comm_stream.
 
-                mapped_param_nodes = [p for p in pg["params"] if p in grad_acc_to_weight]
-                if not mapped_param_nodes:
-                    continue
+        *flat_grads* comes directly from the predecessor BWD/BWD_I task's task_buffer
+        entry.  comm_stream waits on *bwd_event* (ensuring all gradient accumulation is
+        complete) then records an event so _update can wait before stepping.
+        """
+        with torch.cuda.stream(self.comm_stream):
+            self.comm_stream.wait_event(bwd_event)
+            self._start_timing(self.comm_stream, "all_reduce")
+            dist.all_reduce(flat_grads, group=self.dp_group)
+            self._stop_timing(self.comm_stream, "all_reduce")
+            evt = torch.cuda.Event()
+            evt.record(self.comm_stream)
+        self.ar_events[ubid] = evt
 
-                weight_edges = tuple(GradientEdge(p, 0) for p in mapped_param_nodes)
+    def _update(self, *deps):
+        self._start_timing(self.comp_stream, "optim_step")
 
-                with torch.cuda.stream(comp_stream):
-                    gparams = torch.autograd.grad(
-                        outputs=intermediate_edges,
-                        inputs=weight_edges,
-                        grad_outputs=intermediate_edge_grads,
-                        retain_graph=False,
-                    )
+        # Snapshot flat_params and mean grad before stepping, to log the change.
+        # We clone on comp_stream (after all bwd events) then log after synchronize().
+        pre_snapshots: dict = {}  # ubid -> (params_before, mean_grad)
 
-                del pg["grads"]
+        for ubid, optim in self.bucket_optims.items():
+            if optim is None:
+                continue
+            bwd_evt = self.bwd_events.get(ubid)
+            if bwd_evt is not None:
+                self.comp_stream.wait_event(bwd_evt)
+            ar_evt = self.ar_events.get(ubid)
+            if ar_evt is not None:
+                self.comp_stream.wait_event(ar_evt)
 
-                assert len(gparams) == len(mapped_param_nodes)
-                for param_node, dw in zip(mapped_param_nodes, gparams):
-                    if dw is None:
-                        continue
-                    weight = grad_acc_to_weight[param_node]
-                    if weight.grad is None:
-                        weight.grad = dw
-                    else:
-                        weight.grad.add_(dw)
-                    updated_params[id(weight)] = weight
+            flat_params = self.bucket_flat_params.get(ubid)
+            flat_grads = self.bucket_flat_grads.get(ubid)
+            if flat_params is not None:
+                with torch.cuda.stream(self.comp_stream):
+                    params_before = flat_params.clone()
+                    mean_grad = flat_grads.abs().mean() if flat_grads is not None else None
+                pre_snapshots[ubid] = (params_before, mean_grad)
 
-        self.bw_grad_cache[stage_id][mb_idx] = None
-        self.upstream_grad_cache[stage_id][mb_idx] = None
-        self.bw_param_groups[stage_id][mb_idx] = None
-        self.out_activation[stage_id][mb_idx] = None
+            with torch.cuda.stream(self.comp_stream):
+                optim.step()
+        self._stop_timing(self.comp_stream, "optim_step")
 
-    def _update(self, *deps):
-        if self.ar_events:
-            # DAG execution path: wait for in-flight all-reduces then step optimizers.
-            self._start_timing(self.comp_stream, "backward_sync")
-            for ar_evt in self.ar_events.values():
-                self.comp_stream.wait_event(ar_evt)
-            self._stop_timing(self.comp_stream, "backward_sync")
-            self._start_timing(self.comp_stream, "optim_step")
-            for s_id, optim_list in self.bucket_optims.items():
-                for optim in optim_list:
-                    if optim is not None:
-                        optim.step()
-                        optim.zero_grad(set_to_none=False)
-            self._stop_timing(self.comp_stream, "optim_step")
-        else:
-            # Single-device path.
-            self._start_timing(self.comp_stream, "optim_step")
-            for s_id, optim_list in self.bucket_optims.items():
-                for optim in optim_list:
-                    if optim is not None:
-                        optim.step()
-                        optim.zero_grad(set_to_none=False)
-            self._stop_timing(self.comp_stream, "optim_step")
+        losses = self.loss
+        self.loss.clear()
 
         torch.cuda.synchronize()
 
-        losses = self.loss
-        self.loss.clear()
+        # Log mean grad and mean param change per bucket to confirm optimizer is updating.
+        # for ubid, (params_before, mean_grad_t) in pre_snapshots.items():
+        #     flat_params = self.bucket_flat_params[ubid]
+        #     diff = (flat_params - params_before).abs().mean().item()
+        #     mean_grad_val = mean_grad_t.item() if mean_grad_t is not None else float("nan")
+        #     self.logger.info(
+        #         f"[update] rank={self.global_rank} ubid={ubid}: "
+        #         f"mean_grad={mean_grad_val:.4e}  mean_param_diff={diff:.4e}"
+        #     )
+
+        # Process deferred profiling records now that all GPU work is complete.
+        # elapsed_time() is safe to call after synchronize().
+        if self._prof_records:
+            for node, mb_idx, ubid, start_evt, end_evt, mem_before in self._prof_records:
+                t_ms = start_evt.elapsed_time(end_evt)
+                node.profiling_measurements.append(t_ms)
+                self.logger.info(
+                    f"[profiling] rank {self.global_rank} "
+                    f"{node.chunk.type.value} mb{mb_idx} ubid{ubid} "
+                    f"(time step {node.time_step}): time={t_ms:.3f}ms "
+                    f"mem_before={mem_before/1e9:.3f}GB"
+                )
 
         return losses
     
\ No newline at end of file
diff --git a/src/piper_compile.py b/src/piper_compile.py
index f90931f..90e20f7 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -14,8 +14,9 @@
 
 from .piper_actor import _create_actors
 from .piper_utils import piper_metadata, create_logger, LOG_LEVEL
-from .piper_exec import DAGEdge, Schedule2D, CompType, Task, BatchMeta
-from .piper import piper
+from .piper_exec import DAGEdge, PipelineSchedule, CompType, Chunk, BatchMeta, _validate_schedule
+from .piper import piper, piper_exec_dag
+from .piper_graph_transform import compute_critical_path, visualize_dag
 
 logger = create_logger("piper_compile", LOG_LEVEL)
 
@@ -56,158 +57,8 @@ def get(self) -> dict:
 
 
 
-def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdge], num_mbs: int) -> None:
-    """
-    Validate that the schedule respects well-formedness rules and DAG dependencies.
-    
-    Args:
-        schedule: 2D array with one row per device and one column per time step
-        dag_edges: List of DAG edges defining stage dependencies
-        num_mbs: Number of microbatches in the schedule
-        
-    Raises:
-        ValueError: If the schedule violates any validation rules
-    """
-    num_devices, num_steps = len(schedule), len(schedule[0]) if schedule else 0
-    
-    for row in schedule:
-        assert len(row) == num_steps, "Each row must have the same number of time steps"
-    
-    # Check well-formedness: no duplicates, pp_rank matches row, and all stages present
-    all_tasks = set()
-    microbatch_tasks = {}  # mb_idx -> set of (stage_id, type)
-    
-    for pp_rank in range(num_devices):
-        for time_step in range(num_steps):
-            task = schedule[pp_rank][time_step]
-            if task is not None:
-                # Check pp_rank matches row
-                if task.pp_rank != pp_rank:
-                    raise ValueError(
-                        f"Task pp_rank {task.pp_rank} does not match row {pp_rank} "
-                        f"at time step {time_step}"
-                    )
-                
-                # Check for duplicates and track by (stage_id, mb_idx, type) per batch
-                for batch in task.batches:
-                    comp_type = (
-                        task.type
-                        if task.type != CompType.FWD_BWD
-                        else (CompType.FWD if batch is task.batches[0] else CompType.BWD)
-                    )
-                    task_key = (batch.stage_id, batch.mb_idx, comp_type)
-                    if task_key in all_tasks:
-                        raise ValueError(
-                            f"Duplicate task found: stage_id={batch.stage_id}, "
-                            f"mb_idx={batch.mb_idx}, type={comp_type}"
-                        )
-                    all_tasks.add(task_key)
-                    if batch.mb_idx not in microbatch_tasks:
-                        microbatch_tasks[batch.mb_idx] = set()
-                    microbatch_tasks[batch.mb_idx].add((batch.stage_id, comp_type))
-    
-    # Get all required stages from DAG edges
-    all_required_stages = set()
-    for edge in dag_edges:
-        all_required_stages.add(edge.from_stage)
-        all_required_stages.add(edge.to_stage)
-    
-    # Check that each microbatch has all required forward and backward stages
-    for mb_idx, tasks in microbatch_tasks.items():
-        # Find all stages that have forward/backward tasks for this microbatch
-        fwd_stages = {stage_id for stage_id, task_type in tasks if task_type == CompType.FWD}
-        bwd_stages = {
-            stage_id
-            for stage_id, task_type in tasks
-            if task_type in (CompType.BWD, CompType.BWD_I)
-        }
-        
-        # Check that all required stages have forward tasks
-        missing_fwd = all_required_stages - fwd_stages
-        if missing_fwd:
-            raise ValueError(f"Microbatch {mb_idx} missing forward stages: {missing_fwd}")
-
-        # Check that all required stages have backward tasks (BWD or BWD_I)
-        missing_bwd = all_required_stages - bwd_stages
-        if missing_bwd:
-            raise ValueError(
-                f"Microbatch {mb_idx} missing backward stages: {missing_bwd} "
-                f"(need BWD, BWD_I, or FWD_BWD backward for each stage)"
-            )
-    
-    # Check pipeline stage dependencies
-    for mb_idx in range(num_mbs):
-        # Find all tasks for this microbatch
-        fwd_times: dict[int, int] = {}  # stage_id -> time_step
-        bwd_times: dict[int, int] = {}  # stage_id -> time_step (BWD or BWD_I)
-        bwd_w_times: dict[int, int] = {}  # stage_id -> time_step (BWD_W)
-        
-        for pp_rank in range(num_devices):
-            for time_step in range(num_steps):
-                task = schedule[pp_rank][time_step]
-                if task is None:
-                    continue
-                for batch in task.batches:
-                    if batch.mb_idx != mb_idx:
-                        continue
-                    comp_type = (
-                        task.type
-                        if task.type != CompType.FWD_BWD
-                        else (CompType.FWD if batch is task.batches[0] else CompType.BWD)
-                    )
-                    if comp_type == CompType.FWD:
-                        fwd_times[batch.stage_id] = time_step
-                    elif comp_type in (CompType.BWD, CompType.BWD_I):
-                        bwd_times[batch.stage_id] = time_step
-                    elif comp_type == CompType.BWD_W:
-                        bwd_w_times[batch.stage_id] = time_step
-        
-        # Check forward stage ordering: if A -> B, then fwd(A) < fwd(B)
-        for edge in dag_edges:
-            from_stage, to_stage = edge.from_stage, edge.to_stage
-            if from_stage in fwd_times and to_stage in fwd_times:
-                if fwd_times[from_stage] >= fwd_times[to_stage]:
-                    raise ValueError(
-                        f"Forward stage ordering violation for microbatch {mb_idx}: "
-                        f"forward stage {from_stage} (time {fwd_times[from_stage]}) must come "
-                        f"before forward stage {to_stage} (time {fwd_times[to_stage]})"
-                    )
-        
-        # Check forward-backward ordering: fwd(A) < bwd(A)
-        for stage_id in fwd_times:
-            if stage_id in bwd_times:
-                if fwd_times[stage_id] >= bwd_times[stage_id]:
-                    raise ValueError(
-                        f"Forward-backward ordering violation for microbatch {mb_idx}, "
-                        f"stage {stage_id}: forward (time {fwd_times[stage_id]}) must come "
-                        f"before backward (time {bwd_times[stage_id]})"
-                    )
-        
-        # Check backward stage ordering: if A -> B, then bwd(B) < bwd(A)
-        for edge in dag_edges:
-            from_stage, to_stage = edge.from_stage, edge.to_stage
-            if from_stage in bwd_times and to_stage in bwd_times:
-                if bwd_times[to_stage] >= bwd_times[from_stage]:
-                    raise ValueError(
-                        f"Backward stage ordering violation for microbatch {mb_idx}: "
-                        f"backward stage {to_stage} (time {bwd_times[to_stage]}) must come "
-                        f"before backward stage {from_stage} (time {bwd_times[from_stage]})"
-                    )
-        
-        # Check BWD_W must come after BWD_I for same (stage_id, mb_idx)
-        for stage_id in bwd_w_times:
-            if stage_id not in bwd_times:
-                raise ValueError(
-                    f"BWD_W for microbatch {mb_idx} stage {stage_id} has no corresponding "
-                    f"BWD_I or BWD task"
-                )
-            if bwd_w_times[stage_id] <= bwd_times[stage_id]:
-                raise ValueError(
-                    f"BWD_W ordering violation for microbatch {mb_idx}, stage {stage_id}: "
-                    f"BWD_W (time {bwd_w_times[stage_id]}) must come after "
-                    f"BWD_I/BWD (time {bwd_times[stage_id]})"
-                )
-            
+
+
 
 
 def piper_setup(
@@ -218,12 +69,16 @@ def piper_setup(
     optim_fn=None,
     example_inputs=None,
     example_outputs=None,
-    schedule: Schedule2D=None,
+    loss_fn=None,
+    schedule: PipelineSchedule=None,
     naive_gradient_sync=False,
     activation_checkpointing=False,
     bucketing=False,
     pg=None,
     nsight=False,
+    model_flops_per_token: float = None,
+    visualize_dag: bool = True,
+    const_attrs: dict = None,
 ):
     """
     Compile a model with the piper backend.
@@ -238,34 +93,55 @@ def piper_setup(
             pipelined per-param hooks.
     """
 
+    # Clear Dynamo's global compilation cache so that a previous piper_setup
+    # call (e.g. with a different model size or schedule) in the same Python
+    # worker process cannot contaminate this compilation via a stale cache hit.
+    torch._dynamo.reset()
+
     stage_to_device = schedule.stage_to_device()
     assert len(stage_to_device) > 0
     piper_metadata.stage_to_device = stage_to_device
     piper_metadata.use_activation_checkpointing = activation_checkpointing
     piper_metadata.bucketing = bucketing
     piper_metadata.schedule = schedule
+    piper_metadata.visualize_dag = visualize_dag
+
+    # Reset DAG/compile fields so stale data from a prior run never leaks into
+    # this run if the piper backend is somehow not re-invoked.
+    piper_metadata.per_rank_dags = None
+    piper_metadata.full_dag_no_overlap = None
+    piper_metadata.stage_bucket_counts = {}
+    piper_metadata.trainable_bucket_keys = set()
+    piper_metadata.compiled_stage_data = None
+
+    # MFU tracking
+    piper_metadata.model_flops_per_token = model_flops_per_token
+    if model_flops_per_token is not None and example_inputs is not None:
+        dp_degree = int(os.environ.get("PIPER_DP_DEGREE", "1"))
+        # example_inputs[0] is the token index tensor of shape (batch_size, seq_len)
+        piper_metadata.tokens_per_step = example_inputs[0].numel() * dp_degree
 
     dag_edges = []
     # TODO: build dag by analyzing stage dependencies rather than assuming a linear chain
     for stage_id in piper_metadata.stage_to_device.keys():
         if stage_id < len(piper_metadata.stage_to_device.keys()) - 1:
             dag_edges.append(DAGEdge(stage_id, stage_id+1))
-    _validate_schedule(schedule.grid, dag_edges=dag_edges, num_mbs=schedule.num_mbs())
+    _validate_schedule(schedule, dag_edges=dag_edges, num_mbs=schedule.num_mbs())
 
     num_mbs = schedule.num_mbs()
     num_stages = schedule.num_stages()
     num_devices = schedule.num_ranks()
-    
-    _create_actors(
-        num_devices, optim_fn, num_mbs, num_stages,
-        naive_gradient_sync, profile=nsight, stage_to_device=stage_to_device, pg=pg,
-    )
 
     # All dp_ranks must agree on a single master_addr: the IP of the actor with
     # global_rank=0 (pp_rank=0, dp_rank=0).  dp_rank=0 publishes it via a named
     # Ray actor; other dp_ranks wait until it's available.
     import time
     dp_rank = int(os.environ["PIPER_DP_RANK"])
+    _create_actors(
+        num_devices, optim_fn, num_mbs, num_stages,
+        naive_gradient_sync, profile=nsight, stage_to_device=stage_to_device, pg=pg,
+    )
+
     if dp_rank == 0:
         # Create the compiled-data store early so dp_rank>0 can poll for it.
         _CompiledDataStore.options(
@@ -275,7 +151,7 @@ def piper_setup(
         # Get IP and a free port from actor 0's node — it will be the TCPStore server.
         master_addr, master_port = ray.get(piper_metadata.actors[0].get_node_ip_and_free_port.remote())
         _Rank0AddrStore.options(
-            name=_RANK0_ADDR_ACTOR, lifetime="detached", get_if_exists=True
+            name=_RANK0_ADDR_ACTOR, lifetime="detached"
         ).remote(master_addr, master_port)
     else:
         master_addr = master_port = None
@@ -298,6 +174,20 @@ def piper_setup(
         except Exception:
             pass
 
+    # Push non-trainable constant tensor attributes (e.g. freqs_cis, rope_cache, mask)
+    # to all actors so _load_stage can initialize them correctly instead of zero-filling.
+    # Must happen on every dp_rank since each dp_rank owns its own actors.
+    _const_attrs = {
+        k: v.detach().cpu() if isinstance(v, torch.Tensor) else v
+        for k, v in (const_attrs or {}).items()
+    }
+    if _const_attrs:
+        ray.get([
+            actor.load_const_attrs.remote(_const_attrs)
+            for actor in piper_metadata.actors.values()
+        ])
+        logger.debug(f"Pushed {len(_const_attrs)} const attrs to actors: {list(_const_attrs.keys())}")
+
     if dp_rank == 0:
         # --- dp_rank=0: run torch.compile, build DAGs, publish compiled data ---
 
@@ -358,6 +248,7 @@ def piper_setup(
         piper_metadata.stage_bucket_counts = compiled_data["stage_bucket_counts"]
         piper_metadata.trainable_bucket_keys = compiled_data["trainable_bucket_keys"]
         piper_metadata.per_rank_dags = compiled_data["per_rank_dags"]
+        piper_metadata.full_dag_no_overlap = compiled_data.get("full_dag_no_overlap")
 
         # Load the same per-rank DAGs onto this dp_rank's actors.
         ray.get([
@@ -371,6 +262,48 @@ def piper_setup(
     ray.get(piper_metadata.actors[last_stage_rank].load_labels.remote(example_outputs))
     logger.info(f"DP rank {dp_rank} real inputs/labels loaded onto actors.")
 
+    # if loss_fn is not None:
+    #     _NUM_WARMUP = 3
+    #     _NUM_PROFILE = 5
+
+    #     logger.info(f"DP rank {dp_rank}: running {_NUM_WARMUP} warmup iterations before profiling.")
+    #     for _ in range(_NUM_WARMUP):
+    #         piper_exec_dag(loss_fn)
+
+    #     logger.info(f"DP rank {dp_rank}: running {_NUM_PROFILE} profiling iterations.")
+    #     for _ in range(_NUM_PROFILE):
+    #         piper_exec_dag(loss_fn, profiling=True)
+
+    #     # Only dp_rank=0 aggregates timing data and renders the critical-path DAG.
+    #     if dp_rank == 0:
+    #         uid_to_measurements: dict = {}
+    #         for pp_rank in range(len(piper_metadata.per_rank_dags)):
+    #             actor = piper_metadata.actors[pp_rank]
+    #             node_runtimes = ray.get(actor.get_node_runtimes.remote())
+    #             uid_to_measurements.update(node_runtimes)
+
+    #         full_dag = piper_metadata.full_dag_no_overlap
+    #         if full_dag is not None:
+    #             for node in full_dag.nodes:
+    #                 measurements = uid_to_measurements.get(node.uid, [])
+    #                 if measurements:
+    #                     node.profiling_measurements = measurements
+    #                     node.runtime = sum(measurements) / len(measurements)
+
+    #             critical_nodes = compute_critical_path(full_dag)
+    #             logger.info(
+    #                 f"Critical path has {len(critical_nodes)} nodes "
+    #                 f"(of {len(full_dag.nodes)} total)."
+    #             )
+    #             try:
+    #                 visualize_dag(
+    #                     full_dag,
+    #                     output_path="figs/profiled_full_dag",
+    #                     critical_path_nodes=critical_nodes,
+    #                 )
+    #             except Exception as e:
+    #                 logger.warning(f"Profiled DAG visualisation failed: {e}")
+
     logger.info(f"DP rank {dp_rank} done.")
     
 
diff --git a/src/piper_coordinator.py b/src/piper_coordinator.py
index f632eee..9993b4b 100644
--- a/src/piper_coordinator.py
+++ b/src/piper_coordinator.py
@@ -2,6 +2,8 @@
 from typing import Callable
 import os
 
+from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
+
 from .piper_utils import create_logger, LOG_LEVEL
 
 
@@ -10,29 +12,18 @@
 
 # Use manual stage annotations- more stable
 @ray.remote(num_gpus=0.1)
-def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, master_addr, master_port, training_func: Callable, *args, **kwargs):
+def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, training_func: Callable, *args, **kwargs):
     logger = create_logger("piper_coordinator", LOG_LEVEL)
-    logger.debug(f"Running DP rank {dp_rank+1} of {dp_degree} with {master_addr}:{master_port} as master address")
+    logger.debug(f"Running DP rank {dp_rank+1} of {dp_degree}")
 
     os.environ["PIPER_DP_RANK"] = str(dp_rank)
     os.environ["PIPER_DP_DEGREE"] = str(dp_degree)
     os.environ["PIPER_PP_DEGREE"] = str(pp_degree)
     os.environ["PIPER_WORLD_SIZE"] = str(world_size)
-    os.environ["PIPER_MASTER_ADDR"] = str(master_addr)
-    os.environ["PIPER_MASTER_PORT"] = str(master_port)
     os.environ["TORCH_LOGS"] = "+graph_breaks"
     return training_func(*args, **kwargs)
 
 
-def find_free_port():
-    import socket
-
-    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-        s.bind(("0.0.0.0", 0))
-        port = s.getsockname()[1]
-    return port
-
-
 @ray.remote
 class PiperProgramCoordinator:
     """Central Actor that Coordinates all the DP replicas of a single pipeline"""
@@ -41,19 +32,32 @@ def __init__(self, dp_degree, pp_degree):
         self.dp_degree = dp_degree
         self.pp_degree = pp_degree
         self.world_size = dp_degree * pp_degree
-        self.master_port = find_free_port()
 
-    def run_program(self, training_func: Callable, *args, **kwargs):
-        master_addr = ray.util.get_node_ip_address()
+    def run_program(self, training_func: Callable, pg, *args, **kwargs):
+        from .piper_compile import _RANK0_ADDR_ACTOR, _COMPILED_DATA_ACTOR
+        try:
+            ray.kill(ray.get_actor(_RANK0_ADDR_ACTOR))
+        except Exception:
+            pass
+        # Kill any stale compiled-data store from a previous run so that
+        # dp_rank>0 workers cannot read outdated (e.g. wrong-model) stage data.
+        try:
+            ray.kill(ray.get_actor(_COMPILED_DATA_ACTOR))
+        except Exception:
+            pass
+
         return ray.get(
             [
-                run_dp_rank.remote(
+                run_dp_rank.options(
+                    scheduling_strategy=PlacementGroupSchedulingStrategy(
+                        placement_group=pg,
+                        placement_group_bundle_index=dp_rank,
+                    )
+                ).remote(
                     dp_rank,
                     self.dp_degree,
                     self.pp_degree,
                     self.world_size,
-                    master_addr,
-                    self.master_port,
                     training_func,
                     *args,
                     **kwargs,
diff --git a/src/piper_exec.py b/src/piper_exec.py
index 4d5a6d1..9fcdbb7 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -16,6 +16,9 @@
 
 logger = create_logger("piper_exec", LOG_LEVEL)
 
+_uid_counter = itertools.count()
+
+
 class TaskType(Enum):
     FWD = "forward"
     BWD = "backward"
@@ -38,55 +41,165 @@ class BatchMeta(NamedTuple):
     stage_id: int
     mb_idx: int
 
-# Task for 2D schedule grid: one cell (rank, time_step).
-class Task(NamedTuple):
+# Chunk for a schedule: one unit of work on one rank.
+class Chunk(NamedTuple):
     pp_rank: int
     batches: list[BatchMeta]
     type: TaskType
 
     def __repr__(self) -> str:
-        return f"Task(pp_rank={self.pp_rank}, batches={[(batch.stage_id, batch.mb_idx) for batch in self.batches]}, type={self.type})"
+        return f"Chunk(pp_rank={self.pp_rank}, batches={[(batch.stage_id, batch.mb_idx) for batch in self.batches]}, type={self.type})"
+
+
+def logical_time(chunk: "Chunk") -> int:
+    """Logical duration of a chunk: BWD = 2 (fused I+W), all others = 1."""
+    time = 1
+    match chunk.type:
+        case TaskType.BWD:
+            time = 2
+        case TaskType.FWD_BWD:
+            time = 3
+    return time
 
 
 @dataclass
-class Schedule2D:
-    """2D schedule grid: grid[rank][time_step] = Task | None. Execution order: by time_step, then rank descending."""
-    grid: list[list["Task | None"]]
+class PipelineSchedule:
+    """Per-rank ordered chunk lists.
+
+    ``grid[rank]`` is the ordered sequence of :class:`Chunk` objects that rank
+    executes.  There are no ``None`` sentinels — bubbles are implied by data
+    dependencies and are made explicit only when rendering via :meth:`_compute_start_times`.
+    """
+    grid: list[list["Chunk"]]
 
     def stage_to_device(self) -> dict[int, int]:
         stage_to_device = {}
         for row in self.grid:
-            for task in row:
-                if task is not None:
-                    for batch in task.batches:
-                        stage_to_device[batch.stage_id] = task.pp_rank
+            for chunk in row:
+                for batch in chunk.batches:
+                    stage_to_device[batch.stage_id] = chunk.pp_rank
         return stage_to_device
-    
+
     def num_mbs(self) -> int:
         mbs = set()
         for row in self.grid:
-            for task in row:
-                if task is not None:
-                    for batch in task.batches:
-                        mbs.add(batch.mb_idx)
+            for chunk in row:
+                for batch in chunk.batches:
+                    mbs.add(batch.mb_idx)
         return len(mbs)
-    
+
     def num_stages(self) -> int:
         stages = set()
         for row in self.grid:
-            for task in row:
-                if task is not None:
-                    for batch in task.batches:
-                        stages.add(batch.stage_id)
+            for chunk in row:
+                for batch in chunk.batches:
+                    stages.add(batch.stage_id)
         return len(stages)
-    
+
     def num_ranks(self) -> int:
         return len(self.grid)
 
+    def _compute_start_times(self) -> list[list[int]]:
+        """Return start_times[rank][i] = logical start time of chunk i on rank.
+
+        Uses a repeated-pass list-scheduling simulation that respects:
+        * within-rank sequentiality (each chunk starts after the previous finishes)
+        * cross-rank data deps: FWD(s,m) needs FWD(s-1,m); BWD/BWD_I(s,m) needs
+          BWD(s+1,m); BWD_W(s,m) needs BWD_I(s,m) on the same rank.
+        """
+        n_ranks = len(self.grid)
+        num_stages = self.num_stages()
+        rank_free_at = [0] * n_ranks
+        next_idx = [0] * n_ranks
+        start_times: list[list[Optional[int]]] = [[None] * len(row) for row in self.grid]
+        finish: dict = {}   # keyed by ('FWD', s, m), ('BWD', s, m), ('BWDI', rank, s, m)
+        total = sum(len(row) for row in self.grid)
+        scheduled = 0
+
+        while scheduled < total:
+            progress = False
+            for rank in range(n_ranks):
+                if next_idx[rank] >= len(self.grid[rank]):
+                    continue
+                i = next_idx[rank]
+                chunk = self.grid[rank][i]
+                earliest = rank_free_at[rank]
+                ready = True
+
+                for batch in chunk.batches:
+                    s, m = batch.stage_id, batch.mb_idx
+                    t = chunk.type
+                    if t == TaskType.FWD:
+                        if s > 0:
+                            k = ('FWD', s - 1, m)
+                            if k not in finish:
+                                ready = False; break
+                            earliest = max(earliest, finish[k])
+                    elif t in (TaskType.BWD, TaskType.BWD_I):
+                        if s < num_stages - 1:
+                            k = ('BWD', s + 1, m)
+                            if k not in finish:
+                                ready = False; break
+                            earliest = max(earliest, finish[k])
+                    elif t == TaskType.BWD_W:
+                        k = ('BWDI', rank, s, m)
+                        if k not in finish:
+                            ready = False; break
+                        earliest = max(earliest, finish[k])
+                    elif t == TaskType.FWD_BWD:
+                        s0, m0 = chunk.batches[0].stage_id, chunk.batches[0].mb_idx
+                        s1, m1 = chunk.batches[1].stage_id, chunk.batches[1].mb_idx
+                        if s0 > 0:
+                            k = ('FWD', s0 - 1, m0)
+                            if k not in finish:
+                                ready = False; break
+                            earliest = max(earliest, finish[k])
+                        if s1 < num_stages - 1:
+                            k = ('BWD', s1 + 1, m1)
+                            if k not in finish:
+                                ready = False; break
+                            earliest = max(earliest, finish[k])
+                        break  # FWD_BWD has exactly 2 batches, handled above
+
+                if not ready:
+                    continue
+
+                lt = logical_time(chunk)
+                start_times[rank][i] = earliest
+                fin = earliest + lt
+                rank_free_at[rank] = fin
+
+                for batch in chunk.batches:
+                    s, m = batch.stage_id, batch.mb_idx
+                    t = chunk.type
+                    if t == TaskType.FWD:
+                        finish[('FWD', s, m)] = fin
+                    elif t == TaskType.BWD:
+                        finish[('BWD', s, m)] = fin
+                    elif t == TaskType.BWD_I:
+                        finish[('BWD', s, m)] = fin
+                        finish[('BWDI', rank, s, m)] = fin
+                    elif t == TaskType.FWD_BWD:
+                        s0, m0 = chunk.batches[0].stage_id, chunk.batches[0].mb_idx
+                        s1, m1 = chunk.batches[1].stage_id, chunk.batches[1].mb_idx
+                        finish[('FWD', s0, m0)] = earliest + 1
+                        finish[('BWD', s1, m1)] = fin
+                        break
+
+                next_idx[rank] += 1
+                scheduled += 1
+                progress = True
+
+            if not progress:
+                raise ValueError("PipelineSchedule simulation deadlock — check data dependencies")
+
+        return start_times  # type: ignore[return-value]
+
+
 @dataclass
-class TaskNode:
+class Task:
     """A single node in the task DAG, corresponding to one non-None cell of a
-    Schedule2D grid.
+    PipelineSchedule grid.
 
     Edges are of two kinds:
 
@@ -101,27 +214,51 @@ class TaskNode:
       :func:`overlap_a2a_tasks` can introduce forks and joins so the fields
       are lists, mirroring ``data_preds`` / ``data_succs``.
     """
-    task: Task
-    pp_rank: int    # row index in Schedule2D.grid
-    time_step: int  # column index in Schedule2D.grid
+    chunk: Chunk
+    pp_rank: int    # row index in PipelineSchedule.grid
+    time_step: int  # column index in PipelineSchedule.grid
 
-    # Filled in by schedule_to_dag()
-    data_preds: list["TaskNode"] = field(default_factory=list)
-    data_succs: list["TaskNode"] = field(default_factory=list)
-    temporal_preds: list["TaskNode"] = field(default_factory=list)
-    temporal_succs: list["TaskNode"] = field(default_factory=list)
+    # Filled in by expand_chunks_to_dags()
+    data_preds: list["Task"] = field(default_factory=list)
+    data_succs: list["Task"] = field(default_factory=list)
+    temporal_preds: list["Task"] = field(default_factory=list)
+    temporal_succs: list["Task"] = field(default_factory=list)
 
     # For SEND/RECV nodes: the peer pipeline rank
     peer_pp_rank: Optional[int] = None
 
     # Set by graph-transform passes (not meaningful for raw schedule tasks)
-    bucket_id: int = 0
-    a2a_tensor_idx: Optional[int] = None
+    bucket_id: int = 0           # stage-local bucket index (stage_bucket_id); actor must not use this
+    # Task-type-specific metadata (e.g. {"a2a_tensor_idx": int} for A2A tasks).
+    custom_metadata: dict = field(default_factory=dict)
+
+    # The original Chunk from the PipelineSchedule that this task was derived from.
+    # Set by expand_chunks_to_dags; preserved through all subsequent transforms.
+    source_chunk: Optional[Chunk] = None
+
+    # Unique integer ID assigned at construction; survives pickle round-trips.
+    uid: int = field(default_factory=lambda: next(_uid_counter))
+
+    # Profiling data: raw per-iteration GPU times (ms) accumulated by run_dag
+    # when profiling=True.  Set by piper_compile after profiling.
+    profiling_measurements: list = field(default_factory=list)
+
+    # Average of profiling_measurements (ms).  Set by piper_compile.
+    runtime: Optional[float] = None
+
+    # Globally unique bucket index assigned by expand_chunks_to_dags.
+    # The actor uses this to look up bucket_fwd_fns, bucket_fwd_args, etc.
+    # unique_bucket_id is None for non-compute tasks (SEND, RECV, ALL_REDUCE, UPD).
+    unique_bucket_id: Optional[int] = None
+
+    # True on the first BWD/BWD_I bucket of the last pipeline stage.
+    # When set, run_dag applies loss_fn before dispatching the backward method.
+    compute_loss: bool = False
 
     def node_id(self) -> str:
         """Unique string identifier for use as a graph node key."""
-        ttype = self.task.type.value if self.task.type is not None else "none"
-        mb = self.task.batches[0].mb_idx if self.task.batches else "x"
+        ttype = self.chunk.type.value if self.chunk.type is not None else "none"
+        mb = self.chunk.batches[0].mb_idx if self.chunk.batches else "x"
         return f"r{self.pp_rank}_t{self.time_step}_{ttype}_mb{mb}"
 
 
@@ -130,8 +267,9 @@ def _rebuild_task_dag(node_data):
     by TaskDAG.__reduce__.  Must be a module-level function so pickle can find
     it by name."""
     nodes = [
-        TaskNode(task=d[0], pp_rank=d[1], time_step=d[2], peer_pp_rank=d[3],
-                 bucket_id=d[8], a2a_tensor_idx=d[9])
+        Task(chunk=d[0], pp_rank=d[1], time_step=d[2], peer_pp_rank=d[3],
+                 bucket_id=d[8], custom_metadata=d[9], source_chunk=d[11],
+                 unique_bucket_id=d[12], compute_loss=d[13])
         for d in node_data
     ]
     for node, d in zip(nodes, node_data):
@@ -139,18 +277,19 @@ def _rebuild_task_dag(node_data):
         node.data_succs     = [nodes[j] for j in d[5]]
         node.temporal_preds = [nodes[j] for j in d[6]]
         node.temporal_succs = [nodes[j] for j in d[7]]
+        node.uid            = d[10]
     return TaskDAG(nodes=nodes)
 
 
 @dataclass
 class TaskDAG:
-    """DAG of :class:`TaskNode` objects for one training iteration.
+    """DAG of :class:`Task` objects for one training iteration.
 
-    Produced by :func:`~src.piper.schedule_to_dag`.
+    Produced by :func:`~src.piper_graph_transform.expand_chunks_to_dags`.
     """
-    nodes: list[TaskNode]
+    nodes: list[Task]
 
-    def roots(self) -> list[TaskNode]:
+    def roots(self) -> list[Task]:
         """Return nodes that have no predecessors of any kind."""
         return [n for n in self.nodes if not n.data_preds and not n.temporal_preds]
 
@@ -161,12 +300,13 @@ def __reduce__(self):
         idx = {id(n): i for i, n in enumerate(self.nodes)}
         node_data = [
             (
-                n.task, n.pp_rank, n.time_step, n.peer_pp_rank,
+                n.chunk, n.pp_rank, n.time_step, n.peer_pp_rank,
                 [idx[id(p)] for p in n.data_preds],
                 [idx[id(s)] for s in n.data_succs],
                 [idx[id(p)] for p in n.temporal_preds],
                 [idx[id(s)] for s in n.temporal_succs],
-                n.bucket_id, n.a2a_tensor_idx,
+                n.bucket_id, n.custom_metadata, n.uid, n.source_chunk,
+                n.unique_bucket_id, n.compute_loss,
             )
             for n in self.nodes
         ]
@@ -181,113 +321,102 @@ class DAGEdge(NamedTuple):
 def _get_backward_targets(stage_id: int, dag_edges: list[DAGEdge]):
     return [edge for edge in dag_edges if edge.to_stage == stage_id]
 
-def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdge], num_mbs: int) -> None:
+def _validate_schedule(schedule: "PipelineSchedule", dag_edges: list[DAGEdge], num_mbs: int) -> None:
     """
     Validate that the schedule respects well-formedness rules and DAG dependencies.
-    
+
     Args:
-        schedule: 2D array with one row per device and one column per time step
+        schedule: PipelineSchedule (list[list[Chunk]], no Nones)
         dag_edges: List of DAG edges defining stage dependencies
         num_mbs: Number of microbatches in the schedule
-        
+
     Raises:
         ValueError: If the schedule violates any validation rules
     """
-    num_devices, num_steps = len(schedule), len(schedule[0]) if schedule else 0
-    
-    for row in schedule:
-        assert len(row) == num_steps, "Each row must have the same number of time steps"
-    
-    # Check well-formedness: no duplicates, pp_rank matches row, and all stages present
-    all_tasks = set()
-    microbatch_tasks = {}  # mb_idx -> set of (stage_id, type)
-    
-    for pp_rank in range(num_devices):
-        for time_step in range(num_steps):
-            task = schedule[pp_rank][time_step]
-            if task is not None:
-                # Check pp_rank matches row
-                if task.pp_rank != pp_rank:
+    start_times_per_rank = schedule._compute_start_times()
+
+    # chunk id -> logical start time for ordering comparisons
+    chunk_start: dict[int, int] = {}
+    for row, starts in zip(schedule.grid, start_times_per_rank):
+        for chunk, start in zip(row, starts):
+            chunk_start[id(chunk)] = start
+
+    # Check well-formedness: no duplicates, pp_rank matches row, all stages present
+    all_tasks: set = set()
+    microbatch_tasks: dict[int, set] = {}
+
+    for pp_rank, row in enumerate(schedule.grid):
+        for chunk in row:
+            if chunk.pp_rank != pp_rank:
+                raise ValueError(
+                    f"Chunk pp_rank {chunk.pp_rank} does not match row {pp_rank}"
+                )
+            for batch in chunk.batches:
+                comp_type = (
+                    chunk.type
+                    if chunk.type != CompType.FWD_BWD
+                    else (CompType.FWD if batch is chunk.batches[0] else CompType.BWD)
+                )
+                task_key = (batch.stage_id, batch.mb_idx, comp_type)
+                if task_key in all_tasks:
                     raise ValueError(
-                        f"Task pp_rank {task.pp_rank} does not match row {pp_rank} "
-                        f"at time step {time_step}"
+                        f"Duplicate chunk found: stage_id={batch.stage_id}, "
+                        f"mb_idx={batch.mb_idx}, type={comp_type}"
                     )
-                
-                # Check for duplicates and track by (stage_id, mb_idx, type) per batch
-                for batch in task.batches:
-                    comp_type = (
-                        task.type
-                        if task.type != CompType.FWD_BWD
-                        else (CompType.FWD if batch is task.batches[0] else CompType.BWD)
-                    )
-                    task_key = (batch.stage_id, batch.mb_idx, comp_type)
-                    if task_key in all_tasks:
-                        raise ValueError(
-                            f"Duplicate task found: stage_id={batch.stage_id}, "
-                            f"mb_idx={batch.mb_idx}, type={comp_type}"
-                        )
-                    all_tasks.add(task_key)
-                    if batch.mb_idx not in microbatch_tasks:
-                        microbatch_tasks[batch.mb_idx] = set()
-                    microbatch_tasks[batch.mb_idx].add((batch.stage_id, comp_type))
-    
+                all_tasks.add(task_key)
+                if batch.mb_idx not in microbatch_tasks:
+                    microbatch_tasks[batch.mb_idx] = set()
+                microbatch_tasks[batch.mb_idx].add((batch.stage_id, comp_type))
+
     # Get all required stages from DAG edges
     all_required_stages = set()
     for edge in dag_edges:
         all_required_stages.add(edge.from_stage)
         all_required_stages.add(edge.to_stage)
-    
+
     # Check that each microbatch has all required forward and backward stages
     for mb_idx, tasks in microbatch_tasks.items():
-        # Find all stages that have forward/backward tasks for this microbatch
         fwd_stages = {stage_id for stage_id, task_type in tasks if task_type == CompType.FWD}
         bwd_stages = {
             stage_id
             for stage_id, task_type in tasks
             if task_type in (CompType.BWD, CompType.BWD_I)
         }
-        
-        # Check that all required stages have forward tasks
         missing_fwd = all_required_stages - fwd_stages
         if missing_fwd:
             raise ValueError(f"Microbatch {mb_idx} missing forward stages: {missing_fwd}")
-
-        # Check that all required stages have backward tasks (BWD or BWD_I)
         missing_bwd = all_required_stages - bwd_stages
         if missing_bwd:
             raise ValueError(
                 f"Microbatch {mb_idx} missing backward stages: {missing_bwd} "
                 f"(need BWD, BWD_I, or FWD_BWD backward for each stage)"
             )
-    
-    # Check pipeline stage dependencies
+
+    # Check pipeline stage dependencies using logical start times
     for mb_idx in range(num_mbs):
-        # Find all tasks for this microbatch
-        fwd_times: dict[int, int] = {}  # stage_id -> time_step
-        bwd_times: dict[int, int] = {}  # stage_id -> time_step (BWD or BWD_I)
-        bwd_w_times: dict[int, int] = {}  # stage_id -> time_step (BWD_W)
-        
-        for pp_rank in range(num_devices):
-            for time_step in range(num_steps):
-                task = schedule[pp_rank][time_step]
-                if task is None:
-                    continue
-                for batch in task.batches:
+        fwd_times: dict[int, int] = {}   # stage_id -> logical start
+        bwd_times: dict[int, int] = {}   # stage_id -> logical start (BWD or BWD_I)
+        bwd_w_times: dict[int, int] = {} # stage_id -> logical start (BWD_W)
+
+        for row in schedule.grid:
+            for chunk in row:
+                for batch in chunk.batches:
                     if batch.mb_idx != mb_idx:
                         continue
                     comp_type = (
-                        task.type
-                        if task.type != CompType.FWD_BWD
-                        else (CompType.FWD if batch is task.batches[0] else CompType.BWD)
+                        chunk.type
+                        if chunk.type != CompType.FWD_BWD
+                        else (CompType.FWD if batch is chunk.batches[0] else CompType.BWD)
                     )
+                    t = chunk_start[id(chunk)]
                     if comp_type == CompType.FWD:
-                        fwd_times[batch.stage_id] = time_step
+                        fwd_times[batch.stage_id] = t
                     elif comp_type in (CompType.BWD, CompType.BWD_I):
-                        bwd_times[batch.stage_id] = time_step
+                        bwd_times[batch.stage_id] = t
                     elif comp_type == CompType.BWD_W:
-                        bwd_w_times[batch.stage_id] = time_step
-        
-        # Check forward stage ordering: if A -> B, then fwd(A) < fwd(B)
+                        bwd_w_times[batch.stage_id] = t
+
+        # Forward ordering: if A -> B, then fwd(A) < fwd(B)
         for edge in dag_edges:
             from_stage, to_stage = edge.from_stage, edge.to_stage
             if from_stage in fwd_times and to_stage in fwd_times:
@@ -297,8 +426,8 @@ def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdg
                         f"forward stage {from_stage} (time {fwd_times[from_stage]}) must come "
                         f"before forward stage {to_stage} (time {fwd_times[to_stage]})"
                     )
-        
-        # Check forward-backward ordering: fwd(A) < bwd(A)
+
+        # Forward-backward ordering: fwd(A) < bwd(A)
         for stage_id in fwd_times:
             if stage_id in bwd_times:
                 if fwd_times[stage_id] >= bwd_times[stage_id]:
@@ -307,8 +436,8 @@ def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdg
                         f"stage {stage_id}: forward (time {fwd_times[stage_id]}) must come "
                         f"before backward (time {bwd_times[stage_id]})"
                     )
-        
-        # Check backward stage ordering: if A -> B, then bwd(B) < bwd(A)
+
+        # Backward ordering: if A -> B, then bwd(B) < bwd(A)
         for edge in dag_edges:
             from_stage, to_stage = edge.from_stage, edge.to_stage
             if from_stage in bwd_times and to_stage in bwd_times:
@@ -318,8 +447,8 @@ def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdg
                         f"backward stage {to_stage} (time {bwd_times[to_stage]}) must come "
                         f"before backward stage {from_stage} (time {bwd_times[from_stage]})"
                     )
-        
-        # Check BWD_W must come after BWD_I for same (stage_id, mb_idx)
+
+        # BWD_W must come after BWD_I for same (stage_id, mb_idx)
         for stage_id in bwd_w_times:
             if stage_id not in bwd_times:
                 raise ValueError(
@@ -334,42 +463,43 @@ def _validate_schedule(schedule: list[list[Task | None]], dag_edges: list[DAGEdg
                 )
 
 
-def _build_p2p_schedule(schedule, num_stages, num_devices, num_steps, stage_to_device):
+def _build_p2p_schedule(schedule: "PipelineSchedule", num_stages, num_devices, stage_to_device):
     """
-    Build a global p2p schedule by iterating the compute schedule in execution order.
+    Build a global p2p schedule by iterating the compute schedule in logical execution order.
 
     Returns:
         rank_p2p: dict mapping pp_rank -> list of (op_type, stage_from, stage_to, mb_idx, is_fwd)
         p2p_idx_map: dict mapping (pp_rank, stage_from, stage_to, mb_idx, is_fwd, op_type) -> rank_local_idx
     """
-    # Global p2p ops in schedule iteration order. Each entry: (stage_from, stage_to, mb_idx, is_fwd).
-    # Only cross-rank ops are included. Each communication is added exactly once,
+    start_times_per_rank = schedule._compute_start_times()
+
+    # Sort events by (logical_start_time, reverse_rank) to match the original
+    # time-step-first, reverse-rank-second iteration order.
+    events: list[tuple[int, int, int, "Chunk"]] = []
+    for rank, (row, starts) in enumerate(zip(schedule.grid, start_times_per_rank)):
+        for chunk, start in zip(row, starts):
+            events.append((start, -rank, rank, chunk))
+    events.sort(key=lambda e: (e[0], e[1]))
+
+    # Global p2p ops in execution order. Each communication is added exactly once,
     # from the SENDER's compute task, to avoid duplicates.
     p2p_ops = []
-
-    for i in range(num_steps):
-        for j in range(num_devices - 1, -1, -1):
-            if i >= len(schedule[j]):
-                continue
-            task = schedule[j][i]
-            if task is None:
-                continue
-            pp_rank, batches, task_type, *_ = task
-
-            # Forward send (added after forward compute produces output)
-            if task_type in (CompType.FWD, CompType.FWD_BWD):
-                stage_id, mb_idx = batches[0]
-                if stage_id < num_stages - 1 and stage_to_device[stage_id] != stage_to_device[stage_id + 1]:
-                    p2p_ops.append((stage_id, stage_id + 1, mb_idx, True))
-
-            # Backward send (added after backward compute produces gradients)
-            if task_type in (CompType.BWD, CompType.BWD_I, CompType.FWD_BWD):
-                if task_type == CompType.FWD_BWD:
-                    stage_id, mb_idx = batches[1]
-                else:
-                    stage_id, mb_idx = batches[0]
-                if stage_id > 0 and stage_to_device[stage_id] != stage_to_device[stage_id - 1]:
-                    p2p_ops.append((stage_id, stage_id - 1, mb_idx, False))
+    for _, _, pp_rank, chunk in events:
+        task_type = chunk.type
+        batches = chunk.batches
+
+        # Forward send
+        if task_type in (CompType.FWD, CompType.FWD_BWD):
+            stage_id, mb_idx = batches[0].stage_id, batches[0].mb_idx
+            if stage_id < num_stages - 1 and stage_to_device[stage_id] != stage_to_device[stage_id + 1]:
+                p2p_ops.append((stage_id, stage_id + 1, mb_idx, True))
+
+        # Backward send
+        if task_type in (CompType.BWD, CompType.BWD_I, CompType.FWD_BWD):
+            b = batches[1] if task_type == CompType.FWD_BWD else batches[0]
+            stage_id, mb_idx = b.stage_id, b.mb_idx
+            if stage_id > 0 and stage_to_device[stage_id] != stage_to_device[stage_id - 1]:
+                p2p_ops.append((stage_id, stage_id - 1, mb_idx, False))
 
     # Build per-rank schedules and index map
     rank_p2p = defaultdict(list)
@@ -391,7 +521,7 @@ def _build_p2p_schedule(schedule, num_stages, num_devices, num_steps, stage_to_d
 
 
 def piper_exec(
-    schedule: Schedule2D,
+    schedule: PipelineSchedule,
     loss_fn,
     dp_degree=1,
     naive_gradient_sync=False,
@@ -411,21 +541,15 @@ def piper_exec(
     num_mbs = schedule.num_mbs()
     num_devices = schedule.num_ranks()
     num_stages = schedule.num_stages()
-    num_steps = len(schedule.grid[0])
-
-    schedule = schedule.grid
 
-    dag_edges = piper_metadata.dag
-    dag_edges = list(map(lambda e: (DAGEdge(e[0], e[1])), list(piper_metadata.dag)))
+    dag_edges = list(map(lambda e: DAGEdge(e[0], e[1]), list(piper_metadata.dag)))
     _validate_schedule(schedule, dag_edges, num_mbs)
 
     actors = piper_metadata.actors
     stage_to_device = piper_metadata.stage_to_device
 
     # Build p2p schedule and send to actors
-    rank_p2p, p2p_idx_map = _build_p2p_schedule(
-        schedule, num_stages, num_devices, num_steps, stage_to_device
-    )
+    rank_p2p, p2p_idx_map = _build_p2p_schedule(schedule, num_stages, num_devices, stage_to_device)
 
     def _p2p_recv(actor, pp_rank, stage_from, stage_to, mb_idx, is_fwd, dep):
         """Dispatch a recv: _exec_p2p_op for cross-rank, old method for local."""
@@ -446,71 +570,71 @@ def _p2p_send(actor, pp_rank, stage_from, stage_to, mb_idx, is_fwd, dep):
         return actor._exec_bwd_send.remote(stage_from, mb_idx, dep)
 
     ret = []
-    # map pp_rank -> latest dependency
     deps = {}
 
     # Send p2p schedules to actors before dispatch
     for pp_rank in range(num_devices):
-        deps[pp_rank] = actors[pp_rank].set_p2p_schedule.remote(
-            rank_p2p.get(pp_rank, [])
-        )
-
-    for i in range(num_steps):
-        for j in range(num_devices-1, -1, -1):
-            if not i < len(schedule[j]):
-                continue
-            task = schedule[j][i]
-            if task:
-                pp_rank, batches, task_type, *_ = task
-                actor = actors[j]
-                match task_type:
-                    case CompType.UPD:
-                        loss = actor._update.remote(deps[pp_rank])
-                        ret.append(loss)
-                    case CompType.FWD:
-                        stage_id, mb_idx = batches[0]
-                        dep = deps[pp_rank]
-                        if stage_id > 0:
-                            dep = _p2p_recv(actor, pp_rank, stage_id - 1, stage_id, mb_idx, True, dep)
-                        dep = actor._forward.remote(stage_id, mb_idx, dep)
-                        if stage_id < num_stages - 1:
-                            dep = _p2p_send(actor, pp_rank, stage_id, stage_id + 1, mb_idx, True, dep)
-                        deps[pp_rank] = dep
-                    case CompType.BWD:
-                        stage_id, mb_idx = batches[0]
-                        dep = deps[pp_rank]
-                        if stage_id < num_stages - 1:
-                            dep = _p2p_recv(actor, pp_rank, stage_id + 1, stage_id, mb_idx, False, dep)
-                        dep = actor._backward.remote(stage_id, mb_idx, dep, loss_fn=loss_fn)
-                        if stage_id > 0:
-                            dep = _p2p_send(actor, pp_rank, stage_id, stage_id - 1, mb_idx, False, dep)
-                        deps[pp_rank] = dep
-                    case CompType.BWD_I:
-                        stage_id, mb_idx = batches[0]
-                        dep = deps[pp_rank]
-                        if stage_id < num_stages - 1:
-                            dep = _p2p_recv(actor, pp_rank, stage_id + 1, stage_id, mb_idx, False, dep)
-                        dep = actor._backward_input.remote(stage_id, mb_idx, dep, loss_fn=loss_fn)
-                        if stage_id > 0:
-                            dep = _p2p_send(actor, pp_rank, stage_id, stage_id - 1, mb_idx, False, dep)
-                        deps[pp_rank] = dep
-                    case CompType.BWD_W:
-                        stage_id, mb_idx = batches[0]
-                        dep = deps[pp_rank]
-                        dep = actor._backward_weight.remote(stage_id, mb_idx, dep)
-                        deps[pp_rank] = dep
-                    case CompType.FWD_BWD:
-                        fwd_stage_id, fwd_mb_idx = batches[0]
-                        bwd_stage_id, bwd_mb_idx = batches[1]
-                        dep = deps[pp_rank]
-                        if fwd_stage_id > 0:
-                            dep = _p2p_recv(actor, pp_rank, fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, True, dep)
-                        if bwd_stage_id < num_stages - 1:
-                            dep = _p2p_recv(actor, pp_rank, bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False, dep)
-                        dep = actor._forward_backward.remote(fwd_stage_id, fwd_mb_idx, bwd_stage_id, bwd_mb_idx, dep, loss_fn=loss_fn)
-                        if fwd_stage_id < num_stages - 1:
-                            dep = _p2p_send(actor, pp_rank, fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True, dep)
-                        if bwd_stage_id > 0:
-                            dep = _p2p_send(actor, pp_rank, bwd_stage_id, bwd_stage_id - 1, bwd_mb_idx, False, dep)
-                        deps[pp_rank] = dep
+        deps[pp_rank] = actors[pp_rank].set_p2p_schedule.remote(rank_p2p.get(pp_rank, []))
+
+    # Iterate chunks in logical execution order: sorted by (start_time, reverse_rank)
+    start_times_per_rank = schedule._compute_start_times()
+    events: list[tuple[int, int, int, Chunk]] = []
+    for rank, (row, starts) in enumerate(zip(schedule.grid, start_times_per_rank)):
+        for chunk, start in zip(row, starts):
+            events.append((start, -rank, rank, chunk))
+    events.sort(key=lambda e: (e[0], e[1]))
+
+    for _, _, j, chunk in events:
+        pp_rank, batches, task_type, *_ = chunk
+        actor = actors[j]
+        match task_type:
+            case CompType.UPD:
+                loss = actor._update.remote(deps[pp_rank])
+                ret.append(loss)
+            case CompType.FWD:
+                stage_id, mb_idx = batches[0].stage_id, batches[0].mb_idx
+                dep = deps[pp_rank]
+                if stage_id > 0:
+                    dep = _p2p_recv(actor, pp_rank, stage_id - 1, stage_id, mb_idx, True, dep)
+                dep = actor._forward.remote(stage_id, mb_idx, dep)
+                if stage_id < num_stages - 1:
+                    dep = _p2p_send(actor, pp_rank, stage_id, stage_id + 1, mb_idx, True, dep)
+                deps[pp_rank] = dep
+            case CompType.BWD:
+                stage_id, mb_idx = batches[0].stage_id, batches[0].mb_idx
+                dep = deps[pp_rank]
+                if stage_id < num_stages - 1:
+                    dep = _p2p_recv(actor, pp_rank, stage_id + 1, stage_id, mb_idx, False, dep)
+                dep = actor._backward.remote(stage_id, mb_idx, dep, loss_fn=loss_fn)
+                if stage_id > 0:
+                    dep = _p2p_send(actor, pp_rank, stage_id, stage_id - 1, mb_idx, False, dep)
+                deps[pp_rank] = dep
+            case CompType.BWD_I:
+                stage_id, mb_idx = batches[0].stage_id, batches[0].mb_idx
+                dep = deps[pp_rank]
+                if stage_id < num_stages - 1:
+                    dep = _p2p_recv(actor, pp_rank, stage_id + 1, stage_id, mb_idx, False, dep)
+                dep = actor._backward_input.remote(stage_id, mb_idx, dep, loss_fn=loss_fn)
+                if stage_id > 0:
+                    dep = _p2p_send(actor, pp_rank, stage_id, stage_id - 1, mb_idx, False, dep)
+                deps[pp_rank] = dep
+            case CompType.BWD_W:
+                stage_id, mb_idx = batches[0].stage_id, batches[0].mb_idx
+                dep = deps[pp_rank]
+                dep = actor._backward_weight.remote(stage_id, mb_idx, dep)
+                deps[pp_rank] = dep
+            case CompType.FWD_BWD:
+                fwd_stage_id, fwd_mb_idx = batches[0].stage_id, batches[0].mb_idx
+                bwd_stage_id, bwd_mb_idx = batches[1].stage_id, batches[1].mb_idx
+                dep = deps[pp_rank]
+                if fwd_stage_id > 0:
+                    dep = _p2p_recv(actor, pp_rank, fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, True, dep)
+                if bwd_stage_id < num_stages - 1:
+                    dep = _p2p_recv(actor, pp_rank, bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False, dep)
+                dep = actor._forward_backward.remote(fwd_stage_id, fwd_mb_idx, bwd_stage_id, bwd_mb_idx, dep, loss_fn=loss_fn)
+                if fwd_stage_id < num_stages - 1:
+                    dep = _p2p_send(actor, pp_rank, fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True, dep)
+                if bwd_stage_id > 0:
+                    dep = _p2p_send(actor, pp_rank, bwd_stage_id, bwd_stage_id - 1, bwd_mb_idx, False, dep)
+                deps[pp_rank] = dep
     return ray.get(ret)
\ No newline at end of file
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index 35bcf4f..ddc65b4 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -13,7 +13,7 @@
 from torch.autograd import Function
 
 from .piper_utils import create_logger, LOG_LEVEL, piper_metadata
-from .piper_exec import TaskType, Schedule2D, Task, TaskNode, TaskDAG
+from .piper_exec import TaskType, PipelineSchedule, Chunk, Task, TaskDAG, logical_time
 
 logger = create_logger("piper_graph_transform", LOG_LEVEL)
 
@@ -164,8 +164,17 @@ def _profile_and_split_gm(gm, num_stages) -> tuple[fx.GraphModule, list[tuple[in
 
     # Check for cached split from a previous profiling run
     cache_dir = Path.home() / ".cache" / "piper" / "splits"
+
+    def _stable_target(target):
+        """Return a stable string for a node target, avoiding memory addresses."""
+        if isinstance(target, str):
+            return target
+        if callable(target) and hasattr(target, '__module__') and hasattr(target, '__qualname__'):
+            return f"{target.__module__}.{target.__qualname__}"
+        return str(target)
+
     graph_signature = hashlib.sha256(
-        json.dumps([(n.name, n.op, str(n.target)) for n in compute_nodes] + [num_stages]).encode()
+        json.dumps([(n.name, n.op, _stable_target(n.target)) for n in compute_nodes] + [num_stages]).encode()
     ).hexdigest()[:16]
     cache_file = cache_dir / f"{graph_signature}.json"
 
@@ -1420,322 +1429,532 @@ def _wrap_with_send(arg):
 
 
 # ---------------------------------------------------------------------------
-# Schedule → Task DAG
+# Per-chunk task-chain builder (internal helper)
 # ---------------------------------------------------------------------------
 
-def schedule_to_dag(schedule: Schedule2D) -> TaskDAG:
-    """Convert a :class:`Schedule2D` into a :class:`TaskDAG`.
-
-    Two kinds of edges are built:
-
-    **Data-dependency edges** connect tasks that share a ``mb_idx`` and are
-    *adjacent* in time – i.e. no other task carrying the same microbatch index
-    exists at any time step between them.  These edges may go between rows
-    (actors), representing the point-to-point activation / gradient tensors
-    that cross pipeline stage boundaries.
-
-    **Temporal edges** connect every pair of consecutive non-None tasks within
-    the same row (actor).  They model the serialisation constraint that a single
-    actor executes one cell at a time.  A task pair may carry both a temporal
-    edge and a data edge simultaneously.
+def _splice_a2a_nodes(
+    chain: list,
+    boundaries: dict,
+    task_type,
+    pp_rank: int,
+    source_chunk,
+) -> list:
+    """Splice A2A nodes into a bucket chain at all-to-all boundaries.
+
+    For FWD: inserts FWD_A2A after FWD(bkt_id) when bkt_id is a boundary.
+    For BWD/BWD_I: inserts BWD_A2A before BWD(bkt_id) in execution order
+    (i.e. between BWD(bkt_id+1) and BWD(bkt_id)).
+
+    Updates data and temporal edges in-place; returns the new chain list
+    with A2A nodes interleaved.
     """
-    nodes: list[TaskNode] = []
-    grid_nodes: list[list[Optional[TaskNode]]] = []
-    # col_meta records the original 2D schedule position of each node so that
-    # data edges can be constructed from (col_idx, sub_idx) order rather than
-    # time_step.  sub_idx distinguishes the FWD and BWD sub-nodes that expand
-    # from a single FWD_BWD cell (same col_idx, different sub_idx).  This dict
-    # is used only inside schedule_to_dag and is not stored on the nodes.
-    col_meta: dict[int, tuple[int, int]] = {}
-
-    for pp_rank, row in enumerate(schedule.grid):
-        row_nodes: list[Optional[TaskNode]] = []
-        for col_idx, task in enumerate(row):
-            if task is None:
-                row_nodes.append(None)
-            elif task.type == TaskType.FWD_BWD:
-                # Expand inline into a FWD node followed by a BWD node.
-                # Placing both consecutively in row_nodes causes the temporal-edge
-                # pass to chain: prev_cell → fwd_node → bwd_node → next_cell.
-                fwd_batch, bwd_batch = task.batches[0], task.batches[1]
-                fwd_node = TaskNode(
-                    task=Task(pp_rank=pp_rank, batches=[fwd_batch], type=TaskType.FWD),
+    a2a_type = TaskType.FWD_A2A if task_type == TaskType.FWD else TaskType.BWD_A2A
+    new_chain: list = []
+
+    for i, task in enumerate(chain):
+        bkt_id = task.bucket_id
+        is_last = (i == len(chain) - 1)
+
+        if task_type == TaskType.FWD:
+            new_chain.append(task)
+            if bkt_id in boundaries and not is_last:
+                next_task = chain[i + 1]
+                a2a_node = Task(
+                    chunk=Chunk(pp_rank=pp_rank, batches=list(task.chunk.batches), type=a2a_type),
                     pp_rank=pp_rank,
-                    time_step=col_idx,
+                    time_step=0,
+                    bucket_id=bkt_id,
+                    custom_metadata={"a2a_tensor_idx": boundaries[bkt_id]},
+                    source_chunk=source_chunk,
                 )
-                bwd_node = TaskNode(
-                    task=Task(pp_rank=pp_rank, batches=[bwd_batch], type=TaskType.BWD),
+                # Rewire data: task → next_task  ⟹  task → a2a → next_task
+                task.data_succs.remove(next_task)
+                next_task.data_preds.remove(task)
+                task.data_succs.append(a2a_node)
+                a2a_node.data_preds.append(task)
+                a2a_node.data_succs.append(next_task)
+                next_task.data_preds.append(a2a_node)
+                # Rewire temporal
+                task.temporal_succs.remove(next_task)
+                next_task.temporal_preds.remove(task)
+                task.temporal_succs.append(a2a_node)
+                a2a_node.temporal_preds.append(task)
+                a2a_node.temporal_succs.append(next_task)
+                next_task.temporal_preds.append(a2a_node)
+                new_chain.append(a2a_node)
+        else:
+            # BWD / BWD_I: insert A2A before task (i.e. between chain[i-1] and chain[i])
+            if bkt_id in boundaries and i > 0:
+                prev_task = chain[i - 1]
+                a2a_node = Task(
+                    chunk=Chunk(pp_rank=pp_rank, batches=list(task.chunk.batches), type=a2a_type),
                     pp_rank=pp_rank,
-                    time_step=col_idx,
+                    time_step=0,
+                    bucket_id=bkt_id,
+                    custom_metadata={"a2a_tensor_idx": boundaries[bkt_id]},
+                    source_chunk=source_chunk,
                 )
-                col_meta[id(fwd_node)] = (col_idx, 0)
-                col_meta[id(bwd_node)] = (col_idx, 1)
-                nodes.extend([fwd_node, bwd_node])
-                row_nodes.extend([fwd_node, bwd_node])
-            else:
-                node = TaskNode(task=task, pp_rank=pp_rank, time_step=col_idx)
-                col_meta[id(node)] = (col_idx, 0)
-                nodes.append(node)
-                row_nodes.append(node)
-        grid_nodes.append(row_nodes)
-
-    mb_to_nodes: dict[int, list[TaskNode]] = defaultdict(list)
-    seen_per_mb: dict[int, set[int]] = defaultdict(set)
-
-    for node in nodes:
-        # UPD and BWD_W are purely local: UPD has no cross-stage dependency;
-        # BWD_W only depends on its paired BWD_I (same rank/stage/mb) and must
-        # not appear in the cross-rank data chain so that insert_p2p_ops does
-        # not add spurious SEND/RECV nodes for BWD_W.
-        if node.task.type in (TaskType.UPD, TaskType.BWD_W):
-            continue
-        for batch in node.task.batches:
-            mb_idx = batch.mb_idx
-            node_ident = id(node)
-            if node_ident not in seen_per_mb[mb_idx]:
-                seen_per_mb[mb_idx].add(node_ident)
-                mb_to_nodes[mb_idx].append(node)
-
-    for mb_nodes in mb_to_nodes.values():
-        mb_nodes.sort(key=lambda n: (*col_meta[id(n)], n.pp_rank))
-        for i in range(len(mb_nodes) - 1):
-            pred, succ = mb_nodes[i], mb_nodes[i + 1]
-            pred.data_succs.append(succ)
-            succ.data_preds.append(pred)
-
-    # Add explicit BWD_I -> BWD_W data edges for each (rank, stage_id, mb_idx)
-    # pair.  BWD_W is excluded from the mb chain above, but it still needs a
-    # data dependency on its upstream BWD_I so the actor knows to wait for it.
-    bwdi_index: dict = {}  # (pp_rank, stage_id, mb_idx) -> BWD_I node
-    for node in nodes:
-        if node.task.type == TaskType.BWD_I:
-            b = node.task.batches[0]
-            bwdi_index[(node.pp_rank, b.stage_id, b.mb_idx)] = node
-    for node in nodes:
-        if node.task.type == TaskType.BWD_W:
-            b = node.task.batches[0]
-            bwdi = bwdi_index.get((node.pp_rank, b.stage_id, b.mb_idx))
-            if bwdi is not None:
-                bwdi.data_succs.append(node)
-                node.data_preds.append(bwdi)
-
-    for row_nodes in grid_nodes:
-        non_null = [n for n in row_nodes if n is not None]
-        if not non_null:
-            continue
-        for i in range(len(non_null) - 1):
-            pred, succ = non_null[i], non_null[i + 1]
-            pred.temporal_succs.append(succ)
-            succ.temporal_preds.append(pred)
-        # Synthesize a UPD node as the terminal task for this rank's temporal
-        # chain.  time_step is a placeholder; assign_time_steps sets the real
-        # value after all three expansion passes complete.
-        last = non_null[-1]
-        upd_node = TaskNode(
-            task=Task(pp_rank=last.pp_rank, batches=list(last.task.batches), type=TaskType.UPD),
-            pp_rank=last.pp_rank,
-            time_step=0,
+                # Rewire data: prev_task → task  ⟹  prev_task → a2a → task
+                prev_task.data_succs.remove(task)
+                task.data_preds.remove(prev_task)
+                prev_task.data_succs.append(a2a_node)
+                a2a_node.data_preds.append(prev_task)
+                a2a_node.data_succs.append(task)
+                task.data_preds.append(a2a_node)
+                # Rewire temporal
+                prev_task.temporal_succs.remove(task)
+                task.temporal_preds.remove(prev_task)
+                prev_task.temporal_succs.append(a2a_node)
+                a2a_node.temporal_preds.append(prev_task)
+                a2a_node.temporal_succs.append(task)
+                task.temporal_preds.append(a2a_node)
+                new_chain.append(a2a_node)
+            new_chain.append(task)
+
+    return new_chain
+
+
+def _build_chunk_task_chain(
+    chunk,
+    pp_rank: int,
+    col_idx: int,
+    bucket_counts: dict,
+    a2a_info: dict,
+) -> tuple:
+    """Build the expanded task chain for a single schedule chunk.
+
+    For FWD / BWD / BWD_I chunks with bucketing: returns a chain of K bucket
+    tasks with internal data + temporal edges.  A2A nodes are spliced in at
+    boundaries.  For all other chunk types a single task is returned.
+
+    All created tasks have ``source_chunk`` set to *chunk*.
+
+    Returns:
+        ``(all_tasks, head, tail)`` where *head* is the first task in
+        execution order and *tail* is the last.
+    """
+    TIME_SCALE = 1000
+
+    task_type = chunk.type
+    stage_id = chunk.batches[0].stage_id if chunk.batches else None
+    K = bucket_counts.get(stage_id, 1) if stage_id is not None else 1
+
+    # FWD, BWD, BWD_I, BWD_W all support per-bucket tasks
+    if task_type not in (TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W):
+        K = 1
+
+    if K == 1:
+        task = Task(
+            chunk=chunk,
+            pp_rank=pp_rank,
+            time_step=col_idx,
+            source_chunk=chunk,
         )
-        last.temporal_succs.append(upd_node)
-        upd_node.temporal_preds.append(last)
-        nodes.append(upd_node)
+        return [task], task, task
+
+    # --- Bucketed chain ---
+    if task_type == TaskType.FWD:
+        # Execution order: bucket 0, 1, …, K-1
+        chain = [
+            Task(
+                chunk=Chunk(pp_rank=pp_rank, batches=list(chunk.batches), type=TaskType.FWD),
+                pp_rank=pp_rank,
+                time_step=col_idx * TIME_SCALE + i,
+                bucket_id=b,
+                source_chunk=chunk,
+            )
+            for i, b in enumerate(range(K))
+        ]
+    else:
+        # BWD / BWD_I: execution order K-1, K-2, …, 0
+        chain = [
+            Task(
+                chunk=Chunk(pp_rank=pp_rank, batches=list(chunk.batches), type=task_type),
+                pp_rank=pp_rank,
+                time_step=col_idx * TIME_SCALE + i,
+                bucket_id=K - 1 - i,
+                source_chunk=chunk,
+            )
+            for i in range(K)
+        ]
+
+    # Connect consecutive bucket tasks with data + temporal edges
+    for i in range(K - 1):
+        chain[i].data_succs.append(chain[i + 1])
+        chain[i + 1].data_preds.append(chain[i])
+        chain[i].temporal_succs.append(chain[i + 1])
+        chain[i + 1].temporal_preds.append(chain[i])
 
-    return TaskDAG(nodes=nodes)
+    # Insert A2A nodes at boundaries if applicable.
+    # BWD_W (weight gradients) are computed locally and don't need A2A separation.
+    if task_type != TaskType.BWD_W:
+        boundaries = a2a_info.get(stage_id, {}) if stage_id is not None else {}
+        if boundaries:
+            chain = _splice_a2a_nodes(chain, boundaries, task_type, pp_rank, chunk)
 
+    return chain, chain[0], chain[-1]
 
 
 # ---------------------------------------------------------------------------
-# Expand bucketed FWD/BWD tasks into per-bucket task chains
+# expand_chunks_to_dags: build the full task DAG chunk-by-chunk
 # ---------------------------------------------------------------------------
 
-def expand_bucket_tasks(dag: TaskDAG, bucket_counts: dict) -> TaskDAG:
-    """Replace each FWD/BWD TaskNode for a bucketed stage with a chain of
-    FWD / BWD TaskNodes with explicit bucket_id, one per bucket.
+def expand_chunks_to_dags(
+    schedule,
+    bucket_counts: dict,
+    a2a_info: dict,
+    dp_degree: int,
+) -> "TaskDAG":
+    """Build a :class:`TaskDAG` by expanding every schedule chunk in-place.
 
-    For FWD stages the chain executes in bucket order 0, 1, …, K-1.
-    For BWD stages the chain executes in reverse order K-1, K-2, …, 0
-    (highest bucket_id first receives the upstream gradient; bucket_id=0 is
-    last to execute and sends the input gradient to the previous stage).
+    Replaces the former pipeline of ``schedule_to_dag`` →
+    ``expand_bucket_tasks`` → ``expand_a2a_tasks`` →
+    ``insert_p2p_ops`` → ``insert_ar_ops``.
 
-    Incoming data / temporal edges are attached to the first node in the
-    execution chain; outgoing edges are attached to the last node.
+    For each non-``None`` cell of *schedule*, :func:`_build_chunk_task_chain`
+    produces the expanded task chain (with bucketing and A2A nodes already
+    wired).  Cross-chunk data edges, SEND/RECV nodes, and ALL_REDUCE nodes
+    are then added in subsequent passes.
+
+    Every task created here has its ``source_chunk`` field set to the
+    originating :class:`Chunk` from the pipeline schedule.
+
+    The returned :class:`TaskDAG` has data edges and within-chain temporal
+    edges only.  Between-chunk temporal edges are inserted by the separate
+    :func:`add_temporal_dependencies` pass.
 
     Args:
-        dag: Global TaskDAG produced by schedule_to_dag().
-        bucket_counts: Mapping stage_id -> number of buckets. Only stages
-            present in this dict are expanded.
+        schedule: The 2-D pipeline pipeline schedule.
+        bucket_counts: Mapping ``stage_id -> K`` for stages with ``K > 1``
+            buckets.  Stages absent from this dict use a single bucket.
+        a2a_info: Mapping ``stage_id -> {boundary_bucket_id -> tensor_idx}``
+            for expert-parallel stages.  May be empty.
+        dp_degree: Number of data-parallel replicas.  When ``> 1``,
+            ALL_REDUCE nodes are inserted for every BWD/BWD_W bucket.
+            Whether a bucket actually has trainable parameters is checked
+            at runtime in run_dag, keeping the schedule model-agnostic.
 
     Returns:
-        A new TaskDAG with expanded nodes.
+        A new :class:`TaskDAG` containing all compute, A2A, SEND/RECV, and
+        (if *dp_degree* > 1) ALL_REDUCE task nodes.
     """
-    TIME_SCALE = 1000
+    # mb_to_entries[mb_idx] = list of (col_idx, sub_idx, head, tail, pp_rank)
+    # Used to build cross-chunk data edges in time order.
+    mb_to_entries: dict = defaultdict(list)
 
-    # ---- Step 2a: create bucket chains for all expandable nodes ----
-    expandable = [
-        n for n in dag.nodes
-        if n.task.type in (TaskType.FWD, TaskType.BWD, TaskType.BWD_I)
-        and n.task.batches[0].stage_id in bucket_counts
-    ]
+    # (pp_rank, stage_id, mb_idx, bucket_id) -> BWD_I task (for per-bucket BWD_I → BWD_W edges)
+    bwdi_tasks: dict = {}
+
+    all_tasks: list = []
+
+    # ------------------------------------------------------------------
+    # Pass 1: build per-chunk task chains
+    # ------------------------------------------------------------------
+    # Precompute logical start times for cross-rank ordering keys.
+    start_times = schedule._compute_start_times()
+
+    for pp_rank, row in enumerate(schedule.grid):
+        for col_idx, chunk in enumerate(row):
+            if chunk.type == TaskType.FWD_BWD:
+                fwd_batch, bwd_batch = chunk.batches[0], chunk.batches[1]
 
-    # id(orig) -> (first_bkt, last_bkt, chain_list)
-    node_to_first_last: dict = {}
-
-    for orig in expandable:
-        stage_id = orig.task.batches[0].stage_id
-        K = bucket_counts[stage_id]
-
-        if orig.task.type == TaskType.FWD:
-            # Execution order: bucket 0, 1, …, K-1
-            chain = [
-                TaskNode(
-                    task=Task(
-                        pp_rank=orig.pp_rank,
-                        batches=list(orig.task.batches),
-                        type=TaskType.FWD,
-                    ),
-                    pp_rank=orig.pp_rank,
-                    time_step=orig.time_step * TIME_SCALE + i,
-                    bucket_id=b,
+                fwd_sub = Chunk(pp_rank=pp_rank, batches=[fwd_batch], type=TaskType.FWD)
+                fwd_chain, fwd_head, fwd_tail = _build_chunk_task_chain(
+                    fwd_sub, pp_rank, col_idx, bucket_counts, a2a_info
                 )
-                for i, b in enumerate(range(K))
-            ]
-        else:  # BWD / BWD_I: execution order K-1, K-2, …, 0
-            chain = [
-                TaskNode(
-                    task=Task(
-                        pp_rank=orig.pp_rank,
-                        batches=list(orig.task.batches),
-                        type=orig.task.type,   # preserve BWD or BWD_I
-                    ),
-                    pp_rank=orig.pp_rank,
-                    time_step=orig.time_step * TIME_SCALE + i,
-                    bucket_id=K - 1 - i,
+                for t in fwd_chain:
+                    t.source_chunk = chunk  # override to point at original FWD_BWD cell
+
+                bwd_sub = Chunk(pp_rank=pp_rank, batches=[bwd_batch], type=TaskType.BWD)
+                bwd_chain, bwd_head, bwd_tail = _build_chunk_task_chain(
+                    bwd_sub, pp_rank, col_idx, bucket_counts, a2a_info
                 )
-                for i in range(K)
-            ]
-
-        # Data-dep and temporal chain between consecutive bucket nodes
-        for i in range(K - 1):
-            chain[i].data_succs.append(chain[i + 1])
-            chain[i + 1].data_preds.append(chain[i])
-            chain[i].temporal_succs.append(chain[i + 1])
-            chain[i + 1].temporal_preds.append(chain[i])
-
-        first_bkt = chain[0]
-        last_bkt = chain[-1]
-        node_to_first_last[id(orig)] = (first_bkt, last_bkt, chain)
-
-    # ---- Step 2b: redirect all edges ----
-    for orig in expandable:
-        first_bkt, last_bkt, _ = node_to_first_last[id(orig)]
-
-        # Incoming data edges: pred -> orig  =>  new_src -> first_bkt
-        for pred in list(orig.data_preds):
-            if orig in pred.data_succs:
-                pred.data_succs.remove(orig)
-            new_src = node_to_first_last[id(pred)][1] if id(pred) in node_to_first_last else pred
-            if first_bkt not in new_src.data_succs:
-                new_src.data_succs.append(first_bkt)
-                first_bkt.data_preds.append(new_src)
-        orig.data_preds.clear()
-
-        # Outgoing data edges: orig -> succ  =>  last_bkt -> new_dst
-        for succ in list(orig.data_succs):
-            if orig in succ.data_preds:
-                succ.data_preds.remove(orig)
-            new_dst = node_to_first_last[id(succ)][0] if id(succ) in node_to_first_last else succ
-            if new_dst not in last_bkt.data_succs:
-                last_bkt.data_succs.append(new_dst)
-                new_dst.data_preds.append(last_bkt)
-        orig.data_succs.clear()
-
-        # Incoming temporal edges: tp -> orig  =>  new_tp -> first_bkt
-        for tp in list(orig.temporal_preds):
-            tp.temporal_succs.remove(orig)
-            new_tp = node_to_first_last[id(tp)][1] if id(tp) in node_to_first_last else tp
-            new_tp.temporal_succs.append(first_bkt)
-            first_bkt.temporal_preds.append(new_tp)
-        orig.temporal_preds.clear()
-
-        # Outgoing temporal edges: orig -> ts  =>  last_bkt -> new_ts
-        for ts in list(orig.temporal_succs):
-            ts.temporal_preds.remove(orig)
-            new_ts = node_to_first_last[id(ts)][0] if id(ts) in node_to_first_last else ts
-            last_bkt.temporal_succs.append(new_ts)
-            new_ts.temporal_preds.append(last_bkt)
-        orig.temporal_succs.clear()
-
-    # ---- Step 2c: build new node list ----
-    orig_ids = set(id(n) for n in expandable)
-    expanded_nodes = []
-    for n in dag.nodes:
-        if id(n) in orig_ids:
-            expanded_nodes.extend(node_to_first_last[id(n)][2])
-        else:
-            expanded_nodes.append(n)
+                for t in bwd_chain:
+                    t.source_chunk = chunk
 
-    return TaskDAG(nodes=expanded_nodes)
+                # Within-cell ordering: FWD executes before BWD
+                fwd_tail.temporal_succs.append(bwd_head)
+                bwd_head.temporal_preds.append(fwd_tail)
 
+                all_tasks.extend(fwd_chain + bwd_chain)
+                logical_t = start_times[pp_rank][col_idx]
+                mb_to_entries[fwd_batch.mb_idx].append((logical_t, 0, fwd_head, fwd_tail, pp_rank))
+                mb_to_entries[bwd_batch.mb_idx].append((logical_t, 1, bwd_head, bwd_tail, pp_rank))
 
-# ---------------------------------------------------------------------------
-# Insert SEND / RECV nodes for cross-rank data dependencies
-# ---------------------------------------------------------------------------
+            else:
+                tasks, head, tail = _build_chunk_task_chain(
+                    chunk, pp_rank, col_idx, bucket_counts, a2a_info
+                )
+                all_tasks.extend(tasks)
+
+                # UPD and BWD_W are excluded from the cross-stage mb chain
+                if chunk.type not in (TaskType.UPD, TaskType.BWD_W):
+                    logical_t = start_times[pp_rank][col_idx]
+                    for batch in chunk.batches:
+                        mb_to_entries[batch.mb_idx].append(
+                            (logical_t, 0, head, tail, pp_rank)
+                        )
 
-def insert_p2p_ops(dag: TaskDAG) -> list[TaskDAG]:
-    """Transform a :class:`TaskDAG` into one :class:`TaskDAG` per PP rank.
+                # Track per-bucket BWD_I tasks for BWD_I → BWD_W data edges
+                if chunk.type == TaskType.BWD_I and chunk.batches:
+                    b = chunk.batches[0]
+                    for t in tasks:
+                        if t.chunk.type == TaskType.BWD_I:
+                            bwdi_tasks[(pp_rank, b.stage_id, b.mb_idx, t.bucket_id)] = t
 
-    For every cross-rank data-dependency edge ``task1 (rank A) → task2 (rank B)``:
+    # ------------------------------------------------------------------
+    # Pass 1.5: assign unique_bucket_id and compute_loss
+    # ------------------------------------------------------------------
+    # Build a sorted (stage_id, stage_bucket_id) → unique_bucket_id mapping.
+    # Sorting by stage_id ensures the mapping is deterministic and consistent
+    # with the offset computation in piper.py that passes unique_bucket_ids
+    # to _load_stage.
+    _compute_types = (TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W)
+    _stage_bucket_pairs = sorted(
+        {(t.chunk.batches[0].stage_id, t.bucket_id)
+         for t in all_tasks
+         if t.chunk.type in _compute_types and t.chunk.batches},
+        key=lambda x: (x[0], x[1]),
+    )
+    _sb_to_ubid: dict = {sb: i for i, sb in enumerate(_stage_bucket_pairs)}
 
-    1. Remove the data-dep edge.
-    2. Create a SEND node on rank A with a data-dep edge ``task1 → SEND``.
-    3. Create a RECV node on rank B with a data-dep edge ``RECV → task2``.
+    _last_stage_id = max(
+        (t.chunk.batches[0].stage_id
+         for t in all_tasks
+         if t.chunk.type in _compute_types and t.chunk.batches),
+        default=None,
+    )
 
-    SEND / RECV nodes are not inserted into the temporal chain.  Their
-    ``time_step`` is set relative to their compute neighbour: SEND gets
-    ``src.time_step + 1``; RECV gets ``dst.time_step - 1``.
+    for task in all_tasks:
+        if task.chunk.type in _compute_types and task.chunk.batches:
+            s = task.chunk.batches[0].stage_id
+            task.unique_bucket_id = _sb_to_ubid.get((s, task.bucket_id))
+
+    # Set compute_loss=True on the first-to-run BWD/BWD_I task of the last stage.
+    # "First to run" = highest bucket_id (BWD executes in reverse bucket order).
+    if _last_stage_id is not None:
+        _last_stage_K = bucket_counts.get(_last_stage_id, 1)
+        _last_bkt = _last_stage_K - 1
+        for task in all_tasks:
+            if (task.chunk.type in (TaskType.BWD, TaskType.BWD_I)
+                    and task.chunk.batches
+                    and task.chunk.batches[0].stage_id == _last_stage_id
+                    and task.bucket_id == _last_bkt):
+                task.compute_loss = True
 
-    :func:`assign_time_steps` must have been called on *dag* before this
-    function so that the compute nodes carry their final ``time_step`` values.
+    # ------------------------------------------------------------------
+    # Pass 2: per-bucket BWD_I → BWD_W data edges
+    # ------------------------------------------------------------------
+    bwd_w_tasks: dict = {}  # (pp_rank, stage_id, mb_idx, bucket_id) -> Task
+    for task in all_tasks:
+        if task.chunk.type == TaskType.BWD_W and task.chunk.batches:
+            b = task.chunk.batches[0]
+            bwd_w_tasks[(task.pp_rank, b.stage_id, b.mb_idx, task.bucket_id)] = task
+
+    for key, bwdi_task in bwdi_tasks.items():
+        bwdw_task = bwd_w_tasks.get(key)
+        if bwdw_task is not None:
+            bwdi_task.data_succs.append(bwdw_task)
+            bwdw_task.data_preds.append(bwdi_task)
+
+    # ------------------------------------------------------------------
+    # Pass 3: cross-chunk data edges
+    # ------------------------------------------------------------------
+    for mb_idx, entries in mb_to_entries.items():
+        entries.sort(key=lambda e: (e[0], e[1], e[4]))  # col_idx, sub_idx, pp_rank
+        for i in range(len(entries) - 1):
+            _ci, _si, _head_i, tail_i, _ri = entries[i]
+            _cn, _sn, head_n, _tn, _rn = entries[i + 1]
+            tail_i.data_succs.append(head_n)
+            head_n.data_preds.append(tail_i)
 
-    After the transformation no cross-rank edges should remain.  The function
-    asserts this, then splits the node list by ``pp_rank`` and returns a list of
-    :class:`TaskDAG` objects ordered by ascending PP rank.
-    """
-    all_nodes: list[TaskNode] = list(dag.nodes)
 
-    cross_edges: list[tuple[TaskNode, TaskNode]] = [
+    # ------------------------------------------------------------------
+    # Pass 4: SEND / RECV nodes for cross-rank data edges
+    # ------------------------------------------------------------------
+    cross_edges = [
         (src, dst)
-        for src in all_nodes
-        for dst in src.data_succs
+        for src in list(all_tasks)
+        for dst in list(src.data_succs)
         if src.pp_rank != dst.pp_rank
     ]
-
     for src, dst in cross_edges:
         src.data_succs.remove(dst)
         dst.data_preds.remove(src)
 
-        send_node = TaskNode(
-            task=Task(pp_rank=src.pp_rank, batches=list(src.task.batches), type=TaskType.SEND),
+        send_node = Task(
+            chunk=Chunk(pp_rank=src.pp_rank, batches=list(src.chunk.batches), type=TaskType.SEND),
             pp_rank=src.pp_rank,
-            time_step=src.time_step + 1,
+            time_step=0,  # assigned later by assign_time_steps
             peer_pp_rank=dst.pp_rank,
+            source_chunk=src.source_chunk,
         )
         src.data_succs.append(send_node)
         send_node.data_preds.append(src)
 
-        recv_node = TaskNode(
-            task=Task(pp_rank=dst.pp_rank, batches=list(dst.task.batches), type=TaskType.RECV),
+        recv_metadata = {}
+        if dst.chunk.type in (TaskType.BWD, TaskType.BWD_I):
+            recv_metadata["fwd_ubid"] = dst.unique_bucket_id
+        recv_node = Task(
+            chunk=Chunk(pp_rank=dst.pp_rank, batches=list(dst.chunk.batches), type=TaskType.RECV),
             pp_rank=dst.pp_rank,
-            time_step=dst.time_step - 1,
+            time_step=0,  # assigned later by assign_time_steps
             peer_pp_rank=src.pp_rank,
+            source_chunk=dst.source_chunk,
+            custom_metadata=recv_metadata,
         )
         recv_node.data_succs.append(dst)
         dst.data_preds.append(recv_node)
 
-        all_nodes.append(send_node)
-        all_nodes.append(recv_node)
+        all_tasks.append(send_node)
+        all_tasks.append(recv_node)
+
+    # ------------------------------------------------------------------
+    # Pass 5: ALL_REDUCE nodes for data-parallel gradient sync
+    # ------------------------------------------------------------------
+    if dp_degree > 1:
+        bwd_groups: dict = defaultdict(list)
+        for task in all_tasks:
+            if task.chunk.type in (TaskType.BWD, TaskType.BWD_W) and task.chunk.batches:
+                s = task.chunk.batches[0].stage_id
+                key = (s, task.bucket_id)
+                bwd_groups[key].append(task)
+
+        for (stage_id, bucket_id), group in bwd_groups.items():
+            trigger = max(group, key=lambda n: n.time_step)
+            ar_node = Task(
+                chunk=Chunk(
+                    pp_rank=trigger.pp_rank,
+                    batches=list(trigger.chunk.batches),
+                    type=TaskType.ALL_REDUCE,
+                ),
+                pp_rank=trigger.pp_rank,
+                time_step=trigger.time_step + 1,
+                bucket_id=bucket_id,
+                source_chunk=trigger.source_chunk,
+            )
+            trigger.data_succs.append(ar_node)
+            ar_node.data_preds.append(trigger)
+            all_tasks.append(ar_node)
+
+    return TaskDAG(nodes=all_tasks)
 
-    for node in all_nodes:
+
+# ---------------------------------------------------------------------------
+# add_temporal_dependencies: insert temporal edges between adjacent chunks
+# ---------------------------------------------------------------------------
+
+_CRITICAL_PATH_TYPES = frozenset({
+    TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W,
+    TaskType.FWD_BWD, TaskType.FWD_A2A, TaskType.BWD_A2A,
+})
+
+
+def add_temporal_dependencies(dag: "TaskDAG", schedule) -> None:
+    """Insert temporal edges that enforce execution order between adjacent chunks.
+
+    For each row of *schedule*, consecutive non-``None`` chunk pairs ``(c1,
+    c2)`` receive a temporal edge from the last critical-path task of *c1* to
+    the first critical-path task of *c2*.
+
+    The **critical-path chain** for a chunk is the sub-DAG of tasks associated
+    with that chunk whose type is in :data:`_CRITICAL_PATH_TYPES` (i.e.
+    compute and A2A tasks; SEND, RECV, and ALL_REDUCE are excluded).  Head and
+    tail are found by following both data-dependency *and* within-chunk
+    temporal edges.
+
+    A :data:`~TaskType.UPD` node is also synthesised for each pipeline rank
+    and appended to ``dag.nodes`` as the terminal temporal task for that rank.
+
+    Args:
+        dag: :class:`TaskDAG` produced by :func:`expand_chunks_to_dags`.
+            Must contain only data edges and within-chain temporal edges —
+            no between-chunk temporal edges should exist yet.
+        schedule: The :class:`PipelineSchedule` whose grid determines
+            adjacency and row membership.
+    """
+    # Group all tasks by id(source_chunk) for fast lookup.
+    sc_to_tasks: dict = defaultdict(list)
+    for task in dag.nodes:
+        if task.source_chunk is not None:
+            sc_to_tasks[id(task.source_chunk)].append(task)
+
+    def _cp_endpoints(chunk_id: int):
+        """Return (head, tail) of the critical-path sub-DAG for *chunk_id*.
+
+        Uses both data and temporal edges within the filtered task set so
+        that FWD_BWD cells (where FWD and BWD sub-chains are joined only by
+        a temporal edge) are handled correctly.
+        """
+        tasks = [t for t in sc_to_tasks.get(chunk_id, [])
+                 if t.chunk.type in _CRITICAL_PATH_TYPES]
+        if not tasks:
+            return None, None
+        task_ids = {id(t) for t in tasks}
+        head = next(
+            t for t in tasks
+            if not any(id(p) in task_ids
+                       for p in list(t.data_preds) + list(t.temporal_preds))
+        )
+        tail = next(
+            t for t in tasks
+            if not any(id(s) in task_ids
+                       for s in list(t.data_succs) + list(t.temporal_succs))
+        )
+        return head, tail
+
+    new_nodes: list = []
+
+    for pp_rank, row in enumerate(schedule.grid):
+        non_none = list(enumerate(row))
+        if not non_none:
+            continue
+
+        # Between-chunk temporal edges for consecutive pairs
+        for i in range(len(non_none) - 1):
+            _, c1 = non_none[i]
+            _, c2 = non_none[i + 1]
+            _, tail_c1 = _cp_endpoints(id(c1))
+            head_c2, _ = _cp_endpoints(id(c2))
+            if tail_c1 is not None and head_c2 is not None:
+                tail_c1.temporal_succs.append(head_c2)
+                head_c2.temporal_preds.append(tail_c1)
+
+        # UPD node: temporal successor of the last chunk's critical-path tail
+        _, last_chunk = non_none[-1]
+        _, last_tail = _cp_endpoints(id(last_chunk))
+        if last_tail is not None:
+            upd_node = Task(
+                chunk=Chunk(
+                    pp_rank=last_tail.pp_rank,
+                    batches=list(last_tail.chunk.batches),
+                    type=TaskType.UPD,
+                ),
+                pp_rank=last_tail.pp_rank,
+                time_step=0,
+                source_chunk=last_chunk,
+            )
+            last_tail.temporal_succs.append(upd_node)
+            upd_node.temporal_preds.append(last_tail)
+            new_nodes.append(upd_node)
+
+    dag.nodes.extend(new_nodes)
+
+
+# ---------------------------------------------------------------------------
+# split_dag_by_rank: partition unified DAG into per-rank DAGs
+# ---------------------------------------------------------------------------
+
+def split_dag_by_rank(dag: "TaskDAG") -> list:
+    """Split a unified :class:`TaskDAG` into one :class:`TaskDAG` per PP rank.
+
+    Asserts that no cross-rank data or temporal edges remain (they should
+    have been replaced by SEND/RECV nodes in :func:`expand_chunks_to_dags`).
+
+    Returns:
+        List of :class:`TaskDAG` objects ordered by ascending PP rank.
+    """
+    for node in dag.nodes:
         for succ in node.data_succs:
             assert node.pp_rank == succ.pp_rank, (
                 f"Cross-rank data edge still present: "
@@ -1749,77 +1968,25 @@ def insert_p2p_ops(dag: TaskDAG) -> list[TaskDAG]:
                 f"{ts.node_id()} (rank {ts.pp_rank})"
             )
 
-    rank_nodes: dict[int, list[TaskNode]] = defaultdict(list)
-    for node in all_nodes:
+    rank_nodes: dict = defaultdict(list)
+    for node in dag.nodes:
         rank_nodes[node.pp_rank].append(node)
 
     return [TaskDAG(nodes=rank_nodes[r]) for r in sorted(rank_nodes)]
 
 
-def insert_ar_ops(per_rank_dags: list, trainable_bucket_keys: set | None = None) -> list:
-    """Insert ALL_REDUCE nodes into each per-rank DAG for DP gradient sync.
-
-    For each (stage_id, bucket_id) group of BWD/BWD_I nodes, add one ALL_REDUCE
-    after the final microbatch's node.  The actor all-reduces the pre-allocated
-    flat_grads tensor for that (stage, bucket) pair.
-
-    The ALL_REDUCE node has exactly one data edge: from the final microbatch
-    BWD/BWD_I node.  Synchronisation with UPD is handled at the CUDA level via
-    ar_events in _update_impl.
-
-    Args:
-        per_rank_dags: Per-rank TaskDAG list to mutate in-place.
-        trainable_bucket_keys: If provided, only insert ALL_REDUCE for
-            (stage_id, bucket_id) pairs present in this set.  Parameterless
-            buckets (e.g. A2A dispatch/combine segments in MoE models) must be
-            excluded or the actor will crash with a None flat_grads tensor.
-    """
-    for rank_dag in per_rank_dags:
-        new_nodes = []
-
-        # Group BWD/BWD_I nodes by (stage_id, bucket_id).  One ALL_REDUCE is
-        # inserted per group, triggered by the final microbatch's node in that group.
-        bwd_groups: dict = defaultdict(list)
-        for node in rank_dag.nodes:
-            if node.task.type in (TaskType.BWD, TaskType.BWD_I):
-                s = node.task.batches[0].stage_id
-                key = (s, node.bucket_id)
-                if trainable_bucket_keys is not None and key not in trainable_bucket_keys:
-                    continue
-                bwd_groups[key].append(node)
-
-        for (stage_id, bucket_id), group in bwd_groups.items():
-            trigger_node = max(group, key=lambda n: n.time_step)
-            ar_task = Task(
-                pp_rank=trigger_node.pp_rank,
-                batches=list(trigger_node.task.batches),
-                type=TaskType.ALL_REDUCE,
-            )
-            ar_node = TaskNode(
-                task=ar_task,
-                pp_rank=trigger_node.pp_rank,
-                time_step=trigger_node.time_step + 1,
-                bucket_id=bucket_id,
-            )
-            trigger_node.data_succs.append(ar_node)
-            ar_node.data_preds.append(trigger_node)
-            new_nodes.append(ar_node)
-
-        rank_dag.nodes.extend(new_nodes)
-
-    return per_rank_dags
 
 
 # ---------------------------------------------------------------------------
 # Graphviz visualisation
 # ---------------------------------------------------------------------------
 
-def _task_node_label(node: TaskNode) -> str:
-    """Short human-readable label for a TaskNode."""
-    task = node.task
+def _task_node_label(node: Task) -> str:
+    """Short human-readable label for a Task."""
+    chunk = node.chunk
 
-    if task.type in (TaskType.SEND, TaskType.RECV):
-        op = "SEND" if task.type == TaskType.SEND else "RECV"
+    if chunk.type in (TaskType.SEND, TaskType.RECV):
+        op = "SEND" if chunk.type == TaskType.SEND else "RECV"
         return op
 
     type_abbrev = {
@@ -1832,26 +1999,75 @@ def _task_node_label(node: TaskNode) -> str:
         TaskType.ALL_REDUCE: "AR",
         TaskType.FWD_A2A: "F_A2A",
         TaskType.BWD_A2A: "B_A2A",
-    }.get(task.type, "?")
+    }.get(chunk.type, "?")
 
-    if task.type == TaskType.UPD:
+    if chunk.type == TaskType.UPD:
         return type_abbrev
 
-    if task.type == TaskType.ALL_REDUCE:
-        stage_id = task.batches[0].stage_id
+    if chunk.type == TaskType.ALL_REDUCE:
+        stage_id = chunk.batches[0].stage_id
         return "AR"
-    
-    if task.type == TaskType.FWD_A2A or task.type == TaskType.BWD_A2A:
-        return "A2A"
 
-    parts = " + ".join(f"S{b.stage_id} M{b.mb_idx}" for b in task.batches)
+    if chunk.type in (TaskType.FWD_A2A, TaskType.BWD_A2A):
+        return type_abbrev
+
+    parts = " + ".join(f"S{b.stage_id} M{b.mb_idx}" for b in chunk.batches)
     return f"{type_abbrev} {parts}"
 
 
+def compute_critical_path(dag: TaskDAG) -> set:
+    """Return the set of UIDs of nodes on the critical path of *dag*.
+
+    Uses both data-dependency and temporal edges.  Nodes whose ``runtime``
+    field is ``None`` are treated as having zero duration (so they appear on
+    the critical path only when they sit between two critical nodes).
+
+    Returns:
+        A ``set[int]`` of ``node.uid`` values for every critical node.
+    """
+    from collections import deque as _deque2
+
+    # Topological sort (Kahn's algorithm over both edge kinds).
+    in_deg = {n.uid: len(n.data_preds) + len(n.temporal_preds) for n in dag.nodes}
+    queue = _deque2(n for n in dag.nodes if in_deg[n.uid] == 0)
+    topo: list = []
+    while queue:
+        node = queue.popleft()
+        topo.append(node)
+        for s in list(node.data_succs) + list(node.temporal_succs):
+            in_deg[s.uid] -= 1
+            if in_deg[s.uid] == 0:
+                queue.append(s)
+
+    # Forward pass: EST[uid] = earliest start time.
+    est: dict = {}
+    for node in topo:
+        preds = list(node.data_preds) + list(node.temporal_preds)
+        est[node.uid] = max(
+            (est[p.uid] + (p.runtime or 0.0) for p in preds),
+            default=0.0,
+        )
+
+    makespan = max(est[n.uid] + (n.runtime or 0.0) for n in dag.nodes)
+
+    # Backward pass: LST[uid] = latest start time that doesn't extend makespan.
+    lst: dict = {}
+    for node in reversed(topo):
+        w = node.runtime or 0.0
+        succs = list(node.data_succs) + list(node.temporal_succs)
+        lst[node.uid] = (
+            min(lst[s.uid] for s in succs) - w if succs else makespan - w
+        )
+
+    _EPS = 1e-6
+    return {n.uid for n in dag.nodes if abs(lst[n.uid] - est[n.uid]) < _EPS}
+
+
 def visualize_dag(
     dag: TaskDAG,
     output_path: str = "dag",
     fmt: str = "png",
+    critical_path_nodes: "set | None" = None,
 ) -> None:
     """Render a :class:`TaskDAG` as a labelled image using *graphviz*.
 
@@ -1860,6 +2076,14 @@ def visualize_dag(
     * **Dashed grey** – temporal edges (serialisation within one actor).
     * **Solid black** – data-dependency edges.
 
+    Args:
+        dag: The task DAG to render.
+        output_path: Path prefix for the output file (no extension).
+        fmt: Output format passed to graphviz (``"png"``, ``"svg"``, …).
+        critical_path_nodes: Optional set of ``node.uid`` integers.  Nodes
+            whose uid is in this set are outlined in red with a thick border.
+            If ``None`` no critical-path highlighting is applied.
+
     The image is saved to ``{output_path}.{fmt}``.
     If *graphviz* is not installed the function logs a warning and returns.
     """
@@ -1876,9 +2100,9 @@ def visualize_dag(
     # Build a mapping from pp_rank -> sorted list of stage_ids on that rank.
     pp_rank_stages: dict[int, list[int]] = {}
     for n in dag.nodes:
-        if n.task.batches:
-            rank = n.task.pp_rank
-            sid = n.task.batches[0].stage_id
+        if n.chunk.batches:
+            rank = n.chunk.pp_rank
+            sid = n.chunk.batches[0].stage_id
             if rank not in pp_rank_stages:
                 pp_rank_stages[rank] = []
             if sid not in pp_rank_stages[rank]:
@@ -1890,8 +2114,8 @@ def visualize_dag(
         TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W, TaskType.FWD_BWD,
     )
 
-    def _node_fill(node: TaskNode) -> str:
-        t = node.task.type
+    def _node_fill(node: Task) -> str:
+        t = node.chunk.type
         if t in (TaskType.SEND, TaskType.RECV, TaskType.FWD_A2A, TaskType.BWD_A2A, TaskType.ALL_REDUCE):
             return "#FFFFFF"
         if t == TaskType.FWD:
@@ -1902,15 +2126,15 @@ def _node_fill(node: TaskNode) -> str:
             return "#2E86C1"  # blue
         return "#D5D8DC"
 
-    def _node_fontcolor(node: TaskNode) -> str:
-        t = node.task.type
-        if t not in _COMPUTE_TYPES or not node.task.batches:
+    def _node_fontcolor(node: Task) -> str:
+        t = node.chunk.type
+        if t not in _COMPUTE_TYPES or not node.chunk.batches:
             return "black"
-        rank = node.task.pp_rank
+        rank = node.chunk.pp_rank
         stages = pp_rank_stages.get(rank, [])
         if len(stages) < 2:
             return "black"
-        sid = node.task.batches[0].stage_id
+        sid = node.chunk.batches[0].stage_id
         # First stage -> black text, second (and beyond) stage -> white text
         return "black" if sid == stages[0] else "white"
 
@@ -1932,44 +2156,31 @@ def _node_fontcolor(node: TaskNode) -> str:
             if _in_degree[_s.node_id()] == 0:
                 _queue.append(_s)
 
-    dot = graphviz.Digraph("PiperDAG", comment="Piper Task DAG")
+    dot = graphviz.Digraph("PiperDAG", comment="Piper Chunk DAG")
     dot.attr(rankdir="LR", splines="ortho", nodesep="0.4", ranksep="0.6", fontname="Helvetica")
     dot.attr("node", shape="box", style="filled", fontsize="9", fontname="Helvetica")
     dot.attr("edge", fontsize="8", fontname="Helvetica")
 
     for node in dag.nodes:
         label = f"{node.time_step}\n{_task_node_label(node)}"
+        if node.runtime is not None:
+            label += f"\n{node.runtime:.1f}ms"
+        on_critical = critical_path_nodes is not None and node.uid in critical_path_nodes
         dot.node(
             node.node_id(),
             label=label,
             fillcolor=_node_fill(node),
             fontcolor=_node_fontcolor(node),
-            tooltip=repr(node.task),
+            tooltip=repr(node.chunk),
+            color="red" if on_critical else "black",
+            penwidth="3.0" if on_critical else "1.0",
         )
 
-    # Assign column positions so that every temporal edge spans exactly one column.
-    # Assign column positions via BFS from temporal roots.
-    # When the temporal graph has forks/joins (after overlap_a2a_tasks), use
-    # max-predecessor depth to place join nodes correctly.
-    # SEND/RECV/AR nodes have no temporal edges and are left unranked.
-    _tdepth: dict[str, int] = {}
-    _tq: _deque = _deque()
-    for _n in dag.nodes:
-        if not _n.temporal_preds and _n.temporal_succs:
-            _tdepth[_n.node_id()] = 0
-            _tq.append(_n)
-    while _tq:
-        _n = _tq.popleft()
-        for _s in _n.temporal_succs:
-            new_d = _tdepth[_n.node_id()] + 1
-            if _s.node_id() not in _tdepth or _tdepth[_s.node_id()] < new_d:
-                _tdepth[_s.node_id()] = new_d
-                _tq.append(_s)
-
-    depth_to_nodes: dict[int, list[TaskNode]] = defaultdict(list)
+    # Group nodes by time_step so that tasks scheduled at the same step share
+    # a column (graphviz rank="same").
+    depth_to_nodes: dict[int, list[Task]] = defaultdict(list)
     for node in dag.nodes:
-        if node.node_id() in _tdepth:
-            depth_to_nodes[_tdepth[node.node_id()]].append(node)
+        depth_to_nodes[node.time_step].append(node)
 
     for col_nodes in depth_to_nodes.values():
         with dot.subgraph() as sub:
@@ -2005,19 +2216,6 @@ def _node_fontcolor(node: TaskNode) -> str:
     logger.info(f"DAG visualisation saved to {out}")
 
 
-def visualize_schedule(
-    schedule: Schedule2D,
-    output_path: str = "schedule",
-    fmt: str = "png",
-) -> None:
-    """Render a :class:`Schedule2D` as a labelled DAG image using *graphviz*.
-
-    Converts the schedule to a :class:`TaskDAG` via :func:`schedule_to_dag`
-    then delegates to :func:`visualize_dag`.
-    """
-    dag = schedule_to_dag(schedule)
-    visualize_dag(dag, output_path=output_path, fmt=fmt)
-
 
 def print_dag_order(dag: TaskDAG, label: str = "") -> None:
     """Print the topological execution order of nodes in a :class:`TaskDAG`.
@@ -2038,11 +2236,11 @@ def print_dag_order(dag: TaskDAG, label: str = "") -> None:
     step = 0
     while queue:
         node = queue.popleft()
-        task = node.task
-        ttype = task.type.value if task.type is not None else "?"
+        chunk = node.chunk
+        ttype = chunk.type.value if chunk.type is not None else "?"
         batches_str = ", ".join(
-            f"s{b.stage_id} mb{b.mb_idx}" for b in task.batches
-        ) if task.batches else ""
+            f"s{b.stage_id} mb{b.mb_idx}" for b in chunk.batches
+        ) if chunk.batches else ""
         bkt = f" bkt={node.bucket_id}" if node.bucket_id else ""
         print(f"  {step:3d}  rank={node.pp_rank}  {ttype:<14s}  {batches_str}{bkt}")
         step += 1
@@ -2915,181 +3113,132 @@ def _add_ph(nd: fx.Node, name: str, is_input: bool) -> None:
 
 
 # ---------------------------------------------------------------------------
-# expand_a2a_tasks: insert FWD_A2A / BWD_A2A nodes into the task DAG
+# find_overlappable_tasks: identify adjacent task pairs safe to overlap
 # ---------------------------------------------------------------------------
 
-def expand_a2a_tasks(dag: TaskDAG, a2a_info: dict) -> TaskDAG:
-    """Insert FWD_A2A / BWD_A2A task nodes into the DAG between adjacent bucket
-    nodes at all-to-all boundaries.
+def find_overlappable_tasks(schedule: PipelineSchedule) -> list[tuple[Chunk, Chunk]]:
+    """Find pairs of adjacent tasks in the schedule that can be safely overlapped.
 
-    Must be called *after* :func:`expand_bucket_tasks`.
+    A pair ``(t1, t2)`` where t1 precedes t2 on the same pp_rank is overlappable
+    iff all three conditions hold:
 
-    For the forward pass, between FWD(bucket_id=i) and
-    FWD(bucket_id=i+1) a FWD_A2A node is inserted.
+    1. **No data dependency**: t1 and t2 process entirely different microbatches.
+    2. **Upstream deps satisfied before overlap start**: the upstream tasks of *both*
+       t1 and t2 complete by ``min(start(t1), start(t2))``.
+    3. **Downstream deps deferred past overlap end**: the downstream tasks of *both*
+       t1 and t2 start no earlier than ``max(end(t1), end(t2))``.
 
-    For the backward pass (which runs in reverse bucket order), between
-    BWD(bucket_id=i+1) and BWD(bucket_id=i) a BWD_A2A node is
-    inserted.
+    Conditions 2 (for t1) and 3 (for t2) are trivially satisfied by any valid
+    schedule, so the effective non-trivial checks are:
+
+    * ``upstream_end(t2) <= min(start(t1), start(t2))``
+    * ``downstream_start(t1) >= max(end(t1), end(t2))``
 
     Args:
-        dag: TaskDAG produced by :func:`expand_bucket_tasks`.
-        a2a_info: Mapping ``stage_id -> {boundary_bucket_id -> tensor_idx}``.
-            Only stages present here are modified.
+        schedule: The pipeline schedule (``list[list[Chunk]]``, no Nones).
 
     Returns:
-        A new TaskDAG with inserted FWD_A2A / BWD_A2A nodes.
+        A list of ``(t1, t2)`` :class:`Chunk` pairs suitable for passing to
+        :func:`overlap_a2a_tasks`.
     """
-    if not a2a_info:
-        return dag
-
-    # Index FWD/BWD/BWD_I nodes by (stage_id, mb_idx, type, bucket_id)
-    bucket_node_map: dict = {}
-    for node in dag.nodes:
-        t = node.task
-        if t.type in (TaskType.FWD, TaskType.BWD, TaskType.BWD_I):
-            sid = t.batches[0].stage_id
-            mid = t.batches[0].mb_idx
-            bucket_node_map[(sid, mid, t.type, node.bucket_id)] = node
-
-    new_nodes: list[TaskNode] = []
-
-    for node in dag.nodes:
-        t = node.task
-        if t.type not in (TaskType.FWD, TaskType.BWD, TaskType.BWD_I):
-            continue
-        sid = t.batches[0].stage_id
-        if sid not in a2a_info:
-            continue
-        boundaries = a2a_info[sid]
-
-        if t.type == TaskType.FWD:
-            # Insert FWD_A2A after FWD(bucket_id) if it's a boundary
-            bkt_id = node.bucket_id
-            if bkt_id not in boundaries:
+    start_times_per_rank = schedule._compute_start_times()
+
+    # chunk_id -> (logical_start, logical_end)
+    chunk_times: dict[int, tuple[int, int]] = {}
+    for row, starts in zip(schedule.grid, start_times_per_rank):
+        for chunk, start in zip(row, starts):
+            chunk_times[id(chunk)] = (start, start + logical_time(chunk))
+
+    # mb_idx -> sorted list of (start, end) for every chunk across all ranks that
+    # touches that microbatch.
+    mb_timeline: dict[int, list[tuple[int, int]]] = defaultdict(list)
+    for row in schedule.grid:
+        for chunk in row:
+            start, end = chunk_times[id(chunk)]
+            for batch in chunk.batches:
+                mb_timeline[batch.mb_idx].append((start, end))
+    for mb_idx in mb_timeline:
+        mb_timeline[mb_idx].sort()
+
+    def _upstream_end(chunk: Chunk, chunk_start: int) -> int | None:
+        """Latest end time of any chunk that starts before *chunk_start* and shares
+        a microbatch with *chunk*, or ``None`` if no such chunk exists."""
+        best: int | None = None
+        for batch in chunk.batches:
+            for s, e in mb_timeline.get(batch.mb_idx, []):
+                if s < chunk_start:
+                    best = e if best is None else max(best, e)
+        return best
+
+    def _downstream_start(chunk: Chunk, chunk_start: int) -> int | None:
+        """Earliest start time of any chunk that starts after *chunk_start* and
+        shares a microbatch with *chunk*, or ``None`` if no such chunk exists."""
+        best: int | None = None
+        for batch in chunk.batches:
+            for s, e in mb_timeline.get(batch.mb_idx, []):
+                if s > chunk_start:
+                    best = s if best is None else min(best, s)
+        return best
+
+    result: list[tuple[Chunk, Chunk]] = []
+
+    for row, starts in zip(schedule.grid, start_times_per_rank):
+        for i in range(len(row) - 1):
+            t1, t2 = row[i], row[i + 1]
+            start1, end1 = chunk_times[id(t1)]
+            start2, end2 = chunk_times[id(t2)]
+
+            overlap_start = min(start1, start2)
+            overlap_end = max(end1, end2)
+
+            # Condition 1: no shared microbatch.
+            mb1 = {b.mb_idx for b in t1.batches}
+            mb2 = {b.mb_idx for b in t2.batches}
+            if mb1 & mb2:
                 continue
-            tensor_idx = boundaries[bkt_id]
-            mb_idx = t.batches[0].mb_idx
 
-            # Find the successor FWD(bucket_id+1)
-            next_bkt_key = (sid, mb_idx, TaskType.FWD, bkt_id + 1)
-            if next_bkt_key not in bucket_node_map:
+            # Condition 2 (non-trivial part): t2's upstream must finish by overlap_start.
+            up2 = _upstream_end(t2, start2)
+            if up2 is not None and up2 > overlap_start:
                 continue
-            next_node = bucket_node_map[next_bkt_key]
-
-            a2a_node = TaskNode(
-                task=Task(
-                    pp_rank=node.pp_rank,
-                    batches=list(t.batches),
-                    type=TaskType.FWD_A2A,
-                ),
-                pp_rank=node.pp_rank,
-                time_step=0,  # assigned later by assign_time_steps
-                bucket_id=bkt_id,
-                a2a_tensor_idx=tensor_idx,
-            )
 
-            # Rewire data: node -> a2a_node -> next_node  (replacing node -> next_node)
-            if next_node in node.data_succs:
-                node.data_succs.remove(next_node)
-            if node in next_node.data_preds:
-                next_node.data_preds.remove(node)
-
-            node.data_succs.append(a2a_node)
-            a2a_node.data_preds.append(node)
-            a2a_node.data_succs.append(next_node)
-            next_node.data_preds.append(a2a_node)
-
-            # Splice into temporal chain: node → a2a_node → next_node
-            node.temporal_succs.remove(next_node)
-            next_node.temporal_preds.remove(node)
-            node.temporal_succs.append(a2a_node)
-            a2a_node.temporal_preds.append(node)
-            a2a_node.temporal_succs.append(next_node)
-            next_node.temporal_preds.append(a2a_node)
-
-            new_nodes.append(a2a_node)
-
-        else:  # BWD / BWD_I with bucket_id
-            # Insert BWD_A2A after BWD/BWD_I(bucket_id+1) if there's a boundary there
-            # (i.e. between bkt_id+1 and bkt_id in backward execution order)
-            bkt_id = node.bucket_id
-            if bkt_id not in boundaries:
+            # Condition 3 (non-trivial part): t1's downstream must not start until overlap_end.
+            down1 = _downstream_start(t1, start1)
+            if down1 is not None and down1 < overlap_end:
                 continue
-            # This BWD/BWD_I(bkt_id) is the later node in the backward chain.
-            # We need to insert BWD_A2A between BWD/BWD_I(bkt_id+1) and BWD/BWD_I(bkt_id).
-            # The predecessor in the data chain is BWD/BWD_I(bkt_id+1).
-            tensor_idx = boundaries[bkt_id]
-            mb_idx = t.batches[0].mb_idx
-
-            prev_bkt_key = (sid, mb_idx, t.type, bkt_id + 1)
-            if prev_bkt_key not in bucket_node_map:
-                continue
-            prev_node = bucket_node_map[prev_bkt_key]
-
-            a2a_node = TaskNode(
-                task=Task(
-                    pp_rank=node.pp_rank,
-                    batches=list(t.batches),
-                    type=TaskType.BWD_A2A,
-                ),
-                pp_rank=node.pp_rank,
-                time_step=0,  # assigned later by assign_time_steps
-                bucket_id=bkt_id,
-                a2a_tensor_idx=tensor_idx,
-            )
-
-            # Rewire data: prev_node -> a2a_node -> node  (replacing prev_node -> node)
-            if node in prev_node.data_succs:
-                prev_node.data_succs.remove(node)
-            if prev_node in node.data_preds:
-                node.data_preds.remove(prev_node)
-
-            prev_node.data_succs.append(a2a_node)
-            a2a_node.data_preds.append(prev_node)
-            a2a_node.data_succs.append(node)
-            node.data_preds.append(a2a_node)
 
-            # Splice into temporal chain: prev_node → a2a_node → node
-            prev_node.temporal_succs.remove(node)
-            node.temporal_preds.remove(prev_node)
-            prev_node.temporal_succs.append(a2a_node)
-            a2a_node.temporal_preds.append(prev_node)
-            a2a_node.temporal_succs.append(node)
-            node.temporal_preds.append(a2a_node)
+            result.append((t1, t2))
 
-            new_nodes.append(a2a_node)
-
-    all_nodes = list(dag.nodes) + new_nodes
-    return TaskDAG(nodes=all_nodes)
+    return result
 
 # ---------------------------------------------------------------------------
 # Overlap compute and A2A tasks across consecutive task pairs
 # ---------------------------------------------------------------------------
 
-def _find_task_chain_endpoints(dag: TaskDAG, task: Task) -> tuple[TaskNode, TaskNode]:
-    """Return (first_node, last_node) in the temporal chain for the given task.
+def _find_task_chain_endpoints(dag: TaskDAG, chunk: Chunk) -> tuple[Task, Task]:
+    """Return (first_node, last_node) in the temporal chain for the given chunk.
 
     "First" is the node in the group none of whose temporal_preds are also in
     the group; "last" is the node none of whose temporal_succs are in the group.
 
-    The "group" for a task is all DAG nodes sharing the same pp_rank,
-    stage_id, mb_idx, and either the task's compute type or its paired A2A type.
+    The "group" for a chunk is all DAG nodes sharing the same pp_rank,
+    stage_id, mb_idx, and either the chunk's compute type or its paired A2A type.
     """
-    stage_id = task.batches[0].stage_id
-    mb_idx   = task.batches[0].mb_idx
-    compute_type = task.type
+    stage_id = chunk.batches[0].stage_id
+    mb_idx   = chunk.batches[0].mb_idx
+    compute_type = chunk.type
     a2a_type = TaskType.FWD_A2A if compute_type == TaskType.FWD else TaskType.BWD_A2A
 
     group = [
         n for n in dag.nodes
-        if n.pp_rank == task.pp_rank
-        and n.task.batches
-        and n.task.batches[0].stage_id == stage_id
-        and n.task.batches[0].mb_idx   == mb_idx
-        and n.task.type in (compute_type, a2a_type)
+        if n.pp_rank == chunk.pp_rank
+        and n.chunk.batches
+        and n.chunk.batches[0].stage_id == stage_id
+        and n.chunk.batches[0].mb_idx   == mb_idx
+        and n.chunk.type in (compute_type, a2a_type)
     ]
     assert group, (
-        f"No DAG nodes found for task pp_rank={task.pp_rank} "
+        f"No DAG nodes found for chunk pp_rank={chunk.pp_rank} "
         f"stage={stage_id} mb={mb_idx} type={compute_type}"
     )
 
@@ -3105,30 +3254,35 @@ def _find_task_chain_endpoints(dag: TaskDAG, task: Task) -> tuple[TaskNode, Task
     return first, last
 
 
-def overlap_a2a_tasks(dag: TaskDAG, task_pairs: list[tuple[Task, Task]]) -> TaskDAG:
+def overlap_a2a_tasks(dag: TaskDAG, task_pairs: list[tuple[Chunk, Chunk]]) -> TaskDAG:
     """Overlap compute and A2A tasks across each consecutive task pair.
 
     For each pair ``(t1, t2)`` the pre-transform temporal chain contains:
 
         T1_first → … → T1_last → T2_first → … → T2_last
 
-    This function rewires the temporal dependencies so that T2 starts as soon
-    as T1 starts (not after T1 finishes), while still requiring T2_last to
-    complete after T1_last:
+    where each chain has the structure ``C_0 → A2A_0 → C_1 → A2A_1 → … → C_n``
+    with ``C_i`` compute nodes and ``A2A_i`` communication nodes.
+
+    This function rewires the temporal dependencies to interleave the two chains
+    at each A2A boundary:
 
-      1. Remove  T1_last  → T2_first  (from temporal_succs / temporal_preds)
-      2. Add     T1_first → T2_first  (appended to temporal_succs / temporal_preds)
-      3. Add     T1_last  → T2_last   (appended to temporal_succs / temporal_preds)
+      1. Remove  T1_last → T2_first.
+      2. Traverse T1 and T2 alternately.  On each T1 turn, advance T1_current
+         through non-communication nodes until the predecessor of the next A2A
+         (or until the chain is exhausted).  If an A2A was found, add a temporal
+         edge T1_current → T2_current and advance T1_current past the A2A.  Then
+         do the symmetric operation on T2, adding T2_current → T1_current and
+         advancing past T2's A2A.  Repeat until both chains are exhausted (no
+         remaining A2A on either side).
 
-    After :func:`assign_time_steps` (which now does a topological sort), nodes
-    in T1 and T2 that have no other dependency between them receive the *same*
-    ``time_step``, allowing the actor to dispatch them to their respective CUDA
-    streams concurrently — e.g. T1's A2A overlaps with T2's compute and vice
-    versa.
+    The resulting structure allows the actor to overlap A2A communication in one
+    chain with compute in the other chain by dispatching them to separate CUDA
+    streams.
 
     Args:
-        dag: TaskDAG produced by :func:`expand_a2a_tasks`.
-        task_pairs: List of ``(t1, t2)`` pairs where each ``Task`` identifies
+        dag: TaskDAG produced by :func:`expand_chunks_to_dags`.
+        task_pairs: List of ``(t1, t2)`` pairs where each ``Chunk`` identifies
             a compute-task group in the DAG by its pp_rank, stage_id, mb_idx,
             and type.  The two tasks in each pair must be on the same pp_rank
             and T2 must immediately follow T1 in the temporal chain.
@@ -3136,6 +3290,34 @@ def overlap_a2a_tasks(dag: TaskDAG, task_pairs: list[tuple[Task, Task]]) -> Task
     Returns:
         The same ``dag`` object with temporal edges mutated in-place.
     """
+
+    def _is_comm(node: Task) -> bool:
+        return node.chunk.type in (TaskType.FWD_A2A, TaskType.BWD_A2A)
+
+    def _chain_next(node: Task, group_ids: set[int]) -> Task | None:
+        """Return the unique in-group temporal successor of *node*, or None."""
+        succs = [s for s in node.temporal_succs if id(s) in group_ids]
+        assert len(succs) <= 1, (
+            f"overlap_a2a_tasks: expected at most one in-group temporal successor "
+            f"for {node.node_id()}, got {len(succs)}: "
+            f"{[s.node_id() for s in succs]}"
+        )
+        return succs[0] if succs else None
+
+    def _get_group_ids(chunk: Chunk) -> set[int]:
+        compute_type = chunk.type
+        a2a_type = TaskType.FWD_A2A if compute_type == TaskType.FWD else TaskType.BWD_A2A
+        stage_id = chunk.batches[0].stage_id
+        mb_idx = chunk.batches[0].mb_idx
+        return {
+            id(n) for n in dag.nodes
+            if n.pp_rank == chunk.pp_rank
+            and n.chunk.batches
+            and n.chunk.batches[0].stage_id == stage_id
+            and n.chunk.batches[0].mb_idx == mb_idx
+            and n.chunk.type in (compute_type, a2a_type)
+        }
+
     for t1, t2 in task_pairs:
         t1_first, t1_last = _find_task_chain_endpoints(dag, t1)
         t2_first, t2_last = _find_task_chain_endpoints(dag, t2)
@@ -3154,13 +3336,103 @@ def overlap_a2a_tasks(dag: TaskDAG, task_pairs: list[tuple[Task, Task]]) -> Task
         t1_last.temporal_succs.remove(t2_first)
         t2_first.temporal_preds.remove(t1_last)
 
-        # 2. Add T1_first → T2_first
-        t1_first.temporal_succs.append(t2_first)
-        t2_first.temporal_preds.append(t1_first)
-
-        # 3. Add T1_last → T2_last (merged join point)
-        t1_last.temporal_succs.append(t2_last)
-        t2_last.temporal_preds.append(t1_last)
+        t1_group_ids = _get_group_ids(t1)
+        t2_group_ids = _get_group_ids(t2)
+
+        t1_current = t1_first
+        t2_current = t2_first
+        seen_t1_last = False
+        seen_t2_last = False
+
+        while not (seen_t1_last and seen_t2_last):
+            # --- T1 phase ---
+            if not seen_t1_last:
+                # Advance t1_current to the compute node just before the next A2A.
+                nxt = _chain_next(t1_current, t1_group_ids)
+                while nxt is not None and not _is_comm(nxt):
+                    t1_current = nxt
+                    nxt = _chain_next(t1_current, t1_group_ids)
+
+                if nxt is None:
+                    # No A2A remaining in T1; t1_current is at T1_last.
+                    # Create the dep only if T2 has not yet finished: if T2 is
+                    # already done, an earlier T1 node already has a dep to
+                    # T2_last so adding T1_last → T2_last would be redundant.
+                    if not seen_t2_last:
+                        # Capture pre-existing succs before mutating.
+                        t1_pre_succs = list(t1_current.temporal_succs)
+                        t1_current.temporal_succs.append(t2_current)
+                        t2_current.temporal_preds.append(t1_current)
+                        # Propagate T1_last's pre-existing outgoing deps to
+                        # T2_last so that whatever follows T1 in the sequential
+                        # schedule also waits for T2_last (the longer chain).
+                        # Remove the original dep from T1_last since T2_last
+                        # now takes over responsibility for that ordering.
+                        for succ in t1_pre_succs:
+                            t1_current.temporal_succs.remove(succ)
+                            succ.temporal_preds.remove(t1_current)
+                            if succ not in t2_last.temporal_succs:
+                                t2_last.temporal_succs.append(succ)
+                                succ.temporal_preds.append(t2_last)
+                    seen_t1_last = True
+                else:
+                    # t1_current is the compute predecessor of comm node nxt.
+                    # Only create T1_current → T2_current if T2 has not already
+                    # reached its end; otherwise the dep would form a cycle
+                    # (T2's final dep points back into T1's chain).
+                    if not seen_t2_last:
+                        t1_current.temporal_succs.append(t2_current)
+                        t2_current.temporal_preds.append(t1_current)
+                    # Advance T1_current past the A2A node.
+                    after_comm = _chain_next(nxt, t1_group_ids)
+                    assert after_comm is not None and not _is_comm(after_comm), (
+                        f"overlap_a2a_tasks: expected a compute node after T1 A2A "
+                        f"{nxt.node_id()}, got "
+                        f"{after_comm.node_id() if after_comm else None}"
+                    )
+                    t1_current = after_comm
+
+            # --- T2 phase ---
+            if not seen_t2_last:
+                # Advance t2_current to the compute node just before the next A2A.
+                nxt = _chain_next(t2_current, t2_group_ids)
+                while nxt is not None and not _is_comm(nxt):
+                    t2_current = nxt
+                    nxt = _chain_next(t2_current, t2_group_ids)
+
+                if nxt is None:
+                    # No A2A remaining in T2; t2_current is at T2_last.
+                    # Only act if T1 is still running (seen_t1_last=False),
+                    # meaning T2 has fewer A2As than T1.  If seen_t1_last is
+                    # already True the chains had equal A2A counts and T1's
+                    # end-of-chain already established the ordering; creating
+                    # a dep here would form a cycle.
+                    if not seen_t1_last:
+                        t2_pre_succs = list(t2_current.temporal_succs)
+                        t2_current.temporal_succs.append(t1_current)
+                        t1_current.temporal_preds.append(t2_current)
+                        for succ in t2_pre_succs:
+                            t2_current.temporal_succs.remove(succ)
+                            succ.temporal_preds.remove(t2_current)
+                            if succ not in t1_last.temporal_succs:
+                                t1_last.temporal_succs.append(succ)
+                                succ.temporal_preds.append(t1_last)
+                    seen_t2_last = True
+                else:
+                    # Only create T2_current → T1_current if T1 has not already
+                    # reached its end; otherwise the dep would form a cycle
+                    # (T1's end-of-chain dep already points into T2's chain).
+                    if not seen_t1_last:
+                        t2_current.temporal_succs.append(t1_current)
+                        t1_current.temporal_preds.append(t2_current)
+                    # Advance T2_current past the A2A node.
+                    after_comm = _chain_next(nxt, t2_group_ids)
+                    assert after_comm is not None and not _is_comm(after_comm), (
+                        f"overlap_a2a_tasks: expected a compute node after T2 A2A "
+                        f"{nxt.node_id()}, got "
+                        f"{after_comm.node_id() if after_comm else None}"
+                    )
+                    t2_current = after_comm
 
     return dag
 
@@ -3173,8 +3445,8 @@ def assign_time_steps(dag: TaskDAG) -> None:
     """Assign ``time_step`` to every node based on its position in the temporal
     dependency graph.
 
-    Must be called after :func:`expand_a2a_tasks` (and
-    :func:`overlap_a2a_tasks` if used) and before :func:`insert_p2p_ops`.
+    Must be called after :func:`add_temporal_dependencies` (and
+    :func:`overlap_a2a_tasks` if used) and before :func:`split_dag_by_rank`.
 
     Each node's ``time_step`` is ``max(pred.time_step for all temporal preds) + 1``,
     with roots (no temporal predecessors) receiving ``time_step = 0``.  When the
@@ -3183,9 +3455,9 @@ def assign_time_steps(dag: TaskDAG) -> None:
     topological sort correctly assigns shared time steps to concurrent nodes
     and deferred time steps to join points.
 
-    SEND / RECV / ALL_REDUCE nodes are added later by :func:`insert_p2p_ops`
-    and :func:`insert_ar_ops`; they receive ``time_step = adjacent_node ± 1``
-    at insertion time.
+    SEND / RECV nodes have no temporal edges and are assigned after the toposort:
+    ``SEND.time_step = data_pred.time_step + 1`` and
+    ``RECV.time_step = data_succ.time_step - 1``.
     """
     # Kahn's toposort over the temporal graph (temporal_preds / temporal_succs).
     # time_step = max(pred.time_step for all temporal preds) + 1.
@@ -3193,7 +3465,7 @@ def assign_time_steps(dag: TaskDAG) -> None:
     # assignment; forks and joins from overlap_a2a_tasks are handled correctly.
     in_degree: dict[int, int] = {id(n): len(n.temporal_preds) for n in dag.nodes}
     ts_map: dict[int, int] = {}
-    queue: deque[TaskNode] = deque()
+    queue: deque[Task] = deque()
     for node in dag.nodes:
         if in_degree[id(node)] == 0:
             ts_map[id(node)] = 0
@@ -3207,3 +3479,21 @@ def assign_time_steps(dag: TaskDAG) -> None:
             ts_map[id(succ)] = max(ts_map.get(id(succ), 0), node.time_step + 1)
             if in_degree[id(succ)] == 0:
                 queue.append(succ)
+
+    # SEND/RECV/ALL_REDUCE nodes have no temporal edges and are skipped by the toposort.
+    # Assign their time_step relative to their data-edge neighbour.
+
+    # Pass 1: SEND and ALL_REDUCE (depend only on their data predecessor).
+    for node in dag.nodes:
+        if node.chunk.type == TaskType.SEND:
+            assert len(node.data_preds) == 1
+            node.time_step = node.data_preds[0].time_step + 1
+        elif node.chunk.type == TaskType.ALL_REDUCE:
+            assert len(node.data_preds) == 1
+            node.time_step = node.data_preds[0].time_step + 1
+
+    # Pass 2: RECV (depend only on their data successor).
+    for node in dag.nodes:
+        if node.chunk.type == TaskType.RECV:
+            assert len(node.data_succs) == 1
+            node.time_step = node.data_succs[0].time_step - 1
diff --git a/src/piper_utils.py b/src/piper_utils.py
index 6576999..84fea93 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -10,7 +10,7 @@
 from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
 from typing import Any, Optional
 
-LOG_LEVEL = "DEBUG"
+LOG_LEVEL = "INFO"
 
 """ 
 Print the backward graph of a tensor
@@ -337,8 +337,9 @@ class PiperMetadata:
     naive_gradient_sync = False
     use_activation_checkpointing = False
     bucketing = False  # Whether to split stages into per-param-bucket sub-modules
-    schedule = None   # Schedule2D set by piper_setup; used by the piper backend
+    schedule = None   # PipelineSchedule set by piper_setup; used by the piper backend
     task_dag = None   # TaskDAG built from schedule by the piper backend
+    full_dag_no_overlap = None  # Deep copy of the full DAG (pre-P2P-split, no overlap_a2a_tasks); used for profiling and critical-path analysis
     per_rank_dags = None  # Per-rank TaskDAGs built by the piper backend
     stage_bucket_counts: dict = {}   # stage_id -> number of buckets (set by piper backend)
     trainable_bucket_keys: set = set()  # (stage_id, bucket_id) pairs with trainable params
@@ -346,10 +347,83 @@ class PiperMetadata:
     # so they can skip torch.compile entirely.  Contains serialized graph + param metadata
     # (no actual weight values) for all stages, plus the built per-rank TaskDAGs.
     compiled_stage_data: dict = None
+    # MFU tracking: set by piper_setup when model_flops_per_token is provided
+    model_flops_per_token: Optional[float] = None  # FLOPs per token for forward+backward pass
+    tokens_per_step: Optional[int] = None           # Global batch tokens per training step
+    visualize_dag: bool = True  # Whether to render per-rank DAG PNGs after compilation
 
 piper_metadata = PiperMetadata()
 
 
+# ---------------------------------------------------------------------------
+# GPU peak FLOPs lookup for MFU computation
+# ---------------------------------------------------------------------------
+
+# Theoretical peak BF16 tensor-core FLOPs/s for common GPUs.
+# Values are from official NVIDIA spec sheets.
+_GPU_PEAK_FLOPS_BF16: list[tuple[str, float]] = [
+    ("nvidia h200",      1979e12),
+    ("h100 sxm",   989e12),
+    ("h100 pcie",  756e12),
+    ("h100",       989e12),
+    ("a100",       312e12),
+    ("a6000 ada",  362e12),
+    ("rtx 4090",   165e12),
+    ("rtx 3090",   142e12),
+]
+
+
+def get_gpu_peak_flops_bf16() -> Optional[float]:
+    """Return theoretical peak BF16 FLOPs/s for the current CUDA device, or None if unknown."""
+    if not torch.cuda.is_available():
+        return None
+    name = torch.cuda.get_device_properties(torch.cuda.current_device()).name.lower()
+    for key, flops in _GPU_PEAK_FLOPS_BF16:
+        if key in name:
+            return flops
+    return 1979e12  # Default to H200-level FLOPs for unknown devices, since it's better to slightly overestimate than underestimate
+
+
+def compute_transformer_flops_per_token(
+    hidden_dim: int,
+    n_layers: int,
+    ffn_dim: int,
+    n_heads: int,
+    n_kv_heads: int,
+    head_dim: int,
+    activation_checkpointing: bool = False,
+    moe_top_k: int = 1,
+) -> float:
+    """Estimate FLOPs per token for a forward+backward pass of a transformer.
+
+    Uses the standard approximation (sequence-length-independent terms only):
+    - Attention: Q/K/V projections + output projection per layer
+    - FFN (SwiGLU): gate + up + down projections × moe_top_k active experts
+    - Multiply by 3× for fwd+bwd (or 4× with full activation-checkpointing recompute)
+
+    Args:
+        hidden_dim: Model hidden dimension (H).
+        n_layers: Number of transformer layers.
+        ffn_dim: FFN intermediate dimension (per expert for MoE).
+        n_heads: Number of attention heads.
+        n_kv_heads: Number of key/value heads (GQA).
+        head_dim: Dimension per attention head.
+        activation_checkpointing: If True, use 4× multiplier (full recompute).
+        moe_top_k: Number of active experts per token (1 for dense).
+
+    Returns:
+        Estimated FLOPs per token (float).
+    """
+    kv_hidden = n_kv_heads * head_dim
+    # Q proj (H→H) + K proj (H→kv_hidden) + V proj (H→kv_hidden) + out proj (H→H)
+    attn_flops = 2 * hidden_dim * (2 * hidden_dim + 2 * kv_hidden)
+    # SwiGLU FFN: gate_proj + up_proj + down_proj (each H×ffn_dim), scaled by active experts
+    ffn_flops = 6 * hidden_dim * ffn_dim * moe_top_k
+    fwd_total = n_layers * (attn_flops + ffn_flops)
+    multiplier = 4 if activation_checkpointing else 3
+    return fwd_total * multiplier
+
+
 """
 Serialize/deserialize an fx.GraphModule
 """
diff --git a/test/models/llama.py b/test/models/llama.py
index 58cbe2a..288b03f 100644
--- a/test/models/llama.py
+++ b/test/models/llama.py
@@ -420,34 +420,36 @@ def log_size(layer, indent=0):
     #     with torch.fx.traceback.annotate({"stage": 0}):
     #         h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
     #         start_pos = 0
-    #         for layer in self.layers[:self.n_layers//7]:
+    #         for layer in self.layers[:self.n_layers//8]:
     #             h = layer(h, start_pos, self.freqs_cis, self.mask)
 
     #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[self.n_layers//7:2*self.n_layers//7]:
+    #         for layer in self.layers[self.n_layers//8:2*self.n_layers//8]:
     #             h = layer(h, start_pos, self.freqs_cis, self.mask)
 
     #     with torch.fx.traceback.annotate({"stage": 2}):
-    #         for layer in self.layers[2*self.n_layers//7:3*self.n_layers//7]:
+    #         for layer in self.layers[2*self.n_layers//8:3*self.n_layers//8]:
     #             h = layer(h, start_pos, self.freqs_cis, self.mask)
 
     #     with torch.fx.traceback.annotate({"stage": 3}):
-    #         for layer in self.layers[3*self.n_layers//7:4*self.n_layers//7]:
+    #         for layer in self.layers[3*self.n_layers//8:4*self.n_layers//8]:
     #             h = layer(h, start_pos, self.freqs_cis, self.mask)
 
     #     with torch.fx.traceback.annotate({"stage": 4}):
-    #         for layer in self.layers[4*self.n_layers//7:5*self.n_layers//7]:
+    #         for layer in self.layers[4*self.n_layers//8:5*self.n_layers//8]:
     #             h = layer(h, start_pos, self.freqs_cis, self.mask)
 
     #     with torch.fx.traceback.annotate({"stage": 5}):
-    #         for layer in self.layers[5*self.n_layers//7:6*self.n_layers//7]:
+    #         for layer in self.layers[5*self.n_layers//8:6*self.n_layers//8]:
     #             h = layer(h, start_pos, self.freqs_cis, self.mask)
 
     #     with torch.fx.traceback.annotate({"stage": 6}):
-    #         for layer in self.layers[6*self.n_layers//7:]:
+    #         for layer in self.layers[6*self.n_layers//8:7*self.n_layers//8]:
     #             h = layer(h, start_pos, self.freqs_cis, self.mask)
 
     #     with torch.fx.traceback.annotate({"stage": 7}):
+    #         for layer in self.layers[7*self.n_layers//8:]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
     #         h = self.norm(h) if self.norm else h
     #         output = self.output(h).float() if self.output else h
 
@@ -480,31 +482,31 @@ def log_size(layer, indent=0):
 
     #     return output
 
-    """
-    2 STAGES
-    """
-    def forward(self, tokens: torch.Tensor):
-        with torch.fx.traceback.annotate({"stage": 0}):
-            h = self.tok_embeddings(tokens)
-            start_pos = 0
-            for layer in self.layers[:self.n_layers//2]:
-                h = layer(h, start_pos, self.freqs_cis, self.mask)
-        with torch.fx.traceback.annotate({"stage": 1}):
-            for layer in self.layers[self.n_layers//2:]:
-                h = layer(h, start_pos, self.freqs_cis, self.mask)
-            h = self.norm(h)
-            output = self.output(h).float()
-
-        return output
-
     # """
-    # No annotations: auto infer stages
+    # 2 STAGES
     # """
     # def forward(self, tokens: torch.Tensor):
-    #     h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-    #     start_pos = 0
-    #     for layer in self.layers:
-    #         h = layer(h, start_pos, self.freqs_cis, self.mask)
-    #     h = self.norm(h) if self.norm else h
-    #     output = self.output(h).float() if self.output else h
-    #     return output
\ No newline at end of file
+    #     with torch.fx.traceback.annotate({"stage": 0}):
+    #         h = self.tok_embeddings(tokens)
+    #         start_pos = 0
+    #         for layer in self.layers[:self.n_layers//2]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+    #     with torch.fx.traceback.annotate({"stage": 1}):
+    #         for layer in self.layers[self.n_layers//2:]:
+    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
+    #         h = self.norm(h)
+    #         output = self.output(h).float()
+
+    #     return output
+
+    """
+    No annotations: auto infer stages
+    """
+    def forward(self, tokens: torch.Tensor):
+        h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
+        start_pos = 0
+        for layer in self.layers:
+            h = layer(h, start_pos, self.freqs_cis, self.mask)
+        h = self.norm(h) if self.norm else h
+        output = self.output(h).float() if self.output else h
+        return output
\ No newline at end of file
diff --git a/test/models/qwen3.py b/test/models/qwen3.py
index 30e2005..0fd4cf0 100644
--- a/test/models/qwen3.py
+++ b/test/models/qwen3.py
@@ -333,12 +333,24 @@ def __init__(self, config: Qwen3ModelArgs, num_stages: int = 2):
     #     attention_masks: Optional[AttentionMasksType] = None,
     #     positions: Optional[torch.Tensor] = None,
     # ) -> torch.Tensor:
+
+    #     assert len(self.layers) == 5
         
-    #     h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
-    #     for layer in self.layers.values():
-    #         h = layer(h, self.rope_cache, attention_masks, positions)
-    #     h = self.norm(h) if self.norm is not None else h
-    #     output = self.output(h) if self.output is not None else h
+    #     with torch.fx.traceback.annotate({"stage": 0}):
+    #         h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
+    #         h = self.layers["0"](h, self.rope_cache, attention_masks, positions)
+    #         h = self.layers["1"](h, self.rope_cache, attention_masks, positions)
+
+    #     with torch.fx.traceback.annotate({"stage": 1}):
+    #         h = self.layers["2"](h, self.rope_cache, attention_masks, positions)
+
+    #     with torch.fx.traceback.annotate({"stage": 2}):
+    #         h = self.layers["3"](h, self.rope_cache, attention_masks, positions)
+
+    #     with torch.fx.traceback.annotate({"stage": 3}):
+    #         h = self.layers["4"](h, self.rope_cache, attention_masks, positions)
+    #         h = self.norm(h) if self.norm is not None else h
+    #         output = self.output(h) if self.output is not None else h
 
     #     return output
     
diff --git a/test/print_schedules.py b/test/print_schedules.py
index 9dbff23..508e0a5 100644
--- a/test/print_schedules.py
+++ b/test/print_schedules.py
@@ -1,95 +1,38 @@
 """
-Step-by-step test of the Schedule2D → TaskDAG → per-rank DAG pipeline.
+Step-by-step test of the PipelineSchedule → TaskDAG → per-rank DAG pipeline.
 
 Run with:
     python3 -m test.print_schedules
 """
 
-from .schedule_helpers import DUALPIPEV_SEQUENTIAL_MB6_SCHEDULE, INTERLEAVED_1F1B_PP2_MB4_SCHEDULE, build_1f1b_schedule, print_schedule
+from .schedule_helpers import build_1f1b_schedule, build_interleaved_1f1b_schedule, build_zerobubble_schedule, visualize_pipeline_schedule, build_dualpipev_schedule, build_interleaved_zero_bubble
 from src.piper_exec import DAGEdge, TaskType, _validate_schedule
-from src.piper_graph_transform import schedule_to_dag, insert_p2p_ops, visualize_schedule, visualize_dag
+from src.piper_graph_transform import expand_chunks_to_dags, add_temporal_dependencies, split_dag_by_rank, assign_time_steps, visualize_dag
 
 
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
+mbs = 16
+pp = 8
 
-def _type_abbrev(tt: TaskType) -> str:
-    return {
-        TaskType.FWD:     "F",
-        TaskType.BWD:     "B",
-        TaskType.UPD:     "U",
-        TaskType.BWD_I:   "Bi",
-        TaskType.BWD_W:   "Bw",
-        TaskType.FWD_BWD: "FB",
-        TaskType.SEND:    "Snd",
-        TaskType.RECV:    "Rcv",
-    }.get(tt, "?")
+schedule = build_1f1b_schedule(n_mbs=mbs, n_stages=pp)
+_validate_schedule(schedule, dag_edges=(DAGEdge(0, 1), DAGEdge(1, 2), DAGEdge(2, 3)), num_mbs=mbs)
+visualize_pipeline_schedule(schedule, "out/1f1b-template")
 
+schedule = build_interleaved_1f1b_schedule(n_mbs=mbs, pp=pp)
+_validate_schedule(schedule, dag_edges=(DAGEdge(0, 1), DAGEdge(1, 2), DAGEdge(2, 3), DAGEdge(3, 4), DAGEdge(4, 5), DAGEdge(5, 6), DAGEdge(6, 7)), num_mbs=mbs)
+visualize_pipeline_schedule(schedule, "out/interleaved-1f1b-template")
 
-def _node_str(node) -> str:
-    batches = "+".join(f"s{b.stage_id}m{b.mb_idx}" for b in node.task.batches)
-    peer = f" peer=r{node.peer_pp_rank}" if node.peer_pp_rank is not None else ""
-    return f"[r{node.pp_rank} t={node.time_step:+d} {_type_abbrev(node.task.type)} {batches}{peer}]"
+schedule = build_zerobubble_schedule(n_mbs=mbs, pp=pp)
+_validate_schedule(schedule, dag_edges=(DAGEdge(0, 1), DAGEdge(1, 2), DAGEdge(2, 3)), num_mbs=mbs)
+visualize_pipeline_schedule(schedule, "out/zero-bubble-template")
 
+schedule = build_interleaved_zero_bubble(n_mbs=mbs, pp=pp)
+_validate_schedule(schedule, dag_edges=(DAGEdge(0, 1), DAGEdge(1, 2), DAGEdge(2, 3), DAGEdge(3, 4), DAGEdge(4, 5), DAGEdge(5, 6), DAGEdge(6, 7)), num_mbs=mbs)
+visualize_pipeline_schedule(schedule, "out/interleaved-zero-bubble-template")
 
-def print_dag(dag, title: str) -> None:
-    print(f"\n{'='*60}")
-    print(f"  {title}")
-    print(f"{'='*60}")
-    for node in sorted(dag.nodes, key=lambda n: (n.pp_rank, n.time_step)):
-        data_preds  = [_node_str(p) for p in node.data_preds]
-        data_succs  = [_node_str(s) for s in node.data_succs]
-        temp_preds  = [_node_str(p) for p in node.temporal_preds]
-        temp_succs  = [_node_str(s) for s in node.temporal_succs]
-        print(f"  {_node_str(node)}")
-        print(f"    data_preds  : {data_preds or '—'}")
-        print(f"    data_succs  : {data_succs or '—'}")
-        print(f"    temp_preds  : {temp_preds or '—'}")
-        print(f"    temp_succs  : {temp_succs or '—'}")
+schedule = build_dualpipev_schedule(n_mbs=10, pp=4)
+_validate_schedule(schedule, dag_edges=(DAGEdge(0, 1), DAGEdge(1, 2), DAGEdge(2, 3), DAGEdge(3, 4), DAGEdge(4, 5), DAGEdge(5, 6), DAGEdge(6, 7)), num_mbs=8)
+visualize_pipeline_schedule(schedule, "out/dualpipev-template")
 
-
-# ---------------------------------------------------------------------------
-# Step 1: Build Schedule2D
-# ---------------------------------------------------------------------------
-
-print("\n" + "="*60)
-print("  STEP 1 — 1F1B Schedule2D  (PP=2, MB=2)")
-print("="*60)
-schedule = build_1f1b_schedule(n_mbs=2, n_stages=2)
-schedule = DUALPIPEV_SEQUENTIAL_MB6_SCHEDULE
-print_schedule(schedule)
-_validate_schedule(schedule.grid, dag_edges=(DAGEdge(0, 1), DAGEdge(1, 2), DAGEdge(2, 3)), num_mbs=6)
-
-# ---------------------------------------------------------------------------
-# Step 2: Convert to TaskDAG (with cross-rank data edges)
-# ---------------------------------------------------------------------------
-
-dag = schedule_to_dag(schedule)
-print_dag(dag, "STEP 2 — TaskDAG (cross-rank data edges present)")
-
-# Summarise edge counts
-n_data  = sum(len(n.data_succs) for n in dag.nodes)
-n_temp  = sum(len(n.temporal_succs) for n in dag.nodes)
-n_cross = sum(
-    1 for n in dag.nodes for s in n.data_succs if n.pp_rank != s.pp_rank
-)
-print(f"\n  nodes={len(dag.nodes)}  data_edges={n_data}  "
-      f"temporal_edges={n_temp}  cross_rank_data_edges={n_cross}")
-
-# ---------------------------------------------------------------------------
-# Step 3: Insert SEND / RECV nodes (insert_p2p_ops)
-# ---------------------------------------------------------------------------
-
-rank_dags = insert_p2p_ops(dag)
-
-visualize_schedule(schedule, "schedule_1f1b")
-
-for rank, rank_dag in enumerate(rank_dags):
-    visualize_dag(rank_dag, f"rank{rank}_dag")
-    print_dag(rank_dag, f"STEP 3 — Per-rank DAG for PP rank {rank}")
-    n_data = sum(len(n.data_succs) for n in rank_dag.nodes)
-    n_temp = sum(len(n.temporal_succs) for n in rank_dag.nodes)
-    print(f"\n  nodes={len(rank_dag.nodes)}  data_edges={n_data}  temporal_edges={n_temp}")
-
-print()
+schedule = build_dualpipev_schedule(n_mbs=10, pp=4, seq=True)
+_validate_schedule(schedule, dag_edges=(DAGEdge(0, 1), DAGEdge(1, 2), DAGEdge(2, 3), DAGEdge(3, 4), DAGEdge(4, 5), DAGEdge(5, 6), DAGEdge(6, 7)), num_mbs=8)
+visualize_pipeline_schedule(schedule, "out/dualpipev-sequential-template")
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
index 0cd303c..12accfe 100644
--- a/test/schedule_helpers.py
+++ b/test/schedule_helpers.py
@@ -1,652 +1,1952 @@
 
 from src.piper_exec import (
-    Task,
+    Chunk,
     BatchMeta,
     TaskType,
     CompType,  # backwards-compatible alias for TaskType
-    Schedule2D,
-    TaskNode,
+    PipelineSchedule,
+    Task,
     TaskDAG,
+    logical_time,
 )
-from src.piper_graph_transform import schedule_to_dag, visualize_schedule, visualize_dag, insert_p2p_ops
+from src.piper_graph_transform import visualize_dag
 
 
-def _task_label(task: Task) -> str:
-    """Short label for a task: stage:mb:f/b/u, or first+second for dual batches."""
+def _chunk_label(chunk: Chunk) -> str:
+    """Short label for a chunk: stage:mb:f/b/u, or first+second for dual batches."""
     c = (
-        "u" if task.type == CompType.UPD
-        else "f" if task.type == CompType.FWD
-        else "b" if task.type == CompType.BWD
-        else "fb" if task.type == CompType.FWD_BWD
-        else "bi" if task.type == CompType.BWD_I
-        else "bw" if task.type == CompType.BWD_W
+        "u" if chunk.type == CompType.UPD
+        else "f" if chunk.type == CompType.FWD
+        else "b" if chunk.type == CompType.BWD
+        else "fb" if chunk.type == CompType.FWD_BWD
+        else "bi" if chunk.type == CompType.BWD_I
+        else "bw" if chunk.type == CompType.BWD_W
         else "?"
     )
-    if task.type == CompType.UPD:
+    if chunk.type == CompType.UPD:
         label = "   u    "
     else:
-        first = task.batches[0]
+        first = chunk.batches[0]
         label = f"{first.stage_id}:{first.mb_idx}:{c}"
-    if len(task.batches) > 1:
-        second = task.batches[1]
+    if len(chunk.batches) > 1:
+        second = chunk.batches[1]
         label += f"+{second.stage_id}:{second.mb_idx}"
     else:
         label += "    "
     return label
 
 
-def print_schedule(schedule: Schedule2D, path: str = "schedule") -> None:
-    """Print the 2D schedule grid to stdout. Rows = ranks, columns = time steps. None cells shown as ' --- '."""
-    for row in schedule.grid:
-        for task in row:
-            if task is None:
+def print_schedule(schedule: PipelineSchedule) -> None:
+    """Print the schedule as a 2-D timed grid (rows=ranks, columns=logical time)."""
+    start_times_per_rank = schedule._compute_start_times()
+    max_time = max(
+        start + logical_time(chunk)
+        for row, starts in zip(schedule.grid, start_times_per_rank)
+        for chunk, start in zip(row, starts)
+    )
+    for rank, (row, starts) in enumerate(zip(schedule.grid, start_times_per_rank)):
+        # Build a sparse mapping from start_time -> chunk for this rank
+        time_to_chunk = {start: chunk for chunk, start in zip(row, starts)}
+        for t in range(max_time):
+            chunk = time_to_chunk.get(t)
+            if chunk is None:
                 print(" ------ ", end="\t")
             else:
-                print(_task_label(task), end="\t")
+                print(_chunk_label(chunk), end="\t")
         print()
 
-def build_gpipe_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
-    steps = n_mbs + n_stages - 1
-    schedule = [[None] * (steps * 2 + 1) for _ in range(n_stages)]
-    for step in range(steps):
-        for stage_id in range(n_stages):
-            mb_idx = step - stage_id
-            if mb_idx >= 0 and mb_idx < n_mbs:
-                schedule[stage_id][step] = Task(
+
+def build_gpipe_schedule(n_mbs: int, n_stages: int) -> PipelineSchedule:
+    rows: list[list[Chunk]] = [[] for _ in range(n_stages)]
+    for stage_id in range(n_stages):
+        for mb_idx in range(n_mbs):
+            rows[stage_id].append(Chunk(
+                pp_rank=stage_id,
+                batches=[BatchMeta(stage_id=stage_id, mb_idx=mb_idx)],
+                type=CompType.FWD,
+            ))
+    for stage_id in reversed(range(n_stages)):
+        for mb_idx in range(n_mbs):
+            rows[stage_id].append(Chunk(
+                pp_rank=stage_id,
+                batches=[BatchMeta(stage_id=stage_id, mb_idx=mb_idx)],
+                type=CompType.BWD,
+            ))
+    return PipelineSchedule(grid=rows)
+
+
+def build_1f1b_schedule(n_mbs: int, n_stages: int) -> PipelineSchedule:
+    """Standard 1F1B schedule (no virtual stages, BWD = fused)."""
+    rows: list[list[Chunk]] = [[] for _ in range(n_stages)]
+    # Warmup: rank r does r+1 forward passes before first backward
+    fwd_mb = [0] * n_stages
+    bwd_mb = [0] * n_stages
+    for stage_id in range(n_stages):
+        warmup = n_stages - 1 - stage_id
+        for _ in range(min(warmup, n_mbs)):
+            rows[stage_id].append(Chunk(
+                pp_rank=stage_id,
+                batches=[BatchMeta(stage_id=stage_id, mb_idx=fwd_mb[stage_id])],
+                type=CompType.FWD,
+            ))
+            fwd_mb[stage_id] += 1
+    # 1F1B steady state + cooldown
+    for stage_id in range(n_stages):
+        while bwd_mb[stage_id] < n_mbs:
+            if fwd_mb[stage_id] < n_mbs:
+                rows[stage_id].append(Chunk(
                     pp_rank=stage_id,
-                    batches=[BatchMeta(stage_id=stage_id, mb_idx=mb_idx)],
+                    batches=[BatchMeta(stage_id=stage_id, mb_idx=fwd_mb[stage_id])],
                     type=CompType.FWD,
-                )
+                ))
+                fwd_mb[stage_id] += 1
+            rows[stage_id].append(Chunk(
+                pp_rank=stage_id,
+                batches=[BatchMeta(stage_id=stage_id, mb_idx=bwd_mb[stage_id])],
+                type=CompType.BWD,
+            ))
+            bwd_mb[stage_id] += 1
+    return PipelineSchedule(grid=rows)
 
-    for step in range(steps, steps * 2):
-        for stage_id in reversed(range(n_stages)):
-            mb_idx = (step - steps) - (n_stages - stage_id - 1)
-            if mb_idx >= 0 and mb_idx < n_mbs:
-                schedule[stage_id][step] = Task(
-                    pp_rank=stage_id,
-                    batches=[BatchMeta(stage_id=stage_id, mb_idx=mb_idx)],
-                    type=CompType.BWD,
-                )
-
-    for i, stage in enumerate(range(n_stages)):
-        schedule[stage][-i-1] = Task(
-            pp_rank=stage,
-            batches=[BatchMeta(stage_id=stage, mb_idx=0)],
-            type=CompType.UPD,
-        )
-    return Schedule2D(grid=schedule)
-
-
-def build_1f1b_schedule(n_mbs: int, n_stages: int) -> Schedule2D:
-    steps = n_mbs + n_stages - 1
-    schedule = [[None] * (steps * 2) for _ in range(n_stages)]
-    stage_mb = [[0, 0] for _ in range(n_stages)]
-    for step in range(n_stages):
-        for stage_id in range(n_stages):
-            if step >= stage_id:
-                mb_idx = stage_mb[stage_id][0]
-                if mb_idx >= 0 and mb_idx < n_mbs:
-                    schedule[stage_id][step] = Task(
-                        pp_rank=stage_id,
-                        batches=[BatchMeta(stage_id=stage_id, mb_idx=mb_idx)],
-                        type=CompType.FWD,
-                    )
-                    stage_mb[stage_id][0] += 1
-    for step in range(n_stages, 2 * steps):
-        relative_step = step - n_stages
-        for stage_id in range(n_stages):
-            inv_stage = n_stages - stage_id - 1
-            if relative_step >= inv_stage:
-                fwd_or_bwd = 1 - (relative_step + inv_stage) % 2
-                task_type = CompType.FWD if fwd_or_bwd == 0 else CompType.BWD
-                mb_idx = stage_mb[stage_id][fwd_or_bwd]
-                if mb_idx >= 0 and mb_idx < n_mbs:
-                    schedule[stage_id][step] = Task(
-                        pp_rank=stage_id,
-                        batches=[BatchMeta(stage_id=stage_id, mb_idx=mb_idx)],
-                        type=task_type,
-                    )
-                    stage_mb[stage_id][fwd_or_bwd] += 1
-    return Schedule2D(grid=schedule)
-
-
-ZEROBUBBLE_MB4_SCHEDULE = Schedule2D(
+
+def build_interleaved_1f1b_schedule(n_mbs: int, pp: int, v: int = 2) -> PipelineSchedule:
+    """Interleaved 1F1B schedule (v virtual stages per rank, BWD = fused).
+
+    Rank r owns stages r, r+pp, r+2*pp, ..., r+(v-1)*pp.
+    Uses PyTorch's warmup formula: warmup(r) = min((v-1)*pp + 2*(pp-1-r), n_mbs*v).
+    """
+    from collections import defaultdict
+    microbatches_per_round = pp
+
+    def warmup_ops(rank):
+        return min((v - 1) * pp + 2 * (pp - 1 - rank), n_mbs * v)
+
+    def forward_stage_index(step, rank, warmup):
+        local_index = (step // microbatches_per_round) % v
+        return local_index * pp + rank
+
+    def backward_stage_index(step, rank, warmup):
+        local_index = (v - 1 - ((step - warmup) // microbatches_per_round) % v)
+        return local_index * pp + rank
+
+    rows: list[list[Chunk]] = []
+    for rank in range(pp):
+        wu = warmup_ops(rank)
+        microbatch_ops = v * n_mbs
+        fwd_bwd_ops = microbatch_ops - wu
+        cooldown_ops = microbatch_ops - fwd_bwd_ops
+
+        fwd_mb: dict[int, int] = defaultdict(int)
+        bwd_mb: dict[int, int] = defaultdict(int)
+        chunks: list[Chunk] = []
+
+        for op in range(wu + fwd_bwd_ops + cooldown_ops):
+            if op < wu:
+                s = forward_stage_index(op, rank, wu)
+                m = fwd_mb[s]; fwd_mb[s] += 1
+                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(s, m)], type=CompType.FWD))
+            elif op < wu + fwd_bwd_ops:
+                s = forward_stage_index(op, rank, wu)
+                m = fwd_mb[s]; fwd_mb[s] += 1
+                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(s, m)], type=CompType.FWD))
+                s = backward_stage_index(op, rank, wu)
+                m = bwd_mb[s]; bwd_mb[s] += 1
+                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(s, m)], type=CompType.BWD))
+            else:
+                s = backward_stage_index(op, rank, wu)
+                m = bwd_mb[s]; bwd_mb[s] += 1
+                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(s, m)], type=CompType.BWD))
+
+        rows.append(chunks)
+    return PipelineSchedule(grid=rows)
+
+
+def build_zerobubble_schedule(n_mbs: int, pp: int) -> PipelineSchedule:
+    """ZeroBubble (ZB-1) schedule: one stage per rank, backward split into BWD_I + BWD_W.
+
+    Uses PyTorch's ZB warmup formula: warmup(r) = min(pp - 1 - r, n_mbs).
+    BWD_W for microbatch m on rank r is deferred by r slots relative to BWD_I.
+    """
+    from collections import defaultdict
+
+    def warmup_ops(rank):
+        return min(pp - 1 - rank, n_mbs)
+
+    rows: list[list[Chunk]] = []
+    for rank in range(pp):
+        wu = warmup_ops(rank)
+        microbatch_ops = n_mbs
+        fwd_bwd_ops = microbatch_ops - wu
+        cooldown_ops = microbatch_ops - fwd_bwd_ops
+        num_1f1b_microbatches = rank  # W is deferred by this many steps
+
+        fwd_mb = 0
+        bwd_mb = 0
+        w_mb = 0
+        chunks: list[Chunk] = []
+        backward_ops: list[int] = []  # track op indices where BWD_I was emitted
+        weight_op_count = 0
+
+        for op in range(wu + fwd_bwd_ops + cooldown_ops):
+            stage = rank  # single stage per rank
+            if op < wu:
+                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, fwd_mb)], type=CompType.FWD))
+                fwd_mb += 1
+            elif op < wu + fwd_bwd_ops:
+                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, fwd_mb)], type=CompType.FWD))
+                fwd_mb += 1
+                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, bwd_mb)], type=CompType.BWD_I))
+                bwd_mb += 1
+                backward_ops.append(op)
+                if op - wu >= num_1f1b_microbatches:
+                    chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, w_mb)], type=CompType.BWD_W))
+                    w_mb += 1
+                    weight_op_count += 1
+            else:
+                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, bwd_mb)], type=CompType.BWD_I))
+                bwd_mb += 1
+                backward_ops.append(op)
+                if op - wu >= num_1f1b_microbatches:
+                    chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, w_mb)], type=CompType.BWD_W))
+                    w_mb += 1
+                    weight_op_count += 1
+
+        while weight_op_count < len(backward_ops):
+            chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(rank, w_mb)], type=CompType.BWD_W))
+            w_mb += 1
+            weight_op_count += 1
+
+        rows.append(chunks)
+    return PipelineSchedule(grid=rows)
+
+
+
+def build_interleaved_zero_bubble(n_mbs: int, pp: int, v: int = 2) -> PipelineSchedule:
+    """Interleaved ZeroBubble schedule: v virtual stages per rank, backward split into BWD_I + BWD_W.
+
+    Matches torch.distributed.pipelining ScheduleInterleavedZeroBubble op-order exactly.
+
+    Rank r owns stages r, r+pp, r+2*pp, ..., r+(v-1)*pp (same as interleaved 1F1B).
+    Warmup formula (multiply_factor=1, matching torch):
+        warmup(r) = min((v-1)*microbatches_per_round + 1*(pp-1-r), n_mbs*v)
+    where microbatches_per_round = n_mbs // max(1, n_mbs // pp).
+
+    Contrast with interleaved 1F1B which uses multiply_factor=2. The ZeroBubble variant
+    uses multiply_factor=1 because W ops fill the bubble time that would otherwise require
+    extra warmup F ops, so fewer warmup forwards are needed.
+
+    BWD_W is deferred by `rank` 1F1B steps relative to BWD_I (emit W once bwdi_count > rank),
+    matching torch's condition `op - warmup_ops >= rank`. A FIFO weight queue tracks
+    outstanding (stage, mb) pairs whose BWD_I has completed but BWD_W has not yet been emitted.
+
+    Bubble placement: torch inserts explicit None slots (rank pre-padding + pp-rank-1
+    post-warmup Nones). Piper omits explicit Nones; _compute_start_times() recovers the
+    same absolute timing via FWD/BWD dependency constraints.
+    """
+    from collections import defaultdict
+
+    microbatches_per_round = pp  # = n_mbs // max(1, n_mbs // pp) for balanced configs
+
+    def warmup_ops(rank: int) -> int:
+        # multiply_factor=1 (ZeroBubble) vs 2 (1F1B); matches torch's ScheduleInterleavedZeroBubble
+        return min((v - 1) * microbatches_per_round + 1 * (pp - 1 - rank), n_mbs * v)
+
+    def forward_stage_index(step: int, rank: int) -> int:
+        local_index = (step // microbatches_per_round) % v
+        return local_index * pp + rank
+
+    def backward_stage_index(step: int, rank: int, warmup: int) -> int:
+        local_index = (v - 1 - ((step - warmup) // microbatches_per_round) % v)
+        return local_index * pp + rank
+
+    rows: list[list[Chunk]] = []
+    for rank in range(pp):
+        wu = warmup_ops(rank)
+        microbatch_ops = v * n_mbs
+        fwd_bwd_ops = microbatch_ops - wu
+        cooldown_ops = microbatch_ops - fwd_bwd_ops
+
+        fwd_mb: dict[int, int] = defaultdict(int)   # stage_id -> next fwd microbatch index
+        bwd_mb: dict[int, int] = defaultdict(int)   # stage_id -> next bwdi microbatch index
+        chunks: list[Chunk] = []
+        weight_queue: list[tuple[int, int]] = []    # FIFO of (stage_id, mb_idx) pending BWD_W
+        bwdi_count = 0
+        # Defer the first `rank` BWD_Ws — same as non-interleaved ZeroBubble.
+        # This fills the cooldown-phase bubble produced by pipeline depth `rank`.
+        num_deferred = rank
+
+        def _maybe_emit_w() -> None:
+            """Emit the oldest queued BWD_W if the deferral window has passed."""
+            if bwdi_count > num_deferred and weight_queue:
+                ws, wm = weight_queue.pop(0)
+                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(ws, wm)], type=CompType.BWD_W))
+
+        for op in range(wu + fwd_bwd_ops + cooldown_ops):
+            if op < wu:
+                # Warmup: forward only
+                s = forward_stage_index(op, rank)
+                m = fwd_mb[s]; fwd_mb[s] += 1
+                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(s, m)], type=CompType.FWD))
+            elif op < wu + fwd_bwd_ops:
+                # Steady state: FWD then BWD_I, then maybe BWD_W
+                s_fwd = forward_stage_index(op, rank)
+                m_fwd = fwd_mb[s_fwd]; fwd_mb[s_fwd] += 1
+                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(s_fwd, m_fwd)], type=CompType.FWD))
+
+                s_bwd = backward_stage_index(op, rank, wu)
+                m_bwd = bwd_mb[s_bwd]; bwd_mb[s_bwd] += 1
+                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(s_bwd, m_bwd)], type=CompType.BWD_I))
+                weight_queue.append((s_bwd, m_bwd))
+                bwdi_count += 1
+                _maybe_emit_w()
+            else:
+                # Cooldown: BWD_I only, then maybe BWD_W
+                s_bwd = backward_stage_index(op, rank, wu)
+                m_bwd = bwd_mb[s_bwd]; bwd_mb[s_bwd] += 1
+                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(s_bwd, m_bwd)], type=CompType.BWD_I))
+                weight_queue.append((s_bwd, m_bwd))
+                bwdi_count += 1
+                _maybe_emit_w()
+
+        # Drain any remaining deferred BWD_Ws
+        while weight_queue:
+            ws, wm = weight_queue.pop(0)
+            chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(ws, wm)], type=CompType.BWD_W))
+
+        rows.append(chunks)
+    return PipelineSchedule(grid=rows)
+
+
+def build_dualpipev_schedule(n_mbs: int, pp: int, seq: bool = False) -> PipelineSchedule:
+    """DualPipeV schedule: V-layout, 2 virtual stages per rank, zero-bubble optimization.
+
+    Stage assignment (V-layout): rank r owns stage r (stage0) and stage 2*pp-1-r (stage1).
+    Directly ports PyTorch's ScheduleDualPipeV._calculate_single_rank_operations.
+    """
+    rows: list[list[Chunk]] = [[] for _ in range(pp)]
+
+    for rank in range(pp):
+        s0 = rank               # first (outer) stage
+        s1 = 2 * pp - 1 - rank  # second (inner) stage
+        chunks = rows[rank]
+        cnt: dict[tuple[int, str], int] = {}
+        weight_queue: list[tuple[int, int]] = []
+
+        def c(stage: int, key: str) -> int:
+            return cnt.get((stage, key), 0)
+
+        def inc(stage: int, key: str) -> None:
+            cnt[(stage, key)] = c(stage, key) + 1
+
+        def inc_bwd(stage: int) -> None:
+            inc(stage, "i"); inc(stage, "w")
+
+        def fwd(stage: int) -> None:
+            m = c(stage, "f")
+            chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, m)], type=CompType.FWD))
+            inc(stage, "f")
+
+        def bwdi(stage: int) -> None:
+            m = c(stage, "i")
+            chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, m)], type=CompType.BWD_I))
+            weight_queue.append((stage, m))
+            inc(stage, "i")
+
+        def drain_w() -> None:
+            if not weight_queue:
+                return
+            stage, m = weight_queue.pop(0)
+            chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, m)], type=CompType.BWD_W))
+            inc(stage, "w")
+
+        def full_bwd(stage: int) -> None:
+            m = c(stage, "i")
+            chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, m)], type=CompType.BWD))
+            inc_bwd(stage)
+
+        def overlap_fb(fwd_stage: int, bwd_stage: int) -> None:
+            fm = c(fwd_stage, "f")
+            bm = c(bwd_stage, "i")
+            if seq:
+                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(fwd_stage, fm)], type=CompType.FWD))
+                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(bwd_stage, bm)], type=CompType.BWD))
+            else:
+                chunks.append(Chunk(
+                    pp_rank=rank,
+                    batches=[BatchMeta(fwd_stage, fm), BatchMeta(bwd_stage, bm)],
+                    type=CompType.FWD_BWD,
+                ))
+            inc(fwd_stage, "f")
+            inc_bwd(bwd_stage)
+
+        # Phase 1: F0 warmup
+        for _ in range((pp - rank - 1) * 2):
+            fwd(s0)
+
+        # Phase 2: F0F1
+        for _ in range(rank + 1):
+            fwd(s0); fwd(s1)
+
+        # Phase 3: I1 W1 F1 (zero-bubble)
+        for _ in range(pp - rank - 1):
+            bwdi(s1); drain_w(); fwd(s1)
+
+        # Phase 4: Main — F0B1 + F1B0 (overlapped)
+        for i in range(n_mbs - pp * 2 + rank + 1):
+            if i == 0 and rank == pp - 1:
+                fwd(s0); full_bwd(s1)
+            else:
+                overlap_fb(s0, s1)
+            overlap_fb(s1, s0)
+
+        # Phase 5: B1 + F1B0
+        for _ in range(pp - rank - 1):
+            full_bwd(s1); overlap_fb(s1, s0)
+
+        # Phase 6: B1B0 (second half switches to zero-bubble split)
+        enable_zb = False
+        for i in range(rank + 1):
+            if i == (rank + 1) // 2 and rank % 2 == 1:
+                enable_zb = True
+            (bwdi if enable_zb else full_bwd)(s1)
+            if i == (rank + 1) // 2 and rank % 2 == 0:
+                enable_zb = True
+            (bwdi if enable_zb else full_bwd)(s0)
+
+        # Phase 7: W0 B0
+        for _ in range(pp - rank - 1):
+            drain_w()
+            (bwdi if enable_zb else full_bwd)(s0)
+
+        # Phase 8: W0
+        for _ in range(rank + 1):
+            drain_w()
+
+    return PipelineSchedule(grid=rows)
+
+
+def visualize_pipeline_schedule(
+    schedule: PipelineSchedule,
+    output_path: str = "pipeline_schedule",
+    fmt: str = "png",
+) -> None:
+    """Render a PipelineSchedule as a 2-D colored grid.
+
+    Rows = PP ranks.  Columns = logical time steps.
+    Each chunk occupies ``logical_time(chunk)`` consecutive columns.
+    Colors: FWD = yellow, BWD/BWD_I = green, BWD_W = blue, UPD = orange.
+    """
+    import matplotlib
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+    import matplotlib.patches as mpatches
+
+    COLORS = {
+        CompType.FWD:     "#FFD700",   # yellow
+        CompType.BWD:     "#90EE90",   # green
+        CompType.BWD_I:   "#90EE90",   # green
+        CompType.BWD_W:   "#87CEEB",   # blue
+        CompType.UPD:     "#FFA500",   # orange
+        CompType.FWD_BWD: "#DDA0DD",   # plum
+    }
+
+    start_times_per_rank = schedule._compute_start_times()
+    n_ranks = len(schedule.grid)
+    max_time = max(
+        start_times_per_rank[r][i] + logical_time(schedule.grid[r][i])
+        for r in range(n_ranks)
+        for i in range(len(schedule.grid[r]))
+    )
+
+    CELL_W = 1.0   # width per logical time unit
+    CELL_H = 1.0   # height per rank
+    PAD = 0.04     # gap between cells
+
+    fig_w = max_time * CELL_W + 1.5
+    fig_h = n_ranks * CELL_H + 0.8
+    fig, ax = plt.subplots(figsize=(fig_w, fig_h))
+    ax.set_xlim(0, max_time * CELL_W)
+    ax.set_ylim(0, n_ranks * CELL_H)
+    ax.axis("off")
+
+    # Per-rank sorted unique stages, to determine virtual stage index (0=black, 1=white)
+    rank_stages: list[list[int]] = []
+    for row in schedule.grid:
+        seen: list[int] = []
+        for chunk in row:
+            for b in chunk.batches:
+                if b.stage_id not in seen:
+                    seen.append(b.stage_id)
+        rank_stages.append(sorted(seen))
+
+    for rank, (row, starts) in enumerate(zip(schedule.grid, start_times_per_rank)):
+        # rank 0 at top → y increases downward, so flip
+        y = (n_ranks - 1 - rank) * CELL_H
+        stages = rank_stages[rank]
+        for chunk, t in zip(row, starts):
+            lt = logical_time(chunk)
+            color = COLORS.get(chunk.type, "#FFFFFF")
+            x = t * CELL_W
+
+            rect = mpatches.FancyBboxPatch(
+                (x + PAD, y + PAD),
+                lt * CELL_W - 2 * PAD,
+                CELL_H - 2 * PAD,
+                boxstyle="round,pad=0.02",
+                facecolor=color,
+                edgecolor="#555555",
+                linewidth=0.6,
+            )
+            ax.add_patch(rect)
+
+            if chunk.batches:
+                b = chunk.batches[0]
+                label = f"{chunk.type.name}\ns{b.stage_id} m{b.mb_idx}"
+                vstage_idx = stages.index(b.stage_id) if b.stage_id in stages else 0
+            else:
+                label = chunk.type.name
+                vstage_idx = 0
+            font_color = "white" if vstage_idx % 2 == 1 else "black"
+            ax.text(
+                x + lt * CELL_W / 2,
+                y + CELL_H / 2,
+                label,
+                ha="center", va="center",
+                fontsize=7,
+                fontweight="bold",
+                color=font_color,
+            )
+
+    # Rank labels on the left
+    for rank in range(n_ranks):
+        y = (n_ranks - 1 - rank) * CELL_H + CELL_H / 2
+        ax.text(-0.15, y, f"rank {rank}", ha="right", va="center", fontsize=8)
+
+    plt.tight_layout(pad=0.3)
+    out_file = f"{output_path}.{fmt}"
+    plt.savefig(out_file, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+    print(f"Saved schedule visualization to {out_file}")
+
+
+ZEROBUBBLE_MB4_SCHEDULE = PipelineSchedule(
     grid=[
         [
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),          # t0:  F0
-            None,                                                                                   # t1:   -
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),          # t2:  F1
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD_I),        # t3:  I0
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD_W),        # t4:  W0
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),          # t5:  F2
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD_I),        # t6:  I1
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD_W),        # t7:  W1
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),          # t8:  F3
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD_I),        # t9:  I2
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD_W),        # t10: W2
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD_I),        # t11: I3
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD_W),        # t12: W3
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),          # t0:  F0
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),          # t2:  F1
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD_I),        # t3:  I0
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD_W),        # t4:  W0
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),          # t5:  F2
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD_I),        # t6:  I1
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD_W),        # t7:  W1
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),          # t8:  F3
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD_I),        # t9:  I2
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD_W),        # t10: W2
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD_I),        # t11: I3
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD_W),        # t12: W3
         ],
         [
-            None,                                                                                   # t0:  -
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),          # t1:  F0
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD_I),        # t2:  I0
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),          # t3:  F1
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD_I),        # t4:  I1
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD_W),        # t5:  W0
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),          # t6:  F2
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD_I),        # t7:  I2
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD_W),        # t8:  W1
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),          # t9:  F3
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD_I),        # t10: I3
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD_W),        # t11: W2
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD_W),        # t12: W3
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),          # t1:  F0
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD_I),        # t2:  I0
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),          # t3:  F1
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD_I),        # t4:  I1
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD_W),        # t5:  W0
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),          # t6:  F2
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD_I),        # t7:  I2
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD_W),        # t8:  W1
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),          # t9:  F3
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD_I),        # t10: I3
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD_W),        # t11: W2
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD_W),        # t12: W3
         ],
     ])
 
-NO_PP_SCHEDULE = Schedule2D(
+NO_PP_SCHEDULE = PipelineSchedule(
     grid=[
         [
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.UPD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
         ]
     ],
 )
 
 
-DUALPIPEV_MB6_SCHEDULE = Schedule2D(
+DUALPIPEV_MB6_SCHEDULE = PipelineSchedule(
     grid=[
         [
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_I),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_W),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_I),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_W),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2), BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD_BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4), BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD_BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3), BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD_BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5), BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD_BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4), BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD_BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5), BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD_BWD),
-            None,
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_I),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_I),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_W),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_W),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
-            None,
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2), BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4), BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3), BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5), BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4), BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5), BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
         ],
         [
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2), BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD_BWD),
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2), BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD_BWD),
-            None,
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3), BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD_BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3), BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD_BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4), BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD_BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4), BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD_BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5), BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD_BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5), BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD_BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_I),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_W),
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_I),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_I),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_W),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2), BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2), BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3), BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3), BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4), BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4), BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5), BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5), BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_W),
         ]
     ])
 
-DUALPIPEV_SEQUENTIAL_MB6_SCHEDULE = Schedule2D(
+DUALPIPEV_SEQUENTIAL_MB6_SCHEDULE = PipelineSchedule(
     grid=[
         [
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_I),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_W),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_I),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_W),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
-            None,
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
-            None,
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
-            None,
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_I),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_I),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_W),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_W),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
-            None,
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
         ],
         [
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
-            None,
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_I),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_W),
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_I),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_I),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_W),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_W),
         ]
     ])
 
-DUALPIPEV_NOZB_MB6_SCHEDULE = Schedule2D(
+DUALPIPEV_NOZB_MB6_SCHEDULE = PipelineSchedule(
     grid=[
         [
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2), BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD_BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4), BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD_BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3), BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD_BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5), BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD_BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4), BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD_BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5), BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD_BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD),
-            None,
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[], type=CompType.UPD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2), BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4), BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3), BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5), BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4), BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5), BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[], type=CompType.UPD),
         ],
         [
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2), BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD_BWD),
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2), BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD_BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3), BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD_BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3), BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD_BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4), BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD_BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4), BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD_BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5), BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD_BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5), BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD_BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[], type=CompType.UPD),
-            None,
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2), BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2), BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3), BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3), BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4), BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4), BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5), BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5), BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD_BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[], type=CompType.UPD),
         ]
     ])
 
-INTERLEAVED_1F1B_PP2_MB6_SCHEDULE = Schedule2D(
+INTERLEAVED_1F1B_PP2_MB6_SCHEDULE = PipelineSchedule(
     grid=[
         [
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
-            None,
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
-            None,
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[], type=CompType.UPD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[], type=CompType.UPD),
         ],
         [
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[], type=CompType.UPD),
-            None,
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[], type=CompType.UPD),
         ],
     ])
 
-INTERLEAVED_1F1B_PP2_MB4_SCHEDULE = Schedule2D(
+INTERLEAVED_1F1B_PP2_MB4_SCHEDULE = PipelineSchedule(
     grid=[
         [
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
-            None,
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
-            None,
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
         ],
         [
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
-            None,
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
         ],
     ])
 
-INTERLEAVED_GPIPE_PP2_MB4_SCHEDULE = Schedule2D(
+
+INTERLEAVED_GPIPE_PP2_MB4_SCHEDULE = PipelineSchedule(
     grid=[
         [
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
-            None,
-            None,
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[], type=CompType.UPD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[], type=CompType.UPD),
         ],
         [
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[], type=CompType.UPD),
-            None,
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[], type=CompType.UPD),
         ],
     ])
 
-INTERLEAVED_1F1B_PP4_MB8_SCHEDULE = Schedule2D(
+INTERLEAVED_1F1B_PP4_MB8_SCHEDULE = PipelineSchedule(
     grid=[
         [
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
-            None,
-            None,
-            None,
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.FWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
-            None,
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
-            None,
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
-            None,
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.BWD),
-            Task(pp_rank=0, batches=[], type=CompType.UPD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[], type=CompType.UPD),
         ],
         [
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.FWD),
-            None,
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.FWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.BWD),
-            None,
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.BWD),
-            Task(pp_rank=1, batches=[], type=CompType.UPD),
-            None,
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[], type=CompType.UPD),
         ],
         [
-            None,
-            None,
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.FWD),
-            None,
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.FWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.FWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.FWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.FWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.FWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.FWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.BWD),
-            None,
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.BWD),
-            Task(pp_rank=2, batches=[], type=CompType.UPD),
-            None,
-            None,
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[], type=CompType.UPD),
         ],
         [
-            None,
-            None,
-            None,
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.FWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.FWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.FWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.FWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.FWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.FWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.FWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.FWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.FWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.FWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.BWD),
-            Task(pp_rank=3, batches=[], type=CompType.UPD),
-            None,
-            None,
-            None,
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[], type=CompType.UPD),
+        ],
+    ])
+
+
+
+INTERLEAVED_1F1B_PP8_MB16_SCHEDULE = PipelineSchedule(
+    grid=[
+        [  # rank 0, stages 0 and 8
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=8)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=9)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=10)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=11)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=12)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=13)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=14)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=15)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=8)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=9)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=10)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=11)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=12)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=13)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=14)], type=CompType.BWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=15)], type=CompType.BWD),
+        ],
+        [  # rank 1, stages 1 and 9
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=8)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=9)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=10)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=11)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=12)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=13)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=14)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=15)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=8)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=9)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=10)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=11)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=12)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=13)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=14)], type=CompType.BWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=15)], type=CompType.BWD),
+        ],
+        [  # rank 2, stages 2 and 10
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=8)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=9)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=10)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=11)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=12)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=13)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=14)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=15)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=8)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=9)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=10)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=11)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=12)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=13)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=14)], type=CompType.BWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=15)], type=CompType.BWD),
+        ],
+        [  # rank 3, stages 3 and 11
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=8)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=9)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=10)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=11)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=12)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=13)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=14)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=15)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=8)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=9)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=10)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=11)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=12)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=13)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=14)], type=CompType.BWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=15)], type=CompType.BWD),
+        ],
+        [  # rank 4, stages 4 and 12
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=8)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=9)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=10)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=11)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=12)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=13)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=14)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=15)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=8)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=9)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=10)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=11)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=12)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=13)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=14)], type=CompType.BWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=15)], type=CompType.BWD),
+        ],
+        [  # rank 5, stages 5 and 13
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=8)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=9)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=10)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=11)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=12)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=13)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=14)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=15)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=8)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=9)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=10)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=11)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=12)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=13)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=14)], type=CompType.BWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=15)], type=CompType.BWD),
+        ],
+        [  # rank 6, stages 6 and 14
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=8)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=9)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=10)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=11)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=12)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=13)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=14)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=15)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=8)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=9)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=10)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=11)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=12)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=13)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=14)], type=CompType.BWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=15)], type=CompType.BWD),
+        ],
+        [  # rank 7, stages 7 and 15
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=8)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=9)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=10)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=11)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=12)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=13)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=14)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=15)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=8)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=9)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=10)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=11)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=12)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=13)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=14)], type=CompType.BWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=15)], type=CompType.BWD),
+        ],
+    ])
+
+
+ZEROBUBBLE_PP8_MB16_SCHEDULE = PipelineSchedule(
+    grid=[
+        [  # rank 0, stage 0   warmup=7 1f1b=9 cooldown=7
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=8)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=8)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=9)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=9)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=10)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=10)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=11)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=11)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=12)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=12)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=13)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=13)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=14)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=14)], type=CompType.BWD_W),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=15)], type=CompType.BWD_I),
+            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=15)], type=CompType.BWD_W),
+        ],
+        [  # rank 1, stage 1   warmup=6 1f1b=10 cooldown=6
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=8)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=9)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=8)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=10)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=9)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=11)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=10)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=12)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=11)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=13)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=12)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=14)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=13)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=15)], type=CompType.BWD_I),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=14)], type=CompType.BWD_W),
+            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=15)], type=CompType.BWD_W),
+        ],
+        [  # rank 2, stage 2   warmup=5 1f1b=11 cooldown=5
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD_I),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD_I),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD_I),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD_W),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD_I),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD_W),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD_I),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD_W),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_I),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD_W),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.BWD_I),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD_W),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.BWD_I),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_W),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=8)], type=CompType.BWD_I),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.BWD_W),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=9)], type=CompType.BWD_I),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.BWD_W),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=10)], type=CompType.BWD_I),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=8)], type=CompType.BWD_W),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=11)], type=CompType.BWD_I),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=9)], type=CompType.BWD_W),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=12)], type=CompType.BWD_I),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=10)], type=CompType.BWD_W),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=13)], type=CompType.BWD_I),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=11)], type=CompType.BWD_W),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=14)], type=CompType.BWD_I),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=12)], type=CompType.BWD_W),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=15)], type=CompType.BWD_I),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=13)], type=CompType.BWD_W),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=14)], type=CompType.BWD_W),
+            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=15)], type=CompType.BWD_W),
+        ],
+        [  # rank 3, stage 3   warmup=4 1f1b=12 cooldown=4
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_I),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_I),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD_I),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD_I),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_W),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD_I),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_W),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_I),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD_W),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.BWD_I),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD_W),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.BWD_I),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD_W),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=8)], type=CompType.BWD_I),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_W),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=9)], type=CompType.BWD_I),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.BWD_W),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=10)], type=CompType.BWD_I),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.BWD_W),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=11)], type=CompType.BWD_I),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=8)], type=CompType.BWD_W),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=12)], type=CompType.BWD_I),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=9)], type=CompType.BWD_W),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=13)], type=CompType.BWD_I),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=10)], type=CompType.BWD_W),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=14)], type=CompType.BWD_I),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=11)], type=CompType.BWD_W),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=15)], type=CompType.BWD_I),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=12)], type=CompType.BWD_W),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=13)], type=CompType.BWD_W),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=14)], type=CompType.BWD_W),
+            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=15)], type=CompType.BWD_W),
+        ],
+        [  # rank 4, stage 4   warmup=3 1f1b=13 cooldown=3
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.BWD_I),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.BWD_I),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.BWD_I),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.BWD_I),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.BWD_I),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.BWD_W),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.BWD_I),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.BWD_W),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.BWD_I),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.BWD_W),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.BWD_I),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.BWD_W),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=8)], type=CompType.BWD_I),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.BWD_W),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=9)], type=CompType.BWD_I),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.BWD_W),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=10)], type=CompType.BWD_I),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.BWD_W),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=11)], type=CompType.BWD_I),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.BWD_W),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=12)], type=CompType.BWD_I),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=8)], type=CompType.BWD_W),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=13)], type=CompType.BWD_I),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=9)], type=CompType.BWD_W),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=14)], type=CompType.BWD_I),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=10)], type=CompType.BWD_W),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=15)], type=CompType.BWD_I),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=11)], type=CompType.BWD_W),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=12)], type=CompType.BWD_W),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=13)], type=CompType.BWD_W),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=14)], type=CompType.BWD_W),
+            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=15)], type=CompType.BWD_W),
+        ],
+        [  # rank 5, stage 5   warmup=2 1f1b=14 cooldown=2
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.BWD_I),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.BWD_I),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.BWD_I),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.BWD_I),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.BWD_I),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.BWD_I),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.BWD_W),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.BWD_I),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.BWD_W),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.BWD_I),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.BWD_W),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=8)], type=CompType.BWD_I),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.BWD_W),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=9)], type=CompType.BWD_I),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.BWD_W),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=10)], type=CompType.BWD_I),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.BWD_W),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=11)], type=CompType.BWD_I),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.BWD_W),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=12)], type=CompType.BWD_I),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.BWD_W),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=13)], type=CompType.BWD_I),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=8)], type=CompType.BWD_W),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=14)], type=CompType.BWD_I),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=9)], type=CompType.BWD_W),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=15)], type=CompType.BWD_I),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=10)], type=CompType.BWD_W),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=11)], type=CompType.BWD_W),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=12)], type=CompType.BWD_W),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=13)], type=CompType.BWD_W),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=14)], type=CompType.BWD_W),
+            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=15)], type=CompType.BWD_W),
+        ],
+        [  # rank 6, stage 6   warmup=1 1f1b=15 cooldown=1
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.BWD_I),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.BWD_I),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.BWD_I),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.BWD_I),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.BWD_I),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.BWD_I),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.BWD_I),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.BWD_W),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.BWD_I),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.BWD_W),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=8)], type=CompType.BWD_I),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.BWD_W),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=9)], type=CompType.BWD_I),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.BWD_W),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=10)], type=CompType.BWD_I),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.BWD_W),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=11)], type=CompType.BWD_I),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.BWD_W),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=12)], type=CompType.BWD_I),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.BWD_W),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=13)], type=CompType.BWD_I),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.BWD_W),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=14)], type=CompType.BWD_I),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=8)], type=CompType.BWD_W),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=15)], type=CompType.BWD_I),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=9)], type=CompType.BWD_W),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=10)], type=CompType.BWD_W),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=11)], type=CompType.BWD_W),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=12)], type=CompType.BWD_W),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=13)], type=CompType.BWD_W),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=14)], type=CompType.BWD_W),
+            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=15)], type=CompType.BWD_W),
+        ],
+        [  # rank 7, stage 7   warmup=0 1f1b=16 cooldown=0
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.BWD_I),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.BWD_I),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.BWD_I),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.BWD_I),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.BWD_I),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.BWD_I),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.BWD_I),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.BWD_I),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.BWD_W),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=8)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=8)], type=CompType.BWD_I),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.BWD_W),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=9)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=9)], type=CompType.BWD_I),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.BWD_W),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=10)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=10)], type=CompType.BWD_I),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.BWD_W),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=11)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=11)], type=CompType.BWD_I),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.BWD_W),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=12)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=12)], type=CompType.BWD_I),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.BWD_W),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=13)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=13)], type=CompType.BWD_I),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.BWD_W),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=14)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=14)], type=CompType.BWD_I),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.BWD_W),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=15)], type=CompType.FWD),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=15)], type=CompType.BWD_I),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=8)], type=CompType.BWD_W),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=9)], type=CompType.BWD_W),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=10)], type=CompType.BWD_W),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=11)], type=CompType.BWD_W),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=12)], type=CompType.BWD_W),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=13)], type=CompType.BWD_W),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=14)], type=CompType.BWD_W),
+            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=15)], type=CompType.BWD_W),
         ],
     ])
diff --git a/test/test_llama.py b/test/test_llama.py
index 579e5d8..1c0f66e 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -16,21 +16,18 @@
 from src.piper_coordinator import PiperProgramCoordinator
 from src.piper_compile import piper_setup
 from src.piper import piper_exec_dag
-from src.piper_utils import piper_metadata
+from src.piper_utils import piper_metadata, compute_transformer_flops_per_token
 
-from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B, LLAMA_70B
+from .models.llama import Transformer, precompute_freqs_cis, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B, LLAMA_70B
 from .schedule_helpers import (
+    visualize_pipeline_schedule,
     build_1f1b_schedule,
     build_gpipe_schedule,
-    print_schedule,
-    INTERLEAVED_1F1B_PP2_MB4_SCHEDULE,
-    INTERLEAVED_1F1B_PP2_MB6_SCHEDULE,
-    INTERLEAVED_1F1B_PP4_MB8_SCHEDULE,
-    INTERLEAVED_GPIPE_PP2_MB4_SCHEDULE,
+    build_interleaved_1f1b_schedule,
+    build_zerobubble_schedule,
+    build_interleaved_zero_bubble,
+    build_dualpipev_schedule,
     NO_PP_SCHEDULE,
-    DUALPIPEV_MB6_SCHEDULE,
-    DUALPIPEV_NOZB_MB6_SCHEDULE,
-    ZEROBUBBLE_MB4_SCHEDULE,
 )
 
 
@@ -57,35 +54,48 @@ def main(args, pg):
         case "no-pp":
             schedule = NO_PP_SCHEDULE
         case "interleaved-1f1b":
-            if args.pp == 2:
-                if args.mbs == 6:
-                    schedule = INTERLEAVED_1F1B_PP2_MB6_SCHEDULE
-                elif args.mbs == 4:
-                    schedule = INTERLEAVED_1F1B_PP2_MB4_SCHEDULE
-                else:
-                    raise ValueError(f"Unsupported number of microbatches for interleaved-1f1b with PP={args.pp}: {args.mbs}")
-            elif args.pp == 4:
-                assert args.mbs == 8
-                schedule = INTERLEAVED_1F1B_PP4_MB8_SCHEDULE
+            schedule = build_interleaved_1f1b_schedule(args.mbs, args.pp, v=2)
         case "1f1b":
             schedule = build_1f1b_schedule(args.mbs, args.pp)
         case "gpipe":
             schedule = build_gpipe_schedule(args.mbs, args.pp)
         case "interleaved-gpipe":
-            assert args.pp == 2 and args.mbs == 4
-            schedule = INTERLEAVED_GPIPE_PP2_MB4_SCHEDULE
+            assert False
         case "dualpipev":
-            assert args.pp == 2 and args.mbs == 6
-            schedule = DUALPIPEV_MB6_SCHEDULE
+            schedule = build_dualpipev_schedule(args.mbs, args.pp)
         case "dualpipev-nozb":
-            assert args.pp == 2 and args.mbs == 6
-            schedule = DUALPIPEV_NOZB_MB6_SCHEDULE
+            assert False
         case "zerobubble":
-            assert args.pp == 2 and args.mbs == 4
-            schedule = ZEROBUBBLE_MB4_SCHEDULE
+            schedule = build_zerobubble_schedule(args.mbs, args.pp)
+        case "interleaved-zerobubble":
+            schedule = build_interleaved_zero_bubble(args.mbs, args.pp, v=2)
+    
+    visualize_pipeline_schedule(schedule, f"out/{args.schedule}-pp{args.pp}-mb{args.mbs}")
+
+    # Llama FFN hidden dim (SwiGLU): 2/3 * 4 * dim, rounded up to multiple_of
+    _ffn_base = int(2 * 4 * llama_config.dim / 3)
+    if llama_config.ffn_dim_multiplier is not None:
+        _ffn_base = int(llama_config.ffn_dim_multiplier * _ffn_base)
+    _ffn_dim = llama_config.multiple_of * ((_ffn_base + llama_config.multiple_of - 1) // llama_config.multiple_of)
+    n_kv_heads = llama_config.n_kv_heads if llama_config.n_kv_heads is not None else llama_config.n_heads
+    flops_per_token = compute_transformer_flops_per_token(
+        hidden_dim=llama_config.dim,
+        n_layers=llama_config.n_layers,
+        ffn_dim=_ffn_dim,
+        n_heads=llama_config.n_heads,
+        n_kv_heads=n_kv_heads,
+        head_dim=llama_config.dim // llama_config.n_heads,
+        activation_checkpointing=args.activation_checkpointing,
+    )
 
-    print("Schedule:")
-    print_schedule(schedule)
+    freqs_cis = precompute_freqs_cis(
+        llama_config.dim // llama_config.n_heads,
+        args.seq_len,
+        llama_config.rope_theta,
+    )
+    _mask = torch.full((args.seq_len, args.seq_len), float("-inf"))
+    _mask = torch.triu(_mask, diagonal=1)
+    mask = torch.hstack([torch.zeros((args.seq_len, 0)), _mask])
 
     piper_setup(
         Transformer,
@@ -100,11 +110,14 @@ def main(args, pg):
         model_dtype=torch.bfloat16,
         pg=pg,
         nsight=args.nsight,
+        model_flops_per_token=flops_per_token,
+        visualize_dag=not args.no_viz,
+        const_attrs={"freqs_cis": freqs_cis, "mask": mask},
     )
 
     print(f"Running {args.warmup} warmup iterations...")
     for _ in range(args.warmup):
-        piper_exec_dag(loss_fn)
+        piper_exec_dag(loss_fn, log_stats=True, profiling=args.profiling)
         time.sleep(1)
 
     actors = piper_metadata.actors
@@ -113,7 +126,7 @@ def main(args, pg):
     iter_times = []
     for _ in range(args.iters):
         start = time.perf_counter()
-        piper_exec_dag(loss_fn)
+        piper_exec_dag(loss_fn, log_stats=True, profiling=args.profiling)
         end = time.perf_counter()
         iter_times.append(end - start)
         time.sleep(1)
@@ -154,7 +167,7 @@ def parse_args():
     parser.add_argument(
         '--schedule',
         choices=['gpipe', '1f1b', 'interleaved-1f1b', 'interleaved-gpipe',
-                 'dualpipev-nozb', 'dualpipev', 'zerobubble', 'no-pp'],
+                 'dualpipev-nozb', 'dualpipev', 'zerobubble', 'interleaved-zerobubble', 'no-pp'],
         default='1f1b',
     )
     parser.add_argument('--dp', type=int, default=1)
@@ -172,15 +185,26 @@ def parse_args():
                         help='Split stages into per-param-bucket sub-modules for overlapped all-reduce')
     parser.add_argument('--nsight', action='store_true', default=False,
                         help='Whether to use Nsight Systems for tracing')
+    parser.add_argument('--profiling', action='store_true', default=False,
+                        help='Profile each DAG task: log time and memory delta per task')
+    parser.add_argument('--no-viz', action='store_true', default=False,
+                        help='Skip per-rank DAG visualization (speeds up startup for large models)')
     return parser.parse_args()
 
-
+"""
+/m-coriander/coriander/mfris/miniconda3/envs/piper/bin/python \
+    /m-coriander/coriander/mfris/miniconda3/envs/piper/lib/python3.10/site-packages/ray/scripts/scripts.py \
+        start --head \
+        --port 3456 \
+        --temp-dir=/m-coriander/coriander/mfris/piper/ray_tmp \
+        --include-dashboard=false
+"""
 if __name__ == "__main__":
     args = parse_args()
     print(args)
     ray.init(
+        address="10.158.48.71:3456",
         namespace="llama",
-        log_to_driver=True,
         include_dashboard=False,
         _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp",
     )
@@ -188,6 +212,6 @@ def parse_args():
     ray.get(pg.ready(), timeout=600)
     print(placement_group_table(pg))
     piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
-    handles = piper_coordinator.run_program.remote(main, args, pg)
+    handles = piper_coordinator.run_program.remote(main, pg, args, pg)
     ray.get(handles)
     ray.shutdown()
diff --git a/test/test_qwen.py b/test/test_qwen.py
index c5fee44..7a87e34 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -27,18 +27,19 @@
 from src.piper_coordinator import PiperProgramCoordinator
 from src.piper_compile import piper_setup
 from src.piper import piper_exec_dag
-from src.piper_utils import piper_metadata
+from src.piper_utils import piper_metadata, compute_transformer_flops_per_token
 
 from .models.qwen3 import PiperQwen3Model, create_qwen3_config
+from torchtitan.models.qwen3.model.model import precompute_rope_cache
 from .schedule_helpers import (
     build_1f1b_schedule,
     build_gpipe_schedule,
-    print_schedule,
+    build_interleaved_1f1b_schedule,
+    build_zerobubble_schedule,
+    build_interleaved_zero_bubble,
+    build_dualpipev_schedule,
+    visualize_pipeline_schedule,
     NO_PP_SCHEDULE,
-    INTERLEAVED_1F1B_PP2_MB4_SCHEDULE,
-    DUALPIPEV_MB6_SCHEDULE,
-    DUALPIPEV_SEQUENTIAL_MB6_SCHEDULE,
-    ZEROBUBBLE_MB4_SCHEDULE,
 )
 
 
@@ -50,43 +51,62 @@ def main(args, pg):
 
     config = create_qwen3_config(args.model)
 
+    num_stages = args.pp
+    v = 2
     match args.schedule:
         case "no-pp":
             schedule = NO_PP_SCHEDULE
-            num_stages = 1
+        case "interleaved-1f1b":
+            schedule = build_interleaved_1f1b_schedule(args.mbs, args.pp, v=v)
+            num_stages *= v
         case "1f1b":
             schedule = build_1f1b_schedule(args.mbs, args.pp)
-            num_stages = args.pp
         case "gpipe":
             schedule = build_gpipe_schedule(args.mbs, args.pp)
-            num_stages = args.pp
-        case "interleaved-1f1b":
-            schedule = INTERLEAVED_1F1B_PP2_MB4_SCHEDULE
-            num_stages = args.pp * 2
+        case "interleaved-gpipe":
+            assert False
         case "dualpipev":
-            schedule = DUALPIPEV_MB6_SCHEDULE
-            num_stages = args.pp * 2
-        case "dualpipev-seq":
-            schedule = DUALPIPEV_SEQUENTIAL_MB6_SCHEDULE
-            num_stages = args.pp * 2    
+            schedule = build_dualpipev_schedule(args.mbs, args.pp)
+            num_stages *= v
+        case "dualpipev-nozb":
+            assert False
         case "zerobubble":
-            schedule = ZEROBUBBLE_MB4_SCHEDULE
-            num_stages = args.pp
+            schedule = build_zerobubble_schedule(args.mbs, args.pp)
+        case "interleaved-zerobubble":
+            schedule = build_interleaved_zero_bubble(args.mbs, args.pp, v=v)
+            num_stages *= v
 
-    print("Schedule:")
-    print_schedule(schedule)
+    visualize_pipeline_schedule(schedule, f"out/{args.schedule}-pp{args.pp}-mb{args.mbs}")
 
     x = torch.randint(0, config.vocab_size, (batch_size, args.seq_len))
     y = torch.randn(batch_size, args.seq_len, config.vocab_size)
 
     loss_fn = torch.nn.CrossEntropyLoss()
 
+    flops_per_token = compute_transformer_flops_per_token(
+        hidden_dim=config.dim,
+        n_layers=config.n_layers,
+        ffn_dim=config.moe_inter_dim if config.moe_enabled else config.hidden_dim,
+        n_heads=config.n_heads,
+        n_kv_heads=config.n_kv_heads,
+        head_dim=config.head_dim,
+        activation_checkpointing=False,
+        moe_top_k=config.moe_args.top_k if config.moe_enabled else 1,
+    )
+
+    rope_cache = precompute_rope_cache(
+        config.head_dim,
+        config.max_seq_len,
+        config.rope_theta,
+    )
+
     piper_setup(
         PiperQwen3Model,
         model_args=(config, num_stages),
         optim_fn=torch.optim.Adam,
         example_inputs=[x],
         example_outputs=y,
+        loss_fn=loss_fn,
         schedule=schedule,
         naive_gradient_sync=args.naive_grad_sync,
         activation_checkpointing=False,
@@ -94,6 +114,9 @@ def main(args, pg):
         model_dtype=torch.bfloat16,
         pg=pg,
         nsight=args.nsight,
+        model_flops_per_token=flops_per_token,
+        visualize_dag=not args.no_viz,
+        const_attrs={"rope_cache": rope_cache},
     )
 
     print(f"Running {args.warmup} warmup iterations")
@@ -107,7 +130,7 @@ def main(args, pg):
     iter_times = []
     for _ in range(args.iters):
         start = time.perf_counter()
-        losses = piper_exec_dag(loss_fn)
+        losses = piper_exec_dag(loss_fn, log_stats=True, profiling=args.profiling)
         end = time.perf_counter()
         iter_times.append(end - start)
         time.sleep(1)
@@ -161,15 +184,28 @@ def parse_args():
     parser.add_argument("--naive-grad-sync", action="store_true", default=False)
     parser.add_argument("--bucketing", action="store_true", default=False,
                         help="Split stages into per-param-bucket sub-modules for overlapped all-reduce")
+    parser.add_argument("--profiling", action="store_true", default=False,
+                        help="Profile each DAG task: log time and memory delta per task")
     parser.add_argument("--nsight", action="store_true", default=False,
                         help="Whether to use Nsight Systems for tracing")
+    parser.add_argument("--no-viz", action="store_true", default=False,
+                        help="Skip per-rank DAG visualization (speeds up startup for large models)")
     return parser.parse_args()
 
 
+"""
+CUDA_VISIBLE_DEVICES=4,5,6,7 /m-coriander/coriander/mfris/miniconda3/envs/piper/bin/python \
+    /m-coriander/coriander/mfris/miniconda3/envs/piper/lib/python3.10/site-packages/ray/scripts/scripts.py \
+        start --head \
+        --port 4567 \
+        --temp-dir=/m-coriander/coriander/mfris/piper/ray_tmp \
+        --include-dashboard=false
+"""
 if __name__ == "__main__":
     args = parse_args()
     print(args)
     ray.init(
+        address="10.158.48.71:4567",
         namespace="qwen",
         log_to_driver=True,
         include_dashboard=False,
@@ -179,6 +215,6 @@ def parse_args():
     ray.get(pg.ready(), timeout=600)
     print(placement_group_table(pg))
     piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
-    handles = piper_coordinator.run_program.remote(main, args, pg)
+    handles = piper_coordinator.run_program.remote(main, pg, args, pg)
     ray.get(handles)
     ray.shutdown()

From 70e7bdcc115ada4998761a06140c80f1e9d6fc06 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Mon, 13 Apr 2026 20:53:36 +0000
Subject: [PATCH 085/100] add per-bucket AC (split buckets into N ac segments).
 revert flat param buffer back to single buffer per trainable param to avoid
 cuda memory bug. implement optional a2a_ar_no_overlap to avoid EP/DP
 interfrence: orders ar operations after all a2a complete, scheduled on same
 stream to avoid interference. add memory debug logs.

---
 Dockerfile                   |  20 +
 requirements.txt             |   6 +-
 src/piper.py                 |  89 +++-
 src/piper_actor.py           | 777 +++++++++++++++++++++++++++--------
 src/piper_compile.py         |   6 +
 src/piper_graph_transform.py | 286 ++++++++++++-
 src/piper_utils.py           |   5 +-
 test/models/qwen3.py         |   2 +-
 test/test_llama.py           |  29 +-
 test/test_qwen.py            | 184 +++++++--
 10 files changed, 1156 insertions(+), 248 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index 553f037..c2195fe 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,5 +1,25 @@
 FROM rayproject/ray:2.44.1-py310-cu128
 
+USER root
+
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends ca-certificates wget && \
+    . /etc/os-release && \
+    arch="$(dpkg --print-architecture)" && \
+    case "$arch" in \
+        amd64) cuda_arch="x86_64" ;; \
+        arm64) cuda_arch="sbsa" ;; \
+        *) echo "Unsupported architecture: $arch" >&2; exit 1 ;; \
+    esac && \
+    cuda_repo="https://developer.download.nvidia.com/compute/cuda/repos/${ID}${VERSION_ID//./}/${cuda_arch}" && \
+    wget -q "${cuda_repo}/cuda-keyring_1.1-1_all.deb" -O /tmp/cuda-keyring.deb && \
+    dpkg -i /tmp/cuda-keyring.deb && \
+    apt-get update && \
+    apt-get install -y --no-install-recommends cuda-nsight-systems-12-8 && \
+    rm -rf /var/lib/apt/lists/* /tmp/cuda-keyring.deb
+
+USER ray
+
 COPY requirements.txt /tmp/requirements.txt
 RUN pip install --no-cache-dir -r /tmp/requirements.txt \
     --extra-index-url https://download.pytorch.org/whl/cu128
diff --git a/requirements.txt b/requirements.txt
index ee32a24..2d96a59 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,8 @@
+torchtitan
 numpy
-ray[default]==2.44.1
 torch==2.10.0
+click<8.1
 cupy-cuda12x
 bitsandbytes
-graphviz
\ No newline at end of file
+graphviz
+matplotlib
diff --git a/src/piper.py b/src/piper.py
index de6363a..f2b1d2f 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -45,6 +45,7 @@ def scan(module):
     compute_critical_path,
     print_dag_order,
     bucket_stage,
+    apply_activation_checkpointing,
     split_by_a2a,
 )
 from .piper_exec import Chunk, TaskType, BatchMeta
@@ -102,7 +103,7 @@ def piper(gm, example_inputs, **kwargs):
         actor_stages.append((actor, stage_id))
 
         # Split at A2A annotation boundaries (expert-parallel only, requires dp_degree > 1).
-        if dp_degree > 1:
+        if dp_degree > 1 or True:
             a2a_segments, boundary_infos = split_by_a2a(stage_gm, graphargs, input_idxs, param_idxs)
             if boundary_infos:
                 logger.debug(
@@ -127,7 +128,29 @@ def piper(gm, example_inputs, **kwargs):
             else:
                 buckets = [(seg_gm, seg_in, seg_param, seg_args)]
 
-            all_modules.extend(buckets)
+            for bucket_gm, bucket_in, bucket_param, bucket_args in buckets:
+                original_bucket_gm = bucket_gm
+                requested_ac_subgraphs = 1
+                if piper_metadata.use_activation_checkpointing:
+                    requested_ac_subgraphs = piper_metadata.activation_num_checkpoints
+                    ac_gms = apply_activation_checkpointing(
+                        bucket_gm,
+                        bucket_args,
+                        bucket_in,
+                        bucket_param,
+                        piper_metadata.activation_num_checkpoints,
+                    )
+                    if len(ac_gms) > 1:
+                        bucket_gm = ac_gms
+                actual_ac_subgraphs = len(bucket_gm) if isinstance(bucket_gm, list) else 1
+                if piper_metadata.use_activation_checkpointing:
+                    logger.info(
+                        f"[ac_compile_bucket] stage={stage_id} segment={seg_idx} "
+                        f"requested_subgraphs={requested_ac_subgraphs} "
+                        f"actual_subgraphs={actual_ac_subgraphs} "
+                        f"inputs={len(bucket_in)} params={len(bucket_param)}"
+                    )
+                all_modules.append((bucket_gm, bucket_in, bucket_param, bucket_args, original_bucket_gm))
             seg_end_bucket = len(all_modules) - 1  # inclusive index of last bucket in this segment
 
             if seg_idx < len(boundary_infos):
@@ -138,7 +161,7 @@ def piper(gm, example_inputs, **kwargs):
             stage_a2a_boundaries[stage_id] = a2a_boundaries
 
         stage_bucket_counts[stage_id] = len(all_modules)
-        for b_idx, (bgm, b_in, b_param, bargs) in enumerate(all_modules):
+        for b_idx, (bgm, b_in, b_param, bargs, _original_gm) in enumerate(all_modules):
             has_trainable = any(
                 bargs[i] is not None and getattr(bargs[i], "requires_grad", False)
                 for i in b_param
@@ -150,16 +173,43 @@ def piper(gm, example_inputs, **kwargs):
                     f"Stage {stage_id} bucket {b_idx} has no trainable parameters."
                 )
 
-        modules_data = [
-            {
-                "gm_data": _serialize_graphmodule(bgm),
-                "graphargs": bargs,
-                "input_idxs": b_in,
-                "param_idxs": b_param,
-                "triton_constant_args": _collect_triton_constant_args(bgm),
-            }
-            for bgm, b_in, b_param, bargs in all_modules
-        ]
+        modules_data = []
+        for bgm, b_in, b_param, bargs, original_gm in all_modules:
+            placeholder_names = [
+                n.name for n in original_gm.graph.nodes if n.op == "placeholder"
+            ]
+            if isinstance(bgm, list):
+                modules_data.append(
+                    {
+                        "gm_data_list": [_serialize_graphmodule(sgm) for sgm in bgm],
+                        "ac_num_subgraphs": len(bgm),
+                        "ac_requested_subgraphs": piper_metadata.activation_num_checkpoints,
+                        "graphargs": bargs,
+                        "input_idxs": b_in,
+                        "param_idxs": b_param,
+                        "placeholder_names": placeholder_names,
+                        "triton_constant_args_list": [
+                            _collect_triton_constant_args(sgm) for sgm in bgm
+                        ],
+                    }
+                )
+            else:
+                modules_data.append(
+                    {
+                        "gm_data": _serialize_graphmodule(bgm),
+                        "ac_num_subgraphs": 1,
+                        "ac_requested_subgraphs": (
+                            piper_metadata.activation_num_checkpoints
+                            if piper_metadata.use_activation_checkpointing
+                            else 1
+                        ),
+                        "graphargs": bargs,
+                        "input_idxs": b_in,
+                        "param_idxs": b_param,
+                        "placeholder_names": placeholder_names,
+                        "triton_constant_args": _collect_triton_constant_args(bgm),
+                    }
+                )
 
         all_stage_compiled[stage_id] = (modules_data, a2a_boundaries, actor)
 
@@ -206,6 +256,7 @@ def piper(gm, example_inputs, **kwargs):
             piper_metadata.stage_bucket_counts,
             stage_a2a_boundaries,
             dp_degree,
+            a2a_ar_no_overlap=piper_metadata.a2a_ar_no_overlap,
         )
         logger.debug(
             f"Built task DAG: {len(dag.nodes)} nodes, "
@@ -248,8 +299,8 @@ def piper(gm, example_inputs, **kwargs):
                     visualize_dag(per_rank_dag, output_path=f"out/rank{pp_rank}_dag")
                 except Exception as e:
                     logger.warning(f"DAG visualization failed for rank {pp_rank} (DAG may be too large for dot): {e}")
-            # uncomment for debugging DAG construction:
-            # print_dag_order(per_rank_dag, label=f"rank {pp_rank}")
+                print_dag_order(per_rank_dag, label=f"rank {pp_rank}", rank=pp_rank)
+        
         ray.get([
             actors[pp_rank].load_dag.remote(per_rank_dag)
             for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags)
@@ -257,7 +308,6 @@ def piper(gm, example_inputs, **kwargs):
         logger.debug(
             f"Loaded per-rank DAGs onto {len(piper_metadata.per_rank_dags)} actors"
         )
-
         # Publish compiled data so dp_rank > 0 can skip torch.compile.
         piper_metadata.compiled_stage_data = {
             "stages": all_stage_compiled,           # {stage_id: (modules_data, a2a_boundaries)}
@@ -267,12 +317,9 @@ def piper(gm, example_inputs, **kwargs):
             "full_dag_no_overlap": piper_metadata.full_dag_no_overlap,
         }
 
-    example_outputs = original_gm(*example_inputs)
-
-    def callback(*args):
-        # assert False
+    def callback(*args, _gm=gm):
         logger.warning("Should not directly call compiled module, running non-distributed execution")
-        return example_outputs
+        return _gm(*args)
 
     del original_gm
     del example_inputs
diff --git a/src/piper_actor.py b/src/piper_actor.py
index b76b169..e979f4d 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -60,7 +60,7 @@ def _create_actors(
         global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
         nsight_env = {"nsight": {
             "t": "cuda,cudnn,cublas,nvtx",
-            "cuda-event-trace": "false",
+            # "cuda-event-trace": "false",
             "stop-on-exit": "true",
         }} if profile else {}
         nccl_env = {
@@ -151,8 +151,9 @@ def __init__(
         self.labels = None
 
         self.comp_stream = torch.cuda.Stream()
-        self.comm_stream = torch.cuda.Stream()
-        self.a2a_stream = torch.cuda.Stream()
+        self.ar_stream = torch.cuda.Stream()
+        # self.a2a_stream = torch.cuda.Stream()
+        self.a2a_stream = self.ar_stream
         self.p2p_send_stream = torch.cuda.Stream()
         self.p2p_recv_stream = torch.cuda.Stream()
 
@@ -188,15 +189,15 @@ def __init__(
         # "Bucket" here is a globally unique unit of computation; each stage may
         # contribute one or more buckets.  The actor is agnostic to which stage
         # owns a bucket.
-        self.bucket_fwd_fns: dict = {}          # ubid -> fwd function
+        self.bucket_fwd_fns: dict = {}          # ubid -> bucket forward runner
         self.bucket_fwd_args: dict = {}         # ubid -> args list (with None holes for inputs)
         self.bucket_input_idxs: dict = {}       # ubid -> list of input slot indices
         self.bucket_param_idxs: dict = {}       # ubid -> list of param slot indices
         self.bucket_param_names: dict = {}      # ubid -> [FX placeholder name per param]
         self.bucket_optims: dict = {}           # ubid -> optimizer (or None)
-        self.bucket_flat_params: dict = {}      # ubid -> flat param tensor (or None)
-        self.bucket_flat_grads: dict = {}       # ubid -> flat grad tensor (or None)
         self.bucket_trainable_param_idxs: dict = {}  # ubid -> list of trainable param slot indices
+        self.bucket_ac_subgraph_counts: dict = {}    # ubid -> number of sequential AC subgraphs
+        self._logged_bucket_forward_info: set = set()
 
         # Non-trainable constant tensor attributes (e.g. freqs_cis, mask) pushed
         # from the coordinator before compilation so _load_stage can fill them in
@@ -343,11 +344,11 @@ def _join_pp_process_group(self):
             ]
             lo_hi_group = dist.new_group(ranks=group_ranks, backend="nccl")
             hi_lo_group = dist.new_group(ranks=group_ranks, backend="nccl")
-            
+
             if self.global_rank in group_ranks:
                 self.pp_lo_hi = lo_hi_group
                 self.pp_hi_lo = hi_lo_group
-            
+
             # for i in range(len(group_ranks)):
             #     for j in range(i + 1, len(group_ranks)):
             #         rank_lo, rank_hi = group_ranks[i], group_ranks[j]
@@ -378,7 +379,7 @@ def _replace_meta_constants(gm, device):
         from .piper_utils import create_logger, LOG_LEVEL
         from torch._subclasses.fake_tensor import FakeTensor
         logger = create_logger("piper_actor", LOG_LEVEL)
-        
+
         replaced_count = 0
         params_count = 0
         buffers_count = 0
@@ -388,7 +389,7 @@ def _replace_meta_constants(gm, device):
         node_args_count = 0
         node_meta_count = 0
         submodule_count = 0
-        
+
         def is_meta_or_fake(t):
             """Check if tensor is on meta device or is a FakeTensor."""
             if isinstance(t, FakeTensor):
@@ -396,16 +397,16 @@ def is_meta_or_fake(t):
             if isinstance(t, torch.Tensor):
                 return t.device.type == 'meta'
             return False
-        
+
         def replace_meta_tensor(t):
             if is_meta_or_fake(t):
                 return torch.empty_like(t, device=device)
             return t
-        
+
         def replace_in_module(module, module_name=""):
             """Recursively replace meta tensors in a module and all its submodules."""
             nonlocal replaced_count, params_count, buffers_count, get_attr_count, constants_count, attrs_count, node_args_count, node_meta_count, submodule_count
-            
+
             # replace all parameters that are on meta device or FakeTensor
             for name, param in module.named_parameters(recurse=False):
                 if is_meta_or_fake(param):
@@ -416,7 +417,7 @@ def replace_in_module(module, module_name=""):
                     setattr(module, name, torch.nn.Parameter(new_param, requires_grad=param.requires_grad))
                     params_count += 1
                     replaced_count += 1
-            
+
             # replace all buffers that are on meta device or FakeTensor
             for name, buffer in module.named_buffers(recurse=False):
                 if is_meta_or_fake(buffer):
@@ -426,8 +427,8 @@ def replace_in_module(module, module_name=""):
                     setattr(module, name, new_buffer)
                     buffers_count += 1
                     replaced_count += 1
-            
-            # check gm 
+
+            # check gm
             if isinstance(module, fx.GraphModule):
                 # replace in get_attr nodes
                 for node in module.graph.nodes:
@@ -443,7 +444,7 @@ def replace_in_module(module, module_name=""):
                                 replaced_count += 1
                         except AttributeError:
                             pass
-                    
+
                     # check node.meta for tensor values
                     if hasattr(node, 'meta') and isinstance(node.meta, dict):
                         for key, value in node.meta.items():
@@ -453,7 +454,7 @@ def replace_in_module(module, module_name=""):
                                 node.meta[key] = torch.empty_like(value, device=device)
                                 node_meta_count += 1
                                 replaced_count += 1
-                
+
                 # replace in constants dict if it exists
                 if hasattr(module, '_constants'):
                     for key, value in module._constants.items():
@@ -463,7 +464,7 @@ def replace_in_module(module, module_name=""):
                             module._constants[key] = torch.empty_like(value, device=device)
                             constants_count += 1
                             replaced_count += 1
-                
+
                 # traverse all nodes and replace meta tensors in args/kwargs
                 for node in module.graph.nodes:
                     # replace in args
@@ -487,7 +488,7 @@ def replace_in_module(module, module_name=""):
                         else:
                             new_args.append(arg)
                     node.args = tuple(new_args)
-                    
+
                     # replace in kwargs
                     new_kwargs = {}
                     for key, value in node.kwargs.items():
@@ -509,18 +510,18 @@ def replace_in_module(module, module_name=""):
                         else:
                             new_kwargs[key] = value
                     node.kwargs = new_kwargs
-                
+
                 # recompile the GraphModule after modifications
                 module.recompile()
-            
+
             # recursively process all submodules
             for name, submodule in module.named_children():
                 submodule_name = f"{module_name}.{name}" if module_name else name
                 replace_in_module(submodule, submodule_name)
                 submodule_count += 1
-        
+
         replace_in_module(gm)
-        
+
         if replaced_count > 0:
             logger.debug(
                 f"_replace_meta_constants: Replaced {replaced_count} meta device tensors "
@@ -530,7 +531,7 @@ def replace_in_module(module, module_name=""):
             )
         else:
             logger.debug(f"_replace_meta_constants: No meta device tensors found to replace! (checked {submodule_count} submodules)")
-        
+
         return gm
 
     def _load_stage_kernels(self):
@@ -570,7 +571,7 @@ def dummy_kernel_user(tokens, starts, offsets):
                     tokens, starts, offsets,
                     experts_per_rank=1, num_ranks=1, max_len=8, block_size=128
                 )
-            
+
             # Compile the function to trigger kernel registration
             device_idx = int(self.device.split(":")[1]) if ":" in self.device else 0
             with torch.cuda.device(device_idx):
@@ -578,11 +579,11 @@ def dummy_kernel_user(tokens, starts, offsets):
                 dummy_tokens = torch.zeros(8, dtype=torch.int32, device=self.device)
                 dummy_start = torch.zeros(8, dtype=torch.int64, device=self.device)
                 dummy_offsets = torch.zeros(1, dtype=torch.int64, device=self.device)
-                
+
                 # Compile and run to register the kernel
                 compiled_dummy = torch.compile(dummy_kernel_user, mode="reduce-overhead")
                 _ = compiled_dummy(dummy_tokens, dummy_start, dummy_offsets)
-            
+
             # Register the kernel at index 0 in id_to_kernel (kernel_idx=0 is baked into
             # compiled graphs; constant_args are restored separately in _load_stage).
             from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
@@ -637,8 +638,13 @@ def _load_stage(
         try:
             from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
             for bd in modules_data:
-                for idx, args in bd.get("triton_constant_args", {}).items():
-                    kernel_side_table.constant_args[int(idx)] = args
+                if "triton_constant_args_list" in bd:
+                    const_arg_sets = bd.get("triton_constant_args_list", [])
+                else:
+                    const_arg_sets = [bd.get("triton_constant_args", {})]
+                for arg_set in const_arg_sets:
+                    for idx, args in arg_set.items():
+                        kernel_side_table.constant_args[int(idx)] = args
         except Exception as e:
             self.logger.warning(f"Actor {self.global_rank} failed to restore triton constant_args: {e}")
 
@@ -646,19 +652,26 @@ def _load_stage(
 
         for b_idx, bd in enumerate(modules_data):
             ubid: int = bd["unique_bucket_id"]
-            gm = _deserialize_graphmodule(bd["gm_data"])
-            gm = self._replace_meta_constants(gm, self.device)
+            ac_num_subgraphs = int(bd.get("ac_num_subgraphs", 1))
+            ac_requested_subgraphs = int(bd.get("ac_requested_subgraphs", ac_num_subgraphs))
+            if "gm_data_list" in bd:
+                gms = [
+                    self._replace_meta_constants(_deserialize_graphmodule(gm_data), self.device)
+                    for gm_data in bd["gm_data_list"]
+                ]
+            else:
+                gms = [self._replace_meta_constants(_deserialize_graphmodule(bd["gm_data"]), self.device)]
             if b_idx == 0:
-                first_gm = gm
+                first_gm = gms[0]
 
             forward_args = list(bd["graphargs"])
             b_input_idxs = list(bd["input_idxs"])
             b_param_idxs = list(bd["param_idxs"])
+            shared_placeholder_names = list(bd.get("placeholder_names", []))
 
             # Extract FX placeholder names for each param index.
-            gm_placeholders = [n for n in gm.graph.nodes if n.op == 'placeholder']
             self.bucket_param_names[ubid] = [
-                gm_placeholders[i].name if i < len(gm_placeholders) else f"ubid{ubid}_p{i}"
+                shared_placeholder_names[i] if i < len(shared_placeholder_names) else f"ubid{ubid}_p{i}"
                 for i in b_param_idxs
             ]
 
@@ -692,7 +705,7 @@ def _load_stage(
                     # Non-trainable slot: try to fill from const attrs (freqs_cis, mask, …)
                     # before falling back to zeros.  Dynamo names direct model attrs as
                     # "l_self_<attr_name>", so strip that prefix to get the bare name.
-                    ph_name = gm_placeholders[i].name if i < len(gm_placeholders) else ""
+                    ph_name = shared_placeholder_names[i] if i < len(shared_placeholder_names) else ""
                     attr_name = re.sub(r'^l_self_', '', ph_name)
                     const_val = self.model_const_attrs.get(attr_name)
                     if (
@@ -705,54 +718,98 @@ def _load_stage(
                         t.zero_()
                 realized[i] = t
 
-            # Forward function (with optional activation checkpointing).
-            if use_activation_checkpointing:
-                fwd = gm.forward
-                forward_fn = lambda *args, _fn=fwd: torch.utils.checkpoint.checkpoint(
-                    _fn, *args, use_reentrant=False
-                )
-            else:
-                forward_fn = gm.forward
+            shared_name_to_idx = {
+                name: i for i, name in enumerate(shared_placeholder_names)
+            }
+            subgraph_specs = []
+            for gm in gms:
+                sub_placeholder_names = [
+                    n.name for n in gm.graph.nodes if n.op == "placeholder"
+                ]
+                dynamic_names = [
+                    name for name in sub_placeholder_names
+                    if name not in shared_name_to_idx
+                ]
+                subgraph_specs.append((gm.forward, sub_placeholder_names, dynamic_names))
+
+            def _bucket_forward_runner(shared_args, _specs=subgraph_specs, _name_to_idx=shared_name_to_idx, _use_ac=use_activation_checkpointing):
+                out = None
+                for forward_impl, placeholder_names, dynamic_names in _specs:
+                    if len(dynamic_names) == 0:
+                        dynamic_values = []
+                    elif len(dynamic_names) == 1:
+                        dyn_arg = out
+                        if isinstance(dyn_arg, (tuple, list)) and len(dyn_arg) == 1:
+                            dyn_arg = dyn_arg[0]
+                        dynamic_values = [dyn_arg]
+                    else:
+                        if not isinstance(out, (tuple, list)):
+                            raise RuntimeError(
+                                f"Bucket forward expected {len(dynamic_names)} dynamic inputs "
+                                f"but previous subgraph produced {type(out).__name__}"
+                            )
+                        dynamic_values = list(out)
+                        if len(dynamic_values) != len(dynamic_names):
+                            raise RuntimeError(
+                                f"Bucket forward expected {len(dynamic_names)} dynamic inputs "
+                                f"but previous subgraph produced {len(dynamic_values)} values"
+                            )
+                    dynamic_name_to_value = {
+                        name: value for name, value in zip(dynamic_names, dynamic_values)
+                    }
+                    call_args = []
+                    for name in placeholder_names:
+                        if name in dynamic_name_to_value:
+                            call_args.append(dynamic_name_to_value[name])
+                        else:
+                            call_args.append(shared_args[_name_to_idx[name]])
+                    if _use_ac:
+                        out = torch.utils.checkpoint.checkpoint(
+                            forward_impl,
+                            *call_args,
+                            use_reentrant=False,
+                        )
+                    else:
+                        out = forward_impl(*call_args)
+                return out
 
-            self.bucket_fwd_fns[ubid] = forward_fn
+            self.bucket_fwd_fns[ubid] = _bucket_forward_runner
             self.bucket_fwd_args[ubid] = realized
             self.bucket_param_idxs[ubid] = b_param_idxs
+            self.bucket_ac_subgraph_counts[ubid] = ac_num_subgraphs
 
-            # Collect trainable parameters and build a contiguous flat tensor so
-            # a single all-reduce call can sync the entire module's gradients.
+            # Keep trainable params as separate tensors. This avoids flat parameter
+            # and packed-gradient workspaces while debugging memory pressure.
             trainable_idxs = [
                 i for i in b_param_idxs
                 if realized[i] is not None and realized[i].requires_grad
             ]
-            trainable = [realized[i] for i in trainable_idxs]
             self.bucket_trainable_param_idxs[ubid] = trainable_idxs
 
-            if trainable:
-                flat_params = torch.cat([t.detach().view(-1) for t in trainable]).contiguous()
-                flat_params.requires_grad_(False)
-                flat_grads = torch.zeros_like(flat_params)
-                offset = 0
-                for tidx, t in zip(trainable_idxs, trainable):
-                    numel = t.numel()
-                    realized[tidx] = realized[tidx].detach()
-                    realized[tidx].data = flat_params[offset:offset + numel].view(t.shape)
-                    realized[tidx].requires_grad_(True)
-                    offset += numel
-                self.bucket_flat_params[ubid] = flat_params
-                self.bucket_flat_grads[ubid] = flat_grads
-            else:
-                self.bucket_flat_params[ubid] = None
-                self.bucket_flat_grads[ubid] = None
-
             # Optimizer for this module's trainable parameters.
             trainable_for_optim = [realized[i] for i in trainable_idxs]
             optim = self.optim_class(trainable_for_optim, fused=True) if trainable_for_optim else None
             self.bucket_optims[ubid] = optim
 
+            self.logger.debug(
+                f"[ac_bucket] rank={self.global_rank} stage={stage_id} bucket={b_idx} "
+                f"ubid={ubid}: ac_enabled={use_activation_checkpointing} "
+                f"requested_subgraphs={ac_requested_subgraphs} "
+                f"actual_subgraphs={ac_num_subgraphs} inputs={len(b_input_idxs)} "
+                f"params={len(b_param_idxs)} trainable_params={len(trainable_idxs)}"
+            )
+
 
         # Keep first GraphModule for compatibility with external inspection tools.
         self.graph_modules[stage_id] = first_gm
 
+        allocated_gb = torch.cuda.memory_allocated(self.device) / (1024 ** 3)
+        reserved_gb = torch.cuda.memory_reserved(self.device) / (1024 ** 3)
+        self.logger.info(
+            f"_load_stage stage_id={stage_id}: GPU allocated={allocated_gb:.2f} GB, "
+            f"reserved={reserved_gb:.2f} GB"
+        )
+
     # -----------------------------------------------------------------------
     # DAG-based execution
     # -----------------------------------------------------------------------
@@ -763,11 +820,17 @@ def load_dag(self, dag: TaskDAG) -> None:
 
     def get_all_params(self) -> dict:
         """Return {unique_bucket_id: flat_cpu_tensor} for every trainable bucket."""
-        return {
-            ubid: tensor.detach().cpu().clone()
-            for ubid, tensor in self.bucket_flat_params.items()
-            if tensor is not None
-        }
+        result: dict = {}
+        for ubid, trainable_idxs in self.bucket_trainable_param_idxs.items():
+            params = []
+            bucket_args = self.bucket_fwd_args.get(ubid, [])
+            for idx in trainable_idxs:
+                tensor = bucket_args[idx]
+                if tensor is not None:
+                    params.append(tensor.detach().cpu().view(-1))
+            if params:
+                result[ubid] = torch.cat(params).clone()
+        return result
 
     def get_named_params(self) -> dict:
         """Return {fx_placeholder_name: cpu_float32_tensor} for all trainable params."""
@@ -808,7 +871,7 @@ def _timing_stream(self, task_type) -> "torch.cuda.Stream":
         if task_type in (TaskType.FWD_A2A, TaskType.BWD_A2A):
             return self.a2a_stream
         if task_type == TaskType.ALL_REDUCE:
-            return self.comm_stream
+            return self.ar_stream
         return self.comp_stream
 
     def get_node_runtimes(self) -> dict:
@@ -823,6 +886,202 @@ def get_node_runtimes(self) -> dict:
             if node.profiling_measurements
         }
 
+    def _clear_param_grads(self) -> None:
+        """Restore standard PyTorch semantics: inactive params keep grad=None."""
+        for ubid, trainable_idxs in self.bucket_trainable_param_idxs.items():
+            args = self.bucket_fwd_args[ubid]
+            for idx in trainable_idxs:
+                t = args[idx]
+                if t is not None:
+                    t.grad = None
+
+    def _param_has_effective_grad(self, param: torch.Tensor | None) -> bool:
+        """Return True when a param carries a materialized gradient."""
+        return param is not None and param.grad is not None
+
+    def _collect_saved_tensors(
+        self,
+        obj: Any,
+        seen_objs: set[int],
+        seen_tensors: set[int],
+        tensors: list[torch.Tensor],
+    ) -> None:
+        obj_id = id(obj)
+        if obj_id in seen_objs:
+            return
+        seen_objs.add(obj_id)
+
+        if isinstance(obj, torch.Tensor):
+            tensor_id = id(obj)
+            if tensor_id not in seen_tensors:
+                seen_tensors.add(tensor_id)
+                tensors.append(obj)
+            return
+
+        if isinstance(obj, dict):
+            for v in obj.values():
+                self._collect_saved_tensors(v, seen_objs, seen_tensors, tensors)
+            return
+
+        if isinstance(obj, (list, tuple, set)):
+            for v in obj:
+                self._collect_saved_tensors(v, seen_objs, seen_tensors, tensors)
+            return
+
+        if isinstance(obj, Node):
+            for name in dir(obj):
+                if not name.startswith("_saved_"):
+                    continue
+                try:
+                    value = getattr(obj, name)
+                except Exception:
+                    continue
+                self._collect_saved_tensors(value, seen_objs, seen_tensors, tensors)
+
+    def _summarize_param_groups(self, param_groups: list[dict[str, Any]]) -> dict[str, int]:
+        grad_tensor_count = 0
+        grad_numel = 0
+        grad_bytes = 0
+        intermediate_count = 0
+        param_count = 0
+        saved_tensors: list[torch.Tensor] = []
+        seen_objs: set[int] = set()
+        seen_tensors: set[int] = set()
+
+        for param_group in param_groups:
+            intermediates = param_group.get("intermediates", [])
+            grads = param_group.get("grads", [])
+            params = param_group.get("params", [])
+            intermediate_count += len(intermediates)
+            param_count += len(params)
+
+            for grads_tuple in grads:
+                if grads_tuple is None:
+                    continue
+                for grad in grads_tuple:
+                    if grad is None:
+                        continue
+                    grad_tensor_count += 1
+                    grad_numel += grad.numel()
+                    grad_bytes += grad.numel() * grad.element_size()
+
+            for intermediate in intermediates:
+                self._collect_saved_tensors(intermediate, seen_objs, seen_tensors, saved_tensors)
+
+        saved_numel = sum(t.numel() for t in saved_tensors)
+        saved_bytes = sum(t.numel() * t.element_size() for t in saved_tensors)
+
+        return {
+            "groups": len(param_groups),
+            "intermediates": intermediate_count,
+            "params": param_count,
+            "grad_tensors": grad_tensor_count,
+            "grad_numel": grad_numel,
+            "grad_bytes": grad_bytes,
+            "saved_tensors": len(saved_tensors),
+            "saved_numel": saved_numel,
+            "saved_bytes": saved_bytes,
+        }
+
+    def _summarize_outstanding_bwdi(self) -> dict[str, int]:
+        buffers = 0
+        groups = 0
+        params = 0
+        intermediates = 0
+        grad_tensors = 0
+        grad_bytes = 0
+        saved_tensors = 0
+        saved_bytes = 0
+
+        for value in self.task_buffer.values():
+            if not isinstance(value, dict):
+                continue
+            param_groups = value.get("param_groups")
+            if param_groups is None:
+                continue
+            buffers += 1
+            summary = self._summarize_param_groups(param_groups)
+            groups += summary["groups"]
+            params += summary["params"]
+            intermediates += summary["intermediates"]
+            grad_tensors += summary["grad_tensors"]
+            grad_bytes += summary["grad_bytes"]
+            saved_tensors += summary["saved_tensors"]
+            saved_bytes += summary["saved_bytes"]
+
+        return {
+            "buffers": buffers,
+            "groups": groups,
+            "params": params,
+            "intermediates": intermediates,
+            "grad_tensors": grad_tensors,
+            "grad_bytes": grad_bytes,
+            "saved_tensors": saved_tensors,
+            "saved_bytes": saved_bytes,
+        }
+
+    def _accumulate_tensor_bytes(self, obj: Any, seen: set[int]) -> int:
+        if isinstance(obj, torch.Tensor):
+            obj_id = id(obj)
+            if obj_id in seen:
+                return 0
+            seen.add(obj_id)
+            return obj.numel() * obj.element_size()
+
+        if isinstance(obj, dict):
+            return sum(self._accumulate_tensor_bytes(v, seen) for v in obj.values())
+
+        if isinstance(obj, (list, tuple, set)):
+            return sum(self._accumulate_tensor_bytes(v, seen) for v in obj)
+
+        return 0
+
+    def _summarize_task_buffer(self) -> dict[str, float]:
+        categories = {
+            "fwd_mb": 0,
+            "shape_ref": 0,
+            "recv": 0,
+            "bwd_i": 0,
+            "bwd": 0,
+            "a2a": 0,
+            "other": 0,
+        }
+        counts = {k: 0 for k in categories}
+
+        for key, value in self.task_buffer.items():
+            seen: set[int] = set()
+            nbytes = self._accumulate_tensor_bytes(value, seen)
+
+            category = "other"
+            if isinstance(key, tuple) and len(key) == 2 and key[0] == "shape_ref":
+                category = "shape_ref"
+            elif isinstance(key, tuple) and len(key) == 2 and isinstance(key[0], int):
+                category = "fwd_mb"
+            elif isinstance(value, list):
+                category = "recv"
+            elif isinstance(value, dict):
+                if "param_groups" in value:
+                    category = "bwd_i"
+                elif "inp_grads" in value:
+                    category = "bwd"
+                elif "detached_outs" in value:
+                    category = "a2a" if "pre_detach_outs" not in value else "other"
+
+            categories[category] += nbytes
+            counts[category] += 1
+
+        summary = {}
+        total_bytes = 0
+        total_entries = 0
+        for key, nbytes in categories.items():
+            summary[f"{key}_bytes"] = nbytes
+            summary[f"{key}_count"] = counts[key]
+            total_bytes += nbytes
+            total_entries += counts[key]
+        summary["total_bytes"] = total_bytes
+        summary["total_entries"] = total_entries
+        return summary
+
     def run_dag(self, loss_fn=None, profiling: bool = False):
         """Execute the loaded TaskDAG in topological order.
 
@@ -847,22 +1106,7 @@ def run_dag(self, loss_fn=None, profiling: bool = False):
         self.a2a_events = {}
         self.ar_events = {}
         self.bwd_events = {}
-
-        # Point each trainable param's .grad at the appropriate slice of its
-        # bucket's flat_grads tensor so backward accumulates contiguously.
-        for ubid, trainable_idxs in self.bucket_trainable_param_idxs.items():
-            flat_grads = self.bucket_flat_grads.get(ubid)
-            if flat_grads is None:
-                continue
-            flat_grads.zero_()
-            args = self.bucket_fwd_args[ubid]
-            offset = 0
-            for i in trainable_idxs:
-                t = args[i]
-                if t is not None:
-                    numel = t.numel()
-                    t.grad = flat_grads[offset:offset + numel].view(t.shape)
-                    offset += numel
+        self._clear_param_grads()
 
         comp_events: dict = {}  # task_uid -> cuda.Event for compute tasks
 
@@ -898,7 +1142,7 @@ def _ts_key(n):
             mb_idx = batch.mb_idx
             ubid = node.unique_bucket_id  # None for non-compute nodes
 
-            self.logger.info(
+            self.logger.debug(
                 f"run_dag dispatch (time {node.time_step}): {chunk.type.value} "
                 f"mb{mb_idx} ubid={ubid}"
             )
@@ -941,8 +1185,8 @@ def _ts_key(n):
                     else:
                         # BWD recv: pre-allocate based on FWD output shapes.
                         fwd_ubid = node.custom_metadata["fwd_ubid"]
-                        out_with_grad = self.task_buffer[("shape_ref", fwd_ubid)]
-                        recv_tensors = self._exec_recv_bwd(out_with_grad, node.peer_pp_rank)
+                        shape_meta = self.task_buffer[("shape_ref", fwd_ubid)]
+                        recv_tensors = self._exec_recv_bwd(shape_meta, node.peer_pp_rank)
                     self.task_buffer[node.uid] = recv_tensors
                     recv_evt = torch.cuda.Event()
                     recv_evt.record(self.p2p_recv_stream)
@@ -980,8 +1224,15 @@ def _ts_key(n):
                     bwd_buf = dict(self.task_buffer[bwd_pred.uid])
                     # Analog of FWD_A2A change 3: free predecessor buffer after copying.
                     # Skip for BWD_I — its param_groups must survive until BWD_W.
+                    # Skip for BWD that is an ALL_REDUCE trigger — the ALL_REDUCE handler
+                    # will consume and delete the buffer itself.
                     if bwd_pred.chunk.type == TaskType.BWD:
-                        del self.task_buffer[bwd_pred.uid]
+                        has_ar_succ = any(
+                            s.chunk.type == TaskType.ALL_REDUCE
+                            for s in bwd_pred.data_succs
+                        )
+                        if not has_ar_succ:
+                            del self.task_buffer[bwd_pred.uid]
                     inp_grads = list(bwd_buf["inp_grads"])
                     grad_a2a_out = inp_grads[tensor_idx]
                     assert grad_a2a_out is not None, (
@@ -1003,15 +1254,10 @@ def _ts_key(n):
 
                 case TaskType.ALL_REDUCE:
                     bwd_node = node.data_preds[0]
-                    flat_grads = self.task_buffer[bwd_node.uid].get("flat_grads")
-                    if flat_grads is not None:
-                        torch.cuda.nvtx.range_push(f"all_reduce_ubid{bwd_node.unique_bucket_id}")
-                        self._exec_all_reduce(flat_grads, comp_events[bwd_node.uid], bwd_node.unique_bucket_id)
-                        torch.cuda.nvtx.range_pop()
-                    else:
-                        self.logger.debug(
-                            f"ALL_REDUCE skipped: no trainable params at time_step={node.time_step}"
-                        )
+                    torch.cuda.nvtx.range_push(f"all_reduce_ubid{bwd_node.unique_bucket_id}")
+                    self._exec_all_reduce_grads(comp_events[bwd_node.uid], bwd_node.unique_bucket_id)
+                    torch.cuda.nvtx.range_pop()
+                    del self.task_buffer[bwd_node.uid]
 
                 case TaskType.FWD:
                     # Wait on any RECV predecessor.
@@ -1045,10 +1291,9 @@ def _ts_key(n):
                     self._stop_timing(self.comp_stream, "forward")
                     torch.cuda.nvtx.range_pop()
                     self.task_buffer[node.uid] = fwd_out
-                    # Shape ref for BWD RECV: store empty placeholder tensors under a
-                    # tuple key so it never aliases task_buffer[node.uid] (both are ints).
-                    # Allows fwd_out to be freed after BWD_I consumes (ubid, mb_idx).
-                    self.task_buffer[("shape_ref", ubid)] = [torch.empty_like(t) for t in fwd_out["out_with_grad"]]
+                    # Shape ref for BWD RECV: store lightweight metadata (shape, dtype) so
+                    # _exec_recv_bwd can pre-allocate receive buffers without holding live tensors.
+                    self.task_buffer[("shape_ref", ubid)] = [(t.shape, t.dtype) for t in fwd_out["out_with_grad"]]
                     self.task_buffer[(ubid, mb_idx)] = fwd_out  # per-mb FWD data for BWD
                     evt = torch.cuda.Event()
                     evt.record(self.comp_stream)
@@ -1116,8 +1361,15 @@ def _ts_key(n):
                                     d.grad = g
                             # Analog of FWD change 2: free predecessor buffer after consuming
                             # inp_grads.  Only for plain BWD — BWD_I must survive until BWD_W.
+                            # Skip deletion if bwd_pred is an ALL_REDUCE trigger: that task
+                            # will consume and delete the buffer itself.
                             if bwd_pred.chunk.type == TaskType.BWD:
-                                del self.task_buffer[bwd_pred.uid]
+                                has_ar_succ = any(
+                                    s.chunk.type == TaskType.ALL_REDUCE
+                                    for s in bwd_pred.data_succs
+                                )
+                                if not has_ar_succ:
+                                    del self.task_buffer[bwd_pred.uid]
                         else:
                             # Last (or only) stage in backward order, or has a RECV for
                             # upstream grads (cross-rank predecessor): standard backward.
@@ -1136,7 +1388,6 @@ def _ts_key(n):
                     self._stop_timing(self.comp_stream, "backward")
                     torch.cuda.nvtx.range_pop()
                     buf = bwd_out if bwd_out is not None else {}
-                    buf["flat_grads"] = self.bucket_flat_grads.get(ubid)
                     # Full-length inp_grads parallel to fwd_inputs / detached_outs so
                     # BWD_A2A can index with a2a_tensor_idx without filtering offset.
                     fwd_inputs_full = fwd_out.get("fwd_inputs")
@@ -1147,6 +1398,11 @@ def _ts_key(n):
                         else [t.grad for t in (inp_with_grad or [])]
                     )
                     self.task_buffer[node.uid] = buf
+                    # FWD stored the same dict under both node.uid and (ubid, mb_idx).
+                    # Clear the dict in-place so any remaining alias stops keeping
+                    # activations and autograd edges alive through the optimizer step.
+                    fwd_out.clear()
+                    del self.task_buffer[(ubid, mb_idx)]
                     evt = torch.cuda.Event()
                     evt.record(self.comp_stream)
                     comp_events[node.uid] = evt
@@ -1228,19 +1484,34 @@ def _ts_key(n):
                         inp_grads_full = list(dinputs)
                     # Store param_groups for BWD_W (accessed via data_succs edge).
                     self.task_buffer[node.uid] = {
-                        "dinputs": dinputs,
                         "inp_grads": inp_grads_full,
                         "param_groups": param_groups,
-                        "flat_grads": self.bucket_flat_grads.get(ubid),
                     }
+                    pg_summary = self._summarize_param_groups(param_groups)
+                    outstanding = self._summarize_outstanding_bwdi()
+                    self.logger.info(
+                        f"[bwd_i_retain] rank={self.global_rank} ubid={ubid} mb={mb_idx}: "
+                        f"groups={pg_summary['groups']} "
+                        f"params={pg_summary['params']} "
+                        f"intermediates={pg_summary['intermediates']} "
+                        f"grad_tensors={pg_summary['grad_tensors']} "
+                        f"grad_bytes_gb={pg_summary['grad_bytes'] / (1024 ** 3):.2f} "
+                        f"saved_tensors={pg_summary['saved_tensors']} "
+                        f"saved_bytes_gb={pg_summary['saved_bytes'] / (1024 ** 3):.2f} "
+                        f"outstanding_buffers={outstanding['buffers']} "
+                        f"outstanding_grad_bytes_gb={outstanding['grad_bytes'] / (1024 ** 3):.2f} "
+                        f"outstanding_saved_bytes_gb={outstanding['saved_bytes'] / (1024 ** 3):.2f} "
+                        f"alloc_gb={torch.cuda.memory_allocated(self.device) / (1024 ** 3):.2f} "
+                        f"reserved_gb={torch.cuda.memory_reserved(self.device) / (1024 ** 3):.2f}"
+                    )
                     # Propagate input grad to SEND (if this is the first bucket and has predecessors).
                     if dinputs:
                         self.task_buffer[node.uid]["send_output"] = list(dinputs)
-                    # Free per-microbatch FWD data: all tensors from it have been consumed
-                    # by _bucket_backward_input.  This removes the last Python reference to
-                    # fwd_out (task_buffer[ubid] now holds only shape placeholders after
-                    # the FWD task change; task_buffer[node_fwd.uid] was freed by SEND),
-                    # allowing the stage-boundary activation tensors to be GC'd.
+                    # FWD stored the same dict under both node.uid and (ubid, mb_idx).
+                    # Clear the dict in-place so any remaining alias stops keeping
+                    # activations and autograd edges alive through the optimizer step.
+                    fwd_out.clear()
+                    del stage_outputs_or_loss, fwd_out
                     del self.task_buffer[(ubid, mb_idx)]
                     evt = torch.cuda.Event()
                     evt.record(self.comp_stream)
@@ -1255,19 +1526,33 @@ def _ts_key(n):
                     # the wrong task.  Find BWD_I explicitly by type to be safe.
                     bwdi_node = next(p for p in node.data_preds if p.chunk.type == TaskType.BWD_I)
                     param_groups = self.task_buffer[bwdi_node.uid]["param_groups"]
+                    pg_summary = self._summarize_param_groups(param_groups)
+                    outstanding = self._summarize_outstanding_bwdi()
+                    self.logger.info(
+                        f"[bwd_w_begin] rank={self.global_rank} ubid={ubid} mb={mb_idx}: "
+                        f"groups={pg_summary['groups']} "
+                        f"params={pg_summary['params']} "
+                        f"intermediates={pg_summary['intermediates']} "
+                        f"grad_tensors={pg_summary['grad_tensors']} "
+                        f"grad_bytes_gb={pg_summary['grad_bytes'] / (1024 ** 3):.2f} "
+                        f"saved_tensors={pg_summary['saved_tensors']} "
+                        f"saved_bytes_gb={pg_summary['saved_bytes'] / (1024 ** 3):.2f} "
+                        f"outstanding_buffers={outstanding['buffers']} "
+                        f"outstanding_grad_bytes_gb={outstanding['grad_bytes'] / (1024 ** 3):.2f} "
+                        f"outstanding_saved_bytes_gb={outstanding['saved_bytes'] / (1024 ** 3):.2f} "
+                        f"alloc_gb={torch.cuda.memory_allocated(self.device) / (1024 ** 3):.2f} "
+                        f"reserved_gb={torch.cuda.memory_reserved(self.device) / (1024 ** 3):.2f}"
+                    )
                     b_fwd_args = self.bucket_fwd_args[ubid]
                     weights = [b_fwd_args[i] for i in self.bucket_param_idxs[ubid]
                                if b_fwd_args[i] is not None]
                     torch.cuda.nvtx.range_push(f"backward_weight_ubid{ubid}_mb{mb_idx}")
                     self._start_timing(self.comp_stream, "backward_weight")
                     with torch.cuda.stream(self.comp_stream):
-                        self._bucket_backward_weight(iter(weights), param_groups)
+                        self._bucket_backward_weight(iter(weights), param_groups, ubid=ubid, mb_idx=mb_idx)
                     self._stop_timing(self.comp_stream, "backward_weight")
                     torch.cuda.nvtx.range_pop()
-                    # Store flat_grads so ALL_REDUCE can find them when BWD_W is the trigger.
-                    # bucket_flat_grads.get(ubid) is None for non-trainable buckets; the
-                    # ALL_REDUCE handler's "if flat_grads is not None" guard skips those.
-                    self.task_buffer[node.uid] = {"flat_grads": self.bucket_flat_grads.get(ubid)}
+                    self.task_buffer[node.uid] = {}
                     # BWD_I buffer is no longer needed: _bucket_backward_weight already
                     # deleted param_groups["intermediates"] and param_groups["grads"] and
                     # the autograd graph was freed by retain_graph=False.  Drop the shell.
@@ -1290,6 +1575,30 @@ def _ts_key(n):
                 self._prof_records.append((node, mb_idx, ubid, _start_evt, _end_evt, _mem_before))
                 self.logger.info(f"Rank {self.global_rank} task {chunk.type.value} (time {node.time_step}) mem before {_mem_before / 1024 ** 3:.2f} GB after {_mem_after / 1024 ** 3:.2f} GB")
 
+            tb_summary = self._summarize_task_buffer()
+            self.logger.debug(
+                f"[task_mem] rank={self.global_rank} time={node.time_step} "
+                f"task={chunk.type.value} mb={mb_idx} ubid={ubid}: "
+                f"alloc_gb={torch.cuda.memory_allocated(self.device) / (1024 ** 3):.2f} "
+                f"reserved_gb={torch.cuda.memory_reserved(self.device) / (1024 ** 3):.2f} "
+                f"tb_entries={tb_summary['total_entries']} "
+                f"tb_total_gb={tb_summary['total_bytes'] / (1024 ** 3):.2f} "
+                f"fwd_mb_count={tb_summary['fwd_mb_count']} "
+                f"fwd_mb_gb={tb_summary['fwd_mb_bytes'] / (1024 ** 3):.2f} "
+                f"recv_count={tb_summary['recv_count']} "
+                f"recv_gb={tb_summary['recv_bytes'] / (1024 ** 3):.2f} "
+                f"bwd_i_count={tb_summary['bwd_i_count']} "
+                f"bwd_i_gb={tb_summary['bwd_i_bytes'] / (1024 ** 3):.2f} "
+                f"bwd_count={tb_summary['bwd_count']} "
+                f"bwd_gb={tb_summary['bwd_bytes'] / (1024 ** 3):.2f} "
+                f"shape_ref_count={tb_summary['shape_ref_count']} "
+                f"shape_ref_gb={tb_summary['shape_ref_bytes'] / (1024 ** 3):.2f} "
+                f"a2a_count={tb_summary['a2a_count']} "
+                f"a2a_gb={tb_summary['a2a_bytes'] / (1024 ** 3):.2f} "
+                f"other_count={tb_summary['other_count']} "
+                f"other_gb={tb_summary['other_bytes'] / (1024 ** 3):.2f}"
+            )
+
     def _exec_send(self, send_data, peer_pp_rank: int) -> None:
         """Send tensors to peer_pp_rank on p2p_send_stream.
 
@@ -1328,16 +1637,17 @@ def _exec_recv_fwd(self, recv_ubid: int, peer_pp_rank: int) -> list:
             self._stop_timing(self.p2p_recv_stream, "p2p_recv")
         return buf
 
-    def _exec_recv_bwd(self, out_with_grad: list, peer_pp_rank: int) -> list:
+    def _exec_recv_bwd(self, shape_meta: list, peer_pp_rank: int) -> list:
         """Receive BWD upstream gradients from peer_pp_rank.
 
-        Buffer shapes are derived from out_with_grad (FWD outputs saved in task_buffer).
+        Buffer shapes are derived from shape_meta, a list of (shape, dtype) tuples
+        stored in task_buffer[("shape_ref", ubid)] after the corresponding FWD.
         Returns the received gradient list (stored in task_buffer by run_dag).
         """
         global_src_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
         self.logger.debug(f"exec_recv_bwd from global rank {global_src_rank}")
 
-        buf = [torch.empty_like(a) for a in out_with_grad]
+        buf = [torch.empty(shape, dtype=dtype, device=self.device) for shape, dtype in shape_meta]
         with torch.cuda.stream(self.p2p_recv_stream):
             self._start_timing(self.p2p_recv_stream, "p2p_recv")
             pp_group = self.pp_hi_lo if global_src_rank > self.global_rank else self.pp_lo_hi
@@ -1396,9 +1706,17 @@ def _forward_dag(self, ubid: int, mb_idx: int, input_tensors) -> dict:
 
         inp_with_grad = [t for t in fwd_inputs if t is not None and t.requires_grad]
 
+        if ubid not in self._logged_bucket_forward_info:
+            self._logged_bucket_forward_info.add(ubid)
+            self.logger.debug(
+                f"[ac_forward] rank={self.global_rank} ubid={ubid}: "
+                f"ac_subgraphs={self.bucket_ac_subgraph_counts.get(ubid, 1)} "
+                f"inputs={len(input_idxs)}"
+            )
+
         # Run the forward function.
         with torch.cuda.stream(self.comp_stream):
-            output = fwd_fn(*fwd_args)
+            output = fwd_fn(fwd_args)
 
         # Clear input slots so we don't hold stale tensor references.
         for i in input_idxs:
@@ -1526,16 +1844,25 @@ def hook(grad_inputs):
         else:
             dinputs = ()
 
-        for t in stage_outputs_or_loss:
-            t.detach_()
-        # stage_outputs_or_loss = [t.detach() for t in stage_outputs_or_loss]
+        # Match torch.distributed.pipelining.stage_backward_input(): once dinputs
+        # are computed, these outputs are no longer needed by the later weight
+        # pass, so detach them to let autograd release the dead part of the graph.
+        for i, t in enumerate(stage_outputs_or_loss):
+            if not isinstance(t, torch.Tensor):
+                continue
+            try:
+                t.detach_()
+            except RuntimeError:
+                # Some tensors may be views; replace the list entry so the original
+                # graph-carrying tensor can be released once local refs are dropped.
+                stage_outputs_or_loss[i] = t.detach()
 
         for handle in handles:
             handle.remove()
 
         return dinputs, param_groups
 
-    def _bucket_backward_weight(self, weights, param_groups: list) -> None:
+    def _bucket_backward_weight(self, weights, param_groups: list, ubid: int | None = None, mb_idx: int | None = None) -> None:
         """Compute weight gradients for a single bucket (BWD_W).
 
         Matches ``torch.distributed.pipelining.stage_backward_weight`` exactly,
@@ -1569,12 +1896,23 @@ def _bucket_backward_weight(self, weights, param_groups: list) -> None:
             if not weight_edges:
                 continue
 
-            dweights = torch.autograd.grad(
-                valid_edges,
-                weight_edges,
-                grad_outputs=valid_grad_outputs,
-                retain_graph=False,
-            )
+            try:
+                dweights = torch.autograd.grad(
+                    valid_edges,
+                    weight_edges,
+                    grad_outputs=valid_grad_outputs,
+                    retain_graph=False,
+                )
+            except torch.OutOfMemoryError:
+                self.logger.error(
+                    f"[bwd_w_oom] rank={self.global_rank} ubid={ubid} mb={mb_idx}: "
+                    f"group_params={len(param_group['params'])} "
+                    f"group_valid_edges={len(valid_edges)} "
+                    f"group_grad_outputs={len(valid_grad_outputs)} "
+                    f"alloc_gb={torch.cuda.memory_allocated(self.device) / (1024 ** 3):.2f} "
+                    f"reserved_gb={torch.cuda.memory_reserved(self.device) / (1024 ** 3):.2f}"
+                )
+                raise
 
             del param_group["grads"]
 
@@ -1587,28 +1925,66 @@ def _bucket_backward_weight(self, weights, param_groups: list) -> None:
                 else:
                     weight.grad += dw
 
-    def _exec_all_reduce(self, flat_grads: torch.Tensor, bwd_event: torch.cuda.Event, ubid: int) -> None:
-        """Launch an all-reduce for *flat_grads* on comm_stream.
+    def _exec_all_reduce_grads(self, bwd_event: torch.cuda.Event, ubid: int) -> None:
+        """All-reduce each materialized parameter grad for *ubid* on ar_stream."""
+        args = self.bucket_fwd_args.get(ubid, [])
+        trainable_idxs = self.bucket_trainable_param_idxs.get(ubid, [])
+        grad_tensors = []
+        for idx in trainable_idxs:
+            param = args[idx]
+            if param is not None and param.grad is not None:
+                grad_tensors.append((idx, param.grad))
+
+        if not grad_tensors:
+            self.logger.debug(
+                f"[all_reduce_bucket] rank={self.global_rank} ubid={ubid}: "
+                "skipped no materialized grads"
+            )
+            return
 
-        *flat_grads* comes directly from the predecessor BWD/BWD_I task's task_buffer
-        entry.  comm_stream waits on *bwd_event* (ensuring all gradient accumulation is
-        complete) then records an event so _update can wait before stepping.
-        """
-        with torch.cuda.stream(self.comm_stream):
-            self.comm_stream.wait_event(bwd_event)
-            self._start_timing(self.comm_stream, "all_reduce")
-            dist.all_reduce(flat_grads, group=self.dp_group)
-            self._stop_timing(self.comm_stream, "all_reduce")
+        total_numel = sum(grad.numel() for _, grad in grad_tensors)
+        total_bytes = sum(grad.numel() * grad.element_size() for _, grad in grad_tensors)
+        largest_idx, largest_grad = max(grad_tensors, key=lambda item: item[1].numel())
+        largest_bytes = largest_grad.numel() * largest_grad.element_size()
+        self.logger.info(
+            f"[all_reduce_bucket] rank={self.global_rank} ubid={ubid}: "
+            f"mode=per_param grad_tensors={len(grad_tensors)} "
+            f"grad_numel={total_numel} "
+            f"grad_gb={total_bytes / (1024 ** 3):.4f} "
+            f"largest_idx={largest_idx} "
+            f"largest_numel={largest_grad.numel()} "
+            f"largest_gb={largest_bytes / (1024 ** 3):.4f} "
+            f"grad_dtype={largest_grad.dtype} "
+            f"dp_degree={self.dp_degree}"
+        )
+        for grad_order, (idx, grad) in enumerate(grad_tensors):
+            grad_bytes = grad.numel() * grad.element_size()
+            data_ptr_mod_256 = grad.data_ptr() % 256 if grad.is_cuda else -1
+            self.logger.info(
+                f"[all_reduce_grad] rank={self.global_rank} ubid={ubid} "
+                f"order={grad_order} idx={idx}: "
+                f"shape={tuple(grad.shape)} stride={tuple(grad.stride())} "
+                f"numel={grad.numel()} gb={grad_bytes / (1024 ** 3):.4f} "
+                f"dtype={grad.dtype} device={grad.device} "
+                f"contiguous={grad.is_contiguous()} "
+                f"storage_offset={grad.storage_offset()} "
+                f"data_ptr_mod_256={data_ptr_mod_256}"
+            )
+        with torch.cuda.stream(self.ar_stream):
+            self.ar_stream.wait_event(bwd_event)
+            self._start_timing(self.ar_stream, "all_reduce")
+            for _, grad in grad_tensors:
+                dist.all_reduce(grad, group=self.dp_group)
+            self._stop_timing(self.ar_stream, "all_reduce")
             evt = torch.cuda.Event()
-            evt.record(self.comm_stream)
+            evt.record(self.ar_stream)
         self.ar_events[ubid] = evt
 
     def _update(self, *deps):
         self._start_timing(self.comp_stream, "optim_step")
 
-        # Snapshot flat_params and mean grad before stepping, to log the change.
-        # We clone on comp_stream (after all bwd events) then log after synchronize().
-        pre_snapshots: dict = {}  # ubid -> (params_before, mean_grad)
+        pre_step_mem: dict = {}
+        grad_debug: dict = {}
 
         for ubid, optim in self.bucket_optims.items():
             if optim is None:
@@ -1620,16 +1996,70 @@ def _update(self, *deps):
             if ar_evt is not None:
                 self.comp_stream.wait_event(ar_evt)
 
-            flat_params = self.bucket_flat_params.get(ubid)
-            flat_grads = self.bucket_flat_grads.get(ubid)
-            if flat_params is not None:
-                with torch.cuda.stream(self.comp_stream):
-                    params_before = flat_params.clone()
-                    mean_grad = flat_grads.abs().mean() if flat_grads is not None else None
-                pre_snapshots[ubid] = (params_before, mean_grad)
+            params = self.bucket_fwd_args.get(ubid, [])
+            trainable_idxs = self.bucket_trainable_param_idxs.get(ubid, [])
 
-            with torch.cuda.stream(self.comp_stream):
-                optim.step()
+            params_with_grad = 0
+            total_trainable_params = 0
+            total_grad_elems = 0
+            for idx in trainable_idxs:
+                p = params[idx]
+                if p is None:
+                    continue
+                total_trainable_params += p.numel()
+                if p.grad is not None:
+                    params_with_grad += 1
+                    total_grad_elems += p.grad.numel()
+
+            # Exact zero-gradient detection requires scanning each grad tensor and
+            # can allocate a large temporary near peak memory. Treat materialized
+            # grads as active here; structural sparse expert metadata would be
+            # needed to preserve skip-zero semantics without a dense value scan.
+            nonzero_grad_params = params_with_grad
+            nonzero_grad_elems = total_grad_elems
+            zero_grad_params = 0
+
+            state_entries_before = sum(len(optim.state[p]) > 0 for p in optim.param_groups[0]["params"])
+            pre_step_mem[ubid] = (
+                torch.cuda.memory_allocated(self.device),
+                torch.cuda.memory_reserved(self.device),
+            )
+            grad_debug[ubid] = {
+                "num_params": len(trainable_idxs),
+                "params_with_grad": params_with_grad,
+                "nonzero_grad_params": nonzero_grad_params,
+                "total_trainable_params": total_trainable_params,
+                "total_grad_elems": total_grad_elems,
+                "nonzero_grad_elems": nonzero_grad_elems,
+                "zero_grad_params": zero_grad_params,
+                "state_entries_before": state_entries_before,
+            }
+            self.logger.debug(
+                f"[optim_debug_pre] rank={self.global_rank} ubid={ubid}: "
+                f"params={len(trainable_idxs)} "
+                f"params_with_grad={params_with_grad} "
+                f"nonzero_grad_params={nonzero_grad_params} "
+                f"zero_grad_params={zero_grad_params} "
+                f"trainable_elems={total_trainable_params} "
+                f"grad_elems={total_grad_elems} "
+                f"nonzero_grad_elems={nonzero_grad_elems} "
+                f"state_entries_before={state_entries_before} "
+                f"alloc_before_gb={pre_step_mem[ubid][0] / (1024 ** 3):.2f} "
+                f"reserved_before_gb={pre_step_mem[ubid][1] / (1024 ** 3):.2f}"
+            )
+            try:
+                with torch.cuda.stream(self.comp_stream):
+                    optim.step()
+            except torch.OutOfMemoryError:
+                alloc_now = torch.cuda.memory_allocated(self.device)
+                reserv_now = torch.cuda.memory_reserved(self.device)
+                self.logger.error(
+                    f"[optim_debug_oom] rank={self.global_rank} ubid={ubid}: "
+                    f"state_entries_before={state_entries_before} "
+                    f"alloc_now_gb={alloc_now / (1024 ** 3):.2f} "
+                    f"reserved_now_gb={reserv_now / (1024 ** 3):.2f}"
+                )
+                raise
         self._stop_timing(self.comp_stream, "optim_step")
 
         losses = self.loss
@@ -1637,15 +2067,30 @@ def _update(self, *deps):
 
         torch.cuda.synchronize()
 
-        # Log mean grad and mean param change per bucket to confirm optimizer is updating.
-        # for ubid, (params_before, mean_grad_t) in pre_snapshots.items():
-        #     flat_params = self.bucket_flat_params[ubid]
-        #     diff = (flat_params - params_before).abs().mean().item()
-        #     mean_grad_val = mean_grad_t.item() if mean_grad_t is not None else float("nan")
-        #     self.logger.info(
-        #         f"[update] rank={self.global_rank} ubid={ubid}: "
-        #         f"mean_grad={mean_grad_val:.4e}  mean_param_diff={diff:.4e}"
-        #     )
+        for ubid, optim in self.bucket_optims.items():
+            if optim is None or ubid not in grad_debug:
+                continue
+            alloc_before, reserv_before = pre_step_mem[ubid]
+            alloc_after = torch.cuda.memory_allocated(self.device)
+            reserv_after = torch.cuda.memory_reserved(self.device)
+            state_entries_after = sum(len(optim.state[p]) > 0 for p in optim.param_groups[0]["params"])
+            dbg = grad_debug[ubid]
+            self.logger.debug(
+                f"[optim_debug] rank={self.global_rank} ubid={ubid}: "
+                f"params={dbg['num_params']} "
+                f"params_with_grad={dbg['params_with_grad']} "
+                f"nonzero_grad_params={dbg['nonzero_grad_params']} "
+                f"zero_grad_params={dbg['zero_grad_params']} "
+                f"trainable_elems={dbg['total_trainable_params']} "
+                f"grad_elems={dbg['total_grad_elems']} "
+                f"nonzero_grad_elems={dbg['nonzero_grad_elems']} "
+                f"state_entries_before={dbg['state_entries_before']} "
+                f"state_entries_after={state_entries_after} "
+                f"alloc_before_gb={alloc_before / (1024 ** 3):.2f} "
+                f"alloc_after_gb={alloc_after / (1024 ** 3):.2f} "
+                f"reserved_before_gb={reserv_before / (1024 ** 3):.2f} "
+                f"reserved_after_gb={reserv_after / (1024 ** 3):.2f}"
+            )
 
         # Process deferred profiling records now that all GPU work is complete.
         # elapsed_time() is safe to call after synchronize().
@@ -1661,4 +2106,4 @@ def _update(self, *deps):
                 )
 
         return losses
-    
\ No newline at end of file
+
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 90e20f7..659662b 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -73,7 +73,9 @@ def piper_setup(
     schedule: PipelineSchedule=None,
     naive_gradient_sync=False,
     activation_checkpointing=False,
+    num_checkpoints=1,
     bucketing=False,
+    a2a_ar_no_overlap=False,
     pg=None,
     nsight=False,
     model_flops_per_token: float = None,
@@ -91,6 +93,8 @@ def piper_setup(
         schedule: 2D schedule grid (rank x time_step).
         naive_gradient_sync: Use a simple blocking all-reduce instead of
             pipelined per-param hooks.
+        a2a_ar_no_overlap: When true, schedule gradient all-reduces after
+            same-rank A2A operations to avoid NCCL interference.
     """
 
     # Clear Dynamo's global compilation cache so that a previous piper_setup
@@ -102,7 +106,9 @@ def piper_setup(
     assert len(stage_to_device) > 0
     piper_metadata.stage_to_device = stage_to_device
     piper_metadata.use_activation_checkpointing = activation_checkpointing
+    piper_metadata.activation_num_checkpoints = max(1, int(num_checkpoints))
     piper_metadata.bucketing = bucketing
+    piper_metadata.a2a_ar_no_overlap = a2a_ar_no_overlap
     piper_metadata.schedule = schedule
     piper_metadata.visualize_dag = visualize_dag
 
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index ddc65b4..3425457 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -1604,6 +1604,7 @@ def expand_chunks_to_dags(
     bucket_counts: dict,
     a2a_info: dict,
     dp_degree: int,
+    a2a_ar_no_overlap: bool = False,
 ) -> "TaskDAG":
     """Build a :class:`TaskDAG` by expanding every schedule chunk in-place.
 
@@ -1633,6 +1634,9 @@ def expand_chunks_to_dags(
             ALL_REDUCE nodes are inserted for every BWD/BWD_W bucket.
             Whether a bucket actually has trainable parameters is checked
             at runtime in run_dag, keeping the schedule model-agnostic.
+        a2a_ar_no_overlap: When true, adds same-rank temporal dependencies
+            from the last backward compute task to ALL_REDUCE nodes so
+            gradient all-reduces run after backward compute on that actor.
 
     Returns:
         A new :class:`TaskDAG` containing all compute, A2A, SEND/RECV, and
@@ -1837,6 +1841,28 @@ def expand_chunks_to_dags(
             ar_node.data_preds.append(trigger)
             all_tasks.append(ar_node)
 
+    # ------------------------------------------------------------------
+    # Pass 6: Optional backward-compute / ALL_REDUCE no-overlap ordering
+    # ------------------------------------------------------------------
+    if a2a_ar_no_overlap:
+        last_bwd_compute_by_rank: dict = {}
+        ar_by_rank: dict = defaultdict(list)
+        for task in all_tasks:
+            if task.chunk.type in (TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W):
+                last_bwd_compute_by_rank[task.pp_rank] = task
+            elif task.chunk.type == TaskType.ALL_REDUCE:
+                ar_by_rank[task.pp_rank].append(task)
+
+        for pp_rank, ar_tasks in ar_by_rank.items():
+            last_bwd_compute = last_bwd_compute_by_rank.get(pp_rank)
+            if last_bwd_compute is None:
+                continue
+            for ar_task in ar_tasks:
+                if ar_task not in last_bwd_compute.temporal_succs:
+                    last_bwd_compute.temporal_succs.append(ar_task)
+                if last_bwd_compute not in ar_task.temporal_preds:
+                    ar_task.temporal_preds.append(last_bwd_compute)
+
     return TaskDAG(nodes=all_tasks)
 
 
@@ -2217,12 +2243,15 @@ def _node_fontcolor(node: Task) -> str:
 
 
 
-def print_dag_order(dag: TaskDAG, label: str = "") -> None:
-    """Print the topological execution order of nodes in a :class:`TaskDAG`.
+def print_dag_order(dag: TaskDAG, label: str = "", rank: int = 0) -> None:
+    """Write the topological execution order of nodes in a :class:`TaskDAG` to a file.
 
     Uses Kahn's algorithm (same order as ``run_dag``) so the output reflects
     exactly what the actor will execute.  Useful for debugging schedule issues.
+
+    Output is written to ``out/dag_order_rank{rank}``.
     """
+    import os
     from collections import deque as _deque
 
     in_degree = {
@@ -2232,7 +2261,7 @@ def print_dag_order(dag: TaskDAG, label: str = "") -> None:
     queue: _deque = _deque(n for n in dag.nodes if in_degree[id(n)] == 0)
 
     header = f"--- DAG execution order{': ' + label if label else ''} ---"
-    print(header)
+    lines = [header]
     step = 0
     while queue:
         node = queue.popleft()
@@ -2242,13 +2271,18 @@ def print_dag_order(dag: TaskDAG, label: str = "") -> None:
             f"s{b.stage_id} mb{b.mb_idx}" for b in chunk.batches
         ) if chunk.batches else ""
         bkt = f" bkt={node.bucket_id}" if node.bucket_id else ""
-        print(f"  {step:3d}  rank={node.pp_rank}  {ttype:<14s}  {batches_str}{bkt}")
+        lines.append(f"  {step:3d}  rank={node.pp_rank}  {ttype:<14s}  {batches_str}{bkt}")
         step += 1
         for succ in list(node.data_succs) + list(node.temporal_succs):
             in_degree[id(succ)] -= 1
             if in_degree[id(succ)] == 0:
                 queue.append(succ)
-    print("-" * len(header))
+    lines.append("-" * len(header))
+
+    os.makedirs("out", exist_ok=True)
+    out_path = f"out/dag_order_rank{rank}"
+    with open(out_path, "w") as f:
+        f.write("\n".join(lines) + "\n")
 
 
 # ---------------------------------------------------------------------------
@@ -2789,6 +2823,231 @@ def _add_ph(nd: fx.Node, name: str, is_input: bool) -> None:
     return results
 
 
+def apply_activation_checkpointing(
+    stage_gm: fx.GraphModule,
+    graphargs: list,
+    input_idxs: list[int],
+    param_idxs: list[int],
+    num_checkpoints: int,
+) -> list[fx.GraphModule]:
+    """Split a bucket into sequential activation-checkpoint regions.
+
+    The regions are chosen to be roughly balanced by parameter bytes. Live
+    intermediate tensors can flow between adjacent regions as needed, so a
+    later region may receive and forward multiple carried tensors.
+    """
+    num_checkpoints = max(1, int(num_checkpoints))
+    if num_checkpoints <= 1:
+        return [stage_gm]
+    gm_name = getattr(stage_gm, "_get_name", lambda: type(stage_gm).__name__)()
+
+    nodes = list(stage_gm.graph.nodes)
+    node_idx: dict[fx.Node, int] = {nd: i for i, nd in enumerate(nodes)}
+    ph_nodes = [nd for nd in nodes if nd.op == "placeholder"]
+    param_ph_set = {ph_nodes[i] for i in param_idxs if i < len(ph_nodes)}
+    param_ph_list = [ph_nodes[i] for i in param_idxs if i < len(ph_nodes)]
+    input_ph_set = {ph_nodes[i] for i in input_idxs if i < len(ph_nodes)}
+    compute_nodes = [nd for nd in nodes if nd.op not in ("placeholder", "get_attr", "output")]
+
+    if not param_ph_list or len(compute_nodes) < 2:
+        logger.info(
+            f"[ac_split_skip] gm={gm_name} requested_subgraphs={num_checkpoints} "
+            f"params={len(param_ph_list)} compute_nodes={len(compute_nodes)} "
+            "reason=insufficient_params_or_compute"
+        )
+        return [stage_gm]
+
+    compute_set: frozenset[int] = frozenset(node_idx[nd] for nd in compute_nodes)
+
+    def _tensor_meta(nd: fx.Node):
+        ev = nd.meta.get("example_value")
+        if ev is None:
+            ev = nd.meta.get("val")
+        if isinstance(ev, torch.Tensor):
+            return ev
+        return None
+
+    def _param_size(pnd: fx.Node) -> int:
+        i = ph_nodes.index(pnd)
+        if i < len(graphargs) and graphargs[i] is not None and hasattr(graphargs[i], "numel"):
+            return int(graphargs[i].numel() * graphargs[i].element_size())
+        ev = _tensor_meta(pnd)
+        if ev is not None:
+            return int(ev.numel() * ev.element_size())
+        return 0
+
+    def _use_indices(pnd: fx.Node) -> list[int]:
+        return [node_idx[u] for u in pnd.users if node_idx[u] in compute_set]
+
+    param_first_use: dict[fx.Node, int] = {}
+    for pn in param_ph_list:
+        uses = _use_indices(pn)
+        param_first_use[pn] = min(uses) if uses else node_idx[pn]
+
+    param_sizes = {pn: _param_size(pn) for pn in param_ph_list}
+    total_param_bytes = sum(param_sizes.values())
+    if total_param_bytes <= 0:
+        logger.info(
+            f"[ac_split_skip] gm={gm_name} requested_subgraphs={num_checkpoints} "
+            f"params={len(param_ph_list)} compute_nodes={len(compute_nodes)} "
+            "reason=nonpositive_param_bytes"
+        )
+        return [stage_gm]
+
+    sorted_params = sorted(param_ph_list, key=lambda pn: param_first_use[pn])
+    running_bytes = 0
+    next_param = 0
+
+    candidate_cuts: list[tuple[int, int]] = []
+    for cut_node in compute_nodes[:-1]:
+        cut_idx = node_idx[cut_node]
+        while next_param < len(sorted_params) and param_first_use[sorted_params[next_param]] <= cut_idx:
+            running_bytes += param_sizes[sorted_params[next_param]]
+            next_param += 1
+        candidate_cuts.append((cut_idx, running_bytes))
+
+    if len(candidate_cuts) < num_checkpoints - 1:
+        logger.warning(
+            f"[ac_split_reject] gm={gm_name} requested_subgraphs={num_checkpoints} "
+            f"params={len(param_ph_list)} compute_nodes={len(compute_nodes)} "
+            f"candidate_cuts={len(candidate_cuts)} required_cuts={num_checkpoints - 1} "
+            "reason=insufficient_compute_cuts"
+        )
+        return [stage_gm]
+
+    selected_cuts: list[int] = []
+    start = 0
+    for split_idx in range(1, num_checkpoints):
+        target = total_param_bytes * split_idx / num_checkpoints
+        remaining = (num_checkpoints - 1) - split_idx
+        eligible = candidate_cuts[start: len(candidate_cuts) - remaining]
+        if not eligible:
+            logger.warning(
+                f"[ac_split_reject] gm={gm_name} requested_subgraphs={num_checkpoints} "
+                f"candidate_cuts={len(candidate_cuts)} chosen_cuts={len(selected_cuts)} "
+                f"failed_pick={split_idx}/{num_checkpoints - 1} reason=no_eligible_cut"
+            )
+            return [stage_gm]
+        best_rel = min(
+            range(len(eligible)),
+            key=lambda rel: abs(eligible[rel][1] - target),
+        )
+        chosen = eligible[best_rel]
+        selected_cuts.append(chosen[0])
+        start += best_rel + 1
+
+    n_segs = num_checkpoints
+
+    def _seg_of(idx: int) -> int:
+        return sum(1 for cut in selected_cuts if cut < idx)
+
+    node_seg: dict[fx.Node, int] = {}
+    for nd in nodes:
+        if nd.op == "output":
+            node_seg[nd] = n_segs - 1
+        elif nd.op == "placeholder":
+            if nd in param_ph_set:
+                uses = _use_indices(nd)
+                node_seg[nd] = _seg_of(min(uses)) if uses else 0
+            else:
+                user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
+                node_seg[nd] = _seg_of(min(user_idxs)) if user_idxs else 0
+        elif nd.op == "get_attr":
+            user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
+            node_seg[nd] = _seg_of(min(user_idxs)) if user_idxs else 0
+        else:
+            node_seg[nd] = _seg_of(node_idx[nd])
+
+    def _consumer_seg(user: fx.Node) -> int:
+        if user.op == "output":
+            return n_segs - 1
+        return node_seg[user]
+
+    seg_live_inputs: list[list[fx.Node]] = [[] for _ in range(n_segs)]
+    for seg in range(1, n_segs):
+        live_nodes: list[fx.Node] = []
+        seen: set[fx.Node] = set()
+        for nd in compute_nodes:
+            if node_seg[nd] >= seg:
+                continue
+            if any(_consumer_seg(user) >= seg for user in nd.users):
+                if nd not in seen:
+                    seen.add(nd)
+                    live_nodes.append(nd)
+        seg_live_inputs[seg] = live_nodes
+
+    seg_placeholder_inputs: list[list[fx.Node]] = [[] for _ in range(n_segs)]
+    for nd in nodes:
+        if nd.op != "placeholder" or nd in param_ph_set:
+            continue
+        for seg in range(n_segs):
+            if any(node_seg[u] == seg for u in nd.users):
+                seg_placeholder_inputs[seg].append(nd)
+
+    results: list[fx.GraphModule] = []
+    for seg in range(n_segs):
+        sub_g = fx.Graph()
+        remap: dict[fx.Node, fx.Node] = {}
+
+        if seg > 0:
+            for live_idx, boundary_nd in enumerate(seg_live_inputs[seg]):
+                new_ph = sub_g.placeholder(f"_ac_in_{live_idx}")
+                new_ph.type = boundary_nd.type
+                remap[boundary_nd] = new_ph
+
+        for nd in seg_placeholder_inputs[seg]:
+            new_ph = sub_g.placeholder(nd.name)
+            new_ph.type = nd.type
+            remap[nd] = new_ph
+
+        for nd in nodes:
+            if nd.op == "placeholder" and nd in param_ph_set and node_seg[nd] == seg:
+                new_ph = sub_g.placeholder(nd.name)
+                new_ph.type = nd.type
+                remap[nd] = new_ph
+
+        for nd in nodes:
+            if nd.op == "get_attr" and node_seg[nd] == seg:
+                new_ga = sub_g.get_attr(nd.target)
+                new_ga.type = nd.type
+                remap[nd] = new_ga
+
+        try:
+            for nd in nodes:
+                if nd.op in ("placeholder", "get_attr", "output"):
+                    continue
+                if node_seg[nd] != seg:
+                    continue
+                remap[nd] = sub_g.node_copy(nd, arg_transform=lambda x, r=remap: r[x])
+        except KeyError as exc:
+            logger.warning(
+                f"[ac_split_reject] gm={gm_name} requested_subgraphs={num_checkpoints} "
+                f"segment={seg} missing_node={exc} reason=non_local_tensor_during_lowering"
+            )
+            return [stage_gm]
+
+        if seg == n_segs - 1:
+            orig_out = next(nd for nd in nodes if nd.op == "output")
+            sub_g.output(fx.map_arg(orig_out.args[0], lambda x: remap[x]))
+        else:
+            live_outputs = [remap[nd] for nd in seg_live_inputs[seg + 1]]
+            if len(live_outputs) == 1:
+                sub_g.output(live_outputs[0])
+            else:
+                sub_g.output(tuple(live_outputs))
+
+        sub_g.lint()
+        results.append(fx.GraphModule(stage_gm, sub_g))
+
+    logger.info(
+        f"[ac_split_accept] gm={gm_name} requested_subgraphs={num_checkpoints} "
+        f"actual_subgraphs={len(results)} candidate_cuts={len(candidate_cuts)} "
+        f"selected_cuts={selected_cuts} "
+        f"live_counts={[len(seg_live_inputs[i]) for i in range(1, n_segs)]}"
+    )
+    return results
+
+
 # ---------------------------------------------------------------------------
 # split_by_a2a: split an FX GraphModule at all-to-all annotation boundaries
 # ---------------------------------------------------------------------------
@@ -3457,7 +3716,9 @@ def assign_time_steps(dag: TaskDAG) -> None:
 
     SEND / RECV nodes have no temporal edges and are assigned after the toposort:
     ``SEND.time_step = data_pred.time_step + 1`` and
-    ``RECV.time_step = data_succ.time_step - 1``.
+    ``RECV.time_step = data_succ.time_step - 1``. ALL_REDUCE nodes are
+    also constrained by their data predecessor, while preserving any
+    temporal barrier inserted by ``a2a_ar_no_overlap``.
     """
     # Kahn's toposort over the temporal graph (temporal_preds / temporal_succs).
     # time_step = max(pred.time_step for all temporal preds) + 1.
@@ -3480,17 +3741,22 @@ def assign_time_steps(dag: TaskDAG) -> None:
             if in_degree[id(succ)] == 0:
                 queue.append(succ)
 
-    # SEND/RECV/ALL_REDUCE nodes have no temporal edges and are skipped by the toposort.
-    # Assign their time_step relative to their data-edge neighbour.
+    # Assign stream-only communication nodes relative to their data-edge neighbour.
+    # ALL_REDUCE may also have temporal predecessors from a2a_ar_no_overlap;
+    # keep whichever constraint places it later.
 
-    # Pass 1: SEND and ALL_REDUCE (depend only on their data predecessor).
+    # Pass 1: SEND and ALL_REDUCE.
     for node in dag.nodes:
         if node.chunk.type == TaskType.SEND:
             assert len(node.data_preds) == 1
             node.time_step = node.data_preds[0].time_step + 1
         elif node.chunk.type == TaskType.ALL_REDUCE:
             assert len(node.data_preds) == 1
-            node.time_step = node.data_preds[0].time_step + 1
+            data_ready_time = node.data_preds[0].time_step + 1
+            if node.temporal_preds:
+                node.time_step = max(node.time_step, data_ready_time)
+            else:
+                node.time_step = data_ready_time
 
     # Pass 2: RECV (depend only on their data successor).
     for node in dag.nodes:
diff --git a/src/piper_utils.py b/src/piper_utils.py
index 84fea93..9c00f53 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -1,3 +1,4 @@
+import sys
 import queue
 import ray
 import torch
@@ -57,7 +58,7 @@ def create_logger(name: str, log_level: str):
     logger.setLevel(log_level)
     
     if not logger.handlers:
-        handler = logging.StreamHandler()
+        handler = logging.StreamHandler(sys.stdout)
         fmt = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
         handler.setFormatter(logging.Formatter(fmt))
         logger.addHandler(handler)
@@ -336,7 +337,9 @@ class PiperMetadata:
     stage_to_device = dict()
     naive_gradient_sync = False
     use_activation_checkpointing = False
+    activation_num_checkpoints = 1
     bucketing = False  # Whether to split stages into per-param-bucket sub-modules
+    a2a_ar_no_overlap = False  # Whether ALL_REDUCE tasks must wait for all same-rank A2A tasks
     schedule = None   # PipelineSchedule set by piper_setup; used by the piper backend
     task_dag = None   # TaskDAG built from schedule by the piper backend
     full_dag_no_overlap = None  # Deep copy of the full DAG (pre-P2P-split, no overlap_a2a_tasks); used for profiling and critical-path analysis
diff --git a/test/models/qwen3.py b/test/models/qwen3.py
index 0fd4cf0..1460df1 100644
--- a/test/models/qwen3.py
+++ b/test/models/qwen3.py
@@ -159,7 +159,7 @@ def create_qwen3_config(name: str) -> Qwen3ModelArgs:
                 moe_enabled=True,
                 moe_inter_dim=768,
                 moe_args=MoEArgs(
-                    num_experts=64,
+                    num_experts=128,
                     num_shared_experts=0,
                     top_k=8,
                     score_func="softmax",
diff --git a/test/test_llama.py b/test/test_llama.py
index 1c0f66e..d3dc932 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -43,12 +43,13 @@ def main(args, pg):
             llama_config = LLAMA_8B
         case '70b':
             llama_config = LLAMA_70B
-    print(args)
+    logger.info(args)
 
-    loss_fn = torch.nn.CrossEntropyLoss()
+    _ce = torch.nn.CrossEntropyLoss()
+    loss_fn = lambda output, labels: _ce(output.view(-1, output.size(-1)), labels.view(-1))
 
     x = torch.randint(0, llama_config.vocab_size, (args.batch_size, args.seq_len))
-    y = torch.randn((args.batch_size, args.seq_len, llama_config.vocab_size))
+    y = torch.randint(0, llama_config.vocab_size, (args.batch_size, args.seq_len))
 
     match args.schedule:
         case "no-pp":
@@ -106,6 +107,7 @@ def main(args, pg):
         schedule=schedule,
         naive_gradient_sync=args.naive_grad_sync,
         activation_checkpointing=args.activation_checkpointing,
+        num_checkpoints=args.num_checkpoints,
         bucketing=args.bucketing,
         model_dtype=torch.bfloat16,
         pg=pg,
@@ -115,14 +117,14 @@ def main(args, pg):
         const_attrs={"freqs_cis": freqs_cis, "mask": mask},
     )
 
-    print(f"Running {args.warmup} warmup iterations...")
+    logger.info(f"Running {args.warmup} warmup iterations...")
     for _ in range(args.warmup):
         piper_exec_dag(loss_fn, log_stats=True, profiling=args.profiling)
         time.sleep(1)
 
     actors = piper_metadata.actors
 
-    print(f"Running {args.iters} timed iterations...")
+    logger.info(f"Running {args.iters} timed iterations...")
     iter_times = []
     for _ in range(args.iters):
         start = time.perf_counter()
@@ -132,7 +134,7 @@ def main(args, pg):
         time.sleep(1)
 
     dp_rank = int(os.environ['PIPER_DP_RANK'])
-    print(
+    logger.info(
         f"rank {dp_rank} iter time= {np.mean(iter_times):.5f} ± {np.std(iter_times):.5f} s "
         f"({len(iter_times)} samples)\n"
         f"rank {dp_rank} throughput= "
@@ -141,7 +143,7 @@ def main(args, pg):
 
     if args.tracing:
         ray.get([actor.set_tracing.remote(True) for actor in actors.values()])
-        print(f"Running {args.trace_iters} tracing iterations...")
+        logger.info(f"Running {args.trace_iters} tracing iterations...")
         for _ in range(args.trace_iters):
             piper_exec_dag(loss_fn)
             ray.get([actor.flush_timing_events.remote() for actor in actors.values()])
@@ -150,7 +152,7 @@ def main(args, pg):
         for rank, trace_data in trace_data_ret:
             for key in trace_data:
                 all_times = trace_data[key]
-                print(
+                logger.info(
                     f"rank {rank} {key} time= {np.mean(all_times):.3f} ± "
                     f"{np.std(all_times):.3f} ms ({len(all_times)} samples)"
                 )
@@ -158,7 +160,7 @@ def main(args, pg):
     os.makedirs("out", exist_ok=True)
     timeline_filename = f"out/llama-dag-pp{args.pp}-dp{args.dp}-{args.schedule}"
     ray.timeline(timeline_filename)
-    print(f"Ray timeline saved to: {timeline_filename}")
+    logger.info(f"Ray timeline saved to: {timeline_filename}")
 
 
 def parse_args():
@@ -181,6 +183,8 @@ def parse_args():
     parser.add_argument('--tracing', action='store_true', default=False)
     parser.add_argument('--naive-grad-sync', action='store_true', default=False)
     parser.add_argument('--activation-checkpointing', action='store_true', default=False)
+    parser.add_argument('--num-checkpoints', type=int, default=1,
+                        help='Number of sequential activation-checkpoint regions per Piper bucket')
     parser.add_argument('--bucketing', action='store_true', default=False,
                         help='Split stages into per-param-bucket sub-modules for overlapped all-reduce')
     parser.add_argument('--nsight', action='store_true', default=False,
@@ -200,17 +204,18 @@ def parse_args():
         --include-dashboard=false
 """
 if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
     args = parse_args()
-    print(args)
+    logger.info(args)
     ray.init(
-        address="10.158.48.71:3456",
+        address="10.158.48.71:4567",
         namespace="llama",
         include_dashboard=False,
         _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp",
     )
     pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="PACK")
     ray.get(pg.ready(), timeout=600)
-    print(placement_group_table(pg))
+    logger.info(placement_group_table(pg))
     piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
     handles = piper_coordinator.run_program.remote(main, pg, args, pg)
     ray.get(handles)
diff --git a/test/test_qwen.py b/test/test_qwen.py
index 7a87e34..041a391 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -17,8 +17,6 @@
 import os
 import numpy as np
 
-logger = logging.getLogger(__name__)
-
 from ray.util.placement_group import (
     placement_group,
     placement_group_table,
@@ -27,7 +25,7 @@
 from src.piper_coordinator import PiperProgramCoordinator
 from src.piper_compile import piper_setup
 from src.piper import piper_exec_dag
-from src.piper_utils import piper_metadata, compute_transformer_flops_per_token
+from src.piper_utils import piper_metadata, compute_transformer_flops_per_token, create_logger, LOG_LEVEL
 
 from .models.qwen3 import PiperQwen3Model, create_qwen3_config
 from torchtitan.models.qwen3.model.model import precompute_rope_cache
@@ -43,9 +41,70 @@
 )
 
 
+logger = create_logger("test_qwen", LOG_LEVEL)
+
 # from triton.backends.amd.driver import HIPDriver
 # HIPDriver.is_active = staticmethod(lambda: False)
 
+
+def _metrics_output_path(args):
+    return os.path.join(
+        args.output_dir,
+        f"qwen{args.model}-pp{args.pp}-dp{args.dp}-{args.schedule}",
+    )
+
+
+def _build_benchmark_metrics(args, dp_rank, iter_times):
+    mean_iter_time = float(np.mean(iter_times))
+    std_iter_time = float(np.std(iter_times))
+    throughput = float((args.batch_size * args.mbs * args.seq_len) / mean_iter_time)
+
+    return {
+        "dp_rank": dp_rank,
+        "model": args.model,
+        "schedule": args.schedule,
+        "pp": args.pp,
+        "dp": args.dp,
+        "batch_size": args.batch_size,
+        "mbs": args.mbs,
+        "seq_len": args.seq_len,
+        "samples": len(iter_times),
+        "iter_time_mean_s": mean_iter_time,
+        "iter_time_std_s": std_iter_time,
+        "throughput_tokens_per_s": throughput,
+        "iter_times_s": [float(iter_time) for iter_time in iter_times],
+    }
+
+
+def _format_benchmark_log_lines(metrics):
+    rank = metrics["dp_rank"]
+    lines = [
+        (
+            f"rank {rank} iter time= {metrics['iter_time_mean_s']:.5f} +/- "
+            f"{metrics['iter_time_std_s']:.5f} s ({metrics['samples']} samples)"
+        ),
+        f"rank {rank} throughput= {metrics['throughput_tokens_per_s']:.3f} tokens/s",
+    ]
+    for trace in metrics.get("trace_times", []):
+        lines.append(
+            f"rank {trace['rank']} {trace['key']} time= {trace['mean_ms']:.3f} +/- "
+            f"{trace['std_ms']:.3f} ms ({trace['samples']} samples)"
+        )
+    lines.append(f"rank {rank} metrics_json= {json.dumps(metrics, sort_keys=True)}")
+    return lines
+
+
+def _write_benchmark_metrics(args, metrics):
+    metrics_path = _metrics_output_path(args)
+    os.makedirs(os.path.dirname(metrics_path), exist_ok=True)
+    with open(metrics_path, "a", encoding="utf-8") as output_file:
+        for line in _format_benchmark_log_lines(metrics):
+            output_file.write(line + "\n")
+        output_file.write("\n")
+
+    return metrics_path
+
+
 def main(args, pg):
     batch_size = args.batch_size
 
@@ -76,12 +135,13 @@ def main(args, pg):
             schedule = build_interleaved_zero_bubble(args.mbs, args.pp, v=v)
             num_stages *= v
 
-    visualize_pipeline_schedule(schedule, f"out/{args.schedule}-pp{args.pp}-mb{args.mbs}")
+    # visualize_pipeline_schedule(schedule, f"out/{args.schedule}-pp{args.pp}-mb{args.mbs}")
 
     x = torch.randint(0, config.vocab_size, (batch_size, args.seq_len))
-    y = torch.randn(batch_size, args.seq_len, config.vocab_size)
+    y = torch.randint(0, config.vocab_size, (batch_size, args.seq_len))
 
-    loss_fn = torch.nn.CrossEntropyLoss()
+    _ce = torch.nn.CrossEntropyLoss()
+    loss_fn = lambda output, labels: _ce(output.view(-1, output.size(-1)), labels.view(-1))
 
     flops_per_token = compute_transformer_flops_per_token(
         hidden_dim=config.dim,
@@ -109,7 +169,8 @@ def main(args, pg):
         loss_fn=loss_fn,
         schedule=schedule,
         naive_gradient_sync=args.naive_grad_sync,
-        activation_checkpointing=False,
+        activation_checkpointing=args.activation_checkpointing,
+        a2a_ar_no_overlap=True,
         bucketing=args.bucketing,
         model_dtype=torch.bfloat16,
         pg=pg,
@@ -119,14 +180,16 @@ def main(args, pg):
         const_attrs={"rope_cache": rope_cache},
     )
 
-    print(f"Running {args.warmup} warmup iterations")
+    del x, y
+
+    logger.info(f"Running {args.warmup} warmup iterations")
     for _ in range(args.warmup):
         piper_exec_dag(loss_fn)
         time.sleep(1)
 
     actors = piper_metadata.actors
 
-    print(f"Running {args.iters} timed iterations")
+    logger.info(f"Running {args.iters} timed iterations")
     iter_times = []
     for _ in range(args.iters):
         start = time.perf_counter()
@@ -136,33 +199,60 @@ def main(args, pg):
         time.sleep(1)
 
     dp_rank = int(os.environ["PIPER_DP_RANK"])
-    print(
-        f"rank {dp_rank} iter time= {np.mean(iter_times):.5f} ± {np.std(iter_times):.5f} s "
-        f"({len(iter_times)} samples)\n"
-        f"rank {dp_rank} throughput= "
-        f"{(args.batch_size * args.mbs * args.seq_len) / np.mean(iter_times):.3f} tokens/s"
+    metrics = _build_benchmark_metrics(
+        args,
+        dp_rank,
+        iter_times,
     )
+    iter_time_msg, throughput_msg, _ = _format_benchmark_log_lines(metrics)
+    metrics_msg = f"rank {dp_rank} benchmark metrics will be saved by the driver to {_metrics_output_path(args)}"
+    logger.info(iter_time_msg)
+    logger.info(throughput_msg)
+    logger.info(metrics_msg)
+    print(iter_time_msg, flush=True)
+    print(throughput_msg, flush=True)
+    print(metrics_msg, flush=True)
 
     if args.tracing:
         ray.get([actor.set_tracing.remote(True) for actor in actors.values()])
-        print(f"Running {args.trace_iters} tracing iterations...")
+        logger.info(f"Running {args.trace_iters} tracing iterations...")
         for _ in range(args.trace_iters):
             piper_exec_dag(loss_fn)
             ray.get([actor.flush_timing_events.remote() for actor in actors.values()])
             time.sleep(1)
         trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
+        trace_times = []
         for rank, trace_data in trace_data_ret:
             for key in trace_data:
                 all_times = trace_data[key]
-                print(
-                    f"rank {rank} {key} time= {np.mean(all_times):.3f} ± "
-                    f"{np.std(all_times):.3f} ms ({len(all_times)} samples)"
+                trace = {
+                    "rank": rank,
+                    "key": key,
+                    "mean_ms": float(np.mean(all_times)),
+                    "std_ms": float(np.std(all_times)),
+                    "samples": len(all_times),
+                    "times_ms": [float(trace_time) for trace_time in all_times],
+                }
+                trace_times.append(trace)
+                trace_msg = (
+                    f"rank {trace['rank']} {trace['key']} time= {trace['mean_ms']:.3f} +/- "
+                    f"{trace['std_ms']:.3f} ms ({trace['samples']} samples)"
                 )
+                logger.info(trace_msg)
+                print(trace_msg, flush=True)
+        metrics["trace_times"] = trace_times
 
-    os.makedirs("out", exist_ok=True)
-    timeline_filename = f"out/qwen-dag-pp{args.pp}-dp{args.dp}-{args.schedule}"
-    ray.timeline(timeline_filename)
-    print(f"Ray timeline saved to: {timeline_filename}")
+    # os.makedirs("out", exist_ok=True)
+    # timeline_filename = f"out/qwen-dag-pp{args.pp}-dp{args.dp}-{args.schedule}"
+    # ray.timeline(timeline_filename)
+    # print(f"Ray timeline saved to: {timeline_filename}")
+    if args.nsight:
+        logger.info("Stopping Piper actors so Nsight Systems reports are flushed")
+        try:
+            ray.get([actor.__ray_terminate__.remote() for actor in actors.values()])
+        except ray.exceptions.ActorDiedError as exc:
+            logger.info(f"Piper actors stopped for Nsight flush: {exc}")
+    return metrics
 
 
 def parse_args():
@@ -182,6 +272,7 @@ def parse_args():
     parser.add_argument("--trace-iters", type=int, default=3)
     parser.add_argument("--tracing", action="store_true", default=False)
     parser.add_argument("--naive-grad-sync", action="store_true", default=False)
+    parser.add_argument('--activation-checkpointing', action='store_true', default=False)
     parser.add_argument("--bucketing", action="store_true", default=False,
                         help="Split stages into per-param-bucket sub-modules for overlapped all-reduce")
     parser.add_argument("--profiling", action="store_true", default=False,
@@ -190,31 +281,54 @@ def parse_args():
                         help="Whether to use Nsight Systems for tracing")
     parser.add_argument("--no-viz", action="store_true", default=False,
                         help="Skip per-rank DAG visualization (speeds up startup for large models)")
+    parser.add_argument("--address", default="",
+                        help="Ray head address to connect to")
+    parser.add_argument("--port", type=int, default=4567,
+                        help="Ray head port to connect to (default: 4567)")
+    parser.add_argument("--temp-dir", default="/tmp/piper/ray_tmp",
+                        help="Ray temp directory (default: /tmp/piper/ray_tmp)")
+    parser.add_argument("--output-dir", default="out",
+                        help="Directory for benchmark metrics output (default: out)")
     return parser.parse_args()
 
 
 """
-CUDA_VISIBLE_DEVICES=4,5,6,7 /m-coriander/coriander/mfris/miniconda3/envs/piper/bin/python \
-    /m-coriander/coriander/mfris/miniconda3/envs/piper/lib/python3.10/site-packages/ray/scripts/scripts.py \
+python /m-coriander/coriander/mfris/miniconda3/envs/piper/lib/python3.10/site-packages/ray/scripts/scripts.py \
         start --head \
         --port 4567 \
         --temp-dir=/m-coriander/coriander/mfris/piper/ray_tmp \
         --include-dashboard=false
 """
 if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
     args = parse_args()
-    print(args)
-    ray.init(
-        address="10.158.48.71:4567",
-        namespace="qwen",
-        log_to_driver=True,
-        include_dashboard=False,
-        _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp",
-    )
-    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="PACK")
+    metrics_path = _metrics_output_path(args)
+    os.makedirs(os.path.dirname(metrics_path), exist_ok=True)
+    open(metrics_path, "w", encoding="utf-8").close()
+    logger.info(args)
+    if args.address != "":
+        ray.init(
+            address=f"{args.address}:{args.port}",
+            namespace="qwen",
+            log_to_driver=True,
+            include_dashboard=False,
+            _temp_dir=args.temp_dir,
+        )
+    else:
+        ray.init(
+            namespace="qwen",
+            log_to_driver=True,
+            include_dashboard=False,
+            _temp_dir=args.temp_dir,
+        )
+    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="SPREAD")
     ray.get(pg.ready(), timeout=600)
-    print(placement_group_table(pg))
+    logger.info(placement_group_table(pg))
     piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
     handles = piper_coordinator.run_program.remote(main, pg, args, pg)
-    ray.get(handles)
+    dp_metrics = ray.get(handles)
+    open(metrics_path, "w", encoding="utf-8").close()
+    for metrics in sorted(dp_metrics, key=lambda item: item["dp_rank"]):
+        _write_benchmark_metrics(args, metrics)
+    logger.info(f"Benchmark metrics saved to {metrics_path}")
     ray.shutdown()

From 9be79a8b56d3881ea5b010a26cd40f495b56b1c7 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Tue, 14 Apr 2026 06:26:39 +0000
Subject: [PATCH 086/100] merge zero into dag-ir and update to manage
 params/weights per-bucket rather than per-stage. fix slow startup time by
 pipelinine piper backend work across DP ranks

---
 src/piper.py                 |  97 ++++--
 src/piper_actor.py           | 483 ++++++++++++++++++++------
 src/piper_compile.py         | 193 ++++++-----
 src/piper_exec.py            |   8 +-
 src/piper_graph_transform.py | 650 +++++++++++++++++++++--------------
 src/piper_utils.py           |   9 +-
 test/test_llama.py           |   6 +-
 test/test_qwen.py            |   9 +-
 8 files changed, 961 insertions(+), 494 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index f2b1d2f..f5a7334 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -40,6 +40,7 @@ def scan(module):
     split_dag_by_rank,
     assign_time_steps,
     find_overlappable_tasks,
+    insert_zero_ops,
     overlap_a2a_tasks,
     visualize_dag,
     compute_critical_path,
@@ -120,7 +121,13 @@ def piper(gm, example_inputs, **kwargs):
 
         for seg_idx, (seg_gm, seg_in, seg_param, seg_args) in enumerate(a2a_segments):
             if piper_metadata.bucketing and seg_idx % 2 == 0:
-                buckets = bucket_stage(seg_gm, seg_args, seg_in, seg_param)
+                buckets = bucket_stage(
+                    seg_gm,
+                    seg_args,
+                    seg_in,
+                    seg_param,
+                    bucket_size_bytes=piper_metadata.bucket_size,
+                )
                 if len(buckets) > 1:
                     logger.debug(
                         f"Stage {stage_id} segment {seg_idx} bucketed into {len(buckets)} buckets"
@@ -229,25 +236,32 @@ def piper(gm, example_inputs, **kwargs):
         _ubid += stage_bucket_counts[s]
 
     # Now dispatch _load_stage with unique_bucket_ids embedded in modules_data.
-    refs = []
     for stage_id, (modules_data, a2a_boundaries, actor) in all_stage_compiled.items():
-        K = stage_bucket_counts[stage_id]
         ubid_offset = _stage_ubid_offsets[stage_id]
         for b_idx, md in enumerate(modules_data):
             md["unique_bucket_id"] = ubid_offset + b_idx
-        refs.append(actor._load_stage.remote(
+        actor._load_stage.remote(
             stage_id,
             modules_data,
             a2a_boundaries,
             use_activation_checkpointing=piper_metadata.use_activation_checkpointing,
-        ))
+        )
         # Remove actor from stored compiled data (not needed downstream)
         all_stage_compiled[stage_id] = (modules_data, a2a_boundaries)
 
-    ray.get(refs)
+    logger.debug("Sent stages to actors for loading")
 
     piper_metadata.stage_bucket_counts = stage_bucket_counts
     piper_metadata.trainable_bucket_keys = trainable_bucket_keys
+    stage_data = {
+        "stages": all_stage_compiled,
+        "stage_bucket_counts": stage_bucket_counts,
+        "trainable_bucket_keys": trainable_bucket_keys,
+    }
+    compiled_store = getattr(piper_metadata, "compiled_data_store", None)
+    if compiled_store is not None:
+        ray.get(compiled_store.publish_stage_data.remote(stage_data))
+        logger.debug("Published compiled stage data")
 
     # Build task DAG from the schedule stored by piper_setup.
     if piper_metadata.schedule is not None:
@@ -255,34 +269,11 @@ def piper(gm, example_inputs, **kwargs):
             piper_metadata.schedule,
             piper_metadata.stage_bucket_counts,
             stage_a2a_boundaries,
-            dp_degree,
-            a2a_ar_no_overlap=piper_metadata.a2a_ar_no_overlap,
-        )
-        logger.debug(
-            f"Built task DAG: {len(dag.nodes)} nodes, "
-            f"{sum(len(n.data_succs) for n in dag.nodes)} data edges"
         )
+        logger.debug(f"Built task DAG: {len(dag.nodes)} nodes")
 
         add_temporal_dependencies(dag, piper_metadata.schedule)
-        logger.debug(
-            f"Added temporal dependencies: "
-            f"{sum(len(n.temporal_succs) for n in dag.nodes)} temporal edges"
-        )
-
-        if dp_degree > 1 and stage_a2a_boundaries:
-            overlappable = find_overlappable_tasks(piper_metadata.schedule)
-            for t1, t2 in overlappable:
-                logger.info(
-                    f"Found adjacent task pair for A2A/compute overlap: "
-                    f"{t1} -> {t2}"
-                )
-            # if overlappable:
-            #     dag = overlap_a2a_tasks(dag, overlappable)
-            #     logger.debug(
-            #         f"Overlapped {len(overlappable)} adjacent task pair(s) for A2A/compute overlap"
-            #     )
-
-        assign_time_steps(dag)
+        logger.debug(f"Added temporal dependencies")
 
         # Save a deep copy of the full DAG before split_dag_by_rank removes
         # cross-rank data edges.  Used later for profiling and critical-path analysis.
@@ -291,7 +282,31 @@ def piper(gm, example_inputs, **kwargs):
         # Split into per-rank DAGs.
         per_rank_dags = split_dag_by_rank(dag)
 
+        # Insert ZeRO/DP communicatino tasks
+        if dp_degree > 1:
+            per_rank_dags = insert_zero_ops(
+                per_rank_dags,
+                piper_metadata.zero_stage,
+                trainable_bucket_keys=piper_metadata.trainable_bucket_keys,
+            )
+            logger.debug(f"Inserted ZeRO-{piper_metadata.zero_stage} collective nodes")
+
+            overlappable = find_overlappable_tasks(piper_metadata.schedule)
+            for t1, t2 in overlappable:
+                logger.debug(
+                    f"Found adjacent task pair for A2A/compute overlap: "
+                    f"{t1} -> {t2}"
+                )
+                # TODO: overlap a2a tasks
+
+        # Assign time steps
+        for rank_dag in per_rank_dags:
+            assign_time_steps(rank_dag)
+        logger.debug("Assigned time steps")
+
         piper_metadata.per_rank_dags = per_rank_dags
+
+        # Visualize DAG
         actors = piper_metadata.actors
         if piper_metadata.visualize_dag:
             for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags):
@@ -300,14 +315,22 @@ def piper(gm, example_inputs, **kwargs):
                 except Exception as e:
                     logger.warning(f"DAG visualization failed for rank {pp_rank} (DAG may be too large for dot): {e}")
                 print_dag_order(per_rank_dag, label=f"rank {pp_rank}", rank=pp_rank)
-        
-        ray.get([
-            actors[pp_rank].load_dag.remote(per_rank_dag)
-            for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags)
-        ])
+
+        # Send DAG to actors
+        refs = [actors[pp_rank].load_dag.remote(per_rank_dag)
+            for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags)]
+
         logger.debug(
-            f"Loaded per-rank DAGs onto {len(piper_metadata.per_rank_dags)} actors"
+            f"Sent per-rank DAGs onto {len(piper_metadata.per_rank_dags)} actors"
         )
+        dag_data = {
+            "per_rank_dags": per_rank_dags,
+            "full_dag_no_overlap": piper_metadata.full_dag_no_overlap,
+        }
+        if compiled_store is not None:
+            ray.get(compiled_store.publish_dag_data.remote(dag_data))
+            logger.debug("Published compiled DAG data")
+
         # Publish compiled data so dp_rank > 0 can skip torch.compile.
         piper_metadata.compiled_stage_data = {
             "stages": all_stage_compiled,           # {stage_id: (modules_data, a2a_boundaries)}
diff --git a/src/piper_actor.py b/src/piper_actor.py
index e979f4d..a93ad24 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -17,6 +17,7 @@
     _deserialize_graphmodule,
     create_logger,
     LOG_LEVEL,
+    VERBOSE,
     NcclOverlapDetector,
 )
 from .backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
@@ -47,6 +48,8 @@ def _create_actors(
     naive_gradient_sync=False,
     profile=False,
     stage_to_device=None,
+    zero_stage: int = 0,
+    no_nvtx: bool = False,
     pg=None,
 ):
     dp_rank = int(os.environ["PIPER_DP_RANK"])
@@ -87,11 +90,10 @@ def _create_actors(
             dp_degree=dp_degree,
             pp_degree=pp_degree,
             stage_to_device=stage_to_device,
+            zero_stage=zero_stage,
+            no_nvtx=no_nvtx,
         )
         piper_metadata.actors[pp_rank] = actor
-        logger.debug(
-            f"DP rank {dp_rank} created actor {actor} global rank {global_rank}"
-        )
 
 
 def _get_actor(pp_rank):
@@ -113,6 +115,8 @@ def __init__(
         dp_degree=1,
         pp_degree=1,
         stage_to_device=None,
+        zero_stage: int = 0,
+        no_nvtx: bool = False,
     ):
         self.logger = create_logger("piper_actor", LOG_LEVEL)
 
@@ -124,6 +128,8 @@ def __init__(
         self.pp_rank = pp_rank
         self.optim_class = optim_class
         self.naive_gradient_sync = naive_gradient_sync
+        self.zero_stage = int(zero_stage)
+        self.no_nvtx = no_nvtx
 
         self.dp_rank = dp_rank
         self.dp_degree = dp_degree
@@ -183,6 +189,8 @@ def __init__(
         self.recv_events: dict = {}   # task_uid -> cuda.Event (for RECV tasks)
         self.a2a_events: dict = {}    # task_uid -> cuda.Event (for FWD_A2A/BWD_A2A tasks)
         self.ar_events: dict = {}     # unique_bucket_id -> cuda.Event (for ALL_REDUCE tasks)
+        self.rs_events: dict = {}     # unique_bucket_id -> cuda.Event (for REDUCE_SCATTER tasks)
+        self.ag_events: dict = {}     # ALL_GATHER task_uid -> cuda.Event
         self.bwd_events: dict = {}    # unique_bucket_id -> cuda.Event (for BWD/BWD_I/BWD_W tasks)
 
         # Per-bucket data keyed by unique_bucket_id (populated by _load_stage).
@@ -197,6 +205,14 @@ def __init__(
         self.bucket_optims: dict = {}           # ubid -> optimizer (or None)
         self.bucket_trainable_param_idxs: dict = {}  # ubid -> list of trainable param slot indices
         self.bucket_ac_subgraph_counts: dict = {}    # ubid -> number of sequential AC subgraphs
+        self.bucket_flat_params: dict = {}       # ubid -> flat param tensor for ZeRO
+        self.bucket_flat_grads: dict = {}        # ubid -> flat grad tensor for ZeRO
+        self.bucket_shard_params: dict = {}      # ubid -> shard param tensor for ZeRO
+        self.bucket_shard_optims: dict = {}      # ubid -> shard optimizer for ZeRO
+        self.bucket_rs_grads: dict = {}          # ubid -> reduced shard grad tensor for ZeRO-2/3
+        self.param_shard_info: dict = {}         # ubid -> (shard_start, shard_size, orig_numel)
+        self.bucket_param_view_specs: dict = {}  # ubid -> [(param, offset, numel, shape)]
+        self.zero3_full_params_fresh: dict = {}  # ubid -> bool
         self._logged_bucket_forward_info: set = set()
 
         # Non-trainable constant tensor attributes (e.g. freqs_cis, mask) pushed
@@ -232,6 +248,14 @@ def reset_peak_memory(self):
     def get_peak_memory(self):
         return self.global_rank, torch.cuda.max_memory_allocated() / (1024**3)
 
+    def _nvtx_push(self, label: str) -> None:
+        if not self.no_nvtx:
+            torch.cuda.nvtx.range_push(label)
+
+    def _nvtx_pop(self) -> None:
+        if not self.no_nvtx:
+            torch.cuda.nvtx.range_pop()
+
     def load_input(self, inputs):
         self.inputs = [inp.to(self.device) for inp in inputs]
         self.logger.debug(f"Actor {self.global_rank} loaded inputs {len(self.inputs)}")
@@ -287,10 +311,11 @@ def get_node_ip_and_free_port(self):
         return ray.util.get_node_ip_address(), port
 
     def _join_process_groups(self, master_addr, master_port):
-        init_method = f"tcp://{master_addr}:{master_port}"
 
         self.logger.info(f"Actor {self.global_rank} using GPU {os.environ['CUDA_VISIBLE_DEVICES']}, master addr {master_addr}:{master_port}")
 
+        init_method = f"tcp://{master_addr}:{master_port}"
+
         self.device = f"cuda:{self.global_rank % torch.cuda.device_count()}"
         torch.cuda.set_device(self.device)
 
@@ -301,7 +326,7 @@ def _join_process_groups(self, master_addr, master_port):
                 rank=self.global_rank,
                 world_size=self.world_size,
             )
-            self.logger.debug(
+            self.logger.log(VERBOSE,
                 f"Actor {self.global_rank} has GPU {os.environ['CUDA_VISIBLE_DEVICES']}, joined the global process group"
             )
 
@@ -315,7 +340,8 @@ def _join_process_groups(self, master_addr, master_port):
             with torch.cuda.stream(self.comp_stream):
                 _w = torch.zeros(4, 4, device=self.device)
                 torch.mm(_w, _w)
-            torch.cuda.synchronize()
+
+            self.logger.info(f"Actor {self.global_rank} joined process groups")
 
     def _join_dp_process_group(self):
         num_dp_groups = self.world_size // self.dp_degree
@@ -331,7 +357,7 @@ def _join_dp_process_group(self):
             if self.global_rank % num_dp_groups == dp_group_id:
                 self.dp_group = process_group
                 self.ep_group = ep_process_group
-                self.logger.debug(
+                self.logger.log(VERBOSE,
                     f"Global rank {self.global_rank} joined its dp group {dp_group_id} along with ranks {group_ranks}"
                 )
 
@@ -349,24 +375,7 @@ def _join_pp_process_group(self):
                 self.pp_lo_hi = lo_hi_group
                 self.pp_hi_lo = hi_lo_group
 
-            # for i in range(len(group_ranks)):
-            #     for j in range(i + 1, len(group_ranks)):
-            #         rank_lo, rank_hi = group_ranks[i], group_ranks[j]
-            #         pg_lo_to_hi = dist.new_group(ranks=[rank_lo, rank_hi], backend="nccl")
-            #         pg_hi_to_lo = dist.new_group(ranks=[rank_lo, rank_hi], backend="nccl")
-            #         if self.global_rank in (rank_lo, rank_hi):
-            #             self.logger.debug(f"Global rank {self.global_rank} saving pp communicators for ranks {[rank_lo, rank_hi]}")
-            #             self.pp_groups[(rank_lo, rank_hi)] = pg_lo_to_hi
-            #             self.pp_groups[(rank_hi, rank_lo)] = pg_hi_to_lo
-
-        # Warm up both communicators to force eager NCCL initialization.
-        # Without this, NCCL defers init to first use and the entire first
-        # training iteration pays the (very expensive) init cost.
-        dummy = torch.zeros(1, device=self.device)
-        dist.all_reduce(dummy, group=self.pp_lo_hi)
-        dist.all_reduce(dummy, group=self.pp_hi_lo)
-        torch.cuda.synchronize()
-        self.logger.info(f"Global rank {self.global_rank} warmed up pp communicators")
+        self.logger.log(VERBOSE, f"Actor {self.global_rank} created PP communicators")
 
     def shutdown(self):
         dist.destroy_process_group()
@@ -411,7 +420,7 @@ def replace_in_module(module, module_name=""):
             for name, param in module.named_parameters(recurse=False):
                 if is_meta_or_fake(param):
                     full_name = f"{module_name}.{name}" if module_name else name
-                    logger.debug(f"Replacing parameter '{full_name}' from meta/fake to {device} (shape={param.shape}, dtype={param.dtype}, requires_grad={param.requires_grad})")
+                    logger.log(VERBOSE, f"Replacing parameter '{full_name}' from meta/fake to {device} (shape={param.shape}, dtype={param.dtype}, requires_grad={param.requires_grad})")
                     new_param = torch.empty_like(param, device=device)
                     new_param.requires_grad_(param.requires_grad)
                     setattr(module, name, torch.nn.Parameter(new_param, requires_grad=param.requires_grad))
@@ -422,7 +431,7 @@ def replace_in_module(module, module_name=""):
             for name, buffer in module.named_buffers(recurse=False):
                 if is_meta_or_fake(buffer):
                     full_name = f"{module_name}.{name}" if module_name else name
-                    logger.debug(f"Replacing buffer '{full_name}' from meta/fake to {device} (shape={buffer.shape}, dtype={buffer.dtype})")
+                    logger.log(VERBOSE, f"Replacing buffer '{full_name}' from meta/fake to {device} (shape={buffer.shape}, dtype={buffer.dtype})")
                     new_buffer = torch.empty_like(buffer, device=device)
                     setattr(module, name, new_buffer)
                     buffers_count += 1
@@ -437,7 +446,7 @@ def replace_in_module(module, module_name=""):
                             attr_value = getattr(module, node.target)
                             if is_meta_or_fake(attr_value):
                                 full_name = f"{module_name}.{node.target}" if module_name else node.target
-                                logger.debug(f"Replacing get_attr '{full_name}' from meta/fake to {device} (shape={attr_value.shape}, dtype={attr_value.dtype})")
+                                logger.log(VERBOSE, f"Replacing get_attr '{full_name}' from meta/fake to {device} (shape={attr_value.shape}, dtype={attr_value.dtype})")
                                 new_tensor = torch.empty_like(attr_value, device=device)
                                 setattr(module, node.target, new_tensor)
                                 get_attr_count += 1
@@ -450,7 +459,7 @@ def replace_in_module(module, module_name=""):
                         for key, value in node.meta.items():
                             if is_meta_or_fake(value):
                                 full_name = f"{module_name}.{node.name}.meta['{key}']" if module_name else f"{node.name}.meta['{key}']"
-                                logger.debug(f"Replacing node.meta['{key}'] for node '{node.name}' from meta/fake to {device} (shape={value.shape}, dtype={value.dtype})")
+                                logger.log(VERBOSE, f"Replacing node.meta['{key}'] for node '{node.name}' from meta/fake to {device} (shape={value.shape}, dtype={value.dtype})")
                                 node.meta[key] = torch.empty_like(value, device=device)
                                 node_meta_count += 1
                                 replaced_count += 1
@@ -460,7 +469,7 @@ def replace_in_module(module, module_name=""):
                     for key, value in module._constants.items():
                         if is_meta_or_fake(value):
                             full_name = f"{module_name}._constants['{key}']" if module_name else f"_constants['{key}']"
-                            logger.debug(f"Replacing constant '{full_name}' from meta/fake to {device} (shape={value.shape}, dtype={value.dtype})")
+                            logger.log(VERBOSE, f"Replacing constant '{full_name}' from meta/fake to {device} (shape={value.shape}, dtype={value.dtype})")
                             module._constants[key] = torch.empty_like(value, device=device)
                             constants_count += 1
                             replaced_count += 1
@@ -472,14 +481,14 @@ def replace_in_module(module, module_name=""):
                     for arg in node.args:
                         if is_meta_or_fake(arg):
                             full_name = f"{module_name}.{node.name}" if module_name else node.name
-                            logger.debug(f"Replacing meta/fake tensor in node '{full_name}' args (shape={arg.shape}, dtype={arg.dtype})")
+                            logger.log(VERBOSE, f"Replacing meta/fake tensor in node '{full_name}' args (shape={arg.shape}, dtype={arg.dtype})")
                             new_args.append(torch.empty_like(arg, device=device))
                             node_args_count += 1
                             replaced_count += 1
                         elif isinstance(arg, torch.device) and arg.type == 'meta':
                             # replace meta device objects with the actual device
                             full_name = f"{module_name}.{node.name}" if module_name else node.name
-                            logger.debug(f"Replacing meta device object in node '{full_name}' args with {device}")
+                            logger.log(VERBOSE, f"Replacing meta device object in node '{full_name}' args with {device}")
                             new_args.append(torch.device(device))
                             node_args_count += 1
                             replaced_count += 1
@@ -494,14 +503,14 @@ def replace_in_module(module, module_name=""):
                     for key, value in node.kwargs.items():
                         if is_meta_or_fake(value):
                             full_name = f"{module_name}.{node.name}" if module_name else node.name
-                            logger.debug(f"Replacing meta/fake tensor in node '{full_name}' kwargs['{key}'] (shape={value.shape}, dtype={value.dtype})")
+                            logger.log(VERBOSE, f"Replacing meta/fake tensor in node '{full_name}' kwargs['{key}'] (shape={value.shape}, dtype={value.dtype})")
                             new_kwargs[key] = torch.empty_like(value, device=device)
                             node_args_count += 1
                             replaced_count += 1
                         elif isinstance(value, torch.device) and value.type == 'meta':
                             # replace meta device objects with the actual device
                             full_name = f"{module_name}.{node.name}" if module_name else node.name
-                            logger.debug(f"Replacing meta device object in node '{full_name}' kwargs['{key}'] with {device}")
+                            logger.log(VERBOSE, f"Replacing meta device object in node '{full_name}' kwargs['{key}'] with {device}")
                             new_kwargs[key] = torch.device(device)
                             node_args_count += 1
                             replaced_count += 1
@@ -523,14 +532,14 @@ def replace_in_module(module, module_name=""):
         replace_in_module(gm)
 
         if replaced_count > 0:
-            logger.debug(
+            logger.log(VERBOSE,
                 f"_replace_meta_constants: Replaced {replaced_count} meta device tensors "
                 f"(params: {params_count}, buffers: {buffers_count}, get_attr: {get_attr_count}, "
                 f"constants: {constants_count}, attrs: {attrs_count}, node_args: {node_args_count}, "
                 f"node_meta: {node_meta_count}, checked {submodule_count} submodules)"
             )
         else:
-            logger.debug(f"_replace_meta_constants: No meta device tensors found to replace! (checked {submodule_count} submodules)")
+            logger.log(VERBOSE, f"_replace_meta_constants: No meta device tensors found to replace! (checked {submodule_count} submodules)")
 
         return gm
 
@@ -547,7 +556,7 @@ def _load_stage_kernels(self):
                 from torchtitan.models.moe.kernels import generate_permute_indices
                 from torchtitan.models.moe.kernels import fill_indices_wrapper
             import torch._higher_order_ops.triton_kernel_wrap
-            self.logger.debug(f"Actor {self.global_rank} imported and initialized Triton kernel modules")
+            self.logger.log(VERBOSE, f"Actor {self.global_rank} imported and initialized Triton kernel modules")
         except ImportError as e:
             self.logger.warning(
                 f"Actor {self.global_rank} failed to import Triton kernel modules: {e}. "
@@ -556,7 +565,7 @@ def _load_stage_kernels(self):
 
         # HACK: manually register Triton kernel in the side table
         # the graph expects kernel_idx=0, so we need to register it at that index
-        self.logger.debug(f"Manually registering Triton kernel at index 0 on actor {self.global_rank}...")
+        self.logger.log(VERBOSE, f"Manually registering Triton kernel at index 0 on actor {self.global_rank}...")
         try:
             # Import the kernel wrapper
             try:
@@ -592,7 +601,7 @@ def dummy_kernel_user(tokens, starts, offsets):
                 if registered_kernels:
                     max_idx, kernel = max(registered_kernels, key=lambda x: x[0])
                     kernel_side_table.id_to_kernel[0] = kernel
-                    self.logger.debug(
+                    self.logger.log(VERBOSE
                         f"Actor {self.global_rank} registered kernel at id_to_kernel[0] "
                         f"(copied from index {max_idx})"
                     )
@@ -675,7 +684,7 @@ def _load_stage(
                 for i in b_param_idxs
             ]
 
-            self.logger.debug(
+            self.logger.log(VERBOSE,
                 f"Stage {stage_id} bucket {b_idx} (ubid={ubid}) input indices: {b_input_idxs}"
             )
 
@@ -777,21 +786,67 @@ def _bucket_forward_runner(shared_args, _specs=subgraph_specs, _name_to_idx=shar
             self.bucket_fwd_args[ubid] = realized
             self.bucket_param_idxs[ubid] = b_param_idxs
             self.bucket_ac_subgraph_counts[ubid] = ac_num_subgraphs
-
-            # Keep trainable params as separate tensors. This avoids flat parameter
-            # and packed-gradient workspaces while debugging memory pressure.
             trainable_idxs = [
                 i for i in b_param_idxs
                 if realized[i] is not None and realized[i].requires_grad
             ]
             self.bucket_trainable_param_idxs[ubid] = trainable_idxs
 
-            # Optimizer for this module's trainable parameters.
-            trainable_for_optim = [realized[i] for i in trainable_idxs]
-            optim = self.optim_class(trainable_for_optim, fused=True) if trainable_for_optim else None
+            if self.zero_stage > 0 and self.dp_degree > 1 and trainable_idxs:
+                trainable = [realized[i] for i in trainable_idxs]
+                flat_params = torch.cat([p.detach().view(-1) for p in trainable]).contiguous()
+                flat_params.requires_grad_(False)
+                orig_numel = flat_params.numel()
+                dp = self.dp_degree
+                shard_size = (orig_numel + dp - 1) // dp
+                padded_numel = shard_size * dp
+                if padded_numel > orig_numel:
+                    padded = flat_params.new_zeros(padded_numel)
+                    padded[:orig_numel].copy_(flat_params)
+                    flat_params = padded
+
+                offset = 0
+                view_specs = []
+                for idx, p in zip(trainable_idxs, trainable):
+                    numel = p.numel()
+                    realized[idx] = realized[idx].detach()
+                    realized[idx].data = flat_params[offset:offset + numel].view(p.shape)
+                    realized[idx].requires_grad_(True)
+                    view_specs.append((realized[idx], offset, numel, tuple(p.shape)))
+                    offset += numel
+
+                shard_start = self.dp_rank * shard_size
+                if self.zero_stage == 3:
+                    shard_param = flat_params[shard_start:shard_start + shard_size].detach().clone()
+                else:
+                    shard_param = flat_params[shard_start:shard_start + shard_size]
+                shard_param.requires_grad_(True)
+
+                self.bucket_flat_params[ubid] = flat_params
+                self.bucket_flat_grads[ubid] = None if self.zero_stage == 3 else flat_params.new_zeros(padded_numel)
+                self.bucket_shard_params[ubid] = shard_param
+                self.bucket_shard_optims[ubid] = self.optim_class([shard_param])
+                self.bucket_rs_grads[ubid] = flat_params.new_zeros(shard_size)
+                self.param_shard_info[ubid] = (shard_start, shard_size, orig_numel)
+                self.bucket_param_view_specs[ubid] = view_specs
+                self.zero3_full_params_fresh[ubid] = False
+
+                if self.zero_stage == 3:
+                    storage = flat_params.untyped_storage()
+                    if storage.size() != 0:
+                        storage.resize_(0)
+
+                optim = None
+            else:
+                # Keep trainable params as separate tensors for the non-ZeRO path.
+                self.bucket_param_view_specs[ubid] = []
+                self.bucket_flat_params[ubid] = None
+                self.bucket_flat_grads[ubid] = None
+                trainable_for_optim = [realized[i] for i in trainable_idxs]
+                optim = self.optim_class(trainable_for_optim, fused=True) if trainable_for_optim else None
             self.bucket_optims[ubid] = optim
 
-            self.logger.debug(
+            self.logger.log(VERBOSE,
                 f"[ac_bucket] rank={self.global_rank} stage={stage_id} bucket={b_idx} "
                 f"ubid={ubid}: ac_enabled={use_activation_checkpointing} "
                 f"requested_subgraphs={ac_requested_subgraphs} "
@@ -805,8 +860,8 @@ def _bucket_forward_runner(shared_args, _specs=subgraph_specs, _name_to_idx=shar
 
         allocated_gb = torch.cuda.memory_allocated(self.device) / (1024 ** 3)
         reserved_gb = torch.cuda.memory_reserved(self.device) / (1024 ** 3)
-        self.logger.info(
-            f"_load_stage stage_id={stage_id}: GPU allocated={allocated_gb:.2f} GB, "
+        self.logger.debug(
+            f"Actor {self.global_rank} _load_stage stage_id={stage_id}: GPU allocated={allocated_gb:.2f} GB, "
             f"reserved={reserved_gb:.2f} GB"
         )
 
@@ -870,7 +925,7 @@ def _timing_stream(self, task_type) -> "torch.cuda.Stream":
             return self.p2p_recv_stream
         if task_type in (TaskType.FWD_A2A, TaskType.BWD_A2A):
             return self.a2a_stream
-        if task_type == TaskType.ALL_REDUCE:
+        if task_type in (TaskType.ALL_REDUCE, TaskType.REDUCE_SCATTER, TaskType.ALL_GATHER):
             return self.ar_stream
         return self.comp_stream
 
@@ -1105,9 +1160,17 @@ def run_dag(self, loss_fn=None, profiling: bool = False):
         self.recv_events = {}
         self.a2a_events = {}
         self.ar_events = {}
+        self.rs_events = {}
+        if self.zero_stage == 3:
+            self.ag_events = {}
         self.bwd_events = {}
         self._clear_param_grads()
 
+        if self.zero_stage >= 2:
+            with torch.cuda.stream(self.comp_stream):
+                for buf in self.bucket_rs_grads.values():
+                    if buf is not None:
+                        buf.zero_()
         comp_events: dict = {}  # task_uid -> cuda.Event for compute tasks
 
         # profiling: list of (node, start_evt, end_evt, mem_before_bytes)
@@ -1120,14 +1183,29 @@ def run_dag(self, loss_fn=None, profiling: bool = False):
         # RECV overlaps with compute at the same time_step the recv kernel only
         # launches after compute finishes (p2p_recv_stream waits on the compute
         # event), avoiding SM contention from an idle-spinning recv kernel.
-        _SEND_PRIORITY_TYPES = {TaskType.SEND, TaskType.ALL_REDUCE, TaskType.FWD_A2A, TaskType.BWD_A2A}
+        _PRE_COMPUTE_TYPES = {
+            TaskType.SEND,
+            TaskType.ALL_GATHER,
+            TaskType.ALLOC_FULL_GRADS,
+            TaskType.ALLOC_FULL_PARAMS,
+            TaskType.FWD_A2A,
+            TaskType.BWD_A2A,
+        }
+        _POST_COMPUTE_TYPES = {
+            TaskType.ALL_REDUCE,
+            TaskType.REDUCE_SCATTER,
+            TaskType.FREE_FULL_GRADS,
+            TaskType.FREE_FULL_PARAMS,
+        }
 
         def _ts_key(n):
             t = n.chunk.type
-            if t in _SEND_PRIORITY_TYPES:
+            if t in _PRE_COMPUTE_TYPES:
                 sub = 0
-            elif t == TaskType.RECV:
+            elif t in _POST_COMPUTE_TYPES:
                 sub = 2
+            elif t == TaskType.RECV:
+                sub = 3
             else:
                 sub = 1
             return (n.time_step, sub)
@@ -1135,6 +1213,13 @@ def _ts_key(n):
         sorted_nodes = sorted(dag.nodes, key=_ts_key)
         ts_to_comp_event: dict[int, torch.cuda.Event] = {}
 
+        def _wait_for_all_gather(compute_node: Task) -> None:
+            for pred in compute_node.data_preds:
+                if pred.chunk.type == TaskType.ALL_GATHER:
+                    ag_evt = self.ag_events.get(pred.uid)
+                    if ag_evt is not None:
+                        self.comp_stream.wait_event(ag_evt)
+
         for node in sorted_nodes:
 
             chunk = node.chunk
@@ -1142,7 +1227,7 @@ def _ts_key(n):
             mb_idx = batch.mb_idx
             ubid = node.unique_bucket_id  # None for non-compute nodes
 
-            self.logger.debug(
+            self.logger.log(VERBOSE,
                 f"run_dag dispatch (time {node.time_step}): {chunk.type.value} "
                 f"mb{mb_idx} ubid={ubid}"
             )
@@ -1160,7 +1245,9 @@ def _ts_key(n):
                     compute_node = node.data_preds[0]
                     self.p2p_send_stream.wait_event(comp_events[compute_node.uid])
                     send_data = self.task_buffer[compute_node.uid]["send_output"]
+                    self._start_timing(self.p2p_send_stream, "p2p_send")
                     self._exec_send(send_data, node.peer_pp_rank)
+                    self._stop_timing(self.p2p_send_stream, "p2p_send")
                     # Free the send buffer.  For FWD/FWD_A2A the whole entry is stale
                     # after the send (task_buffer[(ubid,mb)] is the BWD consumer).
                     # For BWD/BWD_I/BWD_A2A keep the dict but drop the send tensor —
@@ -1181,12 +1268,16 @@ def _ts_key(n):
                     if compute_node.chunk.type == TaskType.FWD:
                         # FWD recv: pre-allocate based on target bucket's input metadata.
                         recv_ubid = compute_node.unique_bucket_id
+                        self._start_timing(self.p2p_recv_stream, "p2p_recv")
                         recv_tensors = self._exec_recv_fwd(recv_ubid, node.peer_pp_rank)
+                        self._stop_timing(self.p2p_recv_stream, "p2p_recv")
                     else:
                         # BWD recv: pre-allocate based on FWD output shapes.
                         fwd_ubid = node.custom_metadata["fwd_ubid"]
                         shape_meta = self.task_buffer[("shape_ref", fwd_ubid)]
+                        self._start_timing(self.p2p_recv_stream, "p2p_recv")
                         recv_tensors = self._exec_recv_bwd(shape_meta, node.peer_pp_rank)
+                        self._stop_timing(self.p2p_recv_stream, "p2p_recv")
                     self.task_buffer[node.uid] = recv_tensors
                     recv_evt = torch.cuda.Event()
                     recv_evt.record(self.p2p_recv_stream)
@@ -1201,11 +1292,11 @@ def _ts_key(n):
                     # FWD_A2A now owns the copy; original FWD entry is no longer needed.
                     del self.task_buffer[fwd_pred.uid]
                     detached_outs = list(fwd_buf["detached_outs"])
-                    torch.cuda.nvtx.range_push(f"fwd_a2a_ubid{fwd_pred.unique_bucket_id}_mb{mb_idx}")
+                    self._nvtx_push(f"fwd_a2a_ubid{fwd_pred.unique_bucket_id}_mb{mb_idx}")
                     self._start_timing(self.a2a_stream, "fwd_a2a")
                     detached_outs[tensor_idx] = self._exec_a2a(detached_outs[tensor_idx]).requires_grad_(True)
                     self._stop_timing(self.a2a_stream, "fwd_a2a")
-                    torch.cuda.nvtx.range_pop()
+                    self._nvtx_pop()
                     fwd_buf["detached_outs"] = detached_outs
                     self.task_buffer[node.uid] = fwd_buf
                     a2a_evt = torch.cuda.Event()
@@ -1240,11 +1331,11 @@ def _ts_key(n):
                     )
                     if not grad_a2a_out.is_contiguous():
                         grad_a2a_out = grad_a2a_out.contiguous()
-                    torch.cuda.nvtx.range_push(f"bwd_a2a_ubid{bwd_pred.unique_bucket_id}_mb{mb_idx}")
+                    self._nvtx_push(f"bwd_a2a_ubid{bwd_pred.unique_bucket_id}_mb{mb_idx}")
                     self._start_timing(self.a2a_stream, "bwd_a2a")
                     inp_grads[tensor_idx] = self._exec_a2a(grad_a2a_out)
                     self._stop_timing(self.a2a_stream, "bwd_a2a")
-                    torch.cuda.nvtx.range_pop()
+                    self._nvtx_pop()
                     bwd_buf["inp_grads"] = inp_grads
                     self.task_buffer[node.uid] = bwd_buf
                     a2a_evt = torch.cuda.Event()
@@ -1254,11 +1345,69 @@ def _ts_key(n):
 
                 case TaskType.ALL_REDUCE:
                     bwd_node = node.data_preds[0]
-                    torch.cuda.nvtx.range_push(f"all_reduce_ubid{bwd_node.unique_bucket_id}")
-                    self._exec_all_reduce_grads(comp_events[bwd_node.uid], bwd_node.unique_bucket_id)
-                    torch.cuda.nvtx.range_pop()
+                    ar_ubid = bwd_node.unique_bucket_id
+                    self._nvtx_push(f"all_reduce_ubid{bwd_node.unique_bucket_id}")
+                    self.ar_stream.wait_event(comp_events[bwd_node.uid])
+                    self._start_timing(self.ar_stream, "all_reduce")
+                    ar_launched = self._exec_all_reduce_grads(ar_ubid)
+                    self._stop_timing(self.ar_stream, "all_reduce")
+                    if ar_launched:
+                        ar_evt = torch.cuda.Event()
+                        ar_evt.record(self.ar_stream)
+                        self.ar_events[node.uid] = ar_evt
+                    self._nvtx_pop()
                     del self.task_buffer[bwd_node.uid]
 
+                case TaskType.REDUCE_SCATTER:
+                    bwd_node = node.data_preds[0]
+                    rs_ubid = node.unique_bucket_id if node.unique_bucket_id is not None else bwd_node.unique_bucket_id
+                    self._nvtx_push(f"reduce_scatter_ubid{rs_ubid}")
+                    self.ar_stream.wait_event(comp_events[bwd_node.uid])
+                    self._start_timing(self.ar_stream, "reduce_scatter")
+                    self._exec_reduce_scatter(rs_ubid)
+                    self._stop_timing(self.ar_stream, "reduce_scatter")
+                    rs_evt = torch.cuda.Event()
+                    rs_evt.record(self.ar_stream)
+                    self.rs_events[node.uid] = rs_evt
+                    self._nvtx_pop()
+                    _bwd_buf = self.task_buffer.get(bwd_node.uid)
+                    if bwd_node.chunk.type == TaskType.BWD:
+                        self.task_buffer.pop(bwd_node.uid, None)
+                    elif isinstance(_bwd_buf, dict):
+                        _bwd_buf["send_output"] = None
+
+                case TaskType.ALL_GATHER:
+                    self._nvtx_push(f"all_gather_s{batch.stage_id}_b{node.bucket_id}")
+                    self._start_timing(self.ar_stream, "all_gather")
+                    ag_launched = self._exec_all_gather(node.unique_bucket_id)
+                    self._stop_timing(self.ar_stream, "all_gather")
+                    if ag_launched:
+                        ag_evt = torch.cuda.Event()
+                        ag_evt.record(self.ar_stream)
+                        self.ag_events[node.uid] = ag_evt
+                    self._nvtx_pop()
+
+                case TaskType.ALLOC_FULL_GRADS:
+                    self._alloc_full_grads(node.unique_bucket_id)
+
+                case TaskType.FREE_FULL_GRADS:
+                    for pred in node.data_preds:
+                        if pred.chunk.type == TaskType.REDUCE_SCATTER:
+                            rs_evt = self.rs_events.get(pred.uid)
+                            if rs_evt is not None:
+                                self.comp_stream.wait_event(rs_evt)
+                        elif pred.chunk.type == TaskType.ALL_REDUCE:
+                            ar_evt = self.ar_events.get(pred.uid)
+                            if ar_evt is not None:
+                                self.comp_stream.wait_event(ar_evt)
+                    self._free_full_grads(node.unique_bucket_id)
+
+                case TaskType.ALLOC_FULL_PARAMS:
+                    self._alloc_full_params(node.unique_bucket_id)
+
+                case TaskType.FREE_FULL_PARAMS:
+                    self._free_full_params(node.unique_bucket_id)
+
                 case TaskType.FWD:
                     # Wait on any RECV predecessor.
                     recv_pred = next(
@@ -1285,11 +1434,13 @@ def _ts_key(n):
                     else:
                         input_tensors = self.inputs  # first stage, first bucket
 
-                    torch.cuda.nvtx.range_push(f"forward_ubid{ubid}_mb{mb_idx}")
+                    _wait_for_all_gather(node)
+
+                    self._nvtx_push(f"forward_ubid{ubid}_mb{mb_idx}")
                     self._start_timing(self.comp_stream, "forward")
                     fwd_out = self._forward_dag(ubid, mb_idx, input_tensors)
                     self._stop_timing(self.comp_stream, "forward")
-                    torch.cuda.nvtx.range_pop()
+                    self._nvtx_pop()
                     self.task_buffer[node.uid] = fwd_out
                     # Shape ref for BWD RECV: store lightweight metadata (shape, dtype) so
                     # _exec_recv_bwd can pre-allocate receive buffers without holding live tensors.
@@ -1307,6 +1458,7 @@ def _ts_key(n):
                     )
                     if recv_pred is not None and recv_pred.uid in self.recv_events:
                         self.comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
+                    _wait_for_all_gather(node)
 
                     # Resolve outputs and upstream grads via per-mb ubid lookup.
                     fwd_out = self.task_buffer[(ubid, mb_idx)]
@@ -1378,7 +1530,7 @@ def _ts_key(n):
 
                     inp_with_grad = fwd_out.get("inp_with_grad")
 
-                    torch.cuda.nvtx.range_push(f"backward_ubid{ubid}_mb{mb_idx}")
+                    self._nvtx_push(f"backward_ubid{ubid}_mb{mb_idx}")
                     self._start_timing(self.comp_stream, "backward")
                     bwd_out = self._backward_dag(
                         ubid, mb_idx, outputs_or_loss, upstream_grads,
@@ -1386,7 +1538,7 @@ def _ts_key(n):
                         fwd_out.get("out_with_grad"),
                     )
                     self._stop_timing(self.comp_stream, "backward")
-                    torch.cuda.nvtx.range_pop()
+                    self._nvtx_pop()
                     buf = bwd_out if bwd_out is not None else {}
                     # Full-length inp_grads parallel to fwd_inputs / detached_outs so
                     # BWD_A2A can index with a2a_tensor_idx without filtering offset.
@@ -1416,6 +1568,7 @@ def _ts_key(n):
                     )
                     if recv_pred is not None and recv_pred.uid in self.recv_events:
                         self.comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
+                    _wait_for_all_gather(node)
 
                     # Resolve stage_outputs_or_loss and output_grads.
                     fwd_out = self.task_buffer[(ubid, mb_idx)]
@@ -1465,14 +1618,14 @@ def _ts_key(n):
                     weights = [b_fwd_args[i] for i in self.bucket_param_idxs[ubid]
                                if b_fwd_args[i] is not None]
 
-                    torch.cuda.nvtx.range_push(f"backward_input_ubid{ubid}_mb{mb_idx}")
+                    self._nvtx_push(f"backward_input_ubid{ubid}_mb{mb_idx}")
                     self._start_timing(self.comp_stream, "backward_input")
                     with torch.cuda.stream(self.comp_stream):
                         dinputs, param_groups = self._bucket_backward_input(
                             stage_outputs_or_loss, output_grads, input_values, iter(weights)
                         )
                     self._stop_timing(self.comp_stream, "backward_input")
-                    torch.cuda.nvtx.range_pop()
+                    self._nvtx_pop()
                     # Full-length inp_grads parallel to fwd_inputs / detached_outs.
                     fwd_inputs_full = fwd_out.get("fwd_inputs")
                     if fwd_inputs_full is not None:
@@ -1520,6 +1673,7 @@ def _ts_key(n):
                     ts_to_comp_event[node.time_step] = evt
 
                 case TaskType.BWD_W:
+                    _wait_for_all_gather(node)
                     # data_preds[0] is the BWD_I task for bkt=K-1 (head of chain).
                     # For bkt<K-1 the chain-link edge (BWD_W predecessor) is prepended
                     # before Pass 2 appends the BWD_I edge, so data_preds[0] would be
@@ -1546,12 +1700,12 @@ def _ts_key(n):
                     b_fwd_args = self.bucket_fwd_args[ubid]
                     weights = [b_fwd_args[i] for i in self.bucket_param_idxs[ubid]
                                if b_fwd_args[i] is not None]
-                    torch.cuda.nvtx.range_push(f"backward_weight_ubid{ubid}_mb{mb_idx}")
+                    self._nvtx_push(f"backward_weight_ubid{ubid}_mb{mb_idx}")
                     self._start_timing(self.comp_stream, "backward_weight")
                     with torch.cuda.stream(self.comp_stream):
                         self._bucket_backward_weight(iter(weights), param_groups, ubid=ubid, mb_idx=mb_idx)
                     self._stop_timing(self.comp_stream, "backward_weight")
-                    torch.cuda.nvtx.range_pop()
+                    self._nvtx_pop()
                     self.task_buffer[node.uid] = {}
                     # BWD_I buffer is no longer needed: _bucket_backward_weight already
                     # deleted param_groups["intermediates"] and param_groups["grads"] and
@@ -1564,9 +1718,11 @@ def _ts_key(n):
                     ts_to_comp_event[node.time_step] = evt
 
                 case TaskType.UPD:
-                    torch.cuda.nvtx.range_push("update")
+                    self._nvtx_push("update")
+                    self._start_timing(self.comp_stream, "optim_step")
                     self._update()
-                    torch.cuda.nvtx.range_pop()
+                    self._stop_timing(self.comp_stream, "optim_step")
+                    self._nvtx_pop()
 
             if profiling:
                 _end_evt = torch.cuda.Event(enable_timing=True)
@@ -1576,7 +1732,7 @@ def _ts_key(n):
                 self.logger.info(f"Rank {self.global_rank} task {chunk.type.value} (time {node.time_step}) mem before {_mem_before / 1024 ** 3:.2f} GB after {_mem_after / 1024 ** 3:.2f} GB")
 
             tb_summary = self._summarize_task_buffer()
-            self.logger.debug(
+            self.logger.log(VERBOSE,
                 f"[task_mem] rank={self.global_rank} time={node.time_step} "
                 f"task={chunk.type.value} mb={mb_idx} ubid={ubid}: "
                 f"alloc_gb={torch.cuda.memory_allocated(self.device) / (1024 ** 3):.2f} "
@@ -1606,15 +1762,13 @@ def _exec_send(self, send_data, peer_pp_rank: int) -> None:
         compute event before calling this method.
         """
         global_dst_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
-        self.logger.debug(f"exec_send to global rank {global_dst_rank}")
+        self.logger.log(VERBOSE, f"exec_send to global rank {global_dst_rank}")
 
         with torch.cuda.stream(self.p2p_send_stream):
             tensors = send_data if isinstance(send_data, (list, tuple)) else [send_data]
-            self._start_timing(self.p2p_send_stream, "p2p_send")
             pp_group = self.pp_lo_hi if global_dst_rank > self.global_rank else self.pp_hi_lo
             for tensor in tensors:
                 dist.send(tensor, dst=global_dst_rank, group=pp_group)
-            self._stop_timing(self.p2p_send_stream, "p2p_send")
 
     def _exec_recv_fwd(self, recv_ubid: int, peer_pp_rank: int) -> list:
         """Receive FWD activations from peer_pp_rank into pre-allocated buffers.
@@ -1623,18 +1777,16 @@ def _exec_recv_fwd(self, recv_ubid: int, peer_pp_rank: int) -> list:
         Returns the received tensor list (stored in task_buffer by run_dag).
         """
         global_src_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
-        self.logger.debug(f"exec_recv_fwd ubid={recv_ubid} from global rank {global_src_rank}")
+        self.logger.log(VERBOSE, f"exec_recv_fwd ubid={recv_ubid} from global rank {global_src_rank}")
 
         buf = [
             torch.empty(shape, dtype=dtype, requires_grad=requires_grad, device=self.device)
             for shape, dtype, requires_grad in self.forward_input_meta[recv_ubid]
         ]
         with torch.cuda.stream(self.p2p_recv_stream):
-            self._start_timing(self.p2p_recv_stream, "p2p_recv")
             pp_group = self.pp_hi_lo if global_src_rank > self.global_rank else self.pp_lo_hi
             for tensor in buf:
                 dist.recv(tensor, src=global_src_rank, group=pp_group)
-            self._stop_timing(self.p2p_recv_stream, "p2p_recv")
         return buf
 
     def _exec_recv_bwd(self, shape_meta: list, peer_pp_rank: int) -> list:
@@ -1645,15 +1797,13 @@ def _exec_recv_bwd(self, shape_meta: list, peer_pp_rank: int) -> list:
         Returns the received gradient list (stored in task_buffer by run_dag).
         """
         global_src_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
-        self.logger.debug(f"exec_recv_bwd from global rank {global_src_rank}")
+        self.logger.log(VERBOSE, f"exec_recv_bwd from global rank {global_src_rank}")
 
         buf = [torch.empty(shape, dtype=dtype, device=self.device) for shape, dtype in shape_meta]
         with torch.cuda.stream(self.p2p_recv_stream):
-            self._start_timing(self.p2p_recv_stream, "p2p_recv")
             pp_group = self.pp_hi_lo if global_src_rank > self.global_rank else self.pp_lo_hi
             for tensor in buf:
                 dist.recv(tensor, src=global_src_rank, group=pp_group)
-            self._stop_timing(self.p2p_recv_stream, "p2p_recv")
         return buf
 
     def _exec_a2a(self, input_tensor: torch.Tensor) -> torch.Tensor:
@@ -1708,7 +1858,7 @@ def _forward_dag(self, ubid: int, mb_idx: int, input_tensors) -> dict:
 
         if ubid not in self._logged_bucket_forward_info:
             self._logged_bucket_forward_info.add(ubid)
-            self.logger.debug(
+            self.logger.log(VERBOSE,
                 f"[ac_forward] rank={self.global_rank} ubid={ubid}: "
                 f"ac_subgraphs={self.bucket_ac_subgraph_counts.get(ubid, 1)} "
                 f"inputs={len(input_idxs)}"
@@ -1925,8 +2075,20 @@ def _bucket_backward_weight(self, weights, param_groups: list, ubid: int | None
                 else:
                     weight.grad += dw
 
-    def _exec_all_reduce_grads(self, bwd_event: torch.cuda.Event, ubid: int) -> None:
+    def _exec_all_reduce_grads(self, ubid: int) -> bool:
         """All-reduce each materialized parameter grad for *ubid* on ar_stream."""
+        if self.zero_stage == 1 and ubid in self.bucket_flat_grads:
+            flat_grads = self.bucket_flat_grads.get(ubid)
+            if flat_grads is None:
+                self.logger.log(VERBOSE,
+                    f"[all_reduce_bucket] rank={self.global_rank} ubid={ubid}: "
+                    "skipped no ZeRO flat grad buffer"
+                )
+                return False
+            with torch.cuda.stream(self.ar_stream):
+                dist.all_reduce(flat_grads, group=self.dp_group)
+            return True
+
         args = self.bucket_fwd_args.get(ubid, [])
         trainable_idxs = self.bucket_trainable_param_idxs.get(ubid, [])
         grad_tensors = []
@@ -1936,17 +2098,17 @@ def _exec_all_reduce_grads(self, bwd_event: torch.cuda.Event, ubid: int) -> None
                 grad_tensors.append((idx, param.grad))
 
         if not grad_tensors:
-            self.logger.debug(
+            self.logger.log(VERBOSE
                 f"[all_reduce_bucket] rank={self.global_rank} ubid={ubid}: "
                 "skipped no materialized grads"
             )
-            return
+            return False
 
         total_numel = sum(grad.numel() for _, grad in grad_tensors)
         total_bytes = sum(grad.numel() * grad.element_size() for _, grad in grad_tensors)
         largest_idx, largest_grad = max(grad_tensors, key=lambda item: item[1].numel())
         largest_bytes = largest_grad.numel() * largest_grad.element_size()
-        self.logger.info(
+        self.logger.log(VERBOSE,
             f"[all_reduce_bucket] rank={self.global_rank} ubid={ubid}: "
             f"mode=per_param grad_tensors={len(grad_tensors)} "
             f"grad_numel={total_numel} "
@@ -1960,7 +2122,7 @@ def _exec_all_reduce_grads(self, bwd_event: torch.cuda.Event, ubid: int) -> None
         for grad_order, (idx, grad) in enumerate(grad_tensors):
             grad_bytes = grad.numel() * grad.element_size()
             data_ptr_mod_256 = grad.data_ptr() % 256 if grad.is_cuda else -1
-            self.logger.info(
+            self.logger.log(VERBOSE,
                 f"[all_reduce_grad] rank={self.global_rank} ubid={ubid} "
                 f"order={grad_order} idx={idx}: "
                 f"shape={tuple(grad.shape)} stride={tuple(grad.stride())} "
@@ -1971,30 +2133,151 @@ def _exec_all_reduce_grads(self, bwd_event: torch.cuda.Event, ubid: int) -> None
                 f"data_ptr_mod_256={data_ptr_mod_256}"
             )
         with torch.cuda.stream(self.ar_stream):
-            self.ar_stream.wait_event(bwd_event)
-            self._start_timing(self.ar_stream, "all_reduce")
             for _, grad in grad_tensors:
                 dist.all_reduce(grad, group=self.dp_group)
-            self._stop_timing(self.ar_stream, "all_reduce")
-            evt = torch.cuda.Event()
-            evt.record(self.ar_stream)
-        self.ar_events[ubid] = evt
+        return True
+
+    def _alloc_full_params(self, ubid: int | None) -> None:
+        if self.zero_stage != 3:
+            return
+        if ubid is None:
+            return
+        shard_info = self.param_shard_info.get(ubid)
+        if shard_info is None:
+            return
+        _shard_start, shard_size, _orig_numel = shard_info
+        full = self.bucket_flat_params.get(ubid)
+        if full is None:
+            full = self.bucket_shard_params[ubid].new_empty(shard_size * self.dp_degree)
+            self.bucket_flat_params[ubid] = full
+        storage = full.untyped_storage()
+        required_bytes = full.numel() * full.element_size()
+        if storage.size() != required_bytes:
+            storage.resize_(required_bytes)
+        for param, offset, numel, shape in self.bucket_param_view_specs.get(ubid, []):
+            param.data = full[offset:offset + numel].view(shape)
+            param.requires_grad_(True)
+
+    def _free_full_params(self, ubid: int | None) -> None:
+        if self.zero_stage != 3:
+            return
+        if ubid is None:
+            return
+        full = self.bucket_flat_params.get(ubid)
+        if full is None:
+            return
+        storage = full.untyped_storage()
+        if storage.size() != 0:
+            storage.resize_(0)
+        self.zero3_full_params_fresh[ubid] = False
+
+    def _alloc_full_grads(self, ubid: int | None) -> None:
+        if ubid is None:
+            return
+        specs = self.bucket_param_view_specs.get(ubid, [])
+        if not specs:
+            return
+        flat_grads = self.bucket_flat_grads.get(ubid)
+        if flat_grads is None:
+            shard_info = self.param_shard_info.get(ubid)
+            if shard_info is None:
+                return
+            _shard_start, shard_size, _orig_numel = shard_info
+            flat_grads = self.bucket_shard_params[ubid].new_zeros(shard_size * self.dp_degree)
+            self.bucket_flat_grads[ubid] = flat_grads
+        else:
+            flat_grads.zero_()
+        for param, offset, numel, shape in specs:
+            param.grad = flat_grads[offset:offset + numel].view(shape)
+
+    def _free_full_grads(self, ubid: int | None) -> None:
+        if ubid is None:
+            return
+        for param, *_ in self.bucket_param_view_specs.get(ubid, []):
+            param.grad = None
+        if self.zero_stage in (2, 3):
+            self.bucket_flat_grads[ubid] = None
+
+    def _exec_reduce_scatter(self, ubid: int) -> None:
+        with torch.cuda.stream(self.ar_stream):
+            flat_grads = self.bucket_flat_grads.get(ubid)
+            rs_out = self.bucket_rs_grads.get(ubid)
+            assert flat_grads is not None and rs_out is not None, (
+                f"REDUCE_SCATTER dispatched for ubid={ubid} without allocated gradient buffers"
+            )
+            tmp = torch.empty_like(rs_out)
+            dist.reduce_scatter_tensor(tmp, flat_grads, group=self.dp_group)
+            rs_out.add_(tmp)
+
+    def _exec_all_gather(
+        self,
+        ubid: int | None,
+    ):
+        if ubid is None:
+            return False
+        with torch.cuda.stream(self.ar_stream):
+            flat_params = self.bucket_flat_params.get(ubid)
+            shard_in = self.bucket_shard_params.get(ubid)
+            if flat_params is None or shard_in is None:
+                return False
+            if self.zero_stage == 3 and self.zero3_full_params_fresh.get(ubid, False):
+                return False
+            dist.all_gather_into_tensor(flat_params, shard_in, group=self.dp_group)
+            if self.zero_stage == 3:
+                self.zero3_full_params_fresh[ubid] = True
+        return True
 
     def _update(self, *deps):
-        self._start_timing(self.comp_stream, "optim_step")
+        if self.zero_stage > 0:
+            sync_events = self.ar_events if self.zero_stage == 1 else self.rs_events
+            for evt in sync_events.values():
+                self.comp_stream.wait_event(evt)
+
+            for ubid, shard_optim in self.bucket_shard_optims.items():
+                shard_param = self.bucket_shard_params[ubid]
+                shard_start, shard_size, _orig_numel = self.param_shard_info[ubid]
+                if self.zero_stage == 1:
+                    flat_grads = self.bucket_flat_grads.get(ubid)
+                    if flat_grads is None:
+                        continue
+                    shard_param.grad = flat_grads[shard_start:shard_start + shard_size]
+                else:
+                    rs_grad = self.bucket_rs_grads.get(ubid)
+                    if rs_grad is None:
+                        continue
+                    shard_param.grad = rs_grad
+                with torch.cuda.stream(self.comp_stream):
+                    shard_optim.step()
+                shard_param.grad = None
+
+            if self.zero_stage == 3:
+                for ubid, specs in self.bucket_param_view_specs.items():
+                    for param, *_ in specs:
+                        param.grad = None
+                    full = self.bucket_flat_params.get(ubid)
+                    if full is not None:
+                        storage = full.untyped_storage()
+                        if storage.size() != 0:
+                            storage.resize_(0)
+                    self.zero3_full_params_fresh[ubid] = False
+
+            losses = self.loss
+            self.loss.clear()
+            torch.cuda.synchronize()
+            return losses
 
         pre_step_mem: dict = {}
         grad_debug: dict = {}
 
+        for ar_evt in self.ar_events.values():
+            self.comp_stream.wait_event(ar_evt)
+
         for ubid, optim in self.bucket_optims.items():
             if optim is None:
                 continue
             bwd_evt = self.bwd_events.get(ubid)
             if bwd_evt is not None:
                 self.comp_stream.wait_event(bwd_evt)
-            ar_evt = self.ar_events.get(ubid)
-            if ar_evt is not None:
-                self.comp_stream.wait_event(ar_evt)
 
             params = self.bucket_fwd_args.get(ubid, [])
             trainable_idxs = self.bucket_trainable_param_idxs.get(ubid, [])
@@ -2034,7 +2317,7 @@ def _update(self, *deps):
                 "zero_grad_params": zero_grad_params,
                 "state_entries_before": state_entries_before,
             }
-            self.logger.debug(
+            self.logger.log(VERBOSE,
                 f"[optim_debug_pre] rank={self.global_rank} ubid={ubid}: "
                 f"params={len(trainable_idxs)} "
                 f"params_with_grad={params_with_grad} "
@@ -2060,7 +2343,6 @@ def _update(self, *deps):
                     f"reserved_now_gb={reserv_now / (1024 ** 3):.2f}"
                 )
                 raise
-        self._stop_timing(self.comp_stream, "optim_step")
 
         losses = self.loss
         self.loss.clear()
@@ -2075,7 +2357,7 @@ def _update(self, *deps):
             reserv_after = torch.cuda.memory_reserved(self.device)
             state_entries_after = sum(len(optim.state[p]) > 0 for p in optim.param_groups[0]["params"])
             dbg = grad_debug[ubid]
-            self.logger.debug(
+            self.logger.log(VERBOSE,
                 f"[optim_debug] rank={self.global_rank} ubid={ubid}: "
                 f"params={dbg['num_params']} "
                 f"params_with_grad={dbg['params_with_grad']} "
@@ -2106,4 +2388,3 @@ def _update(self, *deps):
                 )
 
         return losses
-
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 659662b..9657d43 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -13,7 +13,7 @@
 from torch._dynamo.backends.debugging import eager
 
 from .piper_actor import _create_actors
-from .piper_utils import piper_metadata, create_logger, LOG_LEVEL
+from .piper_utils import piper_metadata, create_logger, LOG_LEVEL, VERBOSE
 from .piper_exec import DAGEdge, PipelineSchedule, CompType, Chunk, BatchMeta, _validate_schedule
 from .piper import piper, piper_exec_dag
 from .piper_graph_transform import compute_critical_path, visualize_dag
@@ -37,23 +37,57 @@ def get(self):
 class _CompiledDataStore:
     """Named actor used to share compiled stage/DAG data from dp_rank=0 to dp_rank>0.
 
-    dp_rank=0 calls publish() once after compilation.  All other dp_ranks poll
-    is_ready() and then call get() to retrieve the data, avoiding a redundant
-    torch.compile call for large models.
+    Stage data is published as soon as it is serialized so nonzero DP ranks can
+    start _load_stage while dp_rank=0 continues DAG construction.
     """
     def __init__(self):
-        self._data = None
-        self._ready = False
+        self._stage_data = None
+        self._dag_data = None
+        self._stage_ready = False
+        self._dag_ready = False
 
     def publish(self, data: dict):
-        self._data = data
-        self._ready = True
+        self._stage_data = {
+            "stages": data["stages"],
+            "stage_bucket_counts": data["stage_bucket_counts"],
+            "trainable_bucket_keys": data["trainable_bucket_keys"],
+        }
+        self._dag_data = {
+            "per_rank_dags": data["per_rank_dags"],
+            "full_dag_no_overlap": data.get("full_dag_no_overlap"),
+        }
+        self._stage_ready = True
+        self._dag_ready = True
+
+    def publish_stage_data(self, data: dict):
+        self._stage_data = data
+        self._stage_ready = True
+
+    def publish_dag_data(self, data: dict):
+        self._dag_data = data
+        self._dag_ready = True
 
     def is_ready(self) -> bool:
-        return self._ready
+        return self._stage_ready and self._dag_ready
+
+    def is_stage_ready(self) -> bool:
+        return self._stage_ready
+
+    def is_dag_ready(self) -> bool:
+        return self._dag_ready
 
     def get(self) -> dict:
-        return self._data
+        if not self.is_ready():
+            return None
+        result = dict(self._stage_data)
+        result.update(self._dag_data)
+        return result
+
+    def get_stage_data(self) -> dict:
+        return self._stage_data
+
+    def get_dag_data(self) -> dict:
+        return self._dag_data
 
 
 
@@ -75,6 +109,11 @@ def piper_setup(
     activation_checkpointing=False,
     num_checkpoints=1,
     bucketing=False,
+    bucket_size: int = 25 * 1024 * 1024,
+    zero_stage: int = 0,
+    schedule_name: str = "",
+    visualize_dag_render: bool = True,
+    no_nvtx: bool = False,
     a2a_ar_no_overlap=False,
     pg=None,
     nsight=False,
@@ -95,6 +134,8 @@ def piper_setup(
             pipelined per-param hooks.
         a2a_ar_no_overlap: When true, schedule gradient all-reduces after
             same-rank A2A operations to avoid NCCL interference.
+        zero_stage: ZeRO stage: 0 disabled, 1 optimizer-state sharding,
+            2 gradient sharding, 3 parameter and gradient sharding.
     """
 
     # Clear Dynamo's global compilation cache so that a previous piper_setup
@@ -108,6 +149,10 @@ def piper_setup(
     piper_metadata.use_activation_checkpointing = activation_checkpointing
     piper_metadata.activation_num_checkpoints = max(1, int(num_checkpoints))
     piper_metadata.bucketing = bucketing
+    piper_metadata.bucket_size = bucket_size
+    piper_metadata.zero_stage = int(zero_stage)
+    piper_metadata.schedule_name = schedule_name
+    piper_metadata.visualize_dag_render = visualize_dag_render
     piper_metadata.a2a_ar_no_overlap = a2a_ar_no_overlap
     piper_metadata.schedule = schedule
     piper_metadata.visualize_dag = visualize_dag
@@ -119,6 +164,7 @@ def piper_setup(
     piper_metadata.stage_bucket_counts = {}
     piper_metadata.trainable_bucket_keys = set()
     piper_metadata.compiled_stage_data = None
+    piper_metadata.compiled_data_store = None
 
     # MFU tracking
     piper_metadata.model_flops_per_token = model_flops_per_token
@@ -145,20 +191,28 @@ def piper_setup(
     dp_rank = int(os.environ["PIPER_DP_RANK"])
     _create_actors(
         num_devices, optim_fn, num_mbs, num_stages,
-        naive_gradient_sync, profile=nsight, stage_to_device=stage_to_device, pg=pg,
+        naive_gradient_sync, profile=nsight, stage_to_device=stage_to_device,
+        zero_stage=zero_stage, no_nvtx=no_nvtx, pg=pg,
     )
 
     if dp_rank == 0:
         # Create the compiled-data store early so dp_rank>0 can poll for it.
-        _CompiledDataStore.options(
-            name=_COMPILED_DATA_ACTOR, lifetime="detached", get_if_exists=True
+        piper_metadata.compiled_data_store = _CompiledDataStore.options(
+            name=_COMPILED_DATA_ACTOR,
+            lifetime="detached",
+            num_cpus=0,
+            get_if_exists=True,
         ).remote()
+        ray.get(piper_metadata.compiled_data_store.is_ready.remote())
 
         # Get IP and a free port from actor 0's node — it will be the TCPStore server.
         master_addr, master_port = ray.get(piper_metadata.actors[0].get_node_ip_and_free_port.remote())
-        _Rank0AddrStore.options(
-            name=_RANK0_ADDR_ACTOR, lifetime="detached"
+        addr_store = _Rank0AddrStore.options(
+            name=_RANK0_ADDR_ACTOR,
+            lifetime="detached",
+            num_cpus=0,
         ).remote(master_addr, master_port)
+        ray.get(addr_store.get.remote())
     else:
         master_addr = master_port = None
         while master_addr is None:
@@ -167,13 +221,11 @@ def piper_setup(
                 master_addr, master_port = ray.get(store.get.remote())
             except Exception:
                 time.sleep(0.05)
-    logger.debug(f"Master address for process groups: {master_addr}:{master_port}")
-    ray.get(
-        [
-            actor._join_process_groups.remote(master_addr, master_port)
-            for actor in piper_metadata.actors.values()
-        ]
-    )
+
+    logger.debug(f"DP rank {dp_rank}: Master address for process groups: {master_addr}:{master_port}")
+
+    handles = [actor._join_process_groups.remote(master_addr, master_port) for actor in piper_metadata.actors.values()]
+
     if dp_rank == 0:
         try:
             ray.kill(ray.get_actor(_RANK0_ADDR_ACTOR))
@@ -192,7 +244,7 @@ def piper_setup(
             actor.load_const_attrs.remote(_const_attrs)
             for actor in piper_metadata.actors.values()
         ])
-        logger.debug(f"Pushed {len(_const_attrs)} const attrs to actors: {list(_const_attrs.keys())}")
+        logger.log(VERBOSE, f"Pushed {len(_const_attrs)} const attrs to actors: {list(_const_attrs.keys())}")
 
     if dp_rank == 0:
         # --- dp_rank=0: run torch.compile, build DAGs, publish compiled data ---
@@ -210,36 +262,30 @@ def piper_setup(
             param_size_gb = num_params * 4 / (1024**3)
         else:
             raise ValueError(f"Unsupported model dtype: {model_dtype}")
-        print(f"Model size: {num_params/(1e6):.0f} M parameters ({param_size_gb:.2f} GB), dtype: {model_dtype}")
+        logger.info(f"Model size: {num_params/(1e6):.0f} M parameters ({param_size_gb:.2f} GB), dtype: {model_dtype}")
 
         compiled = torch.compile(model, backend=piper, fullgraph=True)
         meta_inputs = [x.to(device="meta") for x in example_inputs]
         _ = compiled(*meta_inputs)
-        logger.info(f"DP rank 0 stage graphs loaded onto actors.")
-
-        # Publish compiled stage data for dp_rank > 0.
-        compiled_store = ray.get_actor(_COMPILED_DATA_ACTOR)
-        ray.get(compiled_store.publish.remote(piper_metadata.compiled_stage_data))
-        logger.info(f"DP rank 0 published compiled stage data.")
 
     else:
-        # --- dp_rank>0: wait for dp_rank=0 to finish, then load directly ---
-        logger.info(f"DP rank {dp_rank} waiting for dp_rank=0 to finish compiling...")
-        compiled_data = None
-        while compiled_data is None:
+        # --- dp_rank>0: wait for stage data first, then load while dp_rank=0 builds DAGs. ---
+        logger.debug(f"DP rank {dp_rank} waiting for dp_rank=0 stage data...")
+        stage_data = None
+        while stage_data is None:
             try:
                 store = ray.get_actor(_COMPILED_DATA_ACTOR)
-                if ray.get(store.is_ready.remote()):
-                    compiled_data = ray.get(store.get.remote())
+                if ray.get(store.is_stage_ready.remote()):
+                    stage_data = ray.get(store.get_stage_data.remote())
             except Exception:
                 pass
-            if compiled_data is None:
+            if stage_data is None:
                 time.sleep(0.2)
-        logger.info(f"DP rank {dp_rank} received compiled stage data, loading onto actors...")
+        logger.debug(f"DP rank {dp_rank} received compiled stage data, loading onto actors...")
 
         # Load stages onto this dp_rank's actors (same serialized graphs, different actor refs).
         refs = []
-        for stage_id, (modules_data, a2a_boundaries) in compiled_data["stages"].items():
+        for stage_id, (modules_data, a2a_boundaries) in stage_data["stages"].items():
             actor_id = piper_metadata.stage_to_device[stage_id]
             actor = piper_metadata.actors[actor_id]
             refs.append(actor._load_stage.remote(
@@ -250,68 +296,37 @@ def piper_setup(
             ))
         ray.get(refs)
 
-        # Restore piper_metadata fields that the piper backend sets on dp_rank=0.
-        piper_metadata.stage_bucket_counts = compiled_data["stage_bucket_counts"]
-        piper_metadata.trainable_bucket_keys = compiled_data["trainable_bucket_keys"]
-        piper_metadata.per_rank_dags = compiled_data["per_rank_dags"]
-        piper_metadata.full_dag_no_overlap = compiled_data.get("full_dag_no_overlap")
+        # Restore stage metadata that the piper backend sets on dp_rank=0.
+        piper_metadata.stage_bucket_counts = stage_data["stage_bucket_counts"]
+        piper_metadata.trainable_bucket_keys = stage_data["trainable_bucket_keys"]
+
+        logger.debug(f"DP rank {dp_rank} waiting for dp_rank=0 DAG data...")
+        dag_data = None
+        while dag_data is None:
+            try:
+                store = ray.get_actor(_COMPILED_DATA_ACTOR)
+                if ray.get(store.is_dag_ready.remote()):
+                    dag_data = ray.get(store.get_dag_data.remote())
+            except Exception:
+                pass
+            if dag_data is None:
+                time.sleep(0.2)
+
+        piper_metadata.per_rank_dags = dag_data["per_rank_dags"]
+        piper_metadata.full_dag_no_overlap = dag_data.get("full_dag_no_overlap")
 
         # Load the same per-rank DAGs onto this dp_rank's actors.
         ray.get([
             piper_metadata.actors[pp_rank].load_dag.remote(per_rank_dag)
             for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags)
         ])
-        logger.info(f"DP rank {dp_rank} stage graphs and DAGs loaded onto actors.")
 
     last_stage_rank = stage_to_device[num_stages - 1]
     ray.get(piper_metadata.actors[0].load_input.remote(example_inputs))
     ray.get(piper_metadata.actors[last_stage_rank].load_labels.remote(example_outputs))
-    logger.info(f"DP rank {dp_rank} real inputs/labels loaded onto actors.")
-
-    # if loss_fn is not None:
-    #     _NUM_WARMUP = 3
-    #     _NUM_PROFILE = 5
-
-    #     logger.info(f"DP rank {dp_rank}: running {_NUM_WARMUP} warmup iterations before profiling.")
-    #     for _ in range(_NUM_WARMUP):
-    #         piper_exec_dag(loss_fn)
-
-    #     logger.info(f"DP rank {dp_rank}: running {_NUM_PROFILE} profiling iterations.")
-    #     for _ in range(_NUM_PROFILE):
-    #         piper_exec_dag(loss_fn, profiling=True)
-
-    #     # Only dp_rank=0 aggregates timing data and renders the critical-path DAG.
-    #     if dp_rank == 0:
-    #         uid_to_measurements: dict = {}
-    #         for pp_rank in range(len(piper_metadata.per_rank_dags)):
-    #             actor = piper_metadata.actors[pp_rank]
-    #             node_runtimes = ray.get(actor.get_node_runtimes.remote())
-    #             uid_to_measurements.update(node_runtimes)
-
-    #         full_dag = piper_metadata.full_dag_no_overlap
-    #         if full_dag is not None:
-    #             for node in full_dag.nodes:
-    #                 measurements = uid_to_measurements.get(node.uid, [])
-    #                 if measurements:
-    #                     node.profiling_measurements = measurements
-    #                     node.runtime = sum(measurements) / len(measurements)
-
-    #             critical_nodes = compute_critical_path(full_dag)
-    #             logger.info(
-    #                 f"Critical path has {len(critical_nodes)} nodes "
-    #                 f"(of {len(full_dag.nodes)} total)."
-    #             )
-    #             try:
-    #                 visualize_dag(
-    #                     full_dag,
-    #                     output_path="figs/profiled_full_dag",
-    #                     critical_path_nodes=critical_nodes,
-    #                 )
-    #             except Exception as e:
-    #                 logger.warning(f"Profiled DAG visualisation failed: {e}")
 
     logger.info(f"DP rank {dp_rank} done.")
-    
+
 
 def piper_shutdown():
     ray.get([actor.shutdown.remote() for actor in piper_metadata.actors.values()])
diff --git a/src/piper_exec.py b/src/piper_exec.py
index 9fcdbb7..f2eb7f0 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -29,6 +29,12 @@ class TaskType(Enum):
     SEND = "send"
     RECV = "recv"
     ALL_REDUCE = "all_reduce"
+    REDUCE_SCATTER = "reduce_scatter"
+    ALL_GATHER = "all_gather"
+    ALLOC_FULL_GRADS = "alloc_full_grads"
+    FREE_FULL_GRADS = "free_full_grads"
+    ALLOC_FULL_PARAMS = "alloc_full_params"
+    FREE_FULL_PARAMS = "free_full_params"
     FWD_A2A = "forward_a2a"
     BWD_A2A = "backward_a2a"
 
@@ -637,4 +643,4 @@ def _p2p_send(actor, pp_rank, stage_from, stage_to, mb_idx, is_fwd, dep):
                 if bwd_stage_id > 0:
                     dep = _p2p_send(actor, pp_rank, bwd_stage_id, bwd_stage_id - 1, bwd_mb_idx, False, dep)
                 deps[pp_rank] = dep
-    return ray.get(ret)
\ No newline at end of file
+    return ray.get(ret)
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index 3425457..6956346 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -12,137 +12,12 @@
 import time
 from torch.autograd import Function
 
-from .piper_utils import create_logger, LOG_LEVEL, piper_metadata
+from .piper_utils import create_logger, LOG_LEVEL, VERBOSE, piper_metadata
 from .piper_exec import TaskType, PipelineSchedule, Chunk, Task, TaskDAG, logical_time
 
 logger = create_logger("piper_graph_transform", LOG_LEVEL)
 
 
-class CommOp:
-    def __init__(self, tensor_id: int, name: str, dep: str, pass_type: str, op: str, group: str):
-        self.tensor = None # this gets set on the actor
-        self.tensor_id = tensor_id
-        self.name = name
-        self.dep = dep # "pre" or "post"
-        self.pass_type = pass_type # "forward" or "backward"
-        self.op = op # "allreduce", "allgather", "scatter", "alltoall"
-        self.group = group # "dp" or "pp"
-
-def _get_dp_comm_ops(inputs, placeholders):
-    dp_comm_ops = []
-    ids = []
-    for p, t in zip(placeholders, inputs):
-        ids.append(id(t))
-        if isinstance(t, torch.nn.Parameter) and t.requires_grad:
-            dp_comm_ops.append(CommOp(id(t), p.name, "post", "backward", "allreduce", "dp"))
-    return dp_comm_ops, ids
-
-class AllToAllSingleFunction(torch.autograd.Function):
-    """
-    Custom autograd function for all_to_all_single that ensures gradients flow correctly.
-    
-    This wraps torch.distributed.all_to_all_single to ensure that gradients
-    from the input tensor properly flow to the output buffer.
-    The backward pass performs another all_to_all_single to reverse the communication.
-    """
-    @staticmethod
-    def forward(ctx, output, input_tensor):
-        """
-        Forward pass: performs all_to_all_single communication.
-
-        Args:
-            ctx: Context for storing information for backward pass
-            output: Output buffer (will be modified in-place)
-            input_tensor: Input tensor to communicate
-            group: Process group (optional)
-        """
-        # Store group for backward pass
-        from .piper_utils import piper_metadata
-        ctx.actor_self = piper_metadata.actor_self
-        ctx.group = ctx.actor_self.ep_group
-        ctx.global_rank = ctx.actor_self.global_rank
-        ctx.stream = ctx.actor_self.a2a_stream
-
-        comp_stream = torch.cuda.current_stream()
-        comm_finished_event = torch.cuda.Event()
-        ctx.actor_self._start_timing(ctx.stream, "fwd_a2a")
-
-        ctx.stream.wait_stream(comp_stream)
-        _ov_token = ctx.actor_self.overlap_detector.before_kernel(ctx.stream, "fwd_a2a", "a2a_stream")
-
-        input_tensor = input_tensor.contiguous()
-        with torch.cuda.stream(ctx.stream):
-            dist.all_to_all_single(output, input_tensor, group=ctx.group)
-            comm_finished_event.record()
-        ctx.actor_self.overlap_detector.after_kernel(ctx.stream, _ov_token)
-
-        ctx.actor_self._stop_timing(ctx.stream, "fwd_a2a")
-
-        # POST-HOOK: wait for bwd to reach its corresponding A2A
-        if ctx.actor_self.overlap_a2a_ops:
-            idx = ctx.actor_self.fwd_a2a_counter
-            ctx.actor_self.fwd_a2a_submitted[idx].set()
-            ctx.actor_self.bwd_a2a_submitted[idx].wait()
-            ctx.actor_self.fwd_a2a_counter += 1
-        comp_stream.wait_event(comm_finished_event)
-
-        return output
-    
-    @staticmethod
-    def backward(ctx, grad_output):
-        """
-        Backward pass: performs reverse all_to_all_single to propagate gradients.
-
-        The backward of all_to_all_single is another all_to_all_single operation
-        that reverses the communication pattern.
-        """
-        logger.debug(f"Dispatch AllToAllSingleFunction backward rank={ctx.global_rank}, shape={grad_output.shape}")
-
-        if grad_output is None:
-            return None, None, None
-
-        comp_stream = torch.cuda.current_stream()
-        comm_finished_event = torch.cuda.Event()
-
-        ctx.actor_self._start_timing(ctx.stream, "bwd_a2a")
-
-        ctx.stream.wait_stream(comp_stream)
-        _ov_token = ctx.actor_self.overlap_detector.before_kernel(ctx.stream, "bwd_a2a", "a2a_stream")
-
-        grad_output = grad_output.contiguous()
-        grad_input = torch.empty_like(grad_output)
-        with torch.cuda.stream(ctx.stream):
-            dist.all_to_all_single(grad_input, grad_output, group=ctx.group)
-            comm_finished_event.record()
-        ctx.actor_self.overlap_detector.after_kernel(ctx.stream, _ov_token)
-
-        ctx.actor_self._stop_timing(ctx.stream, "bwd_a2a")
-
-        # POST-HOOK: wait for fwd to reach the next A2A
-        if ctx.actor_self.overlap_a2a_ops:
-            idx = ctx.actor_self.bwd_a2a_counter
-            ctx.actor_self.bwd_a2a_submitted[idx].set()
-            if idx < len(ctx.actor_self.fwd_a2a_submitted) - 1:
-                ctx.actor_self.fwd_a2a_submitted[idx + 1].wait()
-            ctx.actor_self.bwd_a2a_counter += 1
-        comp_stream.wait_event(comm_finished_event)
-
-        logger.debug(f"Completed AllToAllSingleFunction backward rank={ctx.global_rank}")
-        
-        # Return gradients: grad_output flows to grad_input, None for group
-        return grad_input, grad_input, None
-
-def _dispatch_a2a_single(output: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
-    """
-    Dispatch a Ray remote call to run an all_to_all_single operation
-    using a custom autograd Function. This function is called
-    from the FX graph, so it needs to be allowed in the graph.
-    """
-    return AllToAllSingleFunction.apply(output, input_tensor)
-
-# Allow the dispatch function in the graph
-torch.compiler.allow_in_graph(_dispatch_a2a_single)
-
 def _profile_and_split_gm(gm, num_stages) -> tuple[fx.GraphModule, list[tuple[int, fx.GraphModule, list[int], list]]]:
     """
     Profile each node in the graph module, then split into num_stages stages
@@ -319,7 +194,7 @@ def _log_env_memory():
         # Log the top 10 largest tensors
         active.sort(key=lambda x: x[3], reverse=True)
         for name, shape, dtype, nbytes in active[:10]:
-            logger.debug(f"  {name}: {shape} {dtype} ({nbytes / (1024*1024):.1f} MB)")
+            logger.log(VERBOSE, f"  {name}: {shape} {dtype} ({nbytes / (1024*1024):.1f} MB)")
 
     # Step 3: Profile each compute node with eager memory freeing
     # Use no_grad to prevent autograd graph accumulation — we only need forward timing.
@@ -343,7 +218,7 @@ def _log_env_memory():
                 break
 
         if has_none_input:
-            logger.debug(f"Skipping node {node.name}: predecessor produced None")
+            logger.log(VERBOSE, f"Skipping node {node.name}: predecessor produced None")
             node_times[node.name] = 0.0
             env[node.name] = None
             for inp in node.all_input_nodes:
@@ -457,7 +332,7 @@ def _find_best_split(ideal_idx, stage_start_idx):
         if best_split is not None:
             split_indices.append(best_split)
             time_offset = prefix_times[best_split] - prefix_times[ideal_idx]
-            logger.info(
+            logger.debug(
                 f"Stage {s}/{s+1} boundary: split before node {compute_nodes[best_split].name} "
                 f"(index {best_split}, time offset from ideal: {time_offset:+.3f} ms)"
             )
@@ -470,7 +345,7 @@ def _find_best_split(ideal_idx, stage_start_idx):
         cache_dir.mkdir(parents=True, exist_ok=True)
         split_node_names = [compute_nodes[i].name for i in split_indices]
         cache_file.write_text(json.dumps({"split_node_names": split_node_names}, indent=2))
-        logger.info(f"Saved split cache to {cache_file}")
+        logger.debug(f"Saved split cache to {cache_file}")
     except Exception as e:
         logger.warning(f"Failed to save split cache: {e}")
 
@@ -508,7 +383,7 @@ def _find_best_split(ideal_idx, stage_start_idx):
         stage_times[stage_id] += node_times.get(node.name, 0.0)
         stage_node_counts[stage_id] += 1
     for stage_id in sorted(stage_times.keys()):
-        logger.info(
+        logger.debug(
             f"Stage {stage_id}: {stage_times[stage_id]:.3f} ms "
             f"({stage_node_counts[stage_id]} nodes)"
         )
@@ -554,10 +429,10 @@ def _split_gm_by_stages(gm) -> tuple[fx.GraphModule, list[tuple[int, fx.GraphMod
                     nodes_with_metadata.append((node, stage_annotation_id))
     
     if not nodes_with_metadata:
-        logger.debug("No stage nodes found in graph")
+        logger.log(VERBOSE, "No stage nodes found in graph")
         return gm, []
     else:
-        logger.debug(f"Found stage nodes in graph")
+        logger.log(VERBOSE, f"Found stage nodes in graph")
     
     # Group nodes by stage ID for creating stage modules
     stage_code = defaultdict(list)  # stage_id -> list of all nodes for this stage
@@ -1137,7 +1012,7 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> tuple[fx.GraphModule, int]:
     annotation_blocks = list(blocks_by_key.values())
     
     n_a2a_ops = len(annotation_blocks)
-    logger.debug(f"Found {len(annotation_blocks)} contiguous annotation blocks")
+    logger.log(VERBOSE, f"Found {len(annotation_blocks)} contiguous annotation blocks")
     
     # For each contiguous block, find the output node (the one used outside the annotation)
     # This is the node that should have communication applied to it
@@ -1170,7 +1045,7 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> tuple[fx.GraphModule, int]:
                 last_node = max(nodes_in_block, key=lambda x: node_list.index(x[0]))
                 annotated_output_nodes.append(last_node)
     
-    logger.debug(f"Found {len(annotated_output_nodes)} annotation blocks requiring communication operations")
+    logger.log(VERBOSE, f"Found {len(annotated_output_nodes)} annotation blocks requiring communication operations")
     
     # Create a new graph to build the transformed version
     new_graph = fx.Graph()
@@ -1226,7 +1101,7 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> tuple[fx.GraphModule, int]:
                 reshape_type, reshape_shape = reshape_info
                 if reshape_type == "input":
                     # DEBUG: Log reshape information
-                    logger.debug(f"Applying input reshape {reshape_shape} to node {node.name}")
+                    logger.log(VERBOSE, f"Applying input reshape {reshape_shape} to node {node.name}")
                     if isinstance(reshape_shape, (list, tuple)) and -1 in reshape_shape:
                         logger.warning(f"Input reshape shape contains -1: {reshape_shape}, this may cause shape inference issues")
                     # Reshape before communication
@@ -1264,7 +1139,7 @@ def _insert_a2a_ops(gm: fx.GraphModule) -> tuple[fx.GraphModule, int]:
                 if reshape_type == "output":
                     # Reshape after communication
                     # DEBUG: Log reshape information
-                    logger.debug(f"Applying output reshape {reshape_shape} to node {node.name}, buf_node shape inference needed")
+                    logger.log(VERBOSE, f"Applying output reshape {reshape_shape} to node {node.name}, buf_node shape inference needed")
                     # Log the reshape shape for debugging
                     if isinstance(reshape_shape, (list, tuple)) and -1 in reshape_shape:
                         logger.warning(f"Reshape shape contains -1: {reshape_shape}, this may cause shape inference issues")
@@ -1603,8 +1478,6 @@ def expand_chunks_to_dags(
     schedule,
     bucket_counts: dict,
     a2a_info: dict,
-    dp_degree: int,
-    a2a_ar_no_overlap: bool = False,
 ) -> "TaskDAG":
     """Build a :class:`TaskDAG` by expanding every schedule chunk in-place.
 
@@ -1813,56 +1686,6 @@ def expand_chunks_to_dags(
         all_tasks.append(send_node)
         all_tasks.append(recv_node)
 
-    # ------------------------------------------------------------------
-    # Pass 5: ALL_REDUCE nodes for data-parallel gradient sync
-    # ------------------------------------------------------------------
-    if dp_degree > 1:
-        bwd_groups: dict = defaultdict(list)
-        for task in all_tasks:
-            if task.chunk.type in (TaskType.BWD, TaskType.BWD_W) and task.chunk.batches:
-                s = task.chunk.batches[0].stage_id
-                key = (s, task.bucket_id)
-                bwd_groups[key].append(task)
-
-        for (stage_id, bucket_id), group in bwd_groups.items():
-            trigger = max(group, key=lambda n: n.time_step)
-            ar_node = Task(
-                chunk=Chunk(
-                    pp_rank=trigger.pp_rank,
-                    batches=list(trigger.chunk.batches),
-                    type=TaskType.ALL_REDUCE,
-                ),
-                pp_rank=trigger.pp_rank,
-                time_step=trigger.time_step + 1,
-                bucket_id=bucket_id,
-                source_chunk=trigger.source_chunk,
-            )
-            trigger.data_succs.append(ar_node)
-            ar_node.data_preds.append(trigger)
-            all_tasks.append(ar_node)
-
-    # ------------------------------------------------------------------
-    # Pass 6: Optional backward-compute / ALL_REDUCE no-overlap ordering
-    # ------------------------------------------------------------------
-    if a2a_ar_no_overlap:
-        last_bwd_compute_by_rank: dict = {}
-        ar_by_rank: dict = defaultdict(list)
-        for task in all_tasks:
-            if task.chunk.type in (TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W):
-                last_bwd_compute_by_rank[task.pp_rank] = task
-            elif task.chunk.type == TaskType.ALL_REDUCE:
-                ar_by_rank[task.pp_rank].append(task)
-
-        for pp_rank, ar_tasks in ar_by_rank.items():
-            last_bwd_compute = last_bwd_compute_by_rank.get(pp_rank)
-            if last_bwd_compute is None:
-                continue
-            for ar_task in ar_tasks:
-                if ar_task not in last_bwd_compute.temporal_succs:
-                    last_bwd_compute.temporal_succs.append(ar_task)
-                if last_bwd_compute not in ar_task.temporal_preds:
-                    ar_task.temporal_preds.append(last_bwd_compute)
-
     return TaskDAG(nodes=all_tasks)
 
 
@@ -1946,23 +1769,57 @@ def _cp_endpoints(chunk_id: int):
                 tail_c1.temporal_succs.append(head_c2)
                 head_c2.temporal_preds.append(tail_c1)
 
-        # UPD node: temporal successor of the last chunk's critical-path tail
-        _, last_chunk = non_none[-1]
-        _, last_tail = _cp_endpoints(id(last_chunk))
-        if last_tail is not None:
-            upd_node = Task(
-                chunk=Chunk(
-                    pp_rank=last_tail.pp_rank,
-                    batches=list(last_tail.chunk.batches),
-                    type=TaskType.UPD,
-                ),
-                pp_rank=last_tail.pp_rank,
-                time_step=0,
-                source_chunk=last_chunk,
-            )
-            last_tail.temporal_succs.append(upd_node)
-            upd_node.temporal_preds.append(last_tail)
-            new_nodes.append(upd_node)
+        # UPD node: final optimizer step for this rank.
+        # In the no-DP case, every BWD-type chunk's critical-path tail feeds
+        # into UPD so that UPD correctly waits for ALL backward passes, not
+        # just the last one in the temporal chain.  In the DP/ZeRO case,
+        # _wire_sync_to_upd (called inside insert_zero_ops) additionally
+        # connects each AR/RS → UPD temporal edge.
+        _BWD_CHUNK_TYPES = frozenset({
+            TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W, TaskType.FWD_BWD,
+        })
+
+        # Use the last BWD-type chunk's tail for UPD's pp_rank / batches.
+        ref_tail = None
+        for _, chunk in reversed(non_none):
+            if chunk.type in _BWD_CHUNK_TYPES:
+                _, ref_tail = _cp_endpoints(id(chunk))
+                if ref_tail is not None:
+                    break
+        # Fall back to the very last chunk if no BWD chunk exists.
+        if ref_tail is None:
+            _, ref_tail = _cp_endpoints(id(non_none[-1][1]))
+        if ref_tail is None:
+            continue
+
+        upd_node = Task(
+            chunk=Chunk(
+                pp_rank=ref_tail.pp_rank,
+                batches=list(ref_tail.chunk.batches),
+                type=TaskType.UPD,
+            ),
+            pp_rank=ref_tail.pp_rank,
+            time_step=0,
+            source_chunk=non_none[-1][1],
+        )
+
+        # Wire every BWD chunk tail → UPD.
+        seen_tails: set[int] = set()
+        for _, chunk in non_none:
+            if chunk.type not in _BWD_CHUNK_TYPES:
+                continue
+            _, tail = _cp_endpoints(id(chunk))
+            if tail is not None and id(tail) not in seen_tails:
+                seen_tails.add(id(tail))
+                tail.temporal_succs.append(upd_node)
+                upd_node.temporal_preds.append(tail)
+
+        # If no BWD chunks (FWD-only schedule), fall back to last chunk.
+        if not upd_node.temporal_preds:
+            ref_tail.temporal_succs.append(upd_node)
+            upd_node.temporal_preds.append(ref_tail)
+
+        new_nodes.append(upd_node)
 
     dag.nodes.extend(new_nodes)
 
@@ -2001,6 +1858,223 @@ def split_dag_by_rank(dag: "TaskDAG") -> list:
     return [TaskDAG(nodes=rank_nodes[r]) for r in sorted(rank_nodes)]
 
 
+def _remove_ar_nodes(rank_dag: TaskDAG) -> None:
+    ar_set = {id(n) for n in rank_dag.nodes if n.chunk.type == TaskType.ALL_REDUCE}
+    if not ar_set:
+        return
+    for n in rank_dag.nodes:
+        if id(n) not in ar_set:
+            n.data_preds = [p for p in n.data_preds if id(p) not in ar_set]
+            n.data_succs = [s for s in n.data_succs if id(s) not in ar_set]
+            n.temporal_preds = [p for p in n.temporal_preds if id(p) not in ar_set]
+            n.temporal_succs = [s for s in n.temporal_succs if id(s) not in ar_set]
+    rank_dag.nodes = [n for n in rank_dag.nodes if id(n) not in ar_set]
+
+
+def _zero_task(
+    ref: Task,
+    bucket_id: int,
+    task_type: TaskType,
+    time_step: int = 0,
+    *,
+    ubid: int | None = None,
+    source_chunk: Chunk | None = None,
+) -> Task:
+    return Task(
+        chunk=Chunk(
+            pp_rank=ref.pp_rank,
+            batches=list(ref.chunk.batches),
+            type=task_type,
+        ),
+        pp_rank=ref.pp_rank,
+        time_step=time_step,
+        bucket_id=bucket_id,
+        unique_bucket_id=ubid,
+        source_chunk=source_chunk or ref.source_chunk,
+    )
+
+
+def _bucket_compute_tasks(
+    rank_dag: TaskDAG,
+    trainable_bucket_keys: set | None = None,
+) -> dict[int, list[Task]]:
+    compute_types = {TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W}
+    by_ubid: dict[int, list[Task]] = defaultdict(list)
+    for node in rank_dag.nodes:
+        if node.chunk.type not in compute_types or node.unique_bucket_id is None:
+            continue
+        if trainable_bucket_keys is not None:
+            if not node.chunk.batches:
+                continue
+            stage_id = node.chunk.batches[0].stage_id
+            if (stage_id, node.bucket_id) not in trainable_bucket_keys:
+                continue
+        by_ubid[node.unique_bucket_id].append(node)
+    for nodes in by_ubid.values():
+        nodes.sort(key=lambda n: n.time_step)
+    return by_ubid
+
+
+def _add_data_edge(src: Task, dst: Task) -> None:
+    if dst not in src.data_succs:
+        src.data_succs.append(dst)
+    if src not in dst.data_preds:
+        dst.data_preds.append(src)
+
+
+def _insert_zero_grad_ops(
+    rank_dag: TaskDAG,
+    *,
+    sync_type: TaskType,
+    trainable_bucket_keys: set | None = None,
+) -> None:
+    """Per-compute-node full-gradient lifecycle: G+ → compute → sync → G-.
+
+    Every BWD / BWD_W node gets its own alloc-sync-free triple.  There is
+    no gradient accumulation: each node independently all-reduces or
+    reduce-scatters its gradients immediately after it finishes.
+    """
+    _remove_ar_nodes(rank_dag)
+
+    grad_sync_types = {TaskType.BWD, TaskType.BWD_W}
+    new_nodes: list[Task] = []
+
+    for ubid, bucket_nodes in _bucket_compute_tasks(rank_dag, trainable_bucket_keys).items():
+        for compute_node in bucket_nodes:
+            if compute_node.chunk.type not in grad_sync_types:
+                continue
+            alloc = _zero_task(compute_node, compute_node.bucket_id, TaskType.ALLOC_FULL_GRADS, ubid=ubid)
+            sync = _zero_task(compute_node, compute_node.bucket_id, sync_type, ubid=ubid)
+            free = _zero_task(compute_node, compute_node.bucket_id, TaskType.FREE_FULL_GRADS, ubid=ubid)
+            _add_data_edge(alloc, compute_node)
+            _add_data_edge(compute_node, sync)
+            _add_data_edge(sync, free)
+            new_nodes.extend([alloc, sync, free])
+
+    rank_dag.nodes.extend(new_nodes)
+
+
+def _insert_zero0_ops(rank_dag: TaskDAG) -> None:
+    """ZeRO-0 (plain DP): per-compute-node all-reduce, same policy as ZeRO-1."""
+    _insert_zero_grad_ops(rank_dag, sync_type=TaskType.ALL_REDUCE)
+
+
+def _insert_zero1_ops(
+    rank_dag: TaskDAG,
+    trainable_bucket_keys: set | None = None,
+) -> None:
+    """ZeRO-1: per-compute-node all-reduce."""
+    _insert_zero_grad_ops(
+        rank_dag,
+        sync_type=TaskType.ALL_REDUCE,
+        trainable_bucket_keys=trainable_bucket_keys,
+    )
+
+
+def _insert_zero2_ops(
+    rank_dag: TaskDAG,
+    trainable_bucket_keys: set | None = None,
+) -> None:
+    """ZeRO-2: per-compute-node reduce-scatter."""
+    _insert_zero_grad_ops(
+        rank_dag,
+        sync_type=TaskType.REDUCE_SCATTER,
+        trainable_bucket_keys=trainable_bucket_keys,
+    )
+
+
+def _insert_zero3_ops(
+    rank_dag: TaskDAG,
+    trainable_bucket_keys: set | None = None,
+) -> None:
+    """ZeRO-3: per-compute-node param gather + per-grad-node reduce-scatter.
+
+    * F / B_I  →  P+ → AG → compute → P-
+    * B / B_W  →  (G+ →) and (P+ → AG →) compute (→ RS → G-) and (→ P-)
+    """
+    _remove_ar_nodes(rank_dag)
+
+    # Types that only consume params (no weight-grad accumulation).
+    param_only_types = frozenset({TaskType.FWD, TaskType.BWD_I})
+    # Types that consume params AND accumulate weight gradients.
+    grad_types = frozenset({TaskType.BWD, TaskType.BWD_W})
+
+    new_nodes: list[Task] = []
+
+    for ubid, bucket_nodes in _bucket_compute_tasks(rank_dag, trainable_bucket_keys).items():
+        for compute_node in bucket_nodes:
+            t = compute_node.chunk.type
+            bid = compute_node.bucket_id
+
+            if t in param_only_types:
+                # P+ → AG → compute → P-
+                alloc_p = _zero_task(compute_node, bid, TaskType.ALLOC_FULL_PARAMS, ubid=ubid)
+                ag      = _zero_task(compute_node, bid, TaskType.ALL_GATHER,        ubid=ubid)
+                free_p  = _zero_task(compute_node, bid, TaskType.FREE_FULL_PARAMS,  ubid=ubid)
+                _add_data_edge(alloc_p, ag)
+                _add_data_edge(ag, compute_node)
+                _add_data_edge(compute_node, free_p)
+                new_nodes.extend([alloc_p, ag, free_p])
+
+            elif t in grad_types:
+                # Pre-compute: both G+ and P+→AG feed into compute.
+                alloc_g = _zero_task(compute_node, bid, TaskType.ALLOC_FULL_GRADS,  ubid=ubid)
+                alloc_p = _zero_task(compute_node, bid, TaskType.ALLOC_FULL_PARAMS, ubid=ubid)
+                ag      = _zero_task(compute_node, bid, TaskType.ALL_GATHER,        ubid=ubid)
+                # Post-compute: RS→G- and P- both follow compute.
+                rs      = _zero_task(compute_node, bid, TaskType.REDUCE_SCATTER,    ubid=ubid)
+                free_g  = _zero_task(compute_node, bid, TaskType.FREE_FULL_GRADS,   ubid=ubid)
+                free_p  = _zero_task(compute_node, bid, TaskType.FREE_FULL_PARAMS,  ubid=ubid)
+                _add_data_edge(alloc_g, compute_node)
+                _add_data_edge(alloc_p, ag)
+                _add_data_edge(ag, compute_node)
+                _add_data_edge(compute_node, rs)
+                _add_data_edge(rs, free_g)
+                _add_data_edge(compute_node, free_p)
+                new_nodes.extend([alloc_g, alloc_p, ag, rs, free_g, free_p])
+
+    rank_dag.nodes.extend(new_nodes)
+
+
+def _wire_sync_to_upd(rank_dag: TaskDAG) -> None:
+    """Add temporal edges from every AR/RS node → UPD on this rank.
+
+    In the DP/ZeRO case the optimizer update must not start until all
+    gradient-synchronisation collectives have completed.  This pass wires
+    that constraint so that ``assign_time_steps`` Pass 3 can push UPD's
+    time_step past the last AR/RS.
+    """
+    upd_nodes  = [n for n in rank_dag.nodes if n.chunk.type == TaskType.UPD]
+    sync_nodes = [n for n in rank_dag.nodes
+                  if n.chunk.type in (TaskType.ALL_REDUCE, TaskType.REDUCE_SCATTER)]
+    for upd in upd_nodes:
+        for sync in sync_nodes:
+            if upd not in sync.temporal_succs:
+                sync.temporal_succs.append(upd)
+            if sync not in upd.temporal_preds:
+                upd.temporal_preds.append(sync)
+
+
+def insert_zero_ops(
+    per_rank_dags: list,
+    zero_stage: int,
+    trainable_bucket_keys: set | None = None,
+) -> list:
+    for rank_dag in per_rank_dags:
+        if zero_stage == 0:
+            _insert_zero0_ops(rank_dag)
+        elif zero_stage == 1:
+            _insert_zero1_ops(rank_dag)
+        elif zero_stage == 2:
+            _insert_zero2_ops(rank_dag, trainable_bucket_keys=trainable_bucket_keys)
+        elif zero_stage == 3:
+            _insert_zero3_ops(rank_dag, trainable_bucket_keys=trainable_bucket_keys)
+        else:
+            raise ValueError(f"Unsupported ZeRO stage: {zero_stage}")
+        _wire_sync_to_upd(rank_dag)
+    return per_rank_dags
+
+
 
 
 # ---------------------------------------------------------------------------
@@ -2023,6 +2097,12 @@ def _task_node_label(node: Task) -> str:
         TaskType.BWD_W:   "Bw",
         TaskType.FWD_BWD: "FB",
         TaskType.ALL_REDUCE: "AR",
+        TaskType.REDUCE_SCATTER: "RS",
+        TaskType.ALL_GATHER: "AG",
+        TaskType.ALLOC_FULL_GRADS: "G+",
+        TaskType.FREE_FULL_GRADS: "G-",
+        TaskType.ALLOC_FULL_PARAMS: "P+",
+        TaskType.FREE_FULL_PARAMS: "P-",
         TaskType.FWD_A2A: "F_A2A",
         TaskType.BWD_A2A: "B_A2A",
     }.get(chunk.type, "?")
@@ -2030,9 +2110,17 @@ def _task_node_label(node: Task) -> str:
     if chunk.type == TaskType.UPD:
         return type_abbrev
 
-    if chunk.type == TaskType.ALL_REDUCE:
+    if chunk.type in (
+        TaskType.ALL_REDUCE,
+        TaskType.REDUCE_SCATTER,
+        TaskType.ALL_GATHER,
+        TaskType.ALLOC_FULL_GRADS,
+        TaskType.FREE_FULL_GRADS,
+        TaskType.ALLOC_FULL_PARAMS,
+        TaskType.FREE_FULL_PARAMS,
+    ):
         stage_id = chunk.batches[0].stage_id
-        return "AR"
+        return type_abbrev
 
     if chunk.type in (TaskType.FWD_A2A, TaskType.BWD_A2A):
         return type_abbrev
@@ -2142,7 +2230,12 @@ def visualize_dag(
 
     def _node_fill(node: Task) -> str:
         t = node.chunk.type
-        if t in (TaskType.SEND, TaskType.RECV, TaskType.FWD_A2A, TaskType.BWD_A2A, TaskType.ALL_REDUCE):
+        if t in (
+            TaskType.SEND, TaskType.RECV, TaskType.FWD_A2A, TaskType.BWD_A2A,
+            TaskType.ALL_REDUCE, TaskType.REDUCE_SCATTER, TaskType.ALL_GATHER,
+            TaskType.ALLOC_FULL_GRADS, TaskType.FREE_FULL_GRADS,
+            TaskType.ALLOC_FULL_PARAMS, TaskType.FREE_FULL_PARAMS,
+        ):
             return "#FFFFFF"
         if t == TaskType.FWD:
             return "#FFA500"  # orange
@@ -2850,7 +2943,7 @@ def apply_activation_checkpointing(
     compute_nodes = [nd for nd in nodes if nd.op not in ("placeholder", "get_attr", "output")]
 
     if not param_ph_list or len(compute_nodes) < 2:
-        logger.info(
+        logger.warning(
             f"[ac_split_skip] gm={gm_name} requested_subgraphs={num_checkpoints} "
             f"params={len(param_ph_list)} compute_nodes={len(compute_nodes)} "
             "reason=insufficient_params_or_compute"
@@ -2887,7 +2980,7 @@ def _use_indices(pnd: fx.Node) -> list[int]:
     param_sizes = {pn: _param_size(pn) for pn in param_ph_list}
     total_param_bytes = sum(param_sizes.values())
     if total_param_bytes <= 0:
-        logger.info(
+        logger.warning(
             f"[ac_split_skip] gm={gm_name} requested_subgraphs={num_checkpoints} "
             f"params={len(param_ph_list)} compute_nodes={len(compute_nodes)} "
             "reason=nonpositive_param_bytes"
@@ -3039,7 +3132,7 @@ def _consumer_seg(user: fx.Node) -> int:
         sub_g.lint()
         results.append(fx.GraphModule(stage_gm, sub_g))
 
-    logger.info(
+    logger.debug(
         f"[ac_split_accept] gm={gm_name} requested_subgraphs={num_checkpoints} "
         f"actual_subgraphs={len(results)} candidate_cuts={len(candidate_cuts)} "
         f"selected_cuts={selected_cuts} "
@@ -3120,7 +3213,7 @@ def split_by_a2a(
     if not annotated_nodes:
         return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))], []
 
-    logger.debug(
+    logger.log(VERBOSE,
         f"split_by_a2a: found {len(annotated_nodes)} annotated nodes:\n"
         + "\n".join(
             f"  [{i}] pos={idx} name={nd.name!r} "
@@ -3160,7 +3253,7 @@ def split_by_a2a(
         })
     n_boundaries = len(blocks)
 
-    logger.debug(
+    logger.log(VERBOSE,
         f"split_by_a2a: grouped into {n_boundaries} consecutive blocks:\n"
         + "\n".join(
             f"  block[{i}]: {len(b['nodes'])} nodes "
@@ -3701,33 +3794,58 @@ def _get_group_ids(chunk: Chunk) -> set[int]:
 # ---------------------------------------------------------------------------
 
 def assign_time_steps(dag: TaskDAG) -> None:
-    """Assign ``time_step`` to every node based on its position in the temporal
-    dependency graph.
-
-    Must be called after :func:`add_temporal_dependencies` (and
-    :func:`overlap_a2a_tasks` if used) and before :func:`split_dag_by_rank`.
-
-    Each node's ``time_step`` is ``max(pred.time_step for all temporal preds) + 1``,
-    with roots (no temporal predecessors) receiving ``time_step = 0``.  When the
-    temporal graph is a simple chain this matches the previous sequential
-    assignment; when :func:`overlap_a2a_tasks` introduces forks and joins the
-    topological sort correctly assigns shared time steps to concurrent nodes
-    and deferred time steps to join points.
-
-    SEND / RECV nodes have no temporal edges and are assigned after the toposort:
-    ``SEND.time_step = data_pred.time_step + 1`` and
-    ``RECV.time_step = data_succ.time_step - 1``. ALL_REDUCE nodes are
-    also constrained by their data predecessor, while preserving any
-    temporal barrier inserted by ``a2a_ar_no_overlap``.
+    """Assign ``time_step`` to every node in two passes.
+
+    **Pass 1** – Kahn's toposort over *temporal* edges, restricted to
+    compute / A2A task types (F / B / B_I / B_W / FWD_BWD / F_A2A /
+    B_A2A / UPD).  Non-compute nodes do not contribute to the temporal
+    ordering and are skipped.
+
+    **Pass 2** – assign time steps to the remaining nodes via their
+    direct data-edge neighbours, using BFS propagation from the compute
+    nodes so that chains of non-compute nodes (e.g. P+ → AG → compute
+    or compute → RS → G- → P-) are resolved correctly:
+
+    * RECV / P+ (ALLOC_FULL_PARAMS) / G+ (ALLOC_FULL_GRADS) /
+      AG (ALL_GATHER) → ``direct_data_succ.time_step - 1``
+    * SEND / RS (REDUCE_SCATTER) / AR (ALL_REDUCE) /
+      G- (FREE_FULL_GRADS) / P- (FREE_FULL_PARAMS)
+      → ``direct_data_pred.time_step + 1``
+
+    This function is idempotent and safe to call both on the full DAG
+    (before :func:`split_dag_by_rank`) and on individual per-rank DAGs
+    after :func:`insert_zero_ops` has added the ZeRO collective nodes.
     """
-    # Kahn's toposort over the temporal graph (temporal_preds / temporal_succs).
-    # time_step = max(pred.time_step for all temporal preds) + 1.
-    # When the graph is a simple chain this matches the previous sequential
-    # assignment; forks and joins from overlap_a2a_tasks are handled correctly.
-    in_degree: dict[int, int] = {id(n): len(n.temporal_preds) for n in dag.nodes}
+    _COMPUTE_TYPES = frozenset({
+        TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W,
+        TaskType.FWD_BWD, TaskType.FWD_A2A, TaskType.BWD_A2A, TaskType.UPD,
+    })
+    # Non-compute nodes that sit *before* their data successor: get ts - 1.
+    _DOWNSTREAM_MINUS_ONE = frozenset({
+        TaskType.RECV,
+        TaskType.ALLOC_FULL_PARAMS,
+        TaskType.ALLOC_FULL_GRADS,
+        TaskType.ALL_GATHER,
+    })
+    # Non-compute nodes that sit *after* their data predecessor: get ts + 1.
+    _UPSTREAM_PLUS_ONE = frozenset({
+        TaskType.SEND,
+        TaskType.REDUCE_SCATTER,
+        TaskType.ALL_REDUCE,
+        TaskType.FREE_FULL_GRADS,
+        TaskType.FREE_FULL_PARAMS,
+    })
+
+    # ---- Pass 1: toposort compute nodes via temporal edges ----
+    compute_nodes = [n for n in dag.nodes if n.chunk.type in _COMPUTE_TYPES]
+
+    in_degree: dict[int, int] = {
+        id(n): sum(1 for p in n.temporal_preds if p.chunk.type in _COMPUTE_TYPES)
+        for n in compute_nodes
+    }
     ts_map: dict[int, int] = {}
     queue: deque[Task] = deque()
-    for node in dag.nodes:
+    for node in compute_nodes:
         if in_degree[id(node)] == 0:
             ts_map[id(node)] = 0
             queue.append(node)
@@ -3736,30 +3854,44 @@ def assign_time_steps(dag: TaskDAG) -> None:
         node = queue.popleft()
         node.time_step = ts_map[id(node)]
         for succ in node.temporal_succs:
+            if succ.chunk.type not in _COMPUTE_TYPES:
+                continue
             in_degree[id(succ)] -= 1
             ts_map[id(succ)] = max(ts_map.get(id(succ), 0), node.time_step + 1)
             if in_degree[id(succ)] == 0:
                 queue.append(succ)
 
-    # Assign stream-only communication nodes relative to their data-edge neighbour.
-    # ALL_REDUCE may also have temporal predecessors from a2a_ar_no_overlap;
-    # keep whichever constraint places it later.
-
-    # Pass 1: SEND and ALL_REDUCE.
-    for node in dag.nodes:
-        if node.chunk.type == TaskType.SEND:
-            assert len(node.data_preds) == 1
-            node.time_step = node.data_preds[0].time_step + 1
-        elif node.chunk.type == TaskType.ALL_REDUCE:
-            assert len(node.data_preds) == 1
-            data_ready_time = node.data_preds[0].time_step + 1
-            if node.temporal_preds:
-                node.time_step = max(node.time_step, data_ready_time)
-            else:
-                node.time_step = data_ready_time
-
-    # Pass 2: RECV (depend only on their data successor).
+    # ---- Pass 2: BFS from compute nodes to assign non-compute nodes ----
+    # Forward BFS: compute → _UPSTREAM_PLUS_ONE successors (time_step + 1 per hop).
+    visited: set[int] = {id(n) for n in compute_nodes}
+    bfs: deque[Task] = deque(compute_nodes)
+    while bfs:
+        node = bfs.popleft()
+        for succ in node.data_succs:
+            if succ.chunk.type not in _UPSTREAM_PLUS_ONE or id(succ) in visited:
+                continue
+            visited.add(id(succ))
+            succ.time_step = node.time_step + 1
+            bfs.append(succ)
+
+    # Backward BFS: compute → _DOWNSTREAM_MINUS_ONE predecessors (time_step - 1 per hop).
+    visited2: set[int] = {id(n) for n in compute_nodes}
+    bfs2: deque[Task] = deque(compute_nodes)
+    while bfs2:
+        node = bfs2.popleft()
+        for pred in node.data_preds:
+            if pred.chunk.type not in _DOWNSTREAM_MINUS_ONE or id(pred) in visited2:
+                continue
+            visited2.add(id(pred))
+            pred.time_step = node.time_step - 1
+            bfs2.append(pred)
+
+    # ---- Pass 3: push UPD past any AR/RS temporal predecessors ----
+    # AR/RS nodes are in _UPSTREAM_PLUS_ONE (assigned in Pass 2, not Pass 1).
+    # If _wire_sync_to_upd added AR/RS → UPD temporal edges, UPD must land
+    # at least one step after the latest AR/RS.
     for node in dag.nodes:
-        if node.chunk.type == TaskType.RECV:
-            assert len(node.data_succs) == 1
-            node.time_step = node.data_succs[0].time_step - 1
+        if node.chunk.type == TaskType.UPD:
+            for pred in node.temporal_preds:
+                if pred.chunk.type in _UPSTREAM_PLUS_ONE:
+                    node.time_step = max(node.time_step, pred.time_step + 1)
diff --git a/src/piper_utils.py b/src/piper_utils.py
index 9c00f53..fdfcac2 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -11,7 +11,7 @@
 from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
 from typing import Any, Optional
 
-LOG_LEVEL = "INFO"
+LOG_LEVEL = "DEBUG"
 
 """ 
 Print the backward graph of a tensor
@@ -43,6 +43,7 @@ def _print(t, indent=0):
 Logger utility
 """
 
+VERBOSE = 5
 def create_logger(name: str, log_level: str):
     match log_level:
         case "DEBUG":
@@ -53,6 +54,8 @@ def create_logger(name: str, log_level: str):
             log_level = logging.WARNING
         case "ERROR":
             log_level = logging.ERROR
+        case "VERBOSE":
+            log_level = VERBOSE
     
     logger = logging.getLogger(name)
     logger.setLevel(log_level)
@@ -339,11 +342,15 @@ class PiperMetadata:
     use_activation_checkpointing = False
     activation_num_checkpoints = 1
     bucketing = False  # Whether to split stages into per-param-bucket sub-modules
+    bucket_size: int = 25 * 1024 * 1024  # Target bucket size in bytes
     a2a_ar_no_overlap = False  # Whether ALL_REDUCE tasks must wait for all same-rank A2A tasks
     schedule = None   # PipelineSchedule set by piper_setup; used by the piper backend
     task_dag = None   # TaskDAG built from schedule by the piper backend
     full_dag_no_overlap = None  # Deep copy of the full DAG (pre-P2P-split, no overlap_a2a_tasks); used for profiling and critical-path analysis
     per_rank_dags = None  # Per-rank TaskDAGs built by the piper backend
+    zero_stage: int = 0  # ZeRO stage: 0=disabled, 1=optim states, 2=+gradients, 3=+parameters
+    schedule_name: str = ""  # Human-readable schedule name for debug artifacts
+    visualize_dag_render: bool = True  # If False, save Graphviz source only
     stage_bucket_counts: dict = {}   # stage_id -> number of buckets (set by piper backend)
     trainable_bucket_keys: set = set()  # (stage_id, bucket_id) pairs with trainable params
     # Populated by the piper backend on dp_rank=0; broadcast to dp_rank>0 by piper_setup
diff --git a/test/test_llama.py b/test/test_llama.py
index d3dc932..5e24c77 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -113,7 +113,7 @@ def main(args, pg):
         pg=pg,
         nsight=args.nsight,
         model_flops_per_token=flops_per_token,
-        visualize_dag=not args.no_viz,
+        visualize_dag=args.save_viz,
         const_attrs={"freqs_cis": freqs_cis, "mask": mask},
     )
 
@@ -191,8 +191,8 @@ def parse_args():
                         help='Whether to use Nsight Systems for tracing')
     parser.add_argument('--profiling', action='store_true', default=False,
                         help='Profile each DAG task: log time and memory delta per task')
-    parser.add_argument('--no-viz', action='store_true', default=False,
-                        help='Skip per-rank DAG visualization (speeds up startup for large models)')
+    parser.add_argument('--save-viz', action='store_true', default=False,
+                        help='Save per-rank DAG visualization (slow for models > 1B)')
     return parser.parse_args()
 
 """
diff --git a/test/test_qwen.py b/test/test_qwen.py
index 041a391..b01e188 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -172,11 +172,12 @@ def main(args, pg):
         activation_checkpointing=args.activation_checkpointing,
         a2a_ar_no_overlap=True,
         bucketing=args.bucketing,
+        zero_stage=args.zero_stage,
         model_dtype=torch.bfloat16,
         pg=pg,
         nsight=args.nsight,
         model_flops_per_token=flops_per_token,
-        visualize_dag=not args.no_viz,
+        visualize_dag=args.save_viz,
         const_attrs={"rope_cache": rope_cache},
     )
 
@@ -273,14 +274,16 @@ def parse_args():
     parser.add_argument("--tracing", action="store_true", default=False)
     parser.add_argument("--naive-grad-sync", action="store_true", default=False)
     parser.add_argument('--activation-checkpointing', action='store_true', default=False)
+    parser.add_argument("--zero-stage", type=int, default=0, choices=[0, 1, 2, 3],
+                        help="Apply ZeRO memory optimizations")
     parser.add_argument("--bucketing", action="store_true", default=False,
                         help="Split stages into per-param-bucket sub-modules for overlapped all-reduce")
     parser.add_argument("--profiling", action="store_true", default=False,
                         help="Profile each DAG task: log time and memory delta per task")
     parser.add_argument("--nsight", action="store_true", default=False,
                         help="Whether to use Nsight Systems for tracing")
-    parser.add_argument("--no-viz", action="store_true", default=False,
-                        help="Skip per-rank DAG visualization (speeds up startup for large models)")
+    parser.add_argument("--save-viz", action="store_true", default=False,
+                        help="Save per-rank DAG visualization (slow for models > 1B)")
     parser.add_argument("--address", default="",
                         help="Ray head address to connect to")
     parser.add_argument("--port", type=int, default=4567,

From 99f48f1a00b4493baad13b6622286b023c9cea70 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Tue, 14 Apr 2026 07:07:43 +0000
Subject: [PATCH 087/100] background dag visualization. known bug: illegal
 memory access in zero 3

---
 src/piper.py                 |  40 ++++++----
 src/piper_actor.py           |  21 +++--
 src/piper_graph_transform.py |  29 ++++---
 test/test_llama.py           | 147 ++++++++++++++++++++++++++++++-----
 test/test_qwen.py            |   3 +
 5 files changed, 187 insertions(+), 53 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index f5a7334..6b82441 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -5,6 +5,7 @@
 import gc
 import pickle
 import time
+import threading
 from torch._dynamo.backends.registry import register_backend
 from .piper_utils import _serialize_graphmodule, piper_metadata, create_logger, LOG_LEVEL, get_gpu_peak_flops_bf16
 
@@ -120,18 +121,17 @@ def piper(gm, example_inputs, **kwargs):
         a2a_boundaries: dict = {}  # boundary_bucket_id -> tensor_idx
 
         for seg_idx, (seg_gm, seg_in, seg_param, seg_args) in enumerate(a2a_segments):
-            if piper_metadata.bucketing and seg_idx % 2 == 0:
-                buckets = bucket_stage(
-                    seg_gm,
-                    seg_args,
-                    seg_in,
-                    seg_param,
-                    bucket_size_bytes=piper_metadata.bucket_size,
+            buckets = bucket_stage(
+                seg_gm,
+                seg_args,
+                seg_in,
+                seg_param,
+                bucket_size_bytes=piper_metadata.bucket_size,
+            )
+            if len(buckets) > 1:
+                logger.debug(
+                    f"Stage {stage_id} segment {seg_idx} bucketed into {len(buckets)} buckets of size ~{piper_metadata.bucket_size / (1024 * 1024):.1f}MB"
                 )
-                if len(buckets) > 1:
-                    logger.debug(
-                        f"Stage {stage_id} segment {seg_idx} bucketed into {len(buckets)} buckets"
-                    )
             else:
                 buckets = [(seg_gm, seg_in, seg_param, seg_args)]
 
@@ -309,12 +309,18 @@ def piper(gm, example_inputs, **kwargs):
         # Visualize DAG
         actors = piper_metadata.actors
         if piper_metadata.visualize_dag:
-            for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags):
-                try:
-                    visualize_dag(per_rank_dag, output_path=f"out/rank{pp_rank}_dag")
-                except Exception as e:
-                    logger.warning(f"DAG visualization failed for rank {pp_rank} (DAG may be too large for dot): {e}")
-                print_dag_order(per_rank_dag, label=f"rank {pp_rank}", rank=pp_rank)
+            def _visualize_all(per_rank_dags):
+                for pp_rank, per_rank_dag in enumerate(per_rank_dags):
+                    try:
+                        visualize_dag(per_rank_dag, output_path=f"out/rank{pp_rank}_dag")
+                    except Exception as e:
+                        logger.warning(f"DAG visualization failed for rank {pp_rank} (DAG may be too large for dot): {e}")
+                    print_dag_order(per_rank_dag, label=f"rank {pp_rank}", rank=pp_rank)
+            threading.Thread(
+                target=_visualize_all,
+                args=(piper_metadata.per_rank_dags,),
+                daemon=True,
+            ).start()
 
         # Send DAG to actors
         refs = [actors[pp_rank].load_dag.remote(per_rank_dag)
diff --git a/src/piper_actor.py b/src/piper_actor.py
index a93ad24..d3f19e1 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -271,7 +271,7 @@ def load_const_attrs(self, const_attrs: dict) -> None:
         the actor's device so ``_load_stage`` can copy them directly.
         """
         self.model_const_attrs = {k: v.to(self.device) for k, v in const_attrs.items()}
-        self.logger.debug(
+        self.logger.log(VERBOSE,
             f"Actor {self.global_rank} loaded {len(self.model_const_attrs)} "
             f"constant attrs: {list(self.model_const_attrs.keys())}"
         )
@@ -601,7 +601,7 @@ def dummy_kernel_user(tokens, starts, offsets):
                 if registered_kernels:
                     max_idx, kernel = max(registered_kernels, key=lambda x: x[0])
                     kernel_side_table.id_to_kernel[0] = kernel
-                    self.logger.log(VERBOSE
+                    self.logger.log(VERBOSE,
                         f"Actor {self.global_rank} registered kernel at id_to_kernel[0] "
                         f"(copied from index {max_idx})"
                     )
@@ -1396,16 +1396,25 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                             rs_evt = self.rs_events.get(pred.uid)
                             if rs_evt is not None:
                                 self.comp_stream.wait_event(rs_evt)
+                                rs_evt.synchronize()
                         elif pred.chunk.type == TaskType.ALL_REDUCE:
                             ar_evt = self.ar_events.get(pred.uid)
                             if ar_evt is not None:
                                 self.comp_stream.wait_event(ar_evt)
+                                ar_evt.synchronize()
                     self._free_full_grads(node.unique_bucket_id)
 
                 case TaskType.ALLOC_FULL_PARAMS:
                     self._alloc_full_params(node.unique_bucket_id)
 
                 case TaskType.FREE_FULL_PARAMS:
+                    for pred in node.data_preds:
+                        if pred.uid in comp_events:
+                            # _free_full_params resizes CUDA storage from the host.
+                            # Stream waits are not enough here: the CPU must not
+                            # release the full ZeRO-3 buffer while a compute kernel
+                            # launched on comp_stream may still be reading it.
+                            comp_events[pred.uid].synchronize()
                     self._free_full_params(node.unique_bucket_id)
 
                 case TaskType.FWD:
@@ -1465,7 +1474,8 @@ def _wait_for_all_gather(compute_node: Task) -> None:
 
                     if node.compute_loss:
                         assert loss_fn is not None
-                        outputs_or_loss = [loss_fn(fwd_out["out_with_grad"][0], self.labels)]
+                        with torch.cuda.stream(self.comp_stream):
+                            outputs_or_loss = [loss_fn(fwd_out["out_with_grad"][0], self.labels)]
                         upstream_grads = None
                     elif recv_pred is not None:
                         upstream_grads = self.task_buffer[recv_pred.uid]
@@ -1575,7 +1585,8 @@ def _wait_for_all_gather(compute_node: Task) -> None:
 
                     if node.compute_loss:
                         assert loss_fn is not None
-                        stage_outputs_or_loss = [loss_fn(fwd_out["out_with_grad"][0], self.labels)]
+                        with torch.cuda.stream(self.comp_stream):
+                            stage_outputs_or_loss = [loss_fn(fwd_out["out_with_grad"][0], self.labels)]
                         output_grads = None
                     elif recv_pred is not None:
                         upstream_raw = self.task_buffer[recv_pred.uid]
@@ -2098,7 +2109,7 @@ def _exec_all_reduce_grads(self, ubid: int) -> bool:
                 grad_tensors.append((idx, param.grad))
 
         if not grad_tensors:
-            self.logger.log(VERBOSE
+            self.logger.log(VERBOSE,
                 f"[all_reduce_bucket] rank={self.global_rank} ubid={ubid}: "
                 "skipped no materialized grads"
             )
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index 6956346..1fef110 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -1926,13 +1926,19 @@ def _insert_zero_grad_ops(
     rank_dag: TaskDAG,
     *,
     sync_type: TaskType,
+    use_grad_lifetime: bool = False,
     trainable_bucket_keys: set | None = None,
 ) -> None:
-    """Per-compute-node full-gradient lifecycle: G+ → compute → sync → G-.
+    """Per-compute-node gradient sync.
 
-    Every BWD / BWD_W node gets its own alloc-sync-free triple.  There is
-    no gradient accumulation: each node independently all-reduces or
-    reduce-scatters its gradients immediately after it finishes.
+    Every BWD / BWD_W node gets its own sync immediately after it finishes.
+    There is no gradient accumulation.
+
+    When ``use_grad_lifetime=True`` (ZeRO-2/3 where gradients are sharded),
+    a G+ → compute → sync → G- lifecycle is inserted so that the full
+    gradient buffer is explicitly allocated and freed around each sync.
+    When ``use_grad_lifetime=False`` (ZeRO-0/1 where each rank keeps a full
+    copy of gradients), only compute → sync is wired.
     """
     _remove_ar_nodes(rank_dag)
 
@@ -1943,13 +1949,15 @@ def _insert_zero_grad_ops(
         for compute_node in bucket_nodes:
             if compute_node.chunk.type not in grad_sync_types:
                 continue
-            alloc = _zero_task(compute_node, compute_node.bucket_id, TaskType.ALLOC_FULL_GRADS, ubid=ubid)
             sync = _zero_task(compute_node, compute_node.bucket_id, sync_type, ubid=ubid)
-            free = _zero_task(compute_node, compute_node.bucket_id, TaskType.FREE_FULL_GRADS, ubid=ubid)
-            _add_data_edge(alloc, compute_node)
             _add_data_edge(compute_node, sync)
-            _add_data_edge(sync, free)
-            new_nodes.extend([alloc, sync, free])
+            new_nodes.append(sync)
+            if use_grad_lifetime:
+                alloc = _zero_task(compute_node, compute_node.bucket_id, TaskType.ALLOC_FULL_GRADS, ubid=ubid)
+                free = _zero_task(compute_node, compute_node.bucket_id, TaskType.FREE_FULL_GRADS, ubid=ubid)
+                _add_data_edge(alloc, compute_node)
+                _add_data_edge(sync, free)
+                new_nodes.extend([alloc, free])
 
     rank_dag.nodes.extend(new_nodes)
 
@@ -1975,10 +1983,11 @@ def _insert_zero2_ops(
     rank_dag: TaskDAG,
     trainable_bucket_keys: set | None = None,
 ) -> None:
-    """ZeRO-2: per-compute-node reduce-scatter."""
+    """ZeRO-2: per-compute-node reduce-scatter with full-gradient lifecycle."""
     _insert_zero_grad_ops(
         rank_dag,
         sync_type=TaskType.REDUCE_SCATTER,
+        use_grad_lifetime=True,
         trainable_bucket_keys=trainable_bucket_keys,
     )
 
diff --git a/test/test_llama.py b/test/test_llama.py
index 5e24c77..78d0dcc 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -3,6 +3,7 @@
 import torch
 import time
 import argparse
+import json
 import os
 import numpy as np
 
@@ -31,6 +32,64 @@
 )
 
 
+def _metrics_output_path(args):
+    return os.path.join(
+        args.output_dir,
+        f"llama{args.model}-pp{args.pp}-dp{args.dp}-{args.schedule}",
+    )
+
+
+def _build_benchmark_metrics(args, dp_rank, iter_times):
+    mean_iter_time = float(np.mean(iter_times))
+    std_iter_time = float(np.std(iter_times))
+    throughput = float((args.batch_size * args.mbs * args.seq_len) / mean_iter_time)
+
+    return {
+        "dp_rank": dp_rank,
+        "model": args.model,
+        "schedule": args.schedule,
+        "pp": args.pp,
+        "dp": args.dp,
+        "batch_size": args.batch_size,
+        "mbs": args.mbs,
+        "seq_len": args.seq_len,
+        "samples": len(iter_times),
+        "iter_time_mean_s": mean_iter_time,
+        "iter_time_std_s": std_iter_time,
+        "throughput_tokens_per_s": throughput,
+        "iter_times_s": [float(iter_time) for iter_time in iter_times],
+    }
+
+
+def _format_benchmark_log_lines(metrics):
+    rank = metrics["dp_rank"]
+    lines = [
+        (
+            f"rank {rank} iter time= {metrics['iter_time_mean_s']:.5f} +/- "
+            f"{metrics['iter_time_std_s']:.5f} s ({metrics['samples']} samples)"
+        ),
+        f"rank {rank} throughput= {metrics['throughput_tokens_per_s']:.3f} tokens/s",
+    ]
+    for trace in metrics.get("trace_times", []):
+        lines.append(
+            f"rank {trace['rank']} {trace['key']} time= {trace['mean_ms']:.3f} +/- "
+            f"{trace['std_ms']:.3f} ms ({trace['samples']} samples)"
+        )
+    lines.append(f"rank {rank} metrics_json= {json.dumps(metrics, sort_keys=True)}")
+    return lines
+
+
+def _write_benchmark_metrics(args, metrics):
+    metrics_path = _metrics_output_path(args)
+    os.makedirs(os.path.dirname(metrics_path), exist_ok=True)
+    with open(metrics_path, "a", encoding="utf-8") as output_file:
+        for line in _format_benchmark_log_lines(metrics):
+            output_file.write(line + "\n")
+        output_file.write("\n")
+
+    return metrics_path
+
+
 def main(args, pg):
     match args.model:
         case 'debug':
@@ -109,6 +168,7 @@ def main(args, pg):
         activation_checkpointing=args.activation_checkpointing,
         num_checkpoints=args.num_checkpoints,
         bucketing=args.bucketing,
+        bucket_size=int(args.bucket_size * 1024 * 1024),
         model_dtype=torch.bfloat16,
         pg=pg,
         nsight=args.nsight,
@@ -134,12 +194,15 @@ def main(args, pg):
         time.sleep(1)
 
     dp_rank = int(os.environ['PIPER_DP_RANK'])
-    logger.info(
-        f"rank {dp_rank} iter time= {np.mean(iter_times):.5f} ± {np.std(iter_times):.5f} s "
-        f"({len(iter_times)} samples)\n"
-        f"rank {dp_rank} throughput= "
-        f"{(args.batch_size * args.mbs * args.seq_len) / np.mean(iter_times):.3f} tokens/s"
-    )
+    metrics = _build_benchmark_metrics(args, dp_rank, iter_times)
+    iter_time_msg, throughput_msg, _ = _format_benchmark_log_lines(metrics)
+    metrics_msg = f"rank {dp_rank} benchmark metrics will be saved by the driver to {_metrics_output_path(args)}"
+    logger.info(iter_time_msg)
+    logger.info(throughput_msg)
+    logger.info(metrics_msg)
+    print(iter_time_msg, flush=True)
+    print(throughput_msg, flush=True)
+    print(metrics_msg, flush=True)
 
     if args.tracing:
         ray.get([actor.set_tracing.remote(True) for actor in actors.values()])
@@ -149,18 +212,34 @@ def main(args, pg):
             ray.get([actor.flush_timing_events.remote() for actor in actors.values()])
             time.sleep(1)
         trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
+        trace_times = []
         for rank, trace_data in trace_data_ret:
             for key in trace_data:
                 all_times = trace_data[key]
-                logger.info(
-                    f"rank {rank} {key} time= {np.mean(all_times):.3f} ± "
-                    f"{np.std(all_times):.3f} ms ({len(all_times)} samples)"
+                trace = {
+                    "rank": rank,
+                    "key": key,
+                    "mean_ms": float(np.mean(all_times)),
+                    "std_ms": float(np.std(all_times)),
+                    "samples": len(all_times),
+                    "times_ms": [float(trace_time) for trace_time in all_times],
+                }
+                trace_times.append(trace)
+                trace_msg = (
+                    f"rank {trace['rank']} {trace['key']} time= {trace['mean_ms']:.3f} +/- "
+                    f"{trace['std_ms']:.3f} ms ({trace['samples']} samples)"
                 )
+                logger.info(trace_msg)
+                print(trace_msg, flush=True)
+        metrics["trace_times"] = trace_times
 
-    os.makedirs("out", exist_ok=True)
-    timeline_filename = f"out/llama-dag-pp{args.pp}-dp{args.dp}-{args.schedule}"
-    ray.timeline(timeline_filename)
-    logger.info(f"Ray timeline saved to: {timeline_filename}")
+    if args.nsight:
+        logger.info("Stopping Piper actors so Nsight Systems reports are flushed")
+        try:
+            ray.get([actor.__ray_terminate__.remote() for actor in actors.values()])
+        except ray.exceptions.ActorDiedError as exc:
+            logger.info(f"Piper actors stopped for Nsight flush: {exc}")
+    return metrics
 
 
 def parse_args():
@@ -187,12 +266,22 @@ def parse_args():
                         help='Number of sequential activation-checkpoint regions per Piper bucket')
     parser.add_argument('--bucketing', action='store_true', default=False,
                         help='Split stages into per-param-bucket sub-modules for overlapped all-reduce')
+    parser.add_argument('--bucket-size', type=float, default=25,
+                        help='Bucket size in MB (default: 25)')
     parser.add_argument('--nsight', action='store_true', default=False,
                         help='Whether to use Nsight Systems for tracing')
     parser.add_argument('--profiling', action='store_true', default=False,
                         help='Profile each DAG task: log time and memory delta per task')
     parser.add_argument('--save-viz', action='store_true', default=False,
                         help='Save per-rank DAG visualization (slow for models > 1B)')
+    parser.add_argument('--address', default='',
+                        help='Ray head address to connect to')
+    parser.add_argument('--port', type=int, default=4567,
+                        help='Ray head port to connect to (default: 4567)')
+    parser.add_argument('--temp-dir', default='/tmp/piper/ray_tmp',
+                        help='Ray temp directory (default: /tmp/piper/ray_tmp)')
+    parser.add_argument('--output-dir', default='out',
+                        help='Directory for benchmark metrics output (default: out)')
     return parser.parse_args()
 
 """
@@ -206,17 +295,33 @@ def parse_args():
 if __name__ == "__main__":
     logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
     args = parse_args()
+    metrics_path = _metrics_output_path(args)
+    os.makedirs(os.path.dirname(metrics_path), exist_ok=True)
+    open(metrics_path, "w", encoding="utf-8").close()
     logger.info(args)
-    ray.init(
-        address="10.158.48.71:4567",
-        namespace="llama",
-        include_dashboard=False,
-        _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp",
-    )
-    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="PACK")
+    if args.address != "":
+        ray.init(
+            address=f"{args.address}:{args.port}",
+            namespace="llama",
+            log_to_driver=True,
+            include_dashboard=False,
+            _temp_dir=args.temp_dir,
+        )
+    else:
+        ray.init(
+            namespace="llama",
+            log_to_driver=True,
+            include_dashboard=False,
+            _temp_dir=args.temp_dir,
+        )
+    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="SPREAD")
     ray.get(pg.ready(), timeout=600)
     logger.info(placement_group_table(pg))
     piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
     handles = piper_coordinator.run_program.remote(main, pg, args, pg)
-    ray.get(handles)
+    dp_metrics = ray.get(handles)
+    open(metrics_path, "w", encoding="utf-8").close()
+    for metrics in sorted(dp_metrics, key=lambda item: item["dp_rank"]):
+        _write_benchmark_metrics(args, metrics)
+    logger.info(f"Benchmark metrics saved to {metrics_path}")
     ray.shutdown()
diff --git a/test/test_qwen.py b/test/test_qwen.py
index b01e188..63c6660 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -172,6 +172,7 @@ def main(args, pg):
         activation_checkpointing=args.activation_checkpointing,
         a2a_ar_no_overlap=True,
         bucketing=args.bucketing,
+        bucket_size=int(args.bucket_size * 1024 * 1024),
         zero_stage=args.zero_stage,
         model_dtype=torch.bfloat16,
         pg=pg,
@@ -278,6 +279,8 @@ def parse_args():
                         help="Apply ZeRO memory optimizations")
     parser.add_argument("--bucketing", action="store_true", default=False,
                         help="Split stages into per-param-bucket sub-modules for overlapped all-reduce")
+    parser.add_argument("--bucket-size", type=float, default=25,
+                        help="Bucket size in MB (default: 25)")
     parser.add_argument("--profiling", action="store_true", default=False,
                         help="Profile each DAG task: log time and memory delta per task")
     parser.add_argument("--nsight", action="store_true", default=False,

From 2c6c637ca358c8149af5650b66348d28d37f9b16 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Wed, 15 Apr 2026 23:29:00 +0000
Subject: [PATCH 088/100] zero overlapping performance debugging

---
 src/piper.py                 | 167 ++++++++++----
 src/piper_actor.py           | 408 ++++++++++++++++++++++++++---------
 src/piper_compile.py         |   6 +-
 src/piper_exec.py            |  24 +++
 src/piper_graph_transform.py | 394 +++++++++++++++++++++++++--------
 src/piper_utils.py           |   7 +-
 test/test_llama.py           |   4 +-
 test/test_qwen.py            |   9 +-
 8 files changed, 784 insertions(+), 235 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index 6b82441..7622256 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -40,6 +40,7 @@ def scan(module):
     add_temporal_dependencies,
     split_dag_by_rank,
     assign_time_steps,
+    overlap_zero_ops,
     find_overlappable_tasks,
     insert_zero_ops,
     overlap_a2a_tasks,
@@ -92,6 +93,7 @@ def piper(gm, example_inputs, **kwargs):
     stage_a2a_boundaries: dict = {}
     stage_bucket_counts: dict = {}
     trainable_bucket_keys: set = set()
+    zero_bucket_keys: set = set()
     # Collected for DP broadcasting: stage_id -> (modules_data, a2a_boundaries)
     all_stage_compiled: dict = {}
     # Running counter for assigning globally unique bucket IDs (same ordering as
@@ -105,7 +107,7 @@ def piper(gm, example_inputs, **kwargs):
         actor_stages.append((actor, stage_id))
 
         # Split at A2A annotation boundaries (expert-parallel only, requires dp_degree > 1).
-        if dp_degree > 1 or True:
+        if dp_degree > 1:
             a2a_segments, boundary_infos = split_by_a2a(stage_gm, graphargs, input_idxs, param_idxs)
             if boundary_infos:
                 logger.debug(
@@ -121,22 +123,45 @@ def piper(gm, example_inputs, **kwargs):
         a2a_boundaries: dict = {}  # boundary_bucket_id -> tensor_idx
 
         for seg_idx, (seg_gm, seg_in, seg_param, seg_args) in enumerate(a2a_segments):
-            buckets = bucket_stage(
-                seg_gm,
-                seg_args,
-                seg_in,
-                seg_param,
-                bucket_size_bytes=piper_metadata.bucket_size,
-            )
-            if len(buckets) > 1:
-                logger.debug(
-                    f"Stage {stage_id} segment {seg_idx} bucketed into {len(buckets)} buckets of size ~{piper_metadata.bucket_size / (1024 * 1024):.1f}MB"
-                )
+            # Hack: only bucket even-indexed segments to avoid bucketing MoE subgraphs
+            if piper_metadata.bucket_size is not None:
+                if seg_idx % 2 == 0:
+                    buckets = bucket_stage(
+                        seg_gm,
+                        seg_args,
+                        seg_in,
+                        seg_param,
+                        bucket_size_bytes=piper_metadata.bucket_size,
+                    )
+                    bucket_zero_flags = [True] * len(buckets)
+                else:
+                    bucket_zero_flags = [False]
             else:
                 buckets = [(seg_gm, seg_in, seg_param, seg_args)]
+                if seg_idx % 2 == 0:
+                    bucket_zero_flags = [True]
+                else:
+                    bucket_zero_flags = [False]
 
-            for bucket_gm, bucket_in, bucket_param, bucket_args in buckets:
+            logger.debug(
+                f"Stage {stage_id} segment {seg_idx} bucketed into {len(buckets)} buckets"
+            )
+            for b_idx, (_, _, bp, ba) in enumerate(buckets):
+                for i in bp:
+                    arg = ba[i] if i < len(ba) else None
+                    if arg is not None and hasattr(arg, "numel"):
+                        mb = arg.numel() * arg.element_size() / (1024 * 1024)
+                        logger.debug(
+                            f"  bucket {b_idx} tensor idx={i} shape={tuple(arg.shape)} size={mb:.3f}MB"
+                        )
+
+            for (bucket_gm, bucket_in, bucket_param, bucket_args), apply_zero in zip(
+                buckets, bucket_zero_flags
+            ):
                 original_bucket_gm = bucket_gm
+                shared_placeholder_names = [
+                    n.name for n in original_bucket_gm.graph.nodes if n.op == "placeholder"
+                ]
                 requested_ac_subgraphs = 1
                 if piper_metadata.use_activation_checkpointing:
                     requested_ac_subgraphs = piper_metadata.activation_num_checkpoints
@@ -151,13 +176,23 @@ def piper(gm, example_inputs, **kwargs):
                         bucket_gm = ac_gms
                 actual_ac_subgraphs = len(bucket_gm) if isinstance(bucket_gm, list) else 1
                 if piper_metadata.use_activation_checkpointing:
-                    logger.info(
+                    logger.log(VERBOSE,
                         f"[ac_compile_bucket] stage={stage_id} segment={seg_idx} "
                         f"requested_subgraphs={requested_ac_subgraphs} "
                         f"actual_subgraphs={actual_ac_subgraphs} "
                         f"inputs={len(bucket_in)} params={len(bucket_param)}"
                     )
-                all_modules.append((bucket_gm, bucket_in, bucket_param, bucket_args, original_bucket_gm))
+                all_modules.append(
+                    (
+                        bucket_gm,
+                        bucket_in,
+                        bucket_param,
+                        bucket_args,
+                        original_bucket_gm,
+                        shared_placeholder_names,
+                        apply_zero,
+                    )
+                )
             seg_end_bucket = len(all_modules) - 1  # inclusive index of last bucket in this segment
 
             if seg_idx < len(boundary_infos):
@@ -168,23 +203,35 @@ def piper(gm, example_inputs, **kwargs):
             stage_a2a_boundaries[stage_id] = a2a_boundaries
 
         stage_bucket_counts[stage_id] = len(all_modules)
-        for b_idx, (bgm, b_in, b_param, bargs, _original_gm) in enumerate(all_modules):
+        for b_idx, (
+            bgm,
+            b_in,
+            b_param,
+            bargs,
+            _original_gm,
+            _shared_placeholder_names,
+            apply_zero,
+        ) in enumerate(all_modules):
             has_trainable = any(
                 bargs[i] is not None and getattr(bargs[i], "requires_grad", False)
                 for i in b_param
             )
+            logger.debug(
+                f"[zero_bucket_classify] stage={stage_id} bucket={b_idx} "
+                f"has_trainable={has_trainable} apply_zero={apply_zero} "
+                f"param_idxs={b_param}"
+            )
             if has_trainable:
                 trainable_bucket_keys.add((stage_id, b_idx))
+                if apply_zero:
+                    zero_bucket_keys.add((stage_id, b_idx))
             else:
                 logger.warning(
                     f"Stage {stage_id} bucket {b_idx} has no trainable parameters."
                 )
 
         modules_data = []
-        for bgm, b_in, b_param, bargs, original_gm in all_modules:
-            placeholder_names = [
-                n.name for n in original_gm.graph.nodes if n.op == "placeholder"
-            ]
+        for bgm, b_in, b_param, bargs, original_gm, shared_placeholder_names, apply_zero in all_modules:
             if isinstance(bgm, list):
                 modules_data.append(
                     {
@@ -194,7 +241,8 @@ def piper(gm, example_inputs, **kwargs):
                         "graphargs": bargs,
                         "input_idxs": b_in,
                         "param_idxs": b_param,
-                        "placeholder_names": placeholder_names,
+                        "shared_placeholder_names": shared_placeholder_names,
+                        "apply_zero": apply_zero,
                         "triton_constant_args_list": [
                             _collect_triton_constant_args(sgm) for sgm in bgm
                         ],
@@ -213,7 +261,8 @@ def piper(gm, example_inputs, **kwargs):
                         "graphargs": bargs,
                         "input_idxs": b_in,
                         "param_idxs": b_param,
-                        "placeholder_names": placeholder_names,
+                        "shared_placeholder_names": shared_placeholder_names,
+                        "apply_zero": apply_zero,
                         "triton_constant_args": _collect_triton_constant_args(bgm),
                     }
                 )
@@ -253,10 +302,12 @@ def piper(gm, example_inputs, **kwargs):
 
     piper_metadata.stage_bucket_counts = stage_bucket_counts
     piper_metadata.trainable_bucket_keys = trainable_bucket_keys
+    piper_metadata.zero_bucket_keys = zero_bucket_keys
     stage_data = {
         "stages": all_stage_compiled,
         "stage_bucket_counts": stage_bucket_counts,
         "trainable_bucket_keys": trainable_bucket_keys,
+        "zero_bucket_keys": zero_bucket_keys,
     }
     compiled_store = getattr(piper_metadata, "compiled_data_store", None)
     if compiled_store is not None:
@@ -284,10 +335,13 @@ def piper(gm, example_inputs, **kwargs):
 
         # Insert ZeRO/DP communicatino tasks
         if dp_degree > 1:
+            logger.debug(
+                f"ZeRO bucket filter: {sorted(piper_metadata.zero_bucket_keys)}"
+            )
             per_rank_dags = insert_zero_ops(
                 per_rank_dags,
                 piper_metadata.zero_stage,
-                trainable_bucket_keys=piper_metadata.trainable_bucket_keys,
+                zero_bucket_keys=piper_metadata.zero_bucket_keys,
             )
             logger.debug(f"Inserted ZeRO-{piper_metadata.zero_stage} collective nodes")
 
@@ -297,30 +351,26 @@ def piper(gm, example_inputs, **kwargs):
                     f"Found adjacent task pair for A2A/compute overlap: "
                     f"{t1} -> {t2}"
                 )
-                # TODO: overlap a2a tasks
 
         # Assign time steps
         for rank_dag in per_rank_dags:
             assign_time_steps(rank_dag)
+            # if dp_degree > 1:
+            #     overlap_zero_ops(rank_dag)
         logger.debug("Assigned time steps")
 
         piper_metadata.per_rank_dags = per_rank_dags
 
         # Visualize DAG
         actors = piper_metadata.actors
-        if piper_metadata.visualize_dag:
-            def _visualize_all(per_rank_dags):
-                for pp_rank, per_rank_dag in enumerate(per_rank_dags):
-                    try:
-                        visualize_dag(per_rank_dag, output_path=f"out/rank{pp_rank}_dag")
-                    except Exception as e:
-                        logger.warning(f"DAG visualization failed for rank {pp_rank} (DAG may be too large for dot): {e}")
-                    print_dag_order(per_rank_dag, label=f"rank {pp_rank}", rank=pp_rank)
-            threading.Thread(
-                target=_visualize_all,
-                args=(piper_metadata.per_rank_dags,),
-                daemon=True,
-            ).start()
+        for pp_rank, per_rank_dag in enumerate(per_rank_dags):
+            if piper_metadata.visualize_dag:
+                try:
+                    visualize_dag(per_rank_dag, output_path=f"out/rank{pp_rank}_dag")
+                except Exception as e:
+                    logger.warning(f"DAG visualization failed for rank {pp_rank} (DAG may be too large for dot): {e}")
+            print_dag_order(per_rank_dag, label=f"rank {pp_rank}", rank=pp_rank)
+
 
         # Send DAG to actors
         refs = [actors[pp_rank].load_dag.remote(per_rank_dag)
@@ -342,6 +392,7 @@ def _visualize_all(per_rank_dags):
             "stages": all_stage_compiled,           # {stage_id: (modules_data, a2a_boundaries)}
             "stage_bucket_counts": stage_bucket_counts,
             "trainable_bucket_keys": trainable_bucket_keys,
+            "zero_bucket_keys": zero_bucket_keys,
             "per_rank_dags": per_rank_dags,
             "full_dag_no_overlap": piper_metadata.full_dag_no_overlap,
         }
@@ -395,15 +446,51 @@ def piper_exec_dag(loss_fn, profiling: bool = False, log_stats: bool = False) ->
     step_time = time.perf_counter() - t0
 
     if log_stats or piper_metadata.model_flops_per_token is not None:
-        _log_step_stats(step_time, log_stats, actors)
+        _log_step_stats(step_time, log_stats, actors, results)
 
-    return [item for sublist in results if sublist for item in sublist]
+    losses = []
+    for result in results:
+        if isinstance(result, dict):
+            losses.extend(result.get("losses", []))
+        elif result:
+            losses.extend(result)
+    return losses
 
 
-def _log_step_stats(step_time: float, log_memory: bool, actors: dict) -> None:
+def _log_step_stats(step_time: float, log_memory: bool, actors: dict, results: list | None = None) -> None:
     """Log throughput, MFU, and optionally per-rank peak GPU memory."""
     stats = [f"step_time={step_time:.3f}s"]
 
+    if results:
+        schedule_reduced_payload_bytes = 0
+        schedule_all_reduce_payload_bytes = 0
+        schedule_reduce_scatter_payload_bytes = 0
+        runtime_reduced_payload_bytes = 0
+        runtime_all_reduce_payload_bytes = 0
+        runtime_reduce_scatter_payload_bytes = 0
+        runtime_all_reduce_ops = 0
+        runtime_reduce_scatter_ops = 0
+        for result in results:
+            if not isinstance(result, dict):
+                continue
+            schedule_reduced_payload_bytes += int(result.get("schedule_reduced_payload_bytes", 0))
+            schedule_all_reduce_payload_bytes += int(result.get("schedule_all_reduce_payload_bytes", 0))
+            schedule_reduce_scatter_payload_bytes += int(result.get("schedule_reduce_scatter_payload_bytes", 0))
+            runtime_reduced_payload_bytes += int(result.get("runtime_reduced_payload_bytes", 0))
+            runtime_all_reduce_payload_bytes += int(result.get("runtime_all_reduce_payload_bytes", 0))
+            runtime_reduce_scatter_payload_bytes += int(result.get("runtime_reduce_scatter_payload_bytes", 0))
+            runtime_all_reduce_ops += int(result.get("runtime_all_reduce_ops", 0))
+            runtime_reduce_scatter_ops += int(result.get("runtime_reduce_scatter_ops", 0))
+        stats.append(f"schedule_reduced_payload_total_gb={schedule_reduced_payload_bytes / (1024 ** 3):.6f}")
+        stats.append(f"schedule_all_reduce_payload_gb={schedule_all_reduce_payload_bytes / (1024 ** 3):.6f}")
+        stats.append(f"schedule_reduce_scatter_payload_gb={schedule_reduce_scatter_payload_bytes / (1024 ** 3):.6f}")
+        stats.append(f"runtime_reduced_payload_total_gb={runtime_reduced_payload_bytes / (1024 ** 3):.6f}")
+        stats.append(f"runtime_all_reduce_payload_gb={runtime_all_reduce_payload_bytes / (1024 ** 3):.6f}")
+        stats.append(f"runtime_reduce_scatter_payload_gb={runtime_reduce_scatter_payload_bytes / (1024 ** 3):.6f}")
+        stats.append(f"runtime_all_reduce_ops={runtime_all_reduce_ops}")
+        stats.append(f"runtime_reduce_scatter_ops={runtime_reduce_scatter_ops}")
+        stats.append(f"runtime_payload_matches_schedule={runtime_reduced_payload_bytes == schedule_reduced_payload_bytes}")
+
     tokens = piper_metadata.tokens_per_step
     if tokens is not None:
         stats.append(f"throughput={tokens / step_time:.1f} tok/s")
diff --git a/src/piper_actor.py b/src/piper_actor.py
index d3f19e1..8839944 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -21,7 +21,7 @@
     NcclOverlapDetector,
 )
 from .backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
-from .piper_exec import TaskType, TaskDAG, Task
+from .piper_exec import TaskType, TaskDAG, Task, runtime_sort_key
 
 CLEANUP_MEMORY = False
 
@@ -51,6 +51,7 @@ def _create_actors(
     zero_stage: int = 0,
     no_nvtx: bool = False,
     pg=None,
+    temp_dir: str = None,
 ):
     dp_rank = int(os.environ["PIPER_DP_RANK"])
     world_size = int(os.environ["PIPER_WORLD_SIZE"])
@@ -68,8 +69,11 @@ def _create_actors(
         }} if profile else {}
         nccl_env = {
             "env_vars": {
-                "NCCL_SOCKET_IFNAME": "^lo,docker",
-                "NCCL_DEBUG": "WARN",
+                # "NCCL_SOCKET_IFNAME": "ens32",
+                # "GLOO_SOCKET_IFNAME": "ens32",
+                # "NCCL_PROTO": "simple",
+                # "NCCL_DEBUG": "WARN",
+                **({"TMPDIR": temp_dir} if (profile and temp_dir) else {}),
             }
         }
         actor = PiperActor.options(
@@ -158,7 +162,6 @@ def __init__(
 
         self.comp_stream = torch.cuda.Stream()
         self.ar_stream = torch.cuda.Stream()
-        # self.a2a_stream = torch.cuda.Stream()
         self.a2a_stream = self.ar_stream
         self.p2p_send_stream = torch.cuda.Stream()
         self.p2p_recv_stream = torch.cuda.Stream()
@@ -176,6 +179,14 @@ def __init__(
         self._pending_timing_events: list = []  # (label, start_event, stop_event)
         self.trace_data = defaultdict(list)
         self.memory_tracing_enabled = False
+        self._schedule_reduced_payload_bytes = 0
+        self._schedule_all_reduce_payload_bytes = 0
+        self._schedule_reduce_scatter_payload_bytes = 0
+        self._runtime_reduced_payload_bytes = 0
+        self._runtime_all_reduce_payload_bytes = 0
+        self._runtime_reduce_scatter_payload_bytes = 0
+        self._runtime_all_reduce_ops = 0
+        self._runtime_reduce_scatter_ops = 0
 
         # DAG execution state
         self.dag = None
@@ -184,6 +195,7 @@ def __init__(
         # data the task produced for downstream consumers.
         # Cleared at the start of each run_dag() call.
         self.task_buffer: dict = {}
+        self.task_buffer_refcounts: dict[int, int] = {}
 
         # CUDA events for synchronisation between streams
         self.recv_events: dict = {}   # task_uid -> cuda.Event (for RECV tasks)
@@ -676,7 +688,24 @@ def _load_stage(
             forward_args = list(bd["graphargs"])
             b_input_idxs = list(bd["input_idxs"])
             b_param_idxs = list(bd["param_idxs"])
-            shared_placeholder_names = list(bd.get("placeholder_names", []))
+            apply_zero = bool(bd.get("apply_zero", True))
+            shared_placeholder_names = list(
+                bd.get("shared_placeholder_names", bd.get("placeholder_names", []))
+            )
+            input_placeholder_names = [
+                shared_placeholder_names[i] if 0 <= i < len(shared_placeholder_names) else f"<missing:{i}>"
+                for i in b_input_idxs
+            ]
+            param_placeholder_names = [
+                shared_placeholder_names[i] if 0 <= i < len(shared_placeholder_names) else f"<missing:{i}>"
+                for i in b_param_idxs
+            ]
+            self.logger.log(VERBOSE,
+                f"[load_stage_bucket] rank={self.global_rank} stage={stage_id} "
+                f"ubid={ubid} shared_placeholders={shared_placeholder_names} "
+                f"input_idxs={b_input_idxs} input_placeholders={input_placeholder_names} "
+                f"param_idxs={b_param_idxs} param_placeholders={param_placeholder_names}"
+            )
 
             # Extract FX placeholder names for each param index.
             self.bucket_param_names[ubid] = [
@@ -741,9 +770,17 @@ def _load_stage(
                 ]
                 subgraph_specs.append((gm.forward, sub_placeholder_names, dynamic_names))
 
-            def _bucket_forward_runner(shared_args, _specs=subgraph_specs, _name_to_idx=shared_name_to_idx, _use_ac=use_activation_checkpointing):
+            def _bucket_forward_runner(
+                shared_args,
+                _specs=subgraph_specs,
+                _name_to_idx=shared_name_to_idx,
+                _use_ac=use_activation_checkpointing,
+                _stage_id=stage_id,
+                _ubid=ubid,
+                _shared_placeholder_names=tuple(shared_placeholder_names),
+            ):
                 out = None
-                for forward_impl, placeholder_names, dynamic_names in _specs:
+                for subgraph_idx, (forward_impl, placeholder_names, dynamic_names) in enumerate(_specs):
                     if len(dynamic_names) == 0:
                         dynamic_values = []
                     elif len(dynamic_names) == 1:
@@ -772,6 +809,33 @@ def _bucket_forward_runner(shared_args, _specs=subgraph_specs, _name_to_idx=shar
                             call_args.append(dynamic_name_to_value[name])
                         else:
                             call_args.append(shared_args[_name_to_idx[name]])
+                    try:
+                        self.logger.debug(
+                            f"[bucket_fwd_call] rank={self.global_rank} stage={_stage_id} "
+                            f"ubid={_ubid} subgraph={subgraph_idx} "
+                            f"shared_placeholders={list(_shared_placeholder_names)} "
+                            f"placeholders={placeholder_names} "
+                            f"dynamic_names={dynamic_names}"
+                        )
+                        zero_storage = []
+                        for name, arg in zip(placeholder_names, call_args):
+                            if not isinstance(arg, torch.Tensor):
+                                continue
+                            storage = arg.untyped_storage()
+                            storage_size = storage.size()
+                            if storage_size == 0:
+                                zero_storage.append(
+                                    f"{name}: shape={tuple(arg.shape)} stride={tuple(arg.stride())} "
+                                    f"requires_grad={arg.requires_grad} dtype={arg.dtype} "
+                                    f"device={arg.device} storage_offset={arg.storage_offset()}"
+                                )
+                        if zero_storage:
+                            self.logger.error(
+                                f"[bucket_fwd_zero_storage] rank={self.global_rank} stage={_stage_id} "
+                                f"ubid={_ubid} subgraph={subgraph_idx}: " + " | ".join(zero_storage)
+                            )
+                    except Exception:
+                        self.logger.debug("bucket forward pre-call logging failed", exc_info=True)
                     if _use_ac:
                         out = torch.utils.checkpoint.checkpoint(
                             forward_impl,
@@ -779,7 +843,30 @@ def _bucket_forward_runner(shared_args, _specs=subgraph_specs, _name_to_idx=shar
                             use_reentrant=False,
                         )
                     else:
-                        out = forward_impl(*call_args)
+                        try:
+                            out = forward_impl(*call_args)
+                        except Exception:
+                            tensor_summaries = []
+                            for name, arg in zip(placeholder_names, call_args):
+                                if isinstance(arg, torch.Tensor):
+                                    storage = arg.untyped_storage()
+                                    tensor_summaries.append(
+                                        f"{name}: shape={tuple(arg.shape)} stride={tuple(arg.stride())} "
+                                        f"requires_grad={arg.requires_grad} dtype={arg.dtype} "
+                                        f"device={arg.device} storage_size={storage.size()} "
+                                        f"storage_offset={arg.storage_offset()}"
+                                    )
+                                else:
+                                    tensor_summaries.append(f"{name}: type={type(arg).__name__}")
+                            self.logger.error(
+                                f"[bucket_fwd_exception] rank={self.global_rank} stage={_stage_id} "
+                                f"ubid={_ubid} subgraph={subgraph_idx} "
+                                f"shared_placeholders={list(_shared_placeholder_names)} "
+                                f"placeholders={placeholder_names} "
+                                f"dynamic_names={dynamic_names} "
+                                f"args={' | '.join(tensor_summaries)}"
+                            )
+                            raise
                 return out
 
             self.bucket_fwd_fns[ubid] = _bucket_forward_runner
@@ -792,7 +879,7 @@ def _bucket_forward_runner(shared_args, _specs=subgraph_specs, _name_to_idx=shar
             ]
             self.bucket_trainable_param_idxs[ubid] = trainable_idxs
 
-            if self.zero_stage > 0 and self.dp_degree > 1 and trainable_idxs:
+            if self.zero_stage > 0 and self.dp_degree > 1 and apply_zero and trainable_idxs:
                 trainable = [realized[i] for i in trainable_idxs]
                 flat_params = torch.cat([p.detach().view(-1) for p in trainable]).contiguous()
                 flat_params.requires_grad_(False)
@@ -851,7 +938,8 @@ def _bucket_forward_runner(shared_args, _specs=subgraph_specs, _name_to_idx=shar
                 f"ubid={ubid}: ac_enabled={use_activation_checkpointing} "
                 f"requested_subgraphs={ac_requested_subgraphs} "
                 f"actual_subgraphs={ac_num_subgraphs} inputs={len(b_input_idxs)} "
-                f"params={len(b_param_idxs)} trainable_params={len(trainable_idxs)}"
+                f"params={len(b_param_idxs)} trainable_params={len(trainable_idxs)} "
+                f"apply_zero={apply_zero}"
             )
 
 
@@ -954,6 +1042,49 @@ def _param_has_effective_grad(self, param: torch.Tensor | None) -> bool:
         """Return True when a param carries a materialized gradient."""
         return param is not None and param.grad is not None
 
+    def _record_runtime_reduced_payload(self, op_name: str, num_bytes: int) -> None:
+        if num_bytes <= 0:
+            return
+        self._runtime_reduced_payload_bytes += num_bytes
+        if op_name == "all_reduce":
+            self._runtime_all_reduce_payload_bytes += num_bytes
+            self._runtime_all_reduce_ops += 1
+        elif op_name == "reduce_scatter":
+            self._runtime_reduce_scatter_payload_bytes += num_bytes
+            self._runtime_reduce_scatter_ops += 1
+
+    def _bucket_payload_bytes(self, ubid: int | None, op_name: str) -> int:
+        if ubid is None:
+            return 0
+
+        flat_grads = self.bucket_flat_grads.get(ubid)
+        if flat_grads is not None:
+            return flat_grads.numel() * flat_grads.element_size()
+
+        if op_name == "reduce_scatter":
+            shard_param = self.bucket_shard_params.get(ubid)
+            if shard_param is not None:
+                return shard_param.numel() * shard_param.element_size() * self.dp_degree
+
+        specs = self.bucket_param_view_specs.get(ubid, [])
+        if specs:
+            return sum(param.numel() * param.element_size() for param, *_ in specs)
+
+        args = self.bucket_fwd_args.get(ubid, [])
+        trainable_idxs = self.bucket_trainable_param_idxs.get(ubid, [])
+        return sum(
+            args[idx].numel() * args[idx].element_size()
+            for idx in trainable_idxs
+            if idx < len(args) and args[idx] is not None
+        )
+
+    def _sync_payload_ubid(self, node: Task) -> int | None:
+        if node.unique_bucket_id is not None:
+            return node.unique_bucket_id
+        if node.data_preds:
+            return node.data_preds[0].unique_bucket_id
+        return None
+
     def _collect_saved_tensors(
         self,
         obj: Any,
@@ -1137,6 +1268,25 @@ def _summarize_task_buffer(self) -> dict[str, float]:
         summary["total_entries"] = total_entries
         return summary
 
+    def _init_task_buffer_refcounts(self, dag: TaskDAG) -> None:
+        self.task_buffer_refcounts = {
+            node.uid: len(node.data_succs)
+            for node in dag.nodes
+            if node.data_succs
+        }
+
+    def _release_task_buffer_uid(self, uid: int) -> None:
+        remaining = self.task_buffer_refcounts.get(uid)
+        if remaining is None:
+            self.task_buffer.pop(uid, None)
+            return
+        remaining -= 1
+        if remaining <= 0:
+            self.task_buffer_refcounts.pop(uid, None)
+            self.task_buffer.pop(uid, None)
+        else:
+            self.task_buffer_refcounts[uid] = remaining
+
     def run_dag(self, loss_fn=None, profiling: bool = False):
         """Execute the loaded TaskDAG in topological order.
 
@@ -1157,6 +1307,7 @@ def run_dag(self, loss_fn=None, profiling: bool = False):
 
         # Reset per-iteration state
         self.task_buffer = {}
+        self.task_buffer_refcounts = {}
         self.recv_events = {}
         self.a2a_events = {}
         self.ar_events = {}
@@ -1165,6 +1316,14 @@ def run_dag(self, loss_fn=None, profiling: bool = False):
             self.ag_events = {}
         self.bwd_events = {}
         self._clear_param_grads()
+        self._schedule_reduced_payload_bytes = 0
+        self._schedule_all_reduce_payload_bytes = 0
+        self._schedule_reduce_scatter_payload_bytes = 0
+        self._runtime_reduced_payload_bytes = 0
+        self._runtime_all_reduce_payload_bytes = 0
+        self._runtime_reduce_scatter_payload_bytes = 0
+        self._runtime_all_reduce_ops = 0
+        self._runtime_reduce_scatter_ops = 0
 
         if self.zero_stage >= 2:
             with torch.cuda.stream(self.comp_stream):
@@ -1178,39 +1337,21 @@ def run_dag(self, loss_fn=None, profiling: bool = False):
         # final synchronize() so we never block the CPU mid-dispatch.
         self._prof_records: list = []
 
-        # Sort nodes by time_step with tie-breaking: SEND/AR/A2A first (0),
-        # compute second (1), RECV last (2).  RECV is deferred so that when a
-        # RECV overlaps with compute at the same time_step the recv kernel only
-        # launches after compute finishes (p2p_recv_stream waits on the compute
-        # event), avoiding SM contention from an idle-spinning recv kernel.
-        _PRE_COMPUTE_TYPES = {
-            TaskType.SEND,
-            TaskType.ALL_GATHER,
-            TaskType.ALLOC_FULL_GRADS,
-            TaskType.ALLOC_FULL_PARAMS,
-            TaskType.FWD_A2A,
-            TaskType.BWD_A2A,
-        }
-        _POST_COMPUTE_TYPES = {
-            TaskType.ALL_REDUCE,
-            TaskType.REDUCE_SCATTER,
-            TaskType.FREE_FULL_GRADS,
-            TaskType.FREE_FULL_PARAMS,
-        }
-
-        def _ts_key(n):
-            t = n.chunk.type
-            if t in _PRE_COMPUTE_TYPES:
-                sub = 0
-            elif t in _POST_COMPUTE_TYPES:
-                sub = 2
-            elif t == TaskType.RECV:
-                sub = 3
-            else:
-                sub = 1
-            return (n.time_step, sub)
-
-        sorted_nodes = sorted(dag.nodes, key=_ts_key)
+        sorted_nodes = sorted(dag.nodes, key=runtime_sort_key)
+        self._schedule_all_reduce_payload_bytes = sum(
+            self._bucket_payload_bytes(self._sync_payload_ubid(node), "all_reduce")
+            for node in sorted_nodes
+            if node.chunk.type == TaskType.ALL_REDUCE
+        )
+        self._schedule_reduce_scatter_payload_bytes = sum(
+            self._bucket_payload_bytes(self._sync_payload_ubid(node), "reduce_scatter")
+            for node in sorted_nodes
+            if node.chunk.type == TaskType.REDUCE_SCATTER
+        )
+        self._schedule_reduced_payload_bytes = (
+            self._schedule_all_reduce_payload_bytes + self._schedule_reduce_scatter_payload_bytes
+        )
+        self._init_task_buffer_refcounts(dag)
         ts_to_comp_event: dict[int, torch.cuda.Event] = {}
 
         def _wait_for_all_gather(compute_node: Task) -> None:
@@ -1227,7 +1368,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
             mb_idx = batch.mb_idx
             ubid = node.unique_bucket_id  # None for non-compute nodes
 
-            self.logger.log(VERBOSE,
+            self.logger.debug(
                 f"run_dag dispatch (time {node.time_step}): {chunk.type.value} "
                 f"mb{mb_idx} ubid={ubid}"
             )
@@ -1254,10 +1395,9 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     # BWD_W still needs param_groups (BWD_I), co-located BWD still
                     # needs inp_grads (BWD/BWD_A2A).
                     _send_buf = self.task_buffer.get(compute_node.uid)
-                    if compute_node.chunk.type in (TaskType.FWD, TaskType.FWD_A2A):
-                        del self.task_buffer[compute_node.uid]
-                    elif isinstance(_send_buf, dict):
+                    if isinstance(_send_buf, dict):
                         _send_buf["send_output"] = None
+                    self._release_task_buffer_uid(compute_node.uid)
 
                 case TaskType.RECV:
                     # data_succs[0] is the downstream compute task.
@@ -1289,8 +1429,8 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     tensor_idx = node.custom_metadata["a2a_tensor_idx"]
                     # Pass-through: copy upstream FWD task_buffer, apply A2A only at tensor_idx.
                     fwd_buf = dict(self.task_buffer[fwd_pred.uid])
-                    # FWD_A2A now owns the copy; original FWD entry is no longer needed.
-                    del self.task_buffer[fwd_pred.uid]
+                    # FWD_A2A now owns the copy; release predecessor after consuming it.
+                    self._release_task_buffer_uid(fwd_pred.uid)
                     detached_outs = list(fwd_buf["detached_outs"])
                     self._nvtx_push(f"fwd_a2a_ubid{fwd_pred.unique_bucket_id}_mb{mb_idx}")
                     self._start_timing(self.a2a_stream, "fwd_a2a")
@@ -1313,17 +1453,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     tensor_idx = node.custom_metadata["a2a_tensor_idx"]
                     # Pass-through: copy upstream BWD/BWD_I task_buffer, apply reversed A2A only at tensor_idx.
                     bwd_buf = dict(self.task_buffer[bwd_pred.uid])
-                    # Analog of FWD_A2A change 3: free predecessor buffer after copying.
-                    # Skip for BWD_I — its param_groups must survive until BWD_W.
-                    # Skip for BWD that is an ALL_REDUCE trigger — the ALL_REDUCE handler
-                    # will consume and delete the buffer itself.
-                    if bwd_pred.chunk.type == TaskType.BWD:
-                        has_ar_succ = any(
-                            s.chunk.type == TaskType.ALL_REDUCE
-                            for s in bwd_pred.data_succs
-                        )
-                        if not has_ar_succ:
-                            del self.task_buffer[bwd_pred.uid]
+                    self._release_task_buffer_uid(bwd_pred.uid)
                     inp_grads = list(bwd_buf["inp_grads"])
                     grad_a2a_out = inp_grads[tensor_idx]
                     assert grad_a2a_out is not None, (
@@ -1346,35 +1476,43 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                 case TaskType.ALL_REDUCE:
                     bwd_node = node.data_preds[0]
                     ar_ubid = bwd_node.unique_bucket_id
+                    scheduled_bytes = self._bucket_payload_bytes(ar_ubid, "all_reduce")
                     self._nvtx_push(f"all_reduce_ubid{bwd_node.unique_bucket_id}")
                     self.ar_stream.wait_event(comp_events[bwd_node.uid])
                     self._start_timing(self.ar_stream, "all_reduce")
-                    ar_launched = self._exec_all_reduce_grads(ar_ubid)
+                    runtime_bytes = self._exec_all_reduce_grads(ar_ubid)
                     self._stop_timing(self.ar_stream, "all_reduce")
-                    if ar_launched:
+                    if runtime_bytes > 0:
                         ar_evt = torch.cuda.Event()
                         ar_evt.record(self.ar_stream)
                         self.ar_events[node.uid] = ar_evt
+                    elif scheduled_bytes > 0:
+                        self.logger.warning(
+                            f"[all_reduce_runtime_miss] rank={self.global_rank} "
+                            f"ubid={ar_ubid} scheduled_bytes={scheduled_bytes}"
+                        )
                     self._nvtx_pop()
-                    del self.task_buffer[bwd_node.uid]
+                    self._release_task_buffer_uid(bwd_node.uid)
 
                 case TaskType.REDUCE_SCATTER:
                     bwd_node = node.data_preds[0]
                     rs_ubid = node.unique_bucket_id if node.unique_bucket_id is not None else bwd_node.unique_bucket_id
+                    scheduled_bytes = self._bucket_payload_bytes(rs_ubid, "reduce_scatter")
                     self._nvtx_push(f"reduce_scatter_ubid{rs_ubid}")
                     self.ar_stream.wait_event(comp_events[bwd_node.uid])
                     self._start_timing(self.ar_stream, "reduce_scatter")
-                    self._exec_reduce_scatter(rs_ubid)
+                    runtime_bytes = self._exec_reduce_scatter(rs_ubid)
                     self._stop_timing(self.ar_stream, "reduce_scatter")
                     rs_evt = torch.cuda.Event()
                     rs_evt.record(self.ar_stream)
                     self.rs_events[node.uid] = rs_evt
+                    if runtime_bytes <= 0 and scheduled_bytes > 0:
+                        self.logger.warning(
+                            f"[reduce_scatter_runtime_miss] rank={self.global_rank} "
+                            f"ubid={rs_ubid} scheduled_bytes={scheduled_bytes}"
+                        )
                     self._nvtx_pop()
-                    _bwd_buf = self.task_buffer.get(bwd_node.uid)
-                    if bwd_node.chunk.type == TaskType.BWD:
-                        self.task_buffer.pop(bwd_node.uid, None)
-                    elif isinstance(_bwd_buf, dict):
-                        _bwd_buf["send_output"] = None
+                    self._release_task_buffer_uid(bwd_node.uid)
 
                 case TaskType.ALL_GATHER:
                     self._nvtx_push(f"all_gather_s{batch.stage_id}_b{node.bucket_id}")
@@ -1388,9 +1526,12 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     self._nvtx_pop()
 
                 case TaskType.ALLOC_FULL_GRADS:
+                    self._nvtx_push(f"alloc_full_grads_ubid{node.unique_bucket_id}")
                     self._alloc_full_grads(node.unique_bucket_id)
+                    self._nvtx_pop()
 
                 case TaskType.FREE_FULL_GRADS:
+                    self._nvtx_push(f"free_full_grads_ubid{node.unique_bucket_id}")
                     for pred in node.data_preds:
                         if pred.chunk.type == TaskType.REDUCE_SCATTER:
                             rs_evt = self.rs_events.get(pred.uid)
@@ -1402,12 +1543,16 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                             if ar_evt is not None:
                                 self.comp_stream.wait_event(ar_evt)
                                 ar_evt.synchronize()
+                    self._nvtx_pop()
                     self._free_full_grads(node.unique_bucket_id)
 
                 case TaskType.ALLOC_FULL_PARAMS:
+                    self._nvtx_push(f"alloc_full_params_ubid{node.unique_bucket_id}")
                     self._alloc_full_params(node.unique_bucket_id)
+                    self._nvtx_pop()
 
                 case TaskType.FREE_FULL_PARAMS:
+                    self._nvtx_push(f"free_full_params_ubid{node.unique_bucket_id}")
                     for pred in node.data_preds:
                         if pred.uid in comp_events:
                             # _free_full_params resizes CUDA storage from the host.
@@ -1416,6 +1561,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                             # launched on comp_stream may still be reading it.
                             comp_events[pred.uid].synchronize()
                     self._free_full_params(node.unique_bucket_id)
+                    self._nvtx_pop()
 
                 case TaskType.FWD:
                     # Wait on any RECV predecessor.
@@ -1436,10 +1582,10 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     if fwd_data_pred is not None:
                         # Both FWD and FWD_A2A task_buffer entries carry "detached_outs".
                         input_tensors = self.task_buffer[fwd_data_pred.uid]["detached_outs"]
-                        # Upstream bucket consumed; free its buffer (no further reader).
-                        del self.task_buffer[fwd_data_pred.uid]
+                        self._release_task_buffer_uid(fwd_data_pred.uid)
                     elif recv_pred is not None:
                         input_tensors = self.task_buffer[recv_pred.uid]
+                        self._release_task_buffer_uid(recv_pred.uid)
                     else:
                         input_tensors = self.inputs  # first stage, first bucket
 
@@ -1479,6 +1625,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                         upstream_grads = None
                     elif recv_pred is not None:
                         upstream_grads = self.task_buffer[recv_pred.uid]
+                        self._release_task_buffer_uid(recv_pred.uid)
                         if not isinstance(upstream_grads, (list, tuple)):
                             upstream_grads = [upstream_grads]
                         outputs_or_loss = fwd_out["out_with_grad"]
@@ -1502,6 +1649,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                         for i, (d, g) in enumerate(zip(detached_outs, a2a_buf["inp_grads"])):
                             if d is not None and isinstance(d, torch.Tensor) and d.requires_grad and g is not None:
                                 d.grad = g
+                        self._release_task_buffer_uid(bwd_a2a_pred.uid)
                     else:
                         bwd_pred = next(
                             (p for p in node.data_preds
@@ -1521,17 +1669,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                                 if (d is not None and isinstance(d, torch.Tensor)
                                         and d.requires_grad and g is not None):
                                     d.grad = g
-                            # Analog of FWD change 2: free predecessor buffer after consuming
-                            # inp_grads.  Only for plain BWD — BWD_I must survive until BWD_W.
-                            # Skip deletion if bwd_pred is an ALL_REDUCE trigger: that task
-                            # will consume and delete the buffer itself.
-                            if bwd_pred.chunk.type == TaskType.BWD:
-                                has_ar_succ = any(
-                                    s.chunk.type == TaskType.ALL_REDUCE
-                                    for s in bwd_pred.data_succs
-                                )
-                                if not has_ar_succ:
-                                    del self.task_buffer[bwd_pred.uid]
+                            self._release_task_buffer_uid(bwd_pred.uid)
                         else:
                             # Last (or only) stage in backward order, or has a RECV for
                             # upstream grads (cross-rank predecessor): standard backward.
@@ -1590,6 +1728,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                         output_grads = None
                     elif recv_pred is not None:
                         upstream_raw = self.task_buffer[recv_pred.uid]
+                        self._release_task_buffer_uid(recv_pred.uid)
                         if not isinstance(upstream_raw, (list, tuple)):
                             upstream_raw = [upstream_raw]
                         stage_outputs_or_loss = fwd_out["out_with_grad"]
@@ -1621,6 +1760,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                         ]
                         stage_outputs_or_loss = [p[0] for p in pairs]
                         output_grads = [p[1] for p in pairs]
+                        self._release_task_buffer_uid(bwd_a2a_pred.uid)
 
                     input_values = fwd_out.get("inp_with_grad") or []
 
@@ -1721,7 +1861,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     # BWD_I buffer is no longer needed: _bucket_backward_weight already
                     # deleted param_groups["intermediates"] and param_groups["grads"] and
                     # the autograd graph was freed by retain_graph=False.  Drop the shell.
-                    del self.task_buffer[bwdi_node.uid]
+                    self._release_task_buffer_uid(bwdi_node.uid)
                     evt = torch.cuda.Event()
                     evt.record(self.comp_stream)
                     comp_events[node.uid] = evt
@@ -1937,13 +2077,20 @@ def _backward_dag(
                     outputs_or_loss[0].backward()
         else:
             # Non-last bucket: propagate through the bucket boundary.
-            outputs_bwd = [p for p, d in zip(pre_detach_outs, detached_outs) if d.requires_grad]
-            grads_bwd = [d.grad for p, d in zip(pre_detach_outs, detached_outs) if d.requires_grad]
-            assert all(g is not None for g in grads_bwd), (
-                f"BWD ubid={ubid} mb={mb_idx}: detached boundary has no .grad"
+            bwd_pairs = [
+                (p, d.grad)
+                for p, d in zip(pre_detach_outs, detached_outs)
+                if (isinstance(d, torch.Tensor) and d.requires_grad and d.grad is not None)
+            ]
+            assert bwd_pairs, (
+                f"BWD ubid={ubid} mb={mb_idx}: detached boundary has no tensors "
+                "with a materialized grad"
             )
-            with torch.cuda.stream(comp_stream):
-                torch.autograd.backward(outputs_bwd, grads_bwd)
+            if bwd_pairs:
+                outputs_bwd = [p for p, _g in bwd_pairs]
+                grads_bwd = [g for _p, g in bwd_pairs]
+                with torch.cuda.stream(comp_stream):
+                    torch.autograd.backward(outputs_bwd, grads_bwd)
 
         # Collect stage-input grads for SEND — applies to both last-bucket and
         # non-last-bucket paths (e.g. when downstream of BWD_A2A).
@@ -2086,8 +2233,11 @@ def _bucket_backward_weight(self, weights, param_groups: list, ubid: int | None
                 else:
                     weight.grad += dw
 
-    def _exec_all_reduce_grads(self, ubid: int) -> bool:
-        """All-reduce each materialized parameter grad for *ubid* on ar_stream."""
+    def _exec_all_reduce_grads(self, ubid: int) -> int:
+        """All-reduce each materialized parameter grad for *ubid* on ar_stream.
+
+        Returns the total payload bytes actually dispatched at runtime.
+        """
         if self.zero_stage == 1 and ubid in self.bucket_flat_grads:
             flat_grads = self.bucket_flat_grads.get(ubid)
             if flat_grads is None:
@@ -2095,10 +2245,12 @@ def _exec_all_reduce_grads(self, ubid: int) -> bool:
                     f"[all_reduce_bucket] rank={self.global_rank} ubid={ubid}: "
                     "skipped no ZeRO flat grad buffer"
                 )
-                return False
+                return 0
+            total_bytes = flat_grads.numel() * flat_grads.element_size()
             with torch.cuda.stream(self.ar_stream):
                 dist.all_reduce(flat_grads, group=self.dp_group)
-            return True
+            self._record_runtime_reduced_payload("all_reduce", total_bytes)
+            return total_bytes
 
         args = self.bucket_fwd_args.get(ubid, [])
         trainable_idxs = self.bucket_trainable_param_idxs.get(ubid, [])
@@ -2113,7 +2265,7 @@ def _exec_all_reduce_grads(self, ubid: int) -> bool:
                 f"[all_reduce_bucket] rank={self.global_rank} ubid={ubid}: "
                 "skipped no materialized grads"
             )
-            return False
+            return 0
 
         total_numel = sum(grad.numel() for _, grad in grad_tensors)
         total_bytes = sum(grad.numel() * grad.element_size() for _, grad in grad_tensors)
@@ -2146,7 +2298,8 @@ def _exec_all_reduce_grads(self, ubid: int) -> bool:
         with torch.cuda.stream(self.ar_stream):
             for _, grad in grad_tensors:
                 dist.all_reduce(grad, group=self.dp_group)
-        return True
+        self._record_runtime_reduced_payload("all_reduce", total_bytes)
+        return total_bytes
 
     def _alloc_full_params(self, ubid: int | None) -> None:
         if self.zero_stage != 3:
@@ -2165,9 +2318,28 @@ def _alloc_full_params(self, ubid: int | None) -> None:
         required_bytes = full.numel() * full.element_size()
         if storage.size() != required_bytes:
             storage.resize_(required_bytes)
+        self.logger.debug(
+            f"[zero3_alloc_full_params] rank={self.global_rank} ubid={ubid}: "
+            f"numel={full.numel()} required_bytes={required_bytes} "
+            f"storage_size={storage.size()} fresh={self.zero3_full_params_fresh.get(ubid, False)}"
+        )
         for param, offset, numel, shape in self.bucket_param_view_specs.get(ubid, []):
             param.data = full[offset:offset + numel].view(shape)
             param.requires_grad_(True)
+        param_names = self.bucket_param_names.get(ubid, [])
+        zero_storage = []
+        for i, (param, offset, numel, shape) in enumerate(self.bucket_param_view_specs.get(ubid, [])):
+            p_storage = param.untyped_storage()
+            if p_storage.size() == 0:
+                name = param_names[i] if i < len(param_names) else f"param{i}"
+                zero_storage.append(
+                    f"{name}: offset={offset} numel={numel} shape={shape} stride={tuple(param.stride())}"
+                )
+        if zero_storage:
+            self.logger.error(
+                f"[zero3_alloc_full_params_zero_storage] rank={self.global_rank} ubid={ubid}: "
+                + " | ".join(zero_storage)
+            )
 
     def _free_full_params(self, ubid: int | None) -> None:
         if self.zero_stage != 3:
@@ -2178,6 +2350,10 @@ def _free_full_params(self, ubid: int | None) -> None:
         if full is None:
             return
         storage = full.untyped_storage()
+        self.logger.debug(
+            f"[zero3_free_full_params] rank={self.global_rank} ubid={ubid}: "
+            f"storage_size_before={storage.size()} fresh_before={self.zero3_full_params_fresh.get(ubid, False)}"
+        )
         if storage.size() != 0:
             storage.resize_(0)
         self.zero3_full_params_fresh[ubid] = False
@@ -2209,16 +2385,19 @@ def _free_full_grads(self, ubid: int | None) -> None:
         if self.zero_stage in (2, 3):
             self.bucket_flat_grads[ubid] = None
 
-    def _exec_reduce_scatter(self, ubid: int) -> None:
+    def _exec_reduce_scatter(self, ubid: int) -> int:
         with torch.cuda.stream(self.ar_stream):
             flat_grads = self.bucket_flat_grads.get(ubid)
             rs_out = self.bucket_rs_grads.get(ubid)
             assert flat_grads is not None and rs_out is not None, (
                 f"REDUCE_SCATTER dispatched for ubid={ubid} without allocated gradient buffers"
             )
+            total_bytes = flat_grads.numel() * flat_grads.element_size()
             tmp = torch.empty_like(rs_out)
             dist.reduce_scatter_tensor(tmp, flat_grads, group=self.dp_group)
             rs_out.add_(tmp)
+        self._record_runtime_reduced_payload("reduce_scatter", total_bytes)
+        return total_bytes
 
     def _exec_all_gather(
         self,
@@ -2232,10 +2411,24 @@ def _exec_all_gather(
             if flat_params is None or shard_in is None:
                 return False
             if self.zero_stage == 3 and self.zero3_full_params_fresh.get(ubid, False):
+                self.logger.debug(
+                    f"[zero3_all_gather_skip] rank={self.global_rank} ubid={ubid}: "
+                    f"storage_size={flat_params.untyped_storage().size()} fresh=True"
+                )
                 return False
+            self.logger.debug(
+                f"[zero3_all_gather_begin] rank={self.global_rank} ubid={ubid}: "
+                f"flat_numel={flat_params.numel()} flat_storage={flat_params.untyped_storage().size()} "
+                f"shard_numel={shard_in.numel()} shard_storage={shard_in.untyped_storage().size()} "
+                f"fresh={self.zero3_full_params_fresh.get(ubid, False)}"
+            )
             dist.all_gather_into_tensor(flat_params, shard_in, group=self.dp_group)
             if self.zero_stage == 3:
                 self.zero3_full_params_fresh[ubid] = True
+            self.logger.debug(
+                f"[zero3_all_gather_end] rank={self.global_rank} ubid={ubid}: "
+                f"flat_storage={flat_params.untyped_storage().size()} fresh={self.zero3_full_params_fresh.get(ubid, False)}"
+            )
         return True
 
     def _update(self, *deps):
@@ -2388,6 +2581,17 @@ def _update(self, *deps):
         # Process deferred profiling records now that all GPU work is complete.
         # elapsed_time() is safe to call after synchronize().
         if self._prof_records:
+            self.logger.info(
+                f"[profiling] rank {self.global_rank} "
+                f"schedule_reduced_payload_total_gb={self._schedule_reduced_payload_bytes / (1024 ** 3):.6f} "
+                f"schedule_all_reduce_payload_gb={self._schedule_all_reduce_payload_bytes / (1024 ** 3):.6f} "
+                f"schedule_reduce_scatter_payload_gb={self._schedule_reduce_scatter_payload_bytes / (1024 ** 3):.6f} "
+                f"runtime_reduced_payload_total_gb={self._runtime_reduced_payload_bytes / (1024 ** 3):.6f} "
+                f"runtime_all_reduce_payload_gb={self._runtime_all_reduce_payload_bytes / (1024 ** 3):.6f} "
+                f"runtime_reduce_scatter_payload_gb={self._runtime_reduce_scatter_payload_bytes / (1024 ** 3):.6f} "
+                f"runtime_all_reduce_ops={self._runtime_all_reduce_ops} "
+                f"runtime_reduce_scatter_ops={self._runtime_reduce_scatter_ops}"
+            )
             for node, mb_idx, ubid, start_evt, end_evt, mem_before in self._prof_records:
                 t_ms = start_evt.elapsed_time(end_evt)
                 node.profiling_measurements.append(t_ms)
@@ -2398,4 +2602,14 @@ def _update(self, *deps):
                     f"mem_before={mem_before/1e9:.3f}GB"
                 )
 
-        return losses
+        return {
+            "losses": losses,
+            "schedule_reduced_payload_bytes": self._schedule_reduced_payload_bytes,
+            "schedule_all_reduce_payload_bytes": self._schedule_all_reduce_payload_bytes,
+            "schedule_reduce_scatter_payload_bytes": self._schedule_reduce_scatter_payload_bytes,
+            "runtime_reduced_payload_bytes": self._runtime_reduced_payload_bytes,
+            "runtime_all_reduce_payload_bytes": self._runtime_all_reduce_payload_bytes,
+            "runtime_reduce_scatter_payload_bytes": self._runtime_reduce_scatter_payload_bytes,
+            "runtime_all_reduce_ops": self._runtime_all_reduce_ops,
+            "runtime_reduce_scatter_ops": self._runtime_reduce_scatter_ops,
+        }
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 9657d43..ea33750 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -51,6 +51,7 @@ def publish(self, data: dict):
             "stages": data["stages"],
             "stage_bucket_counts": data["stage_bucket_counts"],
             "trainable_bucket_keys": data["trainable_bucket_keys"],
+            "zero_bucket_keys": data.get("zero_bucket_keys", set()),
         }
         self._dag_data = {
             "per_rank_dags": data["per_rank_dags"],
@@ -117,6 +118,7 @@ def piper_setup(
     a2a_ar_no_overlap=False,
     pg=None,
     nsight=False,
+    temp_dir: str = None,
     model_flops_per_token: float = None,
     visualize_dag: bool = True,
     const_attrs: dict = None,
@@ -148,7 +150,6 @@ def piper_setup(
     piper_metadata.stage_to_device = stage_to_device
     piper_metadata.use_activation_checkpointing = activation_checkpointing
     piper_metadata.activation_num_checkpoints = max(1, int(num_checkpoints))
-    piper_metadata.bucketing = bucketing
     piper_metadata.bucket_size = bucket_size
     piper_metadata.zero_stage = int(zero_stage)
     piper_metadata.schedule_name = schedule_name
@@ -192,7 +193,7 @@ def piper_setup(
     _create_actors(
         num_devices, optim_fn, num_mbs, num_stages,
         naive_gradient_sync, profile=nsight, stage_to_device=stage_to_device,
-        zero_stage=zero_stage, no_nvtx=no_nvtx, pg=pg,
+        zero_stage=zero_stage, no_nvtx=no_nvtx, pg=pg, temp_dir=temp_dir,
     )
 
     if dp_rank == 0:
@@ -299,6 +300,7 @@ def piper_setup(
         # Restore stage metadata that the piper backend sets on dp_rank=0.
         piper_metadata.stage_bucket_counts = stage_data["stage_bucket_counts"]
         piper_metadata.trainable_bucket_keys = stage_data["trainable_bucket_keys"]
+        piper_metadata.zero_bucket_keys = stage_data.get("zero_bucket_keys", set())
 
         logger.debug(f"DP rank {dp_rank} waiting for dp_rank=0 DAG data...")
         dag_data = None
diff --git a/src/piper_exec.py b/src/piper_exec.py
index f2eb7f0..4792578 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -268,6 +268,30 @@ def node_id(self) -> str:
         return f"r{self.pp_rank}_t{self.time_step}_{ttype}_mb{mb}"
 
 
+def runtime_sort_key(node: Task) -> tuple:
+    """Return the actor dispatch ordering key for a task.
+
+    Runtime dispatch order is driven by ``Task.time_step`` first. For tasks that
+    intentionally share a time step, dispatch uses a fixed priority:
+    SEND, A2A, RS/AR, AG, compute, RECV.
+    """
+    priority = {
+        TaskType.SEND: 0,
+        TaskType.FWD_A2A: 1,
+        TaskType.BWD_A2A: 1,
+        TaskType.REDUCE_SCATTER: 2,
+        TaskType.ALL_REDUCE: 2,
+        TaskType.ALL_GATHER: 3,
+        TaskType.FWD: 4,
+        TaskType.BWD: 4,
+        TaskType.BWD_I: 4,
+        TaskType.BWD_W: 4,
+        TaskType.FWD_BWD: 4,
+        TaskType.RECV: 5,
+    }.get(node.chunk.type, 4)
+    return (node.time_step, priority, node.uid)
+
+
 def _rebuild_task_dag(node_data):
     """Reconstruct a TaskDAG from the flat index-based representation produced
     by TaskDAG.__reduce__.  Must be a module-level function so pickle can find
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index 1fef110..cf51ca0 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -13,7 +13,7 @@
 from torch.autograd import Function
 
 from .piper_utils import create_logger, LOG_LEVEL, VERBOSE, piper_metadata
-from .piper_exec import TaskType, PipelineSchedule, Chunk, Task, TaskDAG, logical_time
+from .piper_exec import TaskType, PipelineSchedule, Chunk, Task, TaskDAG, logical_time, runtime_sort_key
 
 logger = create_logger("piper_graph_transform", LOG_LEVEL)
 
@@ -1896,18 +1896,18 @@ def _zero_task(
 
 def _bucket_compute_tasks(
     rank_dag: TaskDAG,
-    trainable_bucket_keys: set | None = None,
+    bucket_filter_keys: set | None = None,
 ) -> dict[int, list[Task]]:
     compute_types = {TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W}
     by_ubid: dict[int, list[Task]] = defaultdict(list)
     for node in rank_dag.nodes:
         if node.chunk.type not in compute_types or node.unique_bucket_id is None:
             continue
-        if trainable_bucket_keys is not None:
+        if bucket_filter_keys is not None:
             if not node.chunk.batches:
                 continue
             stage_id = node.chunk.batches[0].stage_id
-            if (stage_id, node.bucket_id) not in trainable_bucket_keys:
+            if (stage_id, node.bucket_id) not in bucket_filter_keys:
                 continue
         by_ubid[node.unique_bucket_id].append(node)
     for nodes in by_ubid.values():
@@ -1927,7 +1927,7 @@ def _insert_zero_grad_ops(
     *,
     sync_type: TaskType,
     use_grad_lifetime: bool = False,
-    trainable_bucket_keys: set | None = None,
+    zero_bucket_keys: set | None = None,
 ) -> None:
     """Per-compute-node gradient sync.
 
@@ -1945,7 +1945,7 @@ def _insert_zero_grad_ops(
     grad_sync_types = {TaskType.BWD, TaskType.BWD_W}
     new_nodes: list[Task] = []
 
-    for ubid, bucket_nodes in _bucket_compute_tasks(rank_dag, trainable_bucket_keys).items():
+    for ubid, bucket_nodes in _bucket_compute_tasks(rank_dag, zero_bucket_keys).items():
         for compute_node in bucket_nodes:
             if compute_node.chunk.type not in grad_sync_types:
                 continue
@@ -1969,34 +1969,34 @@ def _insert_zero0_ops(rank_dag: TaskDAG) -> None:
 
 def _insert_zero1_ops(
     rank_dag: TaskDAG,
-    trainable_bucket_keys: set | None = None,
+    zero_bucket_keys: set | None = None,
 ) -> None:
     """ZeRO-1: per-compute-node all-reduce."""
     _insert_zero_grad_ops(
         rank_dag,
         sync_type=TaskType.ALL_REDUCE,
-        trainable_bucket_keys=trainable_bucket_keys,
+        zero_bucket_keys=zero_bucket_keys,
     )
 
 
 def _insert_zero2_ops(
     rank_dag: TaskDAG,
-    trainable_bucket_keys: set | None = None,
+    zero_bucket_keys: set | None = None,
 ) -> None:
     """ZeRO-2: per-compute-node reduce-scatter with full-gradient lifecycle."""
     _insert_zero_grad_ops(
         rank_dag,
         sync_type=TaskType.REDUCE_SCATTER,
         use_grad_lifetime=True,
-        trainable_bucket_keys=trainable_bucket_keys,
+        zero_bucket_keys=zero_bucket_keys,
     )
 
 
 def _insert_zero3_ops(
     rank_dag: TaskDAG,
-    trainable_bucket_keys: set | None = None,
+    zero_bucket_keys: set | None = None,
 ) -> None:
-    """ZeRO-3: per-compute-node param gather + per-grad-node reduce-scatter.
+    """ZeRO-3: param gather + per-grad-node reduce-scatter lifetimes.
 
     * F / B_I  →  P+ → AG → compute → P-
     * B / B_W  →  (G+ →) and (P+ → AG →) compute (→ RS → G-) and (→ P-)
@@ -2010,13 +2010,12 @@ def _insert_zero3_ops(
 
     new_nodes: list[Task] = []
 
-    for ubid, bucket_nodes in _bucket_compute_tasks(rank_dag, trainable_bucket_keys).items():
+    for ubid, bucket_nodes in _bucket_compute_tasks(rank_dag, zero_bucket_keys).items():
         for compute_node in bucket_nodes:
             t = compute_node.chunk.type
             bid = compute_node.bucket_id
 
             if t in param_only_types:
-                # P+ → AG → compute → P-
                 alloc_p = _zero_task(compute_node, bid, TaskType.ALLOC_FULL_PARAMS, ubid=ubid)
                 ag      = _zero_task(compute_node, bid, TaskType.ALL_GATHER,        ubid=ubid)
                 free_p  = _zero_task(compute_node, bid, TaskType.FREE_FULL_PARAMS,  ubid=ubid)
@@ -2026,25 +2025,229 @@ def _insert_zero3_ops(
                 new_nodes.extend([alloc_p, ag, free_p])
 
             elif t in grad_types:
-                # Pre-compute: both G+ and P+→AG feed into compute.
-                alloc_g = _zero_task(compute_node, bid, TaskType.ALLOC_FULL_GRADS,  ubid=ubid)
                 alloc_p = _zero_task(compute_node, bid, TaskType.ALLOC_FULL_PARAMS, ubid=ubid)
                 ag      = _zero_task(compute_node, bid, TaskType.ALL_GATHER,        ubid=ubid)
-                # Post-compute: RS→G- and P- both follow compute.
+                alloc_g = _zero_task(compute_node, bid, TaskType.ALLOC_FULL_GRADS,  ubid=ubid)
+                free_p  = _zero_task(compute_node, bid, TaskType.FREE_FULL_PARAMS,  ubid=ubid)
                 rs      = _zero_task(compute_node, bid, TaskType.REDUCE_SCATTER,    ubid=ubid)
                 free_g  = _zero_task(compute_node, bid, TaskType.FREE_FULL_GRADS,   ubid=ubid)
-                free_p  = _zero_task(compute_node, bid, TaskType.FREE_FULL_PARAMS,  ubid=ubid)
-                _add_data_edge(alloc_g, compute_node)
                 _add_data_edge(alloc_p, ag)
                 _add_data_edge(ag, compute_node)
+                _add_data_edge(alloc_g, compute_node)
+                _add_data_edge(compute_node, free_p)
                 _add_data_edge(compute_node, rs)
                 _add_data_edge(rs, free_g)
-                _add_data_edge(compute_node, free_p)
-                new_nodes.extend([alloc_g, alloc_p, ag, rs, free_g, free_p])
+                new_nodes.extend([alloc_p, ag, alloc_g, free_p, rs, free_g])
 
     rank_dag.nodes.extend(new_nodes)
 
 
+def _zero_sequence_compute_nodes(rank_dag: TaskDAG, zero_stage: int) -> list[Task]:
+    if zero_stage == 2:
+        compute_types = {TaskType.BWD, TaskType.BWD_W}
+    elif zero_stage == 3:
+        compute_types = {TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W}
+    else:
+        return []
+    return sorted(
+        [node for node in rank_dag.nodes if node.chunk.type in compute_types],
+        key=lambda node: (node.time_step, node.uid),
+    )
+
+
+def _find_zero_neighbor(node: Task, task_type: TaskType, *, preds: bool, ubid: int | None) -> Task | None:
+    neighbors = node.data_preds if preds else node.data_succs
+    for neighbor in neighbors:
+        if neighbor.chunk.type != task_type:
+            continue
+        if ubid is not None and neighbor.unique_bucket_id != ubid:
+            continue
+        return neighbor
+    return None
+
+
+def _collect_zero_sequence_nodes(compute_node: Task, zero_stage: int) -> dict[TaskType, Task]:
+    ubid = compute_node.unique_bucket_id
+    seq: dict[TaskType, Task] = {compute_node.chunk.type: compute_node}
+    if zero_stage == 2:
+        alloc_g = _find_zero_neighbor(compute_node, TaskType.ALLOC_FULL_GRADS, preds=True, ubid=ubid)
+        rs = _find_zero_neighbor(compute_node, TaskType.REDUCE_SCATTER, preds=False, ubid=ubid)
+        free_g = _find_zero_neighbor(rs, TaskType.FREE_FULL_GRADS, preds=False, ubid=ubid) if rs else None
+        if alloc_g is not None:
+            seq[TaskType.ALLOC_FULL_GRADS] = alloc_g
+        if rs is not None:
+            seq[TaskType.REDUCE_SCATTER] = rs
+        if free_g is not None:
+            seq[TaskType.FREE_FULL_GRADS] = free_g
+        return seq
+
+    alloc_p = _find_zero_neighbor(compute_node, TaskType.ALL_GATHER, preds=True, ubid=ubid)
+    if alloc_p is not None:
+        ag = alloc_p
+        alloc_p = _find_zero_neighbor(ag, TaskType.ALLOC_FULL_PARAMS, preds=True, ubid=ubid)
+        if alloc_p is not None:
+            seq[TaskType.ALLOC_FULL_PARAMS] = alloc_p
+        seq[TaskType.ALL_GATHER] = ag
+    free_p = _find_zero_neighbor(compute_node, TaskType.FREE_FULL_PARAMS, preds=False, ubid=ubid)
+    if free_p is not None:
+        seq[TaskType.FREE_FULL_PARAMS] = free_p
+    if compute_node.chunk.type in {TaskType.BWD, TaskType.BWD_W}:
+        alloc_g = _find_zero_neighbor(compute_node, TaskType.ALLOC_FULL_GRADS, preds=True, ubid=ubid)
+        rs = _find_zero_neighbor(compute_node, TaskType.REDUCE_SCATTER, preds=False, ubid=ubid)
+        free_g = _find_zero_neighbor(rs, TaskType.FREE_FULL_GRADS, preds=False, ubid=ubid) if rs else None
+        if alloc_g is not None:
+            seq[TaskType.ALLOC_FULL_GRADS] = alloc_g
+        if rs is not None:
+            seq[TaskType.REDUCE_SCATTER] = rs
+        if free_g is not None:
+            seq[TaskType.FREE_FULL_GRADS] = free_g
+    return seq
+
+
+def _reachable_downstream(node: Task) -> set[int]:
+    seen: set[int] = set()
+    stack = list(node.data_succs) + list(node.temporal_succs)
+    while stack:
+        cur = stack.pop()
+        cur_id = id(cur)
+        if cur_id in seen:
+            continue
+        seen.add(cur_id)
+        stack.extend(cur.data_succs)
+        stack.extend(cur.temporal_succs)
+    return seen
+
+
+def modify_zero_timesteps(rank_dag: TaskDAG, zero_stage: int) -> None:
+    if zero_stage in (0, 1):
+        return
+
+    for compute_node in _zero_sequence_compute_nodes(rank_dag, zero_stage):
+        base_time_step = compute_node.time_step
+        seq_nodes = _collect_zero_sequence_nodes(compute_node, zero_stage)
+        seq_node_ids = {id(node) for node in seq_nodes.values()}
+        downstream_ids = _reachable_downstream(compute_node)
+
+        for node in rank_dag.nodes:
+            if id(node) in downstream_ids and id(node) not in seq_node_ids:
+                node.time_step += 4
+
+        compute_node.time_step = base_time_step + 2
+        if zero_stage == 2:
+            alloc_g = seq_nodes.get(TaskType.ALLOC_FULL_GRADS)
+            rs = seq_nodes.get(TaskType.REDUCE_SCATTER)
+            free_g = seq_nodes.get(TaskType.FREE_FULL_GRADS)
+            if alloc_g is not None:
+                alloc_g.time_step = base_time_step + 1
+            if rs is not None:
+                rs.time_step = base_time_step + 3
+            if free_g is not None:
+                free_g.time_step = base_time_step + 4
+
+
+def overlap_zero_ops(rank_dag: TaskDAG) -> None:
+    compute_types = frozenset({TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W})
+    zero_chain_types = frozenset({
+        TaskType.ALL_GATHER,
+        TaskType.REDUCE_SCATTER,
+        TaskType.ALL_REDUCE,
+        TaskType.ALLOC_FULL_GRADS,
+        TaskType.FREE_FULL_GRADS,
+        TaskType.ALLOC_FULL_PARAMS,
+        TaskType.FREE_FULL_PARAMS,
+    })
+
+    def _nodes_at_time_step(time_step: int) -> list[Task]:
+        return [node for node in rank_dag.nodes if node.time_step == time_step]
+
+    def _has_overlap_with_types(node: Task, types: frozenset[TaskType]) -> bool:
+        return any(
+            other is not node and other.chunk.type in types and other.time_step == node.time_step
+            for other in rank_dag.nodes
+        )
+
+    def _nearest_compute_before(time_step: int) -> Task | None:
+        candidates = [
+            node for node in rank_dag.nodes
+            if node.chunk.type in compute_types and node.time_step < time_step
+        ]
+        if not candidates:
+            return None
+        return max(candidates, key=lambda node: (node.time_step, -node.uid))
+
+    def _nearest_compute_after(time_step: int) -> Task | None:
+        candidates = [
+            node for node in rank_dag.nodes
+            if node.chunk.type in compute_types and node.time_step > time_step
+        ]
+        if not candidates:
+            return None
+        return min(candidates, key=lambda node: (node.time_step, node.uid))
+
+    def _collect_zero_preds(node: Task) -> list[Task]:
+        result: list[Task] = []
+        seen: set[int] = set()
+        stack = list(node.data_preds)
+        while stack:
+            cur = stack.pop()
+            cur_id = id(cur)
+            if cur_id in seen or cur.chunk.type not in zero_chain_types:
+                continue
+            seen.add(cur_id)
+            result.append(cur)
+            stack.extend(cur.data_preds)
+        return result
+
+    def _collect_zero_succs(node: Task) -> list[Task]:
+        result: list[Task] = []
+        seen: set[int] = set()
+        stack = list(node.data_succs)
+        while stack:
+            cur = stack.pop()
+            cur_id = id(cur)
+            if cur_id in seen or cur.chunk.type not in zero_chain_types:
+                continue
+            seen.add(cur_id)
+            result.append(cur)
+            stack.extend(cur.data_succs)
+        return result
+
+    def _shift_nodes(nodes: list[Task], delta: int) -> None:
+        if delta == 0:
+            return
+        for node in nodes:
+            node.time_step += delta
+
+    ag_nodes = sorted(
+        [node for node in rank_dag.nodes if node.chunk.type == TaskType.ALL_GATHER],
+        key=lambda node: (node.time_step, node.uid),
+    )
+    for ag in ag_nodes:
+        if _has_overlap_with_types(ag, compute_types):
+            continue
+        target = _nearest_compute_before(ag.time_step)
+        if target is None:
+            continue
+        delta = target.time_step - ag.time_step
+        _shift_nodes([ag] + _collect_zero_preds(ag), delta)
+
+    reduce_nodes = sorted(
+        [
+            node for node in rank_dag.nodes
+            if node.chunk.type in (TaskType.REDUCE_SCATTER, TaskType.ALL_REDUCE)
+        ],
+        key=lambda node: (node.time_step, node.uid),
+    )
+    for red in reduce_nodes:
+        if _has_overlap_with_types(red, compute_types):
+            continue
+        target = _nearest_compute_after(red.time_step)
+        if target is None:
+            continue
+        delta = target.time_step - red.time_step
+        _shift_nodes([red] + _collect_zero_succs(red), delta)
+
+
 def _wire_sync_to_upd(rank_dag: TaskDAG) -> None:
     """Add temporal edges from every AR/RS node → UPD on this rank.
 
@@ -2067,17 +2270,17 @@ def _wire_sync_to_upd(rank_dag: TaskDAG) -> None:
 def insert_zero_ops(
     per_rank_dags: list,
     zero_stage: int,
-    trainable_bucket_keys: set | None = None,
+    zero_bucket_keys: set | None = None,
 ) -> list:
     for rank_dag in per_rank_dags:
         if zero_stage == 0:
             _insert_zero0_ops(rank_dag)
         elif zero_stage == 1:
-            _insert_zero1_ops(rank_dag)
+            _insert_zero1_ops(rank_dag, zero_bucket_keys=zero_bucket_keys)
         elif zero_stage == 2:
-            _insert_zero2_ops(rank_dag, trainable_bucket_keys=trainable_bucket_keys)
+            _insert_zero2_ops(rank_dag, zero_bucket_keys=zero_bucket_keys)
         elif zero_stage == 3:
-            _insert_zero3_ops(rank_dag, trainable_bucket_keys=trainable_bucket_keys)
+            _insert_zero3_ops(rank_dag, zero_bucket_keys=zero_bucket_keys)
         else:
             raise ValueError(f"Unsupported ZeRO stage: {zero_stage}")
         _wire_sync_to_upd(rank_dag)
@@ -2346,41 +2549,36 @@ def _node_fontcolor(node: Task) -> str:
 
 
 def print_dag_order(dag: TaskDAG, label: str = "", rank: int = 0) -> None:
-    """Write the topological execution order of nodes in a :class:`TaskDAG` to a file.
+    """Write the execution order of nodes in a :class:`TaskDAG` to a file.
 
-    Uses Kahn's algorithm (same order as ``run_dag``) so the output reflects
-    exactly what the actor will execute.  Useful for debugging schedule issues.
+    Nodes are sorted by :func:`runtime_sort_key`, exactly as in ``run_dag``,
+    so the output reflects exactly what the actor will execute.
+    Useful for debugging schedule issues.
 
     Output is written to ``out/dag_order_rank{rank}``.
     """
     import os
-    from collections import deque as _deque
 
-    in_degree = {
-        id(n): len(n.data_preds) + len(n.temporal_preds)
-        for n in dag.nodes
-    }
-    queue: _deque = _deque(n for n in dag.nodes if in_degree[id(n)] == 0)
+    sorted_nodes = sorted(dag.nodes, key=runtime_sort_key)
 
     header = f"--- DAG execution order{': ' + label if label else ''} ---"
     lines = [header]
-    step = 0
-    while queue:
-        node = queue.popleft()
+    for step, node in enumerate(sorted_nodes):
         chunk = node.chunk
         ttype = chunk.type.value if chunk.type is not None else "?"
         batches_str = ", ".join(
             f"s{b.stage_id} mb{b.mb_idx}" for b in chunk.batches
         ) if chunk.batches else ""
         bkt = f" bkt={node.bucket_id}" if node.bucket_id else ""
-        lines.append(f"  {step:3d}  rank={node.pp_rank}  {ttype:<14s}  {batches_str}{bkt}")
-        step += 1
-        for succ in list(node.data_succs) + list(node.temporal_succs):
-            in_degree[id(succ)] -= 1
-            if in_degree[id(succ)] == 0:
-                queue.append(succ)
+        ubid = f" ubid={node.unique_bucket_id}" if node.unique_bucket_id is not None else ""
+        lines.append(
+            f"  {step:3d}  ts={node.time_step:3d}  rank={node.pp_rank}  {ttype:<14s}  {batches_str}{bkt}{ubid}"
+        )
     lines.append("-" * len(header))
 
+    for line in lines:
+        logger.debug(line)
+
     os.makedirs("out", exist_ok=True)
     out_path = f"out/dag_order_rank{rank}"
     with open(out_path, "w") as f:
@@ -2790,54 +2988,82 @@ def _bucket_at(idx: int) -> int:
         else:
             merged.append((f, l, ms))
 
-    n_segs = len(merged)
-    if n_segs == 1:
-        return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))]
-
-    cut_after = [merged[i][1] for i in range(n_segs - 1)]
+    def _compute_seg_metadata(
+        seg_ranges: list[tuple[int, int, list[fx.Node]]]
+    ) -> tuple[dict[fx.Node, int], list[list[fx.Node]]]:
+        """Return (node_seg, seg_cross_in) for the given segment ranges."""
+        cut_after = [seg_ranges[i][1] for i in range(len(seg_ranges) - 1)]
 
-    def _seg_of(idx: int) -> int:
-        return sum(1 for c in cut_after if c < idx)
+        def _seg_of(idx: int) -> int:
+            return sum(1 for c in cut_after if c < idx)
 
-    # Assign nodes to segments
-    node_seg: dict[fx.Node, int] = {}
-    for nd in nodes:
-        if nd.op == "output":
-            node_seg[nd] = n_segs - 1
-        elif nd.op == "placeholder":
-            if nd in param_ph_set:
+        node_seg: dict[fx.Node, int] = {}
+        for nd in nodes:
+            if nd.op == "output":
+                node_seg[nd] = len(seg_ranges) - 1
+            elif nd.op == "placeholder":
+                if nd in param_ph_set:
+                    user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
+                    node_seg[nd] = _seg_of(min(user_idxs)) if user_idxs else 0
+                else:
+                    node_seg[nd] = 0
+            elif nd.op == "get_attr":
                 user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
                 node_seg[nd] = _seg_of(min(user_idxs)) if user_idxs else 0
             else:
-                node_seg[nd] = 0
-        elif nd.op == "get_attr":
-            user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
-            node_seg[nd] = _seg_of(min(user_idxs)) if user_idxs else 0
-        else:
-            node_seg[nd] = _seg_of(node_idx[nd])
+                node_seg[nd] = _seg_of(node_idx[nd])
 
-    # Cross-segment inputs: compute outputs needed in later segments, plus
-    # non-param placeholders (e.g. freqs_cis, attention_mask) that live in
-    # segment 0 but are consumed by compute nodes in later segments.
-    node_max_user_seg: dict[fx.Node, int] = {}
-    for nd in nodes:
-        if nd.op == "output":
-            continue
-        node_max_user_seg[nd] = (
-            max(node_seg[u] for u in nd.users) if nd.users else node_seg[nd]
-        )
+        node_max_user_seg: dict[fx.Node, int] = {}
+        for nd in nodes:
+            if nd.op == "output":
+                continue
+            node_max_user_seg[nd] = (
+                max(node_seg[u] for u in nd.users) if nd.users else node_seg[nd]
+            )
 
-    seg_cross_in: list[list[fx.Node]] = [[] for _ in range(n_segs)]
-    for nd in nodes:
-        if nd.op in ("get_attr", "output"):
-            continue
-        # Param placeholders are assigned to exactly one segment — not cross-segment.
-        if nd.op == "placeholder" and nd in param_ph_set:
-            continue
-        s = node_seg[nd]
-        mu = node_max_user_seg[nd]
-        for seg in range(s + 1, mu + 1):
-            seg_cross_in[seg].append(nd)
+        seg_cross_in: list[list[fx.Node]] = [[] for _ in range(len(seg_ranges))]
+        for nd in nodes:
+            if nd.op in ("get_attr", "output"):
+                continue
+            if nd.op == "placeholder" and nd in param_ph_set:
+                continue
+            s = node_seg[nd]
+            mu = node_max_user_seg[nd]
+            for seg in range(s + 1, mu + 1):
+                seg_cross_in[seg].append(nd)
+
+        return node_seg, seg_cross_in
+
+    # Enforce a single activation-tensor dependency between adjacent buckets.
+    # If a boundary carries multiple values or only stage-input placeholders,
+    # merge the adjacent buckets until each remaining boundary is a single
+    # non-placeholder tensor produced by the previous bucket.
+    while len(merged) > 1:
+        _node_seg_tmp, seg_cross_in_tmp = _compute_seg_metadata(merged)
+        invalid_boundary = None
+        for seg in range(1, len(merged)):
+            cross_inputs = seg_cross_in_tmp[seg]
+            if len(cross_inputs) != 1 or cross_inputs[0].op == "placeholder":
+                invalid_boundary = seg - 1
+                logger.debug(
+                    "bucket_stage: merging adjacent buckets due to invalid boundary "
+                    f"between segments {seg - 1} and {seg}: "
+                    f"cross_inputs={[n.name for n in cross_inputs]}"
+                )
+                break
+        if invalid_boundary is None:
+            break
+        f0, _l0, ms0 = merged[invalid_boundary]
+        _f1, l1, ms1 = merged[invalid_boundary + 1]
+        merged[invalid_boundary:invalid_boundary + 2] = [
+            (f0, l1, ms0 + ms1)
+        ]
+
+    n_segs = len(merged)
+    if n_segs == 1:
+        return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))]
+
+    node_seg, seg_cross_in = _compute_seg_metadata(merged)
 
     # Build sub-graphs
     results: list[tuple[fx.GraphModule, list[int], list[int], list]] = []
diff --git a/src/piper_utils.py b/src/piper_utils.py
index fdfcac2..add6314 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -56,17 +56,17 @@ def create_logger(name: str, log_level: str):
             log_level = logging.ERROR
         case "VERBOSE":
             log_level = VERBOSE
-    
+
     logger = logging.getLogger(name)
     logger.setLevel(log_level)
-    
+
     if not logger.handlers:
         handler = logging.StreamHandler(sys.stdout)
         fmt = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
         handler.setFormatter(logging.Formatter(fmt))
         logger.addHandler(handler)
         logger.propagate = False
-    
+
     return logger
 
 
@@ -353,6 +353,7 @@ class PiperMetadata:
     visualize_dag_render: bool = True  # If False, save Graphviz source only
     stage_bucket_counts: dict = {}   # stage_id -> number of buckets (set by piper backend)
     trainable_bucket_keys: set = set()  # (stage_id, bucket_id) pairs with trainable params
+    zero_bucket_keys: set = set()  # (stage_id, bucket_id) pairs that should receive ZeRO transforms
     # Populated by the piper backend on dp_rank=0; broadcast to dp_rank>0 by piper_setup
     # so they can skip torch.compile entirely.  Contains serialized graph + param metadata
     # (no actual weight values) for all stages, plus the built per-rank TaskDAGs.
diff --git a/test/test_llama.py b/test/test_llama.py
index 78d0dcc..2bcead1 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -167,11 +167,11 @@ def main(args, pg):
         naive_gradient_sync=args.naive_grad_sync,
         activation_checkpointing=args.activation_checkpointing,
         num_checkpoints=args.num_checkpoints,
-        bucketing=args.bucketing,
         bucket_size=int(args.bucket_size * 1024 * 1024),
         model_dtype=torch.bfloat16,
         pg=pg,
         nsight=args.nsight,
+        temp_dir=args.temp_dir,
         model_flops_per_token=flops_per_token,
         visualize_dag=args.save_viz,
         const_attrs={"freqs_cis": freqs_cis, "mask": mask},
@@ -264,8 +264,6 @@ def parse_args():
     parser.add_argument('--activation-checkpointing', action='store_true', default=False)
     parser.add_argument('--num-checkpoints', type=int, default=1,
                         help='Number of sequential activation-checkpoint regions per Piper bucket')
-    parser.add_argument('--bucketing', action='store_true', default=False,
-                        help='Split stages into per-param-bucket sub-modules for overlapped all-reduce')
     parser.add_argument('--bucket-size', type=float, default=25,
                         help='Bucket size in MB (default: 25)')
     parser.add_argument('--nsight', action='store_true', default=False,
diff --git a/test/test_qwen.py b/test/test_qwen.py
index 63c6660..16774e9 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -171,12 +171,12 @@ def main(args, pg):
         naive_gradient_sync=args.naive_grad_sync,
         activation_checkpointing=args.activation_checkpointing,
         a2a_ar_no_overlap=True,
-        bucketing=args.bucketing,
-        bucket_size=int(args.bucket_size * 1024 * 1024),
+        bucket_size=int(args.bucket_size * 1024 * 1024) if args.bucket_size is not None else None,
         zero_stage=args.zero_stage,
         model_dtype=torch.bfloat16,
         pg=pg,
         nsight=args.nsight,
+        temp_dir=args.temp_dir,
         model_flops_per_token=flops_per_token,
         visualize_dag=args.save_viz,
         const_attrs={"rope_cache": rope_cache},
@@ -277,10 +277,7 @@ def parse_args():
     parser.add_argument('--activation-checkpointing', action='store_true', default=False)
     parser.add_argument("--zero-stage", type=int, default=0, choices=[0, 1, 2, 3],
                         help="Apply ZeRO memory optimizations")
-    parser.add_argument("--bucketing", action="store_true", default=False,
-                        help="Split stages into per-param-bucket sub-modules for overlapped all-reduce")
-    parser.add_argument("--bucket-size", type=float, default=25,
-                        help="Bucket size in MB (default: 25)")
+    parser.add_argument("--bucket-size", type=float, help="Bucket size in MB")
     parser.add_argument("--profiling", action="store_true", default=False,
                         help="Profile each DAG task: log time and memory delta per task")
     parser.add_argument("--nsight", action="store_true", default=False,

From 12aaabe289c4b3dacd1928654d38e207c3f63ca5 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Thu, 16 Apr 2026 18:04:24 +0000
Subject: [PATCH 089/100] flash attention backend for qwen. update sweep
 scripts

---
 src/piper.py                 |  42 ++-------
 src/piper_actor.py           | 169 +++++++++++------------------------
 src/piper_compile.py         |  17 ++++
 src/piper_graph_transform.py | 121 ++++++++++++++++++-------
 src/piper_utils.py           |   6 +-
 test/models/qwen3.py         |  22 ++---
 test/test_qwen.py            |  83 +++++++++++++++--
 7 files changed, 257 insertions(+), 203 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index 7622256..53f43cb 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -333,7 +333,7 @@ def piper(gm, example_inputs, **kwargs):
         # Split into per-rank DAGs.
         per_rank_dags = split_dag_by_rank(dag)
 
-        # Insert ZeRO/DP communicatino tasks
+        # Insert ZeRO/DP communication tasks.
         if dp_degree > 1:
             logger.debug(
                 f"ZeRO bucket filter: {sorted(piper_metadata.zero_bucket_keys)}"
@@ -342,8 +342,12 @@ def piper(gm, example_inputs, **kwargs):
                 per_rank_dags,
                 piper_metadata.zero_stage,
                 zero_bucket_keys=piper_metadata.zero_bucket_keys,
+                gradient_accumulation=piper_metadata.gradient_accumulation,
+            )
+            logger.debug(
+                f"Inserted ZeRO-{piper_metadata.zero_stage} collective nodes "
+                f"(gradient_accumulation={piper_metadata.gradient_accumulation})"
             )
-            logger.debug(f"Inserted ZeRO-{piper_metadata.zero_stage} collective nodes")
 
             overlappable = find_overlappable_tasks(piper_metadata.schedule)
             for t1, t2 in overlappable:
@@ -355,8 +359,8 @@ def piper(gm, example_inputs, **kwargs):
         # Assign time steps
         for rank_dag in per_rank_dags:
             assign_time_steps(rank_dag)
-            # if dp_degree > 1:
-            #     overlap_zero_ops(rank_dag)
+            if piper_metadata.overlap_zero_ops:
+                overlap_zero_ops(rank_dag)
         logger.debug("Assigned time steps")
 
         piper_metadata.per_rank_dags = per_rank_dags
@@ -461,36 +465,6 @@ def _log_step_stats(step_time: float, log_memory: bool, actors: dict, results: l
     """Log throughput, MFU, and optionally per-rank peak GPU memory."""
     stats = [f"step_time={step_time:.3f}s"]
 
-    if results:
-        schedule_reduced_payload_bytes = 0
-        schedule_all_reduce_payload_bytes = 0
-        schedule_reduce_scatter_payload_bytes = 0
-        runtime_reduced_payload_bytes = 0
-        runtime_all_reduce_payload_bytes = 0
-        runtime_reduce_scatter_payload_bytes = 0
-        runtime_all_reduce_ops = 0
-        runtime_reduce_scatter_ops = 0
-        for result in results:
-            if not isinstance(result, dict):
-                continue
-            schedule_reduced_payload_bytes += int(result.get("schedule_reduced_payload_bytes", 0))
-            schedule_all_reduce_payload_bytes += int(result.get("schedule_all_reduce_payload_bytes", 0))
-            schedule_reduce_scatter_payload_bytes += int(result.get("schedule_reduce_scatter_payload_bytes", 0))
-            runtime_reduced_payload_bytes += int(result.get("runtime_reduced_payload_bytes", 0))
-            runtime_all_reduce_payload_bytes += int(result.get("runtime_all_reduce_payload_bytes", 0))
-            runtime_reduce_scatter_payload_bytes += int(result.get("runtime_reduce_scatter_payload_bytes", 0))
-            runtime_all_reduce_ops += int(result.get("runtime_all_reduce_ops", 0))
-            runtime_reduce_scatter_ops += int(result.get("runtime_reduce_scatter_ops", 0))
-        stats.append(f"schedule_reduced_payload_total_gb={schedule_reduced_payload_bytes / (1024 ** 3):.6f}")
-        stats.append(f"schedule_all_reduce_payload_gb={schedule_all_reduce_payload_bytes / (1024 ** 3):.6f}")
-        stats.append(f"schedule_reduce_scatter_payload_gb={schedule_reduce_scatter_payload_bytes / (1024 ** 3):.6f}")
-        stats.append(f"runtime_reduced_payload_total_gb={runtime_reduced_payload_bytes / (1024 ** 3):.6f}")
-        stats.append(f"runtime_all_reduce_payload_gb={runtime_all_reduce_payload_bytes / (1024 ** 3):.6f}")
-        stats.append(f"runtime_reduce_scatter_payload_gb={runtime_reduce_scatter_payload_bytes / (1024 ** 3):.6f}")
-        stats.append(f"runtime_all_reduce_ops={runtime_all_reduce_ops}")
-        stats.append(f"runtime_reduce_scatter_ops={runtime_reduce_scatter_ops}")
-        stats.append(f"runtime_payload_matches_schedule={runtime_reduced_payload_bytes == schedule_reduced_payload_bytes}")
-
     tokens = piper_metadata.tokens_per_step
     if tokens is not None:
         stats.append(f"throughput={tokens / step_time:.1f} tok/s")
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 8839944..5fb23c8 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -52,6 +52,8 @@ def _create_actors(
     no_nvtx: bool = False,
     pg=None,
     temp_dir: str = None,
+    use_inductor: bool = False,
+    ar_a2a_same_stream: bool = False,
 ):
     dp_rank = int(os.environ["PIPER_DP_RANK"])
     world_size = int(os.environ["PIPER_WORLD_SIZE"])
@@ -96,6 +98,8 @@ def _create_actors(
             stage_to_device=stage_to_device,
             zero_stage=zero_stage,
             no_nvtx=no_nvtx,
+            use_inductor=use_inductor,
+            ar_a2a_same_stream=ar_a2a_same_stream,
         )
         piper_metadata.actors[pp_rank] = actor
 
@@ -121,6 +125,8 @@ def __init__(
         stage_to_device=None,
         zero_stage: int = 0,
         no_nvtx: bool = False,
+        use_inductor: bool = False,
+        ar_a2a_same_stream: bool = False,
     ):
         self.logger = create_logger("piper_actor", LOG_LEVEL)
 
@@ -134,6 +140,8 @@ def __init__(
         self.naive_gradient_sync = naive_gradient_sync
         self.zero_stage = int(zero_stage)
         self.no_nvtx = no_nvtx
+        self.use_inductor = bool(use_inductor)
+        self.ar_a2a_same_stream = bool(ar_a2a_same_stream)
 
         self.dp_rank = dp_rank
         self.dp_degree = dp_degree
@@ -162,7 +170,10 @@ def __init__(
 
         self.comp_stream = torch.cuda.Stream()
         self.ar_stream = torch.cuda.Stream()
-        self.a2a_stream = self.ar_stream
+        if self.ar_a2a_same_stream:
+            self.a2a_stream = self.ar_stream
+        else:
+            self.a2a_stream = torch.cuda.Stream()
         self.p2p_send_stream = torch.cuda.Stream()
         self.p2p_recv_stream = torch.cuda.Stream()
 
@@ -179,15 +190,6 @@ def __init__(
         self._pending_timing_events: list = []  # (label, start_event, stop_event)
         self.trace_data = defaultdict(list)
         self.memory_tracing_enabled = False
-        self._schedule_reduced_payload_bytes = 0
-        self._schedule_all_reduce_payload_bytes = 0
-        self._schedule_reduce_scatter_payload_bytes = 0
-        self._runtime_reduced_payload_bytes = 0
-        self._runtime_all_reduce_payload_bytes = 0
-        self._runtime_reduce_scatter_payload_bytes = 0
-        self._runtime_all_reduce_ops = 0
-        self._runtime_reduce_scatter_ops = 0
-
         # DAG execution state
         self.dag = None
 
@@ -675,13 +677,33 @@ def _load_stage(
             ubid: int = bd["unique_bucket_id"]
             ac_num_subgraphs = int(bd.get("ac_num_subgraphs", 1))
             ac_requested_subgraphs = int(bd.get("ac_requested_subgraphs", ac_num_subgraphs))
-            if "gm_data_list" in bd:
-                gms = [
-                    self._replace_meta_constants(_deserialize_graphmodule(gm_data), self.device)
-                    for gm_data in bd["gm_data_list"]
-                ]
-            else:
-                gms = [self._replace_meta_constants(_deserialize_graphmodule(bd["gm_data"]), self.device)]
+            gms = [_deserialize_graphmodule(gm_data) for gm_data in bd["gm_data_list"]] if "gm_data_list" in bd else [_deserialize_graphmodule(bd["gm_data"])]
+            gms = [self._replace_meta_constants(gm, self.device) for gm in gms]
+            if self.use_inductor:
+                compiled_gms = []
+                for subgraph_idx, gm in enumerate(gms):
+                    try:
+                        compiled_gm = torch.compile(gm)
+                        compiled_gms.append(compiled_gm)
+                        self.logger.debug(
+                            f"[load_stage_compile] rank={self.global_rank} stage={stage_id} "
+                            f"ubid={ubid} subgraph={subgraph_idx} compiled=True"
+                        )
+                    except Exception as exc:
+                        self.logger.warning(
+                            f"[load_stage_compile] rank={self.global_rank} stage={stage_id} "
+                            f"ubid={ubid} subgraph={subgraph_idx} compiled=False error={exc}"
+                        )
+                        compiled_gms.append(gm)
+                gms = compiled_gms
+            # if "gm_data_list" in bd:
+            #     gms = [
+            #         self._replace_meta_constants(_deserialize_graphmodule(gm_data), self.device)
+            #         for gm_data in bd["gm_data_list"]
+            #     ]
+            # else:
+            #     gms = [self._replace_meta_constants(_deserialize_graphmodule(bd["gm_data"]), self.device)]
+            
             if b_idx == 0:
                 first_gm = gms[0]
 
@@ -1042,42 +1064,6 @@ def _param_has_effective_grad(self, param: torch.Tensor | None) -> bool:
         """Return True when a param carries a materialized gradient."""
         return param is not None and param.grad is not None
 
-    def _record_runtime_reduced_payload(self, op_name: str, num_bytes: int) -> None:
-        if num_bytes <= 0:
-            return
-        self._runtime_reduced_payload_bytes += num_bytes
-        if op_name == "all_reduce":
-            self._runtime_all_reduce_payload_bytes += num_bytes
-            self._runtime_all_reduce_ops += 1
-        elif op_name == "reduce_scatter":
-            self._runtime_reduce_scatter_payload_bytes += num_bytes
-            self._runtime_reduce_scatter_ops += 1
-
-    def _bucket_payload_bytes(self, ubid: int | None, op_name: str) -> int:
-        if ubid is None:
-            return 0
-
-        flat_grads = self.bucket_flat_grads.get(ubid)
-        if flat_grads is not None:
-            return flat_grads.numel() * flat_grads.element_size()
-
-        if op_name == "reduce_scatter":
-            shard_param = self.bucket_shard_params.get(ubid)
-            if shard_param is not None:
-                return shard_param.numel() * shard_param.element_size() * self.dp_degree
-
-        specs = self.bucket_param_view_specs.get(ubid, [])
-        if specs:
-            return sum(param.numel() * param.element_size() for param, *_ in specs)
-
-        args = self.bucket_fwd_args.get(ubid, [])
-        trainable_idxs = self.bucket_trainable_param_idxs.get(ubid, [])
-        return sum(
-            args[idx].numel() * args[idx].element_size()
-            for idx in trainable_idxs
-            if idx < len(args) and args[idx] is not None
-        )
-
     def _sync_payload_ubid(self, node: Task) -> int | None:
         if node.unique_bucket_id is not None:
             return node.unique_bucket_id
@@ -1085,6 +1071,13 @@ def _sync_payload_ubid(self, node: Task) -> int | None:
             return node.data_preds[0].unique_bucket_id
         return None
 
+    def _sync_payload_ubids(self, node: Task) -> list[int]:
+        sync_ubids = node.custom_metadata.get("sync_ubids")
+        if sync_ubids:
+            return list(sync_ubids)
+        ubid = self._sync_payload_ubid(node)
+        return [ubid] if ubid is not None else []
+
     def _collect_saved_tensors(
         self,
         obj: Any,
@@ -1316,15 +1309,6 @@ def run_dag(self, loss_fn=None, profiling: bool = False):
             self.ag_events = {}
         self.bwd_events = {}
         self._clear_param_grads()
-        self._schedule_reduced_payload_bytes = 0
-        self._schedule_all_reduce_payload_bytes = 0
-        self._schedule_reduce_scatter_payload_bytes = 0
-        self._runtime_reduced_payload_bytes = 0
-        self._runtime_all_reduce_payload_bytes = 0
-        self._runtime_reduce_scatter_payload_bytes = 0
-        self._runtime_all_reduce_ops = 0
-        self._runtime_reduce_scatter_ops = 0
-
         if self.zero_stage >= 2:
             with torch.cuda.stream(self.comp_stream):
                 for buf in self.bucket_rs_grads.values():
@@ -1338,19 +1322,6 @@ def run_dag(self, loss_fn=None, profiling: bool = False):
         self._prof_records: list = []
 
         sorted_nodes = sorted(dag.nodes, key=runtime_sort_key)
-        self._schedule_all_reduce_payload_bytes = sum(
-            self._bucket_payload_bytes(self._sync_payload_ubid(node), "all_reduce")
-            for node in sorted_nodes
-            if node.chunk.type == TaskType.ALL_REDUCE
-        )
-        self._schedule_reduce_scatter_payload_bytes = sum(
-            self._bucket_payload_bytes(self._sync_payload_ubid(node), "reduce_scatter")
-            for node in sorted_nodes
-            if node.chunk.type == TaskType.REDUCE_SCATTER
-        )
-        self._schedule_reduced_payload_bytes = (
-            self._schedule_all_reduce_payload_bytes + self._schedule_reduce_scatter_payload_bytes
-        )
         self._init_task_buffer_refcounts(dag)
         ts_to_comp_event: dict[int, torch.cuda.Event] = {}
 
@@ -1475,42 +1446,31 @@ def _wait_for_all_gather(compute_node: Task) -> None:
 
                 case TaskType.ALL_REDUCE:
                     bwd_node = node.data_preds[0]
-                    ar_ubid = bwd_node.unique_bucket_id
-                    scheduled_bytes = self._bucket_payload_bytes(ar_ubid, "all_reduce")
-                    self._nvtx_push(f"all_reduce_ubid{bwd_node.unique_bucket_id}")
+                    ar_ubids = self._sync_payload_ubids(node)
+                    ar_ubid = ar_ubids[0] if ar_ubids else bwd_node.unique_bucket_id
+                    self._nvtx_push(f"all_reduce_ubid{ar_ubid}")
                     self.ar_stream.wait_event(comp_events[bwd_node.uid])
                     self._start_timing(self.ar_stream, "all_reduce")
-                    runtime_bytes = self._exec_all_reduce_grads(ar_ubid)
+                    runtime_bytes = sum(self._exec_all_reduce_grads(ubid) for ubid in ar_ubids)
                     self._stop_timing(self.ar_stream, "all_reduce")
                     if runtime_bytes > 0:
                         ar_evt = torch.cuda.Event()
                         ar_evt.record(self.ar_stream)
                         self.ar_events[node.uid] = ar_evt
-                    elif scheduled_bytes > 0:
-                        self.logger.warning(
-                            f"[all_reduce_runtime_miss] rank={self.global_rank} "
-                            f"ubid={ar_ubid} scheduled_bytes={scheduled_bytes}"
-                        )
                     self._nvtx_pop()
                     self._release_task_buffer_uid(bwd_node.uid)
 
                 case TaskType.REDUCE_SCATTER:
                     bwd_node = node.data_preds[0]
                     rs_ubid = node.unique_bucket_id if node.unique_bucket_id is not None else bwd_node.unique_bucket_id
-                    scheduled_bytes = self._bucket_payload_bytes(rs_ubid, "reduce_scatter")
                     self._nvtx_push(f"reduce_scatter_ubid{rs_ubid}")
                     self.ar_stream.wait_event(comp_events[bwd_node.uid])
                     self._start_timing(self.ar_stream, "reduce_scatter")
-                    runtime_bytes = self._exec_reduce_scatter(rs_ubid)
+                    self._exec_reduce_scatter(rs_ubid)
                     self._stop_timing(self.ar_stream, "reduce_scatter")
                     rs_evt = torch.cuda.Event()
                     rs_evt.record(self.ar_stream)
                     self.rs_events[node.uid] = rs_evt
-                    if runtime_bytes <= 0 and scheduled_bytes > 0:
-                        self.logger.warning(
-                            f"[reduce_scatter_runtime_miss] rank={self.global_rank} "
-                            f"ubid={rs_ubid} scheduled_bytes={scheduled_bytes}"
-                        )
                     self._nvtx_pop()
                     self._release_task_buffer_uid(bwd_node.uid)
 
@@ -1793,7 +1753,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     }
                     pg_summary = self._summarize_param_groups(param_groups)
                     outstanding = self._summarize_outstanding_bwdi()
-                    self.logger.info(
+                    self.logger.log(VERBOSE,
                         f"[bwd_i_retain] rank={self.global_rank} ubid={ubid} mb={mb_idx}: "
                         f"groups={pg_summary['groups']} "
                         f"params={pg_summary['params']} "
@@ -1833,7 +1793,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     param_groups = self.task_buffer[bwdi_node.uid]["param_groups"]
                     pg_summary = self._summarize_param_groups(param_groups)
                     outstanding = self._summarize_outstanding_bwdi()
-                    self.logger.info(
+                    self.logger.log(VERBOSE,
                         f"[bwd_w_begin] rank={self.global_rank} ubid={ubid} mb={mb_idx}: "
                         f"groups={pg_summary['groups']} "
                         f"params={pg_summary['params']} "
@@ -2249,7 +2209,6 @@ def _exec_all_reduce_grads(self, ubid: int) -> int:
             total_bytes = flat_grads.numel() * flat_grads.element_size()
             with torch.cuda.stream(self.ar_stream):
                 dist.all_reduce(flat_grads, group=self.dp_group)
-            self._record_runtime_reduced_payload("all_reduce", total_bytes)
             return total_bytes
 
         args = self.bucket_fwd_args.get(ubid, [])
@@ -2298,7 +2257,6 @@ def _exec_all_reduce_grads(self, ubid: int) -> int:
         with torch.cuda.stream(self.ar_stream):
             for _, grad in grad_tensors:
                 dist.all_reduce(grad, group=self.dp_group)
-        self._record_runtime_reduced_payload("all_reduce", total_bytes)
         return total_bytes
 
     def _alloc_full_params(self, ubid: int | None) -> None:
@@ -2396,8 +2354,6 @@ def _exec_reduce_scatter(self, ubid: int) -> int:
             tmp = torch.empty_like(rs_out)
             dist.reduce_scatter_tensor(tmp, flat_grads, group=self.dp_group)
             rs_out.add_(tmp)
-        self._record_runtime_reduced_payload("reduce_scatter", total_bytes)
-        return total_bytes
 
     def _exec_all_gather(
         self,
@@ -2581,17 +2537,6 @@ def _update(self, *deps):
         # Process deferred profiling records now that all GPU work is complete.
         # elapsed_time() is safe to call after synchronize().
         if self._prof_records:
-            self.logger.info(
-                f"[profiling] rank {self.global_rank} "
-                f"schedule_reduced_payload_total_gb={self._schedule_reduced_payload_bytes / (1024 ** 3):.6f} "
-                f"schedule_all_reduce_payload_gb={self._schedule_all_reduce_payload_bytes / (1024 ** 3):.6f} "
-                f"schedule_reduce_scatter_payload_gb={self._schedule_reduce_scatter_payload_bytes / (1024 ** 3):.6f} "
-                f"runtime_reduced_payload_total_gb={self._runtime_reduced_payload_bytes / (1024 ** 3):.6f} "
-                f"runtime_all_reduce_payload_gb={self._runtime_all_reduce_payload_bytes / (1024 ** 3):.6f} "
-                f"runtime_reduce_scatter_payload_gb={self._runtime_reduce_scatter_payload_bytes / (1024 ** 3):.6f} "
-                f"runtime_all_reduce_ops={self._runtime_all_reduce_ops} "
-                f"runtime_reduce_scatter_ops={self._runtime_reduce_scatter_ops}"
-            )
             for node, mb_idx, ubid, start_evt, end_evt, mem_before in self._prof_records:
                 t_ms = start_evt.elapsed_time(end_evt)
                 node.profiling_measurements.append(t_ms)
@@ -2604,12 +2549,4 @@ def _update(self, *deps):
 
         return {
             "losses": losses,
-            "schedule_reduced_payload_bytes": self._schedule_reduced_payload_bytes,
-            "schedule_all_reduce_payload_bytes": self._schedule_all_reduce_payload_bytes,
-            "schedule_reduce_scatter_payload_bytes": self._schedule_reduce_scatter_payload_bytes,
-            "runtime_reduced_payload_bytes": self._runtime_reduced_payload_bytes,
-            "runtime_all_reduce_payload_bytes": self._runtime_all_reduce_payload_bytes,
-            "runtime_reduce_scatter_payload_bytes": self._runtime_reduce_scatter_payload_bytes,
-            "runtime_all_reduce_ops": self._runtime_all_reduce_ops,
-            "runtime_reduce_scatter_ops": self._runtime_reduce_scatter_ops,
         }
diff --git a/src/piper_compile.py b/src/piper_compile.py
index ea33750..7056d09 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -112,6 +112,10 @@ def piper_setup(
     bucketing=False,
     bucket_size: int = 25 * 1024 * 1024,
     zero_stage: int = 0,
+    gradient_accumulation: bool = True,
+    use_inductor: bool = False,
+    ar_a2a_same_stream: bool = False,
+    overlap_zero_ops: bool = False,
     schedule_name: str = "",
     visualize_dag_render: bool = True,
     no_nvtx: bool = False,
@@ -138,6 +142,14 @@ def piper_setup(
             same-rank A2A operations to avoid NCCL interference.
         zero_stage: ZeRO stage: 0 disabled, 1 optimizer-state sharding,
             2 gradient sharding, 3 parameter and gradient sharding.
+        gradient_accumulation: When true, delay bucket gradient reductions
+            until the last occurrence of each bucket.
+        use_inductor: When true, actors torch.compile stage GraphModules
+            during _load_stage with the default inductor backend.
+        ar_a2a_same_stream: When true, actors run A2A ops on the same CUDA
+            stream as AR ops.
+        overlap_zero_ops: When true, run overlap_zero_ops on per-rank DAGs
+            after ZeRO collective insertion.
     """
 
     # Clear Dynamo's global compilation cache so that a previous piper_setup
@@ -152,6 +164,10 @@ def piper_setup(
     piper_metadata.activation_num_checkpoints = max(1, int(num_checkpoints))
     piper_metadata.bucket_size = bucket_size
     piper_metadata.zero_stage = int(zero_stage)
+    piper_metadata.gradient_accumulation = bool(gradient_accumulation)
+    piper_metadata.use_inductor = bool(use_inductor)
+    piper_metadata.ar_a2a_same_stream = bool(ar_a2a_same_stream)
+    piper_metadata.overlap_zero_ops = bool(overlap_zero_ops)
     piper_metadata.schedule_name = schedule_name
     piper_metadata.visualize_dag_render = visualize_dag_render
     piper_metadata.a2a_ar_no_overlap = a2a_ar_no_overlap
@@ -194,6 +210,7 @@ def piper_setup(
         num_devices, optim_fn, num_mbs, num_stages,
         naive_gradient_sync, profile=nsight, stage_to_device=stage_to_device,
         zero_stage=zero_stage, no_nvtx=no_nvtx, pg=pg, temp_dir=temp_dir,
+        use_inductor=use_inductor, ar_a2a_same_stream=ar_a2a_same_stream,
     )
 
     if dp_rank == 0:
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index cf51ca0..73ec459 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -1928,17 +1928,13 @@ def _insert_zero_grad_ops(
     sync_type: TaskType,
     use_grad_lifetime: bool = False,
     zero_bucket_keys: set | None = None,
+    gradient_accumulation: bool = True,
 ) -> None:
-    """Per-compute-node gradient sync.
+    """Insert gradient-sync nodes for ZeRO-0/1/2.
 
-    Every BWD / BWD_W node gets its own sync immediately after it finishes.
-    There is no gradient accumulation.
-
-    When ``use_grad_lifetime=True`` (ZeRO-2/3 where gradients are sharded),
-    a G+ → compute → sync → G- lifecycle is inserted so that the full
-    gradient buffer is explicitly allocated and freed around each sync.
-    When ``use_grad_lifetime=False`` (ZeRO-0/1 where each rank keeps a full
-    copy of gradients), only compute → sync is wired.
+    With ``gradient_accumulation=True``, a bucket's sync is attached only to the
+    last BWD/BWD_W occurrence of that bucket. For ZeRO-2, the full-grad
+    lifetime widens to first occurrence → last occurrence.
     """
     _remove_ar_nodes(rank_dag)
 
@@ -1946,55 +1942,77 @@ def _insert_zero_grad_ops(
     new_nodes: list[Task] = []
 
     for ubid, bucket_nodes in _bucket_compute_tasks(rank_dag, zero_bucket_keys).items():
-        for compute_node in bucket_nodes:
-            if compute_node.chunk.type not in grad_sync_types:
-                continue
+        grad_nodes = [node for node in bucket_nodes if node.chunk.type in grad_sync_types]
+        if not grad_nodes:
+            continue
+        first_compute = min(grad_nodes, key=lambda node: (node.time_step, node.uid))
+        last_compute = max(grad_nodes, key=lambda node: (node.time_step, node.uid))
+
+        if gradient_accumulation:
+            sync_sources = [last_compute]
+        else:
+            sync_sources = grad_nodes
+
+        for compute_node in sync_sources:
             sync = _zero_task(compute_node, compute_node.bucket_id, sync_type, ubid=ubid)
             _add_data_edge(compute_node, sync)
             new_nodes.append(sync)
-            if use_grad_lifetime:
-                alloc = _zero_task(compute_node, compute_node.bucket_id, TaskType.ALLOC_FULL_GRADS, ubid=ubid)
-                free = _zero_task(compute_node, compute_node.bucket_id, TaskType.FREE_FULL_GRADS, ubid=ubid)
-                _add_data_edge(alloc, compute_node)
-                _add_data_edge(sync, free)
+
+        if use_grad_lifetime:
+            if gradient_accumulation:
+                alloc = _zero_task(first_compute, first_compute.bucket_id, TaskType.ALLOC_FULL_GRADS, ubid=ubid)
+                free = _zero_task(last_compute, last_compute.bucket_id, TaskType.FREE_FULL_GRADS, ubid=ubid)
+                _add_data_edge(alloc, first_compute)
+                _add_data_edge(new_nodes[-1], free)
                 new_nodes.extend([alloc, free])
+            else:
+                for compute_node in grad_nodes:
+                    sync = next(
+                        node for node in new_nodes
+                        if node.chunk.type == sync_type and node.unique_bucket_id == ubid and compute_node in node.data_preds
+                    )
+                    alloc = _zero_task(compute_node, compute_node.bucket_id, TaskType.ALLOC_FULL_GRADS, ubid=ubid)
+                    free = _zero_task(compute_node, compute_node.bucket_id, TaskType.FREE_FULL_GRADS, ubid=ubid)
+                    _add_data_edge(alloc, compute_node)
+                    _add_data_edge(sync, free)
+                    new_nodes.extend([alloc, free])
 
     rank_dag.nodes.extend(new_nodes)
 
 
-def _insert_zero0_ops(rank_dag: TaskDAG) -> None:
-    """ZeRO-0 (plain DP): per-compute-node all-reduce, same policy as ZeRO-1."""
-    _insert_zero_grad_ops(rank_dag, sync_type=TaskType.ALL_REDUCE)
-
-
 def _insert_zero1_ops(
     rank_dag: TaskDAG,
     zero_bucket_keys: set | None = None,
+    gradient_accumulation: bool = True,
 ) -> None:
-    """ZeRO-1: per-compute-node all-reduce."""
+    """ZeRO-1: all-reduce after each bucket or after its last occurrence."""
     _insert_zero_grad_ops(
         rank_dag,
         sync_type=TaskType.ALL_REDUCE,
         zero_bucket_keys=zero_bucket_keys,
+        gradient_accumulation=gradient_accumulation,
     )
 
 
 def _insert_zero2_ops(
     rank_dag: TaskDAG,
     zero_bucket_keys: set | None = None,
+    gradient_accumulation: bool = True,
 ) -> None:
-    """ZeRO-2: per-compute-node reduce-scatter with full-gradient lifecycle."""
+    """ZeRO-2: reduce-scatter with widened grad lifetime under accumulation."""
     _insert_zero_grad_ops(
         rank_dag,
         sync_type=TaskType.REDUCE_SCATTER,
         use_grad_lifetime=True,
         zero_bucket_keys=zero_bucket_keys,
+        gradient_accumulation=gradient_accumulation,
     )
 
 
 def _insert_zero3_ops(
     rank_dag: TaskDAG,
     zero_bucket_keys: set | None = None,
+    gradient_accumulation: bool = True,
 ) -> None:
     """ZeRO-3: param gather + per-grad-node reduce-scatter lifetimes.
 
@@ -2027,17 +2045,37 @@ def _insert_zero3_ops(
             elif t in grad_types:
                 alloc_p = _zero_task(compute_node, bid, TaskType.ALLOC_FULL_PARAMS, ubid=ubid)
                 ag      = _zero_task(compute_node, bid, TaskType.ALL_GATHER,        ubid=ubid)
-                alloc_g = _zero_task(compute_node, bid, TaskType.ALLOC_FULL_GRADS,  ubid=ubid)
                 free_p  = _zero_task(compute_node, bid, TaskType.FREE_FULL_PARAMS,  ubid=ubid)
-                rs      = _zero_task(compute_node, bid, TaskType.REDUCE_SCATTER,    ubid=ubid)
-                free_g  = _zero_task(compute_node, bid, TaskType.FREE_FULL_GRADS,   ubid=ubid)
                 _add_data_edge(alloc_p, ag)
                 _add_data_edge(ag, compute_node)
-                _add_data_edge(alloc_g, compute_node)
                 _add_data_edge(compute_node, free_p)
+                new_nodes.extend([alloc_p, ag, free_p])
+
+    # Insert grad alloc/reduce/free for grad-producing ZeRO-3 nodes.
+    for ubid, bucket_nodes in _bucket_compute_tasks(rank_dag, zero_bucket_keys).items():
+        grad_nodes = [node for node in bucket_nodes if node.chunk.type in grad_types]
+        if not grad_nodes:
+            continue
+        first_compute = min(grad_nodes, key=lambda node: (node.time_step, node.uid))
+        last_compute = max(grad_nodes, key=lambda node: (node.time_step, node.uid))
+
+        if gradient_accumulation:
+            alloc_g = _zero_task(first_compute, first_compute.bucket_id, TaskType.ALLOC_FULL_GRADS, ubid=ubid)
+            rs = _zero_task(last_compute, last_compute.bucket_id, TaskType.REDUCE_SCATTER, ubid=ubid)
+            free_g = _zero_task(last_compute, last_compute.bucket_id, TaskType.FREE_FULL_GRADS, ubid=ubid)
+            _add_data_edge(alloc_g, first_compute)
+            _add_data_edge(last_compute, rs)
+            _add_data_edge(rs, free_g)
+            new_nodes.extend([alloc_g, rs, free_g])
+        else:
+            for compute_node in grad_nodes:
+                alloc_g = _zero_task(compute_node, compute_node.bucket_id, TaskType.ALLOC_FULL_GRADS, ubid=ubid)
+                rs = _zero_task(compute_node, compute_node.bucket_id, TaskType.REDUCE_SCATTER, ubid=ubid)
+                free_g = _zero_task(compute_node, compute_node.bucket_id, TaskType.FREE_FULL_GRADS, ubid=ubid)
+                _add_data_edge(alloc_g, compute_node)
                 _add_data_edge(compute_node, rs)
                 _add_data_edge(rs, free_g)
-                new_nodes.extend([alloc_p, ag, alloc_g, free_p, rs, free_g])
+                new_nodes.extend([alloc_g, rs, free_g])
 
     rank_dag.nodes.extend(new_nodes)
 
@@ -2271,16 +2309,33 @@ def insert_zero_ops(
     per_rank_dags: list,
     zero_stage: int,
     zero_bucket_keys: set | None = None,
+    gradient_accumulation: bool = True,
 ) -> list:
     for rank_dag in per_rank_dags:
         if zero_stage == 0:
-            _insert_zero0_ops(rank_dag)
+            _insert_zero1_ops(
+                rank_dag,
+                zero_bucket_keys=zero_bucket_keys,
+                gradient_accumulation=gradient_accumulation,
+            )
         elif zero_stage == 1:
-            _insert_zero1_ops(rank_dag, zero_bucket_keys=zero_bucket_keys)
+            _insert_zero1_ops(
+                rank_dag,
+                zero_bucket_keys=zero_bucket_keys,
+                gradient_accumulation=gradient_accumulation,
+            )
         elif zero_stage == 2:
-            _insert_zero2_ops(rank_dag, zero_bucket_keys=zero_bucket_keys)
+            _insert_zero2_ops(
+                rank_dag,
+                zero_bucket_keys=zero_bucket_keys,
+                gradient_accumulation=gradient_accumulation,
+            )
         elif zero_stage == 3:
-            _insert_zero3_ops(rank_dag, zero_bucket_keys=zero_bucket_keys)
+            _insert_zero3_ops(
+                rank_dag,
+                zero_bucket_keys=zero_bucket_keys,
+                gradient_accumulation=gradient_accumulation,
+            )
         else:
             raise ValueError(f"Unsupported ZeRO stage: {zero_stage}")
         _wire_sync_to_upd(rank_dag)
diff --git a/src/piper_utils.py b/src/piper_utils.py
index add6314..8bcb497 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -11,7 +11,7 @@
 from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
 from typing import Any, Optional
 
-LOG_LEVEL = "DEBUG"
+LOG_LEVEL = "INFO"
 
 """ 
 Print the backward graph of a tensor
@@ -349,6 +349,10 @@ class PiperMetadata:
     full_dag_no_overlap = None  # Deep copy of the full DAG (pre-P2P-split, no overlap_a2a_tasks); used for profiling and critical-path analysis
     per_rank_dags = None  # Per-rank TaskDAGs built by the piper backend
     zero_stage: int = 0  # ZeRO stage: 0=disabled, 1=optim states, 2=+gradients, 3=+parameters
+    gradient_accumulation: bool = True  # Delay gradient sync to the last occurrence of each bucket
+    use_inductor: bool = False  # Whether actors should torch.compile stage GraphModules in _load_stage
+    ar_a2a_same_stream: bool = False  # Whether A2A ops should share the AR stream on actors
+    overlap_zero_ops: bool = False  # Whether to apply overlap_zero_ops to per-rank DAGs
     schedule_name: str = ""  # Human-readable schedule name for debug artifacts
     visualize_dag_render: bool = True  # If False, save Graphviz source only
     stage_bucket_counts: dict = {}   # stage_id -> number of buckets (set by piper backend)
diff --git a/test/models/qwen3.py b/test/models/qwen3.py
index 1460df1..bdabaff 100644
--- a/test/models/qwen3.py
+++ b/test/models/qwen3.py
@@ -1,5 +1,6 @@
 from typing import Optional
 import torch
+from torch.nn.attention import sdpa_kernel, SDPBackend
 
 from torchtitan.models.qwen3 import Qwen3Model, Qwen3ModelArgs
 from torchtitan.models.qwen3.model.model import TransformerBlock
@@ -363,17 +364,18 @@ def forward(
         num_layers = len(self.layers)
         layers_per_stage = num_layers // self.num_stages
 
-        for stage_id in range(self.num_stages):
-            with torch.fx.traceback.annotate({"stage": stage_id}):
-                if stage_id == 0:
-                    h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
+        with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
+            for stage_id in range(self.num_stages):
+                with torch.fx.traceback.annotate({"stage": stage_id}):
+                    if stage_id == 0:
+                        h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
 
-                for i in range(stage_id * layers_per_stage, (stage_id + 1) * layers_per_stage):
-                    layer = self.layers[str(i)]
-                    h = layer(h, self.rope_cache, attention_masks, positions)
+                    for i in range(stage_id * layers_per_stage, (stage_id + 1) * layers_per_stage):
+                        layer = self.layers[str(i)]
+                        h = layer(h, self.rope_cache, attention_masks, positions)
 
-                if stage_id == self.num_stages - 1:
-                    h = self.norm(h) if self.norm is not None else h
-                    output = self.output(h) if self.output is not None else h
+                    if stage_id == self.num_stages - 1:
+                        h = self.norm(h) if self.norm is not None else h
+                        output = self.output(h) if self.output is not None else h
 
         return output
diff --git a/test/test_qwen.py b/test/test_qwen.py
index 16774e9..d59b94d 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -50,16 +50,21 @@
 def _metrics_output_path(args):
     return os.path.join(
         args.output_dir,
-        f"qwen{args.model}-pp{args.pp}-dp{args.dp}-{args.schedule}",
+        (
+            f"qwen{args.model}-pp{args.pp}-dp{args.dp}-zero{args.zero_stage}-"
+            f"bs{args.batch_size}-sl{args.seq_len}-ga{int(args.gradient_accumulation)}-"
+            f"aras{int(args.ar_a2a_same_stream)}-ozo{int(args.overlap_zero_ops)}-"
+            f"{args.schedule}"
+        ),
     )
 
 
-def _build_benchmark_metrics(args, dp_rank, iter_times):
+def _build_benchmark_metrics(args, dp_rank, iter_times, peak_memory_stats=None):
     mean_iter_time = float(np.mean(iter_times))
     std_iter_time = float(np.std(iter_times))
     throughput = float((args.batch_size * args.mbs * args.seq_len) / mean_iter_time)
 
-    return {
+    metrics = {
         "dp_rank": dp_rank,
         "model": args.model,
         "schedule": args.schedule,
@@ -68,23 +73,49 @@ def _build_benchmark_metrics(args, dp_rank, iter_times):
         "batch_size": args.batch_size,
         "mbs": args.mbs,
         "seq_len": args.seq_len,
+        "gradient_accumulation": bool(args.gradient_accumulation),
+        "ar_a2a_same_stream": bool(args.ar_a2a_same_stream),
+        "overlap_zero_ops": bool(args.overlap_zero_ops),
         "samples": len(iter_times),
         "iter_time_mean_s": mean_iter_time,
         "iter_time_std_s": std_iter_time,
         "throughput_tokens_per_s": throughput,
         "iter_times_s": [float(iter_time) for iter_time in iter_times],
     }
+    if peak_memory_stats is not None:
+        peak_memory_by_rank = {
+            str(rank): {
+                "peak_memory_bytes": int(max_alloc_bytes),
+                "peak_memory_gb": float(max_alloc_bytes / (1024 ** 3)),
+            }
+            for rank, max_alloc_bytes in sorted(peak_memory_stats)
+        }
+        metrics["peak_memory_by_rank"] = peak_memory_by_rank
+    return metrics
 
 
 def _format_benchmark_log_lines(metrics):
     rank = metrics["dp_rank"]
+    peak_memory_summary = "/".join(
+        f"{mem_stats['peak_memory_gb']:.3f}"
+        for mem_rank, mem_stats in metrics.get("peak_memory_by_rank", {}).items()
+    )
     lines = [
+        (
+            f"{metrics['iter_time_mean_s']:.5f},"
+            f"{metrics['iter_time_std_s']:.5f},"
+            f"{peak_memory_summary}"
+        ),
         (
             f"rank {rank} iter time= {metrics['iter_time_mean_s']:.5f} +/- "
             f"{metrics['iter_time_std_s']:.5f} s ({metrics['samples']} samples)"
         ),
         f"rank {rank} throughput= {metrics['throughput_tokens_per_s']:.3f} tokens/s",
     ]
+    for mem_rank, mem_stats in metrics.get("peak_memory_by_rank", {}).items():
+        lines.append(
+            f"rank {mem_rank} peak memory= {mem_stats['peak_memory_gb']:.3f} GB"
+        )
     for trace in metrics.get("trace_times", []):
         lines.append(
             f"rank {trace['rank']} {trace['key']} time= {trace['mean_ms']:.3f} +/- "
@@ -173,6 +204,7 @@ def main(args, pg):
         a2a_ar_no_overlap=True,
         bucket_size=int(args.bucket_size * 1024 * 1024) if args.bucket_size is not None else None,
         zero_stage=args.zero_stage,
+        gradient_accumulation=args.gradient_accumulation,
         model_dtype=torch.bfloat16,
         pg=pg,
         nsight=args.nsight,
@@ -180,6 +212,9 @@ def main(args, pg):
         model_flops_per_token=flops_per_token,
         visualize_dag=args.save_viz,
         const_attrs={"rope_cache": rope_cache},
+        use_inductor=args.use_inductor,
+        ar_a2a_same_stream=args.ar_a2a_same_stream,
+        overlap_zero_ops=args.overlap_zero_ops,
     )
 
     del x, y
@@ -192,6 +227,7 @@ def main(args, pg):
     actors = piper_metadata.actors
 
     logger.info(f"Running {args.iters} timed iterations")
+    ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
     iter_times = []
     for _ in range(args.iters):
         start = time.perf_counter()
@@ -200,19 +236,24 @@ def main(args, pg):
         iter_times.append(end - start)
         time.sleep(1)
 
+    peak_memory_stats = ray.get(
+        [actor.get_and_reset_peak_memory_stats.remote() for actor in actors.values()]
+    )
+
     dp_rank = int(os.environ["PIPER_DP_RANK"])
     metrics = _build_benchmark_metrics(
         args,
         dp_rank,
         iter_times,
+        peak_memory_stats=peak_memory_stats,
     )
-    iter_time_msg, throughput_msg, _ = _format_benchmark_log_lines(metrics)
+    metric_lines = _format_benchmark_log_lines(metrics)
     metrics_msg = f"rank {dp_rank} benchmark metrics will be saved by the driver to {_metrics_output_path(args)}"
-    logger.info(iter_time_msg)
-    logger.info(throughput_msg)
+    for line in metric_lines:
+        logger.info(line)
     logger.info(metrics_msg)
-    print(iter_time_msg, flush=True)
-    print(throughput_msg, flush=True)
+    for line in metric_lines:
+        print(line, flush=True)
     print(metrics_msg, flush=True)
 
     if args.tracing:
@@ -263,7 +304,7 @@ def parse_args():
     )
     parser.add_argument('--model', choices=['9M', '1B', '9B', '48B', '30B-A3B', '30-A3B-half', '72B'], default='1B',
                         help='Model configuration: 9M, 1B, 9B, 48B, 30B-A3B, 30-A3B-half, or 72B (default: 1B)')
-    parser.add_argument("--schedule", choices=["gpipe", "1f1b", "no-pp", "interleaved-1f1b", "dualpipev", "dualpipev-seq", "zerobubble"], default="1f1b",)
+    parser.add_argument("--schedule", choices=["gpipe", "1f1b", "no-pp", "interleaved-1f1b", "dualpipev", "dualpipev-seq", "zerobubble", "interleaved-zerobubble"], default="1f1b",)
     parser.add_argument("--dp", type=int, default=1)
     parser.add_argument("--pp", type=int, default=2)
     parser.add_argument("--batch-size", type=int, default=4)
@@ -292,6 +333,30 @@ def parse_args():
                         help="Ray temp directory (default: /tmp/piper/ray_tmp)")
     parser.add_argument("--output-dir", default="out",
                         help="Directory for benchmark metrics output (default: out)")
+    parser.add_argument(
+        "--use-inductor",
+        action=argparse.BooleanOptionalAction,
+        default=True,
+        help="Whether actors torch.compile stage GraphModules in _load_stage (default: true)",
+    )
+    parser.add_argument(
+        "--gradient-accumulation",
+        action=argparse.BooleanOptionalAction,
+        default=True,
+        help="Whether to delay bucket gradient reductions to the last occurrence (default: true)",
+    )
+    parser.add_argument(
+        "--ar-a2a-same-stream",
+        action=argparse.BooleanOptionalAction,
+        default=False,
+        help="Whether A2A ops should share the AR CUDA stream on actors (default: false)",
+    )
+    parser.add_argument(
+        "--overlap-zero-ops",
+        action=argparse.BooleanOptionalAction,
+        default=False,
+        help="Whether to run overlap_zero_ops on the per-rank DAGs (default: false)",
+    )
     return parser.parse_args()
 
 

From ddc8a35686c8be5a13b9901f8b23ead516b50d7b Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Sun, 19 Apr 2026 00:10:33 +0000
Subject: [PATCH 090/100] implement chunk overlapping

---
 src/piper.py                 |   72 ++-
 src/piper_actor.py           |  416 ++++++++-----
 src/piper_compile.py         |   10 +
 src/piper_exec.py            |   65 ++-
 src/piper_graph_transform.py | 1062 ++++++++++++++++------------------
 src/piper_utils.py           |    7 +
 test/models/qwen3.py         |   23 +-
 test/test_qwen.py            |   19 +
 8 files changed, 907 insertions(+), 767 deletions(-)

diff --git a/src/piper.py b/src/piper.py
index 53f43cb..6e6c997 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -7,7 +7,14 @@
 import time
 import threading
 from torch._dynamo.backends.registry import register_backend
-from .piper_utils import _serialize_graphmodule, piper_metadata, create_logger, LOG_LEVEL, get_gpu_peak_flops_bf16
+from .piper_utils import (
+    _serialize_graphmodule,
+    piper_metadata,
+    create_logger,
+    LOG_LEVEL,
+    get_gpu_peak_flops_bf16,
+    should_enable_ep,
+)
 
 
 def _collect_triton_constant_args(gm):
@@ -36,22 +43,21 @@ def scan(module):
 from .piper_graph_transform import (
     _split_gm_by_stages,
     _profile_and_split_gm,
+    _fuse_consecutive_zero3_param_sequences,
     expand_chunks_to_dags,
     add_temporal_dependencies,
     split_dag_by_rank,
     assign_time_steps,
-    overlap_zero_ops,
-    find_overlappable_tasks,
+    get_overlappable_tasks,
     insert_zero_ops,
-    overlap_a2a_tasks,
+    overlap_chunks,
     visualize_dag,
-    compute_critical_path,
     print_dag_order,
     bucket_stage,
     apply_activation_checkpointing,
     split_by_a2a,
 )
-from .piper_exec import Chunk, TaskType, BatchMeta
+from .piper_exec import Chunk
 from .piper_actor import _get_actor
 
 logger = create_logger("piper_backend", LOG_LEVEL)
@@ -106,8 +112,8 @@ def piper(gm, example_inputs, **kwargs):
         actor = _get_actor(actor_id)
         actor_stages.append((actor, stage_id))
 
-        # Split at A2A annotation boundaries (expert-parallel only, requires dp_degree > 1).
-        if dp_degree > 1:
+        # Split at A2A annotation boundaries only for multi-DP runs with EP enabled.
+        if dp_degree > 1 and should_enable_ep(dp_degree):
             a2a_segments, boundary_infos = split_by_a2a(stage_gm, graphargs, input_idxs, param_idxs)
             if boundary_infos:
                 logger.debug(
@@ -123,8 +129,9 @@ def piper(gm, example_inputs, **kwargs):
         a2a_boundaries: dict = {}  # boundary_bucket_id -> tensor_idx
 
         for seg_idx, (seg_gm, seg_in, seg_param, seg_args) in enumerate(a2a_segments):
+            buckets = [(seg_gm, seg_in, seg_param, seg_args)]
             # Hack: only bucket even-indexed segments to avoid bucketing MoE subgraphs
-            if piper_metadata.bucket_size is not None:
+            if dp_degree > 1 and piper_metadata.bucket_size is not None:
                 if seg_idx % 2 == 0:
                     buckets = bucket_stage(
                         seg_gm,
@@ -137,21 +144,26 @@ def piper(gm, example_inputs, **kwargs):
                 else:
                     bucket_zero_flags = [False]
             else:
-                buckets = [(seg_gm, seg_in, seg_param, seg_args)]
                 if seg_idx % 2 == 0:
                     bucket_zero_flags = [True]
                 else:
                     bucket_zero_flags = [False]
 
-            logger.debug(
+            logger.info(
                 f"Stage {stage_id} segment {seg_idx} bucketed into {len(buckets)} buckets"
             )
             for b_idx, (_, _, bp, ba) in enumerate(buckets):
+                bucket_total_mb = 0.0
+                for i in bp:
+                    arg = ba[i] if i < len(ba) else None
+                    if arg is not None and hasattr(arg, "numel"):
+                        bucket_total_mb += arg.numel() * arg.element_size() / (1024 * 1024)
+                logger.info(f"  bucket {b_idx} total size={bucket_total_mb:.3f}MB")
                 for i in bp:
                     arg = ba[i] if i < len(ba) else None
                     if arg is not None and hasattr(arg, "numel"):
                         mb = arg.numel() * arg.element_size() / (1024 * 1024)
-                        logger.debug(
+                        logger.info(
                             f"  bucket {b_idx} tensor idx={i} shape={tuple(arg.shape)} size={mb:.3f}MB"
                         )
 
@@ -349,18 +361,27 @@ def piper(gm, example_inputs, **kwargs):
                 f"(gradient_accumulation={piper_metadata.gradient_accumulation})"
             )
 
-            overlappable = find_overlappable_tasks(piper_metadata.schedule)
+        overlappable_by_rank: dict[int, list[tuple[Chunk, Chunk]]] = {}
+        if piper_metadata.overlap_chunks:
+            # overlappable = find_overlappable_tasks(piper_metadata.schedule)
+            overlappable = get_overlappable_tasks(piper_metadata.schedule)
             for t1, t2 in overlappable:
-                logger.debug(
-                    f"Found adjacent task pair for A2A/compute overlap: "
-                    f"{t1} -> {t2}"
+                overlappable_by_rank.setdefault(t1.pp_rank, []).append((t1, t2))
+                logger.debug(f"Found intra-FWD_BWD overlap pair: {t1} -> {t2}")
+
+        for pp_rank, rank_dag in enumerate(per_rank_dags):
+            rank_pairs = overlappable_by_rank.get(pp_rank, [])
+            if rank_pairs:
+                overlap_chunks(rank_dag, rank_pairs)
+            if piper_metadata.zero_stage == 3:
+                _fuse_consecutive_zero3_param_sequences(
+                    rank_dag,
+                    zero_bucket_keys=piper_metadata.zero_bucket_keys,
                 )
 
         # Assign time steps
         for rank_dag in per_rank_dags:
             assign_time_steps(rank_dag)
-            if piper_metadata.overlap_zero_ops:
-                overlap_zero_ops(rank_dag)
         logger.debug("Assigned time steps")
 
         piper_metadata.per_rank_dags = per_rank_dags
@@ -373,7 +394,12 @@ def piper(gm, example_inputs, **kwargs):
                     visualize_dag(per_rank_dag, output_path=f"out/rank{pp_rank}_dag")
                 except Exception as e:
                     logger.warning(f"DAG visualization failed for rank {pp_rank} (DAG may be too large for dot): {e}")
-            print_dag_order(per_rank_dag, label=f"rank {pp_rank}", rank=pp_rank)
+            print_dag_order(
+                per_rank_dag,
+                label=f"rank {pp_rank}",
+                rank=pp_rank,
+                out_dir=piper_metadata.output_dir,
+            )
 
 
         # Send DAG to actors
@@ -484,12 +510,4 @@ def _log_step_stats(step_time: float, log_memory: bool, actors: dict, results: l
                 "Add it to _GPU_PEAK_FLOPS_BF16 in piper_utils.py."
             )
 
-    if log_memory:
-        mem_refs = [
-            actors[pp_rank].get_and_reset_peak_memory_stats.remote()
-            for pp_rank in range(len(piper_metadata.per_rank_dags))
-        ]
-        for global_rank, max_alloc_bytes in ray.get(mem_refs):
-            stats.append(f"rank{global_rank}_peak_mem={max_alloc_bytes / 1e9:.2f}GB")
-
     logger.info("  ".join(stats))
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 5fb23c8..ef04178 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -5,7 +5,6 @@
 import re
 from typing import Any, Dict, List, Optional, Set
 import gc
-import threading
 from torch.nn import Parameter
 from torch.autograd.graph import GradientEdge, Node, set_warn_on_accumulate_grad_stream_mismatch
 import torch.distributed as dist
@@ -71,10 +70,9 @@ def _create_actors(
         }} if profile else {}
         nccl_env = {
             "env_vars": {
-                # "NCCL_SOCKET_IFNAME": "ens32",
-                # "GLOO_SOCKET_IFNAME": "ens32",
-                # "NCCL_PROTO": "simple",
-                # "NCCL_DEBUG": "WARN",
+                "NCCL_SOCKET_IFNAME": "ens32",
+                "GLOO_SOCKET_IFNAME": "ens32",
+                "NCCL_DEBUG": "WARN",
                 **({"TMPDIR": temp_dir} if (profile and temp_dir) else {}),
             }
         }
@@ -224,9 +222,12 @@ def __init__(
         self.bucket_shard_params: dict = {}      # ubid -> shard param tensor for ZeRO
         self.bucket_shard_optims: dict = {}      # ubid -> shard optimizer for ZeRO
         self.bucket_rs_grads: dict = {}          # ubid -> reduced shard grad tensor for ZeRO-2/3
+        self.zero_grad_dtype = torch.float32     # ZeRO gradient buffers stay fp32
         self.param_shard_info: dict = {}         # ubid -> (shard_start, shard_size, orig_numel)
         self.bucket_param_view_specs: dict = {}  # ubid -> [(param, offset, numel, shape)]
         self.zero3_full_params_fresh: dict = {}  # ubid -> bool
+        self.bucket_param_locks: dict = {}       # ubid -> threading.Lock for full-param alloc/free
+        self.bucket_grad_locks: dict = {}        # ubid -> threading.Lock for full-grad alloc/free
         self._logged_bucket_forward_info: set = set()
 
         # Non-trainable constant tensor attributes (e.g. freqs_cis, mask) pushed
@@ -867,7 +868,7 @@ def _bucket_forward_runner(
                     else:
                         try:
                             out = forward_impl(*call_args)
-                        except Exception:
+                        except Exception as exc:
                             tensor_summaries = []
                             for name, arg in zip(placeholder_names, call_args):
                                 if isinstance(arg, torch.Tensor):
@@ -886,7 +887,9 @@ def _bucket_forward_runner(
                                 f"shared_placeholders={list(_shared_placeholder_names)} "
                                 f"placeholders={placeholder_names} "
                                 f"dynamic_names={dynamic_names} "
-                                f"args={' | '.join(tensor_summaries)}"
+                                f"exc_type={type(exc).__name__} exc={exc} "
+                                f"args={' | '.join(tensor_summaries)}",
+                                exc_info=True,
                             )
                             raise
                 return out
@@ -921,6 +924,7 @@ def _bucket_forward_runner(
                     realized[idx] = realized[idx].detach()
                     realized[idx].data = flat_params[offset:offset + numel].view(p.shape)
                     realized[idx].requires_grad_(True)
+                    realized[idx].grad_dtype = self.zero_grad_dtype
                     view_specs.append((realized[idx], offset, numel, tuple(p.shape)))
                     offset += numel
 
@@ -930,15 +934,26 @@ def _bucket_forward_runner(
                 else:
                     shard_param = flat_params[shard_start:shard_start + shard_size]
                 shard_param.requires_grad_(True)
+                # Keep ZeRO comm/storage buffers in fp32, but let the optimizer consume
+                # grads in the shard-param dtype to match Adam's bf16 foreach path.
+                shard_param.grad_dtype = None
 
                 self.bucket_flat_params[ubid] = flat_params
-                self.bucket_flat_grads[ubid] = None if self.zero_stage == 3 else flat_params.new_zeros(padded_numel)
+                self.bucket_flat_grads[ubid] = (
+                    None
+                    if self.zero_stage == 3
+                    else torch.zeros(padded_numel, dtype=self.zero_grad_dtype, device=self.device)
+                )
                 self.bucket_shard_params[ubid] = shard_param
                 self.bucket_shard_optims[ubid] = self.optim_class([shard_param])
-                self.bucket_rs_grads[ubid] = flat_params.new_zeros(shard_size)
+                self.bucket_rs_grads[ubid] = torch.zeros(
+                    shard_size, dtype=self.zero_grad_dtype, device=self.device
+                )
                 self.param_shard_info[ubid] = (shard_start, shard_size, orig_numel)
                 self.bucket_param_view_specs[ubid] = view_specs
                 self.zero3_full_params_fresh[ubid] = False
+                # self.bucket_param_locks[ubid] = threading.Lock()
+                # self.bucket_grad_locks[ubid] = threading.Lock()
 
                 if self.zero_stage == 3:
                     storage = flat_params.untyped_storage()
@@ -951,6 +966,8 @@ def _bucket_forward_runner(
                 self.bucket_param_view_specs[ubid] = []
                 self.bucket_flat_params[ubid] = None
                 self.bucket_flat_grads[ubid] = None
+                self.bucket_param_locks.pop(ubid, None)
+                self.bucket_grad_locks.pop(ubid, None)
                 trainable_for_optim = [realized[i] for i in trainable_idxs]
                 optim = self.optim_class(trainable_for_optim, fused=True) if trainable_for_optim else None
             self.bucket_optims[ubid] = optim
@@ -981,7 +998,7 @@ def _bucket_forward_runner(
 
     def load_dag(self, dag: TaskDAG) -> None:
         """Store the per-rank TaskDAG for subsequent run_dag() calls."""
-        self.dag = dag
+        self.dag = sorted(dag.nodes, key=runtime_sort_key)
 
     def get_all_params(self) -> dict:
         """Return {unique_bucket_id: flat_cpu_tensor} for every trainable bucket."""
@@ -1059,11 +1076,36 @@ def _clear_param_grads(self) -> None:
                 t = args[idx]
                 if t is not None:
                     t.grad = None
-
     def _param_has_effective_grad(self, param: torch.Tensor | None) -> bool:
         """Return True when a param carries a materialized gradient."""
         return param is not None and param.grad is not None
 
+    def _accumulate_zero_param_grads_to_flat(self, ubid: int | None) -> None:
+        """Accumulate ZeRO-managed param grads into the fp32 flat grad buffer."""
+        if ubid is None or self.zero_stage <= 0 or self.dp_degree <= 1:
+            return
+        specs = self.bucket_param_view_specs.get(ubid, [])
+        if not specs:
+            return
+        flat_grads = self.bucket_flat_grads.get(ubid)
+        if flat_grads is None:
+            shard_info = self.param_shard_info.get(ubid)
+            if shard_info is None:
+                return
+            _shard_start, shard_size, _orig_numel = shard_info
+            flat_grads = torch.zeros(
+                shard_size * self.dp_degree,
+                dtype=self.zero_grad_dtype,
+                device=self.device,
+            )
+            self.bucket_flat_grads[ubid] = flat_grads
+        for param, offset, numel, _shape in specs:
+            grad = param.grad
+            if grad is None:
+                continue
+            flat_grads[offset:offset + numel].add_(grad.detach().reshape(-1).to(flat_grads.dtype))
+            param.grad = None
+
     def _sync_payload_ubid(self, node: Task) -> int | None:
         if node.unique_bucket_id is not None:
             return node.unique_bucket_id
@@ -1296,7 +1338,8 @@ def run_dag(self, loss_fn=None, profiling: bool = False):
         - RECV records a recv_event after the dist.recv completes.
         """
         assert self.dag is not None, "load_dag() must be called before run_dag()"
-        dag = self.dag
+        debug_enabled = self.logger.isEnabledFor(logging.DEBUG)
+        verbose_enabled = self.logger.isEnabledFor(VERBOSE)
 
         # Reset per-iteration state
         self.task_buffer = {}
@@ -1309,48 +1352,97 @@ def run_dag(self, loss_fn=None, profiling: bool = False):
             self.ag_events = {}
         self.bwd_events = {}
         self._clear_param_grads()
+        if self.zero_stage >= 1:
+            with torch.cuda.stream(self.comp_stream):
+                for buf in self.bucket_flat_grads.values():
+                    if buf is not None:
+                        buf.zero_()
         if self.zero_stage >= 2:
             with torch.cuda.stream(self.comp_stream):
                 for buf in self.bucket_rs_grads.values():
                     if buf is not None:
                         buf.zero_()
         comp_events: dict = {}  # task_uid -> cuda.Event for compute tasks
-
         # profiling: list of (node, start_evt, end_evt, mem_before_bytes)
         # populated during the loop; elapsed_time() is only called after the
         # final synchronize() so we never block the CPU mid-dispatch.
         self._prof_records: list = []
 
-        sorted_nodes = sorted(dag.nodes, key=runtime_sort_key)
-        self._init_task_buffer_refcounts(dag)
+        self._init_task_buffer_refcounts(self.dag)
         ts_to_comp_event: dict[int, torch.cuda.Event] = {}
 
         def _wait_for_all_gather(compute_node: Task) -> None:
             for pred in compute_node.data_preds:
-                if pred.chunk.type == TaskType.ALL_GATHER:
+                if pred.task_type == TaskType.ALL_GATHER:
                     ag_evt = self.ag_events.get(pred.uid)
                     if ag_evt is not None:
                         self.comp_stream.wait_event(ag_evt)
 
-        for node in sorted_nodes:
-
-            chunk = node.chunk
-            batch = chunk.batches[0]
+        def _metadata_ubids(node: Task, key: str) -> list[int]:
+            value = node.custom_metadata.get(key)
+            if value is True:
+                sync_ubids = self._sync_payload_ubids(node)
+                if sync_ubids:
+                    return sync_ubids
+                return [node.unique_bucket_id] if node.unique_bucket_id is not None else []
+            if isinstance(value, (list, tuple)):
+                return [int(ubid) for ubid in value]
+            return []
+
+        def _run_free_full_grads(free_node: Task) -> None:
+            self._nvtx_push(f"free_full_grads_ubid{free_node.unique_bucket_id}")
+            try:
+                for pred in free_node.data_preds:
+                    if pred.task_type == TaskType.REDUCE_SCATTER:
+                        rs_evt = self.rs_events.get(pred.uid)
+                        if rs_evt is not None:
+                            self.comp_stream.wait_event(rs_evt)
+                            # self.logger.info("synchronizing free_full_grads on thread_id=%s", threading.get_ident())
+                            rs_evt.synchronize()
+                    elif pred.task_type == TaskType.ALL_REDUCE:
+                        ar_evt = self.ar_events.get(pred.uid)
+                        if ar_evt is not None:
+                            self.comp_stream.wait_event(ar_evt)
+                            # self.logger.info("synchronizing free_full_grads on thread_id=%s", threading.get_ident())
+                            ar_evt.synchronize()
+                self._free_full_grads(free_node.unique_bucket_id)
+            finally:
+                self._nvtx_pop()
+
+        def _run_free_full_params(free_node: Task) -> None:
+            self._nvtx_push(f"free_full_params_ubid{free_node.unique_bucket_id}")
+            try:
+                for pred in free_node.data_preds:
+                    if pred.uid in comp_events:
+                        # _free_full_params resizes CUDA storage from the host.
+                        # Stream waits are not enough here: the CPU must not
+                        # release the full ZeRO-3 buffer while a compute kernel
+                        # launched on comp_stream may still be reading it.
+                        comp_events[pred.uid].synchronize()
+                self._free_full_params(free_node.unique_bucket_id)
+            finally:
+                self._nvtx_pop()
+
+        for node in self.dag:
+
+            task_type = node.task_type
+            batch = node.batches[0]
             mb_idx = batch.mb_idx
             ubid = node.unique_bucket_id  # None for non-compute nodes
 
-            self.logger.debug(
-                f"run_dag dispatch (time {node.time_step}): {chunk.type.value} "
-                f"mb{mb_idx} ubid={ubid}"
-            )
+            if debug_enabled:
+                self.logger.debug(
+                    f"run_dag dispatch (time {node.time_step}): {task_type.value} "
+                    f"mb{mb_idx} ubid={ubid}"
+                )
 
             if profiling:
-                _stream = self._timing_stream(chunk.type)
+                _stream = self._timing_stream(task_type)
                 _start_evt = torch.cuda.Event(enable_timing=True)
                 _start_evt.record(_stream)
                 _mem_before = torch.cuda.memory_allocated()
 
-            match chunk.type:
+            match task_type:
 
                 case TaskType.SEND:
                     # data_preds[0] is the compute task whose output we send.
@@ -1376,7 +1468,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     comp_evt = ts_to_comp_event.get(node.time_step)
                     if comp_evt is not None:
                         self.p2p_recv_stream.wait_event(comp_evt)
-                    if compute_node.chunk.type == TaskType.FWD:
+                    if compute_node.task_type == TaskType.FWD:
                         # FWD recv: pre-allocate based on target bucket's input metadata.
                         recv_ubid = compute_node.unique_bucket_id
                         self._start_timing(self.p2p_recv_stream, "p2p_recv")
@@ -1395,7 +1487,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     self.recv_events[node.uid] = recv_evt
 
                 case TaskType.FWD_A2A:
-                    fwd_pred = next(p for p in node.data_preds if p.chunk.type == TaskType.FWD)
+                    fwd_pred = next(p for p in node.data_preds if p.task_type == TaskType.FWD)
                     self.a2a_stream.wait_event(comp_events[fwd_pred.uid])
                     tensor_idx = node.custom_metadata["a2a_tensor_idx"]
                     # Pass-through: copy upstream FWD task_buffer, apply A2A only at tensor_idx.
@@ -1418,7 +1510,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                 case TaskType.BWD_A2A:
                     bwd_pred = next(
                         p for p in node.data_preds
-                        if p.chunk.type in (TaskType.BWD, TaskType.BWD_I)
+                        if p.task_type in (TaskType.BWD, TaskType.BWD_I)
                     )
                     self.a2a_stream.wait_event(comp_events[bwd_pred.uid])
                     tensor_idx = node.custom_metadata["a2a_tensor_idx"]
@@ -1457,6 +1549,9 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                         ar_evt = torch.cuda.Event()
                         ar_evt.record(self.ar_stream)
                         self.ar_events[node.uid] = ar_evt
+                        for free_ubid in _metadata_ubids(node, "zero_free_full_grads_after_ubids"):
+                            ar_evt.synchronize()
+                            self._free_full_grads(free_ubid)
                     self._nvtx_pop()
                     self._release_task_buffer_uid(bwd_node.uid)
 
@@ -1471,10 +1566,15 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     rs_evt = torch.cuda.Event()
                     rs_evt.record(self.ar_stream)
                     self.rs_events[node.uid] = rs_evt
+                    for free_ubid in _metadata_ubids(node, "zero_free_full_grads_after_ubids"):
+                        rs_evt.synchronize()
+                        self._free_full_grads(free_ubid)
                     self._nvtx_pop()
                     self._release_task_buffer_uid(bwd_node.uid)
 
                 case TaskType.ALL_GATHER:
+                    for alloc_ubid in _metadata_ubids(node, "zero_alloc_full_params_before"):
+                        self._alloc_full_params(alloc_ubid)
                     self._nvtx_push(f"all_gather_s{batch.stage_id}_b{node.bucket_id}")
                     self._start_timing(self.ar_stream, "all_gather")
                     ag_launched = self._exec_all_gather(node.unique_bucket_id)
@@ -1491,20 +1591,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     self._nvtx_pop()
 
                 case TaskType.FREE_FULL_GRADS:
-                    self._nvtx_push(f"free_full_grads_ubid{node.unique_bucket_id}")
-                    for pred in node.data_preds:
-                        if pred.chunk.type == TaskType.REDUCE_SCATTER:
-                            rs_evt = self.rs_events.get(pred.uid)
-                            if rs_evt is not None:
-                                self.comp_stream.wait_event(rs_evt)
-                                rs_evt.synchronize()
-                        elif pred.chunk.type == TaskType.ALL_REDUCE:
-                            ar_evt = self.ar_events.get(pred.uid)
-                            if ar_evt is not None:
-                                self.comp_stream.wait_event(ar_evt)
-                                ar_evt.synchronize()
-                    self._nvtx_pop()
-                    self._free_full_grads(node.unique_bucket_id)
+                    _run_free_full_grads(node)
 
                 case TaskType.ALLOC_FULL_PARAMS:
                     self._nvtx_push(f"alloc_full_params_ubid{node.unique_bucket_id}")
@@ -1512,21 +1599,12 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     self._nvtx_pop()
 
                 case TaskType.FREE_FULL_PARAMS:
-                    self._nvtx_push(f"free_full_params_ubid{node.unique_bucket_id}")
-                    for pred in node.data_preds:
-                        if pred.uid in comp_events:
-                            # _free_full_params resizes CUDA storage from the host.
-                            # Stream waits are not enough here: the CPU must not
-                            # release the full ZeRO-3 buffer while a compute kernel
-                            # launched on comp_stream may still be reading it.
-                            comp_events[pred.uid].synchronize()
-                    self._free_full_params(node.unique_bucket_id)
-                    self._nvtx_pop()
+                    _run_free_full_params(node)
 
                 case TaskType.FWD:
                     # Wait on any RECV predecessor.
                     recv_pred = next(
-                        (p for p in node.data_preds if p.chunk.type == TaskType.RECV), None
+                        (p for p in node.data_preds if p.task_type == TaskType.RECV), None
                     )
                     if recv_pred is not None and recv_pred.uid in self.recv_events:
                         self.comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
@@ -1537,7 +1615,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     # FWD(bkt>0): inputs come from prev FWD or FWD_A2A output.
                     fwd_data_pred = next(
                         (p for p in node.data_preds
-                         if p.chunk.type in (TaskType.FWD, TaskType.FWD_A2A)), None
+                         if p.task_type in (TaskType.FWD, TaskType.FWD_A2A)), None
                     )
                     if fwd_data_pred is not None:
                         # Both FWD and FWD_A2A task_buffer entries carry "detached_outs".
@@ -1565,11 +1643,14 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     evt.record(self.comp_stream)
                     comp_events[node.uid] = evt
                     ts_to_comp_event[node.time_step] = evt
+                    if node.custom_metadata.get("zero_free_full_params_after"):
+                        evt.synchronize()
+                        self._free_full_params(ubid)
 
                 case TaskType.BWD:
                     # Wait on upstream grad RECV if present.
                     recv_pred = next(
-                        (p for p in node.data_preds if p.chunk.type == TaskType.RECV), None
+                        (p for p in node.data_preds if p.task_type == TaskType.RECV), None
                     )
                     if recv_pred is not None and recv_pred.uid in self.recv_events:
                         self.comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
@@ -1595,7 +1676,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                         upstream_grads = None  # last stage, no recv
 
                     bwd_a2a_pred = next(
-                        (p for p in node.data_preds if p.chunk.type == TaskType.BWD_A2A), None
+                        (p for p in node.data_preds if p.task_type == TaskType.BWD_A2A), None
                     )
                     if bwd_a2a_pred is not None:
                         a2a_buf = self.task_buffer[bwd_a2a_pred.uid]
@@ -1613,7 +1694,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     else:
                         bwd_pred = next(
                             (p for p in node.data_preds
-                             if p.chunk.type in (TaskType.BWD, TaskType.BWD_I)), None
+                             if p.task_type in (TaskType.BWD, TaskType.BWD_I)), None
                         )
                         if bwd_pred is not None and recv_pred is None:
                             # Co-located boundary-bridge: the downstream BWD stored
@@ -1637,6 +1718,8 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                             detached_outs = None
 
                     inp_with_grad = fwd_out.get("inp_with_grad")
+                    if node.custom_metadata.get("zero_alloc_full_grads_before"):
+                        self._alloc_full_grads(ubid)
 
                     self._nvtx_push(f"backward_ubid{ubid}_mb{mb_idx}")
                     self._start_timing(self.comp_stream, "backward")
@@ -1657,6 +1740,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                         if fwd_inputs_full is not None
                         else [t.grad for t in (inp_with_grad or [])]
                     )
+                    self._accumulate_zero_param_grads_to_flat(ubid)
                     self.task_buffer[node.uid] = buf
                     # FWD stored the same dict under both node.uid and (ubid, mb_idx).
                     # Clear the dict in-place so any remaining alias stops keeping
@@ -1668,11 +1752,14 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     comp_events[node.uid] = evt
                     self.bwd_events[ubid] = evt
                     ts_to_comp_event[node.time_step] = evt
+                    if node.custom_metadata.get("zero_free_full_params_after"):
+                        evt.synchronize()
+                        self._free_full_params(ubid)
 
                 case TaskType.BWD_I:
                     # Wait on upstream grad RECV if present.
                     recv_pred = next(
-                        (p for p in node.data_preds if p.chunk.type == TaskType.RECV), None
+                        (p for p in node.data_preds if p.task_type == TaskType.RECV), None
                     )
                     if recv_pred is not None and recv_pred.uid in self.recv_events:
                         self.comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
@@ -1700,7 +1787,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     # If a BWD_A2A precedes this bucket, use the reversed grad as
                     # output_grads so backward flows correctly through the A2A boundary.
                     bwd_a2a_pred = next(
-                        (p for p in node.data_preds if p.chunk.type == TaskType.BWD_A2A), None
+                        (p for p in node.data_preds if p.task_type == TaskType.BWD_A2A), None
                     )
                     if bwd_a2a_pred is not None and not node.compute_loss and recv_pred is None:
                         a2a_buf = self.task_buffer[bwd_a2a_pred.uid]
@@ -1751,23 +1838,24 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                         "inp_grads": inp_grads_full,
                         "param_groups": param_groups,
                     }
-                    pg_summary = self._summarize_param_groups(param_groups)
-                    outstanding = self._summarize_outstanding_bwdi()
-                    self.logger.log(VERBOSE,
-                        f"[bwd_i_retain] rank={self.global_rank} ubid={ubid} mb={mb_idx}: "
-                        f"groups={pg_summary['groups']} "
-                        f"params={pg_summary['params']} "
-                        f"intermediates={pg_summary['intermediates']} "
-                        f"grad_tensors={pg_summary['grad_tensors']} "
-                        f"grad_bytes_gb={pg_summary['grad_bytes'] / (1024 ** 3):.2f} "
-                        f"saved_tensors={pg_summary['saved_tensors']} "
-                        f"saved_bytes_gb={pg_summary['saved_bytes'] / (1024 ** 3):.2f} "
-                        f"outstanding_buffers={outstanding['buffers']} "
-                        f"outstanding_grad_bytes_gb={outstanding['grad_bytes'] / (1024 ** 3):.2f} "
-                        f"outstanding_saved_bytes_gb={outstanding['saved_bytes'] / (1024 ** 3):.2f} "
-                        f"alloc_gb={torch.cuda.memory_allocated(self.device) / (1024 ** 3):.2f} "
-                        f"reserved_gb={torch.cuda.memory_reserved(self.device) / (1024 ** 3):.2f}"
-                    )
+                    if verbose_enabled:
+                        pg_summary = self._summarize_param_groups(param_groups)
+                        outstanding = self._summarize_outstanding_bwdi()
+                        self.logger.log(VERBOSE,
+                            f"[bwd_i_retain] rank={self.global_rank} ubid={ubid} mb={mb_idx}: "
+                            f"groups={pg_summary['groups']} "
+                            f"params={pg_summary['params']} "
+                            f"intermediates={pg_summary['intermediates']} "
+                            f"grad_tensors={pg_summary['grad_tensors']} "
+                            f"grad_bytes_gb={pg_summary['grad_bytes'] / (1024 ** 3):.2f} "
+                            f"saved_tensors={pg_summary['saved_tensors']} "
+                            f"saved_bytes_gb={pg_summary['saved_bytes'] / (1024 ** 3):.2f} "
+                            f"outstanding_buffers={outstanding['buffers']} "
+                            f"outstanding_grad_bytes_gb={outstanding['grad_bytes'] / (1024 ** 3):.2f} "
+                            f"outstanding_saved_bytes_gb={outstanding['saved_bytes'] / (1024 ** 3):.2f} "
+                            f"alloc_gb={torch.cuda.memory_allocated(self.device) / (1024 ** 3):.2f} "
+                            f"reserved_gb={torch.cuda.memory_reserved(self.device) / (1024 ** 3):.2f}"
+                        )
                     # Propagate input grad to SEND (if this is the first bucket and has predecessors).
                     if dinputs:
                         self.task_buffer[node.uid]["send_output"] = list(dinputs)
@@ -1782,32 +1870,38 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     comp_events[node.uid] = evt
                     self.bwd_events[ubid] = evt
                     ts_to_comp_event[node.time_step] = evt
+                    if node.custom_metadata.get("zero_free_full_params_after"):
+                        evt.synchronize()
+                        self._free_full_params(ubid)
 
                 case TaskType.BWD_W:
                     _wait_for_all_gather(node)
+                    if node.custom_metadata.get("zero_alloc_full_grads_before"):
+                        self._alloc_full_grads(ubid)
                     # data_preds[0] is the BWD_I task for bkt=K-1 (head of chain).
                     # For bkt<K-1 the chain-link edge (BWD_W predecessor) is prepended
                     # before Pass 2 appends the BWD_I edge, so data_preds[0] would be
                     # the wrong task.  Find BWD_I explicitly by type to be safe.
-                    bwdi_node = next(p for p in node.data_preds if p.chunk.type == TaskType.BWD_I)
+                    bwdi_node = next(p for p in node.data_preds if p.task_type == TaskType.BWD_I)
                     param_groups = self.task_buffer[bwdi_node.uid]["param_groups"]
-                    pg_summary = self._summarize_param_groups(param_groups)
-                    outstanding = self._summarize_outstanding_bwdi()
-                    self.logger.log(VERBOSE,
-                        f"[bwd_w_begin] rank={self.global_rank} ubid={ubid} mb={mb_idx}: "
-                        f"groups={pg_summary['groups']} "
-                        f"params={pg_summary['params']} "
-                        f"intermediates={pg_summary['intermediates']} "
-                        f"grad_tensors={pg_summary['grad_tensors']} "
-                        f"grad_bytes_gb={pg_summary['grad_bytes'] / (1024 ** 3):.2f} "
-                        f"saved_tensors={pg_summary['saved_tensors']} "
-                        f"saved_bytes_gb={pg_summary['saved_bytes'] / (1024 ** 3):.2f} "
-                        f"outstanding_buffers={outstanding['buffers']} "
-                        f"outstanding_grad_bytes_gb={outstanding['grad_bytes'] / (1024 ** 3):.2f} "
-                        f"outstanding_saved_bytes_gb={outstanding['saved_bytes'] / (1024 ** 3):.2f} "
-                        f"alloc_gb={torch.cuda.memory_allocated(self.device) / (1024 ** 3):.2f} "
-                        f"reserved_gb={torch.cuda.memory_reserved(self.device) / (1024 ** 3):.2f}"
-                    )
+                    if verbose_enabled:
+                        pg_summary = self._summarize_param_groups(param_groups)
+                        outstanding = self._summarize_outstanding_bwdi()
+                        self.logger.log(VERBOSE,
+                            f"[bwd_w_begin] rank={self.global_rank} ubid={ubid} mb={mb_idx}: "
+                            f"groups={pg_summary['groups']} "
+                            f"params={pg_summary['params']} "
+                            f"intermediates={pg_summary['intermediates']} "
+                            f"grad_tensors={pg_summary['grad_tensors']} "
+                            f"grad_bytes_gb={pg_summary['grad_bytes'] / (1024 ** 3):.2f} "
+                            f"saved_tensors={pg_summary['saved_tensors']} "
+                            f"saved_bytes_gb={pg_summary['saved_bytes'] / (1024 ** 3):.2f} "
+                            f"outstanding_buffers={outstanding['buffers']} "
+                            f"outstanding_grad_bytes_gb={outstanding['grad_bytes'] / (1024 ** 3):.2f} "
+                            f"outstanding_saved_bytes_gb={outstanding['saved_bytes'] / (1024 ** 3):.2f} "
+                            f"alloc_gb={torch.cuda.memory_allocated(self.device) / (1024 ** 3):.2f} "
+                            f"reserved_gb={torch.cuda.memory_reserved(self.device) / (1024 ** 3):.2f}"
+                        )
                     b_fwd_args = self.bucket_fwd_args[ubid]
                     weights = [b_fwd_args[i] for i in self.bucket_param_idxs[ubid]
                                if b_fwd_args[i] is not None]
@@ -1817,6 +1911,7 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                         self._bucket_backward_weight(iter(weights), param_groups, ubid=ubid, mb_idx=mb_idx)
                     self._stop_timing(self.comp_stream, "backward_weight")
                     self._nvtx_pop()
+                    self._accumulate_zero_param_grads_to_flat(ubid)
                     self.task_buffer[node.uid] = {}
                     # BWD_I buffer is no longer needed: _bucket_backward_weight already
                     # deleted param_groups["intermediates"] and param_groups["grads"] and
@@ -1827,6 +1922,9 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                     comp_events[node.uid] = evt
                     self.bwd_events[ubid] = evt
                     ts_to_comp_event[node.time_step] = evt
+                    if node.custom_metadata.get("zero_free_full_params_after"):
+                        evt.synchronize()
+                        self._free_full_params(ubid)
 
                 case TaskType.UPD:
                     self._nvtx_push("update")
@@ -1840,31 +1938,32 @@ def _wait_for_all_gather(compute_node: Task) -> None:
                 _end_evt.record(_stream)
                 _mem_after = torch.cuda.memory_allocated()
                 self._prof_records.append((node, mb_idx, ubid, _start_evt, _end_evt, _mem_before))
-                self.logger.info(f"Rank {self.global_rank} task {chunk.type.value} (time {node.time_step}) mem before {_mem_before / 1024 ** 3:.2f} GB after {_mem_after / 1024 ** 3:.2f} GB")
+                self.logger.info(f"Rank {self.global_rank} task {node.task_type.value} (time {node.time_step}) mem before {_mem_before / 1024 ** 3:.2f} GB after {_mem_after / 1024 ** 3:.2f} GB")
 
-            tb_summary = self._summarize_task_buffer()
-            self.logger.log(VERBOSE,
-                f"[task_mem] rank={self.global_rank} time={node.time_step} "
-                f"task={chunk.type.value} mb={mb_idx} ubid={ubid}: "
-                f"alloc_gb={torch.cuda.memory_allocated(self.device) / (1024 ** 3):.2f} "
-                f"reserved_gb={torch.cuda.memory_reserved(self.device) / (1024 ** 3):.2f} "
-                f"tb_entries={tb_summary['total_entries']} "
-                f"tb_total_gb={tb_summary['total_bytes'] / (1024 ** 3):.2f} "
-                f"fwd_mb_count={tb_summary['fwd_mb_count']} "
-                f"fwd_mb_gb={tb_summary['fwd_mb_bytes'] / (1024 ** 3):.2f} "
-                f"recv_count={tb_summary['recv_count']} "
-                f"recv_gb={tb_summary['recv_bytes'] / (1024 ** 3):.2f} "
-                f"bwd_i_count={tb_summary['bwd_i_count']} "
-                f"bwd_i_gb={tb_summary['bwd_i_bytes'] / (1024 ** 3):.2f} "
-                f"bwd_count={tb_summary['bwd_count']} "
-                f"bwd_gb={tb_summary['bwd_bytes'] / (1024 ** 3):.2f} "
-                f"shape_ref_count={tb_summary['shape_ref_count']} "
-                f"shape_ref_gb={tb_summary['shape_ref_bytes'] / (1024 ** 3):.2f} "
-                f"a2a_count={tb_summary['a2a_count']} "
-                f"a2a_gb={tb_summary['a2a_bytes'] / (1024 ** 3):.2f} "
-                f"other_count={tb_summary['other_count']} "
-                f"other_gb={tb_summary['other_bytes'] / (1024 ** 3):.2f}"
-            )
+            if verbose_enabled:
+                tb_summary = self._summarize_task_buffer()
+                self.logger.log(VERBOSE,
+                    f"[task_mem] rank={self.global_rank} time={node.time_step} "
+                    f"task={node.task_type.value} mb={mb_idx} ubid={ubid}: "
+                    f"alloc_gb={torch.cuda.memory_allocated(self.device) / (1024 ** 3):.2f} "
+                    f"reserved_gb={torch.cuda.memory_reserved(self.device) / (1024 ** 3):.2f} "
+                    f"tb_entries={tb_summary['total_entries']} "
+                    f"tb_total_gb={tb_summary['total_bytes'] / (1024 ** 3):.2f} "
+                    f"fwd_mb_count={tb_summary['fwd_mb_count']} "
+                    f"fwd_mb_gb={tb_summary['fwd_mb_bytes'] / (1024 ** 3):.2f} "
+                    f"recv_count={tb_summary['recv_count']} "
+                    f"recv_gb={tb_summary['recv_bytes'] / (1024 ** 3):.2f} "
+                    f"bwd_i_count={tb_summary['bwd_i_count']} "
+                    f"bwd_i_gb={tb_summary['bwd_i_bytes'] / (1024 ** 3):.2f} "
+                    f"bwd_count={tb_summary['bwd_count']} "
+                    f"bwd_gb={tb_summary['bwd_bytes'] / (1024 ** 3):.2f} "
+                    f"shape_ref_count={tb_summary['shape_ref_count']} "
+                    f"shape_ref_gb={tb_summary['shape_ref_bytes'] / (1024 ** 3):.2f} "
+                    f"a2a_count={tb_summary['a2a_count']} "
+                    f"a2a_gb={tb_summary['a2a_bytes'] / (1024 ** 3):.2f} "
+                    f"other_count={tb_summary['other_count']} "
+                    f"other_gb={tb_summary['other_bytes'] / (1024 ** 3):.2f}"
+                )
 
     def _exec_send(self, send_data, peer_pp_rank: int) -> None:
         """Send tensors to peer_pp_rank on p2p_send_stream.
@@ -1873,7 +1972,8 @@ def _exec_send(self, send_data, peer_pp_rank: int) -> None:
         compute event before calling this method.
         """
         global_dst_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
-        self.logger.log(VERBOSE, f"exec_send to global rank {global_dst_rank}")
+        if self.logger.isEnabledFor(VERBOSE):
+            self.logger.log(VERBOSE, f"exec_send to global rank {global_dst_rank}")
 
         with torch.cuda.stream(self.p2p_send_stream):
             tensors = send_data if isinstance(send_data, (list, tuple)) else [send_data]
@@ -1888,7 +1988,8 @@ def _exec_recv_fwd(self, recv_ubid: int, peer_pp_rank: int) -> list:
         Returns the received tensor list (stored in task_buffer by run_dag).
         """
         global_src_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
-        self.logger.log(VERBOSE, f"exec_recv_fwd ubid={recv_ubid} from global rank {global_src_rank}")
+        if self.logger.isEnabledFor(VERBOSE):
+            self.logger.log(VERBOSE, f"exec_recv_fwd ubid={recv_ubid} from global rank {global_src_rank}")
 
         buf = [
             torch.empty(shape, dtype=dtype, requires_grad=requires_grad, device=self.device)
@@ -1908,7 +2009,8 @@ def _exec_recv_bwd(self, shape_meta: list, peer_pp_rank: int) -> list:
         Returns the received gradient list (stored in task_buffer by run_dag).
         """
         global_src_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
-        self.logger.log(VERBOSE, f"exec_recv_bwd from global rank {global_src_rank}")
+        if self.logger.isEnabledFor(VERBOSE):
+            self.logger.log(VERBOSE, f"exec_recv_bwd from global rank {global_src_rank}")
 
         buf = [torch.empty(shape, dtype=dtype, device=self.device) for shape, dtype in shape_meta]
         with torch.cuda.stream(self.p2p_recv_stream):
@@ -2207,6 +2309,12 @@ def _exec_all_reduce_grads(self, ubid: int) -> int:
                 )
                 return 0
             total_bytes = flat_grads.numel() * flat_grads.element_size()
+            self.logger.log(VERBOSE,
+                f"[collective_payload] op=all_reduce rank={self.global_rank} "
+                f"ubid={ubid} mode=flat numel={flat_grads.numel()} "
+                f"element_size={flat_grads.element_size()} dtype={flat_grads.dtype} "
+                f"payload_bytes={total_bytes}"
+            )
             with torch.cuda.stream(self.ar_stream):
                 dist.all_reduce(flat_grads, group=self.dp_group)
             return total_bytes
@@ -2230,6 +2338,12 @@ def _exec_all_reduce_grads(self, ubid: int) -> int:
         total_bytes = sum(grad.numel() * grad.element_size() for _, grad in grad_tensors)
         largest_idx, largest_grad = max(grad_tensors, key=lambda item: item[1].numel())
         largest_bytes = largest_grad.numel() * largest_grad.element_size()
+        dtype_summary = ",".join(sorted({str(grad.dtype) for _, grad in grad_tensors}))
+        self.logger.log(VERBOSE,
+            f"[collective_payload] op=all_reduce rank={self.global_rank} "
+            f"ubid={ubid} mode=per_param grad_tensors={len(grad_tensors)} "
+            f"numel={total_numel} payload_bytes={total_bytes} dtypes={dtype_summary}"
+        )
         self.logger.log(VERBOSE,
             f"[all_reduce_bucket] rank={self.global_rank} ubid={ubid}: "
             f"mode=per_param grad_tensors={len(grad_tensors)} "
@@ -2302,8 +2416,6 @@ def _alloc_full_params(self, ubid: int | None) -> None:
     def _free_full_params(self, ubid: int | None) -> None:
         if self.zero_stage != 3:
             return
-        if ubid is None:
-            return
         full = self.bucket_flat_params.get(ubid)
         if full is None:
             return
@@ -2317,27 +2429,32 @@ def _free_full_params(self, ubid: int | None) -> None:
         self.zero3_full_params_fresh[ubid] = False
 
     def _alloc_full_grads(self, ubid: int | None) -> None:
-        if ubid is None:
-            return
         specs = self.bucket_param_view_specs.get(ubid, [])
-        if not specs:
-            return
+        assert specs, f"Expected param view specs for ubid={ubid}"
         flat_grads = self.bucket_flat_grads.get(ubid)
         if flat_grads is None:
             shard_info = self.param_shard_info.get(ubid)
             if shard_info is None:
                 return
             _shard_start, shard_size, _orig_numel = shard_info
-            flat_grads = self.bucket_shard_params[ubid].new_zeros(shard_size * self.dp_degree)
+            flat_grads = torch.zeros(
+                shard_size * self.dp_degree,
+                dtype=self.zero_grad_dtype,
+                device=self.device,
+            )
             self.bucket_flat_grads[ubid] = flat_grads
         else:
             flat_grads.zero_()
-        for param, offset, numel, shape in specs:
-            param.grad = flat_grads[offset:offset + numel].view(shape)
+        if self.zero_stage in (2, 3) and self.bucket_rs_grads.get(ubid) is None:
+            shard_info = self.param_shard_info.get(ubid)
+            if shard_info is None:
+                return
+            _shard_start, shard_size, _orig_numel = shard_info
+            self.bucket_rs_grads[ubid] = torch.zeros(
+                shard_size, dtype=self.zero_grad_dtype, device=self.device
+            )
 
     def _free_full_grads(self, ubid: int | None) -> None:
-        if ubid is None:
-            return
         for param, *_ in self.bucket_param_view_specs.get(ubid, []):
             param.grad = None
         if self.zero_stage in (2, 3):
@@ -2346,14 +2463,39 @@ def _free_full_grads(self, ubid: int | None) -> None:
     def _exec_reduce_scatter(self, ubid: int) -> int:
         with torch.cuda.stream(self.ar_stream):
             flat_grads = self.bucket_flat_grads.get(ubid)
+            _shard_start, shard_size, _orig_numel = self.param_shard_info.get(ubid)
+            # add logging here
             rs_out = self.bucket_rs_grads.get(ubid)
+            if flat_grads is None or rs_out is None:
+                if flat_grads is None:
+                    self.logger.warning(f"[reduce_scatter_alloc] rank={self.global_rank} ubid={ubid}: Found no flat grad buffer")
+                    flat_grads = torch.zeros(
+                        shard_size * self.dp_degree,
+                        dtype=self.zero_grad_dtype,
+                        device=self.device,
+                    )
+                    self.bucket_flat_grads[ubid] = flat_grads
+                if rs_out is None:
+                    rs_out = torch.zeros(
+                        shard_size, dtype=self.zero_grad_dtype, device=self.device
+                    )
+                    self.bucket_rs_grads[ubid] = rs_out
             assert flat_grads is not None and rs_out is not None, (
                 f"REDUCE_SCATTER dispatched for ubid={ubid} without allocated gradient buffers"
             )
             total_bytes = flat_grads.numel() * flat_grads.element_size()
+            self.logger.log(VERBOSE,
+                f"[collective_payload] op=reduce_scatter rank={self.global_rank} "
+                f"ubid={ubid} input_numel={flat_grads.numel()} "
+                f"input_element_size={flat_grads.element_size()} input_dtype={flat_grads.dtype} "
+                f"payload_bytes={total_bytes} output_numel={rs_out.numel()} "
+                f"output_element_size={rs_out.element_size()} output_dtype={rs_out.dtype}"
+            )
             tmp = torch.empty_like(rs_out)
-            dist.reduce_scatter_tensor(tmp, flat_grads, group=self.dp_group)
+            flat_grads[_shard_start:_shard_start + shard_size].add_(rs_out)
+            dist.reduce_scatter_tensor(rs_out, flat_grads, group=self.dp_group)
             rs_out.add_(tmp)
+            return total_bytes
 
     def _exec_all_gather(
         self,
@@ -2400,12 +2542,12 @@ def _update(self, *deps):
                     flat_grads = self.bucket_flat_grads.get(ubid)
                     if flat_grads is None:
                         continue
-                    shard_param.grad = flat_grads[shard_start:shard_start + shard_size]
+                    shard_param.grad = flat_grads[shard_start:shard_start + shard_size].to(shard_param.dtype)
                 else:
                     rs_grad = self.bucket_rs_grads.get(ubid)
                     if rs_grad is None:
                         continue
-                    shard_param.grad = rs_grad
+                    shard_param.grad = rs_grad.to(shard_param.dtype)
                 with torch.cuda.stream(self.comp_stream):
                     shard_optim.step()
                 shard_param.grad = None
@@ -2542,7 +2684,7 @@ def _update(self, *deps):
                 node.profiling_measurements.append(t_ms)
                 self.logger.info(
                     f"[profiling] rank {self.global_rank} "
-                    f"{node.chunk.type.value} mb{mb_idx} ubid{ubid} "
+                    f"{node.task_type.value} mb{mb_idx} ubid{ubid} "
                     f"(time step {node.time_step}): time={t_ms:.3f}ms "
                     f"mem_before={mem_before/1e9:.3f}GB"
                 )
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 7056d09..01997de 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -114,8 +114,10 @@ def piper_setup(
     zero_stage: int = 0,
     gradient_accumulation: bool = True,
     use_inductor: bool = False,
+    enable_ep: bool = False,
     ar_a2a_same_stream: bool = False,
     overlap_zero_ops: bool = False,
+    overlap_chunks: bool = False,
     schedule_name: str = "",
     visualize_dag_render: bool = True,
     no_nvtx: bool = False,
@@ -125,6 +127,7 @@ def piper_setup(
     temp_dir: str = None,
     model_flops_per_token: float = None,
     visualize_dag: bool = True,
+    output_dir: str = "out",
     const_attrs: dict = None,
 ):
     """
@@ -146,10 +149,14 @@ def piper_setup(
             until the last occurrence of each bucket.
         use_inductor: When true, actors torch.compile stage GraphModules
             during _load_stage with the default inductor backend.
+        enable_ep: When true, honor expert-parallel A2A annotations by
+            splitting stage graphs and inserting DAG-level A2A tasks.
         ar_a2a_same_stream: When true, actors run A2A ops on the same CUDA
             stream as AR ops.
         overlap_zero_ops: When true, run overlap_zero_ops on per-rank DAGs
             after ZeRO collective insertion.
+        overlap_chunks: When true, apply the chunk-overlap transform to
+            per-rank DAGs before assigning time steps.
     """
 
     # Clear Dynamo's global compilation cache so that a previous piper_setup
@@ -166,13 +173,16 @@ def piper_setup(
     piper_metadata.zero_stage = int(zero_stage)
     piper_metadata.gradient_accumulation = bool(gradient_accumulation)
     piper_metadata.use_inductor = bool(use_inductor)
+    piper_metadata.enable_ep = bool(enable_ep)
     piper_metadata.ar_a2a_same_stream = bool(ar_a2a_same_stream)
     piper_metadata.overlap_zero_ops = bool(overlap_zero_ops)
+    piper_metadata.overlap_chunks = bool(overlap_chunks)
     piper_metadata.schedule_name = schedule_name
     piper_metadata.visualize_dag_render = visualize_dag_render
     piper_metadata.a2a_ar_no_overlap = a2a_ar_no_overlap
     piper_metadata.schedule = schedule
     piper_metadata.visualize_dag = visualize_dag
+    piper_metadata.output_dir = output_dir
 
     # Reset DAG/compile fields so stale data from a prior run never leaks into
     # this run if the piper backend is somehow not re-invoked.
diff --git a/src/piper_exec.py b/src/piper_exec.py
index 4792578..5341954 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -220,7 +220,9 @@ class Task:
       :func:`overlap_a2a_tasks` can introduce forks and joins so the fields
       are lists, mirroring ``data_preds`` / ``data_succs``.
     """
-    chunk: Chunk
+    task_type: TaskType
+    batches: list[BatchMeta]
+    task_pp_rank: int
     pp_rank: int    # row index in PipelineSchedule.grid
     time_step: int  # column index in PipelineSchedule.grid
 
@@ -235,6 +237,7 @@ class Task:
 
     # Set by graph-transform passes (not meaningful for raw schedule tasks)
     bucket_id: int = 0           # stage-local bucket index (stage_bucket_id); actor must not use this
+    resource: str = "compute_stream"
     # Task-type-specific metadata (e.g. {"a2a_tensor_idx": int} for A2A tasks).
     custom_metadata: dict = field(default_factory=dict)
 
@@ -242,6 +245,10 @@ class Task:
     # Set by expand_chunks_to_dags; preserved through all subsequent transforms.
     source_chunk: Optional[Chunk] = None
 
+    # The specific logical chunk that this task belongs to. For FWD_BWD cells,
+    # this distinguishes the derived FWD half from the derived BWD half.
+    associated_chunk: Optional[Chunk] = None
+
     # Unique integer ID assigned at construction; survives pickle round-trips.
     uid: int = field(default_factory=lambda: next(_uid_counter))
 
@@ -263,8 +270,8 @@ class Task:
 
     def node_id(self) -> str:
         """Unique string identifier for use as a graph node key."""
-        ttype = self.chunk.type.value if self.chunk.type is not None else "none"
-        mb = self.chunk.batches[0].mb_idx if self.chunk.batches else "x"
+        ttype = self.task_type.value if self.task_type is not None else "none"
+        mb = self.batches[0].mb_idx if self.batches else "x"
         return f"r{self.pp_rank}_t{self.time_step}_{ttype}_mb{mb}"
 
 
@@ -273,22 +280,26 @@ def runtime_sort_key(node: Task) -> tuple:
 
     Runtime dispatch order is driven by ``Task.time_step`` first. For tasks that
     intentionally share a time step, dispatch uses a fixed priority:
-    SEND, A2A, RS/AR, AG, compute, RECV.
+    SEND, A2A, alloc, AG, compute, RS/AR, free, RECV.
     """
     priority = {
         TaskType.SEND: 0,
-        TaskType.FWD_A2A: 1,
-        TaskType.BWD_A2A: 1,
-        TaskType.REDUCE_SCATTER: 2,
-        TaskType.ALL_REDUCE: 2,
-        TaskType.ALL_GATHER: 3,
-        TaskType.FWD: 4,
-        TaskType.BWD: 4,
-        TaskType.BWD_I: 4,
-        TaskType.BWD_W: 4,
-        TaskType.FWD_BWD: 4,
-        TaskType.RECV: 5,
-    }.get(node.chunk.type, 4)
+        TaskType.FREE_FULL_GRADS: 1,
+        TaskType.FREE_FULL_PARAMS: 1,
+        TaskType.FWD_A2A: 2,
+        TaskType.BWD_A2A: 2,
+        TaskType.REDUCE_SCATTER: 3,
+        TaskType.ALL_REDUCE: 3,
+        TaskType.ALL_GATHER: 4,
+        TaskType.FWD: 5,
+        TaskType.BWD: 5,
+        TaskType.BWD_I: 5,
+        TaskType.BWD_W: 5,
+        TaskType.FWD_BWD: 5,
+        TaskType.ALLOC_FULL_PARAMS: 6,
+        TaskType.ALLOC_FULL_GRADS: 6,
+        TaskType.RECV: 7,
+    }.get(node.task_type, 4)
     return (node.time_step, priority, node.uid)
 
 
@@ -297,17 +308,17 @@ def _rebuild_task_dag(node_data):
     by TaskDAG.__reduce__.  Must be a module-level function so pickle can find
     it by name."""
     nodes = [
-        Task(chunk=d[0], pp_rank=d[1], time_step=d[2], peer_pp_rank=d[3],
-                 bucket_id=d[8], custom_metadata=d[9], source_chunk=d[11],
-                 unique_bucket_id=d[12], compute_loss=d[13])
+        Task(task_type=d[0], batches=d[1], task_pp_rank=d[2], pp_rank=d[3], time_step=d[4], peer_pp_rank=d[5],
+                 bucket_id=d[10], resource=d[11], custom_metadata=d[12], source_chunk=d[14],
+                 associated_chunk=d[15], unique_bucket_id=d[16], compute_loss=d[17])
         for d in node_data
     ]
     for node, d in zip(nodes, node_data):
-        node.data_preds     = [nodes[j] for j in d[4]]
-        node.data_succs     = [nodes[j] for j in d[5]]
-        node.temporal_preds = [nodes[j] for j in d[6]]
-        node.temporal_succs = [nodes[j] for j in d[7]]
-        node.uid            = d[10]
+        node.data_preds     = [nodes[j] for j in d[6]]
+        node.data_succs     = [nodes[j] for j in d[7]]
+        node.temporal_preds = [nodes[j] for j in d[8]]
+        node.temporal_succs = [nodes[j] for j in d[9]]
+        node.uid            = d[13]
     return TaskDAG(nodes=nodes)
 
 
@@ -330,13 +341,13 @@ def __reduce__(self):
         idx = {id(n): i for i, n in enumerate(self.nodes)}
         node_data = [
             (
-                n.chunk, n.pp_rank, n.time_step, n.peer_pp_rank,
+                n.task_type, n.batches, n.task_pp_rank, n.pp_rank, n.time_step, n.peer_pp_rank,
                 [idx[id(p)] for p in n.data_preds],
                 [idx[id(s)] for s in n.data_succs],
                 [idx[id(p)] for p in n.temporal_preds],
                 [idx[id(s)] for s in n.temporal_succs],
-                n.bucket_id, n.custom_metadata, n.uid, n.source_chunk,
-                n.unique_bucket_id, n.compute_loss,
+                n.bucket_id, n.resource, n.custom_metadata, n.uid, n.source_chunk,
+                n.associated_chunk, n.unique_bucket_id, n.compute_loss,
             )
             for n in self.nodes
         ]
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index 73ec459..a11c266 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -13,7 +13,7 @@
 from torch.autograd import Function
 
 from .piper_utils import create_logger, LOG_LEVEL, VERBOSE, piper_metadata
-from .piper_exec import TaskType, PipelineSchedule, Chunk, Task, TaskDAG, logical_time, runtime_sort_key
+from .piper_exec import TaskType, PipelineSchedule, Chunk, Task, TaskDAG, runtime_sort_key
 
 logger = create_logger("piper_graph_transform", LOG_LEVEL)
 
@@ -1335,12 +1335,16 @@ def _splice_a2a_nodes(
             if bkt_id in boundaries and not is_last:
                 next_task = chain[i + 1]
                 a2a_node = Task(
-                    chunk=Chunk(pp_rank=pp_rank, batches=list(task.chunk.batches), type=a2a_type),
+                    task_type=a2a_type,
+                    batches=list(task.batches),
+                    task_pp_rank=pp_rank,
                     pp_rank=pp_rank,
                     time_step=0,
                     bucket_id=bkt_id,
+                    resource="ep_stream",
                     custom_metadata={"a2a_tensor_idx": boundaries[bkt_id]},
                     source_chunk=source_chunk,
+                    associated_chunk=source_chunk,
                 )
                 # Rewire data: task → next_task  ⟹  task → a2a → next_task
                 task.data_succs.remove(next_task)
@@ -1362,12 +1366,16 @@ def _splice_a2a_nodes(
             if bkt_id in boundaries and i > 0:
                 prev_task = chain[i - 1]
                 a2a_node = Task(
-                    chunk=Chunk(pp_rank=pp_rank, batches=list(task.chunk.batches), type=a2a_type),
+                    task_type=a2a_type,
+                    batches=list(task.batches),
+                    task_pp_rank=pp_rank,
                     pp_rank=pp_rank,
                     time_step=0,
                     bucket_id=bkt_id,
+                    resource="ep_stream",
                     custom_metadata={"a2a_tensor_idx": boundaries[bkt_id]},
                     source_chunk=source_chunk,
+                    associated_chunk=source_chunk,
                 )
                 # Rewire data: prev_task → task  ⟹  prev_task → a2a → task
                 prev_task.data_succs.remove(task)
@@ -1420,10 +1428,13 @@ def _build_chunk_task_chain(
 
     if K == 1:
         task = Task(
-            chunk=chunk,
+            task_type=chunk.type,
+            batches=list(chunk.batches),
+            task_pp_rank=chunk.pp_rank,
             pp_rank=pp_rank,
             time_step=col_idx,
             source_chunk=chunk,
+            associated_chunk=chunk,
         )
         return [task], task, task
 
@@ -1432,11 +1443,14 @@ def _build_chunk_task_chain(
         # Execution order: bucket 0, 1, …, K-1
         chain = [
             Task(
-                chunk=Chunk(pp_rank=pp_rank, batches=list(chunk.batches), type=TaskType.FWD),
+                task_type=TaskType.FWD,
+                batches=list(chunk.batches),
+                task_pp_rank=pp_rank,
                 pp_rank=pp_rank,
                 time_step=col_idx * TIME_SCALE + i,
                 bucket_id=b,
                 source_chunk=chunk,
+                associated_chunk=chunk,
             )
             for i, b in enumerate(range(K))
         ]
@@ -1444,11 +1458,14 @@ def _build_chunk_task_chain(
         # BWD / BWD_I: execution order K-1, K-2, …, 0
         chain = [
             Task(
-                chunk=Chunk(pp_rank=pp_rank, batches=list(chunk.batches), type=task_type),
+                task_type=task_type,
+                batches=list(chunk.batches),
+                task_pp_rank=pp_rank,
                 pp_rank=pp_rank,
                 time_step=col_idx * TIME_SCALE + i,
                 bucket_id=K - 1 - i,
                 source_chunk=chunk,
+                associated_chunk=chunk,
             )
             for i in range(K)
         ]
@@ -1576,7 +1593,7 @@ def expand_chunks_to_dags(
                 if chunk.type == TaskType.BWD_I and chunk.batches:
                     b = chunk.batches[0]
                     for t in tasks:
-                        if t.chunk.type == TaskType.BWD_I:
+                        if t.task_type == TaskType.BWD_I:
                             bwdi_tasks[(pp_rank, b.stage_id, b.mb_idx, t.bucket_id)] = t
 
     # ------------------------------------------------------------------
@@ -1588,23 +1605,23 @@ def expand_chunks_to_dags(
     # to _load_stage.
     _compute_types = (TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W)
     _stage_bucket_pairs = sorted(
-        {(t.chunk.batches[0].stage_id, t.bucket_id)
+        {(t.batches[0].stage_id, t.bucket_id)
          for t in all_tasks
-         if t.chunk.type in _compute_types and t.chunk.batches},
+         if t.task_type in _compute_types and t.batches},
         key=lambda x: (x[0], x[1]),
     )
     _sb_to_ubid: dict = {sb: i for i, sb in enumerate(_stage_bucket_pairs)}
 
     _last_stage_id = max(
-        (t.chunk.batches[0].stage_id
+        (t.batches[0].stage_id
          for t in all_tasks
-         if t.chunk.type in _compute_types and t.chunk.batches),
+         if t.task_type in _compute_types and t.batches),
         default=None,
     )
 
     for task in all_tasks:
-        if task.chunk.type in _compute_types and task.chunk.batches:
-            s = task.chunk.batches[0].stage_id
+        if task.task_type in _compute_types and task.batches:
+            s = task.batches[0].stage_id
             task.unique_bucket_id = _sb_to_ubid.get((s, task.bucket_id))
 
     # Set compute_loss=True on the first-to-run BWD/BWD_I task of the last stage.
@@ -1613,9 +1630,9 @@ def expand_chunks_to_dags(
         _last_stage_K = bucket_counts.get(_last_stage_id, 1)
         _last_bkt = _last_stage_K - 1
         for task in all_tasks:
-            if (task.chunk.type in (TaskType.BWD, TaskType.BWD_I)
-                    and task.chunk.batches
-                    and task.chunk.batches[0].stage_id == _last_stage_id
+            if (task.task_type in (TaskType.BWD, TaskType.BWD_I)
+                    and task.batches
+                    and task.batches[0].stage_id == _last_stage_id
                     and task.bucket_id == _last_bkt):
                 task.compute_loss = True
 
@@ -1624,8 +1641,8 @@ def expand_chunks_to_dags(
     # ------------------------------------------------------------------
     bwd_w_tasks: dict = {}  # (pp_rank, stage_id, mb_idx, bucket_id) -> Task
     for task in all_tasks:
-        if task.chunk.type == TaskType.BWD_W and task.chunk.batches:
-            b = task.chunk.batches[0]
+        if task.task_type == TaskType.BWD_W and task.batches:
+            b = task.batches[0]
             bwd_w_tasks[(task.pp_rank, b.stage_id, b.mb_idx, task.bucket_id)] = task
 
     for key, bwdi_task in bwdi_tasks.items():
@@ -1660,24 +1677,32 @@ def expand_chunks_to_dags(
         dst.data_preds.remove(src)
 
         send_node = Task(
-            chunk=Chunk(pp_rank=src.pp_rank, batches=list(src.chunk.batches), type=TaskType.SEND),
+            task_type=TaskType.SEND,
+            batches=list(src.batches),
+            task_pp_rank=src.pp_rank,
             pp_rank=src.pp_rank,
             time_step=0,  # assigned later by assign_time_steps
             peer_pp_rank=dst.pp_rank,
+            resource="pp_stream",
             source_chunk=src.source_chunk,
+            associated_chunk=src.associated_chunk,
         )
         src.data_succs.append(send_node)
         send_node.data_preds.append(src)
 
         recv_metadata = {}
-        if dst.chunk.type in (TaskType.BWD, TaskType.BWD_I):
+        if dst.task_type in (TaskType.BWD, TaskType.BWD_I):
             recv_metadata["fwd_ubid"] = dst.unique_bucket_id
         recv_node = Task(
-            chunk=Chunk(pp_rank=dst.pp_rank, batches=list(dst.chunk.batches), type=TaskType.RECV),
+            task_type=TaskType.RECV,
+            batches=list(dst.batches),
+            task_pp_rank=dst.pp_rank,
             pp_rank=dst.pp_rank,
             time_step=0,  # assigned later by assign_time_steps
             peer_pp_rank=src.pp_rank,
+            resource="pp_stream",
             source_chunk=dst.source_chunk,
+            associated_chunk=dst.associated_chunk,
             custom_metadata=recv_metadata,
         )
         recv_node.data_succs.append(dst)
@@ -1736,7 +1761,7 @@ def _cp_endpoints(chunk_id: int):
         a temporal edge) are handled correctly.
         """
         tasks = [t for t in sc_to_tasks.get(chunk_id, [])
-                 if t.chunk.type in _CRITICAL_PATH_TYPES]
+                 if t.task_type in _CRITICAL_PATH_TYPES]
         if not tasks:
             return None, None
         task_ids = {id(t) for t in tasks}
@@ -1793,11 +1818,9 @@ def _cp_endpoints(chunk_id: int):
             continue
 
         upd_node = Task(
-            chunk=Chunk(
-                pp_rank=ref_tail.pp_rank,
-                batches=list(ref_tail.chunk.batches),
-                type=TaskType.UPD,
-            ),
+            task_type=TaskType.UPD,
+            batches=list(ref_tail.batches),
+            task_pp_rank=ref_tail.pp_rank,
             pp_rank=ref_tail.pp_rank,
             time_step=0,
             source_chunk=non_none[-1][1],
@@ -1859,7 +1882,7 @@ def split_dag_by_rank(dag: "TaskDAG") -> list:
 
 
 def _remove_ar_nodes(rank_dag: TaskDAG) -> None:
-    ar_set = {id(n) for n in rank_dag.nodes if n.chunk.type == TaskType.ALL_REDUCE}
+    ar_set = {id(n) for n in rank_dag.nodes if n.task_type == TaskType.ALL_REDUCE}
     if not ar_set:
         return
     for n in rank_dag.nodes:
@@ -1880,17 +1903,28 @@ def _zero_task(
     ubid: int | None = None,
     source_chunk: Chunk | None = None,
 ) -> Task:
+    resource = "compute_stream"
+    if task_type in {
+        TaskType.ALL_REDUCE,
+        TaskType.REDUCE_SCATTER,
+        TaskType.ALL_GATHER,
+        TaskType.ALLOC_FULL_GRADS,
+        TaskType.FREE_FULL_GRADS,
+        TaskType.ALLOC_FULL_PARAMS,
+        TaskType.FREE_FULL_PARAMS,
+    }:
+        resource = "ep_stream" if piper_metadata.ar_a2a_same_stream else "dp_stream"
     return Task(
-        chunk=Chunk(
-            pp_rank=ref.pp_rank,
-            batches=list(ref.chunk.batches),
-            type=task_type,
-        ),
+        task_type=task_type,
+        batches=list(ref.batches),
+        task_pp_rank=ref.pp_rank,
         pp_rank=ref.pp_rank,
         time_step=time_step,
         bucket_id=bucket_id,
+        resource=resource,
         unique_bucket_id=ubid,
         source_chunk=source_chunk or ref.source_chunk,
+        associated_chunk=ref.associated_chunk,
     )
 
 
@@ -1901,12 +1935,12 @@ def _bucket_compute_tasks(
     compute_types = {TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W}
     by_ubid: dict[int, list[Task]] = defaultdict(list)
     for node in rank_dag.nodes:
-        if node.chunk.type not in compute_types or node.unique_bucket_id is None:
+        if node.task_type not in compute_types or node.unique_bucket_id is None:
             continue
         if bucket_filter_keys is not None:
-            if not node.chunk.batches:
+            if not node.batches:
                 continue
-            stage_id = node.chunk.batches[0].stage_id
+            stage_id = node.batches[0].stage_id
             if (stage_id, node.bucket_id) not in bucket_filter_keys:
                 continue
         by_ubid[node.unique_bucket_id].append(node)
@@ -1922,6 +1956,35 @@ def _add_data_edge(src: Task, dst: Task) -> None:
         dst.data_preds.append(src)
 
 
+def _add_temporal_edge(src: Task, dst: Task) -> None:
+    if dst not in src.temporal_succs:
+        src.temporal_succs.append(dst)
+    if src not in dst.temporal_preds:
+        dst.temporal_preds.append(src)
+
+
+def _remove_data_edge(src: Task, dst: Task) -> None:
+    if dst in src.data_succs:
+        src.data_succs.remove(dst)
+    if src in dst.data_preds:
+        dst.data_preds.remove(src)
+
+
+def _remove_temporal_edge(src: Task, dst: Task) -> None:
+    if dst in src.temporal_succs:
+        src.temporal_succs.remove(dst)
+    if src in dst.temporal_preds:
+        dst.temporal_preds.remove(src)
+
+
+def _append_metadata_ubid(node: Task, key: str, ubid: int | None) -> None:
+    if ubid is None:
+        return
+    values = node.custom_metadata.setdefault(key, [])
+    if ubid not in values:
+        values.append(ubid)
+
+
 def _insert_zero_grad_ops(
     rank_dag: TaskDAG,
     *,
@@ -1942,7 +2005,7 @@ def _insert_zero_grad_ops(
     new_nodes: list[Task] = []
 
     for ubid, bucket_nodes in _bucket_compute_tasks(rank_dag, zero_bucket_keys).items():
-        grad_nodes = [node for node in bucket_nodes if node.chunk.type in grad_sync_types]
+        grad_nodes = [node for node in bucket_nodes if node.task_type in grad_sync_types]
         if not grad_nodes:
             continue
         first_compute = min(grad_nodes, key=lambda node: (node.time_step, node.uid))
@@ -1960,22 +2023,16 @@ def _insert_zero_grad_ops(
 
         if use_grad_lifetime:
             if gradient_accumulation:
-                alloc = _zero_task(first_compute, first_compute.bucket_id, TaskType.ALLOC_FULL_GRADS, ubid=ubid)
-                free = _zero_task(last_compute, last_compute.bucket_id, TaskType.FREE_FULL_GRADS, ubid=ubid)
-                _add_data_edge(alloc, first_compute)
-                _add_data_edge(new_nodes[-1], free)
-                new_nodes.extend([alloc, free])
+                first_compute.custom_metadata["zero_alloc_full_grads_before"] = True
+                _append_metadata_ubid(new_nodes[-1], "zero_free_full_grads_after_ubids", ubid)
             else:
                 for compute_node in grad_nodes:
                     sync = next(
                         node for node in new_nodes
-                        if node.chunk.type == sync_type and node.unique_bucket_id == ubid and compute_node in node.data_preds
+                        if node.task_type == sync_type and node.unique_bucket_id == ubid and compute_node in node.data_preds
                     )
-                    alloc = _zero_task(compute_node, compute_node.bucket_id, TaskType.ALLOC_FULL_GRADS, ubid=ubid)
-                    free = _zero_task(compute_node, compute_node.bucket_id, TaskType.FREE_FULL_GRADS, ubid=ubid)
-                    _add_data_edge(alloc, compute_node)
-                    _add_data_edge(sync, free)
-                    new_nodes.extend([alloc, free])
+                    compute_node.custom_metadata["zero_alloc_full_grads_before"] = True
+                    _append_metadata_ubid(sync, "zero_free_full_grads_after_ubids", ubid)
 
     rank_dag.nodes.extend(new_nodes)
 
@@ -2030,56 +2087,155 @@ def _insert_zero3_ops(
 
     for ubid, bucket_nodes in _bucket_compute_tasks(rank_dag, zero_bucket_keys).items():
         for compute_node in bucket_nodes:
-            t = compute_node.chunk.type
+            t = compute_node.task_type
             bid = compute_node.bucket_id
 
             if t in param_only_types:
-                alloc_p = _zero_task(compute_node, bid, TaskType.ALLOC_FULL_PARAMS, ubid=ubid)
                 ag      = _zero_task(compute_node, bid, TaskType.ALL_GATHER,        ubid=ubid)
-                free_p  = _zero_task(compute_node, bid, TaskType.FREE_FULL_PARAMS,  ubid=ubid)
-                _add_data_edge(alloc_p, ag)
                 _add_data_edge(ag, compute_node)
-                _add_data_edge(compute_node, free_p)
-                new_nodes.extend([alloc_p, ag, free_p])
+                ag.custom_metadata["zero_alloc_full_params_before"] = True
+                compute_node.custom_metadata["zero_free_full_params_after"] = True
+                new_nodes.append(ag)
 
             elif t in grad_types:
-                alloc_p = _zero_task(compute_node, bid, TaskType.ALLOC_FULL_PARAMS, ubid=ubid)
                 ag      = _zero_task(compute_node, bid, TaskType.ALL_GATHER,        ubid=ubid)
-                free_p  = _zero_task(compute_node, bid, TaskType.FREE_FULL_PARAMS,  ubid=ubid)
-                _add_data_edge(alloc_p, ag)
                 _add_data_edge(ag, compute_node)
-                _add_data_edge(compute_node, free_p)
-                new_nodes.extend([alloc_p, ag, free_p])
+                ag.custom_metadata["zero_alloc_full_params_before"] = True
+                compute_node.custom_metadata["zero_free_full_params_after"] = True
+                new_nodes.append(ag)
 
     # Insert grad alloc/reduce/free for grad-producing ZeRO-3 nodes.
     for ubid, bucket_nodes in _bucket_compute_tasks(rank_dag, zero_bucket_keys).items():
-        grad_nodes = [node for node in bucket_nodes if node.chunk.type in grad_types]
+        grad_nodes = [node for node in bucket_nodes if node.task_type in grad_types]
         if not grad_nodes:
             continue
         first_compute = min(grad_nodes, key=lambda node: (node.time_step, node.uid))
         last_compute = max(grad_nodes, key=lambda node: (node.time_step, node.uid))
 
         if gradient_accumulation:
-            alloc_g = _zero_task(first_compute, first_compute.bucket_id, TaskType.ALLOC_FULL_GRADS, ubid=ubid)
             rs = _zero_task(last_compute, last_compute.bucket_id, TaskType.REDUCE_SCATTER, ubid=ubid)
-            free_g = _zero_task(last_compute, last_compute.bucket_id, TaskType.FREE_FULL_GRADS, ubid=ubid)
-            _add_data_edge(alloc_g, first_compute)
             _add_data_edge(last_compute, rs)
-            _add_data_edge(rs, free_g)
-            new_nodes.extend([alloc_g, rs, free_g])
+            first_compute.custom_metadata["zero_alloc_full_grads_before"] = True
+            _append_metadata_ubid(rs, "zero_free_full_grads_after_ubids", ubid)
+            new_nodes.append(rs)
         else:
             for compute_node in grad_nodes:
-                alloc_g = _zero_task(compute_node, compute_node.bucket_id, TaskType.ALLOC_FULL_GRADS, ubid=ubid)
                 rs = _zero_task(compute_node, compute_node.bucket_id, TaskType.REDUCE_SCATTER, ubid=ubid)
-                free_g = _zero_task(compute_node, compute_node.bucket_id, TaskType.FREE_FULL_GRADS, ubid=ubid)
-                _add_data_edge(alloc_g, compute_node)
                 _add_data_edge(compute_node, rs)
-                _add_data_edge(rs, free_g)
-                new_nodes.extend([alloc_g, rs, free_g])
+                compute_node.custom_metadata["zero_alloc_full_grads_before"] = True
+                _append_metadata_ubid(rs, "zero_free_full_grads_after_ubids", ubid)
+                new_nodes.append(rs)
 
     rank_dag.nodes.extend(new_nodes)
 
 
+def _fuse_consecutive_zero3_param_sequences(
+    rank_dag: TaskDAG,
+    *,
+    zero_bucket_keys: set | None = None,
+) -> None:
+    """Fuse ZeRO runtime metadata across consecutive same-ubid compute runs."""
+
+    def _ordered_compute_nodes() -> list[Task]:
+        compute_types = {TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W}
+        selected: list[Task] = []
+        for node in rank_dag.nodes:
+            if node.task_type not in compute_types or node.unique_bucket_id is None:
+                continue
+            if zero_bucket_keys is not None:
+                if not node.batches:
+                    continue
+                stage_id = node.batches[0].stage_id
+                if (stage_id, node.bucket_id) not in zero_bucket_keys:
+                    continue
+            selected.append(node)
+
+        if not selected:
+            return []
+
+        selected_ids = {id(node) for node in selected}
+        in_degree: dict[int, int] = {id(node): 0 for node in selected}
+        succs: dict[int, list[Task]] = {id(node): [] for node in selected}
+        for node in selected:
+            for succ in node.temporal_succs:
+                if id(succ) not in selected_ids:
+                    continue
+                succs[id(node)].append(succ)
+                in_degree[id(succ)] += 1
+
+        ready = sorted(
+            [node for node in selected if in_degree[id(node)] == 0],
+            key=lambda node: (node.time_step, node.uid),
+        )
+        ordered: list[Task] = []
+        while ready:
+            node = ready.pop(0)
+            ordered.append(node)
+            newly_ready: list[Task] = []
+            for succ in sorted(succs[id(node)], key=lambda cur: (cur.time_step, cur.uid)):
+                succ_id = id(succ)
+                in_degree[succ_id] -= 1
+                if in_degree[succ_id] == 0:
+                    newly_ready.append(succ)
+            if newly_ready:
+                ready.extend(newly_ready)
+                ready.sort(key=lambda cur: (cur.time_step, cur.uid))
+
+        if len(ordered) != len(selected):
+            return sorted(selected, key=lambda node: (node.time_step, node.uid))
+        return ordered
+
+    ordered = _ordered_compute_nodes()
+    runs: list[list[Task]] = []
+    cur_run: list[Task] = []
+    for node in ordered:
+        if cur_run and node.unique_bucket_id == cur_run[-1].unique_bucket_id:
+            cur_run.append(node)
+            continue
+        if len(cur_run) > 1:
+            runs.append(cur_run)
+        cur_run = [node]
+    if len(cur_run) > 1:
+        runs.append(cur_run)
+
+    for run in runs:
+        ubid = run[0].unique_bucket_id
+        ag_nodes: list[Task] = []
+        for compute_node in run:
+            ag = _find_zero_neighbor(compute_node, TaskType.ALL_GATHER, preds=True, ubid=ubid)
+            if ag is None:
+                ag_nodes = []
+                break
+            ag_nodes.append(ag)
+        if ag_nodes:
+            for ag in ag_nodes[1:]:
+                ag.custom_metadata.pop("zero_alloc_full_params_before", None)
+            for compute_node in run[:-1]:
+                compute_node.custom_metadata.pop("zero_free_full_params_after", None)
+
+        grad_run = [node for node in run if node.task_type in {TaskType.BWD, TaskType.BWD_W}]
+        if not grad_run:
+            continue
+        rs_nodes: list[Task] = []
+        for compute_node in grad_run:
+            rs = _find_zero_neighbor(compute_node, TaskType.REDUCE_SCATTER, preds=False, ubid=ubid)
+            if rs is None:
+                rs_nodes = []
+                break
+            rs_nodes.append(rs)
+        if not rs_nodes:
+            continue
+        for compute_node in grad_run[1:]:
+            compute_node.custom_metadata.pop("zero_alloc_full_grads_before", None)
+        for rs in rs_nodes[:-1]:
+            ubids = rs.custom_metadata.get("zero_free_full_grads_after_ubids", [])
+            rs.custom_metadata["zero_free_full_grads_after_ubids"] = [
+                cur for cur in ubids if cur != ubid
+            ]
+            if not rs.custom_metadata["zero_free_full_grads_after_ubids"]:
+                rs.custom_metadata.pop("zero_free_full_grads_after_ubids", None)
+
+
 def _zero_sequence_compute_nodes(rank_dag: TaskDAG, zero_stage: int) -> list[Task]:
     if zero_stage == 2:
         compute_types = {TaskType.BWD, TaskType.BWD_W}
@@ -2088,7 +2244,7 @@ def _zero_sequence_compute_nodes(rank_dag: TaskDAG, zero_stage: int) -> list[Tas
     else:
         return []
     return sorted(
-        [node for node in rank_dag.nodes if node.chunk.type in compute_types],
+        [node for node in rank_dag.nodes if node.task_type in compute_types],
         key=lambda node: (node.time_step, node.uid),
     )
 
@@ -2096,7 +2252,7 @@ def _zero_sequence_compute_nodes(rank_dag: TaskDAG, zero_stage: int) -> list[Tas
 def _find_zero_neighbor(node: Task, task_type: TaskType, *, preds: bool, ubid: int | None) -> Task | None:
     neighbors = node.data_preds if preds else node.data_succs
     for neighbor in neighbors:
-        if neighbor.chunk.type != task_type:
+        if neighbor.task_type != task_type:
             continue
         if ubid is not None and neighbor.unique_bucket_id != ubid:
             continue
@@ -2106,7 +2262,7 @@ def _find_zero_neighbor(node: Task, task_type: TaskType, *, preds: bool, ubid: i
 
 def _collect_zero_sequence_nodes(compute_node: Task, zero_stage: int) -> dict[TaskType, Task]:
     ubid = compute_node.unique_bucket_id
-    seq: dict[TaskType, Task] = {compute_node.chunk.type: compute_node}
+    seq: dict[TaskType, Task] = {compute_node.task_type: compute_node}
     if zero_stage == 2:
         alloc_g = _find_zero_neighbor(compute_node, TaskType.ALLOC_FULL_GRADS, preds=True, ubid=ubid)
         rs = _find_zero_neighbor(compute_node, TaskType.REDUCE_SCATTER, preds=False, ubid=ubid)
@@ -2129,7 +2285,7 @@ def _collect_zero_sequence_nodes(compute_node: Task, zero_stage: int) -> dict[Ta
     free_p = _find_zero_neighbor(compute_node, TaskType.FREE_FULL_PARAMS, preds=False, ubid=ubid)
     if free_p is not None:
         seq[TaskType.FREE_FULL_PARAMS] = free_p
-    if compute_node.chunk.type in {TaskType.BWD, TaskType.BWD_W}:
+    if compute_node.task_type in {TaskType.BWD, TaskType.BWD_W}:
         alloc_g = _find_zero_neighbor(compute_node, TaskType.ALLOC_FULL_GRADS, preds=True, ubid=ubid)
         rs = _find_zero_neighbor(compute_node, TaskType.REDUCE_SCATTER, preds=False, ubid=ubid)
         free_g = _find_zero_neighbor(rs, TaskType.FREE_FULL_GRADS, preds=False, ubid=ubid) if rs else None
@@ -2156,136 +2312,6 @@ def _reachable_downstream(node: Task) -> set[int]:
     return seen
 
 
-def modify_zero_timesteps(rank_dag: TaskDAG, zero_stage: int) -> None:
-    if zero_stage in (0, 1):
-        return
-
-    for compute_node in _zero_sequence_compute_nodes(rank_dag, zero_stage):
-        base_time_step = compute_node.time_step
-        seq_nodes = _collect_zero_sequence_nodes(compute_node, zero_stage)
-        seq_node_ids = {id(node) for node in seq_nodes.values()}
-        downstream_ids = _reachable_downstream(compute_node)
-
-        for node in rank_dag.nodes:
-            if id(node) in downstream_ids and id(node) not in seq_node_ids:
-                node.time_step += 4
-
-        compute_node.time_step = base_time_step + 2
-        if zero_stage == 2:
-            alloc_g = seq_nodes.get(TaskType.ALLOC_FULL_GRADS)
-            rs = seq_nodes.get(TaskType.REDUCE_SCATTER)
-            free_g = seq_nodes.get(TaskType.FREE_FULL_GRADS)
-            if alloc_g is not None:
-                alloc_g.time_step = base_time_step + 1
-            if rs is not None:
-                rs.time_step = base_time_step + 3
-            if free_g is not None:
-                free_g.time_step = base_time_step + 4
-
-
-def overlap_zero_ops(rank_dag: TaskDAG) -> None:
-    compute_types = frozenset({TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W})
-    zero_chain_types = frozenset({
-        TaskType.ALL_GATHER,
-        TaskType.REDUCE_SCATTER,
-        TaskType.ALL_REDUCE,
-        TaskType.ALLOC_FULL_GRADS,
-        TaskType.FREE_FULL_GRADS,
-        TaskType.ALLOC_FULL_PARAMS,
-        TaskType.FREE_FULL_PARAMS,
-    })
-
-    def _nodes_at_time_step(time_step: int) -> list[Task]:
-        return [node for node in rank_dag.nodes if node.time_step == time_step]
-
-    def _has_overlap_with_types(node: Task, types: frozenset[TaskType]) -> bool:
-        return any(
-            other is not node and other.chunk.type in types and other.time_step == node.time_step
-            for other in rank_dag.nodes
-        )
-
-    def _nearest_compute_before(time_step: int) -> Task | None:
-        candidates = [
-            node for node in rank_dag.nodes
-            if node.chunk.type in compute_types and node.time_step < time_step
-        ]
-        if not candidates:
-            return None
-        return max(candidates, key=lambda node: (node.time_step, -node.uid))
-
-    def _nearest_compute_after(time_step: int) -> Task | None:
-        candidates = [
-            node for node in rank_dag.nodes
-            if node.chunk.type in compute_types and node.time_step > time_step
-        ]
-        if not candidates:
-            return None
-        return min(candidates, key=lambda node: (node.time_step, node.uid))
-
-    def _collect_zero_preds(node: Task) -> list[Task]:
-        result: list[Task] = []
-        seen: set[int] = set()
-        stack = list(node.data_preds)
-        while stack:
-            cur = stack.pop()
-            cur_id = id(cur)
-            if cur_id in seen or cur.chunk.type not in zero_chain_types:
-                continue
-            seen.add(cur_id)
-            result.append(cur)
-            stack.extend(cur.data_preds)
-        return result
-
-    def _collect_zero_succs(node: Task) -> list[Task]:
-        result: list[Task] = []
-        seen: set[int] = set()
-        stack = list(node.data_succs)
-        while stack:
-            cur = stack.pop()
-            cur_id = id(cur)
-            if cur_id in seen or cur.chunk.type not in zero_chain_types:
-                continue
-            seen.add(cur_id)
-            result.append(cur)
-            stack.extend(cur.data_succs)
-        return result
-
-    def _shift_nodes(nodes: list[Task], delta: int) -> None:
-        if delta == 0:
-            return
-        for node in nodes:
-            node.time_step += delta
-
-    ag_nodes = sorted(
-        [node for node in rank_dag.nodes if node.chunk.type == TaskType.ALL_GATHER],
-        key=lambda node: (node.time_step, node.uid),
-    )
-    for ag in ag_nodes:
-        if _has_overlap_with_types(ag, compute_types):
-            continue
-        target = _nearest_compute_before(ag.time_step)
-        if target is None:
-            continue
-        delta = target.time_step - ag.time_step
-        _shift_nodes([ag] + _collect_zero_preds(ag), delta)
-
-    reduce_nodes = sorted(
-        [
-            node for node in rank_dag.nodes
-            if node.chunk.type in (TaskType.REDUCE_SCATTER, TaskType.ALL_REDUCE)
-        ],
-        key=lambda node: (node.time_step, node.uid),
-    )
-    for red in reduce_nodes:
-        if _has_overlap_with_types(red, compute_types):
-            continue
-        target = _nearest_compute_after(red.time_step)
-        if target is None:
-            continue
-        delta = target.time_step - red.time_step
-        _shift_nodes([red] + _collect_zero_succs(red), delta)
-
-
 def _wire_sync_to_upd(rank_dag: TaskDAG) -> None:
     """Add temporal edges from every AR/RS node → UPD on this rank.
 
@@ -2294,9 +2320,9 @@ def _wire_sync_to_upd(rank_dag: TaskDAG) -> None:
     that constraint so that ``assign_time_steps`` Pass 3 can push UPD's
     time_step past the last AR/RS.
     """
-    upd_nodes  = [n for n in rank_dag.nodes if n.chunk.type == TaskType.UPD]
+    upd_nodes  = [n for n in rank_dag.nodes if n.task_type == TaskType.UPD]
     sync_nodes = [n for n in rank_dag.nodes
-                  if n.chunk.type in (TaskType.ALL_REDUCE, TaskType.REDUCE_SCATTER)]
+                  if n.task_type in (TaskType.ALL_REDUCE, TaskType.REDUCE_SCATTER)]
     for upd in upd_nodes:
         for sync in sync_nodes:
             if upd not in sync.temporal_succs:
@@ -2350,10 +2376,11 @@ def insert_zero_ops(
 
 def _task_node_label(node: Task) -> str:
     """Short human-readable label for a Task."""
-    chunk = node.chunk
+    task_type = node.task_type
+    batches = node.batches
 
-    if chunk.type in (TaskType.SEND, TaskType.RECV):
-        op = "SEND" if chunk.type == TaskType.SEND else "RECV"
+    if task_type in (TaskType.SEND, TaskType.RECV):
+        op = "SEND" if task_type == TaskType.SEND else "RECV"
         return op
 
     type_abbrev = {
@@ -2372,12 +2399,12 @@ def _task_node_label(node: Task) -> str:
         TaskType.FREE_FULL_PARAMS: "P-",
         TaskType.FWD_A2A: "F_A2A",
         TaskType.BWD_A2A: "B_A2A",
-    }.get(chunk.type, "?")
+    }.get(task_type, "?")
 
-    if chunk.type == TaskType.UPD:
+    if task_type == TaskType.UPD:
         return type_abbrev
 
-    if chunk.type in (
+    if task_type in (
         TaskType.ALL_REDUCE,
         TaskType.REDUCE_SCATTER,
         TaskType.ALL_GATHER,
@@ -2386,13 +2413,12 @@ def _task_node_label(node: Task) -> str:
         TaskType.ALLOC_FULL_PARAMS,
         TaskType.FREE_FULL_PARAMS,
     ):
-        stage_id = chunk.batches[0].stage_id
         return type_abbrev
 
-    if chunk.type in (TaskType.FWD_A2A, TaskType.BWD_A2A):
+    if task_type in (TaskType.FWD_A2A, TaskType.BWD_A2A):
         return type_abbrev
 
-    parts = " + ".join(f"S{b.stage_id} M{b.mb_idx}" for b in chunk.batches)
+    parts = " + ".join(f"S{b.stage_id} M{b.mb_idx}" for b in batches)
     return f"{type_abbrev} {parts}"
 
 
@@ -2481,9 +2507,9 @@ def visualize_dag(
     # Build a mapping from pp_rank -> sorted list of stage_ids on that rank.
     pp_rank_stages: dict[int, list[int]] = {}
     for n in dag.nodes:
-        if n.chunk.batches:
-            rank = n.chunk.pp_rank
-            sid = n.chunk.batches[0].stage_id
+        if n.batches:
+            rank = n.task_pp_rank
+            sid = n.batches[0].stage_id
             if rank not in pp_rank_stages:
                 pp_rank_stages[rank] = []
             if sid not in pp_rank_stages[rank]:
@@ -2496,7 +2522,7 @@ def visualize_dag(
     )
 
     def _node_fill(node: Task) -> str:
-        t = node.chunk.type
+        t = node.task_type
         if t in (
             TaskType.SEND, TaskType.RECV, TaskType.FWD_A2A, TaskType.BWD_A2A,
             TaskType.ALL_REDUCE, TaskType.REDUCE_SCATTER, TaskType.ALL_GATHER,
@@ -2513,14 +2539,14 @@ def _node_fill(node: Task) -> str:
         return "#D5D8DC"
 
     def _node_fontcolor(node: Task) -> str:
-        t = node.chunk.type
-        if t not in _COMPUTE_TYPES or not node.chunk.batches:
+        t = node.task_type
+        if t not in _COMPUTE_TYPES or not node.batches:
             return "black"
-        rank = node.chunk.pp_rank
+        rank = node.task_pp_rank
         stages = pp_rank_stages.get(rank, [])
         if len(stages) < 2:
             return "black"
-        sid = node.chunk.batches[0].stage_id
+        sid = node.batches[0].stage_id
         # First stage -> black text, second (and beyond) stage -> white text
         return "black" if sid == stages[0] else "white"
 
@@ -2557,7 +2583,7 @@ def _node_fontcolor(node: Task) -> str:
             label=label,
             fillcolor=_node_fill(node),
             fontcolor=_node_fontcolor(node),
-            tooltip=repr(node.chunk),
+            tooltip=repr(node.source_chunk) if node.source_chunk is not None else node.task_type.value,
             color="red" if on_critical else "black",
             penwidth="3.0" if on_critical else "1.0",
         )
@@ -2603,14 +2629,19 @@ def _node_fontcolor(node: Task) -> str:
 
 
 
-def print_dag_order(dag: TaskDAG, label: str = "", rank: int = 0) -> None:
+def print_dag_order(
+    dag: TaskDAG,
+    label: str = "",
+    rank: int = 0,
+    out_dir: str = "out",
+) -> None:
     """Write the execution order of nodes in a :class:`TaskDAG` to a file.
 
     Nodes are sorted by :func:`runtime_sort_key`, exactly as in ``run_dag``,
     so the output reflects exactly what the actor will execute.
     Useful for debugging schedule issues.
 
-    Output is written to ``out/dag_order_rank{rank}``.
+    Output is written to ``{out_dir}/dag_order_rank{rank}``.
     """
     import os
 
@@ -2619,23 +2650,22 @@ def print_dag_order(dag: TaskDAG, label: str = "", rank: int = 0) -> None:
     header = f"--- DAG execution order{': ' + label if label else ''} ---"
     lines = [header]
     for step, node in enumerate(sorted_nodes):
-        chunk = node.chunk
-        ttype = chunk.type.value if chunk.type is not None else "?"
+        ttype = node.task_type.value if node.task_type is not None else "?"
         batches_str = ", ".join(
-            f"s{b.stage_id} mb{b.mb_idx}" for b in chunk.batches
-        ) if chunk.batches else ""
+            f"s{b.stage_id} mb{b.mb_idx}" for b in node.batches
+        ) if node.batches else ""
         bkt = f" bkt={node.bucket_id}" if node.bucket_id else ""
         ubid = f" ubid={node.unique_bucket_id}" if node.unique_bucket_id is not None else ""
-        lines.append(
-            f"  {step:3d}  ts={node.time_step:3d}  rank={node.pp_rank}  {ttype:<14s}  {batches_str}{bkt}{ubid}"
-        )
+        line = f"  {step:3d}  ts={node.time_step:3d}  rank={node.pp_rank}  {ttype:<14s}  {batches_str}{bkt}{ubid}"
+        lines.append(line)
+        logger.info(line)
     lines.append("-" * len(header))
 
     for line in lines:
         logger.debug(line)
 
-    os.makedirs("out", exist_ok=True)
-    out_path = f"out/dag_order_rank{rank}"
+    os.makedirs(out_dir, exist_ok=True)
+    out_path = os.path.join(out_dir, f"dag_order_rank{rank}")
     with open(out_path, "w") as f:
         f.write("\n".join(lines) + "\n")
 
@@ -3043,10 +3073,66 @@ def _bucket_at(idx: int) -> int:
         else:
             merged.append((f, l, ms))
 
+    alias_methods = {
+        "view", "_unsafe_view", "reshape", "transpose", "permute", "t",
+        "movedim", "moveaxis", "swapdims", "swapaxes",
+        "select", "narrow", "slice", "split", "chunk", "unbind",
+        "unsqueeze", "squeeze", "flatten", "expand", "diagonal",
+        "detach", "alias", "as_strided",
+    }
+    multi_output_alias_methods = {"split", "chunk", "unbind"}
+
+    alias_function_names = {
+        "view", "_unsafe_view", "reshape", "transpose", "permute", "t",
+        "movedim", "moveaxis", "swapdims", "swapaxes",
+        "select", "narrow", "slice", "split", "chunk", "unbind",
+        "unsqueeze", "squeeze", "flatten", "expand", "diagonal",
+        "detach", "alias", "as_strided",
+    }
+    multi_output_alias_function_names = {"split", "chunk", "unbind"}
+    hard_forbid_cross_bucket_prefixes = ("bfloat16_",)
+
+    def _target_name(target: object) -> str | None:
+        if isinstance(target, str):
+            return target
+        return getattr(target, "__name__", None)
+
+    def _alias_passthrough_sources(nd: fx.Node) -> set[fx.Node]:
+        if nd.op == "call_method":
+            if nd.target in alias_methods:
+                base = next(iter(nd.all_input_nodes), None)
+                return set(alias_sources.get(base, set())) if base is not None else set()
+            return set()
+
+        if nd.op != "call_function":
+            return set()
+
+        if nd.target == operator.getitem:
+            base = nd.args[0] if nd.args else None
+            if isinstance(base, fx.Node):
+                return set(alias_sources.get(base, set()))
+            return set()
+
+        name = _target_name(nd.target)
+        if name in alias_function_names:
+            base = next(iter(nd.all_input_nodes), None)
+            return set(alias_sources.get(base, set())) if base is not None else set()
+        return set()
+
+    alias_sources: dict[fx.Node, set[fx.Node]] = {}
+    for nd in nodes:
+        if nd.op == "placeholder":
+            alias_sources[nd] = {nd} if nd in param_ph_set else set()
+            continue
+        if nd.op in ("get_attr", "output"):
+            alias_sources[nd] = set()
+            continue
+        alias_sources[nd] = _alias_passthrough_sources(nd)
+
     def _compute_seg_metadata(
         seg_ranges: list[tuple[int, int, list[fx.Node]]]
-    ) -> tuple[dict[fx.Node, int], list[list[fx.Node]]]:
-        """Return (node_seg, seg_cross_in) for the given segment ranges."""
+    ) -> tuple[dict[fx.Node, int], dict[fx.Node, int], list[list[fx.Node]]]:
+        """Return (node_seg, node_max_user_seg, seg_cross_in) for segment ranges."""
         cut_after = [seg_ranges[i][1] for i in range(len(seg_ranges) - 1)]
 
         def _seg_of(idx: int) -> int:
@@ -3087,39 +3173,37 @@ def _seg_of(idx: int) -> int:
             for seg in range(s + 1, mu + 1):
                 seg_cross_in[seg].append(nd)
 
-        return node_seg, seg_cross_in
+        return node_seg, node_max_user_seg, seg_cross_in
 
-    # Enforce a single activation-tensor dependency between adjacent buckets.
-    # If a boundary carries multiple values or only stage-input placeholders,
-    # merge the adjacent buckets until each remaining boundary is a single
-    # non-placeholder tensor produced by the previous bucket.
-    while len(merged) > 1:
-        _node_seg_tmp, seg_cross_in_tmp = _compute_seg_metadata(merged)
-        invalid_boundary = None
+    n_segs = len(merged)
+    if n_segs == 1:
+        return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))]
+
+    while True:
+        node_seg, node_max_user_seg, seg_cross_in = _compute_seg_metadata(merged)
+        merged_boundary = False
         for seg in range(1, len(merged)):
-            cross_inputs = seg_cross_in_tmp[seg]
-            if len(cross_inputs) != 1 or cross_inputs[0].op == "placeholder":
-                invalid_boundary = seg - 1
-                logger.debug(
-                    "bucket_stage: merging adjacent buckets due to invalid boundary "
-                    f"between segments {seg - 1} and {seg}: "
-                    f"cross_inputs={[n.name for n in cross_inputs]}"
+            alias_crossers = [
+                nd for nd in seg_cross_in[seg]
+                if alias_sources.get(nd) or any(
+                    nd.name.startswith(prefix) for prefix in hard_forbid_cross_bucket_prefixes
                 )
-                break
-        if invalid_boundary is None:
+            ]
+            if not alias_crossers:
+                continue
+            pf, _pl, pms = merged[seg - 1]
+            _cf, cl, cms = merged[seg]
+            merged[seg - 1] = (pf, cl, pms + cms)
+            del merged[seg]
+            merged_boundary = True
+            break
+        if not merged_boundary:
             break
-        f0, _l0, ms0 = merged[invalid_boundary]
-        _f1, l1, ms1 = merged[invalid_boundary + 1]
-        merged[invalid_boundary:invalid_boundary + 2] = [
-            (f0, l1, ms0 + ms1)
-        ]
 
     n_segs = len(merged)
     if n_segs == 1:
         return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))]
 
-    node_seg, seg_cross_in = _compute_seg_metadata(merged)
-
     # Build sub-graphs
     results: list[tuple[fx.GraphModule, list[int], list[int], list]] = []
 
@@ -3755,329 +3839,179 @@ def _add_ph(nd: fx.Node, name: str, is_input: bool) -> None:
 
 
 # ---------------------------------------------------------------------------
-# find_overlappable_tasks: identify adjacent task pairs safe to overlap
+# get_overlappable_tasks / overlap_chunks
 # ---------------------------------------------------------------------------
 
-def find_overlappable_tasks(schedule: PipelineSchedule) -> list[tuple[Chunk, Chunk]]:
-    """Find pairs of adjacent tasks in the schedule that can be safely overlapped.
-
-    A pair ``(t1, t2)`` where t1 precedes t2 on the same pp_rank is overlappable
-    iff all three conditions hold:
-
-    1. **No data dependency**: t1 and t2 process entirely different microbatches.
-    2. **Upstream deps satisfied before overlap start**: the upstream tasks of *both*
-       t1 and t2 complete by ``min(start(t1), start(t2))``.
-    3. **Downstream deps deferred past overlap end**: the downstream tasks of *both*
-       t1 and t2 start no earlier than ``max(end(t1), end(t2))``.
-
-    Conditions 2 (for t1) and 3 (for t2) are trivially satisfied by any valid
-    schedule, so the effective non-trivial checks are:
-
-    * ``upstream_end(t2) <= min(start(t1), start(t2))``
-    * ``downstream_start(t1) >= max(end(t1), end(t2))``
-
-    Args:
-        schedule: The pipeline schedule (``list[list[Chunk]]``, no Nones).
-
-    Returns:
-        A list of ``(t1, t2)`` :class:`Chunk` pairs suitable for passing to
-        :func:`overlap_a2a_tasks`.
-    """
-    start_times_per_rank = schedule._compute_start_times()
-
-    # chunk_id -> (logical_start, logical_end)
-    chunk_times: dict[int, tuple[int, int]] = {}
-    for row, starts in zip(schedule.grid, start_times_per_rank):
-        for chunk, start in zip(row, starts):
-            chunk_times[id(chunk)] = (start, start + logical_time(chunk))
-
-    # mb_idx -> sorted list of (start, end) for every chunk across all ranks that
-    # touches that microbatch.
-    mb_timeline: dict[int, list[tuple[int, int]]] = defaultdict(list)
+def get_overlappable_tasks(schedule: PipelineSchedule) -> list[tuple[Chunk, Chunk]]:
+    """Return one ``(FWD, BWD)`` pair for each ``FWD_BWD`` schedule cell."""
+    result: list[tuple[Chunk, Chunk]] = []
     for row in schedule.grid:
         for chunk in row:
-            start, end = chunk_times[id(chunk)]
-            for batch in chunk.batches:
-                mb_timeline[batch.mb_idx].append((start, end))
-    for mb_idx in mb_timeline:
-        mb_timeline[mb_idx].sort()
-
-    def _upstream_end(chunk: Chunk, chunk_start: int) -> int | None:
-        """Latest end time of any chunk that starts before *chunk_start* and shares
-        a microbatch with *chunk*, or ``None`` if no such chunk exists."""
-        best: int | None = None
-        for batch in chunk.batches:
-            for s, e in mb_timeline.get(batch.mb_idx, []):
-                if s < chunk_start:
-                    best = e if best is None else max(best, e)
-        return best
-
-    def _downstream_start(chunk: Chunk, chunk_start: int) -> int | None:
-        """Earliest start time of any chunk that starts after *chunk_start* and
-        shares a microbatch with *chunk*, or ``None`` if no such chunk exists."""
-        best: int | None = None
-        for batch in chunk.batches:
-            for s, e in mb_timeline.get(batch.mb_idx, []):
-                if s > chunk_start:
-                    best = s if best is None else min(best, s)
-        return best
-
-    result: list[tuple[Chunk, Chunk]] = []
-
-    for row, starts in zip(schedule.grid, start_times_per_rank):
-        for i in range(len(row) - 1):
-            t1, t2 = row[i], row[i + 1]
-            start1, end1 = chunk_times[id(t1)]
-            start2, end2 = chunk_times[id(t2)]
-
-            overlap_start = min(start1, start2)
-            overlap_end = max(end1, end2)
-
-            # Condition 1: no shared microbatch.
-            mb1 = {b.mb_idx for b in t1.batches}
-            mb2 = {b.mb_idx for b in t2.batches}
-            if mb1 & mb2:
+            if chunk.type != TaskType.FWD_BWD or len(chunk.batches) != 2:
                 continue
-
-            # Condition 2 (non-trivial part): t2's upstream must finish by overlap_start.
-            up2 = _upstream_end(t2, start2)
-            if up2 is not None and up2 > overlap_start:
-                continue
-
-            # Condition 3 (non-trivial part): t1's downstream must not start until overlap_end.
-            down1 = _downstream_start(t1, start1)
-            if down1 is not None and down1 < overlap_end:
-                continue
-
-            result.append((t1, t2))
-
+            fwd_batch, bwd_batch = chunk.batches
+            result.append((
+                Chunk(pp_rank=chunk.pp_rank, batches=[fwd_batch], type=TaskType.FWD),
+                Chunk(pp_rank=chunk.pp_rank, batches=[bwd_batch], type=TaskType.BWD),
+            ))
     return result
 
-# ---------------------------------------------------------------------------
-# Overlap compute and A2A tasks across consecutive task pairs
-# ---------------------------------------------------------------------------
-
-def _find_task_chain_endpoints(dag: TaskDAG, chunk: Chunk) -> tuple[Task, Task]:
-    """Return (first_node, last_node) in the temporal chain for the given chunk.
 
-    "First" is the node in the group none of whose temporal_preds are also in
-    the group; "last" is the node none of whose temporal_succs are in the group.
-
-    The "group" for a chunk is all DAG nodes sharing the same pp_rank,
-    stage_id, mb_idx, and either the chunk's compute type or its paired A2A type.
-    """
-    stage_id = chunk.batches[0].stage_id
-    mb_idx   = chunk.batches[0].mb_idx
-    compute_type = chunk.type
-    a2a_type = TaskType.FWD_A2A if compute_type == TaskType.FWD else TaskType.BWD_A2A
-
-    group = [
-        n for n in dag.nodes
-        if n.pp_rank == chunk.pp_rank
-        and n.chunk.batches
-        and n.chunk.batches[0].stage_id == stage_id
-        and n.chunk.batches[0].mb_idx   == mb_idx
-        and n.chunk.type in (compute_type, a2a_type)
-    ]
-    assert group, (
-        f"No DAG nodes found for chunk pp_rank={chunk.pp_rank} "
-        f"stage={stage_id} mb={mb_idx} type={compute_type}"
+def _task_matches_chunk(task: Task, chunk: Chunk) -> bool:
+    return (
+        task.task_type != TaskType.UPD
+        and task.associated_chunk == chunk
     )
 
-    group_ids = {id(n) for n in group}
-    first = next(
-        n for n in group
-        if not any(id(p) in group_ids for p in n.temporal_preds)
-    )
-    last = next(
-        n for n in group
-        if not any(id(s) in group_ids for s in n.temporal_succs)
-    )
-    return first, last
 
+def _tasks_for_chunk(rank_dag: TaskDAG, chunk: Chunk) -> list[Task]:
+    tasks = [node for node in rank_dag.nodes if _task_matches_chunk(node, chunk)]
+    if not tasks:
+        raise ValueError(f"No DAG tasks found for chunk descriptor {chunk}")
+    return tasks
 
-def overlap_a2a_tasks(dag: TaskDAG, task_pairs: list[tuple[Chunk, Chunk]]) -> TaskDAG:
-    """Overlap compute and A2A tasks across each consecutive task pair.
 
-    For each pair ``(t1, t2)`` the pre-transform temporal chain contains:
+def _chunk_head_tasks(tasks: list[Task]) -> list[Task]:
+    task_ids = {id(task) for task in tasks}
+    return [
+        task for task in tasks
+        if not any(id(pred) in task_ids for pred in list(task.data_preds) + list(task.temporal_preds))
+    ]
 
-        T1_first → … → T1_last → T2_first → … → T2_last
 
-    where each chain has the structure ``C_0 → A2A_0 → C_1 → A2A_1 → … → C_n``
-    with ``C_i`` compute nodes and ``A2A_i`` communication nodes.
+def _local_descendant_counts(tasks: list[Task]) -> dict[int, set[int]]:
+    task_ids = {id(task) for task in tasks}
+    descendants: dict[int, set[int]] = {}
+    for task in tasks:
+        seen: set[int] = set()
+        stack = [
+            succ for succ in list(task.data_succs) + list(task.temporal_succs)
+            if id(succ) in task_ids
+        ]
+        while stack:
+            cur = stack.pop()
+            cur_id = id(cur)
+            if cur_id in seen:
+                continue
+            seen.add(cur_id)
+            stack.extend(
+                succ for succ in list(cur.data_succs) + list(cur.temporal_succs)
+                if id(succ) in task_ids
+            )
+        descendants[id(task)] = seen
+    return descendants
 
-    This function rewires the temporal dependencies to interleave the two chains
-    at each A2A boundary:
 
-      1. Remove  T1_last → T2_first.
-      2. Traverse T1 and T2 alternately.  On each T1 turn, advance T1_current
-         through non-communication nodes until the predecessor of the next A2A
-         (or until the chain is exhausted).  If an A2A was found, add a temporal
-         edge T1_current → T2_current and advance T1_current past the A2A.  Then
-         do the symmetric operation on T2, adding T2_current → T1_current and
-         advancing past T2's A2A.  Repeat until both chains are exhausted (no
-         remaining A2A on either side).
+def _overlap_ready_priority(task: Task) -> tuple[int, int, int]:
+    """Tie-break ready tasks during overlap scheduling.
 
-    The resulting structure allows the actor to overlap A2A communication in one
-    chain with compute in the other chain by dispatching them to separate CUDA
-    streams.
+    Lower tuples win. SEND/RECV take priority over collective comm tasks when
+    downstream counts tie, then fall back to stable task construction order.
+    """
+    op_priority = {
+        TaskType.SEND: 0,
+        TaskType.RECV: 0,
+        TaskType.ALL_GATHER: 1,
+        TaskType.REDUCE_SCATTER: 1,
+        TaskType.ALL_REDUCE: 1,
+    }.get(task.task_type, 2)
+    return (op_priority, task.uid)
+
+
+def overlap_chunks(rank_dag: TaskDAG, chunk_pairs: list[tuple[Chunk, Chunk]]) -> None:
+    """Overlap the tasks belonging to each ``(FWD, BWD)`` chunk pair in-place."""
+    for first_chunk, second_chunk in chunk_pairs:
+        first_tasks = _tasks_for_chunk(rank_dag, first_chunk)
+        second_tasks = _tasks_for_chunk(rank_dag, second_chunk)
+        first_ids = {id(task) for task in first_tasks}
+        second_ids = {id(task) for task in second_tasks}
+        pair_ids = first_ids | second_ids
+
+        cross_temporal = [
+            (src, dst)
+            for src in first_tasks
+            for dst in src.temporal_succs
+            if id(dst) in second_ids
+        ]
+        if len(cross_temporal) != 1:
+            raise ValueError(
+                f"Expected exactly one temporal edge between overlapped chunks "
+                f"{first_chunk} and {second_chunk}, found {len(cross_temporal)}"
+            )
+        _remove_temporal_edge(*cross_temporal[0])
 
-    Args:
-        dag: TaskDAG produced by :func:`expand_chunks_to_dags`.
-        task_pairs: List of ``(t1, t2)`` pairs where each ``Chunk`` identifies
-            a compute-task group in the DAG by its pp_rank, stage_id, mb_idx,
-            and type.  The two tasks in each pair must be on the same pp_rank
-            and T2 must immediately follow T1 in the temporal chain.
+        all_tasks = first_tasks + second_tasks
+        descendants = {
+            0: _local_descendant_counts(first_tasks),
+            1: _local_descendant_counts(second_tasks),
+        }
+        chunk_index = {id(task): 0 for task in first_tasks}
+        chunk_index.update({id(task): 1 for task in second_tasks})
+        assigned: set[int] = set()
+        ready = _chunk_head_tasks(first_tasks) + _chunk_head_tasks(second_tasks)
+        ready_ids = {id(task) for task in ready}
+        resource_bins: dict[str, list[Task]] = defaultdict(list)
+        next_chunk_idx = 0
+
+        def _is_locally_ready(task: Task) -> bool:
+            preds = [
+                pred for pred in list(task.data_preds) + list(task.temporal_preds)
+                if id(pred) in pair_ids
+            ]
+            return all(id(pred) in assigned for pred in preds)
+
+        def _remaining_downstream(task: Task) -> int:
+            task_chunk = chunk_index[id(task)]
+            return sum(1 for desc_id in descendants[task_chunk][id(task)] if desc_id not in assigned)
+
+        while len(assigned) < len(all_tasks):
+            ready = [task for task in ready if id(task) not in assigned and _is_locally_ready(task)]
+            if not ready:
+                raise ValueError(
+                    f"No ready tasks remain while overlapping chunks {first_chunk} and {second_chunk}"
+                )
 
-    Returns:
-        The same ``dag`` object with temporal edges mutated in-place.
-    """
+            best_remaining = max(_remaining_downstream(task) for task in ready)
+            candidates = [task for task in ready if _remaining_downstream(task) == best_remaining]
+            preferred = [task for task in candidates if chunk_index[id(task)] == next_chunk_idx]
+            current = min(preferred or candidates, key=_overlap_ready_priority)
 
-    def _is_comm(node: Task) -> bool:
-        return node.chunk.type in (TaskType.FWD_A2A, TaskType.BWD_A2A)
+            assigned.add(id(current))
+            ready_ids.discard(id(current))
+            resource_bins[current.resource].append(current)
+            next_chunk_idx = 1 - chunk_index[id(current)]
 
-    def _chain_next(node: Task, group_ids: set[int]) -> Task | None:
-        """Return the unique in-group temporal successor of *node*, or None."""
-        succs = [s for s in node.temporal_succs if id(s) in group_ids]
-        assert len(succs) <= 1, (
-            f"overlap_a2a_tasks: expected at most one in-group temporal successor "
-            f"for {node.node_id()}, got {len(succs)}: "
-            f"{[s.node_id() for s in succs]}"
-        )
-        return succs[0] if succs else None
-
-    def _get_group_ids(chunk: Chunk) -> set[int]:
-        compute_type = chunk.type
-        a2a_type = TaskType.FWD_A2A if compute_type == TaskType.FWD else TaskType.BWD_A2A
-        stage_id = chunk.batches[0].stage_id
-        mb_idx = chunk.batches[0].mb_idx
-        return {
-            id(n) for n in dag.nodes
-            if n.pp_rank == chunk.pp_rank
-            and n.chunk.batches
-            and n.chunk.batches[0].stage_id == stage_id
-            and n.chunk.batches[0].mb_idx == mb_idx
-            and n.chunk.type in (compute_type, a2a_type)
-        }
+            for succ in list(current.data_succs) + list(current.temporal_succs):
+                succ_id = id(succ)
+                if succ_id not in pair_ids or succ_id in assigned or succ_id in ready_ids:
+                    continue
+                if _is_locally_ready(succ):
+                    ready.append(succ)
+                    ready_ids.add(succ_id)
 
-    for t1, t2 in task_pairs:
-        t1_first, t1_last = _find_task_chain_endpoints(dag, t1)
-        t2_first, t2_last = _find_task_chain_endpoints(dag, t2)
+        for resource_tasks in resource_bins.values():
+            for src, dst in zip(resource_tasks, resource_tasks[1:]):
+                _add_temporal_edge(src, dst)
 
-        # Assert T2 directly follows T1 in the pre-transform temporal chain.
-        assert t2_first in t1_last.temporal_succs, (
-            f"overlap_a2a_tasks: expected T2_first ({t2_first.node_id()}) to be "
-            f"an immediate temporal successor of T1_last ({t1_last.node_id()})"
-        )
-        assert t1_last in t2_first.temporal_preds, (
-            f"overlap_a2a_tasks: expected T1_last ({t1_last.node_id()}) to be "
-            f"a temporal predecessor of T2_first ({t2_first.node_id()})"
-        )
+        compute_tasks = resource_bins.get("compute_stream", [])
+        if not compute_tasks:
+            continue
 
-        # 1. Remove T1_last → T2_first
-        t1_last.temporal_succs.remove(t2_first)
-        t2_first.temporal_preds.remove(t1_last)
-
-        t1_group_ids = _get_group_ids(t1)
-        t2_group_ids = _get_group_ids(t2)
-
-        t1_current = t1_first
-        t2_current = t2_first
-        seen_t1_last = False
-        seen_t2_last = False
-
-        while not (seen_t1_last and seen_t2_last):
-            # --- T1 phase ---
-            if not seen_t1_last:
-                # Advance t1_current to the compute node just before the next A2A.
-                nxt = _chain_next(t1_current, t1_group_ids)
-                while nxt is not None and not _is_comm(nxt):
-                    t1_current = nxt
-                    nxt = _chain_next(t1_current, t1_group_ids)
-
-                if nxt is None:
-                    # No A2A remaining in T1; t1_current is at T1_last.
-                    # Create the dep only if T2 has not yet finished: if T2 is
-                    # already done, an earlier T1 node already has a dep to
-                    # T2_last so adding T1_last → T2_last would be redundant.
-                    if not seen_t2_last:
-                        # Capture pre-existing succs before mutating.
-                        t1_pre_succs = list(t1_current.temporal_succs)
-                        t1_current.temporal_succs.append(t2_current)
-                        t2_current.temporal_preds.append(t1_current)
-                        # Propagate T1_last's pre-existing outgoing deps to
-                        # T2_last so that whatever follows T1 in the sequential
-                        # schedule also waits for T2_last (the longer chain).
-                        # Remove the original dep from T1_last since T2_last
-                        # now takes over responsibility for that ordering.
-                        for succ in t1_pre_succs:
-                            t1_current.temporal_succs.remove(succ)
-                            succ.temporal_preds.remove(t1_current)
-                            if succ not in t2_last.temporal_succs:
-                                t2_last.temporal_succs.append(succ)
-                                succ.temporal_preds.append(t2_last)
-                    seen_t1_last = True
-                else:
-                    # t1_current is the compute predecessor of comm node nxt.
-                    # Only create T1_current → T2_current if T2 has not already
-                    # reached its end; otherwise the dep would form a cycle
-                    # (T2's final dep points back into T1's chain).
-                    if not seen_t2_last:
-                        t1_current.temporal_succs.append(t2_current)
-                        t2_current.temporal_preds.append(t1_current)
-                    # Advance T1_current past the A2A node.
-                    after_comm = _chain_next(nxt, t1_group_ids)
-                    assert after_comm is not None and not _is_comm(after_comm), (
-                        f"overlap_a2a_tasks: expected a compute node after T1 A2A "
-                        f"{nxt.node_id()}, got "
-                        f"{after_comm.node_id() if after_comm else None}"
-                    )
-                    t1_current = after_comm
-
-            # --- T2 phase ---
-            if not seen_t2_last:
-                # Advance t2_current to the compute node just before the next A2A.
-                nxt = _chain_next(t2_current, t2_group_ids)
-                while nxt is not None and not _is_comm(nxt):
-                    t2_current = nxt
-                    nxt = _chain_next(t2_current, t2_group_ids)
-
-                if nxt is None:
-                    # No A2A remaining in T2; t2_current is at T2_last.
-                    # Only act if T1 is still running (seen_t1_last=False),
-                    # meaning T2 has fewer A2As than T1.  If seen_t1_last is
-                    # already True the chains had equal A2A counts and T1's
-                    # end-of-chain already established the ordering; creating
-                    # a dep here would form a cycle.
-                    if not seen_t1_last:
-                        t2_pre_succs = list(t2_current.temporal_succs)
-                        t2_current.temporal_succs.append(t1_current)
-                        t1_current.temporal_preds.append(t2_current)
-                        for succ in t2_pre_succs:
-                            t2_current.temporal_succs.remove(succ)
-                            succ.temporal_preds.remove(t2_current)
-                            if succ not in t1_last.temporal_succs:
-                                t1_last.temporal_succs.append(succ)
-                                succ.temporal_preds.append(t1_last)
-                    seen_t2_last = True
-                else:
-                    # Only create T2_current → T1_current if T1 has not already
-                    # reached its end; otherwise the dep would form a cycle
-                    # (T1's end-of-chain dep already points into T2's chain).
-                    if not seen_t1_last:
-                        t2_current.temporal_succs.append(t1_current)
-                        t1_current.temporal_preds.append(t2_current)
-                    # Advance T2_current past the A2A node.
-                    after_comm = _chain_next(nxt, t2_group_ids)
-                    assert after_comm is not None and not _is_comm(after_comm), (
-                        f"overlap_a2a_tasks: expected a compute node after T2 A2A "
-                        f"{nxt.node_id()}, got "
-                        f"{after_comm.node_id() if after_comm else None}"
-                    )
-                    t2_current = after_comm
+        first_compute = compute_tasks[0]
+        last_compute = compute_tasks[-1]
 
-    return dag
+        for task in compute_tasks:
+            if task is not first_compute:
+                external_preds = [pred for pred in list(task.temporal_preds) if id(pred) not in pair_ids]
+                for pred in external_preds:
+                    _remove_temporal_edge(pred, task)
+                    _add_temporal_edge(pred, first_compute)
 
+            if task is not last_compute:
+                external_succs = [succ for succ in list(task.temporal_succs) if id(succ) not in pair_ids]
+                for succ in external_succs:
+                    _remove_temporal_edge(task, succ)
+                    _add_temporal_edge(last_compute, succ)
 
 # ---------------------------------------------------------------------------
 # Assign time_step from temporal chain position
@@ -4127,10 +4061,10 @@ def assign_time_steps(dag: TaskDAG) -> None:
     })
 
     # ---- Pass 1: toposort compute nodes via temporal edges ----
-    compute_nodes = [n for n in dag.nodes if n.chunk.type in _COMPUTE_TYPES]
+    compute_nodes = [n for n in dag.nodes if n.task_type in _COMPUTE_TYPES]
 
     in_degree: dict[int, int] = {
-        id(n): sum(1 for p in n.temporal_preds if p.chunk.type in _COMPUTE_TYPES)
+        id(n): sum(1 for p in n.temporal_preds if p.task_type in _COMPUTE_TYPES)
         for n in compute_nodes
     }
     ts_map: dict[int, int] = {}
@@ -4144,7 +4078,7 @@ def assign_time_steps(dag: TaskDAG) -> None:
         node = queue.popleft()
         node.time_step = ts_map[id(node)]
         for succ in node.temporal_succs:
-            if succ.chunk.type not in _COMPUTE_TYPES:
+            if succ.task_type not in _COMPUTE_TYPES:
                 continue
             in_degree[id(succ)] -= 1
             ts_map[id(succ)] = max(ts_map.get(id(succ), 0), node.time_step + 1)
@@ -4158,7 +4092,7 @@ def assign_time_steps(dag: TaskDAG) -> None:
     while bfs:
         node = bfs.popleft()
         for succ in node.data_succs:
-            if succ.chunk.type not in _UPSTREAM_PLUS_ONE or id(succ) in visited:
+            if succ.task_type not in _UPSTREAM_PLUS_ONE or id(succ) in visited:
                 continue
             visited.add(id(succ))
             succ.time_step = node.time_step + 1
@@ -4170,7 +4104,7 @@ def assign_time_steps(dag: TaskDAG) -> None:
     while bfs2:
         node = bfs2.popleft()
         for pred in node.data_preds:
-            if pred.chunk.type not in _DOWNSTREAM_MINUS_ONE or id(pred) in visited2:
+            if pred.task_type not in _DOWNSTREAM_MINUS_ONE or id(pred) in visited2:
                 continue
             visited2.add(id(pred))
             pred.time_step = node.time_step - 1
@@ -4181,7 +4115,7 @@ def assign_time_steps(dag: TaskDAG) -> None:
     # If _wire_sync_to_upd added AR/RS → UPD temporal edges, UPD must land
     # at least one step after the latest AR/RS.
     for node in dag.nodes:
-        if node.chunk.type == TaskType.UPD:
+        if node.task_type == TaskType.UPD:
             for pred in node.temporal_preds:
-                if pred.chunk.type in _UPSTREAM_PLUS_ONE:
+                if pred.task_type in _UPSTREAM_PLUS_ONE:
                     node.time_step = max(node.time_step, pred.time_step + 1)
diff --git a/src/piper_utils.py b/src/piper_utils.py
index 8bcb497..bce9759 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -351,8 +351,10 @@ class PiperMetadata:
     zero_stage: int = 0  # ZeRO stage: 0=disabled, 1=optim states, 2=+gradients, 3=+parameters
     gradient_accumulation: bool = True  # Delay gradient sync to the last occurrence of each bucket
     use_inductor: bool = False  # Whether actors should torch.compile stage GraphModules in _load_stage
+    enable_ep: bool = False  # Whether expert-parallel A2A graph splitting/tasks are enabled
     ar_a2a_same_stream: bool = False  # Whether A2A ops should share the AR stream on actors
     overlap_zero_ops: bool = False  # Whether to apply overlap_zero_ops to per-rank DAGs
+    overlap_chunks: bool = False  # Whether to apply the chunk-overlap transform to per-rank DAGs
     schedule_name: str = ""  # Human-readable schedule name for debug artifacts
     visualize_dag_render: bool = True  # If False, save Graphviz source only
     stage_bucket_counts: dict = {}   # stage_id -> number of buckets (set by piper backend)
@@ -366,10 +368,15 @@ class PiperMetadata:
     model_flops_per_token: Optional[float] = None  # FLOPs per token for forward+backward pass
     tokens_per_step: Optional[int] = None           # Global batch tokens per training step
     visualize_dag: bool = True  # Whether to render per-rank DAG PNGs after compilation
+    output_dir: str = "out"  # Base directory for debug artifacts emitted during runs
 
 piper_metadata = PiperMetadata()
 
 
+def should_enable_ep(dp_degree: int) -> bool:
+    return bool(piper_metadata.enable_ep) and dp_degree > 1
+
+
 # ---------------------------------------------------------------------------
 # GPU peak FLOPs lookup for MFU computation
 # ---------------------------------------------------------------------------
diff --git a/test/models/qwen3.py b/test/models/qwen3.py
index bdabaff..15e6cb5 100644
--- a/test/models/qwen3.py
+++ b/test/models/qwen3.py
@@ -15,7 +15,7 @@ def create_qwen3_config(name: str) -> Qwen3ModelArgs:
             return Qwen3ModelArgs(
                 vocab_size=2048,
                 dim=256,
-                n_layers=4,
+                n_layers=8,
                 n_heads=8,
                 n_kv_heads=4,
                 head_dim=32,
@@ -364,18 +364,17 @@ def forward(
         num_layers = len(self.layers)
         layers_per_stage = num_layers // self.num_stages
 
-        with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
-            for stage_id in range(self.num_stages):
-                with torch.fx.traceback.annotate({"stage": stage_id}):
-                    if stage_id == 0:
-                        h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
+        for stage_id in range(self.num_stages):
+            with torch.fx.traceback.annotate({"stage": stage_id}):
+                if stage_id == 0:
+                    h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
 
-                    for i in range(stage_id * layers_per_stage, (stage_id + 1) * layers_per_stage):
-                        layer = self.layers[str(i)]
-                        h = layer(h, self.rope_cache, attention_masks, positions)
+                for i in range(stage_id * layers_per_stage, (stage_id + 1) * layers_per_stage):
+                    layer = self.layers[str(i)]
+                    h = layer(h, self.rope_cache, attention_masks, positions)
 
-                    if stage_id == self.num_stages - 1:
-                        h = self.norm(h) if self.norm is not None else h
-                        output = self.output(h) if self.output is not None else h
+                if stage_id == self.num_stages - 1:
+                    h = self.norm(h) if self.norm is not None else h
+                    output = self.output(h) if self.output is not None else h
 
         return output
diff --git a/test/test_qwen.py b/test/test_qwen.py
index d59b94d..ef4ae97 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -52,8 +52,10 @@ def _metrics_output_path(args):
         args.output_dir,
         (
             f"qwen{args.model}-pp{args.pp}-dp{args.dp}-zero{args.zero_stage}-"
+            f"ep{int(args.ep)}-"
             f"bs{args.batch_size}-sl{args.seq_len}-ga{int(args.gradient_accumulation)}-"
             f"aras{int(args.ar_a2a_same_stream)}-ozo{int(args.overlap_zero_ops)}-"
+            f"och{int(args.overlap_chunks)}-"
             f"{args.schedule}"
         ),
     )
@@ -70,12 +72,14 @@ def _build_benchmark_metrics(args, dp_rank, iter_times, peak_memory_stats=None):
         "schedule": args.schedule,
         "pp": args.pp,
         "dp": args.dp,
+        "ep": bool(args.ep),
         "batch_size": args.batch_size,
         "mbs": args.mbs,
         "seq_len": args.seq_len,
         "gradient_accumulation": bool(args.gradient_accumulation),
         "ar_a2a_same_stream": bool(args.ar_a2a_same_stream),
         "overlap_zero_ops": bool(args.overlap_zero_ops),
+        "overlap_chunks": bool(args.overlap_chunks),
         "samples": len(iter_times),
         "iter_time_mean_s": mean_iter_time,
         "iter_time_std_s": std_iter_time,
@@ -211,10 +215,13 @@ def main(args, pg):
         temp_dir=args.temp_dir,
         model_flops_per_token=flops_per_token,
         visualize_dag=args.save_viz,
+        output_dir=args.output_dir,
         const_attrs={"rope_cache": rope_cache},
         use_inductor=args.use_inductor,
+        enable_ep=args.ep,
         ar_a2a_same_stream=args.ar_a2a_same_stream,
         overlap_zero_ops=args.overlap_zero_ops,
+        overlap_chunks=args.overlap_chunks,
     )
 
     del x, y
@@ -306,6 +313,12 @@ def parse_args():
                         help='Model configuration: 9M, 1B, 9B, 48B, 30B-A3B, 30-A3B-half, or 72B (default: 1B)')
     parser.add_argument("--schedule", choices=["gpipe", "1f1b", "no-pp", "interleaved-1f1b", "dualpipev", "dualpipev-seq", "zerobubble", "interleaved-zerobubble"], default="1f1b",)
     parser.add_argument("--dp", type=int, default=1)
+    parser.add_argument(
+        "--ep",
+        action="store_true",
+        default=False,
+        help="Enable expert-parallel handling of all_to_all annotations",
+    )
     parser.add_argument("--pp", type=int, default=2)
     parser.add_argument("--batch-size", type=int, default=4)
     parser.add_argument("--seq-len", type=int, default=1024)
@@ -357,6 +370,12 @@ def parse_args():
         default=False,
         help="Whether to run overlap_zero_ops on the per-rank DAGs (default: false)",
     )
+    parser.add_argument(
+        "--overlap-chunks",
+        action=argparse.BooleanOptionalAction,
+        default=False,
+        help="Whether to run overlap_chunks on the per-rank DAGs (default: false)",
+    )
     return parser.parse_args()
 
 

From a5e1387bde1b2d06c6b960bf9b19aa6c743f1ec8 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Sun, 19 Apr 2026 00:17:40 +0000
Subject: [PATCH 091/100] update zero e2e script

---
 scripts/copy-logs.sh                         |   52 +
 scripts/export_qwen_sweep_results.py         |  310 ++++
 scripts/nccl_benchmark.py                    |  223 +++
 scripts/plot_qwen_e2e_torchtitan_vs_piper.py |  494 ++++++
 scripts/print_qwen3_9b_output_layer_size.py  |   80 +
 scripts/resync.sh                            |   24 +
 scripts/run-qwen-ec2.sh                      |  301 ++++
 scripts/run_ag_rs_bucket_benchmark.sh        |   77 +
 scripts/run_local.sh                         |   17 +
 scripts/run_nccl_benchmark.sh                |  168 ++
 scripts/run_qwen_e2e_eval.py                 | 1447 ++++++++++++++++++
 scripts/run_qwen_ec2_sweep.py                |  377 +++++
 scripts/test_env.py                          |  161 ++
 13 files changed, 3731 insertions(+)
 create mode 100755 scripts/copy-logs.sh
 create mode 100644 scripts/export_qwen_sweep_results.py
 create mode 100644 scripts/nccl_benchmark.py
 create mode 100644 scripts/plot_qwen_e2e_torchtitan_vs_piper.py
 create mode 100644 scripts/print_qwen3_9b_output_layer_size.py
 create mode 100755 scripts/resync.sh
 create mode 100755 scripts/run-qwen-ec2.sh
 create mode 100644 scripts/run_ag_rs_bucket_benchmark.sh
 create mode 100755 scripts/run_local.sh
 create mode 100755 scripts/run_nccl_benchmark.sh
 create mode 100644 scripts/run_qwen_e2e_eval.py
 create mode 100644 scripts/run_qwen_ec2_sweep.py
 create mode 100644 scripts/test_env.py

diff --git a/scripts/copy-logs.sh b/scripts/copy-logs.sh
new file mode 100755
index 0000000..8edaf18
--- /dev/null
+++ b/scripts/copy-logs.sh
@@ -0,0 +1,52 @@
+#!/usr/bin/env bash
+# Copy training logs and/or Nsight profiles from the EC2 cluster.
+# Requires: SSH_KEY, HEAD_PUBLIC_IP, WORKER1_PRIVATE_IP, WORKER2_PRIVATE_IP, WORKER3_PRIVATE_IP
+
+set -euo pipefail
+
+NSIGHT=false
+NSIGHT_ONLY=false
+OUT_DIR="out/ec2"
+
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        --nsight)       NSIGHT=true; shift ;;
+        --nsight-only)  NSIGHT=true; NSIGHT_ONLY=true; shift ;;
+        --out-dir)      OUT_DIR="$2"; shift 2 ;;
+        *) echo "Unknown argument: $1" >&2; exit 1 ;;
+    esac
+done
+
+SSH="ssh -i $SSH_KEY -o StrictHostKeyChecking=no"
+PROXY="ProxyCommand=ssh -i $SSH_KEY -o StrictHostKeyChecking=no -W %h:%p ubuntu@$HEAD_PUBLIC_IP"
+
+mkdir -p "$OUT_DIR"
+
+if ! $NSIGHT_ONLY; then
+    # Copy training log files from head container.
+    $SSH ubuntu@$HEAD_PUBLIC_IP \
+      "rm -rf /tmp/piper-out && docker cp piper_ray:/tmp/piper/out/. /tmp/piper-out/"
+    scp -r -i "$SSH_KEY" ubuntu@$HEAD_PUBLIC_IP:/tmp/piper-out/. "$OUT_DIR/"
+fi
+
+if $NSIGHT; then
+    # Head node Nsight profile
+    mkdir -p "$OUT_DIR/nsight-head"
+    $SSH ubuntu@$HEAD_PUBLIC_IP \
+      "rm -rf /tmp/nsight-head && mkdir -p /tmp/nsight-head && \
+       docker cp piper_ray:/tmp/piper/ray_tmp/session_latest/logs/nsight/. /tmp/nsight-head/"
+    scp -r -i "$SSH_KEY" ubuntu@$HEAD_PUBLIC_IP:/tmp/nsight-head/. "$OUT_DIR/nsight-head/"
+
+    # Worker Nsight profiles
+    WORKER_IDX=1
+    for WORKER_IP in $WORKER1_PRIVATE_IP $WORKER2_PRIVATE_IP; do
+        mkdir -p "$OUT_DIR/nsight-worker${WORKER_IDX}"
+        $SSH -o "$PROXY" ubuntu@$WORKER_IP \
+          "rm -rf /tmp/nsight-worker && mkdir -p /tmp/nsight-worker && \
+           docker cp piper_ray:/tmp/piper/ray_tmp/session_latest/logs/nsight/. /tmp/nsight-worker/"
+        scp -r -i "$SSH_KEY" \
+          -o "ProxyCommand=ssh -i $SSH_KEY -o StrictHostKeyChecking=no -W %h:%p ubuntu@$HEAD_PUBLIC_IP" \
+          ubuntu@$WORKER_IP:/tmp/nsight-worker/. "$OUT_DIR/nsight-worker${WORKER_IDX}/"
+        WORKER_IDX=$((WORKER_IDX + 1))
+    done
+fi
diff --git a/scripts/export_qwen_sweep_results.py b/scripts/export_qwen_sweep_results.py
new file mode 100644
index 0000000..2fcef84
--- /dev/null
+++ b/scripts/export_qwen_sweep_results.py
@@ -0,0 +1,310 @@
+#!/usr/bin/env python3
+"""Export Qwen sweep results from out/ec2 metrics files to CSV.
+
+By default this targets the completed Piper sweep:
+  --model 9B
+  --schedules 1f1b
+  --pp-mbs 4:8 8:16
+  --dp-values 1 2 4
+  --zero-stages 0 2 3
+  --batch-sizes 8
+
+Each matching experiment produces one CSV row aggregated across dp_ranks.
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import os
+import re
+from collections import defaultdict
+from pathlib import Path
+
+
+DEFAULT_MODEL = "9B"
+DEFAULT_SCHEDULES = ("1f1b",)
+DEFAULT_PP_MBS = ("4:8", "8:16")
+DEFAULT_DP_VALUES = (1, 2, 4)
+DEFAULT_ZERO_STAGES = (0, 2, 3)
+DEFAULT_BATCH_SIZES = (8,)
+
+
+def _parse_pp_mbs(values: list[str]) -> dict[int, set[int]]:
+    result: dict[int, set[int]] = defaultdict(set)
+    for value in values:
+        pp_str, mbs_str = value.split(":", maxsplit=1)
+        result[int(pp_str)].add(int(mbs_str))
+    return result
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Export aggregated Qwen sweep results from out/ec2 metrics files."
+    )
+    parser.add_argument("--metrics-dir", default="out/ec2", help="Directory containing qwen metrics files")
+    parser.add_argument("--output-csv", default="out/ec2/qwen_sweep_results.csv", help="CSV output path")
+    parser.add_argument("--model", default=DEFAULT_MODEL, help="Model to include")
+    parser.add_argument(
+        "--schedules",
+        nargs="+",
+        default=list(DEFAULT_SCHEDULES),
+        help="Schedules to include. Default: 1f1b",
+    )
+    parser.add_argument(
+        "--pp-mbs",
+        nargs="+",
+        default=list(DEFAULT_PP_MBS),
+        metavar="PP:MBS",
+        help="PP/MBS pairs to include. Default: 4:8 8:16",
+    )
+    parser.add_argument(
+        "--dp-values",
+        nargs="+",
+        type=int,
+        default=list(DEFAULT_DP_VALUES),
+        help="DP values to include. Default: 1 2 4",
+    )
+    parser.add_argument(
+        "--zero-stages",
+        nargs="+",
+        type=int,
+        default=list(DEFAULT_ZERO_STAGES),
+        help="ZeRO stages to include. Default: 0 2 3",
+    )
+    parser.add_argument(
+        "--batch-sizes",
+        nargs="+",
+        type=int,
+        default=list(DEFAULT_BATCH_SIZES),
+        help="Batch sizes to include. Default: 8",
+    )
+    return parser.parse_args()
+
+
+def _iter_metrics_records(path: Path) -> list[dict]:
+    records: list[dict] = []
+    if not path.is_file():
+        return records
+    with open(path, "r", encoding="utf-8") as handle:
+        for line in handle:
+            marker = "metrics_json="
+            if marker not in line:
+                continue
+            payload = line.split(marker, 1)[1].strip()
+            try:
+                records.append(json.loads(payload))
+            except json.JSONDecodeError:
+                continue
+    return records
+
+
+_METRICS_NAME_RE = re.compile(
+    r"^qwen(?P<model>.+)-pp(?P<pp>\d+)-dp(?P<dp>\d+)"
+    r"-zero(?P<zero_stage>\d+)-bs(?P<batch_size>\d+)"
+    r"-sl(?P<seq_len>\d+)-ga(?P<gradient_accumulation>[01])"
+    r"-aras(?P<ar_a2a_same_stream>[01])"
+    r"(?:-ozo(?P<overlap_zero_ops>[01]))?"
+    r"-(?P<schedule>[^/]+)$"
+)
+
+
+def _parse_metrics_filename(path: Path) -> dict | None:
+    match = _METRICS_NAME_RE.match(path.name)
+    if match is None:
+        return None
+    data = match.groupdict()
+    return {
+        "model": data["model"],
+        "pp": int(data["pp"]),
+        "dp": int(data["dp"]),
+        "schedule": data["schedule"],
+        "zero_stage": int(data["zero_stage"]),
+        "batch_size": int(data["batch_size"]),
+        "seq_len": int(data["seq_len"]),
+        "gradient_accumulation": bool(int(data["gradient_accumulation"])),
+        "ar_a2a_same_stream": bool(int(data["ar_a2a_same_stream"])),
+        "overlap_zero_ops": bool(int(data["overlap_zero_ops"] or "0")),
+    }
+
+
+def _peak_memory_summary(peak_memory_by_rank: dict[str, dict]) -> str:
+    ordered = []
+    for rank in sorted(peak_memory_by_rank, key=lambda item: int(item)):
+        ordered.append(f"{peak_memory_by_rank[rank]['peak_memory_gb']:.3f}")
+    return "/".join(ordered)
+
+
+def main() -> int:
+    args = parse_args()
+    metrics_dir = Path(args.metrics_dir)
+    output_csv = Path(args.output_csv)
+    output_csv.parent.mkdir(parents=True, exist_ok=True)
+
+    pp_to_mbs = _parse_pp_mbs(args.pp_mbs)
+    schedules = set(args.schedules)
+    dp_values = set(args.dp_values)
+    zero_stages = set(args.zero_stages)
+    batch_sizes = set(args.batch_sizes)
+
+    candidate_files: dict[tuple, tuple[bool, Path]] = {}
+    for path in sorted(metrics_dir.glob("qwen*")):
+        meta = _parse_metrics_filename(path)
+        if meta is None:
+            continue
+        records = _iter_metrics_records(path)
+        if not records:
+            continue
+        first = records[0]
+        model = first.get("model", meta["model"])
+        schedule = first.get("schedule", meta["schedule"])
+        pp = int(first.get("pp", meta["pp"]))
+        dp = int(first.get("dp", meta["dp"]))
+        mbs = int(first.get("mbs"))
+        zero_stage = meta["zero_stage"]
+        batch_size = meta["batch_size"]
+        seq_len = int(first.get("seq_len", meta["seq_len"]))
+        gradient_accumulation = bool(
+            first.get("gradient_accumulation", meta["gradient_accumulation"])
+        )
+        ar_a2a_same_stream = bool(
+            first.get("ar_a2a_same_stream", meta["ar_a2a_same_stream"])
+        )
+        overlap_zero_ops = bool(
+            first.get("overlap_zero_ops", meta["overlap_zero_ops"])
+        )
+
+        if model != args.model:
+            continue
+        if schedule not in schedules:
+            continue
+        if pp not in pp_to_mbs or mbs not in pp_to_mbs[pp]:
+            continue
+        if dp not in dp_values:
+            continue
+        if zero_stage not in zero_stages:
+            continue
+        if batch_size not in batch_sizes:
+            continue
+
+        key = (
+            model,
+            schedule,
+            pp,
+            dp,
+            mbs,
+            zero_stage,
+            batch_size,
+            seq_len,
+            gradient_accumulation,
+            ar_a2a_same_stream,
+            overlap_zero_ops,
+        )
+        if key not in candidate_files:
+            candidate_files[key] = (True, path)
+
+    grouped: dict[tuple, list[dict]] = defaultdict(list)
+    for (
+        model,
+        schedule,
+        pp,
+        dp,
+        mbs,
+        zero_stage,
+        batch_size,
+        seq_len,
+        gradient_accumulation,
+        ar_a2a_same_stream,
+        overlap_zero_ops,
+    ), (_explicit, path) in sorted(candidate_files.items()):
+        records = _iter_metrics_records(path)
+        for record in records:
+            key = (
+                model,
+                schedule,
+                pp,
+                dp,
+                mbs,
+                zero_stage,
+                batch_size,
+                seq_len,
+                gradient_accumulation,
+                ar_a2a_same_stream,
+                overlap_zero_ops,
+            )
+            enriched = dict(record)
+            enriched["_path"] = str(path)
+            enriched["_zero_stage"] = zero_stage
+            grouped[key].append(enriched)
+
+    fieldnames = [
+        "schedule",
+        "pp",
+        "dp",
+        "zero_stage",
+        "gradient_accumulation",
+        "ar_a2a_same_stream",
+        "overlap_zero_ops",
+        "iter_time_mean",
+        "iter_time_stddev",
+        "peak_memory_gb_by_rank",
+    ]
+
+    rows: list[dict[str, object]] = []
+    for key in sorted(grouped):
+        latest_by_dp_rank: dict[int, dict] = {}
+        for record in grouped[key]:
+            latest_by_dp_rank[int(record["dp_rank"])] = record
+        records = [latest_by_dp_rank[rank] for rank in sorted(latest_by_dp_rank)]
+        (
+            model,
+            schedule,
+            pp,
+            dp,
+            mbs,
+            zero_stage,
+            batch_size,
+            seq_len,
+            gradient_accumulation,
+            ar_a2a_same_stream,
+            overlap_zero_ops,
+        ) = key
+        dp_ranks = [int(record["dp_rank"]) for record in records]
+        iter_means = [float(record["iter_time_mean_s"]) for record in records]
+        iter_stds = [float(record["iter_time_std_s"]) for record in records]
+
+        peak_memory_by_rank: dict[str, dict] = {}
+        for record in records:
+            peak_memory_by_rank.update(record.get("peak_memory_by_rank", {}))
+        peak_values = [
+            float(stats["peak_memory_gb"])
+            for rank, stats in sorted(peak_memory_by_rank.items(), key=lambda item: int(item[0]))
+        ]
+
+        rows.append(
+            {
+                "schedule": schedule,
+                "pp": pp,
+                "dp": dp,
+                "zero_stage": zero_stage,
+                "gradient_accumulation": int(gradient_accumulation),
+                "ar_a2a_same_stream": int(ar_a2a_same_stream),
+                "overlap_zero_ops": int(overlap_zero_ops),
+                "iter_time_mean": sum(iter_means) / len(iter_means),
+                "iter_time_stddev": sum(iter_stds) / len(iter_stds),
+                "peak_memory_gb_by_rank": _peak_memory_summary(peak_memory_by_rank),
+            }
+        )
+
+    with open(output_csv, "w", newline="", encoding="utf-8") as handle:
+        writer = csv.DictWriter(handle, fieldnames=fieldnames)
+        writer.writeheader()
+        writer.writerows(rows)
+
+    print(f"Wrote {len(rows)} experiments to {output_csv}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/nccl_benchmark.py b/scripts/nccl_benchmark.py
new file mode 100644
index 0000000..39b3ce3
--- /dev/null
+++ b/scripts/nccl_benchmark.py
@@ -0,0 +1,223 @@
+"""
+NCCL bandwidth benchmark for Qwen3-30B-A3B communication patterns.
+
+Qwen3-30B-A3B config:
+  hidden_size:           2048
+  moe_intermediate_size: 768    (per-expert FFN width)
+  num_experts:           128
+  num_experts_per_tok:   8      (top-k)
+  num_hidden_layers:     48
+  num_attention_heads:   32
+  num_key_value_heads:   4
+  head_dim:              128
+  vocab_size:            151936
+
+All-to-All:
+  Simulates MoE expert dispatch. Each GPU holds hidden states for BATCH_SIZE=64
+  tokens and sends them to every other GPU (each GPU holds num_experts/world_size
+  experts). Total send buffer per GPU: [world_size * BATCH_SIZE, hidden_size].
+
+All-Reduce:
+  Simulates gradient all-reduce of one MoE layer's expert weights:
+    128 experts * 3 projections (up, gate, down) * 2048 * 768 = 604M params
+    In bf16: ~1.15 GB — the dominant per-layer communication cost.
+
+GPU configurations:
+  4 GPUs  — within-node  (GPUs 0-3 on node 0)
+  8 GPUs  — within-node  (all 8 GPUs on node 0)
+  12 GPUs — cross-node   (6 GPUs on each of 2 nodes)
+  16 GPUs — cross-node   (8 GPUs on each of 2 nodes)
+
+Launch (run inside Docker container on head node):
+  # 4 GPUs, single node
+  torchrun --nproc_per_node=4 test/nccl_benchmark.py
+
+  # 8 GPUs, single node
+  torchrun --nproc_per_node=8 test/nccl_benchmark.py
+
+  # 12 GPUs, 2 nodes (run on head; worker connects back)
+  torchrun --nnodes=2 --nproc_per_node=6 \\
+    --rdzv_backend=c10d --rdzv_endpoint=$HEAD_PRIVATE_IP:29500 \\
+    test/nccl_benchmark.py
+
+  # 16 GPUs, 2 nodes
+  torchrun --nnodes=2 --nproc_per_node=8 \\
+    --rdzv_backend=c10d --rdzv_endpoint=$HEAD_PRIVATE_IP:29500 \\
+    test/nccl_benchmark.py
+
+  On the worker node (inside its Docker container), run the same torchrun
+  command for multi-node configurations.
+
+To test EFA interface count impact:
+  FI_EFA_NUM_NETWORK_CARDS=1 torchrun ...   # restrict to 1 EFA NIC
+  torchrun ...                               # use all 4 EFA NICs (default)
+"""
+
+import os
+import time
+
+import torch
+import torch.distributed as dist
+
+# ---------------------------------------------------------------------------
+# Qwen3-30B-A3B model config
+# ---------------------------------------------------------------------------
+HIDDEN_SIZE = 2048
+MOE_INTERMEDIATE_SIZE = 768
+NUM_EXPERTS = 128
+NUM_LAYERS = 48
+
+# ---------------------------------------------------------------------------
+# Benchmark config
+# ---------------------------------------------------------------------------
+BUCKET_BYTES = 25 * 1024 * 1024   # 25 MiB — tensor size for both kernels
+DTYPE = torch.bfloat16
+WARMUP = 10
+ITERS = 50
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+def gb(n_bytes: int) -> float:
+    return n_bytes / 1e9
+
+
+def separator(title: str, width: int = 64) -> str:
+    return f"\n{'─' * width}\n  {title}\n{'─' * width}"
+
+
+# ---------------------------------------------------------------------------
+# All-to-All benchmark
+# ---------------------------------------------------------------------------
+def benchmark_alltoall(rank: int, world_size: int, device: torch.device):
+    """
+    Fixed BUCKET_BYTES total send/recv tensor, split evenly across world_size
+    peers.  Matches the all-reduce tensor size for direct comparison.
+
+    Bandwidth reported:
+      algbw = (world_size - 1) * chunk_bytes / time
+              (data *sent* by one GPU per iteration, excluding self)
+      busbw = algbw * (world_size - 1) / world_size   (nccl-tests convention)
+    """
+    n_elems = BUCKET_BYTES // (torch.finfo(DTYPE).bits // 8)
+    # round down to a multiple of world_size so chunks are equal
+    n_elems = (n_elems // world_size) * world_size
+    send = torch.randn(n_elems, dtype=DTYPE, device=device)
+    recv = torch.empty_like(send)
+    send_chunks = list(send.chunk(world_size))
+    recv_chunks = list(recv.chunk(world_size))
+
+    for _ in range(WARMUP):
+        dist.all_to_all(recv_chunks, send_chunks)
+    torch.cuda.synchronize()
+    dist.barrier()
+    if rank == 0:
+        print(f"  [{time.strftime('%H:%M:%S')}] all-to-all: warmup done, timing {ITERS} iters...", flush=True)
+
+    start = time.perf_counter()
+    for _ in range(ITERS):
+        dist.all_to_all(recv_chunks, send_chunks)
+    torch.cuda.synchronize()
+    elapsed = (time.perf_counter() - start) / ITERS
+
+    total_bytes = send.numel() * send.element_size()
+    chunk_bytes = total_bytes // world_size
+    algbw = (world_size - 1) * chunk_bytes / elapsed
+    busbw = algbw * (world_size - 1) / world_size
+    return elapsed, total_bytes, algbw, busbw
+
+
+# ---------------------------------------------------------------------------
+# All-Reduce benchmark
+# ---------------------------------------------------------------------------
+def benchmark_allreduce(rank: int, world_size: int, device: torch.device):
+    """
+    Gradient of one MoE layer's expert weights:
+      128 experts * (up + gate + down) projections
+      = 128 * 3 * 2048 * 768 = 603,979,776 params  (~1.15 GB bf16)
+
+    Bandwidth reported:
+      algbw = tensor_bytes / time
+      busbw = algbw * 2 * (world_size - 1) / world_size   (ring all-reduce)
+    """
+    n_elems = BUCKET_BYTES // (torch.finfo(DTYPE).bits // 8)
+    tensor = torch.randn(n_elems, dtype=DTYPE, device=device)
+
+    for _ in range(WARMUP):
+        dist.all_reduce(tensor)
+    torch.cuda.synchronize()
+    dist.barrier()
+    if rank == 0:
+        print(f"  [{time.strftime('%H:%M:%S')}] all-reduce: warmup done, timing {ITERS} iters...", flush=True)
+
+    start = time.perf_counter()
+    for _ in range(ITERS):
+        dist.all_reduce(tensor)
+    torch.cuda.synchronize()
+    elapsed = (time.perf_counter() - start) / ITERS
+
+    total_bytes = tensor.numel() * tensor.element_size()
+    algbw = total_bytes / elapsed
+    busbw = algbw * 2 * (world_size - 1) / world_size
+    return elapsed, total_bytes, algbw, busbw
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+def log(rank, msg):
+    """Print a timestamped progress message from rank 0."""
+    if rank == 0:
+        print(f"  [{time.strftime('%H:%M:%S')}] {msg}", flush=True)
+
+
+def main():
+    rank = int(os.environ["RANK"])
+    local_rank = int(os.environ["LOCAL_RANK"])
+    world_size = int(os.environ["WORLD_SIZE"])
+
+    torch.cuda.set_device(local_rank)
+    device = torch.device(f"cuda:{local_rank}")
+
+    log(rank, f"rank {rank}/{world_size} initializing NCCL process group...")
+    dist.init_process_group("nccl", device_id=device)
+    log(rank, "process group ready")
+
+    topology = "within-node" if world_size <= 8 else "cross-node"
+
+    if rank == 0:
+        print(separator(f"NCCL Benchmark — {world_size} GPUs ({topology})"))
+        print(f"  dtype:       {DTYPE}")
+        print(f"  warmup/iters: {WARMUP}/{ITERS}")
+        efa_cards = os.environ.get("FI_EFA_NUM_NETWORK_CARDS", "default (all)")
+        print(f"  FI_EFA_NUM_NETWORK_CARDS: {efa_cards}")
+
+    dist.barrier()
+
+    # --- All-to-All ---
+    log(rank, "all-to-all: warming up...")
+    elapsed, total_bytes, algbw, _busbw = benchmark_alltoall(rank, world_size, device)
+    if rank == 0:
+        print(f"\n  All-to-All")
+        print(f"    tensor:    {total_bytes / 1024**2:.1f} MiB total  ({total_bytes / 1024**2 / world_size:.1f} MiB/peer)")
+        print(f"    time/iter: {elapsed * 1e3:.3f} ms")
+        print(f"    bandwidth: {gb(algbw):.2f} GB/s")
+
+    dist.barrier()
+
+    # --- All-Reduce ---
+    log(rank, "all-reduce: warming up...")
+    elapsed, total_bytes, algbw, _busbw = benchmark_allreduce(rank, world_size, device)
+    if rank == 0:
+        print(f"\n  All-Reduce")
+        print(f"    tensor:    {total_bytes / 1024**2:.1f} MiB")
+        print(f"    time/iter: {elapsed * 1e3:.3f} ms")
+        print(f"    bandwidth: {gb(algbw):.2f} GB/s")
+
+    log(rank, "done")
+    dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/plot_qwen_e2e_torchtitan_vs_piper.py b/scripts/plot_qwen_e2e_torchtitan_vs_piper.py
new file mode 100644
index 0000000..8c0fb7e
--- /dev/null
+++ b/scripts/plot_qwen_e2e_torchtitan_vs_piper.py
@@ -0,0 +1,494 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import argparse
+import csv
+import os
+from dataclasses import dataclass
+import logging
+from pathlib import Path
+from typing import Any
+
+os.environ.setdefault("MPLCONFIGDIR", "/tmp/matplotlib")
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
+logger = logging.getLogger(__name__)
+
+TORCHTITAN_COLOR = "#4C72B0"
+PIPER_COLOR = "#DD8452"
+SWEEPS = ("scalability", "zero", "schedule")
+ZERO_MAP = {"none": 0, "zero1": 1, "zero2": 2, "zero3": 3}
+
+
+@dataclass(frozen=True)
+class CommonRow:
+    source: str
+    sweep: str
+    key: tuple[Any, ...]
+    label: str
+    pp: int
+    dp: int
+    zero_level: int
+    schedule: str
+    mb_size: int
+    seq_len: int
+    status: str
+    iter_time_mean: float | None
+    iter_time_std: float | None
+    peak_memory_values: list[float]
+
+
+@dataclass(frozen=True)
+class PlotSeries:
+    name: str
+    color: str
+    row: CommonRow
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Create unified torchtitan+piper iteration-time and memory plots for matched Qwen e2e sweeps."
+    )
+    parser.add_argument("--torchtitan-results", required=True, help="Path to torchtitan all_results.csv")
+    parser.add_argument("--piper-results", required=True, help="Path to piper all_results.csv")
+    parser.add_argument(
+        "--sweeps",
+        nargs="+",
+        choices=SWEEPS,
+        default=list(SWEEPS),
+        help="Subset of sweeps to compare. Default: scalability zero schedule",
+    )
+    parser.add_argument(
+        "--out-dir",
+        required=True,
+        help="Directory for combined plots and matched CSVs",
+    )
+    return parser.parse_args()
+
+
+
+def parse_torchtitan_peak_memory(summary: str) -> list[float]:
+    summary = summary.strip()
+    if not summary:
+        return []
+    values: list[float] = []
+    for part in summary.split("/"):
+        part = part.strip()
+        if not part:
+            continue
+        try:
+            values.append(float(part))
+        except ValueError:
+            continue
+    return values
+
+
+
+def parse_piper_peak_memory(summary: str) -> list[float]:
+    summary = summary.strip()
+    if not summary:
+        return []
+    values: list[float] = []
+    for part in summary.split(";"):
+        part = part.strip()
+        if not part or ":" not in part:
+            continue
+        _, value_text = part.split(":", maxsplit=1)
+        try:
+            values.append(float(value_text))
+        except ValueError:
+            continue
+    return values
+
+
+
+def torchtitan_status(raw: str) -> str:
+    if raw == "ok":
+        return "success"
+    return raw
+
+
+
+def torchtitan_key(row: dict[str, str]) -> tuple[Any, ...]:
+    sweep = row["sweep"]
+    if sweep == "scalability":
+        return (sweep, int(row["pp"]), int(row["dp"]))
+    if sweep == "zero":
+        return (sweep, ZERO_MAP[row["zero_level"]], int(row["mb_size"]))
+    return (sweep, row["schedule"])
+
+
+
+def torchtitan_label(row: dict[str, str]) -> str:
+    sweep = row["sweep"]
+    if sweep == "scalability":
+        return f"(pp={row['pp']}, dp={row['dp']})"
+    if sweep == "zero":
+        return f"(zero={ZERO_MAP[row['zero_level']]}, mb={row['mb_size']})"
+    return row["schedule"]
+
+
+
+def load_torchtitan_rows(path: Path) -> dict[tuple[Any, ...], CommonRow]:
+    rows: dict[tuple[Any, ...], CommonRow] = {}
+    with path.open("r", newline="", encoding="utf-8") as handle:
+        reader = csv.DictReader(handle)
+        for row in reader:
+            iter_mean_text = row.get("iter_time_mean", "").strip()
+            iter_std_text = row.get("iter_time_stddev", "").strip()
+            iter_time_mean = float(iter_mean_text) if iter_mean_text else None
+            iter_time_std = float(iter_std_text) if iter_std_text else None
+            common = CommonRow(
+                source="torchtitan",
+                sweep=row["sweep"],
+                key=torchtitan_key(row),
+                label=torchtitan_label(row),
+                pp=int(row["pp"]),
+                dp=int(row["dp"]),
+                zero_level=ZERO_MAP[row["zero_level"]],
+                schedule=row["schedule"],
+                mb_size=int(row["mb_size"]),
+                seq_len=int(row["seq_len"]),
+                status=torchtitan_status(row["status"]),
+                iter_time_mean=iter_time_mean,
+                iter_time_std=iter_time_std,
+                peak_memory_values=parse_torchtitan_peak_memory(row.get("peak_memory_gb_by_rank", "")),
+            )
+            rows[common.key] = common
+    return rows
+
+
+
+def piper_key(row: dict[str, str]) -> tuple[Any, ...]:
+    sweep = row["sweep"]
+    if sweep == "scalability":
+        return (sweep, int(row["pp"]), int(row["dp"]))
+    if sweep == "zero":
+        return (sweep, int(row["zero_stage"]), int(row["batch_size"]), int(row["gradient_accumulation"]))
+    return (sweep, row["schedule"])
+
+
+
+def load_piper_rows(path: Path) -> dict[tuple[Any, ...], CommonRow]:
+    rows: dict[tuple[Any, ...], CommonRow] = {}
+    with path.open("r", newline="", encoding="utf-8") as handle:
+        reader = csv.DictReader(handle)
+        for row in reader:
+            iter_time_mean = row.get("iter_time_mean_s", "").strip()
+            iter_time_std = row.get("iter_time_std_s", "").strip()
+            common = CommonRow(
+                source="piper",
+                sweep=row["sweep"],
+                key=piper_key(row),
+                label=row["label"],
+                pp=int(row["pp"]),
+                dp=int(row["dp"]),
+                zero_level=int(row["zero_stage"]),
+                schedule=row["schedule"],
+                mb_size=int(row["batch_size"]),
+                seq_len=int(row["seq_len"]),
+                status=row["status"],
+                iter_time_mean=float(iter_time_mean) if iter_time_mean else None,
+                iter_time_std=float(iter_time_std) if iter_time_std else None,
+                peak_memory_values=parse_piper_peak_memory(row.get("peak_memory_gb_by_rank", "")),
+            )
+            rows[common.key] = common
+    return rows
+
+
+
+def validate_pair_consistency(torchtitan_row: CommonRow, piper_row: CommonRow) -> None:
+    mismatches: list[str] = []
+    for field in ("pp", "dp", "zero_level", "schedule", "mb_size", "seq_len"):
+        tt_value = getattr(torchtitan_row, field)
+        piper_value = getattr(piper_row, field)
+        if tt_value != piper_value:
+            mismatches.append(f"{field}: torchtitan={tt_value!r} piper={piper_value!r}")
+    if mismatches:
+        raise ValueError(
+            "Inconsistent matched experiment for "
+            f"sweep={torchtitan_row.sweep} "
+            f"torchtitan_key={torchtitan_row.key!r} "
+            f"piper_key={piper_row.key!r}: "
+            + ", ".join(mismatches)
+        )
+
+
+
+def save_grouped_iter_time_plot(title: str, grouped_rows: list[list[PlotSeries]], output_path: Path) -> None:
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    labels = [group[0].row.label for group in grouped_rows]
+    xs = list(range(len(grouped_rows)))
+    width = 0.24 if max((len(group) for group in grouped_rows), default=0) >= 3 else 0.34
+
+    success_values = []
+    for group in grouped_rows:
+        for series in group:
+            row = series.row
+            if row.status == "success" and row.iter_time_mean is not None:
+                success_values.append(row.iter_time_mean)
+    marker_y = max(success_values, default=0.0) * 0.05 if success_values else 1.0
+
+    fig, ax = plt.subplots(figsize=(max(10, len(grouped_rows) * 0.9), 6))
+    legend_seen: set[str] = set()
+    for index, group in enumerate(grouped_rows):
+        offsets = centered_offsets(len(group), width)
+        for offset, series in zip(offsets, group):
+            row = series.row
+            xpos = index + offset
+            label = series.name if series.name not in legend_seen else None
+            if row.status == "success" and row.iter_time_mean is not None:
+                ax.bar(
+                    xpos,
+                    row.iter_time_mean,
+                    yerr=row.iter_time_std or 0.0,
+                    width=width * 0.9,
+                    color=series.color,
+                    ecolor="#2F2F2F",
+                    capsize=4,
+                    label=label,
+                )
+            else:
+                ax.scatter(
+                    xpos,
+                    marker_y,
+                    marker="x",
+                    s=120,
+                    color=series.color,
+                    linewidths=2.5,
+                    label=label,
+                )
+            legend_seen.add(series.name)
+
+    ax.set_title(title)
+    ax.set_ylabel("Iteration Time (s)")
+    ax.set_xticks(xs)
+    ax.set_xticklabels(labels, rotation=45, ha="right")
+    ax.grid(axis="y", linestyle="--", alpha=0.35)
+    ax.set_ylim(bottom=0)
+    ax.legend()
+    fig.tight_layout()
+    fig.savefig(output_path, dpi=200)
+    plt.close(fig)
+
+
+
+def save_grouped_memory_plot(title: str, grouped_rows: list[list[PlotSeries]], output_path: Path) -> None:
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    labels = [group[0].row.label for group in grouped_rows]
+    xs = list(range(len(grouped_rows)))
+    spacing = 0.16 if max((len(group) for group in grouped_rows), default=0) >= 3 else 0.18
+
+    success_values = [
+        value
+        for group in grouped_rows
+        for series in group
+        if series.row.status == "success"
+        for value in series.row.peak_memory_values
+    ]
+    marker_y = max(success_values, default=0.0) * 0.05 if success_values else 0.25
+
+    fig, ax = plt.subplots(figsize=(max(10, len(grouped_rows) * 0.9), 6))
+    legend_seen: set[str] = set()
+    for index, group in enumerate(grouped_rows):
+        offsets = centered_offsets(len(group), spacing)
+        for shift, series in zip(offsets, group):
+            row = series.row
+            xpos = index + shift
+            label = series.name if series.name not in legend_seen else None
+            if row.status == "success" and row.peak_memory_values:
+                ax.scatter(
+                    [xpos] * len(row.peak_memory_values),
+                    row.peak_memory_values,
+                    color=series.color,
+                    s=36,
+                    alpha=0.9,
+                    label=label,
+                )
+            else:
+                ax.scatter(
+                    xpos,
+                    marker_y,
+                    marker="x",
+                    s=120,
+                    color=series.color,
+                    linewidths=2.5,
+                    label=label,
+                )
+            legend_seen.add(series.name)
+
+    ax.set_title(title)
+    ax.set_ylabel("Peak Memory (GB)")
+    ax.set_xticks(xs)
+    ax.set_xticklabels(labels, rotation=45, ha="right")
+    ax.grid(axis="y", linestyle="--", alpha=0.35)
+    ax.set_ylim(bottom=0)
+    ax.legend()
+    fig.tight_layout()
+    fig.savefig(output_path, dpi=200)
+    plt.close(fig)
+
+
+
+def centered_offsets(count: int, spacing: float) -> list[float]:
+    midpoint = (count - 1) / 2
+    return [(index - midpoint) * spacing for index in range(count)]
+
+
+
+def build_plot_groups(
+    sweep: str,
+    sweep_pairs: list[tuple[CommonRow, CommonRow]],
+    piper_rows: dict[tuple[Any, ...], CommonRow],
+) -> list[list[PlotSeries]]:
+    if sweep != "zero":
+        return [
+            [
+                PlotSeries(name="torchtitan", color=TORCHTITAN_COLOR, row=tt),
+                PlotSeries(name="piper", color=PIPER_COLOR, row=pp),
+            ]
+            for tt, pp in sweep_pairs
+        ]
+
+    groups: list[list[PlotSeries]] = []
+    for tt, _ in sweep_pairs:
+        zero_stage = tt.key[1]
+        batch_size = tt.key[2]
+        piper_ga_on = piper_rows[(sweep, zero_stage, batch_size, 1)]
+        piper_ga_off = piper_rows[(sweep, zero_stage, batch_size, 0)]
+        groups.append(
+            [
+                PlotSeries(name="torchtitan", color=TORCHTITAN_COLOR, row=tt),
+                PlotSeries(name="piper ga=on", color=PIPER_COLOR, row=piper_ga_on),
+                PlotSeries(name="piper ga=off", color="#55A868", row=piper_ga_off),
+            ]
+        )
+    return groups
+
+
+
+def collect_standard_sweep_pairs(
+    sweep: str,
+    torchtitan_rows: dict[tuple[Any, ...], CommonRow],
+    piper_rows: dict[tuple[Any, ...], CommonRow],
+) -> list[tuple[CommonRow, CommonRow]]:
+    common_keys = sorted(set(torchtitan_rows) & set(piper_rows))
+    pairs: list[tuple[CommonRow, CommonRow]] = []
+    for key in common_keys:
+        torchtitan_row = torchtitan_rows[key]
+        piper_row = piper_rows[key]
+        if torchtitan_row.sweep != sweep or piper_row.sweep != sweep:
+            continue
+        validate_pair_consistency(torchtitan_row, piper_row)
+        pairs.append((torchtitan_row, piper_row))
+    return pairs
+
+
+
+def collect_zero_sweep_pairs(
+    torchtitan_rows: dict[tuple[Any, ...], CommonRow],
+    piper_rows: dict[tuple[Any, ...], CommonRow],
+) -> list[tuple[CommonRow, CommonRow]]:
+    pairs: list[tuple[CommonRow, CommonRow]] = []
+    for key in sorted(torchtitan_rows):
+        tt = torchtitan_rows[key]
+        if tt.sweep != "zero":
+            continue
+        zero_stage = tt.key[1]
+        batch_size = tt.key[2]
+        piper_ga_on_key = ("zero", zero_stage, batch_size, 1)
+        piper_ga_off_key = ("zero", zero_stage, batch_size, 0)
+        if piper_ga_on_key not in piper_rows:
+            logger.warning(
+                "Missing piper zero GA=on result for torchtitan experiment: label=%s key=%r",
+                tt.label,
+                key,
+            )
+            continue
+        if piper_ga_off_key not in piper_rows:
+            logger.warning(
+                "Missing piper zero GA=off result for torchtitan experiment: label=%s key=%r",
+                tt.label,
+                key,
+            )
+            continue
+        piper_ga_on = piper_rows[piper_ga_on_key]
+        piper_ga_off = piper_rows[piper_ga_off_key]
+        validate_pair_consistency(tt, piper_ga_on)
+        validate_pair_consistency(tt, piper_ga_off)
+        pairs.append((tt, piper_ga_on))
+    return pairs
+
+
+
+def write_matched_csv(path: Path, rows: list[tuple[CommonRow, CommonRow]]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.writer(handle)
+        writer.writerow([
+            "sweep",
+            "label",
+            "key",
+            "torchtitan_status",
+            "torchtitan_iter_time_mean_s",
+            "torchtitan_iter_time_std_s",
+            "torchtitan_peak_memory_gb_by_rank",
+            "piper_status",
+            "piper_iter_time_mean_s",
+            "piper_iter_time_std_s",
+            "piper_peak_memory_gb_by_rank",
+        ])
+        for tt, pp in rows:
+            writer.writerow([
+                tt.sweep,
+                tt.label,
+                repr(tt.key),
+                tt.status,
+                tt.iter_time_mean,
+                tt.iter_time_std,
+                "/".join(str(v) for v in tt.peak_memory_values),
+                pp.status,
+                pp.iter_time_mean,
+                pp.iter_time_std,
+                "/".join(str(v) for v in pp.peak_memory_values),
+            ])
+
+
+
+def main() -> int:
+    args = parse_args()
+    torchtitan_rows = load_torchtitan_rows(Path(args.torchtitan_results))
+    piper_rows = load_piper_rows(Path(args.piper_results))
+    out_dir = Path(args.out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    for sweep in args.sweeps:
+        if sweep == "zero":
+            sweep_pairs = collect_zero_sweep_pairs(torchtitan_rows, piper_rows)
+        else:
+            sweep_pairs = collect_standard_sweep_pairs(sweep, torchtitan_rows, piper_rows)
+        if not sweep_pairs:
+            continue
+        plot_groups = build_plot_groups(sweep, sweep_pairs, piper_rows)
+        write_matched_csv(out_dir / f"{sweep}_matched.csv", sweep_pairs)
+        save_grouped_iter_time_plot(
+            f"Qwen {sweep.title()} Iteration Time: Torchtitan vs Piper",
+            plot_groups,
+            out_dir / f"{sweep}_iter_time.png",
+        )
+        save_grouped_memory_plot(
+            f"Qwen {sweep.title()} Memory: Torchtitan vs Piper",
+            plot_groups,
+            out_dir / f"{sweep}_memory.png",
+        )
+        print(f"wrote {sweep} plots and matched csv")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/print_qwen3_9b_output_layer_size.py b/scripts/print_qwen3_9b_output_layer_size.py
new file mode 100644
index 0000000..68dd345
--- /dev/null
+++ b/scripts/print_qwen3_9b_output_layer_size.py
@@ -0,0 +1,80 @@
+#!/usr/bin/env python3
+"""Read the local Qwen3 9B config and print output-layer size in bf16."""
+
+from __future__ import annotations
+
+import sys
+import ast
+from pathlib import Path
+
+import torch
+from torch import nn
+
+
+def _add_repo_paths() -> None:
+    repo_root = Path(__file__).resolve().parents[1]
+    sibling_torchtitan = repo_root.parent / "torchtitan"
+
+    for path in (repo_root, sibling_torchtitan):
+        path_str = str(path)
+        if path.exists() and path_str not in sys.path:
+            sys.path.insert(0, path_str)
+
+
+_add_repo_paths()
+
+
+def _load_local_qwen3_9b_dims() -> tuple[int, int]:
+    repo_root = Path(__file__).resolve().parents[1]
+    config_path = repo_root / "test" / "models" / "qwen3.py"
+    module = ast.parse(config_path.read_text(encoding="utf-8"), filename=str(config_path))
+
+    in_9b_case = False
+    vocab_size = None
+    dim = None
+
+    for node in ast.walk(module):
+        if not isinstance(node, ast.Match):
+            continue
+        for case in node.cases:
+            pattern = case.pattern
+            if isinstance(pattern, ast.MatchValue) and isinstance(pattern.value, ast.Constant):
+                in_9b_case = pattern.value.value == "9B"
+            else:
+                in_9b_case = False
+            if not in_9b_case:
+                continue
+            for stmt in case.body:
+                if not isinstance(stmt, ast.Return) or not isinstance(stmt.value, ast.Call):
+                    continue
+                for kw in stmt.value.keywords:
+                    if kw.arg == "vocab_size" and isinstance(kw.value, ast.Constant):
+                        vocab_size = int(kw.value.value)
+                    elif kw.arg == "dim" and isinstance(kw.value, ast.Constant):
+                        dim = int(kw.value.value)
+            break
+
+    if vocab_size is None or dim is None:
+        raise RuntimeError(f"Failed to read vocab_size/dim for 9B from {config_path}")
+    return vocab_size, dim
+
+
+def main() -> None:
+    vocab_size, dim = _load_local_qwen3_9b_dims()
+    with torch.device("meta"):
+        output_layer = nn.Linear(dim, vocab_size, bias=False, dtype=torch.bfloat16)
+    weight = output_layer.weight
+    num_params = weight.numel()
+    size_bytes = num_params * torch.tensor([], dtype=torch.bfloat16).element_size()
+
+    print(f"model=Qwen3 9B dtype={torch.bfloat16}")
+    print("source=local-config")
+    print(f"output_layer_type={type(output_layer).__name__}")
+    print(f"output_weight_shape={tuple(weight.shape)}")
+    print(f"output_weight_numel={num_params}")
+    print(f"output_weight_size_bytes={size_bytes}")
+    print(f"output_weight_size_mib={size_bytes / (1024 ** 2):.2f}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/resync.sh b/scripts/resync.sh
new file mode 100755
index 0000000..24d2a7d
--- /dev/null
+++ b/scripts/resync.sh
@@ -0,0 +1,24 @@
+rsync -av --delete \
+  --exclude='.git' --exclude='__pycache__' --exclude='.venv' \
+  --exclude='venv' --exclude='out' --exclude='e2e-eval' --exclude='ray_tmp' --exclude='aws' \
+  -e "ssh -i $SSH_KEY -o StrictHostKeyChecking=no" \
+  . ubuntu@$HEAD_PUBLIC_IP:/tmp/piper/
+
+for WORKER_IP in $WORKER1_PRIVATE_IP $WORKER2_PRIVATE_IP $WORKER3_PRIVATE_IP; do
+  rsync -av --delete \
+    --exclude='.git' --exclude='__pycache__' --exclude='.venv' \
+    --exclude='venv' --exclude='out' --exclude='e2e-eval' --exclude='ray_tmp' --exclude='aws' \
+    -e "ssh -i $SSH_KEY -o StrictHostKeyChecking=no -o 'ProxyCommand=ssh -i $SSH_KEY -o StrictHostKeyChecking=no -W %h:%p ubuntu@$HEAD_PUBLIC_IP'" \
+    . ubuntu@$WORKER_IP:/tmp/piper/
+done
+
+ssh -i $SSH_KEY -o StrictHostKeyChecking=no ubuntu@$HEAD_PUBLIC_IP \
+  "docker cp /tmp/piper/. piper_ray:/tmp/piper/"
+
+for WORKER_IP in $WORKER1_PRIVATE_IP $WORKER2_PRIVATE_IP $WORKER3_PRIVATE_IP; do
+  ssh -i $SSH_KEY -o StrictHostKeyChecking=no \
+    -o "ProxyCommand=ssh -i $SSH_KEY -o StrictHostKeyChecking=no -W %h:%p ubuntu@$HEAD_PUBLIC_IP" \
+    ubuntu@$WORKER_IP \
+    "docker cp /tmp/piper/. piper_ray:/tmp/piper/" &
+done
+wait
diff --git a/scripts/run-qwen-ec2.sh b/scripts/run-qwen-ec2.sh
new file mode 100755
index 0000000..942fd93
--- /dev/null
+++ b/scripts/run-qwen-ec2.sh
@@ -0,0 +1,301 @@
+#!/usr/bin/env bash
+# Run piper qwen training on EC2 cluster via SSH.
+#
+# Usage:
+#   ./scripts/run-qwen-ec2.sh [OPTIONS]
+#
+# All options can also be set as environment variables.
+#
+# Options:
+#   --model MODEL    Model size (default: $MODEL or 9B)
+#   --schedule NAME  Pipeline schedule (default: $SCHEDULE or 1f1b)
+#   --pp N           Pipeline parallel degree (default: $PP or 2)
+#   --dp N           Data parallel degree (default: $DP or 1)
+#   --ep             Enable expert parallel (default: off)
+#   --zero-stage N   ZeRO stage 0/1/2/3 (default: $ZERO_STAGE or 0)
+#   --bucket-size N  Bucket size in MB (default: $BUCKET_SIZE if set)
+#   --batch-size N   Global batch size (default: $BATCH_SIZE or 4)
+#   --seq-len N      Sequence length (default: $SEQ_LEN or 512)
+#   --mbs N          Microbatch size (default: $MBS or 4)
+#   --gradient-accumulation / --no-gradient-accumulation
+#                    Delay bucket reductions to the last occurrence (default: on)
+#   --ar-a2a-same-stream / --no-ar-a2a-same-stream
+#                    Run A2A ops on the same CUDA stream as AR ops (default: off)
+#   --overlap-zero-ops / --no-overlap-zero-ops
+#                    Run overlap_zero_ops on the per-rank DAGs (default: off)
+#   --overlap-chunks / --no-overlap-chunks
+#                    Run overlap_chunks on the per-rank DAGs (default: off)
+#   --warmup N       Warmup iterations (default: $WARMUP or 2)
+#   --iters N        Training iterations (default: $ITERS or 5)
+#   --ray-port PORT  Ray port (default: $RAY_PORT or 6379)
+#   --nsight         Enable Nsight profiling (saves to ray_tmp)
+#   --log-to-file    Tee all output to out/ec2/<experiment-name>_<timestamp>.log
+#   --out-dir DIR    Directory for log files (default: out/ec2)
+#   --remote-output-dir DIR
+#                    Directory inside the container for test_qwen outputs (default: /tmp/piper/out)
+#
+# Reads from environment: SSH_KEY, HEAD_PUBLIC_IP, HEAD_PRIVATE_IP
+
+set -euo pipefail
+
+MODEL="${MODEL:-9B}"
+SCHEDULE="${SCHEDULE:-1f1b}"
+PP="${PP:-2}"
+DP="${DP:-1}"
+ZERO_STAGE="${ZERO_STAGE:-0}"
+BUCKET_SIZE="${BUCKET_SIZE:-}"
+BATCH_SIZE="${BATCH_SIZE:-4}"
+SEQ_LEN="${SEQ_LEN:-512}"
+MBS="${MBS:-4}"
+WARMUP="${WARMUP:-2}"
+ITERS="${ITERS:-5}"
+RAY_PORT="${RAY_PORT:-6379}"
+NSIGHT=false
+EP=false
+LOG_TO_FILE=false
+OUT_DIR="out/ec2"
+REMOTE_OUTPUT_DIR="/tmp/piper/out"
+USE_INDUCTOR_ARG=""
+GRADIENT_ACCUMULATION_ARG="--gradient-accumulation"
+AR_A2A_SAME_STREAM_ARG="--no-ar-a2a-same-stream"
+OVERLAP_ZERO_OPS_ARG="--no-overlap-zero-ops"
+OVERLAP_CHUNKS_ARG="--no-overlap-chunks"
+
+SSH_BASE=(-i "$SSH_KEY" -o StrictHostKeyChecking=no)
+PROXY="ProxyCommand=ssh -i $SSH_KEY -o StrictHostKeyChecking=no -W %h:%p ubuntu@$HEAD_PUBLIC_IP"
+
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        --model)       MODEL="$2";       shift 2 ;;
+        --schedule)    SCHEDULE="$2";    shift 2 ;;
+        --pp)          PP="$2";          shift 2 ;;
+        --dp)          DP="$2";          shift 2 ;;
+        --ep)          EP=true;          shift ;;
+        --zero-stage)  ZERO_STAGE="$2";  shift 2 ;;
+        --bucket-size) BUCKET_SIZE="$2"; shift 2 ;;
+        --batch-size)  BATCH_SIZE="$2";  shift 2 ;;
+        --seq-len)     SEQ_LEN="$2";     shift 2 ;;
+        --mbs)         MBS="$2";         shift 2 ;;
+        --gradient-accumulation) GRADIENT_ACCUMULATION_ARG="--gradient-accumulation"; shift ;;
+        --no-gradient-accumulation) GRADIENT_ACCUMULATION_ARG="--no-gradient-accumulation"; shift ;;
+        --ar-a2a-same-stream) AR_A2A_SAME_STREAM_ARG="--ar-a2a-same-stream"; shift ;;
+        --no-ar-a2a-same-stream) AR_A2A_SAME_STREAM_ARG="--no-ar-a2a-same-stream"; shift ;;
+        --overlap-zero-ops) OVERLAP_ZERO_OPS_ARG="--overlap-zero-ops"; shift ;;
+        --no-overlap-zero-ops) OVERLAP_ZERO_OPS_ARG="--no-overlap-zero-ops"; shift ;;
+        --overlap-chunks) OVERLAP_CHUNKS_ARG="--overlap-chunks"; shift ;;
+        --no-overlap-chunks) OVERLAP_CHUNKS_ARG="--no-overlap-chunks"; shift ;;
+        --warmup)      WARMUP="$2";      shift 2 ;;
+        --iters)       ITERS="$2";       shift 2 ;;
+        --ray-port)    RAY_PORT="$2";    shift 2 ;;
+        --nsight)      NSIGHT=true;      shift ;;
+        --use-inductor) USE_INDUCTOR_ARG="--use-inductor"; shift ;;
+        --no-use-inductor) USE_INDUCTOR_ARG="--no-use-inductor"; shift ;;
+        --log-to-file) LOG_TO_FILE=true; shift ;;
+        --out-dir)     OUT_DIR="$2";     shift 2 ;;
+        --remote-output-dir) REMOTE_OUTPUT_DIR="$2"; shift 2 ;;
+        *) echo "Unknown argument: $1" >&2; exit 1 ;;
+    esac
+done
+
+run_head_ssh() {
+    ssh "${SSH_BASE[@]}" "ubuntu@$HEAD_PUBLIC_IP" "$@"
+}
+
+run_worker_ssh() {
+    local worker_ip="$1"
+    shift
+    ssh "${SSH_BASE[@]}" -o "$PROXY" "ubuntu@$worker_ip" "$@"
+}
+
+snapshot_ray_worker_logs() {
+    local node_kind="$1"
+    local node_host="${2:-}"
+    local snapshot_path="$3"
+    local remote_cmd="docker exec piper_ray bash -lc 'for f in /tmp/piper/ray_tmp/session_latest/logs/worker-*.out /tmp/piper/ray_tmp/session_latest/logs/worker-*.err; do [ -f \"\$f\" ] || continue; stat -c \"%n|%s\" \"\$f\"; done'"
+
+    if [[ "$node_kind" == "head" ]]; then
+        run_head_ssh "$remote_cmd" > "$snapshot_path" || true
+    else
+        run_worker_ssh "$node_host" "$remote_cmd" > "$snapshot_path" || true
+    fi
+}
+
+bundle_ray_worker_logs() {
+    local node_label="$1"
+    local node_kind="$2"
+    local node_host="${3:-}"
+    local before_snapshot="$4"
+    local bundle_dir="$5"
+    local after_snapshot
+    local remote_cmd
+
+    mkdir -p "$bundle_dir/$node_label"
+    after_snapshot="$(mktemp)"
+    snapshot_ray_worker_logs "$node_kind" "$node_host" "$after_snapshot"
+
+    declare -A before_sizes=()
+    while IFS='|' read -r path size; do
+        [[ -n "$path" ]] || continue
+        before_sizes["$path"]="$size"
+    done < "$before_snapshot"
+
+    while IFS='|' read -r path size; do
+        [[ -n "$path" ]] || continue
+        local before_size="${before_sizes[$path]:-0}"
+        if [[ "$size" == "$before_size" ]]; then
+            continue
+        fi
+        local fetch_from=1
+        if [[ "$size" =~ ^[0-9]+$ ]] && [[ "$before_size" =~ ^[0-9]+$ ]] && (( size > before_size )); then
+            fetch_from=$((before_size + 1))
+        fi
+
+        local dest="$bundle_dir/$node_label/$(basename "$path")"
+        remote_cmd="docker exec piper_ray bash -lc 'tail -c +$fetch_from \"${path}\"'"
+        if [[ "$node_kind" == "head" ]]; then
+            run_head_ssh "$remote_cmd" > "$dest" || true
+        else
+            run_worker_ssh "$node_host" "$remote_cmd" > "$dest" || true
+        fi
+    done < "$after_snapshot"
+
+    rm -f "$after_snapshot"
+}
+
+BUCKET_SIZE_LABEL=""
+if [[ -n "$BUCKET_SIZE" ]]; then
+    BUCKET_SIZE_LABEL="-bucket${BUCKET_SIZE}"
+fi
+
+NSIGHT_LABEL=""
+if $NSIGHT; then
+    NSIGHT_LABEL="-nsight1"
+fi
+
+GRADIENT_ACCUMULATION_LABEL=1
+if [[ "$GRADIENT_ACCUMULATION_ARG" == "--no-gradient-accumulation" ]]; then
+    GRADIENT_ACCUMULATION_LABEL=0
+fi
+
+AR_A2A_SAME_STREAM_LABEL=0
+if [[ "$AR_A2A_SAME_STREAM_ARG" == "--ar-a2a-same-stream" ]]; then
+    AR_A2A_SAME_STREAM_LABEL=1
+fi
+
+OVERLAP_ZERO_OPS_LABEL=0
+if [[ "$OVERLAP_ZERO_OPS_ARG" == "--overlap-zero-ops" ]]; then
+    OVERLAP_ZERO_OPS_LABEL=1
+fi
+
+OVERLAP_CHUNKS_LABEL=0
+if [[ "$OVERLAP_CHUNKS_ARG" == "--overlap-chunks" ]]; then
+    OVERLAP_CHUNKS_LABEL=1
+fi
+
+EP_LABEL=0
+if $EP; then
+    EP_LABEL=1
+fi
+
+EXPERIMENT_NAME="qwen${MODEL}-sched_${SCHEDULE}-pp${PP}-dp${DP}-ep${EP_LABEL}-zero${ZERO_STAGE}${BUCKET_SIZE_LABEL}${NSIGHT_LABEL}-bs${BATCH_SIZE}-sl${SEQ_LEN}-mbs${MBS}-ga${GRADIENT_ACCUMULATION_LABEL}-aras${AR_A2A_SAME_STREAM_LABEL}-ozo${OVERLAP_ZERO_OPS_LABEL}-och${OVERLAP_CHUNKS_LABEL}"
+
+if $LOG_TO_FILE; then
+    mkdir -p "$OUT_DIR"
+    LOG_FILE="$OUT_DIR/${EXPERIMENT_NAME}_$(date +%Y%m%d_%H%M%S).log"
+    echo "Logging to $LOG_FILE"
+    exec > >(tee -a "$LOG_FILE") 2>&1
+fi
+
+ZERO_ARG=""
+if [[ "$ZERO_STAGE" -gt 0 ]]; then
+    ZERO_ARG="--zero-stage $ZERO_STAGE"
+fi
+
+BUCKET_SIZE_ARG=""
+if [[ -n "$BUCKET_SIZE" ]]; then
+    BUCKET_SIZE_ARG="--bucket-size $BUCKET_SIZE"
+fi
+
+NSIGHT_ARG=""
+if $NSIGHT; then
+    NSIGHT_ARG="--nsight"
+fi
+
+RUN_ID="$(date +%Y%m%d_%H%M%S)"
+REMOTE_RUN_DIR="${REMOTE_OUTPUT_DIR}/runner-fetch/${EXPERIMENT_NAME}_${RUN_ID}"
+REMOTE_LOG="${REMOTE_RUN_DIR}/test_qwen.log"
+LOCAL_CLUSTER_LOG="$OUT_DIR/${EXPERIMENT_NAME}_${RUN_ID}.cluster.log"
+LOCAL_RAY_LOG_BUNDLE_DIR="$OUT_DIR/${EXPERIMENT_NAME}_${RUN_ID}.ray-logs"
+SNAPSHOT_DIR="$(mktemp -d)"
+HEAD_SNAPSHOT="$SNAPSHOT_DIR/head.snapshot"
+WORKER_LOG_NODES=()
+
+for worker_ip in "${WORKER1_PRIVATE_IP:-}" "${WORKER2_PRIVATE_IP:-}" "${WORKER3_PRIVATE_IP:-}"; do
+    if [[ -n "$worker_ip" ]]; then
+        WORKER_LOG_NODES+=("$worker_ip")
+    fi
+done
+
+mkdir -p "$OUT_DIR"
+echo "Experiment name: $EXPERIMENT_NAME"
+echo "Remote run dir: $REMOTE_RUN_DIR"
+echo "Remote test_qwen log: $REMOTE_LOG"
+echo "Local fetched log: $LOCAL_CLUSTER_LOG"
+echo "Local Ray worker log bundle: $LOCAL_RAY_LOG_BUNDLE_DIR"
+
+snapshot_ray_worker_logs "head" "" "$HEAD_SNAPSHOT"
+for worker_idx in "${!WORKER_LOG_NODES[@]}"; do
+    snapshot_ray_worker_logs "worker" "${WORKER_LOG_NODES[$worker_idx]}" "$SNAPSHOT_DIR/worker$((worker_idx + 1)).snapshot"
+done
+
+set +e
+run_head_ssh \
+  "docker exec -e RAY_DEDUP_LOGS=0 -w /tmp/piper -e PYTHONPATH=/tmp/piper piper_ray \
+   bash -lc 'mkdir -p $REMOTE_OUTPUT_DIR $REMOTE_RUN_DIR && python3 -m test.test_qwen \
+     --model $MODEL \
+     --schedule $SCHEDULE \
+     --pp $PP \
+     --dp $DP \
+     $( $EP && echo --ep ) \
+     $ZERO_ARG \
+     $BUCKET_SIZE_ARG \
+     --batch-size $BATCH_SIZE \
+     --seq-len $SEQ_LEN \
+     --mbs $MBS \
+     $GRADIENT_ACCUMULATION_ARG \
+     $AR_A2A_SAME_STREAM_ARG \
+     $OVERLAP_ZERO_OPS_ARG \
+     $OVERLAP_CHUNKS_ARG \
+     --warmup $WARMUP \
+     --iters $ITERS \
+     --address $HEAD_PRIVATE_IP \
+     --port $RAY_PORT \
+     --output-dir $REMOTE_OUTPUT_DIR \
+     $USE_INDUCTOR_ARG \
+     $NSIGHT_ARG \
+     --temp-dir /tmp/piper/ray_tmp > $REMOTE_LOG 2>&1'"
+RUN_STATUS=$?
+
+run_head_ssh "docker exec piper_ray bash -lc 'test -f $REMOTE_LOG && cat $REMOTE_LOG'" > "$LOCAL_CLUSTER_LOG"
+FETCH_STATUS=$?
+
+bundle_ray_worker_logs "head" "head" "" "$HEAD_SNAPSHOT" "$LOCAL_RAY_LOG_BUNDLE_DIR"
+for worker_idx in "${!WORKER_LOG_NODES[@]}"; do
+    bundle_ray_worker_logs \
+        "worker$((worker_idx + 1))" \
+        "worker" \
+        "${WORKER_LOG_NODES[$worker_idx]}" \
+        "$SNAPSHOT_DIR/worker$((worker_idx + 1)).snapshot" \
+        "$LOCAL_RAY_LOG_BUNDLE_DIR"
+done
+set -e
+
+rm -rf "$SNAPSHOT_DIR"
+
+if [[ $FETCH_STATUS -ne 0 ]]; then
+    echo "Failed to fetch remote log from $REMOTE_LOG" >&2
+    exit $RUN_STATUS
+fi
+
+cat "$LOCAL_CLUSTER_LOG"
+exit $RUN_STATUS
diff --git a/scripts/run_ag_rs_bucket_benchmark.sh b/scripts/run_ag_rs_bucket_benchmark.sh
new file mode 100644
index 0000000..a02da16
--- /dev/null
+++ b/scripts/run_ag_rs_bucket_benchmark.sh
@@ -0,0 +1,77 @@
+#!/usr/bin/env bash
+# Run the AR vs RS bucket microbenchmark across 2 nodes × 1 GPU.
+#
+# Requires:
+#   SSH_KEY, HEAD_PUBLIC_IP, HEAD_PRIVATE_IP, and either WORKER1_PRIVATE_IP or WORKER_PRIVATE_IP
+#
+# Usage:
+#   ./scripts/run_ag_rs_bucket_benchmark.sh [--warmup N] [--iters N] [--dtype DTYPE]
+
+set -euo pipefail
+
+: "${SSH_KEY:?SSH_KEY must be set}"
+: "${HEAD_PUBLIC_IP:?HEAD_PUBLIC_IP must be set}"
+: "${HEAD_PRIVATE_IP:?HEAD_PRIVATE_IP must be set}"
+
+WORKER_IP="${WORKER1_PRIVATE_IP:-${WORKER_PRIVATE_IP:-}}"
+: "${WORKER_IP:?WORKER1_PRIVATE_IP or WORKER_PRIVATE_IP must be set}"
+
+WARMUP=10
+ITERS=50
+DTYPE=bfloat16
+RDZV_PORT="${RDZV_PORT:-29500}"
+
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        --warmup) WARMUP="$2"; shift 2 ;;
+        --iters) ITERS="$2"; shift 2 ;;
+        --dtype) DTYPE="$2"; shift 2 ;;
+        *) echo "Unknown argument: $1" >&2; exit 1 ;;
+    esac
+done
+
+SSH_CFG=$(mktemp /tmp/ag-rs-ssh-cfg-XXXXXX)
+trap 'rm -f "$SSH_CFG"' EXIT
+cat > "$SSH_CFG" <<EOF
+Host head
+  HostName $HEAD_PUBLIC_IP
+  User ubuntu
+  IdentityFile $SSH_KEY
+  StrictHostKeyChecking no
+
+Host worker
+  HostName $WORKER_IP
+  User ubuntu
+  IdentityFile $SSH_KEY
+  StrictHostKeyChecking no
+  ProxyCommand ssh -F $SSH_CFG -W %h:%p head
+EOF
+
+ssh_head()   { ssh -F "$SSH_CFG" head "$@"; }
+ssh_worker() { ssh -F "$SSH_CFG" worker "$@"; }
+
+BASE_ENV="-e NCCL_SOCKET_IFNAME=ens32 -e GLOO_SOCKET_IFNAME=ens32"
+BENCH_CMD="docker exec $BASE_ENV -w /tmp/piper piper_ray bash -lc 'torchrun --nnodes=2 --nproc_per_node=1 --node_rank=%NODE_RANK% --master_addr=$HEAD_PRIVATE_IP --master_port=$RDZV_PORT test/allgather_reduce_scatter_bucket_bench.py --warmup $WARMUP --iters $ITERS --dtype $DTYPE'"
+
+tmpout=$(mktemp)
+trap 'rm -f "$SSH_CFG" "$tmpout"' EXIT
+
+echo "Starting AR/RS bucket benchmark on head=$HEAD_PUBLIC_IP worker=$WORKER_IP port=$RDZV_PORT"
+
+ssh_head "${BENCH_CMD//%NODE_RANK%/0}" 2>&1 | tee "$tmpout" &
+HEAD_PID=$!
+
+ssh_worker "${BENCH_CMD//%NODE_RANK%/1}" >/dev/null 2>&1 &
+WORKER_PID=$!
+
+set +e
+wait "$HEAD_PID"
+HEAD_STATUS=$?
+wait "$WORKER_PID"
+WORKER_STATUS=$?
+set -e
+
+if [[ $HEAD_STATUS -ne 0 || $WORKER_STATUS -ne 0 ]]; then
+    echo "Benchmark failed: head_status=$HEAD_STATUS worker_status=$WORKER_STATUS" >&2
+    exit 1
+fi
diff --git a/scripts/run_local.sh b/scripts/run_local.sh
new file mode 100755
index 0000000..3711777
--- /dev/null
+++ b/scripts/run_local.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+# Run piper inside Docker locally. Usage:
+#   ./run_local.sh test.test_env
+#   ./run_local.sh test.test_mixtral --model tiny --schedule 1f1b --pp 2 --dp 1 --mbs 4
+
+set -e
+
+MODULE="$1"
+shift
+
+docker run --rm \
+    --gpus all \
+    --shm-size=10g \
+    --network host \
+    -v "$(pwd)":/tmp/piper \
+    piper:latest \
+    bash -c "cd /tmp/piper && ray start --head --num-cpus=$(nproc) --num-gpus=$(nvidia-smi -L | wc -l) && python -m $MODULE $*"
diff --git a/scripts/run_nccl_benchmark.sh b/scripts/run_nccl_benchmark.sh
new file mode 100755
index 0000000..4319f45
--- /dev/null
+++ b/scripts/run_nccl_benchmark.sh
@@ -0,0 +1,168 @@
+#!/usr/bin/env bash
+# Run NCCL benchmark combinations and print a summary table.
+#
+# EFA experiments:    4 GPU configs × 2 NIC settings = 8 experiments
+# Socket experiments: 4 GPU configs × 1              = 4 experiments
+#   GPU counts:  4 (within-node), 8 (within-node), 12 (cross-node), 16 (cross-node)
+#   EFA NICs:    FI_EFA_NUM_NETWORK_CARDS = 1, 4  (ignored for socket)
+#
+# Requires these env vars (set from aws-manual-setup.md Variables section):
+#   SSH_KEY, HEAD_PUBLIC_IP, WORKER_PRIVATE_IP, HEAD_PRIVATE_IP
+#
+# Run from local machine:
+#   ./scripts/run_nccl_benchmark.sh
+
+set -euo pipefail
+
+: "${SSH_KEY:?SSH_KEY must be set}"
+: "${HEAD_PUBLIC_IP:?HEAD_PUBLIC_IP must be set}"
+: "${WORKER_PRIVATE_IP:?WORKER_PRIVATE_IP must be set}"
+: "${HEAD_PRIVATE_IP:?HEAD_PRIVATE_IP must be set}"
+
+RDZV_PORT=29500
+LOGFILE=$(mktemp /tmp/nccl_benchmark_XXXXXX.log)
+echo "Raw output → $LOGFILE"
+echo ""
+
+# Write an SSH config so ProxyCommand quoting is clean
+SSH_CFG=$(mktemp /tmp/ssh-cfg-XXXXXX)
+trap "rm -f $SSH_CFG" EXIT
+cat > "$SSH_CFG" <<EOF
+Host head
+  HostName $HEAD_PUBLIC_IP
+  User ubuntu
+  IdentityFile $SSH_KEY
+  StrictHostKeyChecking no
+
+Host worker
+  HostName $WORKER_PRIVATE_IP
+  User ubuntu
+  IdentityFile $SSH_KEY
+  StrictHostKeyChecking no
+  ProxyCommand ssh -F $SSH_CFG -W %h:%p head
+EOF
+
+ssh_head()   { ssh -F "$SSH_CFG" head "$@"; }
+ssh_worker() { ssh -F "$SSH_CFG" worker "$@"; }
+
+# Results: indexed by [gpus,efa_cards,transport] → "a2a_algbw a2a_busbw ar_algbw ar_busbw"
+declare -A RESULTS
+
+BASE_ENV="-e NCCL_SOCKET_IFNAME=ens32 -e GLOO_SOCKET_IFNAME=ens32 -e NCCL_PROTO=simple"
+
+# Build extra env vars for a given transport and NIC count.
+# transport: "efa" or "socket"
+# efa: NIC count (ignored for socket)
+extra_env() {
+    local transport=$1 efa=$2
+    if [[ $transport == socket ]]; then
+        echo "-e NCCL_NET=Socket"
+    else
+        echo "-e FI_EFA_NUM_NETWORK_CARDS=$efa"
+    fi
+}
+
+run_single_node() {
+    local gpus=$1 efa=$2 transport=$3
+    local nic_label; [[ $transport == socket ]] && nic_label="-" || nic_label="$efa"
+    printf "  %-16s %-7s NICs=%-2s  running..." "within-node ${gpus}G" "[$transport]" "$nic_label"
+    out=$(ssh_head "docker exec \
+        $BASE_ENV $(extra_env $transport $efa) \
+        -w /tmp/piper piper_ray \
+        torchrun --nproc_per_node=$gpus scripts/nccl_benchmark.py" 2>&1)
+    echo "$out" >> "$LOGFILE"
+    echo "  done"
+    parse_results "$out" "$gpus" "$efa" "$transport"
+}
+
+run_multi_node() {
+    local gpus_per_node=$1 efa=$2 transport=$3
+    local total=$((gpus_per_node * 2))
+    local nic_label; [[ $transport == socket ]] && nic_label="-" || nic_label="$efa"
+    printf "  %-16s %-7s NICs=%-2s  running..." "cross-node ${total}G" "[$transport]" "$nic_label"
+
+    local tmpout
+    tmpout=$(mktemp)
+    ssh_head "docker exec \
+        $BASE_ENV $(extra_env $transport $efa) \
+        -w /tmp/piper piper_ray \
+        torchrun --nnodes=2 --nproc_per_node=$gpus_per_node \
+        --node_rank=0 \
+        --master_addr=$HEAD_PRIVATE_IP --master_port=$RDZV_PORT \
+        scripts/nccl_benchmark.py" > "$tmpout" 2>&1 &
+    HEAD_PID=$!
+
+    ssh_worker "docker exec \
+        $BASE_ENV $(extra_env $transport $efa) \
+        -w /tmp/piper piper_ray \
+        torchrun --nnodes=2 --nproc_per_node=$gpus_per_node \
+        --node_rank=1 \
+        --master_addr=$HEAD_PRIVATE_IP --master_port=$RDZV_PORT \
+        scripts/nccl_benchmark.py" >/dev/null 2>&1 &
+    WORKER_PID=$!
+
+    wait $HEAD_PID
+    out=$(cat "$tmpout")
+    rm -f "$tmpout"
+    echo "$out" >> "$LOGFILE"
+    echo "  done"
+    wait $WORKER_PID
+
+    parse_results "$out" "$total" "$efa" "$transport"
+    RDZV_PORT=$((RDZV_PORT + 1))
+}
+
+parse_results() {
+    local out=$1 gpus=$2 efa=$3 transport=$4
+    local a2a_time a2a_bw ar_time ar_bw
+    a2a_time=$(echo "$out" | grep -A6 "All-to-All" | grep "time/iter" | awk '{print $2}')
+    a2a_bw=$(  echo "$out" | grep -A6 "All-to-All" | grep "bandwidth" | awk '{print $2}')
+    ar_time=$( echo "$out" | grep -A6 "All-Reduce"  | grep "time/iter" | awk '{print $2}')
+    ar_bw=$(   echo "$out" | grep -A6 "All-Reduce"  | grep "bandwidth" | awk '{print $2}')
+    RESULTS["${gpus},${efa},${transport}"]="${a2a_time:-N/A} ${a2a_bw:-N/A} ${ar_time:-N/A} ${ar_bw:-N/A}"
+}
+
+# ── Run all experiments ───────────────────────────────────────────────────────
+
+echo "── EFA ──────────────────────────────────────────────────────────────────"
+for efa in 1 4; do
+    run_single_node  4  $efa  efa
+    run_single_node  8  $efa  efa
+    run_multi_node   6  $efa  efa   # 12 GPUs total
+    run_multi_node   8  $efa  efa   # 16 GPUs total
+done
+
+echo ""
+echo "── Socket ───────────────────────────────────────────────────────────────"
+run_single_node  4  1  socket
+run_single_node  8  1  socket
+run_multi_node   6  1  socket   # 12 GPUs total
+run_multi_node   8  1  socket   # 16 GPUs total
+
+# ── Summary table ─────────────────────────────────────────────────────────────
+
+echo ""
+echo "═══════════════════════════════════════════════════════════════════════════════"
+echo "  NCCL Benchmark Summary (GB/s)"
+echo "═══════════════════════════════════════════════════════════════════════════════"
+printf "  %-18s  %-7s  %-5s  %-22s  %-22s\n" \
+    "config" "net" "NICs" "All-to-All (ms / GB/s)" "All-Reduce (ms / GB/s)"
+echo "  ──────────────────  ───────  ─────  ──────────────────────  ──────────────────────"
+
+for gpus in 4 8 12 16; do
+    if   [[ $gpus -le 8 ]]; then label="within-node ${gpus}G"
+    else                         label="cross-node  ${gpus}G"
+    fi
+    for efa in 1 4; do
+        key="${gpus},${efa},efa"
+        read -r a2a_time a2a_bw ar_time ar_bw <<< "${RESULTS[$key]:-N/A N/A N/A N/A}"
+        printf "  %-18s  %-7s  %-5s  %-10s %-10s  %-10s %-10s\n" \
+            "$label" "efa" "$efa" "$a2a_time" "$a2a_bw" "$ar_time" "$ar_bw"
+    done
+    key="${gpus},1,socket"
+    read -r a2a_time a2a_bw ar_time ar_bw <<< "${RESULTS[$key]:-N/A N/A N/A N/A}"
+    printf "  %-18s  %-7s  %-5s  %-10s %-10s  %-10s %-10s\n" \
+        "$label" "socket" "-" "$a2a_time" "$a2a_bw" "$ar_time" "$ar_bw"
+done
+echo "═══════════════════════════════════════════════════════════════════════════════"
+echo "  Full output: $LOGFILE"
diff --git a/scripts/run_qwen_e2e_eval.py b/scripts/run_qwen_e2e_eval.py
new file mode 100644
index 0000000..64de748
--- /dev/null
+++ b/scripts/run_qwen_e2e_eval.py
@@ -0,0 +1,1447 @@
+#!/usr/bin/env python3
+"""Run three end-to-end Qwen EC2 eval sweeps and generate plots/CSVs.
+
+This script wraps scripts/run-qwen-ec2.sh, stores per-experiment logs under
+e2e-eval/, copies the matching metrics file out of the remote piper_ray
+container, aggregates throughput statistics, and saves one CSV and one plot for
+each requested sweep.
+
+Defaults that were not fully specified in the request are made explicit here:
+  - model: 9B
+  - default batch size for non-ZeRO sweeps: 4
+  - seq len: 512
+  - warmup / iters: 3 / 10
+  - scalability sweep PP->MBS mapping: 4->8, 8->16
+  - base schedule for scalability and ZeRO sweeps: 1f1b
+  - schedule sweep ZeRO stage: 0
+  - schedule sweep MBS: 16
+  - ZeRO sweep batch sizes: 4, 8, 16
+  - ZeRO sweep num microbatches: 2 * pp
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import math
+import os
+import re
+import shutil
+import subprocess
+import sys
+from dataclasses import dataclass
+from pathlib import Path
+from datetime import datetime, timezone
+from typing import Iterable
+
+
+OOM_PATTERNS = (
+    "out of memory",
+    "torch.cuda.outofmemoryerror",
+    "cuda out of memory",
+    "oom-killed",
+    "std::bad_alloc",
+    "failed to cuda calloc async",
+)
+
+
+@dataclass(frozen=True)
+class Experiment:
+    sweep: str
+    schedule: str
+    pp: int
+    dp: int
+    ep: bool
+    zero_stage: int
+    bucket_size_mb: float | None
+    batch_size: int
+    seq_len: int
+    mbs: int
+    gradient_accumulation: bool
+    ar_a2a_same_stream: bool
+    overlap_zero_ops: bool
+    overlap_chunks: bool
+    nsight: bool
+
+    def slug(self) -> str:
+        bucket_part = (
+            f"-bucket{_format_bucket_size(self.bucket_size_mb)}"
+            if self.bucket_size_mb is not None
+            else ""
+        )
+        nsight_part = "-nsight1" if self.nsight else ""
+        return (
+            f"{self.sweep}-sched_{self.schedule}-pp{self.pp}-dp{self.dp}-"
+            f"ep{int(self.ep)}-zero{self.zero_stage}{bucket_part}{nsight_part}-bs{self.batch_size}-sl{self.seq_len}-"
+            f"mbs{self.mbs}-ga{int(self.gradient_accumulation)}-"
+            f"aras{int(self.ar_a2a_same_stream)}-ozo{int(self.overlap_zero_ops)}-"
+            f"och{int(self.overlap_chunks)}"
+        )
+
+    def label(self) -> str:
+        if self.sweep == "scalability":
+            return f"(pp={self.pp}, dp={self.dp})"
+        if self.sweep == "zero":
+            ga = "on" if self.gradient_accumulation else "off"
+            return f"(zero={self.zero_stage}, ga={ga}, batch_size={self.batch_size})"
+        return self.schedule
+
+    def key(self) -> tuple[object, ...]:
+        return (
+            self.sweep,
+            self.schedule,
+            self.pp,
+            self.dp,
+            int(self.ep),
+            self.zero_stage,
+            self.bucket_size_mb,
+            self.batch_size,
+            self.seq_len,
+            self.mbs,
+            int(self.gradient_accumulation),
+            int(self.ar_a2a_same_stream),
+            int(self.overlap_zero_ops),
+            int(self.overlap_chunks),
+            int(self.nsight),
+        )
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Run three EC2 Qwen end-to-end sweeps and save plots/CSVs."
+    )
+    parser.add_argument("--model", default="1B")
+    parser.add_argument("--pp", type=int, default=8, help="Default PP degree for non-scalability sweeps.")
+    parser.add_argument("--dp", type=int, default=1, help="Default DP degree for non-scalability sweeps.")
+    parser.add_argument("--batch-size", type=int, default=4)
+    parser.add_argument(
+        "--bucket-size",
+        type=float,
+        default=None,
+        help="Bucket size in MB for scalability and ZeRO sweeps. Default: unset (no bucketing).",
+    )
+    parser.add_argument("--seq-len", type=int, default=512)
+    parser.add_argument("--warmup", type=int, default=3)
+    parser.add_argument("--iters", type=int, default=10)
+    parser.add_argument("--ray-port", type=int, default=6379)
+    parser.add_argument(
+        "--remote-output-dir",
+        default="/tmp/piper/out",
+        help="Directory inside the remote piper_ray container where test_qwen writes outputs.",
+    )
+    parser.add_argument(
+        "--out-dir",
+        default="e2e-eval",
+        help="Base directory where timestamped eval run folders are created.",
+    )
+    parser.add_argument(
+        "--run-name",
+        help="Optional run directory name under --out-dir. Defaults to a UTC timestamp.",
+    )
+    parser.add_argument("--runner", default="scripts/run-qwen-ec2.sh")
+    parser.add_argument("--base-schedule", default="1f1b")
+    parser.add_argument(
+        "--scalability-pp-mbs",
+        nargs="+",
+        default=["4:8", "8:16"],
+        metavar="PP:MBS",
+        help="PP->MBS mapping for the scalability sweep.",
+    )
+    parser.add_argument(
+        "--zero-stages",
+        nargs="+",
+        type=int,
+        default=[1,2,3],
+    )
+    parser.add_argument(
+        "--zero-batch-sizes",
+        nargs="+",
+        type=int,
+        default=[16,32,64],
+    )
+    parser.add_argument(
+        "--zero-grad-acc-settings",
+        nargs="+",
+        choices=["on", "off"],
+        default=["off"],
+        help="Gradient accumulation settings to include in the zero sweep. "
+             "Examples: --zero-grad-acc-settings on | off | off on",
+    )
+    parser.add_argument(
+        "--schedule-values",
+        nargs="+",
+        default=["1f1b", "interleaved-1f1b", "interleaved-zerobubble", "dualpipev"],
+    )
+    parser.add_argument("--schedule-mbs", type=int, default=16)
+    parser.add_argument("--schedule-zero-stage", type=int, default=0)
+    parser.add_argument(
+        "--nsight",
+        action="store_true",
+        help="Run each experiment with Nsight enabled and fetch matching Nsight artifacts.",
+    )
+    parser.add_argument(
+        "--use-inductor",
+        dest="use_inductor",
+        action=argparse.BooleanOptionalAction,
+        default=None,
+        help="Override whether to pass --use-inductor through to test_qwen. "
+             "By default this is disabled for schedule sweeps and enabled for zero/scalability sweeps.",
+    )
+    parser.add_argument(
+        "--gradient-accumulation",
+        dest="gradient_accumulation",
+        action=argparse.BooleanOptionalAction,
+        default=True,
+        help="Whether to pass gradient accumulation through to test_qwen (default: true).",
+    )
+    parser.add_argument(
+        "--overlap-chunks",
+        dest="overlap_chunks",
+        action=argparse.BooleanOptionalAction,
+        default=False,
+        help="Whether to pass overlap_chunks through to test_qwen (default: false).",
+    )
+    parser.add_argument(
+        "--no-udse-inductor",
+        dest="use_inductor",
+        action="store_false",
+        help=argparse.SUPPRESS,
+    )
+    parser.add_argument(
+        "--no-resume",
+        action="store_true",
+        help="Do not reuse completed rows from an existing all_results.csv.",
+    )
+    parser.add_argument(
+        "--sweeps",
+        nargs="+",
+        choices=["scalability", "zero", "schedule"],
+        default=["scalability", "zero", "schedule"],
+        help="Subset of e2e sweeps to run. Default: scalability zero schedule",
+    )
+    parser.add_argument("--dry-run", action="store_true")
+    return parser.parse_args()
+
+
+def _format_bucket_size(bucket_size_mb: float | None) -> str:
+    if bucket_size_mb is None:
+        return "none"
+    return f"{bucket_size_mb:g}"
+
+
+def _parse_pp_mbs(values: list[str]) -> list[tuple[int, int]]:
+    parsed: list[tuple[int, int]] = []
+    for value in values:
+        pp_text, mbs_text = value.split(":", maxsplit=1)
+        parsed.append((int(pp_text), int(mbs_text)))
+    return parsed
+
+
+def _metrics_name(model: str, exp: Experiment) -> str:
+    return (
+        f"qwen{model}-pp{exp.pp}-dp{exp.dp}-zero{exp.zero_stage}-"
+        f"ep{int(exp.ep)}-"
+        f"bs{exp.batch_size}-sl{exp.seq_len}-ga{int(exp.gradient_accumulation)}-"
+        f"aras{int(exp.ar_a2a_same_stream)}-ozo{int(exp.overlap_zero_ops)}-"
+        f"och{int(exp.overlap_chunks)}-"
+        f"{exp.schedule}"
+    )
+
+
+def _remote_experiment_output_dir(args: argparse.Namespace, exp: Experiment) -> str:
+    return f"{args.remote_output_dir.rstrip('/')}/{exp.slug()}"
+
+
+def _metrics_remote_paths(
+    model: str,
+    exp: Experiment,
+    remote_output_dir: str,
+) -> list[str]:
+    return [
+        f"{remote_output_dir}/{_metrics_name(model, exp)}",
+        (
+            f"{remote_output_dir}/qwen{model}-pp{exp.pp}-dp{exp.dp}-zero{exp.zero_stage}-"
+            f"bs{exp.batch_size}-sl{exp.seq_len}-ga{int(exp.gradient_accumulation)}-"
+            f"aras{int(exp.ar_a2a_same_stream)}-ozo{int(exp.overlap_zero_ops)}-"
+            f"och{int(exp.overlap_chunks)}-"
+            f"{exp.schedule}"
+        ),
+    ]
+
+
+def _runner_command(args: argparse.Namespace, exp: Experiment, fetch_dir: Path) -> list[str]:
+    remote_output_dir = _remote_experiment_output_dir(args, exp)
+    command = [
+        str(Path(args.runner).resolve()),
+        "--model", args.model,
+        "--schedule", exp.schedule,
+        "--pp", str(exp.pp),
+        "--dp", str(exp.dp),
+        "--zero-stage", str(exp.zero_stage),
+        "--batch-size", str(exp.batch_size),
+        "--seq-len", str(exp.seq_len),
+        "--mbs", str(exp.mbs),
+        "--gradient-accumulation" if exp.gradient_accumulation else "--no-gradient-accumulation",
+        "--ar-a2a-same-stream" if exp.ar_a2a_same_stream else "--no-ar-a2a-same-stream",
+        "--overlap-zero-ops" if exp.overlap_zero_ops else "--no-overlap-zero-ops",
+        "--overlap-chunks" if exp.overlap_chunks else "--no-overlap-chunks",
+        "--warmup", str(args.warmup),
+        "--iters", str(args.iters),
+        "--ray-port", str(args.ray_port),
+        "--out-dir", str(fetch_dir),
+        "--remote-output-dir", remote_output_dir,
+    ]
+    if exp.bucket_size_mb is not None:
+        command.extend(["--bucket-size", _format_bucket_size(exp.bucket_size_mb)])
+    if exp.ep:
+        command.append("--ep")
+    if exp.nsight:
+        command.append("--nsight")
+    use_inductor = args.use_inductor
+    if use_inductor is None:
+        use_inductor = exp.sweep != "schedule"
+    if use_inductor:
+        command.append("--use-inductor")
+    else:
+        command.append("--no-use-inductor")
+    return command
+
+
+def _ssh_head_command(remote_command: str) -> list[str]:
+    ssh_key = os.environ["SSH_KEY"]
+    head_public_ip = os.environ["HEAD_PUBLIC_IP"]
+    return [
+        "ssh",
+        "-i",
+        ssh_key,
+        "-o",
+        "StrictHostKeyChecking=no",
+        f"ubuntu@{head_public_ip}",
+        remote_command,
+    ]
+
+
+def _ssh_worker_command(worker_ip: str, remote_command: str) -> list[str]:
+    ssh_key = os.environ["SSH_KEY"]
+    head_public_ip = os.environ["HEAD_PUBLIC_IP"]
+    return [
+        "ssh",
+        "-i",
+        ssh_key,
+        "-o",
+        "StrictHostKeyChecking=no",
+        "-o",
+        (
+            "ProxyCommand="
+            f"ssh -i {ssh_key} -o StrictHostKeyChecking=no -W %h:%p ubuntu@{head_public_ip}"
+        ),
+        f"ubuntu@{worker_ip}",
+        remote_command,
+    ]
+
+
+def _fetch_remote_metrics(
+    model: str,
+    exp: Experiment,
+    destination: Path,
+    remote_output_dir: str,
+) -> bool:
+    for remote_path in _metrics_remote_paths(model, exp, remote_output_dir):
+        remote_command = (
+            "docker exec piper_ray bash -lc "
+            + json.dumps(f"test -f {remote_path} && cat {remote_path}")
+        )
+        result = subprocess.run(
+            _ssh_head_command(remote_command),
+            check=False,
+            capture_output=True,
+            text=True,
+        )
+        if result.returncode != 0 or not result.stdout.strip():
+            continue
+
+        destination.parent.mkdir(parents=True, exist_ok=True)
+        destination.write_text(result.stdout, encoding="utf-8")
+        return True
+
+    return False
+
+
+def _fetch_remote_dag_order_logs(
+    args: argparse.Namespace,
+    exp: Experiment,
+    exp_logs_dir: Path,
+) -> list[Path]:
+    copied_paths: list[Path] = []
+    remote_base = _remote_experiment_output_dir(args, exp)
+    for rank in range(exp.pp):
+        remote_path = f"{remote_base}/dag_order_rank{rank}"
+        result = subprocess.run(
+            _ssh_head_command(
+                "docker exec piper_ray bash -lc "
+                + json.dumps(f"test -f {remote_path} && cat {remote_path}")
+            ),
+            check=False,
+            capture_output=True,
+            text=True,
+        )
+        if result.returncode != 0 or not result.stdout:
+            continue
+
+        destination = exp_logs_dir / f"dag-order-rank{rank}.log"
+        destination.write_text(result.stdout, encoding="utf-8")
+        copied_paths.append(destination)
+
+    return copied_paths
+
+
+def _strip_ansi(text: str) -> str:
+    return re.sub(r"\x1b\[[0-9;]*m", "", text)
+
+
+def _read_text_lossy(path: Path) -> str:
+    return path.read_text(encoding="utf-8", errors="replace")
+
+
+def _parse_head_private_ip(log_path: Path) -> str | None:
+    for line in _read_text_lossy(log_path).splitlines():
+        clean = _strip_ansi(line)
+        match = re.search(r"Namespace\(.*address='([^']+)'", clean)
+        if match:
+            return match.group(1)
+    return None
+
+
+def _node_ip_map_from_env(log_path: Path) -> dict[str, str]:
+    mapping: dict[str, str] = {}
+    head_private_ip = os.environ.get("HEAD_PRIVATE_IP") or _parse_head_private_ip(log_path)
+    if head_private_ip:
+        mapping[head_private_ip] = "head"
+    for idx in range(1, 4):
+        worker_ip = os.environ.get(f"WORKER{idx}_PRIVATE_IP")
+        if worker_ip:
+            mapping[worker_ip] = f"worker{idx}"
+    return mapping
+
+
+def _extract_experiment_pids_by_node(log_path: Path) -> dict[str, set[str]]:
+    pid_pattern = re.compile(r"pid=(\d+)(?:, ip=([0-9.]+))?")
+    ip_to_label = _node_ip_map_from_env(log_path)
+    pids_by_node: dict[str, set[str]] = {}
+
+    for line in _read_text_lossy(log_path).splitlines():
+        clean = _strip_ansi(line)
+        for pid, ip in pid_pattern.findall(clean):
+            if ip:
+                node_label = ip_to_label.get(ip, f"node-{ip}")
+            else:
+                node_label = "head"
+            pids_by_node.setdefault(node_label, set()).add(pid)
+
+    return pids_by_node
+
+
+def _iter_remote_nodes(log_path: Path) -> list[tuple[str, str, str | None]]:
+    nodes: list[tuple[str, str, str | None]] = [("head", "head", None)]
+    ip_to_label = _node_ip_map_from_env(log_path)
+    for ip, label in sorted(ip_to_label.items(), key=lambda item: item[1]):
+        if label == "head":
+            continue
+        nodes.append((label, "worker", ip))
+    return nodes
+
+
+def _scp_from_remote(
+    *,
+    node_kind: str,
+    remote_host: str | None,
+    remote_path: str,
+    destination: Path,
+) -> bool:
+    ssh_key = os.environ["SSH_KEY"]
+    head_public_ip = os.environ["HEAD_PUBLIC_IP"]
+    command = [
+        "scp",
+        "-r",
+        "-i",
+        ssh_key,
+        "-o",
+        "StrictHostKeyChecking=no",
+    ]
+    if node_kind == "head":
+        command.extend([f"ubuntu@{head_public_ip}:{remote_path}", str(destination)])
+    else:
+        command.extend(
+            [
+                "-o",
+                (
+                    "ProxyCommand="
+                    f"ssh -i {ssh_key} -o StrictHostKeyChecking=no -W %h:%p ubuntu@{head_public_ip}"
+                ),
+                f"ubuntu@{remote_host}:{remote_path}",
+                str(destination),
+            ]
+        )
+    result = subprocess.run(command, check=False, capture_output=True, text=True)
+    return result.returncode == 0
+
+
+def _stage_remote_nsight_dir(
+    *,
+    node_kind: str,
+    remote_host: str | None,
+    remote_tmp_dir: str,
+    destination: Path,
+) -> bool:
+    remote_command = (
+        "rm -rf "
+        + remote_tmp_dir
+        + " && mkdir -p "
+        + remote_tmp_dir
+        + " && docker cp piper_ray:/tmp/piper/ray_tmp/session_latest/logs/nsight/. "
+        + remote_tmp_dir
+        + "/"
+    )
+    run_command = (
+        _ssh_head_command(remote_command)
+        if node_kind == "head"
+        else _ssh_worker_command(str(remote_host), remote_command)
+    )
+    result = subprocess.run(run_command, check=False, capture_output=True, text=True)
+    if result.returncode != 0:
+        return False
+    destination.parent.mkdir(parents=True, exist_ok=True)
+    return _scp_from_remote(
+        node_kind=node_kind,
+        remote_host=remote_host,
+        remote_path=f"{remote_tmp_dir}/.",
+        destination=destination,
+    )
+
+
+def _matching_nsight_paths(paths: Iterable[str], pids: set[str]) -> list[str]:
+    matches: list[str] = []
+    for path in paths:
+        basename = Path(path).name
+        if any(pid in basename or pid in path for pid in pids):
+            matches.append(path)
+    return sorted(set(matches))
+
+
+def _copy_experiment_nsight_profiles(
+    exp: Experiment,
+    log_path: Path,
+    exp_logs_dir: Path,
+) -> list[Path]:
+    if not exp.nsight:
+        return []
+
+    pids_by_node = _extract_experiment_pids_by_node(log_path)
+    if not pids_by_node:
+        return []
+
+    copied_paths: list[Path] = []
+    exp_nsight_dir = exp_logs_dir / "nsight"
+    stage_root = exp_nsight_dir / ".stage"
+    shutil.rmtree(stage_root, ignore_errors=True)
+    for node_label, node_kind, remote_host in _iter_remote_nodes(log_path):
+        node_pids = pids_by_node.get(node_label, set())
+        if not node_pids:
+            continue
+        local_stage_dir = stage_root / node_label
+        remote_tmp_dir = f"/tmp/piper-nsight-{exp_logs_dir.name}-{node_label}"
+        if not _stage_remote_nsight_dir(
+            node_kind=node_kind,
+            remote_host=remote_host,
+            remote_tmp_dir=remote_tmp_dir,
+            destination=local_stage_dir,
+        ):
+            continue
+        staged_paths = [str(path.relative_to(local_stage_dir)) for path in local_stage_dir.rglob("*.nsys-rep")]
+        for relative_path in _matching_nsight_paths(staged_paths, node_pids):
+            source = local_stage_dir / relative_path
+            destination = exp_nsight_dir / node_label / Path(relative_path).name
+            destination.parent.mkdir(parents=True, exist_ok=True)
+            shutil.copy2(source, destination)
+            copied_paths.append(destination)
+
+    pid_manifest = {
+        node_label: sorted(pids)
+        for node_label, pids in sorted(pids_by_node.items())
+        if pids
+    }
+    if pid_manifest:
+        exp_nsight_dir.mkdir(parents=True, exist_ok=True)
+        (exp_nsight_dir / "matched_pids.json").write_text(
+            json.dumps(pid_manifest, indent=2, sort_keys=True),
+            encoding="utf-8",
+        )
+
+    shutil.rmtree(stage_root, ignore_errors=True)
+
+    return copied_paths
+
+
+def _recover_metrics_from_cluster_log(
+    exp: Experiment,
+    cluster_log_path: Path,
+    destination: Path,
+    expected_iters: int,
+) -> bool:
+    if not cluster_log_path.is_file():
+        return False
+
+    dp_rank_by_pid: dict[str, int] = {}
+    iter_times_by_pid: dict[str, list[float]] = {}
+    peak_memory_by_pid: dict[str, dict[str, dict[str, float | int]]] = {}
+
+    for raw_line in cluster_log_path.read_text(encoding="utf-8", errors="replace").splitlines():
+        line = _strip_ansi(raw_line)
+
+        dp_rank_match = re.search(r"run_dp_rank pid=(\d+).*DP rank (\d+) done\.", line)
+        if dp_rank_match:
+            dp_rank_by_pid[dp_rank_match.group(1)] = int(dp_rank_match.group(2))
+
+        step_match = re.search(r"run_dp_rank pid=(\d+).*step_time=([0-9.]+)s", line)
+        if step_match:
+            pid = step_match.group(1)
+            iter_times_by_pid.setdefault(pid, []).append(float(step_match.group(2)))
+
+            rank_memories = {
+                rank: {
+                    "peak_memory_gb": float(value),
+                    "peak_memory_bytes": int(float(value) * (1024 ** 3)),
+                }
+                for rank, value in re.findall(r"rank(\d+)_peak_mem=([0-9.]+)GB", line)
+            }
+            if rank_memories:
+                peak_memory_by_pid[pid] = rank_memories
+
+    if not iter_times_by_pid:
+        return False
+
+    for index, pid in enumerate(sorted(iter_times_by_pid, key=int)):
+        dp_rank_by_pid.setdefault(pid, index)
+
+    records: list[dict[str, object]] = []
+    for pid, iter_times in sorted(iter_times_by_pid.items(), key=lambda item: dp_rank_by_pid[item[0]]):
+        timed_iter_times = iter_times[-expected_iters:]
+        iter_time_mean = sum(timed_iter_times) / len(timed_iter_times)
+        iter_time_variance = sum((value - iter_time_mean) ** 2 for value in timed_iter_times) / len(timed_iter_times)
+        iter_time_std = math.sqrt(iter_time_variance)
+        throughput_mean = tokens_per_iter = exp.batch_size * exp.mbs * exp.seq_len
+        throughput_mean = tokens_per_iter / iter_time_mean
+        records.append(
+            {
+                "dp_rank": dp_rank_by_pid[pid],
+                "model": exp.batch_size,
+                "schedule": exp.schedule,
+                "pp": exp.pp,
+                "dp": exp.dp,
+                "ep": exp.ep,
+                "batch_size": exp.batch_size,
+                "mbs": exp.mbs,
+                "seq_len": exp.seq_len,
+                "gradient_accumulation": exp.gradient_accumulation,
+                "ar_a2a_same_stream": exp.ar_a2a_same_stream,
+                "overlap_zero_ops": exp.overlap_zero_ops,
+                "overlap_chunks": exp.overlap_chunks,
+                "samples": len(timed_iter_times),
+                "iter_time_mean_s": iter_time_mean,
+                "iter_time_std_s": iter_time_std,
+                "throughput_tokens_per_s": throughput_mean,
+                "iter_times_s": timed_iter_times,
+                "peak_memory_by_rank": peak_memory_by_pid.get(pid, {}),
+            }
+        )
+
+    destination.parent.mkdir(parents=True, exist_ok=True)
+    with open(destination, "w", encoding="utf-8") as handle:
+        for record in records:
+            handle.write(f"rank {record['dp_rank']} metrics_json= {json.dumps(record, sort_keys=True)}\n")
+    return True
+
+
+def _recover_metrics_from_fetch_logs(
+    args: argparse.Namespace,
+    exp: Experiment,
+    fetch_dir: Path,
+    destination: Path,
+) -> bool:
+    bucket_part = (
+        f"-bucket{_format_bucket_size(exp.bucket_size_mb)}"
+        if exp.bucket_size_mb is not None
+        else ""
+    )
+    nsight_part = "-nsight1" if exp.nsight else ""
+    pattern = (
+        f"qwen{args.model}-sched_{exp.schedule}-pp{exp.pp}-dp{exp.dp}-"
+        f"zero{exp.zero_stage}{bucket_part}{nsight_part}-bs{exp.batch_size}-sl{exp.seq_len}-"
+        f"mbs{exp.mbs}-ga{int(exp.gradient_accumulation)}-"
+        f"aras{int(exp.ar_a2a_same_stream)}-ozo{int(exp.overlap_zero_ops)}-"
+        f"och{int(exp.overlap_chunks)}_*.cluster.log"
+    )
+    candidates = sorted(fetch_dir.glob(pattern))
+    if not candidates:
+        return False
+    return _recover_metrics_from_cluster_log(exp, candidates[-1], destination, args.iters)
+
+
+def _parse_metrics_records(metrics_path: Path) -> list[dict]:
+    records: list[dict] = []
+    if not metrics_path.is_file():
+        return records
+
+    marker = "metrics_json="
+    for line in metrics_path.read_text(encoding="utf-8").splitlines():
+        if marker not in line:
+            continue
+        payload = line.split(marker, maxsplit=1)[1].strip()
+        try:
+            records.append(json.loads(payload))
+        except json.JSONDecodeError:
+            continue
+    return records
+
+
+def _row_from_metrics(
+    exp: Experiment,
+    log_path: Path,
+    metrics_path: Path,
+) -> dict[str, object] | None:
+    records = _parse_metrics_records(metrics_path)
+    if not records:
+        return None
+
+    row: dict[str, object] = {
+        "sweep": exp.sweep,
+        "label": exp.label(),
+        "schedule": exp.schedule,
+        "pp": exp.pp,
+        "dp": exp.dp,
+        "ep": int(exp.ep),
+        "zero_stage": exp.zero_stage,
+        "bucket_size_mb": _format_bucket_size(exp.bucket_size_mb) if exp.bucket_size_mb is not None else "",
+        "batch_size": exp.batch_size,
+        "seq_len": exp.seq_len,
+        "mbs": exp.mbs,
+        "gradient_accumulation": int(exp.gradient_accumulation),
+        "ar_a2a_same_stream": int(exp.ar_a2a_same_stream),
+        "overlap_zero_ops": int(exp.overlap_zero_ops),
+        "overlap_chunks": int(exp.overlap_chunks),
+        "nsight": int(exp.nsight),
+        "status": "success",
+        "failure_reason": "",
+        "log_path": str(log_path),
+        "metrics_path": str(metrics_path),
+    }
+    row.update(_aggregate_metrics(records))
+    return row
+
+
+def _aggregate_metrics(records: list[dict]) -> dict[str, float | int | str]:
+    latest_by_dp_rank: dict[int, dict] = {}
+    for record in records:
+        latest_by_dp_rank[int(record["dp_rank"])] = record
+
+    selected = [latest_by_dp_rank[rank] for rank in sorted(latest_by_dp_rank)]
+    weighted_samples = 0
+    weighted_iter_time_mean = 0.0
+    weighted_iter_time_std = 0.0
+    weighted_throughput_mean = 0.0
+    weighted_throughput_std = 0.0
+    peak_memory_gb_max = 0.0
+    peak_memory_by_rank_summary: list[str] = []
+
+    for record in selected:
+        batch_size = int(record["batch_size"])
+        mbs = int(record["mbs"])
+        seq_len = int(record["seq_len"])
+        tokens_per_iter = batch_size * mbs * seq_len
+        samples = int(record.get("samples", len(record.get("iter_times_s", []))))
+        if samples <= 0:
+            continue
+
+        iter_time_mean = record.get("iter_time_mean_s")
+        if iter_time_mean is None:
+            iter_times = [float(iter_time) for iter_time in record.get("iter_times_s", [])]
+            if not iter_times:
+                continue
+            iter_time_mean = sum(iter_times) / len(iter_times)
+        iter_time_mean = float(iter_time_mean)
+
+        iter_time_std = record.get("iter_time_std_s")
+        if iter_time_std is None:
+            iter_times = [float(iter_time) for iter_time in record.get("iter_times_s", [])]
+            if iter_times:
+                variance = sum((value - iter_time_mean) ** 2 for value in iter_times) / len(iter_times)
+                iter_time_std = math.sqrt(variance)
+            else:
+                iter_time_std = 0.0
+        iter_time_std = float(iter_time_std)
+
+        throughput_mean = record.get("throughput_tokens_per_s")
+        if throughput_mean is None:
+            throughput_mean = tokens_per_iter / iter_time_mean
+        throughput_mean = float(throughput_mean)
+
+        # Use the reported iteration-time stddev from the metrics file rather than
+        # recomputing throughput variance from raw samples.
+        throughput_std = throughput_mean * (iter_time_std / iter_time_mean) if iter_time_mean > 0 else 0.0
+
+        weighted_samples += samples
+        weighted_iter_time_mean += samples * iter_time_mean
+        weighted_iter_time_std += samples * iter_time_std
+        weighted_throughput_mean += samples * throughput_mean
+        weighted_throughput_std += samples * throughput_std
+
+        peak_memory_by_rank = record.get("peak_memory_by_rank", {})
+        for rank, stats in sorted(peak_memory_by_rank.items(), key=lambda item: int(item[0])):
+            peak_gb = float(stats["peak_memory_gb"])
+            peak_memory_gb_max = max(peak_memory_gb_max, peak_gb)
+            peak_memory_by_rank_summary.append(f"{rank}:{peak_gb:.3f}")
+
+    if weighted_samples == 0:
+        return {
+            "samples": 0,
+            "iter_time_mean_s": 0.0,
+            "iter_time_std_s": 0.0,
+            "throughput_mean_tokens_per_s": 0.0,
+            "throughput_std_tokens_per_s": 0.0,
+            "peak_memory_gb_max": peak_memory_gb_max,
+            "peak_memory_gb_by_rank": ";".join(peak_memory_by_rank_summary),
+        }
+
+    return {
+        "samples": weighted_samples,
+        "iter_time_mean_s": weighted_iter_time_mean / weighted_samples,
+        "iter_time_std_s": weighted_iter_time_std / weighted_samples,
+        "throughput_mean_tokens_per_s": weighted_throughput_mean / weighted_samples,
+        "throughput_std_tokens_per_s": weighted_throughput_std / weighted_samples,
+        "peak_memory_gb_max": peak_memory_gb_max,
+        "peak_memory_gb_by_rank": ";".join(peak_memory_by_rank_summary),
+    }
+
+
+def _looks_like_oom(log_path: Path) -> bool:
+    log_text = log_path.read_text(encoding="utf-8", errors="replace").lower()
+    return any(pattern in log_text for pattern in OOM_PATTERNS)
+
+
+def _run_experiment(
+    args: argparse.Namespace,
+    exp: Experiment,
+    index: int,
+    total: int,
+    logs_dir: Path,
+    metrics_dir: Path,
+    fetch_dir: Path,
+) -> dict[str, object]:
+    exp_logs_dir = logs_dir / exp.slug()
+    exp_logs_dir.mkdir(parents=True, exist_ok=True)
+    log_path = exp_logs_dir / "run.log"
+    metrics_path = metrics_dir / f"{exp.slug()}.metrics"
+    command = _runner_command(args, exp, fetch_dir)
+
+    print(
+        f"[{index}/{total}] RUNNING {exp.sweep}: schedule={exp.schedule} pp={exp.pp} "
+        f"dp={exp.dp} zero={exp.zero_stage} bucket_size_mb={exp.bucket_size_mb} "
+        f"batch_size={exp.batch_size} mbs={exp.mbs} "
+        f"ga={'on' if exp.gradient_accumulation else 'off'} nsight={'on' if exp.nsight else 'off'}"
+    )
+    sys.stdout.flush()
+
+    if args.dry_run:
+        return {
+            "sweep": exp.sweep,
+            "label": exp.label(),
+            "schedule": exp.schedule,
+            "pp": exp.pp,
+            "dp": exp.dp,
+            "ep": int(exp.ep),
+            "zero_stage": exp.zero_stage,
+            "bucket_size_mb": _format_bucket_size(exp.bucket_size_mb) if exp.bucket_size_mb is not None else "",
+            "batch_size": exp.batch_size,
+            "seq_len": exp.seq_len,
+            "mbs": exp.mbs,
+            "gradient_accumulation": int(exp.gradient_accumulation),
+            "ar_a2a_same_stream": int(exp.ar_a2a_same_stream),
+            "overlap_zero_ops": int(exp.overlap_zero_ops),
+            "overlap_chunks": int(exp.overlap_chunks),
+            "nsight": int(exp.nsight),
+            "status": "dry-run",
+            "failure_reason": "",
+            "log_path": str(log_path),
+            "metrics_path": str(metrics_path),
+        }
+
+    with open(log_path, "w", encoding="utf-8") as handle:
+        result = subprocess.run(
+            command,
+            check=False,
+            stdout=handle,
+            stderr=subprocess.STDOUT,
+            text=True,
+        )
+
+    metrics_found = _fetch_remote_metrics(
+        args.model,
+        exp,
+        metrics_path,
+        _remote_experiment_output_dir(args, exp),
+    )
+    dag_order_paths = _fetch_remote_dag_order_logs(args, exp, exp_logs_dir)
+    nsight_paths = _copy_experiment_nsight_profiles(exp, log_path, exp_logs_dir)
+    if not metrics_found:
+        metrics_found = _recover_metrics_from_fetch_logs(args, exp, fetch_dir, metrics_path)
+    records = _parse_metrics_records(metrics_path) if metrics_found else []
+
+    status = "success"
+    failure_reason = ""
+    aggregated: dict[str, object] = {}
+    if result.returncode != 0:
+        if _looks_like_oom(log_path):
+            status = "oom"
+            failure_reason = "oom"
+        else:
+            status = "failed"
+            failure_reason = f"runner_exit_{result.returncode}"
+    elif not records:
+        status = "failed"
+        failure_reason = "missing_metrics"
+    else:
+        aggregated = _aggregate_metrics(records)
+
+    print(
+        f"[{index}/{total}] {status.upper()} {exp.sweep}: {exp.label()} "
+        f"log={log_path}"
+        + (
+            f" dag_logs={','.join(str(path.name) for path in dag_order_paths)}"
+            if dag_order_paths
+            else ""
+        )
+        + (
+            f" nsight_dir={exp_logs_dir / 'nsight'}"
+            if nsight_paths
+            else ""
+        )
+    )
+    sys.stdout.flush()
+
+    row: dict[str, object] = {
+        "sweep": exp.sweep,
+        "label": exp.label(),
+        "schedule": exp.schedule,
+        "pp": exp.pp,
+        "dp": exp.dp,
+        "zero_stage": exp.zero_stage,
+        "bucket_size_mb": _format_bucket_size(exp.bucket_size_mb) if exp.bucket_size_mb is not None else "",
+        "batch_size": exp.batch_size,
+        "seq_len": exp.seq_len,
+        "mbs": exp.mbs,
+        "gradient_accumulation": int(exp.gradient_accumulation),
+        "ar_a2a_same_stream": int(exp.ar_a2a_same_stream),
+        "overlap_zero_ops": int(exp.overlap_zero_ops),
+        "overlap_chunks": int(exp.overlap_chunks),
+        "nsight": int(exp.nsight),
+        "status": status,
+        "failure_reason": failure_reason,
+        "log_path": str(log_path),
+        "metrics_path": str(metrics_path) if metrics_found else "",
+    }
+    row.update(aggregated)
+    return row
+
+
+def _write_csv(path: Path, rows: list[dict[str, object]]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    fieldnames = [
+        "sweep",
+        "label",
+        "schedule",
+        "pp",
+        "dp",
+        "ep",
+        "zero_stage",
+        "bucket_size_mb",
+        "batch_size",
+        "seq_len",
+        "mbs",
+        "gradient_accumulation",
+        "ar_a2a_same_stream",
+        "overlap_zero_ops",
+        "overlap_chunks",
+        "nsight",
+        "status",
+        "failure_reason",
+        "samples",
+        "iter_time_mean_s",
+        "iter_time_std_s",
+        "throughput_mean_tokens_per_s",
+        "throughput_std_tokens_per_s",
+        "peak_memory_gb_max",
+        "peak_memory_gb_by_rank",
+        "log_path",
+        "metrics_path",
+    ]
+    with open(path, "w", newline="", encoding="utf-8") as handle:
+        writer = csv.DictWriter(handle, fieldnames=fieldnames)
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def _row_key(row: dict[str, object]) -> tuple[object, ...]:
+    return (
+        row["sweep"],
+        row["schedule"],
+        int(row["pp"]),
+        int(row["dp"]),
+        int(row.get("ep", 0)),
+        int(row["zero_stage"]),
+        float(row["bucket_size_mb"]) if str(row.get("bucket_size_mb", "")).strip() else None,
+        int(row["batch_size"]),
+        int(row["seq_len"]),
+        int(row["mbs"]),
+        int(row["gradient_accumulation"]),
+        int(row["ar_a2a_same_stream"]),
+        int(row["overlap_zero_ops"]),
+        int(row.get("overlap_chunks", 0)),
+        int(row.get("nsight", 0)),
+    )
+
+
+def _load_existing_rows(path: Path) -> list[dict[str, object]]:
+    if not path.is_file():
+        return []
+    with open(path, "r", newline="", encoding="utf-8") as handle:
+        return list(csv.DictReader(handle))
+
+
+def _hydrate_existing_rows(existing_rows: list[dict[str, object]]) -> list[dict[str, object]]:
+    hydrated: list[dict[str, object]] = []
+    for row in existing_rows:
+        metrics_path_text = str(row.get("metrics_path", "")).strip()
+        if row.get("status") in {"success", "oom"} or not metrics_path_text:
+            hydrated.append(row)
+            continue
+
+        metrics_path = Path(metrics_path_text)
+        records = _parse_metrics_records(metrics_path)
+        if not records:
+            hydrated.append(row)
+            continue
+
+        aggregated = _aggregate_metrics(records)
+        updated = dict(row)
+        updated["status"] = "success"
+        updated["failure_reason"] = ""
+        updated.update(aggregated)
+        hydrated.append(updated)
+    return hydrated
+
+
+def _completed_keys_from_metrics(
+    experiments: list[Experiment],
+    metrics_dir: Path,
+    existing_by_key: dict[tuple[object, ...], dict[str, object]],
+) -> set[tuple[object, ...]]:
+    completed: set[tuple[object, ...]] = set()
+    for exp in experiments:
+        existing_row = existing_by_key.get(exp.key())
+        if existing_row is not None and existing_row.get("status") not in {"success", "oom", "dry-run", ""}:
+            continue
+        metrics_path = metrics_dir / f"{exp.slug()}.metrics"
+        records = _parse_metrics_records(metrics_path)
+        if records:
+            completed.add(exp.key())
+    return completed
+
+
+def _save_bar_plot(
+    title: str,
+    xlabel: str,
+    rows: list[dict[str, object]],
+    output_path: Path,
+    *,
+    allow_oom_markers: bool = False,
+) -> None:
+    import matplotlib
+
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    labels = [str(row["label"]) for row in rows]
+    xs = list(range(len(rows)))
+
+    success_rows = [row for row in rows if row["status"] == "success"]
+    success_max = max(
+        (float(row["iter_time_mean_s"]) for row in success_rows),
+        default=0.0,
+    )
+    oom_marker_y = success_max * 0.05 if success_max > 0 else 1.0
+
+    fig, ax = plt.subplots(figsize=(max(10, len(rows) * 0.8), 6))
+    for index, row in enumerate(rows):
+        status = row["status"]
+        if status == "success":
+            ax.bar(
+                index,
+                float(row["iter_time_mean_s"]),
+                yerr=float(row["iter_time_std_s"]),
+                color="#4C72B0",
+                ecolor="#2F2F2F",
+                capsize=4,
+            )
+        elif allow_oom_markers and status == "oom":
+            ax.scatter(index, oom_marker_y, marker="x", s=120, color="red", linewidths=2.5)
+        else:
+            ax.scatter(index, oom_marker_y, marker="x", s=100, color="black", linewidths=2.0)
+
+    ax.set_title(title)
+    ax.set_xlabel(xlabel)
+    ax.set_ylabel("Iteration Time (s)")
+    ax.set_xticks(xs)
+    ax.set_xticklabels(labels, rotation=45, ha="right")
+    ax.grid(axis="y", linestyle="--", alpha=0.35)
+    ax.set_ylim(bottom=0)
+    fig.tight_layout()
+    fig.savefig(output_path, dpi=200)
+    plt.close(fig)
+
+
+def _parse_peak_memory_values(row: dict[str, object]) -> list[float]:
+    summary = str(row.get("peak_memory_gb_by_rank", "")).strip()
+    if not summary:
+        return []
+
+    values: list[float] = []
+    for item in summary.split(";"):
+        item = item.strip()
+        if not item or ":" not in item:
+            continue
+        _rank, value_text = item.split(":", maxsplit=1)
+        try:
+            values.append(float(value_text))
+        except ValueError:
+            continue
+    return values
+
+
+def _save_memory_plot(
+    title: str,
+    xlabel: str,
+    rows: list[dict[str, object]],
+    output_path: Path,
+    *,
+    allow_oom_markers: bool = False,
+) -> None:
+    import matplotlib
+
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    labels = [str(row["label"]) for row in rows]
+    xs = list(range(len(rows)))
+
+    success_values = [
+        value
+        for row in rows
+        if row["status"] == "success"
+        for value in _parse_peak_memory_values(row)
+    ]
+    marker_y = max(success_values, default=0.0) * 0.05 if success_values else 0.25
+
+    fig, ax = plt.subplots(figsize=(max(10, len(rows) * 0.8), 6))
+    for index, row in enumerate(rows):
+        status = row["status"]
+        if status == "success":
+            values = _parse_peak_memory_values(row)
+            if values:
+                ax.scatter(
+                    [index] * len(values),
+                    values,
+                    color="#DD8452",
+                    s=36,
+                    alpha=0.9,
+                )
+        elif allow_oom_markers and status == "oom":
+            ax.scatter(index, marker_y, marker="x", s=120, color="red", linewidths=2.5)
+        else:
+            ax.scatter(index, marker_y, marker="x", s=100, color="black", linewidths=2.0)
+
+    ax.set_title(title)
+    ax.set_xlabel(xlabel)
+    ax.set_ylabel("Peak Memory (GB)")
+    ax.set_xticks(xs)
+    ax.set_xticklabels(labels, rotation=45, ha="right")
+    ax.grid(axis="y", linestyle="--", alpha=0.35)
+    ax.set_ylim(bottom=0)
+    fig.tight_layout()
+    fig.savefig(output_path, dpi=200)
+    plt.close(fig)
+
+
+def _build_experiments(args: argparse.Namespace) -> list[Experiment]:
+    experiments: list[Experiment] = []
+    enabled_sweeps = set(args.sweeps)
+
+    if "scalability" in enabled_sweeps:
+        for pp, mbs in _parse_pp_mbs(args.scalability_pp_mbs):
+            for dp in (1, 2):
+                experiments.append(
+                    Experiment(
+                        sweep="scalability",
+                        schedule=args.base_schedule,
+                        pp=pp,
+                        dp=dp,
+                        ep=0,
+                        zero_stage=0,
+                        bucket_size_mb=args.bucket_size,
+                        batch_size=args.batch_size,
+                        seq_len=args.seq_len,
+                        mbs=mbs,
+                        gradient_accumulation=bool(args.gradient_accumulation),
+                        ar_a2a_same_stream=False,
+                        overlap_zero_ops=True,
+                        overlap_chunks=bool(args.overlap_chunks),
+                        nsight=bool(args.nsight),
+                    )
+                )
+
+    if "zero" in enabled_sweeps:
+        zero_grad_acc_settings = [setting == "on" for setting in args.zero_grad_acc_settings]
+        for zero_stage in args.zero_stages:
+            pp = args.pp
+            mbs = 2 * pp
+            for batch_size in args.zero_batch_sizes:
+                for grad_acc in zero_grad_acc_settings:
+                    experiments.append(
+                        Experiment(
+                            sweep="zero",
+                            schedule=args.base_schedule,
+                            pp=pp,
+                            dp=args.dp,
+                            ep=0,
+                            zero_stage=zero_stage,
+                            bucket_size_mb=args.bucket_size,
+                            batch_size=batch_size,
+                            seq_len=args.seq_len,
+                            mbs=mbs,
+                            gradient_accumulation=grad_acc,
+                            ar_a2a_same_stream=False,
+                            overlap_zero_ops=True,
+                            overlap_chunks=bool(args.overlap_chunks),
+                            nsight=bool(args.nsight),
+                        )
+                )
+
+    if "schedule" in enabled_sweeps:
+        for schedule in args.schedule_values:
+            experiments.append(
+                Experiment(
+                    sweep="schedule",
+                    schedule=schedule,
+                    pp=args.pp,
+                    dp=args.dp,
+                    ep=args.dp,
+                    zero_stage=args.schedule_zero_stage,
+                    bucket_size_mb=args.bucket_size,
+                    batch_size=args.batch_size,
+                    seq_len=args.seq_len,
+                    mbs=args.schedule_mbs,
+                    gradient_accumulation=bool(args.gradient_accumulation),
+                    ar_a2a_same_stream=False,
+                    overlap_zero_ops=True,
+                    overlap_chunks=bool(args.overlap_chunks),
+                    nsight=bool(args.nsight),
+                )
+            )
+
+    return experiments
+
+
+def _require_env_vars() -> None:
+    missing = [name for name in ("SSH_KEY", "HEAD_PUBLIC_IP") if not os.environ.get(name)]
+    if missing:
+        raise SystemExit(
+            "Missing required environment variables: " + ", ".join(missing)
+        )
+
+
+def _default_run_name() -> str:
+    return datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
+
+
+def main() -> int:
+    args = parse_args()
+    _require_env_vars()
+
+    runner_path = Path(args.runner).resolve()
+    if not runner_path.is_file():
+        raise SystemExit(f"Runner not found: {runner_path}")
+
+    out_root = Path(args.out_dir)
+    run_name = args.run_name or _default_run_name()
+    out_dir = out_root / run_name
+    logs_dir = out_dir / "logs"
+    metrics_dir = out_dir / "metrics"
+    fetch_dir = out_dir / "runner-fetch"
+    plots_dir = out_dir / "plots"
+    all_results_path = out_dir / "all_results.csv"
+    for directory in (logs_dir, metrics_dir, fetch_dir, plots_dir):
+        directory.mkdir(parents=True, exist_ok=True)
+
+    experiments = _build_experiments(args)
+    total = len(experiments)
+    existing_rows = [] if args.no_resume else _hydrate_existing_rows(_load_existing_rows(all_results_path))
+    rows: list[dict[str, object]] = list(existing_rows)
+    existing_by_key = {_row_key(row): row for row in existing_rows}
+    completed_keys = {
+        key
+        for key, row in existing_by_key.items()
+        if row.get("status") in {"success", "oom"}
+    }
+    completed_keys |= _completed_keys_from_metrics(experiments, metrics_dir, existing_by_key)
+
+    for exp in experiments:
+        existing_row = existing_by_key.get(exp.key())
+        if existing_row is not None and existing_row.get("status") in {"success", "oom", "dry-run"}:
+            continue
+        metrics_path = metrics_dir / f"{exp.slug()}.metrics"
+        if not metrics_path.is_file():
+            recovered = _recover_metrics_from_fetch_logs(args, exp, fetch_dir, metrics_path)
+            if not recovered:
+                continue
+        cached_row = _row_from_metrics(exp, logs_dir / exp.slug() / "run.log", metrics_path)
+        if cached_row is None:
+            continue
+        existing_by_key[exp.key()] = cached_row
+
+    rows = list(existing_by_key.values())
+    completed_keys = {
+        key
+        for key, row in existing_by_key.items()
+        if row.get("status") in {"success", "oom", "dry-run"}
+    }
+
+    print(f"Starting e2e eval with {total} experiments")
+    print(f"Run directory: {out_dir}")
+    use_inductor_summary = (
+        "auto(schedule=0,zero/scalability=1)"
+        if args.use_inductor is None
+        else str(int(args.use_inductor))
+    )
+
+    print(
+        "Config: "
+        f"model={args.model} batch_size={args.batch_size} seq_len={args.seq_len} "
+        f"warmup={args.warmup} iters={args.iters} base_schedule={args.base_schedule} "
+        f"schedule_zero_stage={args.schedule_zero_stage} schedule_mbs={args.schedule_mbs} "
+        f"zero_batch_sizes={args.zero_batch_sizes} zero_grad_acc_settings={args.zero_grad_acc_settings} "
+        f"bucket_size_mb={_format_bucket_size(args.bucket_size)} "
+        f"zero_mbs=2*pp nsight={int(args.nsight)} use_inductor={use_inductor_summary} "
+        f"gradient_accumulation={int(args.gradient_accumulation)}"
+    )
+    if existing_rows and not args.no_resume:
+        print(
+            f"Resume: loaded {len(existing_rows)} existing rows, "
+            f"skipping {len(completed_keys)} completed experiments"
+        )
+    sys.stdout.flush()
+
+    failures = 0
+    oom_failures = 0
+    interrupted = False
+    for index, exp in enumerate(experiments, start=1):
+        if exp.key() in completed_keys:
+            print(f"[{index}/{total}] SKIP completed {exp.sweep}: {exp.label()}")
+            sys.stdout.flush()
+            continue
+        try:
+            row = _run_experiment(args, exp, index, total, logs_dir, metrics_dir, fetch_dir)
+        except KeyboardInterrupt:
+            interrupted = True
+            print(f"\nInterrupted during {exp.sweep}: {exp.label()}")
+            sys.stdout.flush()
+            break
+
+        existing_by_key[_row_key(row)] = row
+        rows = list(existing_by_key.values())
+        if row["status"] == "oom":
+            oom_failures += 1
+        elif row["status"] not in ("success", "dry-run"):
+            failures += 1
+        completed = len(
+            [experiment for experiment in experiments if experiment.key() in {_row_key(item) for item in rows}]
+        )
+        print(
+            f"Progress: completed={completed}/{total} "
+            f"success={sum(1 for item in rows if item['status'] == 'success')} "
+            f"oom={sum(1 for item in rows if item['status'] == 'oom')} "
+            f"failed={sum(1 for item in rows if item['status'] not in ('success', 'oom', 'dry-run'))}"
+        )
+        sys.stdout.flush()
+        _write_csv(all_results_path, rows)
+
+    _write_csv(all_results_path, rows)
+
+    for sweep in ("scalability", "zero", "schedule"):
+        sweep_rows = [row for row in rows if row["sweep"] == sweep]
+        _write_csv(out_dir / f"{sweep}_results.csv", sweep_rows)
+
+    if args.dry_run:
+        print(f"Dry run finished. Planned artifacts are rooted at {out_dir}")
+        return 0
+
+    _save_bar_plot(
+        "Scalability Sweep",
+        "(pp, dp)",
+        [row for row in rows if row["sweep"] == "scalability"],
+        plots_dir / "scalability.png",
+    )
+    _save_bar_plot(
+        "ZeRO Sweep",
+        "(zero, ga, batch_size)",
+        [row for row in rows if row["sweep"] == "zero"],
+        plots_dir / "zero.png",
+        allow_oom_markers=True,
+    )
+    _save_bar_plot(
+        "Schedule Sweep",
+        "schedule",
+        [row for row in rows if row["sweep"] == "schedule"],
+        plots_dir / "schedule.png",
+    )
+    _save_memory_plot(
+        "Scalability Sweep Memory",
+        "(pp, dp)",
+        [row for row in rows if row["sweep"] == "scalability"],
+        plots_dir / "scalability_memory.png",
+    )
+    _save_memory_plot(
+        "ZeRO Sweep Memory",
+        "(zero, ga, batch_size)",
+        [row for row in rows if row["sweep"] == "zero"],
+        plots_dir / "zero_memory.png",
+        allow_oom_markers=True,
+    )
+    _save_memory_plot(
+        "Schedule Sweep Memory",
+        "schedule",
+        [row for row in rows if row["sweep"] == "schedule"],
+        plots_dir / "schedule_memory.png",
+    )
+
+    if interrupted:
+        print(f"Interrupted. Partial results were saved to {out_dir}")
+        return 130
+
+    print(f"Finished. Results are in {out_dir}")
+    return 0 if failures == 0 else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/run_qwen_ec2_sweep.py b/scripts/run_qwen_ec2_sweep.py
new file mode 100644
index 0000000..10418df
--- /dev/null
+++ b/scripts/run_qwen_ec2_sweep.py
@@ -0,0 +1,377 @@
+#!/usr/bin/env python3
+"""Run a configurable sweep over scripts/run-qwen-ec2.sh.
+
+Default sweep:
+  SCHEDULE in {1f1b}
+  (PP, MBS) in {(4, 8), (8, 16)}
+  DP in {2, 4}
+  ZERO_STAGE in {0, 1, 2, 3}
+  BATCH_SIZE in {8, 16, 32}
+  SEQ_LEN in {512}
+  GRAD_ACC in {on, off}
+  AR_A2A_SAME_STREAM in {on, off}
+  OVERLAP_ZERO_OPS in {on, off}
+  OVERLAP_CHUNKS in {off}
+
+Each dimension can be overridden from the command line.
+"""
+
+from __future__ import annotations
+
+import argparse
+import itertools
+import shlex
+import subprocess
+import sys
+import traceback
+from pathlib import Path
+
+
+DEFAULT_PP_MBS = ("4:8", "8:16")
+DEFAULT_SCHEDULES = ("1f1b",)
+DEFAULT_DP_VALUES = (1, 2, 4)
+DEFAULT_ZERO_STAGES = (0, 1, 2, 3)
+DEFAULT_BATCH_SIZES = (16, 32, 64)
+DEFAULT_SEQ_LENS = (512,)
+DEFAULT_GRADIENT_ACCUMULATION_VALUES = ("on", "off")
+DEFAULT_AR_A2A_SAME_STREAM_VALUES = ("on", "off")
+DEFAULT_OVERLAP_ZERO_OPS_VALUES = ("on", "off")
+DEFAULT_OVERLAP_CHUNKS_VALUES = ("off",)
+
+
+def _parse_pp_mbs(values: list[str]) -> list[tuple[int, int]]:
+    parsed: list[tuple[int, int]] = []
+    for value in values:
+        try:
+            pp_str, mbs_str = value.split(":", maxsplit=1)
+            parsed.append((int(pp_str), int(mbs_str)))
+        except ValueError as exc:
+            raise argparse.ArgumentTypeError(
+                f"Invalid --pp-mbs value '{value}'. Expected format PP:MBS, e.g. 4:8."
+            ) from exc
+    return parsed
+
+
+def _build_command(
+    script_path: Path,
+    *,
+    model: str,
+    schedule: str,
+    pp: int,
+    dp: int,
+    zero_stage: int,
+    batch_size: int,
+    seq_len: int,
+    mbs: int,
+    gradient_accumulation: bool,
+    ar_a2a_same_stream: bool,
+    overlap_zero_ops: bool,
+    overlap_chunks: bool,
+    warmup: int,
+    iters: int,
+    extra_args: list[str],
+) -> list[str]:
+    command = [
+        str(script_path),
+        "--model", model,
+        "--schedule", schedule,
+        "--pp", str(pp),
+        "--dp", str(dp),
+        "--zero-stage", str(zero_stage),
+        "--batch-size", str(batch_size),
+        "--seq-len", str(seq_len),
+        "--mbs", str(mbs),
+        "--gradient-accumulation" if gradient_accumulation else "--no-gradient-accumulation",
+        "--ar-a2a-same-stream" if ar_a2a_same_stream else "--no-ar-a2a-same-stream",
+        "--overlap-zero-ops" if overlap_zero_ops else "--no-overlap-zero-ops",
+        "--overlap-chunks" if overlap_chunks else "--no-overlap-chunks",
+        "--warmup", str(warmup),
+        "--iters", str(iters),
+    ]
+    command.extend(extra_args)
+    return command
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Sweep scripts/run-qwen-ec2.sh over configurable dimension values."
+    )
+    parser.add_argument("--model", default="1B", help="Model passed to run-qwen-ec2.sh")
+    parser.add_argument(
+        "--schedules",
+        nargs="+",
+        default=list(DEFAULT_SCHEDULES),
+        help="Pipeline schedules to sweep. Default: 1f1b",
+    )
+    parser.add_argument(
+        "--pp-mbs",
+        nargs="+",
+        default=list(DEFAULT_PP_MBS),
+        metavar="PP:MBS",
+        help="Pairs of pipeline degree and microbatch count. Default: 4:8 8:16",
+    )
+    parser.add_argument(
+        "--dp-values",
+        nargs="+",
+        type=int,
+        default=list(DEFAULT_DP_VALUES),
+        help="DP values to sweep. Default: 2 4",
+    )
+    parser.add_argument(
+        "--zero-stages",
+        nargs="+",
+        type=int,
+        default=list(DEFAULT_ZERO_STAGES),
+        help="ZeRO stages to sweep. Default: 0 1 2 3",
+    )
+    parser.add_argument(
+        "--batch-sizes",
+        nargs="+",
+        type=int,
+        default=list(DEFAULT_BATCH_SIZES),
+        help="Batch sizes to sweep. Default: 8 16 32",
+    )
+    parser.add_argument(
+        "--seq-lens",
+        nargs="+",
+        type=int,
+        default=list(DEFAULT_SEQ_LENS),
+        help="Sequence lengths to sweep. Default: 512",
+    )
+    parser.add_argument(
+        "--gradient-accumulation-values",
+        nargs="+",
+        choices=["on", "off"],
+        default=list(DEFAULT_GRADIENT_ACCUMULATION_VALUES),
+        help="Gradient accumulation settings to sweep. Default: on off",
+    )
+    parser.add_argument(
+        "--ar-a2a-same-stream-values",
+        nargs="+",
+        choices=["on", "off"],
+        default=list(DEFAULT_AR_A2A_SAME_STREAM_VALUES),
+        help="AR/A2A same-stream settings to sweep. Default: on off",
+    )
+    parser.add_argument(
+        "--overlap-zero-ops-values",
+        nargs="+",
+        choices=["on", "off"],
+        default=list(DEFAULT_OVERLAP_ZERO_OPS_VALUES),
+        help="overlap_zero_ops settings to sweep. Default: on off",
+    )
+    parser.add_argument(
+        "--overlap-chunks-values",
+        nargs="+",
+        choices=["on", "off"],
+        default=list(DEFAULT_OVERLAP_CHUNKS_VALUES),
+        help="overlap_chunks settings to sweep. Default: off",
+    )
+    parser.add_argument("--warmup", type=int, default=3, help="Warmup iterations")
+    parser.add_argument("--iters", type=int, default=10, help="Timed iterations")
+    parser.add_argument(
+        "--script",
+        default="scripts/run-qwen-ec2.sh",
+        help="Path to the underlying runner script",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Print commands without executing them",
+    )
+    parser.add_argument(
+        "--continue-on-error",
+        action="store_true",
+        default=True,
+        help="Deprecated: the sweep now always continues after a failed run",
+    )
+    parser.add_argument(
+        "extra_args",
+        nargs=argparse.REMAINDER,
+        help="Extra arguments forwarded to run-qwen-ec2.sh. Prefix with '--'.",
+    )
+    return parser.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    pp_mbs_values = _parse_pp_mbs(args.pp_mbs)
+    script_path = Path(args.script).resolve()
+    if not script_path.is_file():
+        print(f"Runner script not found: {script_path}", file=sys.stderr)
+        return 1
+
+    extra_args = list(args.extra_args)
+    if extra_args and extra_args[0] == "--":
+        extra_args = extra_args[1:]
+
+    combinations: list[tuple[str, tuple[int, int], int, int, int, int, bool, bool, bool, bool]] = []
+    for schedule, (pp, mbs), dp, batch_size, seq_len, gradient_accumulation, ar_a2a_same_stream, overlap_zero_ops, overlap_chunks in itertools.product(
+        args.schedules,
+        pp_mbs_values,
+        args.dp_values,
+        args.batch_sizes,
+        args.seq_lens,
+        [value == "on" for value in args.gradient_accumulation_values],
+        [value == "on" for value in args.ar_a2a_same_stream_values],
+        [value == "on" for value in args.overlap_zero_ops_values],
+        [value == "on" for value in args.overlap_chunks_values],
+    ):
+        zero_stages = args.zero_stages if dp > 1 else [0]
+        for zero_stage in zero_stages:
+            combinations.append(
+                (
+                    schedule,
+                    (pp, mbs),
+                    dp,
+                    zero_stage,
+                    batch_size,
+                    seq_len,
+                    gradient_accumulation,
+                    ar_a2a_same_stream,
+                    overlap_zero_ops,
+                    overlap_chunks,
+                )
+            )
+
+    total = len(combinations)
+    failures: list[dict[str, object]] = []
+    for index, (
+        schedule,
+        (pp, mbs),
+        dp,
+        zero_stage,
+        batch_size,
+        seq_len,
+        gradient_accumulation,
+        ar_a2a_same_stream,
+        overlap_zero_ops,
+        overlap_chunks,
+    ) in enumerate(
+        combinations, start=1
+    ):
+        command = _build_command(
+            script_path,
+            model=args.model,
+            schedule=schedule,
+            pp=pp,
+            dp=dp,
+            zero_stage=zero_stage,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            mbs=mbs,
+            gradient_accumulation=gradient_accumulation,
+            ar_a2a_same_stream=ar_a2a_same_stream,
+            overlap_zero_ops=overlap_zero_ops,
+            overlap_chunks=overlap_chunks,
+            warmup=args.warmup,
+            iters=args.iters,
+            extra_args=extra_args,
+        )
+        print(
+            f"[{index}/{total}] schedule={schedule} pp={pp} mbs={mbs} dp={dp} zero_stage={zero_stage} "
+            f"batch_size={batch_size} seq_len={seq_len} "
+            f"gradient_accumulation={'on' if gradient_accumulation else 'off'} "
+            f"ar_a2a_same_stream={'on' if ar_a2a_same_stream else 'off'} "
+            f"overlap_zero_ops={'on' if overlap_zero_ops else 'off'} "
+            f"overlap_chunks={'on' if overlap_chunks else 'off'}"
+        )
+        print("  " + " ".join(shlex.quote(part) for part in command))
+
+        if args.dry_run:
+            continue
+
+        try:
+            result = subprocess.run(command, check=False)
+            if result.returncode == 0:
+                continue
+            failures.append(
+                {
+                    "index": index,
+                    "schedule": schedule,
+                    "pp": pp,
+                    "mbs": mbs,
+                    "dp": dp,
+                    "zero_stage": zero_stage,
+                    "batch_size": batch_size,
+                    "seq_len": seq_len,
+                    "gradient_accumulation": gradient_accumulation,
+                    "ar_a2a_same_stream": ar_a2a_same_stream,
+                    "overlap_zero_ops": overlap_zero_ops,
+                    "overlap_chunks": overlap_chunks,
+                    "returncode": result.returncode,
+                    "command": command,
+                }
+            )
+            print(f"  command failed with exit code {result.returncode}, continuing", file=sys.stderr)
+        except KeyboardInterrupt:
+            print("\nSweep interrupted by user.", file=sys.stderr)
+            failures.append(
+                {
+                    "index": index,
+                    "schedule": schedule,
+                    "pp": pp,
+                    "mbs": mbs,
+                    "dp": dp,
+                    "zero_stage": zero_stage,
+                    "batch_size": batch_size,
+                    "seq_len": seq_len,
+                    "gradient_accumulation": gradient_accumulation,
+                    "ar_a2a_same_stream": ar_a2a_same_stream,
+                    "overlap_zero_ops": overlap_zero_ops,
+                    "overlap_chunks": overlap_chunks,
+                    "returncode": "interrupted",
+                    "command": command,
+                }
+            )
+            break
+        except Exception as exc:
+            failures.append(
+                {
+                    "index": index,
+                    "schedule": schedule,
+                    "pp": pp,
+                    "mbs": mbs,
+                    "dp": dp,
+                    "zero_stage": zero_stage,
+                    "batch_size": batch_size,
+                    "seq_len": seq_len,
+                    "gradient_accumulation": gradient_accumulation,
+                    "ar_a2a_same_stream": ar_a2a_same_stream,
+                    "overlap_zero_ops": overlap_zero_ops,
+                    "overlap_chunks": overlap_chunks,
+                    "returncode": "exception",
+                    "command": command,
+                    "error": repr(exc),
+                }
+            )
+            print(f"  command raised {type(exc).__name__}: {exc}", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+    if failures:
+        print("\nSweep completed with failures:", file=sys.stderr)
+        for failure in failures:
+            print(
+                f"  [{failure['index']}/{total}] "
+                f"schedule={failure['schedule']} pp={failure['pp']} mbs={failure['mbs']} dp={failure['dp']} "
+                f"zero_stage={failure['zero_stage']} batch_size={failure['batch_size']} "
+                f"seq_len={failure['seq_len']} "
+                f"gradient_accumulation={'on' if failure['gradient_accumulation'] else 'off'} "
+                f"ar_a2a_same_stream={'on' if failure['ar_a2a_same_stream'] else 'off'} "
+                f"overlap_zero_ops={'on' if failure['overlap_zero_ops'] else 'off'} "
+                f"overlap_chunks={'on' if failure['overlap_chunks'] else 'off'} "
+                f"status={failure['returncode']}",
+                file=sys.stderr,
+            )
+            print(
+                "    " + " ".join(shlex.quote(part) for part in failure["command"]),
+                file=sys.stderr,
+            )
+            if "error" in failure:
+                print(f"    error={failure['error']}", file=sys.stderr)
+        return 1
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/test_env.py b/scripts/test_env.py
new file mode 100644
index 0000000..85ebd1a
--- /dev/null
+++ b/scripts/test_env.py
@@ -0,0 +1,161 @@
+"""Smoke test to verify the Ray + CUDA environment is set up correctly."""
+import sys
+import signal
+
+def timeout_handler(signum, frame):
+    raise TimeoutError("timed out")
+
+def check(name, fn, timeout_sec=30):
+    sys.stdout.flush()
+    try:
+        signal.signal(signal.SIGALRM, timeout_handler)
+        signal.alarm(timeout_sec)
+        result = fn()
+        signal.alarm(0)
+        print(f"  [PASS] {name}: {result}", flush=True)
+        return True
+    except Exception as e:
+        signal.alarm(0)
+        print(f"  [FAIL] {name}: {e}", flush=True)
+        return False
+
+def main():
+    ok = True
+
+    print("Python", flush=True)
+    ok &= check("version", lambda: sys.version.split()[0])
+
+    print("PyTorch", flush=True)
+    import torch
+    ok &= check("version", lambda: torch.__version__)
+    ok &= check("CUDA available", lambda: torch.cuda.is_available())
+    ok &= check("CUDA version", lambda: torch.version.cuda)
+    ok &= check("GPU count", lambda: torch.cuda.device_count())
+    if torch.cuda.is_available():
+        ok &= check("GPU name", lambda: torch.cuda.get_device_name(0))
+        ok &= check("tensor on GPU", lambda: torch.zeros(1, device="cuda").device)
+
+    print("NCCL", flush=True)
+    ok &= check("available", lambda: torch.cuda.nccl.is_available(torch.randn(1, device="cuda")))
+    ok &= check("version", lambda: ".".join(str(v) for v in torch.cuda.nccl.version()))
+
+    print("CuPy", flush=True)
+    import cupy
+    ok &= check("version", lambda: cupy.__version__)
+
+    print("EFA", flush=True)
+    import os, subprocess
+    def check_efa_devices():
+        devs = os.listdir("/dev/infiniband") if os.path.isdir("/dev/infiniband") else []
+        if not devs:
+            raise RuntimeError("no devices under /dev/infiniband")
+        return devs
+    def check_fi_info():
+        result = subprocess.run(["fi_info", "-p", "efa"], capture_output=True, text=True)
+        if result.returncode != 0:
+            raise RuntimeError(result.stderr.strip() or "fi_info -p efa returned non-zero")
+        providers = [l for l in result.stdout.splitlines() if l.startswith("provider:")]
+        return f"{len(providers)} EFA provider(s) found"
+    ok &= check("devices", check_efa_devices)
+    try:
+        result = check_fi_info()
+        print(f"  [PASS] fi_info: {result}", flush=True)
+    except FileNotFoundError:
+        print("  [WARN] fi_info: not installed in container (host-side tool); skipping", flush=True)
+    except Exception as e:
+        print(f"  [FAIL] fi_info: {e}", flush=True)
+        ok = False
+    print("  [INFO] To confirm NCCL uses EFA: set NCCL_DEBUG=INFO before running a job and look for", flush=True)
+    print("         'NET/OFI Initializing aws-ofi-nccl' and 'Using EFA' in the output.", flush=True)
+
+    print("Piper imports", flush=True)
+    ok &= check("piper_compile", lambda: (__import__("src.piper_compile"), "ok")[1])
+    ok &= check("piper_exec", lambda: (__import__("src.piper_exec"), "ok")[1])
+    ok &= check("piper_actor", lambda: (__import__("src.piper_actor"), "ok")[1])
+
+    print("Ray", flush=True)
+    import ray
+    ok &= check("version", lambda: ray.__version__)
+    ok &= check("init", lambda: (ray.init(ignore_reinit_error=True), "connected")[1])
+    ok &= check("resources", lambda: {k: v for k, v in ray.cluster_resources().items() if k in ("CPU", "GPU")})
+
+    print("Multi-node", flush=True)
+    from ray.util.placement_group import placement_group, remove_placement_group
+    active_nodes = [n for n in ray.nodes() if n["Alive"]]
+    print(f"  [INFO] {len(active_nodes)} active node(s) in cluster", flush=True)
+    if len(active_nodes) < 2:
+        print("  [SKIP] strict_spread: need 2 nodes, only 1 available", flush=True)
+    else:
+        @ray.remote
+        def get_node_id():
+            return ray.get_runtime_context().get_node_id()
+
+        def test_strict_spread():
+            pg = placement_group([{"CPU": 1}, {"CPU": 1}], strategy="STRICT_SPREAD")
+            ray.get(pg.ready(), timeout=30)
+            ids = ray.get([
+                get_node_id.options(placement_group=pg, placement_group_bundle_index=0).remote(),
+                get_node_id.options(placement_group=pg, placement_group_bundle_index=1).remote(),
+            ])
+            remove_placement_group(pg)
+            if ids[0] == ids[1]:
+                raise RuntimeError(f"both actors landed on the same node")
+            return f"actors on 2 different nodes"
+
+        ok &= check("strict_spread", test_strict_spread, timeout_sec=60)
+
+        @ray.remote(num_gpus=1)
+        class NCCLWorker:
+            def get_ip(self):
+                return ray.util.get_node_ip_address()
+
+            def init_and_allreduce(self, rank, world_size, master_addr, master_port):
+                import torch
+                import torch.distributed as dist
+                import os
+                os.environ["MASTER_ADDR"] = master_addr
+                os.environ["MASTER_PORT"] = str(master_port)
+                os.environ.setdefault("NCCL_SOCKET_IFNAME", "ens32")
+                os.environ.setdefault("GLOO_SOCKET_IFNAME", "ens32")
+                os.environ.setdefault("NCCL_PROTO", "simple")
+                # Send value (rank+1) so rank 0 sends 1.0 and rank 1 sends 2.0;
+                # after all-reduce the sum should be 3.0 on both ranks.
+                dist.init_process_group(
+                    backend="nccl",
+                    init_method="env://",
+                    world_size=world_size,
+                    rank=rank,
+                )
+                t = torch.full((4,), float(rank + 1), device="cuda")
+                dist.all_reduce(t)
+                result = t[0].item()
+                dist.destroy_process_group()
+                return result
+
+        def test_cross_node_nccl():
+            pg = placement_group([{"CPU": 1, "GPU": 1}, {"CPU": 1, "GPU": 1}], strategy="STRICT_SPREAD")
+            ray.get(pg.ready(), timeout=30)
+            w0 = NCCLWorker.options(placement_group=pg, placement_group_bundle_index=0).remote()
+            w1 = NCCLWorker.options(placement_group=pg, placement_group_bundle_index=1).remote()
+            master_addr = ray.get(w0.get_ip.remote())
+            master_port = 29500
+            results = ray.get([
+                w0.init_and_allreduce.remote(0, 2, master_addr, master_port),
+                w1.init_and_allreduce.remote(1, 2, master_addr, master_port),
+            ], timeout=90)
+            remove_placement_group(pg)
+            expected = 3.0  # 1.0 + 2.0
+            if results[0] != expected or results[1] != expected:
+                raise RuntimeError(f"unexpected all-reduce results: {results}, expected {expected} on both ranks")
+            return f"all-reduce across 2 nodes: {results[0]} == {results[1]} == {expected}"
+
+        ok &= check("cross_node_nccl", test_cross_node_nccl, timeout_sec=120)
+
+    ray.shutdown()
+
+    print(flush=True)
+    print("ALL PASSED" if ok else "SOME CHECKS FAILED", flush=True)
+    sys.exit(0 if ok else 1)
+
+if __name__ == "__main__":
+    main()

From 8655cb68130be40f7d987efe917b553de3d0f110 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Sun, 19 Apr 2026 00:54:35 +0000
Subject: [PATCH 092/100] typo

---
 scripts/run_qwen_e2e_eval.py |   2 +-
 src/piper_actor.py           | 112 ++++++++++++++++++++---------------
 src/piper_exec.py            |   9 +--
 src/piper_graph_transform.py |  13 ++++
 src/piper_utils.py           |   2 +-
 5 files changed, 84 insertions(+), 54 deletions(-)

diff --git a/scripts/run_qwen_e2e_eval.py b/scripts/run_qwen_e2e_eval.py
index 64de748..e837fb8 100644
--- a/scripts/run_qwen_e2e_eval.py
+++ b/scripts/run_qwen_e2e_eval.py
@@ -164,7 +164,7 @@ def parse_args() -> argparse.Namespace:
         "--zero-grad-acc-settings",
         nargs="+",
         choices=["on", "off"],
-        default=["off"],
+        default=["on"],
         help="Gradient accumulation settings to include in the zero sweep. "
              "Examples: --zero-grad-acc-settings on | off | off on",
     )
diff --git a/src/piper_actor.py b/src/piper_actor.py
index ef04178..9cd896c 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -70,8 +70,8 @@ def _create_actors(
         }} if profile else {}
         nccl_env = {
             "env_vars": {
-                "NCCL_SOCKET_IFNAME": "ens32",
-                "GLOO_SOCKET_IFNAME": "ens32",
+                # "NCCL_SOCKET_IFNAME": "ens32",
+                # "GLOO_SOCKET_IFNAME": "ens32",
                 "NCCL_DEBUG": "WARN",
                 **({"TMPDIR": temp_dir} if (profile and temp_dir) else {}),
             }
@@ -167,6 +167,7 @@ def __init__(
         self.labels = None
 
         self.comp_stream = torch.cuda.Stream()
+        self.overlapped_comp_stream = torch.cuda.Stream()
         self.ar_stream = torch.cuda.Stream()
         if self.ar_a2a_same_stream:
             self.a2a_stream = self.ar_stream
@@ -190,6 +191,7 @@ def __init__(
         self.memory_tracing_enabled = False
         # DAG execution state
         self.dag = None
+        self.sorted_dag_nodes = None
 
         # Unified inter-task buffer.  Keyed by task.uid (int), storing whatever
         # data the task produced for downstream consumers.
@@ -350,11 +352,12 @@ def _join_process_groups(self, master_addr, master_port):
             if self.pp_degree > 1:
                 self._join_pp_process_group()
 
-            # Force cuBLAS context initialization on comp_stream so the first
-            # backward pass does not encounter a "no current CUDA context" warning.
-            with torch.cuda.stream(self.comp_stream):
-                _w = torch.zeros(4, 4, device=self.device)
-                torch.mm(_w, _w)
+            # Force cuBLAS context initialization on both compute streams so the
+            # first backward pass does not encounter a "no current CUDA context" warning.
+            for compute_stream in (self.comp_stream, self.overlapped_comp_stream):
+                with torch.cuda.stream(compute_stream):
+                    _w = torch.zeros(4, 4, device=self.device)
+                    torch.mm(_w, _w)
 
             self.logger.info(f"Actor {self.global_rank} joined process groups")
 
@@ -998,7 +1001,8 @@ def _bucket_forward_runner(
 
     def load_dag(self, dag: TaskDAG) -> None:
         """Store the per-rank TaskDAG for subsequent run_dag() calls."""
-        self.dag = sorted(dag.nodes, key=runtime_sort_key)
+        self.dag = dag
+        self.sorted_dag_nodes = sorted(dag.nodes, key=runtime_sort_key)
 
     def get_all_params(self) -> dict:
         """Return {unique_bucket_id: flat_cpu_tensor} for every trainable bucket."""
@@ -1044,8 +1048,17 @@ def get_bucket_fwd_counts(self) -> dict:
         """Return the set of unique_bucket_ids loaded on this actor."""
         return list(self.bucket_fwd_fns.keys())
 
-    def _timing_stream(self, task_type) -> "torch.cuda.Stream":
+    def _compute_stream_for_id(self, compute_stream_id: str) -> "torch.cuda.Stream":
+        if compute_stream_id == "overlapped_comp_stream":
+            return self.overlapped_comp_stream
+        return self.comp_stream
+
+    def _compute_stream_for_task(self, task: Task) -> "torch.cuda.Stream":
+        return self._compute_stream_for_id(task.compute_stream_id)
+
+    def _timing_stream(self, task) -> "torch.cuda.Stream":
         """Return the CUDA stream that drives work for the given task type."""
+        task_type = task.task_type
         if task_type == TaskType.SEND:
             return self.p2p_send_stream
         if task_type == TaskType.RECV:
@@ -1054,7 +1067,7 @@ def _timing_stream(self, task_type) -> "torch.cuda.Stream":
             return self.a2a_stream
         if task_type in (TaskType.ALL_REDUCE, TaskType.REDUCE_SCATTER, TaskType.ALL_GATHER):
             return self.ar_stream
-        return self.comp_stream
+        return self._compute_stream_for_task(task)
 
     def get_node_runtimes(self) -> dict:
         """Return ``{uid: profiling_measurements}`` for every profiled DAG node.
@@ -1338,6 +1351,7 @@ def run_dag(self, loss_fn=None, profiling: bool = False):
         - RECV records a recv_event after the dist.recv completes.
         """
         assert self.dag is not None, "load_dag() must be called before run_dag()"
+        assert self.sorted_dag_nodes is not None, "load_dag() must initialize sorted node order"
         debug_enabled = self.logger.isEnabledFor(logging.DEBUG)
         verbose_enabled = self.logger.isEnabledFor(VERBOSE)
 
@@ -1369,14 +1383,15 @@ def run_dag(self, loss_fn=None, profiling: bool = False):
         self._prof_records: list = []
 
         self._init_task_buffer_refcounts(self.dag)
-        ts_to_comp_event: dict[int, torch.cuda.Event] = {}
+        last_comp_event_by_stream: dict[str, torch.cuda.Event] = {}
 
         def _wait_for_all_gather(compute_node: Task) -> None:
+            compute_stream = self._compute_stream_for_task(compute_node)
             for pred in compute_node.data_preds:
                 if pred.task_type == TaskType.ALL_GATHER:
                     ag_evt = self.ag_events.get(pred.uid)
                     if ag_evt is not None:
-                        self.comp_stream.wait_event(ag_evt)
+                        compute_stream.wait_event(ag_evt)
 
         def _metadata_ubids(node: Task, key: str) -> list[int]:
             value = node.custom_metadata.get(key)
@@ -1423,12 +1438,13 @@ def _run_free_full_params(free_node: Task) -> None:
             finally:
                 self._nvtx_pop()
 
-        for node in self.dag:
+        for node in self.sorted_dag_nodes:
 
             task_type = node.task_type
             batch = node.batches[0]
             mb_idx = batch.mb_idx
             ubid = node.unique_bucket_id  # None for non-compute nodes
+            node_comp_stream = self._compute_stream_for_task(node)
 
             if debug_enabled:
                 self.logger.debug(
@@ -1437,7 +1453,7 @@ def _run_free_full_params(free_node: Task) -> None:
                 )
 
             if profiling:
-                _stream = self._timing_stream(task_type)
+                _stream = self._timing_stream(node)
                 _start_evt = torch.cuda.Event(enable_timing=True)
                 _start_evt.record(_stream)
                 _mem_before = torch.cuda.memory_allocated()
@@ -1465,7 +1481,7 @@ def _run_free_full_params(free_node: Task) -> None:
                 case TaskType.RECV:
                     # data_succs[0] is the downstream compute task.
                     compute_node = node.data_succs[0]
-                    comp_evt = ts_to_comp_event.get(node.time_step)
+                    comp_evt = last_comp_event_by_stream.get(node.compute_stream_id)
                     if comp_evt is not None:
                         self.p2p_recv_stream.wait_event(comp_evt)
                     if compute_node.task_type == TaskType.FWD:
@@ -1505,7 +1521,7 @@ def _run_free_full_params(free_node: Task) -> None:
                     a2a_evt = torch.cuda.Event()
                     a2a_evt.record(self.a2a_stream)
                     self.a2a_events[node.uid] = a2a_evt
-                    self.comp_stream.wait_event(a2a_evt)
+                    node_comp_stream.wait_event(a2a_evt)
 
                 case TaskType.BWD_A2A:
                     bwd_pred = next(
@@ -1534,7 +1550,7 @@ def _run_free_full_params(free_node: Task) -> None:
                     a2a_evt = torch.cuda.Event()
                     a2a_evt.record(self.a2a_stream)
                     self.a2a_events[node.uid] = a2a_evt
-                    self.comp_stream.wait_event(a2a_evt)
+                    node_comp_stream.wait_event(a2a_evt)
 
                 case TaskType.ALL_REDUCE:
                     bwd_node = node.data_preds[0]
@@ -1607,7 +1623,7 @@ def _run_free_full_params(free_node: Task) -> None:
                         (p for p in node.data_preds if p.task_type == TaskType.RECV), None
                     )
                     if recv_pred is not None and recv_pred.uid in self.recv_events:
-                        self.comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
+                        node_comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
 
                     # Gather input tensors from task_buffer.
                     # FWD(bkt=0 of first stage): inputs come from self.inputs.
@@ -1630,9 +1646,9 @@ def _run_free_full_params(free_node: Task) -> None:
                     _wait_for_all_gather(node)
 
                     self._nvtx_push(f"forward_ubid{ubid}_mb{mb_idx}")
-                    self._start_timing(self.comp_stream, "forward")
-                    fwd_out = self._forward_dag(ubid, mb_idx, input_tensors)
-                    self._stop_timing(self.comp_stream, "forward")
+                    self._start_timing(node_comp_stream, "forward")
+                    fwd_out = self._forward_dag(ubid, mb_idx, input_tensors, node_comp_stream)
+                    self._stop_timing(node_comp_stream, "forward")
                     self._nvtx_pop()
                     self.task_buffer[node.uid] = fwd_out
                     # Shape ref for BWD RECV: store lightweight metadata (shape, dtype) so
@@ -1640,9 +1656,9 @@ def _run_free_full_params(free_node: Task) -> None:
                     self.task_buffer[("shape_ref", ubid)] = [(t.shape, t.dtype) for t in fwd_out["out_with_grad"]]
                     self.task_buffer[(ubid, mb_idx)] = fwd_out  # per-mb FWD data for BWD
                     evt = torch.cuda.Event()
-                    evt.record(self.comp_stream)
+                    evt.record(node_comp_stream)
                     comp_events[node.uid] = evt
-                    ts_to_comp_event[node.time_step] = evt
+                    last_comp_event_by_stream[node.compute_stream_id] = evt
                     if node.custom_metadata.get("zero_free_full_params_after"):
                         evt.synchronize()
                         self._free_full_params(ubid)
@@ -1653,7 +1669,7 @@ def _run_free_full_params(free_node: Task) -> None:
                         (p for p in node.data_preds if p.task_type == TaskType.RECV), None
                     )
                     if recv_pred is not None and recv_pred.uid in self.recv_events:
-                        self.comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
+                        node_comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
                     _wait_for_all_gather(node)
 
                     # Resolve outputs and upstream grads via per-mb ubid lookup.
@@ -1661,7 +1677,7 @@ def _run_free_full_params(free_node: Task) -> None:
 
                     if node.compute_loss:
                         assert loss_fn is not None
-                        with torch.cuda.stream(self.comp_stream):
+                        with torch.cuda.stream(node_comp_stream):
                             outputs_or_loss = [loss_fn(fwd_out["out_with_grad"][0], self.labels)]
                         upstream_grads = None
                     elif recv_pred is not None:
@@ -1722,13 +1738,14 @@ def _run_free_full_params(free_node: Task) -> None:
                         self._alloc_full_grads(ubid)
 
                     self._nvtx_push(f"backward_ubid{ubid}_mb{mb_idx}")
-                    self._start_timing(self.comp_stream, "backward")
+                    self._start_timing(node_comp_stream, "backward")
                     bwd_out = self._backward_dag(
                         ubid, mb_idx, outputs_or_loss, upstream_grads,
                         pre_detach_outs, detached_outs, inp_with_grad,
                         fwd_out.get("out_with_grad"),
+                        node_comp_stream,
                     )
-                    self._stop_timing(self.comp_stream, "backward")
+                    self._stop_timing(node_comp_stream, "backward")
                     self._nvtx_pop()
                     buf = bwd_out if bwd_out is not None else {}
                     # Full-length inp_grads parallel to fwd_inputs / detached_outs so
@@ -1748,10 +1765,10 @@ def _run_free_full_params(free_node: Task) -> None:
                     fwd_out.clear()
                     del self.task_buffer[(ubid, mb_idx)]
                     evt = torch.cuda.Event()
-                    evt.record(self.comp_stream)
+                    evt.record(node_comp_stream)
                     comp_events[node.uid] = evt
                     self.bwd_events[ubid] = evt
-                    ts_to_comp_event[node.time_step] = evt
+                    last_comp_event_by_stream[node.compute_stream_id] = evt
                     if node.custom_metadata.get("zero_free_full_params_after"):
                         evt.synchronize()
                         self._free_full_params(ubid)
@@ -1762,7 +1779,7 @@ def _run_free_full_params(free_node: Task) -> None:
                         (p for p in node.data_preds if p.task_type == TaskType.RECV), None
                     )
                     if recv_pred is not None and recv_pred.uid in self.recv_events:
-                        self.comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
+                        node_comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
                     _wait_for_all_gather(node)
 
                     # Resolve stage_outputs_or_loss and output_grads.
@@ -1770,7 +1787,7 @@ def _run_free_full_params(free_node: Task) -> None:
 
                     if node.compute_loss:
                         assert loss_fn is not None
-                        with torch.cuda.stream(self.comp_stream):
+                        with torch.cuda.stream(node_comp_stream):
                             stage_outputs_or_loss = [loss_fn(fwd_out["out_with_grad"][0], self.labels)]
                         output_grads = None
                     elif recv_pred is not None:
@@ -1817,12 +1834,12 @@ def _run_free_full_params(free_node: Task) -> None:
                                if b_fwd_args[i] is not None]
 
                     self._nvtx_push(f"backward_input_ubid{ubid}_mb{mb_idx}")
-                    self._start_timing(self.comp_stream, "backward_input")
-                    with torch.cuda.stream(self.comp_stream):
+                    self._start_timing(node_comp_stream, "backward_input")
+                    with torch.cuda.stream(node_comp_stream):
                         dinputs, param_groups = self._bucket_backward_input(
                             stage_outputs_or_loss, output_grads, input_values, iter(weights)
                         )
-                    self._stop_timing(self.comp_stream, "backward_input")
+                    self._stop_timing(node_comp_stream, "backward_input")
                     self._nvtx_pop()
                     # Full-length inp_grads parallel to fwd_inputs / detached_outs.
                     fwd_inputs_full = fwd_out.get("fwd_inputs")
@@ -1866,10 +1883,10 @@ def _run_free_full_params(free_node: Task) -> None:
                     del stage_outputs_or_loss, fwd_out
                     del self.task_buffer[(ubid, mb_idx)]
                     evt = torch.cuda.Event()
-                    evt.record(self.comp_stream)
+                    evt.record(node_comp_stream)
                     comp_events[node.uid] = evt
                     self.bwd_events[ubid] = evt
-                    ts_to_comp_event[node.time_step] = evt
+                    last_comp_event_by_stream[node.compute_stream_id] = evt
                     if node.custom_metadata.get("zero_free_full_params_after"):
                         evt.synchronize()
                         self._free_full_params(ubid)
@@ -1906,10 +1923,10 @@ def _run_free_full_params(free_node: Task) -> None:
                     weights = [b_fwd_args[i] for i in self.bucket_param_idxs[ubid]
                                if b_fwd_args[i] is not None]
                     self._nvtx_push(f"backward_weight_ubid{ubid}_mb{mb_idx}")
-                    self._start_timing(self.comp_stream, "backward_weight")
-                    with torch.cuda.stream(self.comp_stream):
+                    self._start_timing(node_comp_stream, "backward_weight")
+                    with torch.cuda.stream(node_comp_stream):
                         self._bucket_backward_weight(iter(weights), param_groups, ubid=ubid, mb_idx=mb_idx)
-                    self._stop_timing(self.comp_stream, "backward_weight")
+                    self._stop_timing(node_comp_stream, "backward_weight")
                     self._nvtx_pop()
                     self._accumulate_zero_param_grads_to_flat(ubid)
                     self.task_buffer[node.uid] = {}
@@ -1918,10 +1935,10 @@ def _run_free_full_params(free_node: Task) -> None:
                     # the autograd graph was freed by retain_graph=False.  Drop the shell.
                     self._release_task_buffer_uid(bwdi_node.uid)
                     evt = torch.cuda.Event()
-                    evt.record(self.comp_stream)
+                    evt.record(node_comp_stream)
                     comp_events[node.uid] = evt
                     self.bwd_events[ubid] = evt
-                    ts_to_comp_event[node.time_step] = evt
+                    last_comp_event_by_stream[node.compute_stream_id] = evt
                     if node.custom_metadata.get("zero_free_full_params_after"):
                         evt.synchronize()
                         self._free_full_params(ubid)
@@ -2030,7 +2047,7 @@ def _exec_a2a(self, input_tensor: torch.Tensor) -> torch.Tensor:
             dist.all_to_all_single(output_buf, input_tensor, group=self.ep_group)
         return output_buf
 
-    def _forward_dag(self, ubid: int, mb_idx: int, input_tensors) -> dict:
+    def _forward_dag(self, ubid: int, mb_idx: int, input_tensors, compute_stream) -> dict:
         """Run the forward function for bucket *ubid* and return a result dict.
 
         The result dict always contains:
@@ -2078,7 +2095,7 @@ def _forward_dag(self, ubid: int, mb_idx: int, input_tensors) -> dict:
             )
 
         # Run the forward function.
-        with torch.cuda.stream(self.comp_stream):
+        with torch.cuda.stream(compute_stream):
             output = fwd_fn(fwd_args)
 
         # Clear input slots so we don't hold stale tensor references.
@@ -2117,6 +2134,7 @@ def _backward_dag(
         detached_outs,
         inp_with_grad,
         out_with_grad,
+        compute_stream,
     ) -> dict | None:
         """Fused backward pass (BWD) for a single bucket.
 
@@ -2127,15 +2145,13 @@ def _backward_dag(
         Returns a dict with ``"send_output"`` (input grad list) when this is the
         first-bucket-of-stage and the stage has a predecessor, or None otherwise.
         """
-        comp_stream = self.comp_stream
-
         if pre_detach_outs is None:
             # Last bucket (or only bucket): backward from stage outputs or loss.
             if upstream_grads is not None:
-                with torch.cuda.stream(comp_stream):
+                with torch.cuda.stream(compute_stream):
                     torch.autograd.backward(outputs_or_loss, upstream_grads)
             else:
-                with torch.cuda.stream(comp_stream):
+                with torch.cuda.stream(compute_stream):
                     outputs_or_loss[0].backward()
         else:
             # Non-last bucket: propagate through the bucket boundary.
@@ -2151,7 +2167,7 @@ def _backward_dag(
             if bwd_pairs:
                 outputs_bwd = [p for p, _g in bwd_pairs]
                 grads_bwd = [g for _p, g in bwd_pairs]
-                with torch.cuda.stream(comp_stream):
+                with torch.cuda.stream(compute_stream):
                     torch.autograd.backward(outputs_bwd, grads_bwd)
 
         # Collect stage-input grads for SEND — applies to both last-bucket and
diff --git a/src/piper_exec.py b/src/piper_exec.py
index 5341954..c9fdaf1 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -238,6 +238,7 @@ class Task:
     # Set by graph-transform passes (not meaningful for raw schedule tasks)
     bucket_id: int = 0           # stage-local bucket index (stage_bucket_id); actor must not use this
     resource: str = "compute_stream"
+    compute_stream_id: str = "comp_stream"
     # Task-type-specific metadata (e.g. {"a2a_tensor_idx": int} for A2A tasks).
     custom_metadata: dict = field(default_factory=dict)
 
@@ -309,8 +310,8 @@ def _rebuild_task_dag(node_data):
     it by name."""
     nodes = [
         Task(task_type=d[0], batches=d[1], task_pp_rank=d[2], pp_rank=d[3], time_step=d[4], peer_pp_rank=d[5],
-                 bucket_id=d[10], resource=d[11], custom_metadata=d[12], source_chunk=d[14],
-                 associated_chunk=d[15], unique_bucket_id=d[16], compute_loss=d[17])
+                 bucket_id=d[10], resource=d[11], compute_stream_id=d[12], custom_metadata=d[13], source_chunk=d[15],
+                 associated_chunk=d[16], unique_bucket_id=d[17], compute_loss=d[18])
         for d in node_data
     ]
     for node, d in zip(nodes, node_data):
@@ -318,7 +319,7 @@ def _rebuild_task_dag(node_data):
         node.data_succs     = [nodes[j] for j in d[7]]
         node.temporal_preds = [nodes[j] for j in d[8]]
         node.temporal_succs = [nodes[j] for j in d[9]]
-        node.uid            = d[13]
+        node.uid            = d[14]
     return TaskDAG(nodes=nodes)
 
 
@@ -346,7 +347,7 @@ def __reduce__(self):
                 [idx[id(s)] for s in n.data_succs],
                 [idx[id(p)] for p in n.temporal_preds],
                 [idx[id(s)] for s in n.temporal_succs],
-                n.bucket_id, n.resource, n.custom_metadata, n.uid, n.source_chunk,
+                n.bucket_id, n.resource, n.compute_stream_id, n.custom_metadata, n.uid, n.source_chunk,
                 n.associated_chunk, n.unique_bucket_id, n.compute_loss,
             )
             for n in self.nodes
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index a11c266..c81ac94 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -1345,6 +1345,7 @@ def _splice_a2a_nodes(
                     custom_metadata={"a2a_tensor_idx": boundaries[bkt_id]},
                     source_chunk=source_chunk,
                     associated_chunk=source_chunk,
+                    compute_stream_id=task.compute_stream_id,
                 )
                 # Rewire data: task → next_task  ⟹  task → a2a → next_task
                 task.data_succs.remove(next_task)
@@ -1376,6 +1377,7 @@ def _splice_a2a_nodes(
                     custom_metadata={"a2a_tensor_idx": boundaries[bkt_id]},
                     source_chunk=source_chunk,
                     associated_chunk=source_chunk,
+                    compute_stream_id=task.compute_stream_id,
                 )
                 # Rewire data: prev_task → task  ⟹  prev_task → a2a → task
                 prev_task.data_succs.remove(task)
@@ -1435,6 +1437,7 @@ def _build_chunk_task_chain(
             time_step=col_idx,
             source_chunk=chunk,
             associated_chunk=chunk,
+            compute_stream_id="comp_stream",
         )
         return [task], task, task
 
@@ -1451,6 +1454,7 @@ def _build_chunk_task_chain(
                 bucket_id=b,
                 source_chunk=chunk,
                 associated_chunk=chunk,
+                compute_stream_id="comp_stream",
             )
             for i, b in enumerate(range(K))
         ]
@@ -1466,6 +1470,7 @@ def _build_chunk_task_chain(
                 bucket_id=K - 1 - i,
                 source_chunk=chunk,
                 associated_chunk=chunk,
+                compute_stream_id="comp_stream",
             )
             for i in range(K)
         ]
@@ -1686,6 +1691,7 @@ def expand_chunks_to_dags(
             resource="pp_stream",
             source_chunk=src.source_chunk,
             associated_chunk=src.associated_chunk,
+            compute_stream_id=src.compute_stream_id,
         )
         src.data_succs.append(send_node)
         send_node.data_preds.append(src)
@@ -1703,6 +1709,7 @@ def expand_chunks_to_dags(
             resource="pp_stream",
             source_chunk=dst.source_chunk,
             associated_chunk=dst.associated_chunk,
+            compute_stream_id=dst.compute_stream_id,
             custom_metadata=recv_metadata,
         )
         recv_node.data_succs.append(dst)
@@ -1824,6 +1831,7 @@ def _cp_endpoints(chunk_id: int):
             pp_rank=ref_tail.pp_rank,
             time_step=0,
             source_chunk=non_none[-1][1],
+            compute_stream_id="comp_stream",
         )
 
         # Wire every BWD chunk tail → UPD.
@@ -1922,6 +1930,7 @@ def _zero_task(
         time_step=time_step,
         bucket_id=bucket_id,
         resource=resource,
+        compute_stream_id=ref.compute_stream_id,
         unique_bucket_id=ubid,
         source_chunk=source_chunk or ref.source_chunk,
         associated_chunk=ref.associated_chunk,
@@ -3923,6 +3932,10 @@ def overlap_chunks(rank_dag: TaskDAG, chunk_pairs: list[tuple[Chunk, Chunk]]) ->
     for first_chunk, second_chunk in chunk_pairs:
         first_tasks = _tasks_for_chunk(rank_dag, first_chunk)
         second_tasks = _tasks_for_chunk(rank_dag, second_chunk)
+        for task in first_tasks:
+            task.compute_stream_id = "comp_stream"
+        for task in second_tasks:
+            task.compute_stream_id = "overlapped_comp_stream"
         first_ids = {id(task) for task in first_tasks}
         second_ids = {id(task) for task in second_tasks}
         pair_ids = first_ids | second_ids
diff --git a/src/piper_utils.py b/src/piper_utils.py
index bce9759..0808951 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -11,7 +11,7 @@
 from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
 from typing import Any, Optional
 
-LOG_LEVEL = "INFO"
+LOG_LEVEL = "DEBUG"
 
 """ 
 Print the backward graph of a tensor

From 3674a50ac159a361a098a0e2434fc68b3ed35383 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Sun, 19 Apr 2026 07:20:49 +0000
Subject: [PATCH 093/100] adjust run_dag priorities

---
 .gitignore                   |   1 -
 scripts/run_qwen_e2e_eval.py | 175 +++++++++++++++++++++++++++++------
 src/piper_exec.py            |  26 +++---
 src/piper_utils.py           |   2 +-
 4 files changed, 161 insertions(+), 43 deletions(-)

diff --git a/.gitignore b/.gitignore
index c66457b..6cc66f3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,4 +9,3 @@ docs/
 figs/
 logs/
 .vscode/
-scripts/
diff --git a/scripts/run_qwen_e2e_eval.py b/scripts/run_qwen_e2e_eval.py
index e837fb8..6c7b33a 100644
--- a/scripts/run_qwen_e2e_eval.py
+++ b/scripts/run_qwen_e2e_eval.py
@@ -249,6 +249,22 @@ def _metrics_name(model: str, exp: Experiment) -> str:
     )
 
 
+def _runner_experiment_name(model: str, exp: Experiment) -> str:
+    bucket_part = (
+        f"-bucket{_format_bucket_size(exp.bucket_size_mb)}"
+        if exp.bucket_size_mb is not None
+        else ""
+    )
+    nsight_part = "-nsight1" if exp.nsight else ""
+    return (
+        f"qwen{model}-sched_{exp.schedule}-pp{exp.pp}-dp{exp.dp}-"
+        f"ep{int(exp.ep)}-zero{exp.zero_stage}{bucket_part}{nsight_part}-"
+        f"bs{exp.batch_size}-sl{exp.seq_len}-mbs{exp.mbs}-"
+        f"ga{int(exp.gradient_accumulation)}-aras{int(exp.ar_a2a_same_stream)}-"
+        f"ozo{int(exp.overlap_zero_ops)}-och{int(exp.overlap_chunks)}"
+    )
+
+
 def _remote_experiment_output_dir(args: argparse.Namespace, exp: Experiment) -> str:
     return f"{args.remote_output_dir.rstrip('/')}/{exp.slug()}"
 
@@ -270,6 +286,15 @@ def _metrics_remote_paths(
     ]
 
 
+def _cluster_log_candidates(
+    model: str,
+    exp: Experiment,
+    fetch_dir: Path,
+) -> list[Path]:
+    pattern = f"{_runner_experiment_name(model, exp)}_*.cluster.log"
+    return sorted(fetch_dir.glob(pattern))
+
+
 def _runner_command(args: argparse.Namespace, exp: Experiment, fetch_dir: Path) -> list[str]:
     remote_output_dir = _remote_experiment_output_dir(args, exp)
     command = [
@@ -346,8 +371,18 @@ def _fetch_remote_metrics(
     exp: Experiment,
     destination: Path,
     remote_output_dir: str,
+    remote_metrics_path: str | None = None,
 ) -> bool:
-    for remote_path in _metrics_remote_paths(model, exp, remote_output_dir):
+    remote_paths: list[str] = []
+    if remote_metrics_path:
+        remote_paths.append(remote_metrics_path)
+    remote_paths.extend(_metrics_remote_paths(model, exp, remote_output_dir))
+
+    seen: set[str] = set()
+    for remote_path in remote_paths:
+        if remote_path in seen:
+            continue
+        seen.add(remote_path)
         remote_command = (
             "docker exec piper_ray bash -lc "
             + json.dumps(f"test -f {remote_path} && cat {remote_path}")
@@ -368,6 +403,24 @@ def _fetch_remote_metrics(
     return False
 
 
+def _experiment_fetch_dir(fetch_dir: Path, exp: Experiment) -> Path:
+    return fetch_dir / exp.slug()
+
+
+def _fetched_metrics_path(fetch_dir: Path, exp: Experiment, remote_metrics_path: str | None) -> Path:
+    if remote_metrics_path:
+        return _experiment_fetch_dir(fetch_dir, exp) / Path(remote_metrics_path).name
+    return _experiment_fetch_dir(fetch_dir, exp) / f"{exp.slug()}.metrics"
+
+
+def _copy_local_metrics(source: Path, destination: Path) -> bool:
+    if not source.is_file():
+        return False
+    destination.parent.mkdir(parents=True, exist_ok=True)
+    shutil.copyfile(source, destination)
+    return True
+
+
 def _fetch_remote_dag_order_logs(
     args: argparse.Namespace,
     exp: Experiment,
@@ -592,6 +645,25 @@ def _recover_metrics_from_cluster_log(
     if not cluster_log_path.is_file():
         return False
 
+    records: list[dict] = []
+    marker = "metrics_json="
+    for raw_line in cluster_log_path.read_text(encoding="utf-8", errors="replace").splitlines():
+        line = _strip_ansi(raw_line)
+        if marker not in line:
+            continue
+        payload = line.split(marker, maxsplit=1)[1].strip()
+        try:
+            records.append(json.loads(payload))
+        except json.JSONDecodeError:
+            continue
+
+    if records:
+        destination.parent.mkdir(parents=True, exist_ok=True)
+        with open(destination, "w", encoding="utf-8") as handle:
+            for record in records:
+                handle.write(f"rank {record['dp_rank']} metrics_json= {json.dumps(record, sort_keys=True)}\n")
+        return True
+
     dp_rank_by_pid: dict[str, int] = {}
     iter_times_by_pid: dict[str, list[float]] = {}
     peak_memory_by_pid: dict[str, dict[str, dict[str, float | int]]] = {}
@@ -669,25 +741,39 @@ def _recover_metrics_from_fetch_logs(
     fetch_dir: Path,
     destination: Path,
 ) -> bool:
-    bucket_part = (
-        f"-bucket{_format_bucket_size(exp.bucket_size_mb)}"
-        if exp.bucket_size_mb is not None
-        else ""
-    )
-    nsight_part = "-nsight1" if exp.nsight else ""
-    pattern = (
-        f"qwen{args.model}-sched_{exp.schedule}-pp{exp.pp}-dp{exp.dp}-"
-        f"zero{exp.zero_stage}{bucket_part}{nsight_part}-bs{exp.batch_size}-sl{exp.seq_len}-"
-        f"mbs{exp.mbs}-ga{int(exp.gradient_accumulation)}-"
-        f"aras{int(exp.ar_a2a_same_stream)}-ozo{int(exp.overlap_zero_ops)}-"
-        f"och{int(exp.overlap_chunks)}_*.cluster.log"
-    )
-    candidates = sorted(fetch_dir.glob(pattern))
+    fetched_dir = _experiment_fetch_dir(fetch_dir, exp)
+    if fetched_dir.is_dir():
+        for candidate in sorted(fetched_dir.glob("*")):
+            if _copy_local_metrics(candidate, destination) and _parse_metrics_records(destination):
+                return True
+
+    candidates = _cluster_log_candidates(args.model, exp, fetch_dir)
     if not candidates:
         return False
     return _recover_metrics_from_cluster_log(exp, candidates[-1], destination, args.iters)
 
 
+def _extract_remote_metrics_path(log_path: Path) -> str | None:
+    if not log_path.is_file():
+        return None
+
+    metrics_path: str | None = None
+    for raw_line in log_path.read_text(encoding="utf-8", errors="replace").splitlines():
+        line = _strip_ansi(raw_line)
+        for pattern in (
+            r"benchmark metrics will be saved by the driver to (\S+)",
+            r"Benchmark metrics saved to (\S+)",
+        ):
+            match = re.search(pattern, line)
+            if match:
+                metrics_path = match.group(1)
+    return metrics_path
+
+
+def _extract_remote_metrics_path_from_cluster_log(cluster_log_path: Path) -> str | None:
+    return _extract_remote_metrics_path(cluster_log_path)
+
+
 def _parse_metrics_records(metrics_path: Path) -> list[dict]:
     records: list[dict] = []
     if not metrics_path.is_file():
@@ -840,6 +926,8 @@ def _run_experiment(
 ) -> dict[str, object]:
     exp_logs_dir = logs_dir / exp.slug()
     exp_logs_dir.mkdir(parents=True, exist_ok=True)
+    exp_fetch_dir = _experiment_fetch_dir(fetch_dir, exp)
+    exp_fetch_dir.mkdir(parents=True, exist_ok=True)
     log_path = exp_logs_dir / "run.log"
     metrics_path = metrics_dir / f"{exp.slug()}.metrics"
     command = _runner_command(args, exp, fetch_dir)
@@ -885,12 +973,34 @@ def _run_experiment(
             text=True,
         )
 
-    metrics_found = _fetch_remote_metrics(
-        args.model,
-        exp,
-        metrics_path,
-        _remote_experiment_output_dir(args, exp),
-    )
+    remote_metrics_path = _extract_remote_metrics_path(log_path)
+    if remote_metrics_path is None:
+        cluster_log_candidates = _cluster_log_candidates(args.model, exp, fetch_dir)
+        if cluster_log_candidates:
+            remote_metrics_path = _extract_remote_metrics_path_from_cluster_log(cluster_log_candidates[-1])
+
+    fetched_metrics_path = _fetched_metrics_path(fetch_dir, exp, remote_metrics_path)
+    metrics_found = False
+    if remote_metrics_path is not None:
+        metrics_found = _fetch_remote_metrics(
+            args.model,
+            exp,
+            fetched_metrics_path,
+            _remote_experiment_output_dir(args, exp),
+            remote_metrics_path=remote_metrics_path,
+        )
+        if metrics_found:
+            _copy_local_metrics(fetched_metrics_path, metrics_path)
+    if not metrics_found:
+        metrics_found = _fetch_remote_metrics(
+            args.model,
+            exp,
+            fetched_metrics_path,
+            _remote_experiment_output_dir(args, exp),
+            remote_metrics_path=None,
+        )
+        if metrics_found:
+            _copy_local_metrics(fetched_metrics_path, metrics_path)
     dag_order_paths = _fetch_remote_dag_order_logs(args, exp, exp_logs_dir)
     nsight_paths = _copy_experiment_nsight_profiles(exp, log_path, exp_logs_dir)
     if not metrics_found:
@@ -900,18 +1010,21 @@ def _run_experiment(
     status = "success"
     failure_reason = ""
     aggregated: dict[str, object] = {}
-    if result.returncode != 0:
+    if records:
+        aggregated = _aggregate_metrics(records)
+        if result.returncode != 0 and _looks_like_oom(log_path):
+            status = "oom"
+            failure_reason = "oom"
+    elif result.returncode != 0:
         if _looks_like_oom(log_path):
             status = "oom"
             failure_reason = "oom"
         else:
             status = "failed"
             failure_reason = f"runner_exit_{result.returncode}"
-    elif not records:
+    else:
         status = "failed"
         failure_reason = "missing_metrics"
-    else:
-        aggregated = _aggregate_metrics(records)
 
     print(
         f"[{index}/{total}] {status.upper()} {exp.sweep}: {exp.label()} "
@@ -935,6 +1048,7 @@ def _run_experiment(
         "schedule": exp.schedule,
         "pp": exp.pp,
         "dp": exp.dp,
+        "ep": int(exp.ep),
         "zero_stage": exp.zero_stage,
         "bucket_size_mb": _format_bucket_size(exp.bucket_size_mb) if exp.bucket_size_mb is not None else "",
         "batch_size": exp.batch_size,
@@ -1044,6 +1158,7 @@ def _hydrate_existing_rows(existing_rows: list[dict[str, object]]) -> list[dict[
 def _completed_keys_from_metrics(
     experiments: list[Experiment],
     metrics_dir: Path,
+    fetch_dir: Path,
     existing_by_key: dict[tuple[object, ...], dict[str, object]],
 ) -> set[tuple[object, ...]]:
     completed: set[tuple[object, ...]] = set()
@@ -1053,6 +1168,14 @@ def _completed_keys_from_metrics(
             continue
         metrics_path = metrics_dir / f"{exp.slug()}.metrics"
         records = _parse_metrics_records(metrics_path)
+        if not records:
+            fetched_dir = _experiment_fetch_dir(fetch_dir, exp)
+            if fetched_dir.is_dir():
+                for candidate in sorted(fetched_dir.glob("*")):
+                    records = _parse_metrics_records(candidate)
+                    if records:
+                        _copy_local_metrics(candidate, metrics_path)
+                        break
         if records:
             completed.add(exp.key())
     return completed
@@ -1304,7 +1427,7 @@ def main() -> int:
         for key, row in existing_by_key.items()
         if row.get("status") in {"success", "oom"}
     }
-    completed_keys |= _completed_keys_from_metrics(experiments, metrics_dir, existing_by_key)
+    completed_keys |= _completed_keys_from_metrics(experiments, metrics_dir, fetch_dir, existing_by_key)
 
     for exp in experiments:
         existing_row = existing_by_key.get(exp.key())
diff --git a/src/piper_exec.py b/src/piper_exec.py
index c9fdaf1..e6e0d78 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -285,21 +285,17 @@ def runtime_sort_key(node: Task) -> tuple:
     """
     priority = {
         TaskType.SEND: 0,
-        TaskType.FREE_FULL_GRADS: 1,
-        TaskType.FREE_FULL_PARAMS: 1,
-        TaskType.FWD_A2A: 2,
-        TaskType.BWD_A2A: 2,
-        TaskType.REDUCE_SCATTER: 3,
-        TaskType.ALL_REDUCE: 3,
-        TaskType.ALL_GATHER: 4,
-        TaskType.FWD: 5,
-        TaskType.BWD: 5,
-        TaskType.BWD_I: 5,
-        TaskType.BWD_W: 5,
-        TaskType.FWD_BWD: 5,
-        TaskType.ALLOC_FULL_PARAMS: 6,
-        TaskType.ALLOC_FULL_GRADS: 6,
-        TaskType.RECV: 7,
+        TaskType.FWD_A2A: 1,
+        TaskType.BWD_A2A: 1,
+        TaskType.REDUCE_SCATTER: 2,
+        TaskType.ALL_REDUCE: 2,
+        TaskType.ALL_GATHER: 3,
+        TaskType.FWD: 4,
+        TaskType.BWD: 4,
+        TaskType.BWD_I: 4,
+        TaskType.BWD_W: 4,
+        TaskType.FWD_BWD: 4,
+        TaskType.RECV: 5,
     }.get(node.task_type, 4)
     return (node.time_step, priority, node.uid)
 
diff --git a/src/piper_utils.py b/src/piper_utils.py
index 0808951..bce9759 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -11,7 +11,7 @@
 from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
 from typing import Any, Optional
 
-LOG_LEVEL = "DEBUG"
+LOG_LEVEL = "INFO"
 
 """ 
 Print the backward graph of a tensor

From 655a42d988dc422d8ee3577ba4314a406ec2cba8 Mon Sep 17 00:00:00 2001
From: Shubham Tiwari <shubhamtiwaribits@gmail.com>
Date: Wed, 22 Apr 2026 01:00:34 +0000
Subject: [PATCH 094/100] zero2 startup memory fix

---
 .gitignore                   |   2 +
 check_gpus.sh                |  26 +++++++
 run_piper_remote.sh          |  83 +++++++++++++++++++++
 scripts/run-qwen-ec2.sh      |  11 +++
 scripts/run_qwen_e2e_eval.py |  64 ++++++++++++++++
 src/piper_actor.py           | 140 +++++++++++++++++++++++++++++++++--
 src/piper_compile.py         |   2 +
 test/test_qwen.py            |  89 +++++++++++++++++++---
 8 files changed, 399 insertions(+), 18 deletions(-)
 create mode 100644 check_gpus.sh
 create mode 100644 run_piper_remote.sh

diff --git a/.gitignore b/.gitignore
index 6cc66f3..178f010 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,3 +9,5 @@ docs/
 figs/
 logs/
 .vscode/
+e2e-eval/
+
diff --git a/check_gpus.sh b/check_gpus.sh
new file mode 100644
index 0000000..67c0906
--- /dev/null
+++ b/check_gpus.sh
@@ -0,0 +1,26 @@
+export SSH_KEY=/home/shubham/.ssh/mfris-us-west-2.pem
+export HEAD_PUBLIC_IP=100.22.89.85
+export HEAD_PRIVATE_IP=10.0.79.139
+export WORKER1_PRIVATE_IP=10.0.71.178
+export WORKER2_PRIVATE_IP=10.0.70.34
+export WORKER3_PRIVATE_IP=10.0.69.189
+
+for ENTRY in "head:$HEAD_PUBLIC_IP" \
+             "worker1:$WORKER1_PRIVATE_IP" \
+             "worker2:$WORKER2_PRIVATE_IP" \
+             "worker3:$WORKER3_PRIVATE_IP"; do
+  LABEL=${ENTRY%%:*}; HOST=${ENTRY#*:}
+
+  if [[ "$LABEL" == "head" ]]; then
+    SSH_OPTS=(-i "$SSH_KEY" -o StrictHostKeyChecking=no)
+  else
+    SSH_OPTS=(-i "$SSH_KEY" -o StrictHostKeyChecking=no \
+              -o "ProxyCommand=ssh -i $SSH_KEY -o StrictHostKeyChecking=no -W %h:%p ubuntu@$HEAD_PUBLIC_IP")
+  fi
+
+  echo ""
+  echo "============================================================"
+  echo "=== $LABEL ($HOST) ==="
+  echo "============================================================"
+  ssh "${SSH_OPTS[@]}" ubuntu@$HOST "echo 'Hostname: \$(hostname)'; nvidia-smi"
+done
diff --git a/run_piper_remote.sh b/run_piper_remote.sh
new file mode 100644
index 0000000..98c74b0
--- /dev/null
+++ b/run_piper_remote.sh
@@ -0,0 +1,83 @@
+export PATH_TO_PIPER=/m-coriander/coriander/shubham/moe-scheduling/piper/piper
+export IMAGE=mfris/piper:latest
+# export SSH_KEY=/m-coriander/coriander/shubham/ray-autoscaler_us-east-2.pem
+export SSH_KEY=/home/shubham/.ssh/mfris-us-west-2.pem
+
+export HEAD_PUBLIC_IP=100.22.89.85
+export HEAD_PRIVATE_IP=10.0.79.139
+export WORKER1_PRIVATE_IP=10.0.71.178
+export WORKER2_PRIVATE_IP=10.0.70.34
+export WORKER3_PRIVATE_IP=10.0.69.189
+export WORKERS="$WORKER1_PRIVATE_IP $WORKER2_PRIVATE_IP $WORKER3_PRIVATE_IP"
+
+
+./scripts/resync.sh
+
+
+python scripts/run_qwen_e2e_eval.py \
+  --model 9B \
+  --sweeps zero \
+  --zero-stages 2 3 \
+  --zero-grad-acc-settings off \
+  --bucket-size 25 \
+  --batch-size 4 \
+  --zero-batch-sizes 2 4 8 12 16 24 28 32 \
+  --seq-len 128 \
+  --memory-profile \
+  --pp 2 \
+  --dp 4 \
+  --warmup 2 \
+  --iters 2
+
+# python scripts/run_qwen_e2e_eval.py \
+#   --model 9B \
+#   --sweeps zero \
+#   --zero-stages 2 \
+#   --zero-grad-acc-settings off \
+#   --bucket-size 25 \
+#   --batch-size 4 \
+#   --zero-batch-sizes 8 12 16 \
+#   --seq-len 128 \
+#   --memory-profile \
+#   --pp 2 \
+#   --dp 4 \
+#   --warmup 2 \
+#   --iters 2
+
+# LOCAL_DIR=/m-coriander/coriander/shubham/moe-scheduling/piper/piper/e2e-eval/20260419_235909/logs/zero-sched_1f1b-pp4-dp2-ep0-zero3-bucket25-bs32-sl512-mbs8-ga0-aras0-ozo1-och0/memory_snapshots
+# mkdir -p "$LOCAL_DIR"
+# REMOTE_DIR=/tmp/piper/out/zero-sched_1f1b-pp4-dp2-ep0-zero3-bucket25-bs32-sl512-mbs8-ga0-aras0-ozo1-och0/memory_snapshots
+
+# for ENTRY in "head:$HEAD_PUBLIC_IP" \
+#              "worker1:$WORKER1_PRIVATE_IP" \
+#              "worker2:$WORKER2_PRIVATE_IP" \
+#              "worker3:$WORKER3_PRIVATE_IP"; do
+#   LABEL=${ENTRY%%:*}; HOST=${ENTRY#*:}
+#   [[ -z "$HOST" ]] && continue
+
+#   if [[ "$LABEL" == "head" ]]; then
+#     SSH_OPTS=()
+#   else
+#     SSH_OPTS=(-o "ProxyCommand=ssh -i $SSH_KEY -o StrictHostKeyChecking=no -W %h:%p ubuntu@$HEAD_PUBLIC_IP")
+#   fi
+
+#   STAGE=/tmp/zero3_snaps_${LABEL}
+#   echo "=== $LABEL ($HOST) ==="
+#   ssh -i "$SSH_KEY" -o StrictHostKeyChecking=no "${SSH_OPTS[@]}" ubuntu@$HOST \
+#       "rm -rf $STAGE && mkdir -p $STAGE && \
+#        docker cp piper_ray:$REMOTE_DIR $STAGE/ 2>/dev/null; \
+#        ls $STAGE/memory_snapshots/ 2>/dev/null"
+
+#   scp -r -i "$SSH_KEY" -o StrictHostKeyChecking=no "${SSH_OPTS[@]}" \
+#       "ubuntu@$HOST:$STAGE/memory_snapshots/." "$LOCAL_DIR/" 2>/dev/null \
+#       && echo "  fetched from $LABEL" \
+#       || echo "  no snapshots on $LABEL (skip)"
+# done
+
+# echo "=== Final ==="
+# ls -la "$LOCAL_DIR"
+
+
+
+
+
diff --git a/scripts/run-qwen-ec2.sh b/scripts/run-qwen-ec2.sh
index 942fd93..2a9fddc 100755
--- a/scripts/run-qwen-ec2.sh
+++ b/scripts/run-qwen-ec2.sh
@@ -60,6 +60,9 @@ GRADIENT_ACCUMULATION_ARG="--gradient-accumulation"
 AR_A2A_SAME_STREAM_ARG="--no-ar-a2a-same-stream"
 OVERLAP_ZERO_OPS_ARG="--no-overlap-zero-ops"
 OVERLAP_CHUNKS_ARG="--no-overlap-chunks"
+MEMORY_PROFILE=false
+MEMORY_PROFILE_DIR=""
+MEMORY_PROFILE_ARG=""
 
 SSH_BASE=(-i "$SSH_KEY" -o StrictHostKeyChecking=no)
 PROXY="ProxyCommand=ssh -i $SSH_KEY -o StrictHostKeyChecking=no -W %h:%p ubuntu@$HEAD_PUBLIC_IP"
@@ -91,6 +94,8 @@ while [[ $# -gt 0 ]]; do
         --use-inductor) USE_INDUCTOR_ARG="--use-inductor"; shift ;;
         --no-use-inductor) USE_INDUCTOR_ARG="--no-use-inductor"; shift ;;
         --log-to-file) LOG_TO_FILE=true; shift ;;
+        --memory-profile) MEMORY_PROFILE=true; shift ;;
+        --memory-profile-dir) MEMORY_PROFILE_DIR="$2"; shift 2 ;;
         --out-dir)     OUT_DIR="$2";     shift 2 ;;
         --remote-output-dir) REMOTE_OUTPUT_DIR="$2"; shift 2 ;;
         *) echo "Unknown argument: $1" >&2; exit 1 ;;
@@ -230,6 +235,11 @@ SNAPSHOT_DIR="$(mktemp -d)"
 HEAD_SNAPSHOT="$SNAPSHOT_DIR/head.snapshot"
 WORKER_LOG_NODES=()
 
+if $MEMORY_PROFILE; then
+    MEM_DIR="${MEMORY_PROFILE_DIR:-$REMOTE_OUTPUT_DIR/memory_snapshots/${EXPERIMENT_NAME}_${RUN_ID}}"
+    MEMORY_PROFILE_ARG="--memory-profile --memory-profile-dir $MEM_DIR"
+fi
+
 for worker_ip in "${WORKER1_PRIVATE_IP:-}" "${WORKER2_PRIVATE_IP:-}" "${WORKER3_PRIVATE_IP:-}"; do
     if [[ -n "$worker_ip" ]]; then
         WORKER_LOG_NODES+=("$worker_ip")
@@ -273,6 +283,7 @@ run_head_ssh \
      --output-dir $REMOTE_OUTPUT_DIR \
      $USE_INDUCTOR_ARG \
      $NSIGHT_ARG \
+     $MEMORY_PROFILE_ARG \
      --temp-dir /tmp/piper/ray_tmp > $REMOTE_LOG 2>&1'"
 RUN_STATUS=$?
 
diff --git a/scripts/run_qwen_e2e_eval.py b/scripts/run_qwen_e2e_eval.py
index 6c7b33a..5c5c632 100644
--- a/scripts/run_qwen_e2e_eval.py
+++ b/scripts/run_qwen_e2e_eval.py
@@ -220,6 +220,17 @@ def parse_args() -> argparse.Namespace:
         default=["scalability", "zero", "schedule"],
         help="Subset of e2e sweeps to run. Default: scalability zero schedule",
     )
+    parser.add_argument(
+        "--memory-profile",
+        action="store_true",
+        help="Pass --memory-profile through to run-qwen-ec2.sh / test_qwen.",
+    )
+    parser.add_argument(
+        "--memory-profile-dir",
+        default=None,
+        help="Optional directory on the remote for pickles; forwarded if set.",
+    )
+
     parser.add_argument("--dry-run", action="store_true")
     return parser.parse_args()
 
@@ -330,6 +341,12 @@ def _runner_command(args: argparse.Namespace, exp: Experiment, fetch_dir: Path)
         command.append("--use-inductor")
     else:
         command.append("--no-use-inductor")
+    
+    if args.memory_profile:
+        command.append("--memory-profile")
+        mem_dir = args.memory_profile_dir or f"{remote_output_dir}/memory_snapshots"
+        command.extend(["--memory-profile-dir", mem_dir])
+
     return command
 
 
@@ -448,6 +465,49 @@ def _fetch_remote_dag_order_logs(
 
     return copied_paths
 
+def _fetch_remote_memory_snapshots(
+    args: argparse.Namespace,
+    exp: Experiment,
+    destination: Path,
+    log_path: Path,
+) -> bool:
+    """Copy memory_snapshots/ from piper_ray on every node (head + workers) into destination."""
+    if not args.memory_profile:
+        return False
+
+    remote_snap = f"{_remote_experiment_output_dir(args, exp)}/memory_snapshots"
+    destination.mkdir(parents=True, exist_ok=True)
+
+    any_ok = False
+    for node_label, node_kind, remote_host in _iter_remote_nodes(log_path):
+        stage = f"/tmp/piper_mem_stage_{exp.slug().replace('/', '_')}_{node_label}"
+        remote_command = (
+            f"rm -rf {stage} && mkdir -p {stage} && "
+            f"docker cp piper_ray:{remote_snap} {stage}/ 2>/dev/null || exit 0"
+        )
+        ssh_cmd = (
+            _ssh_head_command(remote_command)
+            if node_kind == "head"
+            else _ssh_worker_command(str(remote_host), remote_command)
+        )
+        subprocess.run(ssh_cmd, check=False, capture_output=True, text=True)
+
+        ok = _scp_from_remote(
+            node_kind=node_kind,
+            remote_host=remote_host,
+            remote_path=f"{stage}/memory_snapshots/.",
+            destination=destination,
+        )
+        any_ok = any_ok or ok
+
+        cleanup_cmd = (
+            _ssh_head_command(f"rm -rf {stage}")
+            if node_kind == "head"
+            else _ssh_worker_command(str(remote_host), f"rm -rf {stage}")
+        )
+        subprocess.run(cleanup_cmd, check=False, capture_output=True, text=True)
+
+    return any_ok
 
 def _strip_ansi(text: str) -> str:
     return re.sub(r"\x1b\[[0-9;]*m", "", text)
@@ -1002,6 +1062,10 @@ def _run_experiment(
         if metrics_found:
             _copy_local_metrics(fetched_metrics_path, metrics_path)
     dag_order_paths = _fetch_remote_dag_order_logs(args, exp, exp_logs_dir)
+    if args.memory_profile:
+        snap_dest = exp_logs_dir / "memory_snapshots"
+        _fetch_remote_memory_snapshots(args, exp, snap_dest, log_path)
+
     nsight_paths = _copy_experiment_nsight_profiles(exp, log_path, exp_logs_dir)
     if not metrics_found:
         metrics_found = _recover_metrics_from_fetch_logs(args, exp, fetch_dir, metrics_path)
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 9cd896c..cd9cdd6 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -53,6 +53,7 @@ def _create_actors(
     temp_dir: str = None,
     use_inductor: bool = False,
     ar_a2a_same_stream: bool = False,
+    memory_profile: bool = False,
 ):
     dp_rank = int(os.environ["PIPER_DP_RANK"])
     world_size = int(os.environ["PIPER_WORLD_SIZE"])
@@ -98,6 +99,7 @@ def _create_actors(
             no_nvtx=no_nvtx,
             use_inductor=use_inductor,
             ar_a2a_same_stream=ar_a2a_same_stream,
+            memory_profile=memory_profile,
         )
         piper_metadata.actors[pp_rank] = actor
 
@@ -125,6 +127,7 @@ def __init__(
         no_nvtx: bool = False,
         use_inductor: bool = False,
         ar_a2a_same_stream: bool = False,
+        memory_profile: bool = False,
     ):
         self.logger = create_logger("piper_actor", LOG_LEVEL)
 
@@ -133,6 +136,12 @@ def __init__(
         # Suppress the spurious stream-mismatch warning.
         set_warn_on_accumulate_grad_stream_mismatch(False)
 
+
+        if memory_profile:
+            torch.cuda.memory._record_memory_history(
+                enabled="all", context="all", stacks="all", max_entries=5_000_000,
+            )
+
         self.pp_rank = pp_rank
         self.optim_class = optim_class
         self.naive_gradient_sync = naive_gradient_sync
@@ -265,6 +274,63 @@ def reset_peak_memory(self):
     def get_peak_memory(self):
         return self.global_rank, torch.cuda.max_memory_allocated() / (1024**3)
 
+
+    def start_memory_recording(self, max_entries: int = 100000) -> None:
+        """Begin recording CUDA memory allocation history on this actor's GPU."""
+        torch.cuda.memory._record_memory_history(
+            enabled="all",
+            context="all",
+            stacks="all",
+            max_entries=max_entries,
+        )
+        self.logger.info(
+            f"Actor {self.global_rank}: started CUDA memory recording "
+            f"(max_entries={max_entries})"
+        )
+
+    def dump_memory_snapshot(self, path: str) -> str:
+        """Dump the recorded CUDA memory snapshot to a pickle file.
+
+        Returns the path to the written file.
+        """
+        os.makedirs(path, exist_ok=True)
+        filepath = os.path.join(path, f"rank{self.global_rank}_memory.pickle")
+        torch.cuda.memory._dump_snapshot(filepath)
+        torch.cuda.memory._record_memory_history(enabled=None)
+        self.logger.info(
+            f"Actor {self.global_rank}: dumped memory snapshot to {filepath}"
+        )
+        return filepath
+
+    def set_oom_snapshot_dir(self, path: str) -> None:
+        """Tell this actor where to drop a memory snapshot if run_dag hits a CUDA OOM."""
+        os.makedirs(path, exist_ok=True)
+        self._oom_snapshot_dir = path
+
+    def _log_mem(self, label: str, extra: str = "") -> None:
+        """Emit a single-line memory trajectory marker to the actor log.
+
+        Captures current/reserved/peak allocator stats plus retry/OOM counters.
+        Cheap: no CUDA sync, just reads allocator counters.
+        """
+        try:
+            alloc = torch.cuda.memory_allocated() / (1024 ** 3)
+            reserv = torch.cuda.memory_reserved() / (1024 ** 3)
+            max_a = torch.cuda.max_memory_allocated() / (1024 ** 3)
+            max_r = torch.cuda.max_memory_reserved() / (1024 ** 3)
+            stats = torch.cuda.memory_stats()
+            active = stats.get("active_bytes.all.current", 0) / (1024 ** 3)
+            retries = stats.get("num_alloc_retries", 0)
+            ooms = stats.get("num_ooms", 0)
+            self.logger.info(
+                f"[mem] rank={self.global_rank} stage={getattr(self, 'pp_rank', '?')} "
+                f"label={label} allocated={alloc:.3f}GB reserved={reserv:.3f}GB "
+                f"max_alloc={max_a:.3f}GB max_res={max_r:.3f}GB active={active:.3f}GB "
+                f"retries={retries} ooms={ooms} {extra}"
+            )
+        except Exception as e:
+            self.logger.warning(f"[mem] log failed (label={label}): {e}")
+
     def _nvtx_push(self, label: str) -> None:
         if not self.no_nvtx:
             torch.cuda.nvtx.range_push(label)
@@ -944,7 +1010,7 @@ def _bucket_forward_runner(
                 self.bucket_flat_params[ubid] = flat_params
                 self.bucket_flat_grads[ubid] = (
                     None
-                    if self.zero_stage == 3
+                    if self.zero_stage in (2, 3)
                     else torch.zeros(padded_numel, dtype=self.zero_grad_dtype, device=self.device)
                 )
                 self.bucket_shard_params[ubid] = shard_param
@@ -988,12 +1054,7 @@ def _bucket_forward_runner(
         # Keep first GraphModule for compatibility with external inspection tools.
         self.graph_modules[stage_id] = first_gm
 
-        allocated_gb = torch.cuda.memory_allocated(self.device) / (1024 ** 3)
-        reserved_gb = torch.cuda.memory_reserved(self.device) / (1024 ** 3)
-        self.logger.debug(
-            f"Actor {self.global_rank} _load_stage stage_id={stage_id}: GPU allocated={allocated_gb:.2f} GB, "
-            f"reserved={reserved_gb:.2f} GB"
-        )
+        self._log_mem(f"post_load_stage_{stage_id}")
 
     # -----------------------------------------------------------------------
     # DAG-based execution
@@ -1336,6 +1397,48 @@ def _release_task_buffer_uid(self, uid: int) -> None:
             self.task_buffer_refcounts[uid] = remaining
 
     def run_dag(self, loss_fn=None, profiling: bool = False):
+        """Thin wrapper around ``_run_dag_body`` that dumps a CUDA memory
+        snapshot if the body raises ``torch.cuda.OutOfMemoryError``.
+
+        The dump is written to ``self._oom_snapshot_dir`` (set via
+        ``set_oom_snapshot_dir``) or ``/tmp/piper_oom_snapshots`` as a fallback.
+        The original exception is re-raised after the snapshot attempt so the
+        caller (driver) still sees the OOM.
+        """
+        # Mark the entire iteration boundary for the memory-viz timeline.
+        iter_idx = getattr(self, "_iter_counter", 0)
+        self._iter_counter = iter_idx + 1
+        self._nvtx_push(f"iter_{iter_idx}_rank_{self.global_rank}")
+        try:
+            self._log_mem(f"iter_{iter_idx}_start")
+            return self._run_dag_body(loss_fn=loss_fn, profiling=profiling)
+        except torch.cuda.OutOfMemoryError as oom_exc:
+            oom_dir = getattr(self, "_oom_snapshot_dir", None) or "/tmp/piper_oom_snapshots"
+            try:
+                os.makedirs(oom_dir, exist_ok=True)
+                filepath = os.path.join(
+                    oom_dir, f"oom_rank{self.global_rank}_memory.pickle"
+                )
+                torch.cuda.memory._dump_snapshot(filepath)
+                self.logger.error(
+                    f"[OOM] rank={self.global_rank} iter={iter_idx} "
+                    f"dumped snapshot to {filepath}: {oom_exc}"
+                )
+            except Exception as dump_exc:
+                self.logger.error(
+                    f"[OOM] rank={self.global_rank} iter={iter_idx} "
+                    f"snapshot dump FAILED: {dump_exc} (original OOM: {oom_exc})"
+                )
+            try:
+                torch.cuda.memory._record_memory_history(enabled=None)
+            except Exception:
+                pass
+            self._log_mem(f"iter_{iter_idx}_oom")
+            raise
+        finally:
+            self._nvtx_pop()
+
+    def _run_dag_body(self, loss_fn=None, profiling: bool = False):
         """Execute the loaded TaskDAG in topological order.
 
         All inter-task data is routed through ``task_buffer``, keyed by the
@@ -1354,7 +1457,6 @@ def run_dag(self, loss_fn=None, profiling: bool = False):
         assert self.sorted_dag_nodes is not None, "load_dag() must initialize sorted node order"
         debug_enabled = self.logger.isEnabledFor(logging.DEBUG)
         verbose_enabled = self.logger.isEnabledFor(VERBOSE)
-
         # Reset per-iteration state
         self.task_buffer = {}
         self.task_buffer_refcounts = {}
@@ -1452,6 +1554,24 @@ def _run_free_full_params(free_node: Task) -> None:
                     f"mb{mb_idx} ubid={ubid}"
                 )
 
+            # Timeline marker visible in NVTX traces & PyTorch memory-viz.
+            _task_label = f"{task_type.value}_mb{mb_idx}_ubid{ubid}_t{node.time_step}"
+            self._nvtx_push(_task_label)
+            _log_heavy_task = task_type in (
+                TaskType.FWD,
+                TaskType.BWD,
+                TaskType.BWD_I,
+                TaskType.BWD_W,
+                TaskType.FWD_BWD,
+                TaskType.UPD,
+                TaskType.ALLOC_FULL_PARAMS,
+                TaskType.FREE_FULL_PARAMS,
+                TaskType.ALLOC_FULL_GRADS,
+                TaskType.FREE_FULL_GRADS,
+            )
+            if _log_heavy_task:
+                self._log_mem(f"before_{_task_label}")
+
             if profiling:
                 _stream = self._timing_stream(node)
                 _start_evt = torch.cuda.Event(enable_timing=True)
@@ -1957,6 +2077,10 @@ def _run_free_full_params(free_node: Task) -> None:
                 self._prof_records.append((node, mb_idx, ubid, _start_evt, _end_evt, _mem_before))
                 self.logger.info(f"Rank {self.global_rank} task {node.task_type.value} (time {node.time_step}) mem before {_mem_before / 1024 ** 3:.2f} GB after {_mem_after / 1024 ** 3:.2f} GB")
 
+            if _log_heavy_task:
+                self._log_mem(f"after_{_task_label}")
+            self._nvtx_pop()
+
             if verbose_enabled:
                 tb_summary = self._summarize_task_buffer()
                 self.logger.log(VERBOSE,
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 01997de..57b4a58 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -129,6 +129,7 @@ def piper_setup(
     visualize_dag: bool = True,
     output_dir: str = "out",
     const_attrs: dict = None,
+    memory_profile: bool = False,
 ):
     """
     Compile a model with the piper backend.
@@ -221,6 +222,7 @@ def piper_setup(
         naive_gradient_sync, profile=nsight, stage_to_device=stage_to_device,
         zero_stage=zero_stage, no_nvtx=no_nvtx, pg=pg, temp_dir=temp_dir,
         use_inductor=use_inductor, ar_a2a_same_stream=ar_a2a_same_stream,
+        memory_profile=memory_profile,
     )
 
     if dp_rank == 0:
diff --git a/test/test_qwen.py b/test/test_qwen.py
index ef4ae97..85e811a 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -216,6 +216,7 @@ def main(args, pg):
         model_flops_per_token=flops_per_token,
         visualize_dag=args.save_viz,
         output_dir=args.output_dir,
+        memory_profile=args.memory_profile,
         const_attrs={"rope_cache": rope_cache},
         use_inductor=args.use_inductor,
         enable_ep=args.ep,
@@ -226,22 +227,78 @@ def main(args, pg):
 
     del x, y
 
+    actors = piper_metadata.actors
+
+    # Configure OOM snapshot destination on every actor so a CUDA OOM inside
+    # run_dag still produces a snapshot for pytorch.org/memory_viz.
+    mem_dir = args.memory_profile_dir or os.path.join(args.output_dir, "memory_snapshots")
+    os.makedirs(mem_dir, exist_ok=True)
+    ray.get([actor.set_oom_snapshot_dir.remote(mem_dir) for actor in actors.values()])
+
+    def _safe_dump_snapshots(reason: str, timeout: float = 30.0) -> None:
+        if not args.memory_profile:
+            return
+        refs_by_rank = {
+            rank: actor.dump_memory_snapshot.remote(mem_dir)
+            for rank, actor in actors.items()
+        }
+        # Use ray.wait with a timeout so a single NCCL-watchdog-hung actor
+        # cannot block the driver for the full NCCL timeout (~10 min).
+        ready, pending = ray.wait(
+            list(refs_by_rank.values()),
+            num_returns=len(refs_by_rank),
+            timeout=timeout,
+        )
+        for ref in ready:
+            try:
+                path = ray.get(ref)
+                logger.info(f"Memory snapshot saved ({reason}): {path}")
+            except Exception as exc:
+                logger.error(f"Snapshot dump failed on an actor ({reason}): {exc}")
+        if pending:
+            stuck_ranks = [
+                rank for rank, ref in refs_by_rank.items() if ref in pending
+            ]
+            logger.error(
+                f"Snapshot dump timed out after {timeout}s on ranks={stuck_ranks} "
+                f"(likely NCCL watchdog hang); skipping."
+            )
+
     logger.info(f"Running {args.warmup} warmup iterations")
-    for _ in range(args.warmup):
-        piper_exec_dag(loss_fn)
-        time.sleep(1)
+    try:
+        for _ in range(args.warmup):
+            piper_exec_dag(loss_fn)
+            time.sleep(1)
+    except Exception:
+        _safe_dump_snapshots("warmup_exception")
+        raise
 
-    actors = piper_metadata.actors
+    if args.memory_profile:
+        logger.info(f"Memory profiling enabled — snapshots will be saved under {mem_dir}")
+        try:
+            piper_exec_dag(loss_fn, log_stats=True)
+        finally:
+            _safe_dump_snapshots("profiling_iter")
 
     logger.info(f"Running {args.iters} timed iterations")
     ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
+    # Record post-reset / pre-training memory state on every actor so the log
+    # shows the baseline right before timed iterations begin.
+    try:
+        ray.get([actor._log_mem.remote("pre_timed_iters") for actor in actors.values()])
+    except Exception as log_exc:
+        logger.warning(f"pre_timed_iters _log_mem failed: {log_exc}")
     iter_times = []
-    for _ in range(args.iters):
-        start = time.perf_counter()
-        losses = piper_exec_dag(loss_fn, log_stats=True, profiling=args.profiling)
-        end = time.perf_counter()
-        iter_times.append(end - start)
-        time.sleep(1)
+    try:
+        for _ in range(args.iters):
+            start = time.perf_counter()
+            losses = piper_exec_dag(loss_fn, log_stats=True, profiling=args.profiling)
+            end = time.perf_counter()
+            iter_times.append(end - start)
+            time.sleep(1)
+    except Exception:
+        _safe_dump_snapshots("timed_iter_exception")
+        raise
 
     peak_memory_stats = ray.get(
         [actor.get_and_reset_peak_memory_stats.remote() for actor in actors.values()]
@@ -376,6 +433,18 @@ def parse_args():
         default=False,
         help="Whether to run overlap_chunks on the per-rank DAGs (default: false)",
     )
+    parser.add_argument(
+        "--memory-profile",
+        action="store_true",
+        default=False,
+        help="Record a CUDA memory snapshot for one iteration after warmup (PyTorch memory viz)",
+    )
+    parser.add_argument(
+        "--memory-profile-dir",
+        type=str,
+        default="",
+        help="Directory for rank*_memory.pickle (default: <output-dir>/memory_snapshots)",
+    )
     return parser.parse_args()
 
 

From 05c196c1b9bb62602938329ea124034fcedf365b Mon Sep 17 00:00:00 2001
From: Yi Pan <conlesspan@outlook.com>
Date: Thu, 23 Apr 2026 23:44:41 +0800
Subject: [PATCH 095/100] enable pp to be the outer dimension

---
 scripts/run-qwen-ec2.sh  | 16 +++++++++++++++-
 src/piper_actor.py       |  8 +++++++-
 src/piper_compile.py     |  3 ++-
 src/piper_coordinator.py |  9 +++++++--
 test/test_qwen.py        | 36 ++++++++++++++++++++++++++++++++++--
 5 files changed, 65 insertions(+), 7 deletions(-)

diff --git a/scripts/run-qwen-ec2.sh b/scripts/run-qwen-ec2.sh
index 2a9fddc..de9359d 100755
--- a/scripts/run-qwen-ec2.sh
+++ b/scripts/run-qwen-ec2.sh
@@ -25,6 +25,11 @@
 #                    Run overlap_zero_ops on the per-rank DAGs (default: off)
 #   --overlap-chunks / --no-overlap-chunks
 #                    Run overlap_chunks on the per-rank DAGs (default: off)
+#   --pp-outer / --no-pp-outer
+#                    Use PP as the outer placement dim (one pipeline stage per
+#                    node, all DP replicas colocated). Makes per-stage EP/DP
+#                    collectives intra-node. Requires dp >= pp for sensible
+#                    packing. Default: off (legacy PP-inner layout).
 #   --warmup N       Warmup iterations (default: $WARMUP or 2)
 #   --iters N        Training iterations (default: $ITERS or 5)
 #   --ray-port PORT  Ray port (default: $RAY_PORT or 6379)
@@ -60,6 +65,7 @@ GRADIENT_ACCUMULATION_ARG="--gradient-accumulation"
 AR_A2A_SAME_STREAM_ARG="--no-ar-a2a-same-stream"
 OVERLAP_ZERO_OPS_ARG="--no-overlap-zero-ops"
 OVERLAP_CHUNKS_ARG="--no-overlap-chunks"
+PP_OUTER_ARG="--no-pp-outer"
 MEMORY_PROFILE=false
 MEMORY_PROFILE_DIR=""
 MEMORY_PROFILE_ARG=""
@@ -87,6 +93,8 @@ while [[ $# -gt 0 ]]; do
         --no-overlap-zero-ops) OVERLAP_ZERO_OPS_ARG="--no-overlap-zero-ops"; shift ;;
         --overlap-chunks) OVERLAP_CHUNKS_ARG="--overlap-chunks"; shift ;;
         --no-overlap-chunks) OVERLAP_CHUNKS_ARG="--no-overlap-chunks"; shift ;;
+        --pp-outer) PP_OUTER_ARG="--pp-outer"; shift ;;
+        --no-pp-outer) PP_OUTER_ARG="--no-pp-outer"; shift ;;
         --warmup)      WARMUP="$2";      shift 2 ;;
         --iters)       ITERS="$2";       shift 2 ;;
         --ray-port)    RAY_PORT="$2";    shift 2 ;;
@@ -197,12 +205,17 @@ if [[ "$OVERLAP_CHUNKS_ARG" == "--overlap-chunks" ]]; then
     OVERLAP_CHUNKS_LABEL=1
 fi
 
+PP_OUTER_LABEL=0
+if [[ "$PP_OUTER_ARG" == "--pp-outer" ]]; then
+    PP_OUTER_LABEL=1
+fi
+
 EP_LABEL=0
 if $EP; then
     EP_LABEL=1
 fi
 
-EXPERIMENT_NAME="qwen${MODEL}-sched_${SCHEDULE}-pp${PP}-dp${DP}-ep${EP_LABEL}-zero${ZERO_STAGE}${BUCKET_SIZE_LABEL}${NSIGHT_LABEL}-bs${BATCH_SIZE}-sl${SEQ_LEN}-mbs${MBS}-ga${GRADIENT_ACCUMULATION_LABEL}-aras${AR_A2A_SAME_STREAM_LABEL}-ozo${OVERLAP_ZERO_OPS_LABEL}-och${OVERLAP_CHUNKS_LABEL}"
+EXPERIMENT_NAME="qwen${MODEL}-sched_${SCHEDULE}-pp${PP}-dp${DP}-ep${EP_LABEL}-zero${ZERO_STAGE}${BUCKET_SIZE_LABEL}${NSIGHT_LABEL}-bs${BATCH_SIZE}-sl${SEQ_LEN}-mbs${MBS}-ga${GRADIENT_ACCUMULATION_LABEL}-aras${AR_A2A_SAME_STREAM_LABEL}-ozo${OVERLAP_ZERO_OPS_LABEL}-och${OVERLAP_CHUNKS_LABEL}-ppo${PP_OUTER_LABEL}"
 
 if $LOG_TO_FILE; then
     mkdir -p "$OUT_DIR"
@@ -276,6 +289,7 @@ run_head_ssh \
      $AR_A2A_SAME_STREAM_ARG \
      $OVERLAP_ZERO_OPS_ARG \
      $OVERLAP_CHUNKS_ARG \
+     $PP_OUTER_ARG \
      --warmup $WARMUP \
      --iters $ITERS \
      --address $HEAD_PRIVATE_IP \
diff --git a/src/piper_actor.py b/src/piper_actor.py
index cd9cdd6..bc7c0ef 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -54,6 +54,7 @@ def _create_actors(
     use_inductor: bool = False,
     ar_a2a_same_stream: bool = False,
     memory_profile: bool = False,
+    pp_outer: bool = False,
 ):
     dp_rank = int(os.environ["PIPER_DP_RANK"])
     world_size = int(os.environ["PIPER_WORLD_SIZE"])
@@ -77,12 +78,17 @@ def _create_actors(
                 **({"TMPDIR": temp_dir} if (profile and temp_dir) else {}),
             }
         }
+        # When pp_outer=True, one bundle corresponds to one pipeline stage and
+        # holds all DP replicas for that stage (placement group shape is
+        # [{"GPU": dp}] * pp). Otherwise one bundle is one DP replica holding
+        # all PP ranks (shape is [{"GPU": pp}] * dp).
+        bundle_index = pp_rank if pp_outer else dp_rank
         actor = PiperActor.options(
             num_gpus=0.6,
             runtime_env={**nsight_env, **nccl_env},
             scheduling_strategy=PlacementGroupSchedulingStrategy(
                 placement_group=pg,
-                placement_group_bundle_index=dp_rank,
+                placement_group_bundle_index=bundle_index,
             ),
         ).remote(
             pp_rank,
diff --git a/src/piper_compile.py b/src/piper_compile.py
index 57b4a58..ccf704e 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -130,6 +130,7 @@ def piper_setup(
     output_dir: str = "out",
     const_attrs: dict = None,
     memory_profile: bool = False,
+    pp_outer: bool = False,
 ):
     """
     Compile a model with the piper backend.
@@ -222,7 +223,7 @@ def piper_setup(
         naive_gradient_sync, profile=nsight, stage_to_device=stage_to_device,
         zero_stage=zero_stage, no_nvtx=no_nvtx, pg=pg, temp_dir=temp_dir,
         use_inductor=use_inductor, ar_a2a_same_stream=ar_a2a_same_stream,
-        memory_profile=memory_profile,
+        memory_profile=memory_profile, pp_outer=pp_outer,
     )
 
     if dp_rank == 0:
diff --git a/src/piper_coordinator.py b/src/piper_coordinator.py
index 9993b4b..ee3f391 100644
--- a/src/piper_coordinator.py
+++ b/src/piper_coordinator.py
@@ -28,10 +28,13 @@ def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, training_func: Callab
 class PiperProgramCoordinator:
     """Central Actor that Coordinates all the DP replicas of a single pipeline"""
 
-    def __init__(self, dp_degree, pp_degree):
+    def __init__(self, dp_degree, pp_degree, pp_outer: bool = False):
         self.dp_degree = dp_degree
         self.pp_degree = pp_degree
         self.world_size = dp_degree * pp_degree
+        # pp_outer=True means one PP stage per node (placement bundles keyed by
+        # pp_rank). In that mode DP drivers are spread across the pp bundles.
+        self.pp_outer = pp_outer
 
     def run_program(self, training_func: Callable, pg, *args, **kwargs):
         from .piper_compile import _RANK0_ADDR_ACTOR, _COMPILED_DATA_ACTOR
@@ -51,7 +54,9 @@ def run_program(self, training_func: Callable, pg, *args, **kwargs):
                 run_dp_rank.options(
                     scheduling_strategy=PlacementGroupSchedulingStrategy(
                         placement_group=pg,
-                        placement_group_bundle_index=dp_rank,
+                        placement_group_bundle_index=(
+                            dp_rank % self.pp_degree if self.pp_outer else dp_rank
+                        ),
                     )
                 ).remote(
                     dp_rank,
diff --git a/test/test_qwen.py b/test/test_qwen.py
index 85e811a..b090cbf 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -223,6 +223,7 @@ def main(args, pg):
         ar_a2a_same_stream=args.ar_a2a_same_stream,
         overlap_zero_ops=args.overlap_zero_ops,
         overlap_chunks=args.overlap_chunks,
+        pp_outer=args.pp_outer,
     )
 
     del x, y
@@ -433,6 +434,17 @@ def parse_args():
         default=False,
         help="Whether to run overlap_chunks on the per-rank DAGs (default: false)",
     )
+    parser.add_argument(
+        "--pp-outer",
+        action=argparse.BooleanOptionalAction,
+        default=False,
+        help=(
+            "Use PP as the outer placement dim (one pipeline stage per node, "
+            "all DP replicas for that stage colocated). Makes per-stage EP/DP "
+            "collectives intra-node at the cost of inter-node PP P2P. "
+            "Default: false (one DP replica per node, PP inner)."
+        ),
+    )
     parser.add_argument(
         "--memory-profile",
         action="store_true",
@@ -477,10 +489,30 @@ def parse_args():
             include_dashboard=False,
             _temp_dir=args.temp_dir,
         )
-    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="SPREAD")
+    if args.pp_outer:
+        # PP is the outer placement dim: one bundle per pipeline stage, pinned
+        # to its own node via STRICT_SPREAD. Each bundle reserves dp GPUs so
+        # all DP replicas at a given stage colocate on one node, making
+        # per-stage EP AllToAll and DP AllReduce intra-node.
+        # CPU per bundle = dp (one per actor; Ray's actor scheduling default is
+        # num_cpus=1) + ceil(dp/pp) slack for the run_dp_rank driver tasks that
+        # land in this bundle (see PiperProgramCoordinator.run_program).
+        drivers_per_bundle = (args.dp + args.pp - 1) // args.pp
+        pg = placement_group(
+            [{"CPU": args.dp + drivers_per_bundle, "GPU": args.dp}] * args.pp,
+            strategy="STRICT_SPREAD",
+        )
+    else:
+        # Legacy layout: one bundle per DP replica holding all PP ranks.
+        pg = placement_group(
+            [{"CPU": args.pp, "GPU": args.pp}] * args.dp,
+            strategy="SPREAD",
+        )
     ray.get(pg.ready(), timeout=600)
     logger.info(placement_group_table(pg))
-    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
+    piper_coordinator = PiperProgramCoordinator.remote(
+        pp_degree=args.pp, dp_degree=args.dp, pp_outer=args.pp_outer,
+    )
     handles = piper_coordinator.run_program.remote(main, pg, args, pg)
     dp_metrics = ray.get(handles)
     open(metrics_path, "w", encoding="utf-8").close()

From 18a7f07f9690976fb764ecf8419868c2c1a79646 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Thu, 23 Apr 2026 16:35:56 +0000
Subject: [PATCH 096/100] add nsight backtrace to runtime env

---
 src/piper_actor.py | 27 +++++++++++++++------------
 test/test_qwen.py  | 42 +++++++++++++++---------------------------
 2 files changed, 30 insertions(+), 39 deletions(-)

diff --git a/src/piper_actor.py b/src/piper_actor.py
index cd9cdd6..994544d 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -66,13 +66,16 @@ def _create_actors(
         global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
         nsight_env = {"nsight": {
             "t": "cuda,cudnn,cublas,nvtx",
-            # "cuda-event-trace": "false",
+            "sample": "process-tree",
+            "backtrace": "dwarf",
+            "cudabacktrace": "sync:0,memory:0",
+            "python-backtrace": "cuda",
             "stop-on-exit": "true",
         }} if profile else {}
         nccl_env = {
             "env_vars": {
-                # "NCCL_SOCKET_IFNAME": "ens32",
-                # "GLOO_SOCKET_IFNAME": "ens32",
+                "NCCL_SOCKET_IFNAME": "ens32",
+                "GLOO_SOCKET_IFNAME": "ens32",
                 "NCCL_DEBUG": "WARN",
                 **({"TMPDIR": temp_dir} if (profile and temp_dir) else {}),
             }
@@ -322,7 +325,7 @@ def _log_mem(self, label: str, extra: str = "") -> None:
             active = stats.get("active_bytes.all.current", 0) / (1024 ** 3)
             retries = stats.get("num_alloc_retries", 0)
             ooms = stats.get("num_ooms", 0)
-            self.logger.info(
+            self.logger.debug(
                 f"[mem] rank={self.global_rank} stage={getattr(self, 'pp_rank', '?')} "
                 f"label={label} allocated={alloc:.3f}GB reserved={reserv:.3f}GB "
                 f"max_alloc={max_a:.3f}GB max_res={max_r:.3f}GB active={active:.3f}GB "
@@ -1054,7 +1057,7 @@ def _bucket_forward_runner(
         # Keep first GraphModule for compatibility with external inspection tools.
         self.graph_modules[stage_id] = first_gm
 
-        self._log_mem(f"post_load_stage_{stage_id}")
+        # self._log_mem(f"post_load_stage_{stage_id}")
 
     # -----------------------------------------------------------------------
     # DAG-based execution
@@ -1410,7 +1413,7 @@ def run_dag(self, loss_fn=None, profiling: bool = False):
         self._iter_counter = iter_idx + 1
         self._nvtx_push(f"iter_{iter_idx}_rank_{self.global_rank}")
         try:
-            self._log_mem(f"iter_{iter_idx}_start")
+            # self._log_mem(f"iter_{iter_idx}_start")
             return self._run_dag_body(loss_fn=loss_fn, profiling=profiling)
         except torch.cuda.OutOfMemoryError as oom_exc:
             oom_dir = getattr(self, "_oom_snapshot_dir", None) or "/tmp/piper_oom_snapshots"
@@ -1433,7 +1436,7 @@ def run_dag(self, loss_fn=None, profiling: bool = False):
                 torch.cuda.memory._record_memory_history(enabled=None)
             except Exception:
                 pass
-            self._log_mem(f"iter_{iter_idx}_oom")
+            # self._log_mem(f"iter_{iter_idx}_oom")
             raise
         finally:
             self._nvtx_pop()
@@ -1569,8 +1572,8 @@ def _run_free_full_params(free_node: Task) -> None:
                 TaskType.ALLOC_FULL_GRADS,
                 TaskType.FREE_FULL_GRADS,
             )
-            if _log_heavy_task:
-                self._log_mem(f"before_{_task_label}")
+            # if _log_heavy_task:
+                # self._log_mem(f"before_{_task_label}")
 
             if profiling:
                 _stream = self._timing_stream(node)
@@ -2077,8 +2080,8 @@ def _run_free_full_params(free_node: Task) -> None:
                 self._prof_records.append((node, mb_idx, ubid, _start_evt, _end_evt, _mem_before))
                 self.logger.info(f"Rank {self.global_rank} task {node.task_type.value} (time {node.time_step}) mem before {_mem_before / 1024 ** 3:.2f} GB after {_mem_after / 1024 ** 3:.2f} GB")
 
-            if _log_heavy_task:
-                self._log_mem(f"after_{_task_label}")
+            # if _log_heavy_task:
+                # self._log_mem(f"after_{_task_label}")
             self._nvtx_pop()
 
             if verbose_enabled:
@@ -2166,7 +2169,7 @@ def _exec_a2a(self, input_tensor: torch.Tensor) -> torch.Tensor:
         Used for both FWD_A2A and BWD_A2A — the operation is symmetric.
         Stream waits and event recording are handled by run_dag.
         """
-        output_buf = torch.empty_like(input_tensor)
+        output_buf = torch.empty_like(input_tensor, device=self.device)
         with torch.cuda.stream(self.a2a_stream):
             dist.all_to_all_single(output_buf, input_tensor, group=self.ep_group)
         return output_buf
diff --git a/test/test_qwen.py b/test/test_qwen.py
index 85e811a..a9fbc1f 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -265,40 +265,28 @@ def _safe_dump_snapshots(reason: str, timeout: float = 30.0) -> None:
             )
 
     logger.info(f"Running {args.warmup} warmup iterations")
-    try:
-        for _ in range(args.warmup):
-            piper_exec_dag(loss_fn)
-            time.sleep(1)
-    except Exception:
-        _safe_dump_snapshots("warmup_exception")
-        raise
+    for _ in range(args.warmup):
+        piper_exec_dag(loss_fn)
+        time.sleep(1)
 
-    if args.memory_profile:
-        logger.info(f"Memory profiling enabled — snapshots will be saved under {mem_dir}")
-        try:
-            piper_exec_dag(loss_fn, log_stats=True)
-        finally:
-            _safe_dump_snapshots("profiling_iter")
+    # if args.memory_profile:
+    #     logger.info(f"Memory profiling enabled — snapshots will be saved under {mem_dir}")
+    #     try:
+    #         piper_exec_dag(loss_fn, log_stats=True)
+    #     finally:
+    #         _safe_dump_snapshots("profiling_iter")
 
     logger.info(f"Running {args.iters} timed iterations")
     ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
     # Record post-reset / pre-training memory state on every actor so the log
     # shows the baseline right before timed iterations begin.
-    try:
-        ray.get([actor._log_mem.remote("pre_timed_iters") for actor in actors.values()])
-    except Exception as log_exc:
-        logger.warning(f"pre_timed_iters _log_mem failed: {log_exc}")
     iter_times = []
-    try:
-        for _ in range(args.iters):
-            start = time.perf_counter()
-            losses = piper_exec_dag(loss_fn, log_stats=True, profiling=args.profiling)
-            end = time.perf_counter()
-            iter_times.append(end - start)
-            time.sleep(1)
-    except Exception:
-        _safe_dump_snapshots("timed_iter_exception")
-        raise
+    for _ in range(args.iters):
+        start = time.perf_counter()
+        losses = piper_exec_dag(loss_fn, log_stats=True, profiling=args.profiling)
+        end = time.perf_counter()
+        iter_times.append(end - start)
+        time.sleep(1)
 
     peak_memory_stats = ray.get(
         [actor.get_and_reset_peak_memory_stats.remote() for actor in actors.values()]

From 303c7ed319ae4d8f91eef2bbc91e6c1fa7490f7e Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Mon, 1 Jun 2026 01:00:06 +0000
Subject: [PATCH 097/100] debug schedule directives

---
 .gitignore                                    |    3 +-
 README.md                                     |    8 +-
 check_gpus.sh                                 |   26 -
 logs/piper_zero0_20260309_220353.log          |   49 -
 logs/piper_zero1_20260309_145713.log          |   35 -
 logs/piper_zero1_20260309_215755.log          |   46 -
 logs/piper_zero1_20260309_224153.log          |   77 -
 logs/piper_zero1_20260311_173310.log          |   61 -
 logs/piper_zero2_20260311_173556.log          |   61 -
 src/piper.py                                  | 3268 ++++++++++++++---
 src/piper_actor.py                            | 2518 +++++--------
 src/piper_compile.py                          |  345 +-
 src/piper_coordinator.py                      |   35 +-
 src/piper_exec.py                             |  662 +---
 src/piper_graph_transform.py                  | 2955 ++-------------
 src/piper_schedule.py                         |  127 +
 src/piper_utils.py                            |  404 +-
 test/all2all_bench.py                         |  126 -
 test/models/qwen3.py                          |   63 +-
 test/print_schedules.py                       |   38 -
 test/schedule_helpers.py                      | 1952 ----------
 test/schedules/build_order_directives.py      |  638 ++++
 test/schedules/build_schedule.py              |   16 +
 test/schedules/llama_pp.json                  |   14 +
 test/schedules/qwen_pp2.json                  |   14 +
 test/schedules/qwen_pp2_dp2_ep2.json          |   38 +
 .../schedules/qwen_pp2_dp2_ep2_dualpipev.json |   74 +
 .../qwen_pp2_dp2_ep2_interleaved.json         |   50 +
 test/schedules/qwen_pp2_interleaved.json      |   26 +
 test/schedules/qwen_pp4.json                  |   26 +
 test/schedules/qwen_pp_dp.json                |   26 +
 test/schedules/qwen_pp_dp_ep_overlap.json     |   52 +
 test/schedules/qwen_pp_dp_ep_zero2.json       |   40 +
 test/schedules/qwen_pp_dp_ep_zero3.json       |   42 +
 test/schedules/qwen_pp_dp_zero2.json          |   28 +
 test/schedules/qwen_pp_dp_zero3.json          |   34 +
 test/test_backward_split.py                   |  144 -
 {src => test}/test_graph_transform.py         |    4 +-
 test/test_harness.py                          |  634 ++++
 test/test_llama.py                            |  394 +-
 test/test_order_directive.py                  |  174 +
 test/test_qwen.py                             |  427 +--
 test/test_qwen_shubham.py                     |  262 --
 test/test_tracing.py                          |  290 --
 44 files changed, 6539 insertions(+), 9767 deletions(-)
 delete mode 100644 check_gpus.sh
 delete mode 100644 logs/piper_zero0_20260309_220353.log
 delete mode 100644 logs/piper_zero1_20260309_145713.log
 delete mode 100644 logs/piper_zero1_20260309_215755.log
 delete mode 100644 logs/piper_zero1_20260309_224153.log
 delete mode 100644 logs/piper_zero1_20260311_173310.log
 delete mode 100644 logs/piper_zero2_20260311_173556.log
 create mode 100644 src/piper_schedule.py
 delete mode 100644 test/all2all_bench.py
 delete mode 100644 test/print_schedules.py
 delete mode 100644 test/schedule_helpers.py
 create mode 100644 test/schedules/build_order_directives.py
 create mode 100644 test/schedules/build_schedule.py
 create mode 100644 test/schedules/llama_pp.json
 create mode 100644 test/schedules/qwen_pp2.json
 create mode 100644 test/schedules/qwen_pp2_dp2_ep2.json
 create mode 100644 test/schedules/qwen_pp2_dp2_ep2_dualpipev.json
 create mode 100644 test/schedules/qwen_pp2_dp2_ep2_interleaved.json
 create mode 100644 test/schedules/qwen_pp2_interleaved.json
 create mode 100644 test/schedules/qwen_pp4.json
 create mode 100644 test/schedules/qwen_pp_dp.json
 create mode 100644 test/schedules/qwen_pp_dp_ep_overlap.json
 create mode 100644 test/schedules/qwen_pp_dp_ep_zero2.json
 create mode 100644 test/schedules/qwen_pp_dp_ep_zero3.json
 create mode 100644 test/schedules/qwen_pp_dp_zero2.json
 create mode 100644 test/schedules/qwen_pp_dp_zero3.json
 delete mode 100644 test/test_backward_split.py
 rename {src => test}/test_graph_transform.py (98%)
 create mode 100644 test/test_harness.py
 create mode 100644 test/test_order_directive.py
 delete mode 100644 test/test_qwen_shubham.py
 delete mode 100644 test/test_tracing.py

diff --git a/.gitignore b/.gitignore
index 178f010..6b55f73 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,4 +10,5 @@ figs/
 logs/
 .vscode/
 e2e-eval/
-
+.codex
+ec2-trust-policy.json
diff --git a/README.md b/README.md
index be7fe34..653920b 100644
--- a/README.md
+++ b/README.md
@@ -43,11 +43,7 @@ else:
 ```
 
 ## Training Llama in Piper
-The llama test program `test/test_llama.py` supports GPipe, 1F1B and interleaved 1F1B schedules for 2 or 4 devices.
-The `test/models/llama.py` file has example `forward` methods for one stage, two stage, and four stage partitions. 
-Ensure that the correct `forward` method is uncommented for the desired schedule (e.g. two stage for 1F1B on 2 devices, four stage for interleaved 1F1B on two devices). 
-DP training can also be turned on with the `dp_degree` flag.
-Run the Llama test program for the 1F1B schedule for two devices:
+The llama test program `test/test_llama.py` uses JSON schedule directives.
 ```
-python3 -m test.test_llama --model LLAMA_DEBUG --schedule 1f1b --num_stages 2 --pp_degree 2 --dp_degree 1
+python3 -m test.test_llama --model debug --schedule-directives-file test/schedules/llama_pp_only.json
 ```
diff --git a/check_gpus.sh b/check_gpus.sh
deleted file mode 100644
index 67c0906..0000000
--- a/check_gpus.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-export SSH_KEY=/home/shubham/.ssh/mfris-us-west-2.pem
-export HEAD_PUBLIC_IP=100.22.89.85
-export HEAD_PRIVATE_IP=10.0.79.139
-export WORKER1_PRIVATE_IP=10.0.71.178
-export WORKER2_PRIVATE_IP=10.0.70.34
-export WORKER3_PRIVATE_IP=10.0.69.189
-
-for ENTRY in "head:$HEAD_PUBLIC_IP" \
-             "worker1:$WORKER1_PRIVATE_IP" \
-             "worker2:$WORKER2_PRIVATE_IP" \
-             "worker3:$WORKER3_PRIVATE_IP"; do
-  LABEL=${ENTRY%%:*}; HOST=${ENTRY#*:}
-
-  if [[ "$LABEL" == "head" ]]; then
-    SSH_OPTS=(-i "$SSH_KEY" -o StrictHostKeyChecking=no)
-  else
-    SSH_OPTS=(-i "$SSH_KEY" -o StrictHostKeyChecking=no \
-              -o "ProxyCommand=ssh -i $SSH_KEY -o StrictHostKeyChecking=no -W %h:%p ubuntu@$HEAD_PUBLIC_IP")
-  fi
-
-  echo ""
-  echo "============================================================"
-  echo "=== $LABEL ($HOST) ==="
-  echo "============================================================"
-  ssh "${SSH_OPTS[@]}" ubuntu@$HOST "echo 'Hostname: \$(hostname)'; nvidia-smi"
-done
diff --git a/logs/piper_zero0_20260309_220353.log b/logs/piper_zero0_20260309_220353.log
deleted file mode 100644
index 1ac61fd..0000000
--- a/logs/piper_zero0_20260309_220353.log
+++ /dev/null
@@ -1,49 +0,0 @@
-2026-03-09 22:04:05,239	INFO worker.py:2007 -- Started a local Ray instance.
-/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
-  warnings.warn(
-(raylet) [2026-03-09 22:04:15,210 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39113 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2199411) Namespace(model='3b', schedule='interleaved-1f1b', dp=1, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=0)
-(raylet) [2026-03-09 22:04:25,223 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.3912 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 22:04:35,236 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39117 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 22:04:45,248 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39119 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2199411) Schedule:
-(run_dp_rank pid=2199411) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
-(run_dp_rank pid=2199411)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
-(raylet) [2026-03-09 22:04:55,261 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.3911 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(PiperActor pid=2199505) 2026-03-09 22:04:57,202 - piper_actor - INFO - Initializing Ray actor 1 GPU 2
-(PiperActor pid=2199385) 2026-03-09 22:04:57,312 - piper_actor - INFO - Initializing Ray actor 0 GPU 1
-(raylet) [2026-03-09 22:05:05,273 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39103 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2199411) 2026-03-09 22:05:10,435 - piper_compile - INFO - DP rank 1 compiling...
-(raylet) [2026-03-09 22:05:15,286 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39008 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2199411) Stage 0 time measured on controller: 139.18 ms
-(raylet) [2026-03-09 22:05:25,303 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39006 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 22:05:35,317 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39005 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2199411) Stage 1 time measured on controller: 139.23 ms
-(raylet) [2026-03-09 22:05:45,352 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39004 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2199411) Stage 2 time measured on controller: 139.14 ms
-(raylet) [2026-03-09 22:05:55,365 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39005 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2199411) Stage 3 time measured on controller: 130.44 ms
-(raylet) [2026-03-09 22:06:05,381 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.39001 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2199411) 2026-03-09 22:06:11,892 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
-(run_dp_rank pid=2199411) 2026-03-09 22:06:11,892 - piper_compile - INFO - DP rank 1 done.
-(run_dp_rank pid=2199411) Running 5 warmup iterations...
-(PiperActor pid=2199505) [rank1]:[W309 22:06:12.849148849 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=2199385) [rank0]:[W309 22:06:12.238779712 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=2199385) [rank0]:[W309 22:06:14.032574556 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=2199505) [rank1]:[W309 22:06:14.450212103 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(raylet) [2026-03-09 22:06:15,403 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.38807 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2199411) Warmup 0 completed
-(run_dp_rank pid=2199411) Warmup 1 completed
-(raylet) [2026-03-09 22:06:25,422 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.38807 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2199411) Warmup 2 completed
-(run_dp_rank pid=2199411) Warmup 3 completed
-(raylet) [2026-03-09 22:06:35,435 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.38807 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2199411) Warmup 4 completed
-(run_dp_rank pid=2199411) Running 10 timed iterations...
-(raylet) [2026-03-09 22:06:45,449 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.38803 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 22:06:55,462 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.38803 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 22:07:05,475 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.38792 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 22:07:15,489 E 2199192 2199240] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-03-59_335754_2198288 is over 95% full, available space: 1.38627 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2199411) rank 0 iter time= 4.137 ± 0.176 s (10 samples)
-(run_dp_rank pid=2199411) rank 0 throughput= 3959.889 tokens/sec (10 samples)
-(run_dp_rank pid=2199411) Ray timeline saved to: out/3b-pp2-dp1-interleaved-1f1b.json
diff --git a/logs/piper_zero1_20260309_145713.log b/logs/piper_zero1_20260309_145713.log
deleted file mode 100644
index 225cf65..0000000
--- a/logs/piper_zero1_20260309_145713.log
+++ /dev/null
@@ -1,35 +0,0 @@
-2026-03-09 14:57:25,301	INFO worker.py:2007 -- Started a local Ray instance.
-/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
-  warnings.warn(
-(raylet) [2026-03-09 14:57:34,292 E 2687383 2687424] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_14-57-18_705671_2685485 is over 95% full, available space: 1.66463 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2687594) Namespace(model='debug', schedule='interleaved-1f1b', dp=1, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=1)
-(run_dp_rank pid=2687594) Schedule:
-(run_dp_rank pid=2687594) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
-(run_dp_rank pid=2687594)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
-(PiperActor pid=2687577) 2026-03-09 14:57:43,760 - piper_actor - INFO - Initializing Ray actor 1 GPU 2
-(PiperActor pid=2687579) 2026-03-09 14:57:43,832 - piper_actor - INFO - Initializing Ray actor 0 GPU 1
-(raylet) [2026-03-09 14:57:44,307 E 2687383 2687424] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_14-57-18_705671_2685485 is over 95% full, available space: 1.66446 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2687594) 2026-03-09 14:57:46,381 - piper_compile - INFO - DP rank 1 compiling...
-(run_dp_rank pid=2687594) Stage 0 time measured on controller: 4.06 ms
-(run_dp_rank pid=2687594) Stage 1 time measured on controller: 4.02 ms
-(run_dp_rank pid=2687594) Stage 2 time measured on controller: 2.70 ms
-(run_dp_rank pid=2687594) Stage 3 time measured on controller: 0.19 ms
-(run_dp_rank pid=2687594) 2026-03-09 14:57:51,188 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
-(run_dp_rank pid=2687594) 2026-03-09 14:57:51,188 - piper_compile - INFO - DP rank 1 done.
-(run_dp_rank pid=2687594) Running 5 warmup iterations...
-(PiperActor pid=2687577) [rank1]:[W309 14:57:51.031928771 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=2687579) [rank0]:[W309 14:57:51.293357587 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=2687579) [rank0]:[W309 14:57:53.799416879 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=2687577) [rank1]:[W309 14:57:53.989258762 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=2687579) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
-(PiperActor pid=2687579)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
-(raylet) [2026-03-09 14:57:54,325 E 2687383 2687424] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_14-57-18_705671_2685485 is over 95% full, available space: 1.66444 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2687594) Warmup 0 completed
-(run_dp_rank pid=2687594) Warmup 1 completed
-(run_dp_rank pid=2687594) Warmup 2 completed
-(run_dp_rank pid=2687594) Warmup 3 completed
-(run_dp_rank pid=2687594) Warmup 4 completed
-(run_dp_rank pid=2687594) Running 10 timed iterations...
-(run_dp_rank pid=2687594) rank 0 iter time= 0.141 ± 0.004 s (10 samples)
-(run_dp_rank pid=2687594) rank 0 throughput= 116525.096 tokens/sec (10 samples)
-(run_dp_rank pid=2687594) Ray timeline saved to: out/debug-pp2-dp1-interleaved-1f1b.json
diff --git a/logs/piper_zero1_20260309_215755.log b/logs/piper_zero1_20260309_215755.log
deleted file mode 100644
index 00fd4ad..0000000
--- a/logs/piper_zero1_20260309_215755.log
+++ /dev/null
@@ -1,46 +0,0 @@
-2026-03-09 21:58:07,111	INFO worker.py:2007 -- Started a local Ray instance.
-/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
-  warnings.warn(
-(raylet) [2026-03-09 21:58:17,050 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39233 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2104175) Namespace(model='3b', schedule='interleaved-1f1b', dp=1, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=1)
-(raylet) [2026-03-09 21:58:27,062 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39219 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 21:58:37,074 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39219 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 21:58:47,089 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39672 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2104175) Schedule:
-(run_dp_rank pid=2104175) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
-(run_dp_rank pid=2104175)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
-(PiperActor pid=2103889) 2026-03-09 21:58:54,673 - piper_actor - INFO - Initializing Ray actor 0 GPU 1
-(PiperActor pid=2104562) 2026-03-09 21:58:54,700 - piper_actor - INFO - Initializing Ray actor 1 GPU 2
-(raylet) [2026-03-09 21:58:57,114 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39722 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2104175) 2026-03-09 21:59:02,110 - piper_compile - INFO - DP rank 1 compiling...
-(raylet) [2026-03-09 21:59:07,143 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39717 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2104175) Stage 0 time measured on controller: 139.14 ms
-(raylet) [2026-03-09 21:59:17,156 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39128 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2104175) Stage 1 time measured on controller: 139.16 ms
-(raylet) [2026-03-09 21:59:27,168 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39614 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2104175) Stage 2 time measured on controller: 139.19 ms
-(run_dp_rank pid=2104175) Stage 3 time measured on controller: 130.44 ms
-(raylet) [2026-03-09 21:59:37,181 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39616 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2104175) 2026-03-09 21:59:40,227 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
-(run_dp_rank pid=2104175) 2026-03-09 21:59:40,228 - piper_compile - INFO - DP rank 1 done.
-(run_dp_rank pid=2104175) Running 5 warmup iterations...
-(PiperActor pid=2104562) [rank1]:[W309 21:59:40.080240337 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=2103889) [rank0]:[W309 21:59:41.541619488 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=2103889) [rank0]:[W309 21:59:42.156095816 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=2104562) [rank1]:[W309 21:59:42.264797203 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(raylet) [2026-03-09 21:59:47,195 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39615 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2104175) Warmup 0 completed
-(run_dp_rank pid=2104175) Warmup 1 completed
-(run_dp_rank pid=2104175) Warmup 2 completed
-(raylet) [2026-03-09 21:59:57,209 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39614 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2104175) Warmup 3 completed
-(run_dp_rank pid=2104175) Warmup 4 completed
-(run_dp_rank pid=2104175) Running 10 timed iterations...
-(raylet) [2026-03-09 22:00:07,223 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39604 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 22:00:17,236 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39409 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 22:00:27,249 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.3941 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 22:00:37,261 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.3941 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2104175) rank 0 iter time= 4.044 ± 0.003 s (10 samples)
-(run_dp_rank pid=2104175) rank 0 throughput= 4051.131 tokens/sec (10 samples)
-(run_dp_rank pid=2104175) Ray timeline saved to: out/3b-pp2-dp1-interleaved-1f1b.json
-(raylet) [2026-03-09 22:00:47,277 E 2103369 2103496] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_21-58-01_435280_2082542 is over 95% full, available space: 1.39404 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
diff --git a/logs/piper_zero1_20260309_224153.log b/logs/piper_zero1_20260309_224153.log
deleted file mode 100644
index 319aeeb..0000000
--- a/logs/piper_zero1_20260309_224153.log
+++ /dev/null
@@ -1,77 +0,0 @@
-2026-03-09 22:42:05,154	INFO worker.py:2007 -- Started a local Ray instance.
-/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
-  warnings.warn(
-(raylet) [2026-03-09 22:42:15,095 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47324 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2875347) Namespace(model='3b', schedule='interleaved-1f1b', dp=2, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=1)
-(run_dp_rank pid=2875351) Namespace(model='3b', schedule='interleaved-1f1b', dp=2, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=1)
-(raylet) [2026-03-09 22:42:25,109 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47821 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 22:42:35,124 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47819 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 22:42:45,139 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.4782 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2875351) Schedule:
-(run_dp_rank pid=2875351) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
-(run_dp_rank pid=2875351)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
-(run_dp_rank pid=2875347) Schedule:
-(run_dp_rank pid=2875347) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
-(run_dp_rank pid=2875347)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
-(PiperActor pid=2875383) 2026-03-09 22:42:52,618 - piper_actor - INFO - Initializing Ray actor 0 GPU 1
-(PiperActor pid=2875392) 2026-03-09 22:42:52,713 - piper_actor - INFO - Initializing Ray actor 1 GPU 2
-(PiperActor pid=2875350) 2026-03-09 22:42:52,729 - piper_actor - INFO - Initializing Ray actor 3 GPU 4
-(PiperActor pid=2875389) 2026-03-09 22:42:52,729 - piper_actor - INFO - Initializing Ray actor 2 GPU 3
-(raylet) [2026-03-09 22:42:55,154 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47817 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2875347) 2026-03-09 22:43:04,908 - piper_compile - INFO - DP rank 2 compiling...
-(run_dp_rank pid=2875351) 2026-03-09 22:43:04,907 - piper_compile - INFO - DP rank 1 compiling...
-(raylet) [2026-03-09 22:43:05,170 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47808 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2875347) Stage 0 time measured on controller: 307.10 ms
-(run_dp_rank pid=2875351) Stage 0 time measured on controller: 308.00 ms
-(raylet) [2026-03-09 22:43:15,185 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47709 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2875347) Stage 1 time measured on controller: 139.19 ms
-(run_dp_rank pid=2875351) Stage 1 time measured on controller: 139.20 ms
-(raylet) [2026-03-09 22:43:25,200 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47702 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2875347) Stage 2 time measured on controller: 139.17 ms
-(run_dp_rank pid=2875351) Stage 2 time measured on controller: 159.96 ms
-(run_dp_rank pid=2875347) Stage 3 time measured on controller: 130.45 ms
-(run_dp_rank pid=2875351) Stage 3 time measured on controller: 130.44 ms
-(raylet) [2026-03-09 22:43:35,215 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47704 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2875347) 2026-03-09 22:43:40,820 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
-(run_dp_rank pid=2875347) 2026-03-09 22:43:40,820 - piper_compile - INFO - DP rank 2 done.
-(run_dp_rank pid=2875347) Running 5 warmup iterations...
-(PiperActor pid=2875350) [rank3]:[W309 22:43:41.798146621 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=2875389) [rank2]:[W309 22:43:42.757082704 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(run_dp_rank pid=2875351) 2026-03-09 22:43:44,807 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
-(run_dp_rank pid=2875351) 2026-03-09 22:43:44,808 - piper_compile - INFO - DP rank 1 done.
-(raylet) [2026-03-09 22:43:45,230 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47703 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2875351) Running 5 warmup iterations...
-(PiperActor pid=2875392) [rank1]:[W309 22:43:45.885325467 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=2875383) [rank0]:[W309 22:43:46.907987648 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=2875383) [rank0]:[W309 22:43:50.874415843 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=2875392) [rank1]:[W309 22:43:50.264331469 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=2875389) [rank2]:[W309 22:43:54.931219822 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(raylet) [2026-03-09 22:43:55,246 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47702 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(PiperActor pid=2875350) [rank3]:[W309 22:43:55.821927540 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(raylet) [2026-03-09 22:44:05,262 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47699 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 22:44:15,282 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47501 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2875347) Warmup 0 completed
-(run_dp_rank pid=2875351) Warmup 0 completed
-(raylet) [2026-03-09 22:44:25,297 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47503 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2875347) Warmup 1 completed
-(run_dp_rank pid=2875351) Warmup 1 completed
-(run_dp_rank pid=2875347) Warmup 2 completed
-(run_dp_rank pid=2875351) Warmup 2 completed
-(run_dp_rank pid=2875347) Warmup 3 completed
-(run_dp_rank pid=2875351) Warmup 3 completed
-(raylet) [2026-03-09 22:44:35,312 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47501 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2875347) Warmup 4 completed
-(run_dp_rank pid=2875347) Running 10 timed iterations...
-(run_dp_rank pid=2875351) Warmup 4 completed
-(run_dp_rank pid=2875351) Running 10 timed iterations...
-(raylet) [2026-03-09 22:44:45,329 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47501 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 22:44:55,344 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47501 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 22:45:05,396 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47491 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(raylet) [2026-03-09 22:45:15,411 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47386 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
-(run_dp_rank pid=2875347) rank 1 iter time= 4.109 ± 0.021 s (10 samples)
-(run_dp_rank pid=2875347) rank 1 throughput= 3987.090 tokens/sec (10 samples)
-(run_dp_rank pid=2875351) rank 0 iter time= 4.110 ± 0.022 s (10 samples)
-(run_dp_rank pid=2875351) rank 0 throughput= 3986.833 tokens/sec (10 samples)
-(run_dp_rank pid=2875347) Ray timeline saved to: out/3b-pp2-dp2-interleaved-1f1b.json
-(run_dp_rank pid=2875351) Ray timeline saved to: out/3b-pp2-dp2-interleaved-1f1b.json
-(raylet) [2026-03-09 22:45:25,427 E 2875194 2875230] (raylet) file_system_monitor.cc:116: /tmp/ray/ray/session_2026-03-09_22-41-59_542505_2873013 is over 95% full, available space: 1.47376 GB; capacity: 69.9375 GB. Object creation will fail if spilling is required.
diff --git a/logs/piper_zero1_20260311_173310.log b/logs/piper_zero1_20260311_173310.log
deleted file mode 100644
index 3afa87d..0000000
--- a/logs/piper_zero1_20260311_173310.log
+++ /dev/null
@@ -1,61 +0,0 @@
-2026-03-11 17:33:22,766	INFO worker.py:2007 -- Started a local Ray instance.
-/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
-  warnings.warn(
-(run_dp_rank pid=4034041) Namespace(model='debug', schedule='interleaved-1f1b', dp=2, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=1)
-(run_dp_rank pid=4034042) Namespace(model='debug', schedule='interleaved-1f1b', dp=2, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=1)
-(run_dp_rank pid=4034042) Schedule:
-(run_dp_rank pid=4034042) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
-(run_dp_rank pid=4034042)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
-(run_dp_rank pid=4034041) Schedule:
-(run_dp_rank pid=4034041) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
-(run_dp_rank pid=4034041)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
-(PiperActor pid=4034029) 2026-03-11 17:33:48,959 - piper_actor - INFO - Initializing Ray actor 2 GPU 2
-(PiperActor pid=4034032) 2026-03-11 17:33:49,004 - piper_actor - INFO - Initializing Ray actor 0 GPU 1
-(PiperActor pid=4034044) 2026-03-11 17:33:48,998 - piper_actor - INFO - Initializing Ray actor 1 GPU 4
-(PiperActor pid=4034038) 2026-03-11 17:33:49,038 - piper_actor - INFO - Initializing Ray actor 3 GPU 3
-(run_dp_rank pid=4034042) 2026-03-11 17:33:53,980 - piper_compile - INFO - DP rank 2 compiling...
-(run_dp_rank pid=4034041) 2026-03-11 17:33:54,098 - piper_compile - INFO - DP rank 1 compiling...
-(run_dp_rank pid=4034042) Stage 0 time measured on controller: 5.30 ms
-(run_dp_rank pid=4034041) Stage 0 time measured on controller: 5.79 ms
-(run_dp_rank pid=4034042) Stage 1 time measured on controller: 4.97 ms
-(run_dp_rank pid=4034041) Stage 1 time measured on controller: 5.04 ms
-(run_dp_rank pid=4034042) Stage 2 time measured on controller: 5.24 ms
-(run_dp_rank pid=4034041) Stage 2 time measured on controller: 3.91 ms
-(run_dp_rank pid=4034041) Stage 3 time measured on controller: 0.09 ms
-(run_dp_rank pid=4034042) Stage 3 time measured on controller: 0.21 ms
-(run_dp_rank pid=4034042) 2026-03-11 17:33:58,667 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
-(run_dp_rank pid=4034042) 2026-03-11 17:33:58,667 - piper_compile - INFO - DP rank 2 done.
-(run_dp_rank pid=4034041) 2026-03-11 17:33:58,686 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
-(run_dp_rank pid=4034041) 2026-03-11 17:33:58,686 - piper_compile - INFO - DP rank 1 done.
-(run_dp_rank pid=4034042) Running 5 warmup iterations...
-(run_dp_rank pid=4034041) Running 5 warmup iterations...
-(PiperActor pid=4034038) [rank3]:[W311 17:33:58.492119629 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=4034044) [rank1]:[W311 17:33:58.504022547 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=4034029) [rank2]:[W311 17:33:59.876989456 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=4034032) [rank0]:[W311 17:33:59.882813165 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=4034029) [rank2]:[W311 17:34:03.718212031 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=4034038) [rank3]:[W311 17:34:03.017809408 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=4034032) [rank0]:[W311 17:34:05.838320263 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=4034029) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
-(PiperActor pid=4034029)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
-(PiperActor pid=4034044) [rank1]:[W311 17:34:05.134825782 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=4034032) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
-(PiperActor pid=4034032)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
-(run_dp_rank pid=4034042) Warmup 0 completed
-(run_dp_rank pid=4034041) Warmup 0 completed
-(run_dp_rank pid=4034042) Warmup 1 completed
-(run_dp_rank pid=4034041) Warmup 1 completed
-(run_dp_rank pid=4034042) Warmup 2 completed
-(run_dp_rank pid=4034041) Warmup 2 completed
-(run_dp_rank pid=4034042) Warmup 3 completed
-(run_dp_rank pid=4034041) Warmup 3 completed
-(run_dp_rank pid=4034042) Warmup 4 completed
-(run_dp_rank pid=4034042) Running 10 timed iterations...
-(run_dp_rank pid=4034041) Warmup 4 completed
-(run_dp_rank pid=4034041) Running 10 timed iterations...
-(run_dp_rank pid=4034042) rank 1 iter time= 0.158 ± 0.005 s (10 samples)
-(run_dp_rank pid=4034042) rank 1 throughput= 103772.089 tokens/sec (10 samples)
-(run_dp_rank pid=4034041) rank 0 iter time= 0.158 ± 0.005 s (10 samples)
-(run_dp_rank pid=4034041) rank 0 throughput= 103752.690 tokens/sec (10 samples)
-(run_dp_rank pid=4034042) Ray timeline saved to: out/debug-pp2-dp2-interleaved-1f1b.json
-(run_dp_rank pid=4034041) Ray timeline saved to: out/debug-pp2-dp2-interleaved-1f1b.json
diff --git a/logs/piper_zero2_20260311_173556.log b/logs/piper_zero2_20260311_173556.log
deleted file mode 100644
index 523765f..0000000
--- a/logs/piper_zero2_20260311_173556.log
+++ /dev/null
@@ -1,61 +0,0 @@
-2026-03-11 17:36:08,845	INFO worker.py:2007 -- Started a local Ray instance.
-/m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/ray/_private/worker.py:2046: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
-  warnings.warn(
-(run_dp_rank pid=4069408) Namespace(model='debug', schedule='interleaved-1f1b', dp=2, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=2)
-(run_dp_rank pid=4069410) Namespace(model='debug', schedule='interleaved-1f1b', dp=2, pp=2, batch_size=16, mbs=4, seq_len=256, warmup=5, iters=10, tracing=False, naive_gradient_sync=False, zero_stage=2)
-(run_dp_rank pid=4069408) Schedule:
-(run_dp_rank pid=4069408) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
-(run_dp_rank pid=4069408)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
-(run_dp_rank pid=4069410) Schedule:
-(run_dp_rank pid=4069410) 0:0:f    	0:1:f    	2:0:f    	2:1:f    	0:2:f    	0:3:f    	2:0:b    	 ------ 	2:1:b    	2:2:f    	0:0:b    	2:3:f    	0:1:b    	 ------ 	2:2:b    	2:3:b    	0:2:b    	0:3:b    	   u        	
-(run_dp_rank pid=4069410)  ------ 	1:0:f    	1:1:f    	3:0:f    	3:0:b    	3:1:f    	3:1:b    	1:2:f    	1:0:b    	1:3:f    	1:1:b    	3:2:f    	3:2:b    	3:3:f    	3:3:b    	1:2:b    	1:3:b    	   u        	 ------ 	
-(PiperActor pid=4069412) 2026-03-11 17:36:34,163 - piper_actor - INFO - Initializing Ray actor 2 GPU 1
-(PiperActor pid=4069404) 2026-03-11 17:36:34,186 - piper_actor - INFO - Initializing Ray actor 3 GPU 2
-(PiperActor pid=4069394) 2026-03-11 17:36:34,433 - piper_actor - INFO - Initializing Ray actor 1 GPU 4
-(PiperActor pid=4069409) 2026-03-11 17:36:34,356 - piper_actor - INFO - Initializing Ray actor 0 GPU 3
-(run_dp_rank pid=4069408) 2026-03-11 17:36:39,073 - piper_compile - INFO - DP rank 2 compiling...
-(run_dp_rank pid=4069410) 2026-03-11 17:36:39,292 - piper_compile - INFO - DP rank 1 compiling...
-(run_dp_rank pid=4069408) Stage 0 time measured on controller: 5.43 ms
-(run_dp_rank pid=4069410) Stage 0 time measured on controller: 6.10 ms
-(run_dp_rank pid=4069408) Stage 1 time measured on controller: 4.88 ms
-(run_dp_rank pid=4069410) Stage 1 time measured on controller: 6.16 ms
-(run_dp_rank pid=4069408) Stage 2 time measured on controller: 3.13 ms
-(run_dp_rank pid=4069410) Stage 2 time measured on controller: 3.34 ms
-(run_dp_rank pid=4069408) Stage 3 time measured on controller: 0.09 ms
-(run_dp_rank pid=4069410) Stage 3 time measured on controller: 0.35 ms
-(run_dp_rank pid=4069408) 2026-03-11 17:36:43,655 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
-(run_dp_rank pid=4069408) 2026-03-11 17:36:43,655 - piper_compile - INFO - DP rank 2 done.
-(run_dp_rank pid=4069410) 2026-03-11 17:36:43,939 - piper_backend - WARNING - Should not directly call compiled module, running non-distributed execution
-(run_dp_rank pid=4069410) 2026-03-11 17:36:43,939 - piper_compile - INFO - DP rank 1 done.
-(run_dp_rank pid=4069408) Running 5 warmup iterations...
-(PiperActor pid=4069404) [rank3]:[W311 17:36:43.436084544 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(run_dp_rank pid=4069410) Running 5 warmup iterations...
-(PiperActor pid=4069412) [rank2]:[W311 17:36:44.797272508 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=4069394) [rank1]:[W311 17:36:44.761425137 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=4069409) [rank0]:[W311 17:36:44.134249353 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=4069412) [rank2]:[W311 17:36:46.041164441 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=4069404) [rank3]:[W311 17:36:46.353801336 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=4069412) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
-(PiperActor pid=4069412)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
-(PiperActor pid=4069409) [rank0]:[W311 17:36:50.040816664 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=4069394) [rank1]:[W311 17:36:50.416971321 ProcessGroupNCCL.cpp:4068] Warning: An unbatched P2P op (send/recv) was called on this ProcessGroup with size 2.  In lazy initialization mode, this will result in a new 2-rank NCCL communicator to be created. (function operator())
-(PiperActor pid=4069409) /m-coriander/coriander/wxdeng/conda-envs/piper_ref/lib/python3.10/site-packages/torch/autograd/graph.py:865: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:330.)
-(PiperActor pid=4069409)   return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
-(run_dp_rank pid=4069410) Warmup 0 completed
-(run_dp_rank pid=4069408) Warmup 0 completed
-(run_dp_rank pid=4069410) Warmup 1 completed
-(run_dp_rank pid=4069408) Warmup 1 completed
-(run_dp_rank pid=4069410) Warmup 2 completed
-(run_dp_rank pid=4069408) Warmup 2 completed
-(run_dp_rank pid=4069410) Warmup 3 completed
-(run_dp_rank pid=4069408) Warmup 3 completed
-(run_dp_rank pid=4069410) Warmup 4 completed
-(run_dp_rank pid=4069410) Running 10 timed iterations...
-(run_dp_rank pid=4069408) Warmup 4 completed
-(run_dp_rank pid=4069408) Running 10 timed iterations...
-(run_dp_rank pid=4069410) rank 0 iter time= 0.150 ± 0.006 s (10 samples)
-(run_dp_rank pid=4069410) rank 0 throughput= 108998.506 tokens/sec (10 samples)
-(run_dp_rank pid=4069408) rank 1 iter time= 0.150 ± 0.005 s (10 samples)
-(run_dp_rank pid=4069408) rank 1 throughput= 109151.695 tokens/sec (10 samples)
-(run_dp_rank pid=4069410) Ray timeline saved to: out/debug-pp2-dp2-interleaved-1f1b.json
-(run_dp_rank pid=4069408) Ray timeline saved to: out/debug-pp2-dp2-interleaved-1f1b.json
diff --git a/src/piper.py b/src/piper.py
index 6e6c997..0a353ac 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -1,482 +1,2879 @@
-import ray
-import torch
-import torch.fx as fx
 import os
-import gc
-import pickle
 import time
-import threading
+from collections import Counter
+from dataclasses import dataclass, field
+from typing import Any
+
+import ray
+import torch.fx as fx
 from torch._dynamo.backends.registry import register_backend
-from .piper_utils import (
-    _serialize_graphmodule,
-    piper_metadata,
-    create_logger,
-    LOG_LEVEL,
-    get_gpu_peak_flops_bf16,
-    should_enable_ep,
-)
-
-
-def _collect_triton_constant_args(gm):
-    """Return {constant_args_idx: args_dict} for every triton_kernel_wrapper node in gm."""
+
+from .piper_graph_transform import _profile_and_split_gm, _split_gm_by_stages, split_by_a2a, bucket_stage
+from .piper_exec import TaskType
+from .piper_utils import LOG_LEVEL, create_logger, piper_metadata
+
+logger = create_logger("piper_backend", LOG_LEVEL)
+_ANY_TAG_INDEX = "__ANY_TAG_INDEX__"
+_FORWARD_PASS = "F"
+_FUSED_BWD_PASS = "B"
+_BWD_INPUT_PASS = "BI"
+_BWD_WEIGHT_PASS = "BW"
+_DEFAULT_STREAM = "default_stream"
+_VALID_ORDER_PASSES = frozenset({
+    _FORWARD_PASS,
+    _FUSED_BWD_PASS,
+    _BWD_INPUT_PASS,
+    _BWD_WEIGHT_PASS,
+})
+
+
+def _is_backward_activation_subkind(subkind: str | None) -> bool:
+    return subkind in ("BWD", "BWD_I")
+
+
+def _is_backward_weight_subkind(subkind: str | None) -> bool:
+    return subkind in ("BWD", "BWD_W")
+
+
+def _is_backward_compute_subkind(subkind: str | None) -> bool:
+    return subkind in ("BWD", "BWD_I", "BWD_W")
+
+def _collect_triton_constant_args(gm: fx.GraphModule) -> dict[int, Any]:
     try:
         from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
     except ImportError:
         return {}
+    out: dict[int, Any] = {}
+    for node in gm.graph.nodes:
+        if node.op != "call_function":
+            continue
+        target_str = getattr(node.target, "__name__", "") or str(node.target)
+        if "triton_kernel_wrapper" not in target_str:
+            continue
+        idx = node.kwargs.get("constant_args_idx")
+        if idx is not None and idx in kernel_side_table.constant_args:
+            out[int(idx)] = kernel_side_table.constant_args[idx]
+    return out
+
+
+@dataclass(frozen=True)
+class TrainingDAGEdge:
+    src_uid: str
+    dst_uid: str
+    dep_kind: str  # "data" | "temporal"
+    tensor_name: str | None = None
+
+
+@dataclass
+class TrainingDAGNode:
+    uid: str
+    node_kind: str  # "COMPUTE" | "SEND_COMM" | "RECV_COMM" | "REDUCE_COMM" | "ALL_GATHER_COMM" | "REDUCE_SCATTER_COMM" | "A2A_COMM" | "ORDER_DUMMY"
+    compute_subkind: str | None  # "FWD" | "BWD" | "BWD_I" | "BWD_W" when node_kind == "COMPUTE"
+    tag: dict[str, int | None]
+    device: list[int] | None
+    stream: str
+    node_meta: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class TrainingDAG:
+    nodes: dict[str, TrainingDAGNode] = field(default_factory=dict)
+    edges: list[TrainingDAGEdge] = field(default_factory=list)
+    succs: dict[str, set[str]] = field(default_factory=dict)
+    preds: dict[str, set[str]] = field(default_factory=dict)
+    edge_keys: set[tuple[str, str, str, str | None]] = field(default_factory=set)
+
+    def add_node(self, node: TrainingDAGNode) -> None:
+        if node.uid in self.nodes:
+            raise ValueError(f"Duplicate node uid: {node.uid}")
+        self.nodes[node.uid] = node
+        self.succs[node.uid] = set()
+        self.preds[node.uid] = set()
+
+    def add_edge(self, edge: TrainingDAGEdge) -> None:
+        if edge.src_uid not in self.nodes or edge.dst_uid not in self.nodes:
+            raise ValueError(f"Edge references unknown node: {edge}")
+        edge_key = (edge.src_uid, edge.dst_uid, edge.dep_kind, edge.tensor_name)
+        if edge_key in self.edge_keys:
+            return
+        self.edge_keys.add(edge_key)
+        self.edges.append(edge)
+        self.succs[edge.src_uid].add(edge.dst_uid)
+        self.preds[edge.dst_uid].add(edge.src_uid)
+
+
+
+def _flatten_output_nodes(out_arg: Any) -> list[fx.Node]:
+    out_nodes: list[fx.Node] = []
+
+    def _walk(v: Any) -> None:
+        if isinstance(v, fx.Node):
+            out_nodes.append(v)
+        elif isinstance(v, (tuple, list)):
+            for x in v:
+                _walk(x)
+        elif isinstance(v, dict):
+            for x in v.values():
+                _walk(x)
+
+    _walk(out_arg)
+    return out_nodes
+
+
+
+def _node_io_names(seg_gm: fx.GraphModule) -> tuple[list[str], list[str]]:
+    inputs = [n.name for n in seg_gm.graph.nodes if n.op == "placeholder"]
+    out_node = next((n for n in seg_gm.graph.nodes if n.op == "output"), None)
+    if out_node is None:
+        return inputs, []
+    outputs = [n.name for n in _flatten_output_nodes(out_node.args[0])]
+    return inputs, outputs
+
+
+def _infer_ep_idx(segment_idx: int) -> int | None:
+    """Heuristic: odd A2A segments are expert regions, even segments are non-expert."""
+    if segment_idx % 2 == 0:
+        return None
+    return segment_idx // 2
+
+
+def _format_tag(tag: dict[str, int | None]) -> str:
+    if not tag:
+        return "(no-tags)"
+    keys = sorted(tag.keys(), key=lambda k: (k != "PP", k != "EP", k))
+    return ", ".join(f"{k}={tag[k]}" for k in keys)
+
+
+def _with_pass_tag(tag: dict[str, Any], pass_value: str | None) -> dict[str, Any]:
+    t = dict(tag)
+    t["PASS"] = pass_value
+    return t
+
 
-    result = {}
-
-    def scan(module):
-        if isinstance(module, fx.GraphModule):
-            for node in module.graph.nodes:
-                if node.op == "call_function":
-                    target_str = getattr(node.target, "__name__", "") or str(node.target)
-                    if "triton_kernel_wrapper" in target_str:
-                        idx = node.kwargs.get("constant_args_idx")
-                        if idx is not None and idx in kernel_side_table.constant_args:
-                            result[idx] = kernel_side_table.constant_args[idx]
-        for child in module.children():
-            scan(child)
-
-    scan(gm)
-    return result
-from .piper_graph_transform import (
-    _split_gm_by_stages,
-    _profile_and_split_gm,
-    _fuse_consecutive_zero3_param_sequences,
-    expand_chunks_to_dags,
-    add_temporal_dependencies,
-    split_dag_by_rank,
-    assign_time_steps,
-    get_overlappable_tasks,
-    insert_zero_ops,
-    overlap_chunks,
-    visualize_dag,
-    print_dag_order,
-    bucket_stage,
-    apply_activation_checkpointing,
-    split_by_a2a,
-)
-from .piper_exec import Chunk
-from .piper_actor import _get_actor
+def _format_device(device: list[int] | None) -> str:
+    return "None" if device is None else str(list(device))
 
-logger = create_logger("piper_backend", LOG_LEVEL)
 
+def _debug_render_training_dag(training_dag: TrainingDAG, output_path: str = "out/training_dag") -> None:
+    """Render TrainingDAG with node labels as tags and data-dependency edges."""
+    try:
+        import graphviz
+    except ImportError:
+        logger.warning("graphviz Python package not installed; skipping TrainingDAG render")
+        return
+
+    dot = graphviz.Digraph("TrainingDAG", comment="Training DAG (tag-labelled)")
+    dot.attr(rankdir="LR")
+    topo_levels = _topological_levels(training_dag)
+
+    def _format_node_meta_lines(node: TrainingDAGNode) -> str:
+        keys = (
+            "bucket_key",
+            "zero_alloc_full_grads_before",
+            "zero_free_full_params_after",
+        )
+        lines = [
+            f"{key}={node.node_meta[key]!r}"
+            for key in keys
+            if key in node.node_meta
+        ]
+        return "\\n".join(lines)
+
+    for uid, node in training_dag.nodes.items():
+        topo_label = f"topo={topo_levels.get(uid, '?')}"
+        meta_label = _format_node_meta_lines(node)
+        meta_suffix = f"\\n{meta_label}" if meta_label else ""
+        if node.node_kind in (
+            "SEND_COMM",
+            "RECV_COMM",
+            "REDUCE_COMM",
+            "ALL_GATHER_COMM",
+            "REDUCE_SCATTER_COMM",
+            "A2A_COMM",
+        ):
+            label = (
+                f"{topo_label}\\n{node.node_kind}\\n{_format_tag(node.tag)}\\n"
+                f"{_format_device(node.device)}\\nstream={node.stream}"
+                f"{meta_suffix}"
+            )
+            shape = "box"
+        else:
+            subkind = node.compute_subkind or "COMPUTE"
+            label = (
+                f"{topo_label}\\n{subkind}\\n{_format_tag(node.tag)}\\n{_format_device(node.device)}\\nstream={node.stream}"
+                f"{meta_suffix}"
+            )
+            shape = "ellipse"
+        dot.node(uid, label=label, shape=shape)
+
+    nodes_by_level: dict[int, list[str]] = {}
+    for uid, level in topo_levels.items():
+        nodes_by_level.setdefault(level, []).append(uid)
+    for level, uids in sorted(nodes_by_level.items()):
+        with dot.subgraph(name=f"topo_{level}") as sub:
+            sub.attr(rank="same")
+            for uid in sorted(uids):
+                sub.node(uid)
+    level_reps = [
+        sorted(uids)[0]
+        for _level, uids in sorted(nodes_by_level.items())
+        if uids
+    ]
+    for src_uid, dst_uid in zip(level_reps, level_reps[1:]):
+        dot.edge(src_uid, dst_uid, style="invis", weight="100")
+
+    for edge in training_dag.edges:
+        if edge.dep_kind == "data":
+            dot.edge(edge.src_uid, edge.dst_uid)
+        elif edge.dep_kind == "temporal":
+            dot.edge(edge.src_uid, edge.dst_uid, style="dashed", color="blue")
+
+    out = dot.render(output_path, format="png", cleanup=True)
+    logger.info("TrainingDAG debug graph saved to %s", out)
+
+
+def _log_training_dag_dependencies(training_dag: TrainingDAG) -> None:
+    """Log predecessor/successor tags for each TrainingDAG node."""
+    def _sort_key(item: tuple[str, TrainingDAGNode]) -> tuple[int, int]:
+        _uid, node = item
+        if node.node_kind == "COMPUTE":
+            return (int(node.node_meta.get("stage_id", 10**9)), int(node.node_meta.get("segment_id", 10**9)))
+        return (10**9, 10**9)
+
+    for uid, node in sorted(training_dag.nodes.items(), key=_sort_key):
+        pred_tags = [
+            _format_tag(training_dag.nodes[p].tag)
+            for p in sorted(training_dag.preds.get(uid, []), key=lambda x: x)
+        ]
+        succ_tags = [
+            _format_tag(training_dag.nodes[s].tag)
+            for s in sorted(training_dag.succs.get(uid, []), key=lambda x: x)
+        ]
+        logger.debug(
+            "TrainingDAG node=%s kind=%s subkind=%s tag=(%s) device=%s stream=%s preds=[%s] succs=[%s]",
+            uid,
+            node.node_kind,
+            node.compute_subkind,
+            _format_tag(node.tag),
+            _format_device(node.device),
+            node.stream,
+            ", ".join(pred_tags),
+            ", ".join(succ_tags),
+        )
 
-# ---------------------------------------------------------------------------
-# Piper torch.compile backend
-# ---------------------------------------------------------------------------
 
-@register_backend
-def piper(gm, example_inputs, **kwargs):
+def _training_dag_task_type(node: TrainingDAGNode) -> TaskType:
+    if node.node_kind == "COMPUTE":
+        if node.compute_subkind == "FWD":
+            return TaskType.FWD
+        if node.compute_subkind == "BWD":
+            return TaskType.BWD
+        if node.compute_subkind == "BWD_I":
+            return TaskType.BWD_I
+        if node.compute_subkind == "BWD_W":
+            return TaskType.BWD_W
+    if node.node_kind == "UPD":
+        return TaskType.UPD
+    if node.node_kind == "SEND_COMM":
+        return TaskType.SEND
+    if node.node_kind == "RECV_COMM":
+        return TaskType.RECV
+    if node.node_kind == "ALL_GATHER_COMM":
+        return TaskType.ALL_GATHER
+    if node.node_kind == "REDUCE_SCATTER_COMM":
+        return TaskType.REDUCE_SCATTER
+    if node.node_kind == "REDUCE_COMM":
+        return TaskType.ALL_REDUCE
+    if node.node_kind == "A2A_COMM":
+        return TaskType.FWD_A2A if node.tag.get("PASS") == "F" else TaskType.BWD_A2A
+    if node.node_kind == "ORDER_DUMMY":
+        return TaskType.ORDER_DUMMY
+    return TaskType.FWD
+
+
+def print_training_dag_order(
+    dag: TrainingDAG,
+    label: str = "",
+    rank: int = 0,
+    out_dir: str = "out",
+) -> None:
+    """Write and log the actor dispatch order for a TrainingDAG."""
+    topo_levels = _topological_levels(dag)
+    topo = _serial_topological_order(dag, topo_levels)
+    data_preds: dict[str, list[str]] = {uid: [] for uid in dag.nodes}
+    data_succs: dict[str, list[str]] = {uid: [] for uid in dag.nodes}
+    temporal_preds: dict[str, list[str]] = {uid: [] for uid in dag.nodes}
+    temporal_succs: dict[str, list[str]] = {uid: [] for uid in dag.nodes}
+    for e in dag.edges:
+        if e.dep_kind == "data":
+            data_preds[e.dst_uid].append(e.src_uid)
+            data_succs[e.src_uid].append(e.dst_uid)
+        else:
+            temporal_preds[e.dst_uid].append(e.src_uid)
+            temporal_succs[e.src_uid].append(e.dst_uid)
+
+    header = f"--- TrainingDAG execution order{': ' + label if label else ''} ---"
+    lines = [header]
+    for uid in topo:
+        node = dag.nodes[uid]
+        task_type = _training_dag_task_type(node)
+        meta = node.node_meta
+        extra = []
+        for key in ("bucket_key", "fwd_uid", "peer_pp_rank", "from_uid", "to_uid"):
+            if key in meta and meta[key] is not None:
+                extra.append(f"{key}={meta[key]}")
+        extra_str = ("  " + " ".join(extra)) if extra else ""
+        line = (
+            f"  topo={topo_levels[uid]:<3d}  "
+            f"{task_type.value:<14s}  kind={node.node_kind:<19s}  uid={node.uid:<24s}  "
+            f"tag=({_format_tag(node.tag)})  stream={node.stream}{extra_str}  "
+            f"data_preds={sorted(data_preds[node.uid])} data_succs={sorted(data_succs[node.uid])}  "
+            f"temp_preds={sorted(temporal_preds[node.uid])} temp_succs={sorted(temporal_succs[node.uid])}"
+        )
+        lines.append(line)
 
-    # gm.print_readable()
+    lines.append("-" * len(header))
 
-    original_gm = gm
-    num_stages = len(piper_metadata.stage_to_device.keys())
+    os.makedirs(out_dir, exist_ok=True)
+    out_path = os.path.join(out_dir, f"training_dag_order_rank{rank}")
+    with open(out_path, "w") as f:
+        f.write("\n".join(lines) + "\n")
+    logger.info("TrainingDAG execution order saved to %s", out_path)
 
-    # Check if the graph has stage annotations
-    has_annotations = any(
-        isinstance(node.meta.get('custom'), dict) and node.meta['custom'].get('stage') is not None
-        for node in gm.graph.nodes
+
+def build_training_dag(
+    stage_submodules: list[tuple[int, fx.GraphModule, list[int], list[int], list, list]],
+    *,
+    enable_ep: bool,
+    dp_degree: int,
+    stage_tag_name: str = "PP",
+) -> TrainingDAG:
+    """Build a TrainingDAG where each node is one stage/a2a segment GraphModule."""
+    dag = TrainingDAG()
+
+    global_prev_uid: str | None = None
+
+    for stage_id, stage_gm, input_idxs, param_idxs, graphargs, _placeholders in stage_submodules:
+        if enable_ep and dp_degree > 1:
+            segments, boundary_infos = split_by_a2a(stage_gm, graphargs, input_idxs, param_idxs)
+        else:
+            segments = [(stage_gm, input_idxs, param_idxs, graphargs)]
+            boundary_infos = []
+
+        for seg_id, (seg_gm, seg_in, seg_param, seg_args) in enumerate(segments):
+            uid = f"s{stage_id}.seg{seg_id}"
+            tag: dict[str, int | None] = {stage_tag_name: stage_id}
+            if seg_id % 2 == 1:
+                boundary_name = None
+                if seg_id - 1 < len(boundary_infos):
+                    boundary_name = boundary_infos[seg_id - 1].get("name")
+                if boundary_name:
+                    tag[boundary_name] = _infer_ep_idx(seg_id)
+            tag = _with_pass_tag(tag, "F")
+            in_names, out_names = _node_io_names(seg_gm)
+            boundary_after = boundary_infos[seg_id] if seg_id < len(boundary_infos) else None
+            node = TrainingDAGNode(
+                uid=uid,
+                node_kind="COMPUTE",
+                compute_subkind="FWD",
+                tag=tag,
+                device=None,
+                stream="default_stream",
+                node_meta={
+                    "bucket_key": uid,
+                    "stage_id": stage_id,
+                    "segment_id": seg_id,
+                    "gm": seg_gm,
+                    "input_idxs": list(seg_in),
+                    "param_idxs": list(seg_param),
+                    "graphargs": list(seg_args),
+                    "input_names": in_names,
+                    "output_names": out_names,
+                    "a2a_boundary_after": boundary_after,
+                    "triton_constant_args": _collect_triton_constant_args(seg_gm),
+                },
+            )
+            dag.add_node(node)
+
+            # Explicit activation-flow ordering in stage/segment order.
+            if global_prev_uid is not None:
+                dag.add_edge(TrainingDAGEdge(src_uid=global_prev_uid, dst_uid=uid, dep_kind="data"))
+            global_prev_uid = uid
+
+    # Build backward-pass nodes and dependencies by reversing forward data edges.
+    fwd_compute_uids = [
+        uid for uid, node in dag.nodes.items()
+        if node.node_kind == "COMPUTE" and node.compute_subkind == "FWD"
+    ]
+    fwd_uid_to_bwd_uid: dict[str, str] = {}
+    for fwd_uid in fwd_compute_uids:
+        fwd_node = dag.nodes[fwd_uid]
+        bwd_uid = f"{fwd_uid}.bwd"
+        fwd_uid_to_bwd_uid[fwd_uid] = bwd_uid
+        dag.add_node(
+            TrainingDAGNode(
+                uid=bwd_uid,
+                node_kind="COMPUTE",
+                compute_subkind="BWD",
+                tag=_with_pass_tag(fwd_node.tag, "B"),
+                device=fwd_node.device,
+                stream="default_stream",
+                node_meta={
+                    "fwd_uid": fwd_uid,
+                    "bucket_key": fwd_node.node_meta.get("bucket_key", fwd_uid),
+                    "compute_loss": False,
+                },
+            )
+        )
+
+    fwd_edges = [
+        e for e in dag.edges
+        if e.dep_kind == "data"
+        and dag.nodes[e.src_uid].node_kind == "COMPUTE"
+        and dag.nodes[e.dst_uid].node_kind == "COMPUTE"
+        and dag.nodes[e.src_uid].compute_subkind == "FWD"
+        and dag.nodes[e.dst_uid].compute_subkind == "FWD"
+    ]
+
+    # Reverse every forward dependency for the backward pass:
+    # FWD:  u -> v    ==>    BWD: v' -> u'
+    for e in fwd_edges:
+        bwd_src = fwd_uid_to_bwd_uid[e.dst_uid]
+        bwd_dst = fwd_uid_to_bwd_uid[e.src_uid]
+        dag.add_edge(
+            TrainingDAGEdge(
+                src_uid=bwd_src,
+                dst_uid=bwd_dst,
+                dep_kind="data",
+                tensor_name=e.tensor_name,
+            )
+        )
+
+    # Bridge from forward phase into backward phase:
+    # last_fwd -> first_bwd (where first_bwd is the BWD node paired with last_fwd).
+    if fwd_compute_uids:
+        def _fwd_sort_key(uid: str) -> tuple[int, int]:
+            n = dag.nodes[uid]
+            return (
+                int(n.node_meta.get("stage_id", -1)),
+                int(n.node_meta.get("segment_id", -1)),
+            )
+
+        last_fwd_uid = max(fwd_compute_uids, key=_fwd_sort_key)
+        first_bwd_uid = fwd_uid_to_bwd_uid[last_fwd_uid]
+        dag.add_edge(
+            TrainingDAGEdge(
+                src_uid=last_fwd_uid,
+                dst_uid=first_bwd_uid,
+                dep_kind="data",
+                tensor_name=None,
+            )
+        )
+
+        # Mark loss computation point: first BWD node (earliest in BWD execution).
+        if first_bwd_uid in dag.nodes:
+            dag.nodes[first_bwd_uid].node_meta["compute_loss"] = True
+
+    # Add explicit UPD node with temporal dependency from every BWD compute node.
+    # UPD participates in the compute temporal chain and is not scheduled from
+    # data edges.
+    upd_uid = "upd.0"
+    dag.add_node(
+        TrainingDAGNode(
+            uid=upd_uid,
+            node_kind="UPD",
+            compute_subkind=None,
+            tag={"PASS": None},
+            device=None,
+            stream="default_stream",
+            node_meta={},
+        )
+    )
+    for uid, node in list(dag.nodes.items()):
+        if node.node_kind == "COMPUTE" and node.compute_subkind == "BWD":
+            dag.add_edge(
+                TrainingDAGEdge(
+                    src_uid=uid,
+                    dst_uid=upd_uid,
+                    dep_kind="temporal",
+                    tensor_name=None,
+                )
+            )
+
+    return dag
+
+
+def _match_filter(tag: dict[str, int | None], flt: dict[str, Any]) -> bool:
+    for k, v in flt.items():
+        if k not in tag:
+            return False
+        if v == _ANY_TAG_INDEX:
+            # "*" wildcard matches any concrete index for this tag, but not None.
+            if tag[k] is None:
+                return False
+        elif tag[k] != v:
+            return False
+    return True
+
+
+def _apply_split_backward_stencil(
+    dag: TrainingDAG,
+    split_base_keys: set[tuple[tuple[str, Any], ...]],
+) -> None:
+    if not split_base_keys:
+        return
+    split_filters = [dict(key) for key in split_base_keys]
+    for node in list(dag.nodes.values()):
+        if (
+            node.node_kind != "COMPUTE"
+            or node.compute_subkind != "BWD"
+            or not any(_match_filter(node.tag, flt) for flt in split_filters)
+        ):
+            continue
+        _split_fused_bwd_node(dag, node.uid)
+
+
+def _split_fused_bwd_node(dag: TrainingDAG, bwd_uid: str) -> None:
+    bwd_i = dag.nodes[bwd_uid]
+    if bwd_i.node_kind != "COMPUTE" or bwd_i.compute_subkind != "BWD":
+        raise ValueError(f"expected fused BWD compute node, got {bwd_uid}: {bwd_i}")
+    bwd_w_uid = f"{bwd_uid}.bw"
+    if bwd_w_uid in dag.nodes:
+        raise ValueError(f"duplicate BWD_W uid while splitting {bwd_uid}: {bwd_w_uid}")
+
+    bwd_i.compute_subkind = "BWD_I"
+    bwd_i.tag = _with_pass_tag(bwd_i.tag, _BWD_INPUT_PASS)
+
+    bwd_w_meta = dict(bwd_i.node_meta)
+    bwd_w_meta["compute_loss"] = False
+    dag.add_node(
+        TrainingDAGNode(
+            uid=bwd_w_uid,
+            node_kind="COMPUTE",
+            compute_subkind="BWD_W",
+            tag=_with_pass_tag(bwd_i.tag, _BWD_WEIGHT_PASS),
+            device=list(bwd_i.device) if bwd_i.device is not None else None,
+            stream=bwd_i.stream,
+            node_meta=bwd_w_meta,
+        )
     )
 
-    logger.debug(f"Graph has stage annotations: {has_annotations}")
+    for edge in [
+        e for e in list(dag.edges)
+        if e.dep_kind == "temporal"
+        and e.src_uid == bwd_uid
+        and dag.nodes[e.dst_uid].node_kind == "UPD"
+    ]:
+        _remove_edge(dag, edge)
+        dag.add_edge(
+            TrainingDAGEdge(
+                src_uid=bwd_w_uid,
+                dst_uid=edge.dst_uid,
+                dep_kind="temporal",
+                tensor_name=edge.tensor_name,
+            )
+        )
 
-    if has_annotations:
-        top_level_gm, submodules = _split_gm_by_stages(gm)
-    else:
-        logger.info(f"No stage annotations found, profiling graph to split into {num_stages} stages")
-        top_level_gm, submodules = _profile_and_split_gm(gm, num_stages)
-
-    dp_degree = int(os.environ['PIPER_DP_DEGREE'])
-
-    del top_level_gm
-
-    refs = []
-    actor_stages = []
-    # stage_id -> {boundary_bucket_id -> tensor_idx} collected for DAG expansion
-    stage_a2a_boundaries: dict = {}
-    stage_bucket_counts: dict = {}
-    trainable_bucket_keys: set = set()
-    zero_bucket_keys: set = set()
-    # Collected for DP broadcasting: stage_id -> (modules_data, a2a_boundaries)
-    all_stage_compiled: dict = {}
-    # Running counter for assigning globally unique bucket IDs (same ordering as
-    # expand_chunks_to_dags Pass 1.5, which sorts by stage_id).  Populated after
-    # all stages are compiled so we can compute offsets in a second pass.
-    _stage_ubid_offsets: dict = {}  # stage_id -> first unique_bucket_id for that stage
-
-    for (stage_id, stage_gm, input_idxs, param_idxs, graphargs, placeholders) in submodules:
-        actor_id = piper_metadata.stage_to_device[stage_id]
-        actor = _get_actor(actor_id)
-        actor_stages.append((actor, stage_id))
-
-        # Split at A2A annotation boundaries only for multi-DP runs with EP enabled.
-        if dp_degree > 1 and should_enable_ep(dp_degree):
-            a2a_segments, boundary_infos = split_by_a2a(stage_gm, graphargs, input_idxs, param_idxs)
-            if boundary_infos:
-                logger.debug(
-                    f"Stage {stage_id} split into {len(a2a_segments)} A2A segments "
-                    f"with {len(boundary_infos)} boundaries"
+    dag.add_edge(
+        TrainingDAGEdge(
+            src_uid=bwd_uid,
+            dst_uid=bwd_w_uid,
+            dep_kind="data",
+            tensor_name=None,
+        )
+    )
+
+
+def _normalize_filter_devices_directive(
+    directive: Any
+) -> tuple[str, list[dict[str, Any]], list[int], str | None, str | None, str | None, bool, bool, int | None]:
+    if isinstance(directive, dict):
+        op = directive.get("op")
+        if op not in ("place", "replicate", "shard"):
+            raise ValueError(f"Unsupported directive op: {op}")
+        if "filter" not in directive:
+            raise ValueError(
+                f"{op} directive requires 'filter' (singular), not 'filters': {directive}"
+            )
+        if "filters" in directive:
+            raise ValueError(
+                f"{op} directive uses deprecated 'filters'; use 'filter' (singular): {directive}"
+            )
+        filter_spec = directive.get("filter")
+        devices = directive.get("devices", directive.get("device"))
+        stream = directive.get("stream")
+        gather_stream = directive.get("gather_stream")
+        reduce_stream = directive.get("reduce_stream")
+        if op == "replicate":
+            if stream is not None:
+                raise ValueError(
+                    f"replicate directive does not accept 'stream'; use 'gather_stream' and/or 'reduce_stream': {directive}"
                 )
         else:
-            a2a_segments = [(stage_gm, input_idxs, param_idxs, graphargs)]
-            boundary_infos = []
+            if gather_stream is not None or reduce_stream is not None:
+                raise ValueError(
+                    f"{op} directive does not accept 'gather_stream'/'reduce_stream'; use 'stream': {directive}"
+                )
+        shard_params = bool(directive.get("shard_params", False))
+        shard_grads = bool(directive.get("shard_grads", False))
+        bucket_size_raw = directive.get("bucket_size", None)
+        bucket_size = None if bucket_size_raw is None else int(bucket_size_raw)
+        if not isinstance(devices, list) or not devices:
+            raise ValueError(f"place directive requires non-empty devices list: {directive}")
+        n = _normalize_filter_spec(filter_spec, directive)
+        return (
+            op,
+            [n],
+            [int(d) for d in devices],
+            (None if stream is None else str(stream)),
+            (None if gather_stream is None else str(gather_stream)),
+            (None if reduce_stream is None else str(reduce_stream)),
+            shard_params,
+            shard_grads,
+            bucket_size,
+        )
+
+    raise ValueError(f"Schedule directives must be JSON objects, got: {type(directive)}")
+
+
+def _normalize_filter_spec(filter_spec: Any, directive: Any) -> dict[str, Any]:
+    """Normalize a single filter spec into {tag_name: value}."""
+    def _norm_value(k: str, v: Any) -> Any:
+        if k == "PASS":
+            if not isinstance(v, str):
+                raise ValueError(
+                    f"'PASS' filter value must be a string in {{'F','B','BI','BW'}}: {directive}"
+                )
+            if v not in ("F", "B", "BI", "BW"):
+                raise ValueError(
+                    f"Invalid 'PASS' filter value '{v}'. Allowed: 'F', 'B', 'BI', 'BW': {directive}"
+                )
+            return v
+        if v == "*":
+            return _ANY_TAG_INDEX
+        if v is None:
+            return None
+        if isinstance(v, int):
+            return int(v)
+        if isinstance(v, str):
+            # Keep pass-like symbolic tags (e.g., "FWD"/"BWD") as strings.
+            try:
+                return int(v)
+            except ValueError:
+                return v
+        return v
+
+    if isinstance(filter_spec, dict):
+        out: dict[str, Any] = {}
+        for k, v in filter_spec.items():
+            if not isinstance(k, str):
+                raise ValueError(f"Filter keys must be strings: {directive}")
+            out[k] = _norm_value(k, v)
+        return out
+    if isinstance(filter_spec, list) and all(
+        isinstance(item, (list, tuple)) and len(item) == 2
+        for item in filter_spec
+    ):
+        out: dict[str, Any] = {}
+        for k, v in filter_spec:
+            if not isinstance(k, str):
+                raise ValueError(f"Filter tuple tag must be a string: {directive}")
+            out[k] = _norm_value(k, v)
+        return out
+    raise ValueError(
+        f"Unsupported filter format. Expected dict or list[tuple[str, value]]: {directive}"
+    )
+
+
+def _iter_filter_specs(spec: Any):
+    if isinstance(spec, dict):
+        yield spec
+        return
+    if isinstance(spec, list):
+        if all(
+            isinstance(item, (list, tuple))
+            and len(item) == 2
+            and isinstance(item[0], str)
+            for item in spec
+        ):
+            yield spec
+            return
+        for item in spec:
+            yield from _iter_filter_specs(item)
+
+
+def _directive_mentions_tag(directive: dict[str, Any], tag_name: str) -> bool:
+    specs = []
+    if "filter" in directive:
+        specs.append(directive["filter"])
+    if isinstance(directive.get("filters"), list):
+        specs.append(directive["filters"])
+    for spec in _iter_filter_specs(specs):
+        if isinstance(spec, dict) and tag_name in spec:
+            return True
+        if isinstance(spec, list):
+            for item in spec:
+                if isinstance(item, tuple) and len(item) == 2 and item[0] == tag_name:
+                    return True
+                if isinstance(item, list) and len(item) == 2 and item[0] == tag_name:
+                    return True
+    return False
+
+
+def _directives_enable_ep(directives: Any) -> bool:
+    if not isinstance(directives, list):
+        return False
+    return any(
+        isinstance(directive, dict)
+        and (directive.get("op") == "shard" or _directive_mentions_tag(directive, "EP"))
+        for directive in directives
+    )
+
+
+def _normalize_split_directive(directive: Any) -> tuple[dict[str, Any], str, int]:
+    if not isinstance(directive, dict):
+        raise ValueError(f"split directive must be a dict: {directive}")
+    if directive.get("op") != "split":
+        raise ValueError(f"Unsupported split directive op: {directive}")
+    if "filter" not in directive:
+        raise ValueError(f"split directive requires 'filter': {directive}")
+    dim_name = directive.get("dim_name")
+    if not isinstance(dim_name, str) or not dim_name:
+        raise ValueError(f"split directive requires non-empty string dim_name: {directive}")
+    num_microbatches = int(directive.get("num_microbatches", 0))
+    if num_microbatches <= 0:
+        raise ValueError(f"split directive requires num_microbatches > 0: {directive}")
+    flt = _normalize_filter_spec(directive["filter"], directive)
+    return flt, dim_name, num_microbatches
+
+
+def _is_filter_spec(spec: Any) -> bool:
+    if isinstance(spec, dict):
+        return True
+    if isinstance(spec, list):
+        return all(
+            isinstance(item, (list, tuple))
+            and len(item) == 2
+            and isinstance(item[0], str)
+            for item in spec
+        )
+    return False
+
+
+def _normalize_order_directive(directive: Any) -> list[list[dict[str, Any]]]:
+    if not isinstance(directive, dict):
+        raise ValueError(f"order directive must be a dict: {directive}")
+    if directive.get("op") != "order":
+        raise ValueError(f"Unsupported order directive op: {directive}")
+    raw_filters = directive.get("filters")
+    if not isinstance(raw_filters, list) or len(raw_filters) < 2:
+        raise ValueError(
+            f"order directive requires filters list with at least 2 groups: {directive}"
+        )
+
+    filter_groups: list[list[dict[str, Any]]] = []
+    for group_idx, raw_group in enumerate(raw_filters):
+        if _is_filter_spec(raw_group):
+            raw_group_items = [raw_group]
+        elif isinstance(raw_group, list) and raw_group:
+            raw_group_items = raw_group
+        else:
+            raise ValueError(
+                f"order directive filter group[{group_idx}] must be a non-empty list "
+                f"of filters: {directive}"
+            )
+
+        group = []
+        for raw_filter in raw_group_items:
+            if not _is_filter_spec(raw_filter):
+                raise ValueError(
+                    f"order directive filter group[{group_idx}] contains invalid "
+                    f"filter spec: {raw_filter}"
+                )
+            flt = _normalize_filter_spec(raw_filter, directive)
+            pass_value = flt.get("PASS")
+            if pass_value is not None and pass_value not in _VALID_ORDER_PASSES:
+                raise ValueError(
+                    f"order directive has unsupported pass={pass_value!r}; "
+                    f"expected one of {sorted(_VALID_ORDER_PASSES)}"
+                )
+            group.append(flt)
+        filter_groups.append(group)
+
+    return filter_groups
+
+
+def _filter_key_without(flt: dict[str, Any], ignored_keys: set[str]) -> tuple[tuple[str, Any], ...]:
+    return tuple(sorted((k, v) for k, v in flt.items() if k not in ignored_keys))
+
 
-        # Apply parameter bucketing only to even-indexed segments (segments outside A2A pairs).
-        all_modules: list = []
-        a2a_boundaries: dict = {}  # boundary_bucket_id -> tensor_idx
-
-        for seg_idx, (seg_gm, seg_in, seg_param, seg_args) in enumerate(a2a_segments):
-            buckets = [(seg_gm, seg_in, seg_param, seg_args)]
-            # Hack: only bucket even-indexed segments to avoid bucketing MoE subgraphs
-            if dp_degree > 1 and piper_metadata.bucket_size is not None:
-                if seg_idx % 2 == 0:
-                    buckets = bucket_stage(
-                        seg_gm,
-                        seg_args,
-                        seg_in,
-                        seg_param,
-                        bucket_size_bytes=piper_metadata.bucket_size,
+def _validate_split_backward_order_stencil(
+    directives: list[Any],
+) -> set[tuple[tuple[str, Any], ...]]:
+    """Validate BI/BW order entries and return base filter keys to split.
+
+    This is intentionally narrow: it validates only the current split-backward
+    stencil until the schedule JSON grows a full schema validation pass.
+    """
+    split_keys: set[tuple[tuple[str, Any], ...]] = set()
+    split_entries: dict[tuple[tuple[str, Any], ...], list[tuple[int, tuple[int, int], str]]] = {}
+
+    for directive_idx, raw in enumerate(directives):
+        if not isinstance(raw, dict) or raw.get("op") != "order":
+            continue
+        filter_groups = _normalize_order_directive(raw)
+        seen_in_row: dict[tuple[tuple[str, Any], ...], dict[str, tuple[int, int]]] = {}
+        for group_idx, group in enumerate(filter_groups):
+            for filter_idx, flt in enumerate(group):
+                filter_pos = (group_idx, filter_idx)
+                pass_value = flt.get("PASS")
+                if pass_value is None:
+                    continue
+                if pass_value == _FUSED_BWD_PASS:
+                    continue
+                if pass_value not in (_BWD_INPUT_PASS, _BWD_WEIGHT_PASS):
+                    continue
+                key = _filter_key_without(flt, {"PASS"})
+                by_pass = seen_in_row.setdefault(key, {})
+                if pass_value in by_pass:
+                    raise ValueError(
+                        f"order directive[{directive_idx}] has duplicate split-backward "
+                        f"{pass_value} entry for {dict(key)} at indices "
+                        f"{by_pass[pass_value]} and {filter_pos}"
                     )
-                    bucket_zero_flags = [True] * len(buckets)
-                else:
-                    bucket_zero_flags = [False]
+                by_pass[pass_value] = filter_pos
+                split_keys.add(key)
+                split_entries.setdefault(key, []).append((directive_idx, filter_pos, pass_value))
+
+    for key, entries in sorted(split_entries.items()):
+        bi = [entry for entry in entries if entry[2] == _BWD_INPUT_PASS]
+        bw = [entry for entry in entries if entry[2] == _BWD_WEIGHT_PASS]
+        if len(bi) != 1 or len(bw) != 1:
+            raise ValueError(
+                f"split backward order entries for {dict(key)} must contain exactly "
+                f"one BI and one BW; got BI={len(bi)} BW={len(bw)}"
+            )
+        if bi[0][0] != bw[0][0]:
+            raise ValueError(
+                f"split backward order entries for {dict(key)} must appear in the "
+                "same order directive row"
+            )
+        if bi[0][1] > bw[0][1]:
+            raise ValueError(
+                f"split backward order entries for {dict(key)} must place BI before BW"
+            )
+
+    return split_keys
+
+
+def _iter_data_edges(dag: TrainingDAG) -> list[TrainingDAGEdge]:
+    return [e for e in dag.edges if e.dep_kind == "data"]
+
+
+def _remove_edge(dag: TrainingDAG, edge: TrainingDAGEdge) -> None:
+    edge_key = (edge.src_uid, edge.dst_uid, edge.dep_kind, edge.tensor_name)
+    if edge_key not in dag.edge_keys:
+        return
+    dag.edge_keys.remove(edge_key)
+    dag.edges = [
+        e for e in dag.edges
+        if not (
+            e.src_uid == edge.src_uid
+            and e.dst_uid == edge.dst_uid
+            and e.dep_kind == edge.dep_kind
+            and e.tensor_name == edge.tensor_name
+        )
+    ]
+    if edge.src_uid in dag.succs:
+        dag.succs[edge.src_uid].discard(edge.dst_uid)
+    if edge.dst_uid in dag.preds:
+        dag.preds[edge.dst_uid].discard(edge.src_uid)
+
+
+def _device_to_physical_pp_rank(dag: TrainingDAG) -> dict[tuple[int, ...], int]:
+    device_keys = sorted({
+        tuple(sorted(node.device))
+        for node in dag.nodes.values()
+        if node.device is not None
+    })
+    return {key: idx for idx, key in enumerate(device_keys)}
+
+
+def _insert_send_recv_comm_nodes(dag: TrainingDAG, comm_stream: str | None = None) -> None:
+    data_edges = _iter_data_edges(dag)
+    device_to_pp_rank = _device_to_physical_pp_rank(dag)
+    send_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "SEND_COMM")
+    recv_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "RECV_COMM")
+    for edge in data_edges:
+        src = dag.nodes[edge.src_uid]
+        dst = dag.nodes[edge.dst_uid]
+        if src.node_kind != "COMPUTE" or dst.node_kind != "COMPUTE":
+            continue
+        if src.device is None or dst.device is None:
+            raise ValueError(f"cannot insert send/recv for unplaced edge {src.uid} -> {dst.uid}")
+        if src.device == dst.device:
+            continue
+        src_device_key = tuple(sorted(src.device))
+        dst_device_key = tuple(sorted(dst.device))
+        send_uid = f"send.{send_idx}"
+        send_idx += 1
+        recv_uid = f"recv.{recv_idx}"
+        recv_idx += 1
+        stream_name = comm_stream if comm_stream is not None else "pp_stream"
+        send_node = TrainingDAGNode(
+            uid=send_uid,
+            node_kind="SEND_COMM",
+            compute_subkind=None,
+            tag=dict(src.tag),
+            device=(None if src.device is None else list(src.device)),
+            stream=stream_name,
+            node_meta={
+                "from_uid": src.uid,
+                "to_uid": dst.uid,
+                "peer_pp_rank": device_to_pp_rank[dst_device_key],
+                "bucket_key": src.node_meta.get("bucket_key"),
+            },
+        )
+        recv_node = TrainingDAGNode(
+            uid=recv_uid,
+            node_kind="RECV_COMM",
+            compute_subkind=None,
+            tag=dict(dst.tag),
+            device=(None if dst.device is None else list(dst.device)),
+            stream=stream_name,
+            node_meta={
+                "from_uid": src.uid,
+                "to_uid": dst.uid,
+                "peer_pp_rank": device_to_pp_rank[src_device_key],
+                "bucket_key": dst.node_meta.get("bucket_key"),
+            },
+        )
+        dag.add_node(send_node)
+        dag.add_node(recv_node)
+        _remove_edge(dag, edge)
+        dag.add_edge(TrainingDAGEdge(src_uid=src.uid, dst_uid=send_uid, dep_kind="data", tensor_name=edge.tensor_name))
+        dag.add_edge(TrainingDAGEdge(src_uid=recv_uid, dst_uid=dst.uid, dep_kind="data", tensor_name=edge.tensor_name))
+
+
+def _replicate_update_nodes_by_device(dag: TrainingDAG) -> None:
+    """Replicate UPD nodes per upstream device set so split components stay homogeneous."""
+    upd_nodes = [n for n in list(dag.nodes.values()) if n.node_kind == "UPD" and ".rep" not in n.uid]
+    for upd in upd_nodes:
+        in_edges = [e for e in list(dag.edges) if e.dep_kind == "temporal" and e.dst_uid == upd.uid]
+        by_dev: dict[tuple[int, ...], list[TrainingDAGEdge]] = {}
+        for e in in_edges:
+            src = dag.nodes[e.src_uid]
+            if src.device is None:
+                continue
+            key = tuple(sorted(src.device))
+            by_dev.setdefault(key, []).append(e)
+        if len(by_dev) <= 1:
+            if len(by_dev) == 1:
+                upd.device = list(next(iter(by_dev.keys())))
+            continue
+        created_uids: list[str] = []
+        for i_upd, (dev_key, dev_edges) in enumerate(sorted(by_dev.items(), key=lambda kv: kv[0])):
+            nu = upd.uid if i_upd == 0 else f"{upd.uid}.rep{i_upd}"
+            if i_upd == 0:
+                upd.device = list(dev_key)
             else:
-                if seg_idx % 2 == 0:
-                    bucket_zero_flags = [True]
-                else:
-                    bucket_zero_flags = [False]
+                dag.add_node(
+                    TrainingDAGNode(
+                        uid=nu,
+                        node_kind="UPD",
+                        compute_subkind=None,
+                        tag=dict(upd.tag),
+                        device=list(dev_key),
+                        stream=upd.stream,
+                        node_meta=dict(upd.node_meta),
+                    )
+                )
+            created_uids.append(nu)
+            for e in dev_edges:
+                _remove_edge(dag, e)
+                dag.add_edge(
+                    TrainingDAGEdge(
+                        src_uid=e.src_uid,
+                        dst_uid=nu,
+                        dep_kind=e.dep_kind,
+                        tensor_name=e.tensor_name,
+                    )
+                )
+        # Duplicate outgoing edges of the original UPD to all replicas.
+        out_edges = [e for e in list(dag.edges) if e.dep_kind == "temporal" and e.src_uid == upd.uid]
+        if out_edges:
+            for e in out_edges:
+                for nu in created_uids[1:]:
+                    dag.add_edge(
+                        TrainingDAGEdge(
+                            src_uid=nu,
+                            dst_uid=e.dst_uid,
+                            dep_kind=e.dep_kind,
+                            tensor_name=e.tensor_name,
+                        )
+                    )
 
+
+def _remove_node_and_incident_edges(dag: TrainingDAG, uid: str) -> None:
+    incident = [e for e in dag.edges if e.src_uid == uid or e.dst_uid == uid]
+    for e in incident:
+        _remove_edge(dag, e)
+    dag.nodes.pop(uid, None)
+    dag.succs.pop(uid, None)
+    dag.preds.pop(uid, None)
+    for s in dag.succs.values():
+        s.discard(uid)
+    for p in dag.preds.values():
+        p.discard(uid)
+
+
+def _rewire_bwd_successors_through_sync(
+    dag: TrainingDAG,
+    bwd_uid: str,
+    sync_uid: str,
+) -> None:
+    """Add update ordering through a gradient sync comm node.
+
+    BWD data successors carry activation gradients, including P2P sends to an
+    upstream pipeline stage.  Gradient synchronization is for parameter grads,
+    so it must not be inserted into those data paths.  UPD dependencies are
+    temporal; add sync -> UPD temporal edges so the
+    update waits for gradient synchronization as well as the BWD compute.
+    """
+    upd_temporal_outs = [
+        e for e in list(dag.edges)
+        if e.dep_kind == "temporal"
+        and e.src_uid == bwd_uid
+        and dag.nodes[e.dst_uid].node_kind == "UPD"
+    ]
+    for e in upd_temporal_outs:
+        dag.add_edge(
+            TrainingDAGEdge(
+                src_uid=sync_uid,
+                dst_uid=e.dst_uid,
+                dep_kind="temporal",
+                tensor_name=None,
+            )
+        )
+
+
+def _bucket_matched_fwd_nodes(
+    dag: TrainingDAG,
+    filters: list[dict[str, Any]],
+    bucket_size_mb: int,
+) -> None:
+    if bucket_size_mb <= 0:
+        raise ValueError(f"bucket_size must be > 0 MB, got {bucket_size_mb}")
+    bucket_size_bytes = int(bucket_size_mb) * 1024 * 1024
+
+    fwd_targets = [
+        uid for uid, node in dag.nodes.items()
+        if node.node_kind == "COMPUTE"
+        and node.compute_subkind == "FWD"
+        and any(_match_filter(node.tag, flt) for flt in filters)
+    ]
+
+    # Deterministic rewrite order.
+    fwd_targets.sort(key=lambda uid: (
+        int(dag.nodes[uid].node_meta.get("stage_id", 10**9)),
+        int(dag.nodes[uid].node_meta.get("segment_id", 10**9)),
+    ))
+
+    for fwd_uid in fwd_targets:
+        if fwd_uid not in dag.nodes:
+            continue
+        fwd_node = dag.nodes[fwd_uid]
+        bwd_uid = f"{fwd_uid}.bwd"
+        bwd_node = dag.nodes.get(bwd_uid)
+        if bwd_node is None or bwd_node.node_kind != "COMPUTE" or bwd_node.compute_subkind != "BWD":
+            raise ValueError(f"Expected corresponding BWD node for {fwd_uid}, missing {bwd_uid}")
+
+        gm = fwd_node.node_meta.get("gm")
+        graphargs = fwd_node.node_meta.get("graphargs")
+        input_idxs = fwd_node.node_meta.get("input_idxs")
+        param_idxs = fwd_node.node_meta.get("param_idxs")
+        if gm is None or graphargs is None or input_idxs is None or param_idxs is None:
+            continue
+
+        def _param_bytes(gargs: list, pidxs: list[int]) -> int:
+            total = 0
+            for i in pidxs:
+                if i < 0 or i >= len(gargs):
+                    continue
+                t = gargs[i]
+                if t is not None and hasattr(t, "numel") and hasattr(t, "element_size"):
+                    total += int(t.numel()) * int(t.element_size())
+            return total
+
+        def _placeholder_names(module: fx.GraphModule) -> list[str]:
+            return [n.name for n in module.graph.nodes if n.op == "placeholder"]
+
+        def _param_names(module: fx.GraphModule, pidxs: list[int]) -> list[str]:
+            names = _placeholder_names(module)
+            return [
+                names[i] if 0 <= i < len(names) else f"<invalid-param-idx:{i}>"
+                for i in pidxs
+            ]
+
+        pre_bytes = _param_bytes(graphargs, param_idxs)
+        logger.info(
+            "bucket_stage pre-size node=%s stage=%s seg=%s bucket_size_value=%d "
+            "bucket_size_bytes_passed=%d bucket_size_mib_passed=%.6f param_bytes=%d",
+            fwd_uid,
+            fwd_node.node_meta.get("stage_id"),
+            fwd_node.node_meta.get("segment_id"),
+            int(bucket_size_mb),
+            bucket_size_bytes,
+            bucket_size_bytes / (1024 * 1024),
+            pre_bytes,
+        )
+
+        try:
+            buckets = bucket_stage(
+                gm,
+                graphargs,
+                input_idxs,
+                param_idxs,
+                bucket_size_bytes=bucket_size_bytes,
+                debug_name=fwd_uid,
+            )
+        except Exception as exc:
+            logger.warning(
+                "replicate bucketing skipped for node %s (stage=%s seg=%s): %s",
+                fwd_uid,
+                fwd_node.node_meta.get("stage_id"),
+                fwd_node.node_meta.get("segment_id"),
+                exc,
+            )
+            continue
+        if len(buckets) <= 1:
+            continue
+        original_param_names = set(_param_names(gm, param_idxs))
+        lowered_param_counts: Counter[str] = Counter()
+        for bi, (_bgm, _bin, b_param, b_args) in enumerate(buckets):
+            post_bytes = _param_bytes(b_args, b_param)
+            bucket_param_names = _param_names(_bgm, b_param)
+            lowered_param_counts.update(bucket_param_names)
             logger.info(
-                f"Stage {stage_id} segment {seg_idx} bucketed into {len(buckets)} buckets"
-            )
-            for b_idx, (_, _, bp, ba) in enumerate(buckets):
-                bucket_total_mb = 0.0
-                for i in bp:
-                    arg = ba[i] if i < len(ba) else None
-                    if arg is not None and hasattr(arg, "numel"):
-                        bucket_total_mb += arg.numel() * arg.element_size() / (1024 * 1024)
-                logger.info(f"  bucket {b_idx} total size={bucket_total_mb:.3f}MB")
-                for i in bp:
-                    arg = ba[i] if i < len(ba) else None
-                    if arg is not None and hasattr(arg, "numel"):
-                        mb = arg.numel() * arg.element_size() / (1024 * 1024)
-                        logger.info(
-                            f"  bucket {b_idx} tensor idx={i} shape={tuple(arg.shape)} size={mb:.3f}MB"
+                "bucket_stage bucket-size node=%s bucket=%d/%d param_bytes=%d params=%d names=%s",
+                fwd_uid,
+                bi,
+                len(buckets),
+                post_bytes,
+                len(bucket_param_names),
+                bucket_param_names,
+            )
+        missing_param_names = sorted(original_param_names - set(lowered_param_counts))
+        unknown_param_names = sorted(set(lowered_param_counts) - original_param_names)
+        duplicate_param_names = sorted(
+            name for name, count in lowered_param_counts.items() if count != 1
+        )
+        logger.info(
+            "bucket_stage lowered-ownership node=%s original_params=%d lowered_param_refs=%d "
+            "unique_lowered_params=%d missing_params=%d duplicate_params=%d unknown_params=%d",
+            fwd_uid,
+            len(original_param_names),
+            sum(lowered_param_counts.values()),
+            len(lowered_param_counts),
+            len(missing_param_names),
+            len(duplicate_param_names),
+            len(unknown_param_names),
+        )
+        if missing_param_names or duplicate_param_names or unknown_param_names:
+            logger.warning(
+                "bucket_stage lowered-ownership-invalid node=%s missing=%s duplicates=%s unknown=%s",
+                fwd_uid,
+                missing_param_names[:16],
+                duplicate_param_names[:16],
+                unknown_param_names[:16],
+            )
+
+        # Snapshot old incident edges before rewrite.
+        in_fwd = [e for e in dag.edges if e.dst_uid == fwd_uid]
+        out_fwd = [e for e in dag.edges if e.src_uid == fwd_uid]
+        in_bwd = [e for e in dag.edges if e.dst_uid == bwd_uid]
+        out_bwd = [e for e in dag.edges if e.src_uid == bwd_uid]
+
+        orig_stage = int(fwd_node.node_meta.get("stage_id", -1))
+        orig_seg = int(fwd_node.node_meta.get("segment_id", -1))
+
+        fwd_bucket_uids: list[str] = []
+        bwd_bucket_uids: list[str] = []
+        for bi, (bgm, b_in, b_param, b_args) in enumerate(buckets):
+            f_uid = f"{fwd_uid}.bucket{bi}"
+            b_uid = f"{f_uid}.bwd"
+            fwd_bucket_uids.append(f_uid)
+            bwd_bucket_uids.append(b_uid)
+            dag.add_node(
+                TrainingDAGNode(
+                    uid=f_uid,
+                    node_kind="COMPUTE",
+                    compute_subkind="FWD",
+                    tag=_with_pass_tag(fwd_node.tag, "F"),
+                    device=list(fwd_node.device) if fwd_node.device is not None else None,
+                    stream=fwd_node.stream,
+                    node_meta={
+                        "stage_id": orig_stage,
+                        "segment_id": orig_seg * 1000 + bi,
+                        "gm": bgm,
+                        "input_idxs": list(b_in),
+                        "param_idxs": list(b_param),
+                        "graphargs": list(b_args),
+                        "input_names": [n.name for n in bgm.graph.nodes if n.op == "placeholder"],
+                        "output_names": [],
+                        "a2a_boundary_after": None,
+                        "bucket_idx": bi,
+                        "num_buckets": len(buckets),
+                        "bucket_key": f_uid,
+                    },
+                )
+            )
+            dag.add_node(
+                TrainingDAGNode(
+                    uid=b_uid,
+                    node_kind="COMPUTE",
+                    compute_subkind="BWD",
+                    tag=_with_pass_tag(fwd_node.tag, "B"),
+                    device=list(bwd_node.device) if bwd_node.device is not None else None,
+                    stream=bwd_node.stream,
+                    node_meta={
+                        "fwd_uid": f_uid,
+                        "bucket_idx": bi,
+                        "num_buckets": len(buckets),
+                        "bucket_key": f_uid,
+                        "compute_loss": bool(bwd_node.node_meta.get("compute_loss", False)) and bi == (len(buckets) - 1),
+                    },
+                )
+            )
+
+        first_fwd = fwd_bucket_uids[0]
+        last_fwd = fwd_bucket_uids[-1]
+        # Mirror backward bucket chain in reverse order.
+        first_bwd = bwd_bucket_uids[-1]
+        last_bwd = bwd_bucket_uids[0]
+
+        def _remap(uid: str) -> str:
+            if uid == fwd_uid:
+                return last_fwd
+            if uid == bwd_uid:
+                return first_bwd
+            return uid
+
+        # Remove originals and their incident edges.
+        _remove_node_and_incident_edges(dag, fwd_uid)
+        _remove_node_and_incident_edges(dag, bwd_uid)
+
+        # Re-attach incoming/outgoing edges at bucket chain boundaries.
+        for e in in_fwd:
+            dag.add_edge(TrainingDAGEdge(src_uid=_remap(e.src_uid), dst_uid=first_fwd, dep_kind=e.dep_kind, tensor_name=e.tensor_name))
+        for e in out_fwd:
+            dst = first_bwd if e.dst_uid == bwd_uid else e.dst_uid
+            dag.add_edge(TrainingDAGEdge(src_uid=last_fwd, dst_uid=dst, dep_kind=e.dep_kind, tensor_name=e.tensor_name))
+        for e in in_bwd:
+            dag.add_edge(TrainingDAGEdge(src_uid=_remap(e.src_uid), dst_uid=first_bwd, dep_kind=e.dep_kind, tensor_name=e.tensor_name))
+        for e in out_bwd:
+            dag.add_edge(TrainingDAGEdge(src_uid=last_bwd, dst_uid=_remap(e.dst_uid), dep_kind=e.dep_kind, tensor_name=e.tensor_name))
+
+        # Internal chain edges.
+        for i in range(len(fwd_bucket_uids) - 1):
+            dag.add_edge(TrainingDAGEdge(src_uid=fwd_bucket_uids[i], dst_uid=fwd_bucket_uids[i + 1], dep_kind="data", tensor_name=None))
+        for i in range(len(bwd_bucket_uids) - 1, 0, -1):
+            dag.add_edge(TrainingDAGEdge(src_uid=bwd_bucket_uids[i], dst_uid=bwd_bucket_uids[i - 1], dep_kind="data", tensor_name=None))
+
+
+def _insert_reduce_comm_nodes(
+    dag: TrainingDAG,
+    filters: list[dict[str, Any]],
+    devices: list[int],
+    comm_stream: str | None = None,
+) -> None:
+    reduce_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "REDUCE_COMM")
+    expected = sorted(int(d) for d in devices)
+    for node in list(dag.nodes.values()):
+        if node.node_kind != "COMPUTE" or not _is_backward_weight_subkind(node.compute_subkind):
+            continue
+        if not any(_match_filter(node.tag, flt) for flt in filters):
+            continue
+        if node.device is None:
+            raise ValueError(
+                f"replicate requires placed backward weight-gradient nodes, but node {node.uid} has device=None; "
+                f"expected devices={devices}"
+            )
+        got = sorted(int(d) for d in node.device)
+        if got != expected:
+            raise ValueError(
+                f"replicate device mismatch for node {node.uid}: "
+                f"expected devices={devices}, node_devices={node.device}"
+            )
+        reduce_uid = f"reduce.{reduce_idx}"
+        reduce_idx += 1
+        reduce_node = TrainingDAGNode(
+            uid=reduce_uid,
+            node_kind="REDUCE_COMM",
+            compute_subkind=None,
+            tag=dict(node.tag),
+            device=list(node.device),
+            stream=comm_stream if comm_stream is not None else "default_stream",
+            node_meta={
+                "bwd_uid": node.uid,
+                "bucket_key": node.node_meta.get("bucket_key"),
+            },
+        )
+        dag.add_node(reduce_node)
+        dag.add_edge(
+            TrainingDAGEdge(
+                src_uid=node.uid,
+                dst_uid=reduce_uid,
+                dep_kind="data",
+                tensor_name=None,
+            )
+        )
+        _rewire_bwd_successors_through_sync(dag, node.uid, reduce_uid)
+
+
+def _node_has_trainable_params(dag: TrainingDAG, node: TrainingDAGNode) -> bool:
+    meta = node.node_meta
+    if _is_backward_compute_subkind(node.compute_subkind):
+        fwd_uid = meta.get("fwd_uid")
+        if isinstance(fwd_uid, str) and fwd_uid in dag.nodes:
+            meta = dag.nodes[fwd_uid].node_meta
+    graphargs = meta.get("graphargs")
+    param_idxs = meta.get("param_idxs")
+    if graphargs is None or param_idxs is None:
+        return True
+    return any(
+        0 <= int(i) < len(graphargs)
+        and graphargs[int(i)] is not None
+        and bool(getattr(graphargs[int(i)], "requires_grad", False))
+        for i in param_idxs
+    )
+
+
+def _insert_all_gather_comm_nodes(
+    dag: TrainingDAG,
+    filters: list[dict[str, Any]],
+    devices: list[int],
+    comm_stream: str | None = None,
+) -> None:
+    ag_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "ALL_GATHER_COMM")
+    expected = sorted(int(d) for d in devices)
+    for node in list(dag.nodes.values()):
+        if (
+            node.node_kind != "COMPUTE"
+            or node.compute_subkind not in ("FWD", "BWD", "BWD_I", "BWD_W")
+        ):
+            continue
+        if not any(_match_filter(node.tag, flt) for flt in filters):
+            continue
+        if not _node_has_trainable_params(dag, node):
+            continue
+        if node.device is None:
+            raise ValueError(
+                f"replicate(shard_params=True) requires placed nodes, but node {node.uid} has device=None; "
+                f"expected devices={devices}"
+            )
+        got = sorted(int(d) for d in node.device)
+        if got != expected:
+            raise ValueError(
+                f"replicate device mismatch for node {node.uid}: "
+                f"expected devices={devices}, node_devices={node.device}"
+            )
+        ag_uid = f"all_gather.{ag_idx}"
+        ag_idx += 1
+        ag_tag = dict(node.tag)
+        ag_node = TrainingDAGNode(
+            uid=ag_uid,
+            node_kind="ALL_GATHER_COMM",
+            compute_subkind=None,
+            tag=ag_tag,
+            device=list(node.device),
+            stream=comm_stream if comm_stream is not None else "default_stream",
+            node_meta={
+                "compute_uid": node.uid,
+                "bucket_key": node.node_meta.get("bucket_key"),
+            },
+        )
+        dag.add_node(ag_node)
+        node.node_meta["zero_free_full_params_after"] = True
+        if node.compute_subkind == "BWD_W":
+            for pred_uid in sorted(dag.preds.get(node.uid, set())):
+                pred = dag.nodes[pred_uid]
+                if pred.node_kind == "COMPUTE" and pred.compute_subkind == "BWD_I":
+                    dag.add_edge(
+                        TrainingDAGEdge(
+                            src_uid=pred.uid,
+                            dst_uid=ag_uid,
+                            dep_kind="data",
+                            tensor_name=None,
                         )
+                    )
+        # ALL_GATHER is a pre-dependency of compute: AG -> COMPUTE
+        dag.add_edge(
+            TrainingDAGEdge(
+                src_uid=ag_uid,
+                dst_uid=node.uid,
+                dep_kind="data",
+                tensor_name=None,
+            )
+        )
 
-            for (bucket_gm, bucket_in, bucket_param, bucket_args), apply_zero in zip(
-                buckets, bucket_zero_flags
-            ):
-                original_bucket_gm = bucket_gm
-                shared_placeholder_names = [
-                    n.name for n in original_bucket_gm.graph.nodes if n.op == "placeholder"
-                ]
-                requested_ac_subgraphs = 1
-                if piper_metadata.use_activation_checkpointing:
-                    requested_ac_subgraphs = piper_metadata.activation_num_checkpoints
-                    ac_gms = apply_activation_checkpointing(
-                        bucket_gm,
-                        bucket_args,
-                        bucket_in,
-                        bucket_param,
-                        piper_metadata.activation_num_checkpoints,
+
+def _insert_reduce_scatter_comm_nodes(
+    dag: TrainingDAG,
+    filters: list[dict[str, Any]],
+    devices: list[int],
+    comm_stream: str | None = None,
+) -> None:
+    rs_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "REDUCE_SCATTER_COMM")
+    expected = sorted(int(d) for d in devices)
+    for node in list(dag.nodes.values()):
+        if node.node_kind != "COMPUTE" or not _is_backward_weight_subkind(node.compute_subkind):
+            continue
+        if not any(_match_filter(node.tag, flt) for flt in filters):
+            continue
+        if not _node_has_trainable_params(dag, node):
+            continue
+        if node.device is None:
+            raise ValueError(
+                f"replicate(shard_grads/shard_params) requires placed backward weight-gradient nodes, but node {node.uid} has device=None; "
+                f"expected devices={devices}"
+            )
+        got = sorted(int(d) for d in node.device)
+        if got != expected:
+            raise ValueError(
+                f"replicate device mismatch for node {node.uid}: "
+                f"expected devices={devices}, node_devices={node.device}"
+            )
+        rs_uid = f"reduce_scatter.{rs_idx}"
+        rs_idx += 1
+        rs_node = TrainingDAGNode(
+            uid=rs_uid,
+            node_kind="REDUCE_SCATTER_COMM",
+            compute_subkind=None,
+            tag=dict(node.tag),
+            device=list(node.device),
+            stream=comm_stream if comm_stream is not None else "default_stream",
+            node_meta={
+                "bwd_uid": node.uid,
+                "bucket_key": node.node_meta.get("bucket_key"),
+            },
+        )
+        node.node_meta["zero_alloc_full_grads_before"] = True
+        if node.node_meta.pop("zero_free_full_params_after", None) is not None:
+            rs_node.node_meta["zero_free_full_params_after"] = True
+        dag.add_node(rs_node)
+        dag.add_edge(
+            TrainingDAGEdge(
+                src_uid=node.uid,
+                dst_uid=rs_uid,
+                dep_kind="data",
+                tensor_name=None,
+            )
+        )
+        _rewire_bwd_successors_through_sync(dag, node.uid, rs_uid)
+
+
+def _insert_shard_a2a_comm_nodes(
+    dag: TrainingDAG,
+    filters: list[dict[str, Any]],
+    devices: list[int],
+    comm_stream: str | None = None,
+) -> None:
+    expected = sorted(int(d) for d in devices)
+    a2a_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "A2A_COMM")
+
+    matched_uids = [
+        uid for uid, node in dag.nodes.items()
+        if node.node_kind == "COMPUTE" and any(_match_filter(node.tag, flt) for flt in filters)
+    ]
+
+    def _a2a_boundary_for_edge(src_node: TrainingDAGNode, dst_node: TrainingDAGNode) -> dict[str, Any]:
+        # Backward data edges are reversed from the forward graph:
+        #     FWD: B -> A    becomes    BWD: A.bwd -> B.bwd
+        # The A2A tensor position is defined by the original forward producer,
+        # which is the fwd node paired with the backward edge destination.
+        if (
+            _is_backward_activation_subkind(src_node.compute_subkind)
+            and _is_backward_activation_subkind(dst_node.compute_subkind)
+        ):
+            fwd_producer_uid = dst_node.node_meta.get("fwd_uid")
+            if not isinstance(fwd_producer_uid, str) or fwd_producer_uid not in dag.nodes:
+                raise ValueError(
+                    f"A2A_COMM could not resolve forward producer for BWD edge "
+                    f"{src_node.uid}->{dst_node.uid}: fwd_uid={fwd_producer_uid!r}"
+                )
+            producer = dag.nodes[fwd_producer_uid]
+        else:
+            producer = src_node
+
+        binfo = producer.node_meta.get("a2a_boundary_after")
+        if not isinstance(binfo, dict) or binfo.get("tensor_idx") is None:
+            raise ValueError(
+                f"A2A_COMM missing tensor_idx for edge {src_node.uid}->{dst_node.uid}; "
+                f"producer={producer.uid} boundary_info={binfo!r}"
+            )
+        return binfo
+
+    for uid in matched_uids:
+        if uid not in dag.nodes:
+            continue
+        node = dag.nodes[uid]
+        if node.device is None or sorted(int(d) for d in node.device) != expected:
+            raise ValueError(
+                f"shard requires matched node {uid} to have devices={devices}, got node_devices={node.device}"
+            )
+
+        if node.compute_subkind == "FWD":
+            node.node_meta["apply_zero"] = False
+        elif _is_backward_compute_subkind(node.compute_subkind):
+            fwd_uid = node.node_meta.get("fwd_uid")
+            if isinstance(fwd_uid, str) and fwd_uid in dag.nodes:
+                dag.nodes[fwd_uid].node_meta["apply_zero"] = False
+
+        # Shard replaces replicate-style grad/param sync comms around matched
+        # compute nodes; remove existing gather/reduce comm nodes attached to
+        # this compute node before inserting A2A edges.
+        removable_sync_kinds = {"ALL_GATHER_COMM", "REDUCE_COMM", "REDUCE_SCATTER_COMM"}
+        removable_comm_uids: set[str] = set()
+        for e in list(dag.edges):
+            if e.dep_kind != "data":
+                continue
+            if e.src_uid == uid and e.dst_uid in dag.nodes:
+                other = dag.nodes[e.dst_uid]
+                if other.node_kind in removable_sync_kinds:
+                    removable_comm_uids.add(other.uid)
+            elif e.dst_uid == uid and e.src_uid in dag.nodes:
+                other = dag.nodes[e.src_uid]
+                if other.node_kind in removable_sync_kinds:
+                    removable_comm_uids.add(other.uid)
+        for comm_uid in sorted(removable_comm_uids):
+            if comm_uid not in dag.nodes:
+                continue
+            # Bypass removed sync-comm nodes so dataflow remains connected:
+            # pred -> COMM -> succ  ==>  pred -> succ
+            in_comm = [e for e in list(dag.edges) if e.dep_kind == "data" and e.dst_uid == comm_uid]
+            out_comm = [e for e in list(dag.edges) if e.dep_kind == "data" and e.src_uid == comm_uid]
+            for ie in in_comm:
+                for oe in out_comm:
+                    dag.add_edge(
+                        TrainingDAGEdge(
+                            src_uid=ie.src_uid,
+                            dst_uid=oe.dst_uid,
+                            dep_kind="data",
+                            tensor_name=(oe.tensor_name if oe.tensor_name is not None else ie.tensor_name),
+                        )
                     )
-                    if len(ac_gms) > 1:
-                        bucket_gm = ac_gms
-                actual_ac_subgraphs = len(bucket_gm) if isinstance(bucket_gm, list) else 1
-                if piper_metadata.use_activation_checkpointing:
-                    logger.log(VERBOSE,
-                        f"[ac_compile_bucket] stage={stage_id} segment={seg_idx} "
-                        f"requested_subgraphs={requested_ac_subgraphs} "
-                        f"actual_subgraphs={actual_ac_subgraphs} "
-                        f"inputs={len(bucket_in)} params={len(bucket_param)}"
+            _remove_node_and_incident_edges(dag, comm_uid)
+        if removable_comm_uids:
+            node.node_meta.pop("zero_free_full_params_after", None)
+        if node.compute_subkind == "BWD_W":
+            continue
+
+        def _is_a2a_compute_edge(e: TrainingDAGEdge) -> bool:
+            src = dag.nodes[e.src_uid]
+            dst = dag.nodes[e.dst_uid]
+            if src.node_kind != "COMPUTE" or dst.node_kind != "COMPUTE":
+                return False
+            if node.compute_subkind == "FWD":
+                return src.compute_subkind == "FWD" and dst.compute_subkind == "FWD"
+            if _is_backward_activation_subkind(node.compute_subkind):
+                return (
+                    _is_backward_activation_subkind(src.compute_subkind)
+                    and _is_backward_activation_subkind(dst.compute_subkind)
+                )
+            return False
+
+        incoming = [
+            e for e in list(dag.edges)
+            if e.dep_kind == "data"
+            and e.dst_uid == uid
+            and _is_a2a_compute_edge(e)
+        ]
+        outgoing = [
+            e for e in list(dag.edges)
+            if e.dep_kind == "data"
+            and e.src_uid == uid
+            and _is_a2a_compute_edge(e)
+        ]
+
+        for e in incoming:
+            src = dag.nodes[e.src_uid]
+            if src.device is None or sorted(int(d) for d in src.device) != expected:
+                raise ValueError(
+                    f"shard precondition failed for incoming edge {e.src_uid}->{uid}: "
+                    f"upstream compute must be replicated on devices={devices}, got {src.device}"
+                )
+            if src.tag.get("PASS") != node.tag.get("PASS"):
+                raise ValueError(
+                    f"A2A_COMM requires matching compute pass tags; "
+                    f"incoming edge {e.src_uid}->{uid} has src.PASS={src.tag.get('PASS')} "
+                    f"dst.PASS={node.tag.get('PASS')}"
+                )
+        for e in outgoing:
+            dst = dag.nodes[e.dst_uid]
+            if dst.device is None or sorted(int(d) for d in dst.device) != expected:
+                raise ValueError(
+                    f"shard precondition failed for outgoing edge {uid}->{e.dst_uid}: "
+                    f"downstream compute must be replicated on devices={devices}, got {dst.device}"
+                )
+            if dst.tag.get("PASS") != node.tag.get("PASS"):
+                raise ValueError(
+                    f"A2A_COMM requires matching compute pass tags; "
+                    f"outgoing edge {uid}->{e.dst_uid} has src.PASS={node.tag.get('PASS')} "
+                    f"dst.PASS={dst.tag.get('PASS')}"
+                )
+
+        for e in incoming:
+            src_node = dag.nodes[e.src_uid]
+            binfo = _a2a_boundary_for_edge(src_node, node)
+            a2a_tensor_idx = binfo["tensor_idx"]
+            comm_uid = f"a2a.{a2a_idx}"
+            a2a_idx += 1
+            comm_node = TrainingDAGNode(
+                uid=comm_uid,
+                node_kind="A2A_COMM",
+                compute_subkind=None,
+                tag=dict(node.tag),
+                device=list(node.device) if node.device is not None else None,
+                stream=comm_stream if comm_stream is not None else "default_stream",
+                node_meta={
+                    "direction": "incoming",
+                    "target_uid": uid,
+                    "a2a_tensor_idx": a2a_tensor_idx,
+                    "bucket_key": node.node_meta.get("bucket_key", src_node.node_meta.get("bucket_key")),
+                },
+            )
+            dag.add_node(comm_node)
+            _remove_edge(dag, e)
+            dag.add_edge(TrainingDAGEdge(src_uid=e.src_uid, dst_uid=comm_uid, dep_kind="data", tensor_name=e.tensor_name))
+            dag.add_edge(TrainingDAGEdge(src_uid=comm_uid, dst_uid=uid, dep_kind="data", tensor_name=e.tensor_name))
+
+        for e in outgoing:
+            dst_node = dag.nodes[e.dst_uid]
+            binfo = _a2a_boundary_for_edge(node, dst_node)
+            a2a_tensor_idx = binfo["tensor_idx"]
+            comm_uid = f"a2a.{a2a_idx}"
+            a2a_idx += 1
+            comm_node = TrainingDAGNode(
+                uid=comm_uid,
+                node_kind="A2A_COMM",
+                compute_subkind=None,
+                tag=dict(node.tag),
+                device=list(node.device) if node.device is not None else None,
+                stream=comm_stream if comm_stream is not None else "default_stream",
+                node_meta={
+                    "direction": "outgoing",
+                    "source_uid": uid,
+                    "a2a_tensor_idx": a2a_tensor_idx,
+                    "bucket_key": node.node_meta.get("bucket_key", dst_node.node_meta.get("bucket_key")),
+                },
+            )
+            dag.add_node(comm_node)
+            _remove_edge(dag, e)
+            dag.add_edge(TrainingDAGEdge(src_uid=uid, dst_uid=comm_uid, dep_kind="data", tensor_name=e.tensor_name))
+            dag.add_edge(TrainingDAGEdge(src_uid=comm_uid, dst_uid=e.dst_uid, dep_kind="data", tensor_name=e.tensor_name))
+
+
+def _topological_order(dag: TrainingDAG) -> list[str]:
+    in_deg: dict[str, int] = {uid: 0 for uid in dag.nodes}
+    out_edges_by_src: dict[str, list[TrainingDAGEdge]] = {uid: [] for uid in dag.nodes}
+    for e in dag.edges:
+        if e.dst_uid in in_deg:
+            in_deg[e.dst_uid] += 1
+        if e.src_uid in out_edges_by_src:
+            out_edges_by_src[e.src_uid].append(e)
+    q = sorted(uid for uid, d in in_deg.items() if d == 0)
+    out: list[str] = []
+    while q:
+        cur_level = q
+        q = []
+        for u in cur_level:
+            out.append(u)
+            # Decrement per outgoing edge (not per unique successor), because we
+            # can have parallel edges between the same pair (e.g., data + temporal).
+            for e in out_edges_by_src.get(u, []):
+                v = e.dst_uid
+                if v not in in_deg:
+                    continue
+                in_deg[v] -= 1
+                if in_deg[v] == 0:
+                    q.append(v)
+        q.sort()
+    if len(out) != len(dag.nodes):
+        remaining = [uid for uid, d in in_deg.items() if d > 0]
+        raise ValueError(
+            "TrainingDAG topological sort failed: unresolved incoming edges remain; "
+            f"possible real cycle or malformed parallel-edge bookkeeping. Remaining nodes: {remaining[:8]}"
+        )
+    return out
+
+
+def _topological_levels(dag: TrainingDAG) -> dict[str, int]:
+    """Return each node's topological level.
+
+    Source nodes are level 0. Every other node is one level after its latest
+    predecessor, so independent successors can share the same level.
+    """
+    topo = _topological_order(dag)
+    levels: dict[str, int] = {}
+    for uid in topo:
+        pred_levels = [
+            levels[pred_uid]
+            for pred_uid in dag.preds.get(uid, set())
+            if pred_uid in levels
+        ]
+        levels[uid] = (max(pred_levels) + 1) if pred_levels else 0
+    return levels
+
+
+def _serial_topological_order(
+    dag: TrainingDAG,
+    topo_levels: dict[str, int] | None = None,
+) -> list[str]:
+    """Serialize topological levels into a deterministic dispatch order.
+
+    Nodes with lower topological levels always come first. Within a level,
+    priority order is: SEND > other comm > compute > RECV.
+    """
+    if topo_levels is None:
+        topo_levels = _topological_levels(dag)
+
+    base_order = _topological_order(dag)
+    topo_idx = {uid: i for i, uid in enumerate(base_order)}
+
+    _OTHER_COMM_KINDS = {
+        "REDUCE_COMM",
+        "ALL_GATHER_COMM",
+        "REDUCE_SCATTER_COMM",
+        "A2A_COMM",
+    }
+
+    def node_priority(uid: str) -> int:
+        kind = dag.nodes[uid].node_kind
+        if kind == "SEND_COMM":
+            return 0
+        if kind in _OTHER_COMM_KINDS:
+            return 1
+        if kind == "RECV_COMM":
+            return 3
+        return 2
+
+    return sorted(
+        dag.nodes,
+        key=lambda uid: (topo_levels[uid], node_priority(uid), topo_idx[uid]),
+    )
+
+
+def _has_path(dag: TrainingDAG, src_uid: str, dst_uid: str) -> bool:
+    seen: set[str] = set()
+    stack = [src_uid]
+    while stack:
+        uid = stack.pop()
+        if uid == dst_uid:
+            return True
+        if uid in seen:
+            continue
+        seen.add(uid)
+        stack.extend(dag.succs.get(uid, set()))
+    return False
+
+
+def _resolve_default_stream_order(dag: TrainingDAG) -> None:
+    """Create a total ordering over default-stream COMPUTE nodes.
+
+    Whenever multiple default-stream compute nodes share a topological level,
+    chain them with temporal edges in descending order of downstream
+    dependencies (more downstream nodes -> earlier in the chain). Downstream
+    count is the size of each compute node's transitive successor set, which in
+    a well-formed training DAG terminates at UPD.
+
+    The new edges shift topological levels, so after each chain insertion we
+    recompute levels and rescan from the earliest level. The pass terminates
+    when every default-stream compute node sits at a unique level.
+    """
+    def _is_default_compute(uid: str) -> bool:
+        node = dag.nodes[uid]
+        return node.stream == _DEFAULT_STREAM and node.node_kind == "COMPUTE"
+
+    compute_uids = [uid for uid in dag.nodes if _is_default_compute(uid)]
+    if len(compute_uids) < 2:
+        return
+
+    def _downstream_count(uid: str) -> int:
+        seen: set[str] = set()
+        stack = list(dag.succs.get(uid, set()))
+        while stack:
+            v = stack.pop()
+            if v in seen:
+                continue
+            seen.add(v)
+            stack.extend(dag.succs.get(v, set()))
+        return len(seen)
+
+    while True:
+        topo_levels = _topological_levels(dag)
+        by_level: dict[int, list[str]] = {}
+        for uid in compute_uids:
+            by_level.setdefault(topo_levels[uid], []).append(uid)
+
+        conflict_level: int | None = None
+        for level in sorted(by_level):
+            if len(by_level[level]) > 1:
+                conflict_level = level
+                break
+        if conflict_level is None:
+            return
+
+        # Same-level nodes have no path between them (otherwise their levels
+        # would differ), so chaining them in any direction is acyclic. Order by
+        # downstream count desc; break ties by uid for determinism.
+        group = by_level[conflict_level]
+        ordered = sorted(group, key=lambda u: (-_downstream_count(u), u))
+        for src, dst in zip(ordered, ordered[1:]):
+            dag.add_edge(
+                TrainingDAGEdge(
+                    src_uid=src,
+                    dst_uid=dst,
+                    dep_kind="temporal",
+                    tensor_name=None,
+                )
+            )
+
+
+def resolve_total_order_per_stream(dag: TrainingDAG) -> None:
+    """Serialize the default stream, then add temporal edges so each non-default
+    logical stream is a strict chain.
+
+    The default-stream pass runs first so the per-non-default-stream anchors
+    below see the post-serialization topological order of default-stream nodes.
+
+    Non-default stream nodes are ordered by the topological order of the
+    default-stream nodes they directly depend on or are depended on by.
+    """
+    _resolve_default_stream_order(dag)
+
+    topo = _topological_order(dag)
+    topo_idx = {uid: i for i, uid in enumerate(topo)}
+    topo_levels = _topological_levels(dag)
+    default_uids = [
+        uid for uid in topo
+        if dag.nodes[uid].stream == _DEFAULT_STREAM
+    ]
+    streams = sorted({
+        n.stream for n in dag.nodes.values()
+        if n.stream != _DEFAULT_STREAM
+    })
+    if not default_uids or not streams:
+        return
+
+    for stream in streams:
+        associated_by_default: dict[str, list[str]] = {uid: [] for uid in default_uids}
+        default_anchors_by_stream_uid: dict[str, list[str]] = {}
+        for edge in dag.edges:
+            src = dag.nodes[edge.src_uid]
+            dst = dag.nodes[edge.dst_uid]
+            if src.stream == _DEFAULT_STREAM and dst.stream == stream:
+                default_anchors_by_stream_uid.setdefault(edge.dst_uid, []).append(edge.src_uid)
+            elif src.stream == stream and dst.stream == _DEFAULT_STREAM:
+                default_anchors_by_stream_uid.setdefault(edge.src_uid, []).append(edge.dst_uid)
+
+        for stream_uid in {
+            uid for uid, node in dag.nodes.items()
+            if node.stream == stream
+        }:
+            default_anchors = default_anchors_by_stream_uid.get(stream_uid, [])
+            if not default_anchors:
+                continue
+            anchor_uid = min(default_anchors, key=lambda uid: (topo_levels[uid], topo_idx[uid]))
+            associated_by_default.setdefault(anchor_uid, []).append(stream_uid)
+
+        current_uid: str | None = None
+        for default_uid in default_uids:
+            stream_uids = sorted(
+                set(associated_by_default.get(default_uid, [])),
+                key=lambda u: (topo_levels[u], topo_idx[u]),
+            )
+            for next_uid in stream_uids:
+                if current_uid is not None and current_uid != next_uid:
+                    if _has_path(dag, next_uid, current_uid):
+                        raise ValueError(
+                            "resolve_total_order_per_stream would create a cycle while ordering "
+                            f"stream={stream}: {current_uid} -> {next_uid}"
+                        )
+                    if not _has_path(dag, current_uid, next_uid):
+                        dag.add_edge(
+                            TrainingDAGEdge(
+                                src_uid=current_uid,
+                                dst_uid=next_uid,
+                                dep_kind="temporal",
+                                tensor_name=None,
+                            )
+                        )
+                current_uid = next_uid
+
+
+def _prune_zero_lifetime_metadata(dag: TrainingDAG) -> list[list[str]]:
+    """Prune redundant ZeRO param/grad lifetimes across same-bucket compute chains.
+
+    Returns the union of gradient and parameter chains used for pruning, one
+    list per chain ordered by topo. The caller passes these to
+    ``_add_inter_chain_temporal_edges`` *after*
+    ``resolve_total_order_per_stream`` runs, so the inter-chain check sees the
+    post-stream-serialization topo levels for the gather_stream and
+    reduce_stream nodes.
+    """
+    topo_levels = _topological_levels(dag)
+    topo = _serial_topological_order(dag, topo_levels)
+
+    def _is_compute(uid: str) -> bool:
+        node = dag.nodes[uid]
+        return (
+            node.node_kind == "COMPUTE"
+            and node.compute_subkind in ("FWD", "BWD", "BWD_I", "BWD_W")
+        )
+
+    def _chain_bucket(uid: str) -> Any:
+        return dag.nodes[uid].node_meta.get("bucket_key")
+
+    def _compute_uids_with(topo_order: list[str], predicate) -> list[str]:
+        return [
+            uid for uid in topo_order
+            if uid in dag.nodes and _is_compute(uid) and predicate(uid)
+        ]
+
+    def _has_direct_compute_dependency(src_uid: str, dst_uid: str) -> bool:
+        return any(
+            e.src_uid == src_uid
+            and e.dst_uid == dst_uid
+            and e.dep_kind in ("data", "temporal")
+            and e.src_uid in dag.nodes
+            and e.dst_uid in dag.nodes
+            and _is_compute(e.src_uid)
+            and _is_compute(e.dst_uid)
+            for e in dag.edges
+        )
+
+    def _build_chains(candidates: list[str], topo_order: list[str]) -> list[list[str]]:
+        chains: list[list[str]] = []
+        by_bucket: dict[Any, list[str]] = {}
+        for uid in candidates:
+            by_bucket.setdefault(_chain_bucket(uid), []).append(uid)
+        topo_idx = {uid: i for i, uid in enumerate(topo_order)}
+        for bucket, uids in sorted(by_bucket.items(), key=lambda item: str(item[0])):
+            uid_set = set(uids)
+            links: dict[str, set[str]] = {uid: set() for uid in uids}
+            for e in dag.edges:
+                if (
+                    e.dep_kind in ("data", "temporal")
+                    and e.src_uid in uid_set
+                    and e.dst_uid in uid_set
+                    and _is_compute(e.src_uid)
+                    and _is_compute(e.dst_uid)
+                ):
+                    links[e.src_uid].add(e.dst_uid)
+                    links[e.dst_uid].add(e.src_uid)
+
+            seen: set[str] = set()
+            for uid in uids:
+                if uid in seen:
+                    continue
+                component: list[str] = []
+                stack = [uid]
+                seen.add(uid)
+                while stack:
+                    cur = stack.pop()
+                    component.append(cur)
+                    for nxt in sorted(links[cur], key=lambda u: topo_idx[u]):
+                        if nxt not in seen:
+                            seen.add(nxt)
+                            stack.append(nxt)
+                chains.append(sorted(component, key=lambda u: topo_idx[u]))
+        return chains
+
+    def _data_preds(uid: str) -> list[TrainingDAGEdge]:
+        return [
+            e for e in list(dag.edges)
+            if e.dep_kind == "data" and e.dst_uid == uid
+        ]
+
+    def _data_succs(uid: str) -> list[TrainingDAGEdge]:
+        return [
+            e for e in list(dag.edges)
+            if e.dep_kind == "data" and e.src_uid == uid
+        ]
+
+    def _all_gather_preds(uid: str) -> list[str]:
+        return sorted(
+            e.src_uid
+            for e in dag.edges
+            if e.dep_kind == "data"
+            and e.dst_uid == uid
+            and e.src_uid in dag.nodes
+            and dag.nodes[e.src_uid].node_kind == "ALL_GATHER_COMM"
+        )
+
+    def _reduce_scatter_succs(uid: str) -> list[str]:
+        return sorted(
+            e.dst_uid
+            for e in dag.edges
+            if e.dep_kind == "data"
+            and e.src_uid == uid
+            and e.dst_uid in dag.nodes
+            and dag.nodes[e.dst_uid].node_kind == "REDUCE_SCATTER_COMM"
+        )
+
+    def _remove_all_gather(ag_uid: str, compute_uid: str, bucket: Any) -> None:
+        if ag_uid not in dag.nodes:
+            return
+        in_ag = _data_preds(ag_uid)
+        out_ag = _data_succs(ag_uid)
+        for ie in in_ag:
+            for oe in out_ag:
+                dag.add_edge(
+                    TrainingDAGEdge(
+                        src_uid=ie.src_uid,
+                        dst_uid=oe.dst_uid,
+                        dep_kind="data",
+                        tensor_name=(oe.tensor_name if oe.tensor_name is not None else ie.tensor_name),
                     )
-                all_modules.append(
-                    (
-                        bucket_gm,
-                        bucket_in,
-                        bucket_param,
-                        bucket_args,
-                        original_bucket_gm,
-                        shared_placeholder_names,
-                        apply_zero,
+                )
+        _remove_node_and_incident_edges(dag, ag_uid)
+
+    def _remove_reduce_scatter(rs_uid: str, compute_uid: str, bucket: Any) -> None:
+        if rs_uid not in dag.nodes:
+            return
+        _remove_node_and_incident_edges(dag, rs_uid)
+
+    has_grad_sync = any(
+        n.node_kind in ("REDUCE_COMM", "REDUCE_SCATTER_COMM")
+        for n in dag.nodes.values()
+    )
+    has_all_gathers = any(n.node_kind == "ALL_GATHER_COMM" for n in dag.nodes.values())
+
+    grad_chains: list[list[str]] = []
+    param_chains: list[list[str]] = []
+
+    # Gradient lifetimes: keep one full-grad allocation and one reduce-scatter
+    # per directly-connected same-bucket gradient chain.
+    if has_grad_sync:
+        grad_candidates = _compute_uids_with(
+            topo,
+            lambda uid: bool(dag.nodes[uid].node_meta.get("zero_alloc_full_grads_before"))
+        )
+        if grad_candidates:
+            grad_chains = _build_chains(grad_candidates, topo)
+            covered = {uid for chain in grad_chains for uid in chain}
+            assert covered == set(grad_candidates), (
+                "zero_lifetime_prune: gradient mode did not assign every grad-allocation "
+                f"compute node to a chain; missing={sorted(set(grad_candidates) - covered)}"
+            )
+
+            for chain in grad_chains:
+                bucket = _chain_bucket(chain[0])
+                assert all(_chain_bucket(uid) == bucket for uid in chain), (
+                    f"zero_lifetime_prune: gradient chain has mixed buckets: "
+                    f"{[(uid, _chain_bucket(uid)) for uid in chain]}"
+                )
+                logger.info(
+                    "zero_lifetime_prune_grad_chain bucket=%s nodes=%s keep_alloc_for=%s keep_reduce_scatter_for=%s",
+                    bucket,
+                    chain,
+                    chain[0],
+                    chain[-1],
+                )
+
+                for uid in chain:
+                    rs_uids = _reduce_scatter_succs(uid)
+                    assert rs_uids, (
+                        f"zero_lifetime_prune: gradient chain node {uid} has "
+                        "zero_alloc_full_grads_before but no outgoing reduce-scatter"
                     )
+
+                for uid in chain[1:]:
+                    dag.nodes[uid].node_meta.pop("zero_alloc_full_grads_before", None)
+                for uid in chain[:-1]:
+                    for rs_uid in _reduce_scatter_succs(uid):
+                        _remove_reduce_scatter(rs_uid, uid, bucket)
+
+                assert dag.nodes[chain[0]].node_meta.get("zero_alloc_full_grads_before"), (
+                    f"zero_lifetime_prune: gradient chain root {chain[0]} lost grad allocation"
                 )
-            seg_end_bucket = len(all_modules) - 1  # inclusive index of last bucket in this segment
-
-            if seg_idx < len(boundary_infos):
-                binfo = boundary_infos[seg_idx]
-                a2a_boundaries[seg_end_bucket] = binfo["tensor_idx"]
-
-        if a2a_boundaries:
-            stage_a2a_boundaries[stage_id] = a2a_boundaries
-
-        stage_bucket_counts[stage_id] = len(all_modules)
-        for b_idx, (
-            bgm,
-            b_in,
-            b_param,
-            bargs,
-            _original_gm,
-            _shared_placeholder_names,
-            apply_zero,
-        ) in enumerate(all_modules):
-            has_trainable = any(
-                bargs[i] is not None and getattr(bargs[i], "requires_grad", False)
-                for i in b_param
-            )
-            logger.debug(
-                f"[zero_bucket_classify] stage={stage_id} bucket={b_idx} "
-                f"has_trainable={has_trainable} apply_zero={apply_zero} "
-                f"param_idxs={b_param}"
-            )
-            if has_trainable:
-                trainable_bucket_keys.add((stage_id, b_idx))
-                if apply_zero:
-                    zero_bucket_keys.add((stage_id, b_idx))
-            else:
-                logger.warning(
-                    f"Stage {stage_id} bucket {b_idx} has no trainable parameters."
-                )
-
-        modules_data = []
-        for bgm, b_in, b_param, bargs, original_gm, shared_placeholder_names, apply_zero in all_modules:
-            if isinstance(bgm, list):
-                modules_data.append(
-                    {
-                        "gm_data_list": [_serialize_graphmodule(sgm) for sgm in bgm],
-                        "ac_num_subgraphs": len(bgm),
-                        "ac_requested_subgraphs": piper_metadata.activation_num_checkpoints,
-                        "graphargs": bargs,
-                        "input_idxs": b_in,
-                        "param_idxs": b_param,
-                        "shared_placeholder_names": shared_placeholder_names,
-                        "apply_zero": apply_zero,
-                        "triton_constant_args_list": [
-                            _collect_triton_constant_args(sgm) for sgm in bgm
-                        ],
-                    }
+                for prev_uid, uid in zip(chain, chain[1:]):
+                    assert _has_direct_compute_dependency(prev_uid, uid), (
+                        f"zero_lifetime_prune: gradient chain is not directly linked: "
+                        f"{prev_uid} does not have a direct compute dependency to {uid}; chain={chain}"
+                    )
+                    assert not dag.nodes[uid].node_meta.get("zero_alloc_full_grads_before"), (
+                        f"zero_lifetime_prune: non-root gradient chain node {uid} still allocates grads"
+                    )
+                live_rs = [
+                    (uid, rs_uid)
+                    for uid in chain
+                    for rs_uid in _reduce_scatter_succs(uid)
+                ]
+                assert live_rs, (
+                    f"zero_lifetime_prune: gradient chain has no live reduce-scatter; chain={chain}"
+                )
+                assert all(uid == chain[-1] for uid, _rs_uid in live_rs), (
+                    f"zero_lifetime_prune: only chain tail {chain[-1]} should keep reduce-scatter, "
+                    f"but live reduce-scatters are {live_rs}"
                 )
-            else:
-                modules_data.append(
-                    {
-                        "gm_data": _serialize_graphmodule(bgm),
-                        "ac_num_subgraphs": 1,
-                        "ac_requested_subgraphs": (
-                            piper_metadata.activation_num_checkpoints
-                            if piper_metadata.use_activation_checkpointing
-                            else 1
-                        ),
-                        "graphargs": bargs,
-                        "input_idxs": b_in,
-                        "param_idxs": b_param,
-                        "shared_placeholder_names": shared_placeholder_names,
-                        "apply_zero": apply_zero,
-                        "triton_constant_args": _collect_triton_constant_args(bgm),
-                    }
-                )
-
-        all_stage_compiled[stage_id] = (modules_data, a2a_boundaries, actor)
-
-        del stage_gm
-        del graphargs
-        del placeholders
-        gc.collect()
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-
-    # Compute globally unique bucket IDs for each (stage_id, stage_bucket_id) pair.
-    # Stages are sorted by stage_id to match the ordering in expand_chunks_to_dags
-    # Pass 1.5, ensuring DAG task.unique_bucket_id matches actor data structure keys.
-    _ubid = 0
-    for s in sorted(stage_bucket_counts.keys()):
-        _stage_ubid_offsets[s] = _ubid
-        _ubid += stage_bucket_counts[s]
-
-    # Now dispatch _load_stage with unique_bucket_ids embedded in modules_data.
-    for stage_id, (modules_data, a2a_boundaries, actor) in all_stage_compiled.items():
-        ubid_offset = _stage_ubid_offsets[stage_id]
-        for b_idx, md in enumerate(modules_data):
-            md["unique_bucket_id"] = ubid_offset + b_idx
-        actor._load_stage.remote(
-            stage_id,
-            modules_data,
-            a2a_boundaries,
-            use_activation_checkpointing=piper_metadata.use_activation_checkpointing,
-        )
-        # Remove actor from stored compiled data (not needed downstream)
-        all_stage_compiled[stage_id] = (modules_data, a2a_boundaries)
-
-    logger.debug("Sent stages to actors for loading")
-
-    piper_metadata.stage_bucket_counts = stage_bucket_counts
-    piper_metadata.trainable_bucket_keys = trainable_bucket_keys
-    piper_metadata.zero_bucket_keys = zero_bucket_keys
-    stage_data = {
-        "stages": all_stage_compiled,
-        "stage_bucket_counts": stage_bucket_counts,
-        "trainable_bucket_keys": trainable_bucket_keys,
-        "zero_bucket_keys": zero_bucket_keys,
-    }
-    compiled_store = getattr(piper_metadata, "compiled_data_store", None)
-    if compiled_store is not None:
-        ray.get(compiled_store.publish_stage_data.remote(stage_data))
-        logger.debug("Published compiled stage data")
-
-    # Build task DAG from the schedule stored by piper_setup.
-    if piper_metadata.schedule is not None:
-        dag = expand_chunks_to_dags(
-            piper_metadata.schedule,
-            piper_metadata.stage_bucket_counts,
-            stage_a2a_boundaries,
-        )
-        logger.debug(f"Built task DAG: {len(dag.nodes)} nodes")
-
-        add_temporal_dependencies(dag, piper_metadata.schedule)
-        logger.debug(f"Added temporal dependencies")
-
-        # Save a deep copy of the full DAG before split_dag_by_rank removes
-        # cross-rank data edges.  Used later for profiling and critical-path analysis.
-        piper_metadata.full_dag_no_overlap = pickle.loads(pickle.dumps(dag))
-
-        # Split into per-rank DAGs.
-        per_rank_dags = split_dag_by_rank(dag)
-
-        # Insert ZeRO/DP communication tasks.
-        if dp_degree > 1:
-            logger.debug(
-                f"ZeRO bucket filter: {sorted(piper_metadata.zero_bucket_keys)}"
-            )
-            per_rank_dags = insert_zero_ops(
-                per_rank_dags,
-                piper_metadata.zero_stage,
-                zero_bucket_keys=piper_metadata.zero_bucket_keys,
-                gradient_accumulation=piper_metadata.gradient_accumulation,
-            )
-            logger.debug(
-                f"Inserted ZeRO-{piper_metadata.zero_stage} collective nodes "
-                f"(gradient_accumulation={piper_metadata.gradient_accumulation})"
-            )
-
-        overlappable_by_rank: dict[int, list[tuple[Chunk, Chunk]]] = {}
-        if piper_metadata.overlap_chunks:
-            # overlappable = find_overlappable_tasks(piper_metadata.schedule)
-            overlappable = get_overlappable_tasks(piper_metadata.schedule)
-            for t1, t2 in overlappable:
-                overlappable_by_rank.setdefault(t1.pp_rank, []).append((t1, t2))
-                logger.debug(f"Found intra-FWD_BWD overlap pair: {t1} -> {t2}")
-
-        for pp_rank, rank_dag in enumerate(per_rank_dags):
-            rank_pairs = overlappable_by_rank.get(pp_rank, [])
-            if rank_pairs:
-                overlap_chunks(rank_dag, rank_pairs)
-            if piper_metadata.zero_stage == 3:
-                _fuse_consecutive_zero3_param_sequences(
-                    rank_dag,
-                    zero_bucket_keys=piper_metadata.zero_bucket_keys,
-                )
-
-        # Assign time steps
-        for rank_dag in per_rank_dags:
-            assign_time_steps(rank_dag)
-        logger.debug("Assigned time steps")
-
-        piper_metadata.per_rank_dags = per_rank_dags
-
-        # Visualize DAG
-        actors = piper_metadata.actors
-        for pp_rank, per_rank_dag in enumerate(per_rank_dags):
-            if piper_metadata.visualize_dag:
-                try:
-                    visualize_dag(per_rank_dag, output_path=f"out/rank{pp_rank}_dag")
-                except Exception as e:
-                    logger.warning(f"DAG visualization failed for rank {pp_rank} (DAG may be too large for dot): {e}")
-            print_dag_order(
-                per_rank_dag,
-                label=f"rank {pp_rank}",
-                rank=pp_rank,
-                out_dir=piper_metadata.output_dir,
-            )
-
-
-        # Send DAG to actors
-        refs = [actors[pp_rank].load_dag.remote(per_rank_dag)
-            for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags)]
 
-        logger.debug(
-            f"Sent per-rank DAGs onto {len(piper_metadata.per_rank_dags)} actors"
+    # Parameter lifetimes: keep one all-gather and one full-param free per
+    # directly-connected same-bucket parameter chain.  This pass intentionally
+    # does not change grad allocation metadata or remove reduce-scatters.
+    if has_all_gathers:
+        topo = _serial_topological_order(dag, _topological_levels(dag))
+        ag_by_compute = {uid: _all_gather_preds(uid) for uid in topo if uid in dag.nodes and _is_compute(uid)}
+        ag_by_compute = {uid: ags for uid, ags in ag_by_compute.items() if ags}
+
+        candidates = _compute_uids_with(topo, lambda uid: uid in ag_by_compute)
+        param_chains = _build_chains(candidates, topo)
+
+        covered = {uid for chain in param_chains for uid in chain}
+        assert covered == set(candidates), (
+            "zero_lifetime_prune: parameter mode did not assign every all-gathered "
+            f"compute node to a chain; missing={sorted(set(candidates) - covered)}"
         )
-        dag_data = {
-            "per_rank_dags": per_rank_dags,
-            "full_dag_no_overlap": piper_metadata.full_dag_no_overlap,
-        }
-        if compiled_store is not None:
-            ray.get(compiled_store.publish_dag_data.remote(dag_data))
-            logger.debug("Published compiled DAG data")
-
-        # Publish compiled data so dp_rank > 0 can skip torch.compile.
-        piper_metadata.compiled_stage_data = {
-            "stages": all_stage_compiled,           # {stage_id: (modules_data, a2a_boundaries)}
-            "stage_bucket_counts": stage_bucket_counts,
-            "trainable_bucket_keys": trainable_bucket_keys,
-            "zero_bucket_keys": zero_bucket_keys,
-            "per_rank_dags": per_rank_dags,
-            "full_dag_no_overlap": piper_metadata.full_dag_no_overlap,
-        }
 
-    def callback(*args, _gm=gm):
-        logger.warning("Should not directly call compiled module, running non-distributed execution")
-        return _gm(*args)
+        for chain in param_chains:
+            bucket = _chain_bucket(chain[0])
+            assert all(_chain_bucket(uid) == bucket for uid in chain), (
+                f"zero_lifetime_prune: parameter chain has mixed buckets: "
+                f"{[(uid, _chain_bucket(uid)) for uid in chain]}"
+            )
 
-    del original_gm
-    del example_inputs
+            for uid in chain[1:]:
+                for ag_uid in ag_by_compute[uid]:
+                    _remove_all_gather(ag_uid, uid, bucket)
 
-    gc.collect()
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
+            tail = chain[-1]
+            for uid in chain:
+                dag.nodes[uid].node_meta.pop("zero_free_full_params_after", None)
+                for rs_uid in _reduce_scatter_succs(uid):
+                    dag.nodes[rs_uid].node_meta.pop("zero_free_full_params_after", None)
+            dag.nodes[tail].node_meta["zero_free_full_params_after"] = True
 
-    return callback
+            remaining_ag = {uid: _all_gather_preds(uid) for uid in chain}
+            assert remaining_ag[chain[0]], (
+                f"zero_lifetime_prune: parameter chain root {chain[0]} lost all-gather"
+            )
+            for prev_uid, uid in zip(chain, chain[1:]):
+                assert _has_direct_compute_dependency(prev_uid, uid), (
+                    f"zero_lifetime_prune: parameter chain is not directly linked: "
+                    f"{prev_uid} does not have a direct compute dependency to {uid}; chain={chain}"
+                )
+                assert not remaining_ag[uid], (
+                    f"zero_lifetime_prune: non-root parameter chain node {uid} still "
+                    f"has all-gather predecessors {remaining_ag[uid]}; chain={chain}"
+                )
+            assert dag.nodes[tail].node_meta.get("zero_free_full_params_after"), (
+                f"zero_lifetime_prune: parameter chain tail {tail} should free params"
+            )
+            for uid in chain[:-1]:
+                assert not dag.nodes[uid].node_meta.get("zero_free_full_params_after"), (
+                    f"zero_lifetime_prune: non-tail parameter chain node {uid} still frees params"
+                )
+            stale_rs_param_frees = [
+                rs_uid
+                for uid in chain
+                for rs_uid in _reduce_scatter_succs(uid)
+                if dag.nodes[rs_uid].node_meta.get("zero_free_full_params_after")
+            ]
+            assert not stale_rs_param_frees, (
+                f"zero_lifetime_prune: reduce-scatters still own param free metadata: "
+                f"{stale_rs_param_frees}; chain={chain}"
+            )
 
+    return grad_chains + param_chains
 
-# ---------------------------------------------------------------------------
-# DAG-based execution entry point
-# ---------------------------------------------------------------------------
 
-def piper_exec_dag(loss_fn, profiling: bool = False, log_stats: bool = False) -> list:
-    """Execute one training step using the per-rank TaskDAG and :meth:`run_dag`.
+def _add_inter_chain_temporal_edges(
+    dag: TrainingDAG,
+    chains: list[list[str]],
+) -> None:
+    """For each consecutive same-bucket chain pair (parameter or gradient),
+    force the next chain's bucket materialization to wait for the prior chain's
+    bucket free.
 
-    Per-rank DAGs are built and loaded onto actors by the piper backend during
-    compilation.  This function just invokes ``run_dag`` in parallel on all actors.
+    Parameter chains: prev_tail compute frees full params, next root's
+    all-gather predecessor materializes them. Edge: prev_tail -> ag_uid.
 
-    Args:
-        loss_fn: Loss function passed through to each actor's run_dag.
-        profiling: If True, each actor times every node and accumulates the
-            measurement into ``node.profiling_measurements``.
-        log_stats: If True, log step time, throughput, MFU (when
-            ``model_flops_per_token`` was set in :func:`piper_setup`), and
-            peak GPU memory for each rank.
+    Gradient chains: prev_tail's reduce-scatter successor frees full grads,
+    next root compute allocates them. Edge: rs_uid -> next_root.
 
-    Returns:
-        Flat list of per-microbatch losses collected from all actors.
+    Without these edges ``_serial_topological_order``'s other-comm-before-
+    compute priority can dispatch the next bucket op before the prior free
+    runs. Must be called after ``resolve_total_order_per_stream`` so the
+    gather_stream and reduce_stream chains have populated topo levels.
     """
-    assert piper_metadata.per_rank_dags is not None, (
-        "per_rank_dags is None — ensure the model was compiled with the piper backend "
-        "before calling piper_exec_dag()"
+    if not chains:
+        return
+
+    topo_levels = _topological_levels(dag)
+
+    def _chain_bucket(uid: str) -> Any:
+        return dag.nodes[uid].node_meta.get("bucket_key")
+
+    def _all_gather_preds(uid: str) -> list[str]:
+        return sorted(
+            e.src_uid
+            for e in dag.edges
+            if e.dep_kind == "data"
+            and e.dst_uid == uid
+            and e.src_uid in dag.nodes
+            and dag.nodes[e.src_uid].node_kind == "ALL_GATHER_COMM"
+        )
+
+    def _reduce_scatter_succs(uid: str) -> list[str]:
+        return sorted(
+            e.dst_uid
+            for e in dag.edges
+            if e.dep_kind == "data"
+            and e.src_uid == uid
+            and e.dst_uid in dag.nodes
+            and dag.nodes[e.dst_uid].node_kind == "REDUCE_SCATTER_COMM"
+        )
+
+    def _maybe_add_edge(src_uid: str, dst_uid: str) -> None:
+        if topo_levels.get(src_uid, 0) < topo_levels.get(dst_uid, 0):
+            return
+        if _has_path(dag, src_uid, dst_uid):
+            return
+        if _has_path(dag, dst_uid, src_uid):
+            return
+        dag.add_edge(TrainingDAGEdge(
+            src_uid=src_uid,
+            dst_uid=dst_uid,
+            dep_kind="temporal",
+            tensor_name=None,
+        ))
+
+    # Group chains by type (param vs grad) and bucket, then within each group
+    # pair consecutive chains by root topo and add the type-specific edge.
+    param_by_bucket: dict[Any, list[list[str]]] = {}
+    grad_by_bucket: dict[Any, list[list[str]]] = {}
+    for chain in chains:
+        bucket = _chain_bucket(chain[0])
+        if _all_gather_preds(chain[0]):
+            param_by_bucket.setdefault(bucket, []).append(chain)
+        if dag.nodes[chain[0]].node_meta.get("zero_alloc_full_grads_before"):
+            grad_by_bucket.setdefault(bucket, []).append(chain)
+
+    for bucket_chains in param_by_bucket.values():
+        bucket_chains.sort(key=lambda c: topo_levels.get(c[0], 0))
+        for prev_chain, next_chain in zip(bucket_chains, bucket_chains[1:]):
+            for ag_uid in _all_gather_preds(next_chain[0]):
+                _maybe_add_edge(prev_chain[-1], ag_uid)
+
+    for bucket_chains in grad_by_bucket.values():
+        bucket_chains.sort(key=lambda c: topo_levels.get(c[0], 0))
+        for prev_chain, next_chain in zip(bucket_chains, bucket_chains[1:]):
+            for rs_uid in _reduce_scatter_succs(prev_chain[-1]):
+                _maybe_add_edge(rs_uid, next_chain[0])
+
+
+def _apply_split_directive(
+    dag: TrainingDAG,
+    flt: dict[str, Any],
+    dim_name: str,
+    num_microbatches: int,
+) -> None:
+    matched = {uid for uid, n in dag.nodes.items() if _match_filter(n.tag, flt)}
+    if not matched:
+        logger.warning("split(filter=%s): no nodes matched", flt)
+        return
+
+    # UPD nodes are global reduction/update sinks and must not be duplicated
+    # by microbatch split; duplicated predecessors should continue to target
+    # the same UPD node(s).
+    matched = {uid for uid in matched if dag.nodes[uid].node_kind != "UPD"}
+    if not matched:
+        logger.warning("split(filter=%s): only UPD nodes matched; nothing to split", flt)
+        return
+
+    topo = _topological_order(dag)
+    idx = {u: i for i, u in enumerate(topo)}
+    mpos = sorted(idx[u] for u in matched)
+    lo, hi = mpos[0], mpos[-1]
+    interleaved = [
+        u for u in topo[lo:hi + 1]
+        if u not in matched and dag.nodes[u].node_kind != "UPD"
+    ]
+    if interleaved:
+        raise ValueError(
+            f"split requires a contiguous sub-DAG; found interleaved non-matching nodes: {interleaved[:8]}"
+        )
+
+    incoming_boundary = [e for e in list(dag.edges) if e.dst_uid in matched and e.src_uid not in matched]
+    outgoing_boundary = [e for e in list(dag.edges) if e.src_uid in matched and e.dst_uid not in matched]
+
+    sources = {
+        u for u in matched
+        if not any(pred in matched for pred in dag.preds.get(u, set()))
+    }
+    sinks = {
+        u for u in matched
+        if not any(succ in matched for succ in dag.succs.get(u, set()))
+    }
+
+    # Ensure outside edges only touch source/sink boundaries.
+    for e in incoming_boundary:
+        if dag.nodes[e.src_uid].node_kind == "UPD" or dag.nodes[e.dst_uid].node_kind == "UPD":
+            continue
+        if e.dst_uid not in sources:
+            raise ValueError(
+                f"split boundary violation: incoming outside edge targets non-source node {e.dst_uid}"
+            )
+    for e in outgoing_boundary:
+        if dag.nodes[e.src_uid].node_kind == "UPD" or dag.nodes[e.dst_uid].node_kind == "UPD":
+            continue
+        if e.src_uid not in sinks:
+            raise ValueError(
+                f"split boundary violation: outgoing outside edge starts at non-sink node {e.src_uid}"
+            )
+
+    inside_edges = [e for e in list(dag.edges) if e.src_uid in matched and e.dst_uid in matched]
+
+    # Tag originals as microbatch 0.
+    for uid in matched:
+        dag.nodes[uid].tag[dim_name] = 0
+
+    # Create copies for microbatches 1..N-1.
+    copy_uid: dict[tuple[str, int], str] = {}
+    for mb in range(1, num_microbatches):
+        for uid in topo:
+            if uid not in matched:
+                continue
+            nu = f"{uid}.split{dim_name}{mb}"
+            copy_uid[(uid, mb)] = nu
+
+    uid_meta_fields = (
+        "fwd_uid",
+        "compute_uid",
+        "bwd_uid",
+        "source_uid",
+        "target_uid",
+        "src_uid",
+        "dst_uid",
+        "from_uid",
+        "to_uid",
     )
+    for mb in range(1, num_microbatches):
+        for uid in topo:
+            if uid not in matched:
+                continue
+            n = dag.nodes[uid]
+            nu = copy_uid[(uid, mb)]
+            copied_meta = dict(n.node_meta)
+            for k in uid_meta_fields:
+                v = copied_meta.get(k)
+                if isinstance(v, str) and v in matched:
+                    copied_meta[k] = copy_uid[(v, mb)]
+            dag.add_node(
+                TrainingDAGNode(
+                    uid=nu,
+                    node_kind=n.node_kind,
+                    compute_subkind=n.compute_subkind,
+                    tag={**n.tag, dim_name: mb},
+                    device=list(n.device) if n.device is not None else None,
+                    stream=n.stream,
+                    node_meta=copied_meta,
+                )
+            )
+
+    # Duplicate internal sub-DAG edges for each copied microbatch.
+    for mb in range(1, num_microbatches):
+        for e in inside_edges:
+            dag.add_edge(
+                TrainingDAGEdge(
+                    src_uid=copy_uid[(e.src_uid, mb)],
+                    dst_uid=copy_uid[(e.dst_uid, mb)],
+                    dep_kind=e.dep_kind,
+                    tensor_name=e.tensor_name,
+                )
+            )
+
+    # Duplicate incoming edges onto copied sources.
+    for mb in range(1, num_microbatches):
+        for e in incoming_boundary:
+            dag.add_edge(
+                TrainingDAGEdge(
+                    src_uid=e.src_uid,
+                    dst_uid=copy_uid[(e.dst_uid, mb)],
+                    dep_kind=e.dep_kind,
+                    tensor_name=e.tensor_name,
+                )
+            )
+
+    # Duplicate outgoing edges from copied sinks.
+    for mb in range(1, num_microbatches):
+        for e in outgoing_boundary:
+            dag.add_edge(
+                TrainingDAGEdge(
+                    src_uid=copy_uid[(e.src_uid, mb)],
+                    dst_uid=e.dst_uid,
+                    dep_kind=e.dep_kind,
+                    tensor_name=e.tensor_name,
+                )
+            )
+
+
+def _match_set_contiguous_subdag(
+    dag: TrainingDAG,
+    matched: set[str],
+    *,
+    context: str,
+) -> tuple[set[str], set[str], set[str], set[str]]:
+    if not matched:
+        raise ValueError(f"{context}: filter matched zero nodes")
+    # Contiguity should be dependency-based, not tied to one arbitrary
+    # topological ordering (which can interleave independent branches).
+    # A non-matching node is considered interleaving iff it is on some path
+    # between matched nodes: matched -> ... -> node -> ... -> matched.
+    interleaved: list[str] = []
+    for u in dag.nodes.keys():
+        if u in matched:
+            continue
+        # Has matched ancestor?
+        has_matched_ancestor = False
+        seen = {u}
+        stack = list(dag.preds.get(u, set()))
+        while stack:
+            cur = stack.pop()
+            if cur in seen:
+                continue
+            seen.add(cur)
+            if cur in matched:
+                has_matched_ancestor = True
+                break
+            stack.extend(dag.preds.get(cur, set()))
+        if not has_matched_ancestor:
+            continue
+
+        # Has matched descendant?
+        has_matched_descendant = False
+        seen = {u}
+        stack = list(dag.succs.get(u, set()))
+        while stack:
+            cur = stack.pop()
+            if cur in seen:
+                continue
+            seen.add(cur)
+            if cur in matched:
+                has_matched_descendant = True
+                break
+            stack.extend(dag.succs.get(cur, set()))
+        if has_matched_descendant:
+            interleaved.append(u)
+
+    if interleaved:
+        raise ValueError(
+            f"{context}: matched nodes are not contiguous; interleaved non-matching nodes: {interleaved[:8]}"
+        )
+
+    sources = {
+        u for u in matched
+        if not any(pred in matched for pred in dag.preds.get(u, set()))
+    }
+    sinks = {
+        u for u in matched
+        if not any(succ in matched for succ in dag.succs.get(u, set()))
+    }
+    if not sources or not sinks:
+        raise ValueError(f"{context}: failed to identify non-empty source/sink sets")
+    compute_nodes = {u for u in matched if dag.nodes[u].node_kind == "COMPUTE"}
+
+    def _has_upstream_compute(u: str) -> bool:
+        seen = {u}
+        stack = list(dag.preds.get(u, set()))
+        while stack:
+            cur = stack.pop()
+            if cur in seen or cur not in matched:
+                continue
+            seen.add(cur)
+            if dag.nodes[cur].node_kind == "COMPUTE":
+                return True
+            stack.extend(dag.preds.get(cur, set()))
+        return False
+
+    def _has_downstream_compute(u: str) -> bool:
+        seen = {u}
+        stack = list(dag.succs.get(u, set()))
+        while stack:
+            cur = stack.pop()
+            if cur in seen or cur not in matched:
+                continue
+            seen.add(cur)
+            if dag.nodes[cur].node_kind == "COMPUTE":
+                return True
+            stack.extend(dag.succs.get(cur, set()))
+        return False
+
+    compute_sources = {u for u in compute_nodes if not _has_upstream_compute(u)}
+    compute_sinks = {u for u in compute_nodes if not _has_downstream_compute(u)}
+    if not compute_sources or not compute_sinks:
+        raise ValueError(f"{context}: failed to identify non-empty compute source/sink sets")
+
+    return sources, sinks, compute_sources, compute_sinks
+
+
+@dataclass(frozen=True)
+class _OrderSegment:
+    source_uids: set[str]
+    sink_uids: set[str]
+
+
+def _single_device_for_nodes(dag: TrainingDAG, uids: set[str]) -> list[int] | None:
+    device_keys = {
+        tuple(n.device)
+        for uid in uids
+        for n in [dag.nodes[uid]]
+        if n.device is not None
+    }
+    if len(device_keys) == 1:
+        return list(next(iter(device_keys)))
+    return None
+
+
+def _add_order_dummy_node(
+    dag: TrainingDAG,
+    *,
+    uid: str,
+    directive_idx: int,
+    group_idx: int,
+    role: str,
+    grouped_uids: set[str],
+) -> str:
+    dag.add_node(
+        TrainingDAGNode(
+            uid=uid,
+            node_kind="ORDER_DUMMY",
+            compute_subkind=None,
+            tag={"order": directive_idx, "group": group_idx},
+            device=_single_device_for_nodes(dag, grouped_uids),
+            stream=_DEFAULT_STREAM,
+            node_meta={"role": role},
+        )
+    )
+    return uid
+
+
+def _add_temporal_order_edge(dag: TrainingDAG, src_uid: str, dst_uid: str) -> None:
+    logger.debug("Adding temporal dependency edge for order directive: %s -> %s", src_uid, dst_uid)
+    dag.add_edge(
+        TrainingDAGEdge(
+            src_uid=src_uid,
+            dst_uid=dst_uid,
+            dep_kind="temporal",
+            tensor_name=None,
+        )
+    )
+
+
+def _apply_order_directive(
+    dag: TrainingDAG,
+    filter_groups: list[list[dict[str, Any]]],
+    *,
+    directive_idx: int,
+) -> None:
+    segments: list[_OrderSegment] = []
+
+    for group_idx, group in enumerate(filter_groups):
+        group_sources: list[set[str]] = []
+        group_sinks: list[set[str]] = []
+        grouped_uids: set[str] = set()
+        for filter_idx, flt in enumerate(group):
+            matched = {
+                uid for uid, n in dag.nodes.items()
+                if n.node_kind != "ORDER_DUMMY" and _match_filter(n.tag, flt)
+            }
+            _srcs, _snks, csrcs, csnks = _match_set_contiguous_subdag(
+                dag,
+                matched,
+                context=f"order filter[{group_idx}][{filter_idx}]={flt}",
+            )
+            group_sources.append(csrcs)
+            group_sinks.append(csnks)
+            grouped_uids.update(matched)
+
+        if len(group) == 1:
+            segments.append(_OrderSegment(source_uids=group_sources[0], sink_uids=group_sinks[0]))
+            continue
+
+        src_uid = _add_order_dummy_node(
+            dag,
+            uid=f"order.{directive_idx}.group{group_idx}.source",
+            directive_idx=directive_idx,
+            group_idx=group_idx,
+            role="source",
+            grouped_uids=grouped_uids,
+        )
+        sink_uid = _add_order_dummy_node(
+            dag,
+            uid=f"order.{directive_idx}.group{group_idx}.sink",
+            directive_idx=directive_idx,
+            group_idx=group_idx,
+            role="sink",
+            grouped_uids=grouped_uids,
+        )
+        for csrcs in group_sources:
+            for v in csrcs:
+                _add_temporal_order_edge(dag, src_uid, v)
+        for csnks in group_sinks:
+            for u in csnks:
+                _add_temporal_order_edge(dag, u, sink_uid)
+        segments.append(_OrderSegment(source_uids={src_uid}, sink_uids={sink_uid}))
+
+    for i in range(len(segments) - 1):
+        for u in segments[i].sink_uids:
+            for v in segments[i + 1].source_uids:
+                _add_temporal_order_edge(dag, u, v)
+
+
+def apply_schedule_directives(training_dag: TrainingDAG, directives: list[Any] | None) -> None:
+    if not directives:
+        return
+    split_backward_keys = _validate_split_backward_order_stencil(directives)
+
+    # Microbatch expansion must run before split-backward lowering so schedules
+    # can mix fused B and split BI/BW for the same bucket on different MBs.
+    for i, raw in enumerate(directives):
+        if not isinstance(raw, dict) or raw.get("op") != "split":
+            continue
+        flt, dim_name, num_microbatches = _normalize_split_directive(raw)
+        logger.info(
+            "Applying directive[%d]: split(filter=%s, dim_name=%s, num_microbatches=%d)",
+            i, flt, dim_name, num_microbatches
+        )
+        _apply_split_directive(training_dag, flt, dim_name, num_microbatches)
+
+    _apply_split_backward_stencil(training_dag, split_backward_keys)
+    place_streams: set[str] = set()
+    for i, raw in enumerate(directives):
+        if isinstance(raw, dict) and raw.get("op") != "place":
+            continue
+        op, filters, devices, stream, _gather_stream, _reduce_stream, shard_params, shard_grads, bucket_size = _normalize_filter_devices_directive(raw)
+        logger.info(
+            "Applying directive[%d]: %s(filters=%s, devices=%s, stream=%s, shard_params=%s, shard_grads=%s, bucket_size=%s)",
+            i, op, filters, devices, stream, shard_params, shard_grads, bucket_size
+        )
+        for node in training_dag.nodes.values():
+            if node.node_kind not in ("COMPUTE", "UPD"):
+                continue
+            if any(_match_filter(node.tag, flt) for flt in filters):
+                node.device = list(devices)
+        if stream is not None:
+            place_streams.add(stream)
+
+    if place_streams:
+        if len(place_streams) != 1:
+            raise ValueError(f"place directives must agree on stream, got {sorted(place_streams)}")
+        place_stream = next(iter(place_streams))
+    else:
+        place_stream = None
+    _replicate_update_nodes_by_device(training_dag)
+    _insert_send_recv_comm_nodes(training_dag, comm_stream=place_stream)
+
+    for i, raw in enumerate(directives):
+        if isinstance(raw, dict) and raw.get("op") in ("place", "split", "order"):
+            continue
+        op, filters, devices, stream, gather_stream, reduce_stream, shard_params, shard_grads, bucket_size = _normalize_filter_devices_directive(raw)
+        logger.info(
+            "Applying directive[%d]: %s(filters=%s, devices=%s, stream=%s, gather_stream=%s, reduce_stream=%s, shard_params=%s, shard_grads=%s, bucket_size=%s)",
+            i, op, filters, devices, stream, gather_stream, reduce_stream, shard_params, shard_grads, bucket_size
+        )
+        if op == "replicate":
+            if bucket_size is not None:
+                _bucket_matched_fwd_nodes(training_dag, filters, int(bucket_size))
+            if shard_params:
+                _insert_all_gather_comm_nodes(training_dag, filters, devices, comm_stream=gather_stream)
+                _insert_reduce_scatter_comm_nodes(training_dag, filters, devices, comm_stream=reduce_stream)
+            elif shard_grads:
+                _insert_reduce_scatter_comm_nodes(training_dag, filters, devices, comm_stream=reduce_stream)
+            else:
+                _insert_reduce_comm_nodes(training_dag, filters, devices, comm_stream=reduce_stream)
+        elif op == "shard":
+            _insert_shard_a2a_comm_nodes(training_dag, filters, devices, comm_stream=stream)
+        else:
+            raise ValueError(f"Unsupported directive op after normalization: {op}")
+
+    # Final pass for order directives (add temporal dependencies across sub-DAGs).
+    for i, raw in enumerate(directives):
+        if not isinstance(raw, dict) or raw.get("op") != "order":
+            continue
+        filter_groups = _normalize_order_directive(raw)
+        logger.info("Applying directive[%d]: order(filters=%s)", i, filter_groups)
+        _apply_order_directive(training_dag, filter_groups, directive_idx=i)
+
+
+def _split_global_training_dag_by_pp_rank(training_dag: TrainingDAG) -> list[TrainingDAG]:
+    """Split the global DAG into per-device-set disconnected DAGs."""
+    # SEND/RECV pairs intentionally have no edge between them, so cross-rank
+    # placement dependencies separate into disconnected local components here.
+    undirected: dict[str, set[str]] = {uid: set() for uid in training_dag.nodes}
+    for e in training_dag.edges:
+        if e.src_uid in undirected and e.dst_uid in undirected:
+            undirected[e.src_uid].add(e.dst_uid)
+            undirected[e.dst_uid].add(e.src_uid)
+
+    components: list[set[str]] = []
+    seen: set[str] = set()
+    for uid in training_dag.nodes:
+        if uid in seen:
+            continue
+        comp: set[str] = set()
+        stack = [uid]
+        seen.add(uid)
+        while stack:
+            cur = stack.pop()
+            comp.add(cur)
+            for nxt in undirected.get(cur, set()):
+                if nxt not in seen:
+                    seen.add(nxt)
+                    stack.append(nxt)
+        components.append(comp)
+
+    # Validate each component is device-homogeneous and component device-sets are distinct.
+    comp_device_keys: list[tuple[int, ...]] = []
+    for ci, comp in enumerate(components):
+        device_keys = {
+            tuple(sorted(node.device)) for uid in comp for node in [training_dag.nodes[uid]] if node.device is not None
+        }
+        if not device_keys:
+            raise ValueError(f"component[{ci}] has no device assignment after P2P split")
+        if len(device_keys) != 1:
+            raise ValueError(
+                f"component[{ci}] is not device-homogeneous; device sets present: {sorted(device_keys)}"
+            )
+        comp_device_keys.append(next(iter(device_keys)))
+    if len(set(comp_device_keys)) != len(comp_device_keys):
+        raise ValueError(
+            f"expected distinct device sets across split components, got {comp_device_keys}"
+        )
+
+    # Materialize each component as a standalone TrainingDAG.
+    subdags: list[TrainingDAG] = []
+    for comp in components:
+        sub = TrainingDAG()
+        for uid in comp:
+            n = training_dag.nodes[uid]
+            sub.add_node(
+                TrainingDAGNode(
+                    uid=n.uid,
+                    node_kind=n.node_kind,
+                    compute_subkind=n.compute_subkind,
+                    tag=dict(n.tag),
+                    device=(None if n.device is None else list(n.device)),
+                    stream=n.stream,
+                    node_meta=dict(n.node_meta),
+                )
+            )
+        for e in training_dag.edges:
+            if e.src_uid in comp and e.dst_uid in comp:
+                sub.add_edge(
+                    TrainingDAGEdge(
+                        src_uid=e.src_uid,
+                        dst_uid=e.dst_uid,
+                        dep_kind=e.dep_kind,
+                        tensor_name=e.tensor_name,
+                    )
+                )
+        subdags.append(sub)
+
+    def _dag_device_key(d: TrainingDAG) -> tuple[int, ...]:
+        keys = {tuple(sorted(n.device)) for n in d.nodes.values() if n.device is not None}
+        if len(keys) != 1:
+            raise ValueError(f"sub-DAG should have exactly one device key, got {keys}")
+        return next(iter(keys))
+
+    subdags.sort(key=_dag_device_key)
+    return subdags
+
+
+@register_backend
+def piper(gm, example_inputs, **kwargs):
+    """TrainingDAG backend: split by stage/a2a and lower schedule directives."""
+    del example_inputs, kwargs
+
+    schedule_info = getattr(piper_metadata, "schedule_info", {}) or {}
+    schedule_directives = getattr(piper_metadata, "schedule_directives", None)
+    num_stages = int(schedule_info.get("num_stages", schedule_info["pp_degree"]))
+    has_annotations = any(
+        isinstance(node.meta.get("custom"), dict) and node.meta["custom"].get("stage") is not None
+        for node in gm.graph.nodes
+    )
+
+    stage_tag_name = "PP"
+    if has_annotations:
+        for node in gm.graph.nodes:
+            custom = node.meta.get("custom")
+            if isinstance(custom, dict) and custom.get("stage") is not None:
+                if isinstance(custom.get("name"), str) and custom.get("name"):
+                    stage_tag_name = custom["name"]
+                break
+    if has_annotations:
+        _top_level_gm, stage_submodules = _split_gm_by_stages(gm)
+    else:
+        logger.info(
+            "No stage annotations found, profiling graph to split into %d stages",
+            num_stages,
+        )
+        _top_level_gm, stage_submodules = _profile_and_split_gm(gm, num_stages)
+
+    # Build and store the new directed DAG representation for later scheduling transforms.
+    dp_degree = int(os.environ.get("PIPER_DP_DEGREE", "1"))
+    training_dag = build_training_dag(
+        stage_submodules,
+        enable_ep=_directives_enable_ep(schedule_directives),
+        dp_degree=dp_degree,
+        stage_tag_name=stage_tag_name,
+    )
+    apply_schedule_directives(
+        training_dag,
+        schedule_directives,
+    )
+    piper_metadata.training_dag = training_dag
+    per_pp_training_dags = _split_global_training_dag_by_pp_rank(training_dag)
+    output_dir = getattr(piper_metadata, "output_dir", "out")
+    for i, subdag in enumerate(per_pp_training_dags):
+        zero_chains = _prune_zero_lifetime_metadata(subdag)
+        resolve_total_order_per_stream(subdag)
+        _add_inter_chain_temporal_edges(subdag, zero_chains)
+        _log_training_dag_dependencies(subdag)
+        print_training_dag_order(subdag, label=f"pp{i}", rank=i, out_dir=output_dir)
+        _debug_render_training_dag(subdag, output_path=os.path.join(output_dir, f"training_dag_pp{i}"))
+    piper_metadata.per_pp_training_dags = per_pp_training_dags
+
+    logger.info(
+        "piper: built TrainingDAG with %d nodes and %d edges, split into %d per-PP DAG(s)",
+        len(training_dag.nodes),
+        len(training_dag.edges),
+        len(per_pp_training_dags),
+    )
+
+    def callback(*args, _gm=gm):
+        logger.warning(
+            "piper compiled callback invoked directly; running local graph execution"
+        )
+        return _gm(*args)
+
+    return callback
+
+
+def piper_exec_dag(loss_fn, log_stats: bool = False) -> list:
+    """Execute one training step using the loaded per-rank TrainingDAG."""
     actors = piper_metadata.actors
     run_refs = [
-        actors[pp_rank].run_dag.remote(loss_fn=loss_fn, profiling=profiling)
-        for pp_rank in range(len(piper_metadata.per_rank_dags))
+        actor.run_dag.remote(loss_fn=loss_fn)
+        for actor in actors.values()
     ]
     t0 = time.perf_counter()
     results = ray.get(run_refs)
     step_time = time.perf_counter() - t0
 
-    if log_stats or piper_metadata.model_flops_per_token is not None:
-        _log_step_stats(step_time, log_stats, actors, results)
+    if log_stats:
+        _log_step_stats(step_time, log_stats, actors)
 
     losses = []
     for result in results:
@@ -487,27 +2884,12 @@ def piper_exec_dag(loss_fn, profiling: bool = False, log_stats: bool = False) ->
     return losses
 
 
-def _log_step_stats(step_time: float, log_memory: bool, actors: dict, results: list | None = None) -> None:
+def _log_step_stats(step_time: float, log_memory: bool, actors: dict) -> None:
     """Log throughput, MFU, and optionally per-rank peak GPU memory."""
     stats = [f"step_time={step_time:.3f}s"]
 
-    tokens = piper_metadata.tokens_per_step
+    tokens = getattr(piper_metadata, "tokens_per_step", None)
     if tokens is not None:
         stats.append(f"throughput={tokens / step_time:.1f} tok/s")
 
-    flops_per_token = piper_metadata.model_flops_per_token
-    if tokens is not None and flops_per_token is not None:
-        peak_flops = 1979e12
-        if peak_flops is not None:
-            dp_degree = int(os.environ.get("PIPER_DP_DEGREE", "1"))
-            total_gpus = len(actors) * dp_degree
-            achieved_flops = flops_per_token * tokens / step_time
-            mfu = achieved_flops / (peak_flops * total_gpus)
-            stats.append(f"MFU={mfu:.2%}")
-        else:
-            logger.warning(
-                "MFU not computed: GPU peak FLOPs unknown for this device. "
-                "Add it to _GPU_PEAK_FLOPS_BF16 in piper_utils.py."
-            )
-
     logger.info("  ".join(stats))
diff --git a/src/piper_actor.py b/src/piper_actor.py
index 1d870a1..d047a37 100644
--- a/src/piper_actor.py
+++ b/src/piper_actor.py
@@ -3,36 +3,29 @@
 import logging
 import os
 import re
-from typing import Any, Dict, List, Optional, Set
+from typing import Any, Dict, List
 import gc
+from concurrent.futures import Future, ThreadPoolExecutor
 from torch.nn import Parameter
-from torch.autograd.graph import GradientEdge, Node, set_warn_on_accumulate_grad_stream_mismatch
+from torch.autograd.graph import GradientEdge, Node
+from torch.autograd.graph import set_warn_on_accumulate_grad_stream_mismatch
 import torch.distributed as dist
 from collections import defaultdict
 
 from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
 
 from .piper_utils import (
+    _serialize_graphmodule,
     _deserialize_graphmodule,
     create_logger,
     LOG_LEVEL,
-    VERBOSE,
-    NcclOverlapDetector,
 )
 from .backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
-from .piper_exec import TaskType, TaskDAG, Task, runtime_sort_key
+from .piper_exec import TaskType
+from .piper import _serial_topological_order, _training_dag_task_type
 
 CLEANUP_MEMORY = False
 
-# Disable AOT autograd's donated-buffer optimization so that retain_graph=True
-# works during the split BWD_I pass (which must keep the graph alive for BWD_W).
-# try:
-#     import torch._functorch.config as _tmp_fc
-#     _tmp_fc.donated_buffer = False
-#     del _tmp_fc  # remove from module globals so Ray/cloudpickle never sees it
-# except (ImportError, AttributeError):
-#     pass
-
 logger = create_logger("piper_actor", LOG_LEVEL)
 
 def _get_rank(pp_rank, dp_rank, pp_degree):
@@ -42,18 +35,11 @@ def _get_rank(pp_rank, dp_rank, pp_degree):
 def _create_actors(
     num_actors,
     optim_class,
-    num_mbs,
-    num_stages,
-    naive_gradient_sync=False,
     profile=False,
-    stage_to_device=None,
-    zero_stage: int = 0,
     no_nvtx: bool = False,
     pg=None,
     temp_dir: str = None,
     use_inductor: bool = False,
-    ar_a2a_same_stream: bool = False,
-    memory_profile: bool = False,
     pp_outer: bool = False,
 ):
     dp_rank = int(os.environ["PIPER_DP_RANK"])
@@ -75,9 +61,10 @@ def _create_actors(
         }} if profile else {}
         nccl_env = {
             "env_vars": {
-                "NCCL_SOCKET_IFNAME": "ens32",
-                "GLOO_SOCKET_IFNAME": "ens32",
-                "NCCL_DEBUG": "WARN",
+                # "NCCL_SOCKET_IFNAME": "ens32",
+                # "GLOO_SOCKET_IFNAME": "ens32",
+                # "NCCL_P2P_DISABLE": "1",
+                # "NCCL_DEBUG": "INFO",
                 **({"TMPDIR": temp_dir} if (profile and temp_dir) else {}),
             }
         }
@@ -97,27 +84,15 @@ def _create_actors(
             pp_rank,
             optim_class,
             world_size,
-            num_mbs,
-            num_stages,
-            naive_gradient_sync,
             dp_rank=dp_rank,
             dp_degree=dp_degree,
             pp_degree=pp_degree,
-            stage_to_device=stage_to_device,
-            zero_stage=zero_stage,
             no_nvtx=no_nvtx,
             use_inductor=use_inductor,
-            ar_a2a_same_stream=ar_a2a_same_stream,
-            memory_profile=memory_profile,
         )
         piper_metadata.actors[pp_rank] = actor
 
 
-def _get_actor(pp_rank):
-    from .piper_utils import piper_metadata
-
-    return piper_metadata.actors[pp_rank]
-
 @ray.remote
 class PiperActor:
     def __init__(
@@ -125,18 +100,11 @@ def __init__(
         pp_rank,
         optim_class,
         world_size,
-        num_mbs,
-        num_stages,
-        naive_gradient_sync=False,
         dp_rank=0,
         dp_degree=1,
         pp_degree=1,
-        stage_to_device=None,
-        zero_stage: int = 0,
         no_nvtx: bool = False,
         use_inductor: bool = False,
-        ar_a2a_same_stream: bool = False,
-        memory_profile: bool = False,
     ):
         self.logger = create_logger("piper_actor", LOG_LEVEL)
 
@@ -145,28 +113,16 @@ def __init__(
         # Suppress the spurious stream-mismatch warning.
         set_warn_on_accumulate_grad_stream_mismatch(False)
 
-
-        if memory_profile:
-            torch.cuda.memory._record_memory_history(
-                enabled="all", context="all", stacks="all", max_entries=5_000_000,
-            )
-
         self.pp_rank = pp_rank
         self.optim_class = optim_class
-        self.naive_gradient_sync = naive_gradient_sync
-        self.zero_stage = int(zero_stage)
         self.no_nvtx = no_nvtx
         self.use_inductor = bool(use_inductor)
-        self.ar_a2a_same_stream = bool(ar_a2a_same_stream)
 
         self.dp_rank = dp_rank
         self.dp_degree = dp_degree
         self.pp_degree = pp_degree
         self.world_size = world_size
 
-        self.num_mbs = num_mbs
-        self.num_stages = num_stages
-        self.stage_to_device = stage_to_device or {}
         self.dp_group = None
         self.ep_group = None  # separate NCCL communicator for expert-parallel (all2all) ops
         # Per-direction communicators: (src_global_rank, dst_global_rank) -> ProcessGroup
@@ -184,15 +140,7 @@ def __init__(
         self.input = None
         self.labels = None
 
-        self.comp_stream = torch.cuda.Stream()
-        self.overlapped_comp_stream = torch.cuda.Stream()
-        self.ar_stream = torch.cuda.Stream()
-        if self.ar_a2a_same_stream:
-            self.a2a_stream = self.ar_stream
-        else:
-            self.a2a_stream = torch.cuda.Stream()
-        self.p2p_send_stream = torch.cuda.Stream()
-        self.p2p_recv_stream = torch.cuda.Stream()
+        self.streams: dict[str, torch.cuda.Stream] = {}
 
         # map stage id -> original GraphModule (for hook registration)
         self.graph_modules = dict()
@@ -203,14 +151,14 @@ def __init__(
         # accumuate loss for each microbatch
         self.loss = []
 
-        self.tracing = False
-        self._pending_timing_events: list = []  # (label, start_event, stop_event)
-        self.trace_data = defaultdict(list)
-        self.memory_tracing_enabled = False
         # DAG execution state
         self.dag = None
         self.sorted_dag_nodes = None
 
+        # PyTorch profiler state (enabled on demand via start_pytorch_profiler).
+        self._pytorch_profiler_enabled = False
+        self._torch_profiler = None
+
         # Unified inter-task buffer.  Keyed by task.uid (int), storing whatever
         # data the task produced for downstream consumers.
         # Cleared at the start of each run_dag() call.
@@ -220,12 +168,12 @@ def __init__(
         # CUDA events for synchronisation between streams
         self.recv_events: dict = {}   # task_uid -> cuda.Event (for RECV tasks)
         self.a2a_events: dict = {}    # task_uid -> cuda.Event (for FWD_A2A/BWD_A2A tasks)
-        self.ar_events: dict = {}     # unique_bucket_id -> cuda.Event (for ALL_REDUCE tasks)
-        self.rs_events: dict = {}     # unique_bucket_id -> cuda.Event (for REDUCE_SCATTER tasks)
+        self.ar_events: dict = {}     # task_uid -> cuda.Event (for ALL_REDUCE tasks)
+        self.rs_events: dict = {}     # task_uid -> cuda.Event (for REDUCE_SCATTER tasks)
         self.ag_events: dict = {}     # ALL_GATHER task_uid -> cuda.Event
-        self.bwd_events: dict = {}    # unique_bucket_id -> cuda.Event (for BWD/BWD_I/BWD_W tasks)
+        self.bwd_events: dict = {}    # bucket_key -> cuda.Event (for BWD/BWD_I/BWD_W tasks)
 
-        # Per-bucket data keyed by unique_bucket_id (populated by _load_stage).
+        # Per-bucket data keyed by bucket_key (populated by _load_stage).
         # "Bucket" here is a globally unique unit of computation; each stage may
         # contribute one or more buckets.  The actor is agnostic to which stage
         # owns a bucket.
@@ -237,116 +185,108 @@ def __init__(
         self.bucket_optims: dict = {}           # ubid -> optimizer (or None)
         self.bucket_trainable_param_idxs: dict = {}  # ubid -> list of trainable param slot indices
         self.bucket_ac_subgraph_counts: dict = {}    # ubid -> number of sequential AC subgraphs
-        self.bucket_flat_params: dict = {}       # ubid -> flat param tensor for ZeRO
-        self.bucket_flat_grads: dict = {}        # ubid -> flat grad tensor for ZeRO
-        self.bucket_shard_params: dict = {}      # ubid -> shard param tensor for ZeRO
-        self.bucket_shard_optims: dict = {}      # ubid -> shard optimizer for ZeRO
-        self.bucket_rs_grads: dict = {}          # ubid -> reduced shard grad tensor for ZeRO-2/3
-        self.zero_grad_dtype = torch.float32     # ZeRO gradient buffers stay fp32
+        self.bucket_flat_params: dict = {}       # ubid -> full flat param tensor for sharded params
+        self.bucket_flat_grads: dict = {}        # ubid -> flat grad tensor for sharded grads
+        self.bucket_shard_params: dict = {}      # ubid -> local optimizer shard param tensor
+        self.bucket_shard_optims: dict = {}      # ubid -> local shard optimizer
+        self.bucket_rs_grads: dict = {}          # ubid -> reduced local shard grad tensor
+        self.grad_buffer_dtype = torch.float32   # sharded gradient buffers stay fp32
         self.param_shard_info: dict = {}         # ubid -> (shard_start, shard_size, orig_numel)
         self.bucket_param_view_specs: dict = {}  # ubid -> [(param, offset, numel, shape)]
-        self.zero3_full_params_fresh: dict = {}  # ubid -> bool
-        self.bucket_param_locks: dict = {}       # ubid -> threading.Lock for full-param alloc/free
-        self.bucket_grad_locks: dict = {}        # ubid -> threading.Lock for full-grad alloc/free
-        self._logged_bucket_forward_info: set = set()
+        self.full_params_fresh: dict = {}        # ubid -> bool
+        self.param_sharded_ubids: set = set()
+        self.grad_sharded_ubids: set = set()
+        self.zero_managed_ubids: set = set()
+        self.cleanup_executor = ThreadPoolExecutor(max_workers=1)
+        self.pending_param_frees: dict[Any, Future] = {}
+        self.pending_grad_frees: dict[Any, Future] = {}
 
         # Non-trainable constant tensor attributes (e.g. freqs_cis, mask) pushed
         # from the coordinator before compilation so _load_stage can fill them in
         # instead of zero-initializing.  Keyed by bare attribute name (e.g. "freqs_cis").
         self.model_const_attrs: dict = {}
 
-        from .piper_utils import piper_metadata
-        piper_metadata.actor_self = self
-
-    def get_trace_data(self) -> dict:
-        return self.global_rank, self.trace_data
-
     def get_and_reset_peak_memory_stats(self) -> tuple:
         """Return (global_rank, max_memory_allocated_bytes) and reset peak stats."""
         max_alloc = torch.cuda.max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()
         return self.global_rank, max_alloc
 
-    def clear_trace_data(self) -> None:
-        self.trace_data.clear()
-        self._pending_timing_events.clear()
-
-    def set_tracing(self, enabled: bool) -> None:
-        self.tracing = enabled
-        self.logger.info(
-            f"Actor {self.global_rank}: Tracing {'enabled' if enabled else 'disabled'}"
-        )
-
     def reset_peak_memory(self):
         torch.cuda.reset_peak_memory_stats()
 
-    def get_peak_memory(self):
-        return self.global_rank, torch.cuda.max_memory_allocated() / (1024**3)
+    def _nvtx_push(self, label: str) -> None:
+        if not self.no_nvtx:
+            torch.cuda.nvtx.range_push(label)
 
+    def _nvtx_pop(self) -> None:
+        if not self.no_nvtx:
+            torch.cuda.nvtx.range_pop()
 
-    def start_memory_recording(self, max_entries: int = 100000) -> None:
-        """Begin recording CUDA memory allocation history on this actor's GPU."""
-        torch.cuda.memory._record_memory_history(
-            enabled="all",
-            context="all",
-            stacks="all",
-            max_entries=max_entries,
-        )
-        self.logger.info(
-            f"Actor {self.global_rank}: started CUDA memory recording "
-            f"(max_entries={max_entries})"
+    def start_pytorch_profiler(self) -> None:
+        """Begin a torch.profiler session spanning the next run_dag iterations.
+
+        Each node's execution in _run_dag_body is wrapped in a record_function
+        labelled the same as its NVTX range, so the resulting trace identifies
+        per-node work.
+        """
+        self._torch_profiler = torch.profiler.profile(
+            activities=[
+                torch.profiler.ProfilerActivity.CPU,
+                torch.profiler.ProfilerActivity.CUDA,
+            ],
         )
+        self._torch_profiler.__enter__()
+        self._pytorch_profiler_enabled = True
+        self.logger.info(f"Actor {self.global_rank}: PyTorch profiler started")
 
-    def dump_memory_snapshot(self, path: str) -> str:
-        """Dump the recorded CUDA memory snapshot to a pickle file.
+    def stop_pytorch_profiler(self, profile_dir: str) -> str:
+        """End the profiler session and export a chrome trace.
 
-        Returns the path to the written file.
+        The file is named ``dp{dp_rank}_pp{pp_rank}.json`` inside *profile_dir*
+        so the harness can group same-dp-rank actors. Returns the written path.
         """
-        os.makedirs(path, exist_ok=True)
-        filepath = os.path.join(path, f"rank{self.global_rank}_memory.pickle")
-        torch.cuda.memory._dump_snapshot(filepath)
-        torch.cuda.memory._record_memory_history(enabled=None)
+        self._pytorch_profiler_enabled = False
+        self._torch_profiler.__exit__(None, None, None)
+        os.makedirs(profile_dir, exist_ok=True)
+        filepath = os.path.join(
+            profile_dir, f"dp{self.dp_rank}_pp{self.pp_rank}.json"
+        )
+        self._torch_profiler.export_chrome_trace(filepath)
+        self._torch_profiler = None
         self.logger.info(
-            f"Actor {self.global_rank}: dumped memory snapshot to {filepath}"
+            f"Actor {self.global_rank}: PyTorch profiler trace written to {filepath}"
         )
         return filepath
 
-    def set_oom_snapshot_dir(self, path: str) -> None:
-        """Tell this actor where to drop a memory snapshot if run_dag hits a CUDA OOM."""
-        os.makedirs(path, exist_ok=True)
-        self._oom_snapshot_dir = path
+    def _rf_enter(self, label: str):
+        """Enter a profiler record_function for *label* (no-op unless profiling).
 
-    def _log_mem(self, label: str, extra: str = "") -> None:
-        """Emit a single-line memory trajectory marker to the actor log.
+        Returns the entered context (or None) to pass to _rf_exit, mirroring the
+        _nvtx_push/_nvtx_pop pattern so the large match block need not be
+        re-indented under a ``with``.
 
-        Captures current/reserved/peak allocator stats plus retry/OOM counters.
-        Cheap: no CUDA sync, just reads allocator counters.
+        While profiling, we also disable multithreaded autograd for the duration
+        of the task. record_function pushes its range onto the *calling* thread's
+        thread-local stack, but the autograd engine normally runs backward on a
+        separate per-device worker thread, so kernels it launches fall outside the
+        label window and appear un-labelled on the GPU streams. Running backward on
+        the calling thread makes the label span every backward kernel. This perturbs
+        timing slightly, which is acceptable for a diagnostic profiling run.
         """
-        try:
-            alloc = torch.cuda.memory_allocated() / (1024 ** 3)
-            reserv = torch.cuda.memory_reserved() / (1024 ** 3)
-            max_a = torch.cuda.max_memory_allocated() / (1024 ** 3)
-            max_r = torch.cuda.max_memory_reserved() / (1024 ** 3)
-            stats = torch.cuda.memory_stats()
-            active = stats.get("active_bytes.all.current", 0) / (1024 ** 3)
-            retries = stats.get("num_alloc_retries", 0)
-            ooms = stats.get("num_ooms", 0)
-            self.logger.debug(
-                f"[mem] rank={self.global_rank} stage={getattr(self, 'pp_rank', '?')} "
-                f"label={label} allocated={alloc:.3f}GB reserved={reserv:.3f}GB "
-                f"max_alloc={max_a:.3f}GB max_res={max_r:.3f}GB active={active:.3f}GB "
-                f"retries={retries} ooms={ooms} {extra}"
-            )
-        except Exception as e:
-            self.logger.warning(f"[mem] log failed (label={label}): {e}")
-
-    def _nvtx_push(self, label: str) -> None:
-        if not self.no_nvtx:
-            torch.cuda.nvtx.range_push(label)
-
-    def _nvtx_pop(self) -> None:
-        if not self.no_nvtx:
-            torch.cuda.nvtx.range_pop()
+        if not self._pytorch_profiler_enabled:
+            return None
+        mt = torch.autograd.set_multithreading_enabled(False)
+        mt.__enter__()
+        rf = torch.profiler.record_function(label)
+        rf.__enter__()
+        return (rf, mt)
+
+    def _rf_exit(self, rf) -> None:
+        if rf is not None:
+            rf_ctx, mt = rf
+            rf_ctx.__exit__(None, None, None)
+            mt.__exit__(None, None, None)
 
     def load_input(self, inputs):
         self.inputs = [inp.to(self.device) for inp in inputs]
@@ -363,36 +303,6 @@ def load_const_attrs(self, const_attrs: dict) -> None:
         the actor's device so ``_load_stage`` can copy them directly.
         """
         self.model_const_attrs = {k: v.to(self.device) for k, v in const_attrs.items()}
-        self.logger.log(VERBOSE,
-            f"Actor {self.global_rank} loaded {len(self.model_const_attrs)} "
-            f"constant attrs: {list(self.model_const_attrs.keys())}"
-        )
-
-    def _start_timing(self, stream, label):
-        if self.tracing:
-            start = torch.cuda.Event(enable_timing=True)
-            stop = torch.cuda.Event(enable_timing=True)
-            start.record(stream)
-            self._pending_timing_events.append((label, start, stop))
-
-    def _stop_timing(self, stream, label):
-        if self.tracing:
-            for i in range(len(self._pending_timing_events) - 1, -1, -1):
-                lbl, _start, stop = self._pending_timing_events[i]
-                if lbl == label:
-                    stop.record(stream)
-                    break
-
-    def flush_timing_events(self) -> None:
-        """Synchronize all pending timing events and accumulate elapsed times.
-        Call this once per iteration after GPU work completes, instead of
-        synchronizing in _stop_timing which would serialize GPU execution."""
-        if not self.tracing or not self._pending_timing_events:
-            return
-        torch.cuda.synchronize()
-        for label, start, stop in self._pending_timing_events:
-            self.trace_data[label].append(start.elapsed_time(stop))
-        self._pending_timing_events.clear()
 
     def get_node_ip_and_free_port(self):
         import socket
@@ -418,22 +328,12 @@ def _join_process_groups(self, master_addr, master_port):
                 rank=self.global_rank,
                 world_size=self.world_size,
             )
-            self.logger.log(VERBOSE,
-                f"Actor {self.global_rank} has GPU {os.environ['CUDA_VISIBLE_DEVICES']}, joined the global process group"
-            )
 
             if self.dp_degree > 1:
                 self._join_dp_process_group()
             if self.pp_degree > 1:
                 self._join_pp_process_group()
 
-            # Force cuBLAS context initialization on both compute streams so the
-            # first backward pass does not encounter a "no current CUDA context" warning.
-            for compute_stream in (self.comp_stream, self.overlapped_comp_stream):
-                with torch.cuda.stream(compute_stream):
-                    _w = torch.zeros(4, 4, device=self.device)
-                    torch.mm(_w, _w)
-
             self.logger.info(f"Actor {self.global_rank} joined process groups")
 
     def _join_dp_process_group(self):
@@ -450,9 +350,6 @@ def _join_dp_process_group(self):
             if self.global_rank % num_dp_groups == dp_group_id:
                 self.dp_group = process_group
                 self.ep_group = ep_process_group
-                self.logger.log(VERBOSE,
-                    f"Global rank {self.global_rank} joined its dp group {dp_group_id} along with ranks {group_ranks}"
-                )
 
     def _join_pp_process_group(self):
         num_pp_groups = self.world_size // self.pp_degree
@@ -468,248 +365,69 @@ def _join_pp_process_group(self):
                 self.pp_lo_hi = lo_hi_group
                 self.pp_hi_lo = hi_lo_group
 
-        self.logger.log(VERBOSE, f"Actor {self.global_rank} created PP communicators")
-
-    def shutdown(self):
-        dist.destroy_process_group()
-
+    def _derive_dag_bucket_modes(self, training_dag: Any) -> None:
+        self.param_sharded_ubids = set()
+        self.grad_sharded_ubids = set()
+        for node in training_dag.nodes.values():
+            meta = getattr(node, "node_meta", {}) or {}
+            ubid = meta.get("bucket_key")
+            if ubid is None:
+                continue
+            if node.node_kind == "ALL_GATHER_COMM":
+                self.param_sharded_ubids.add(ubid)
+            elif node.node_kind == "REDUCE_SCATTER_COMM":
+                self.grad_sharded_ubids.add(ubid)
+        self.zero_managed_ubids = self.param_sharded_ubids | self.grad_sharded_ubids
 
     @staticmethod
-    def _replace_meta_constants(gm, device):
-        """Replace any constants on meta device with tensors on the actual device."""
+    def _node_meta(node: Any) -> dict:
+        meta = getattr(node, "node_meta", None)
+        return meta if isinstance(meta, dict) else {}
+
+    def _node_bucket_key(self, node: Any) -> Any | None:
+        return self._node_meta(node).get("bucket_key")
+
+    def _relocate_meta_devices(self, gm) -> None:
+        """Rewrite baked ``torch.device('meta')`` literals to the actor device.
+
+        Meta-device compilation captures whatever device was current at trace
+        time (inside ``type_as`` / ``.to(device=x.device)`` / device-aware
+        factory ops like ``torch.zeros(..., device=...)``). That literal
+        round-trips through serialization as ``meta`` and would otherwise make
+        those ops emit meta tensors at runtime, mismatching the CUDA inputs.
+        Lifted params/buffers/inputs are unaffected (placed on device directly),
+        so this only touches device literals embedded in node args/kwargs.
+        """
         import torch.fx as fx
-        from .piper_utils import create_logger, LOG_LEVEL
-        from torch._subclasses.fake_tensor import FakeTensor
-        logger = create_logger("piper_actor", LOG_LEVEL)
-
-        replaced_count = 0
-        params_count = 0
-        buffers_count = 0
-        get_attr_count = 0
-        constants_count = 0
-        attrs_count = 0
-        node_args_count = 0
-        node_meta_count = 0
-        submodule_count = 0
-
-        def is_meta_or_fake(t):
-            """Check if tensor is on meta device or is a FakeTensor."""
-            if isinstance(t, FakeTensor):
-                return True
-            if isinstance(t, torch.Tensor):
-                return t.device.type == 'meta'
-            return False
-
-        def replace_meta_tensor(t):
-            if is_meta_or_fake(t):
-                return torch.empty_like(t, device=device)
-            return t
-
-        def replace_in_module(module, module_name=""):
-            """Recursively replace meta tensors in a module and all its submodules."""
-            nonlocal replaced_count, params_count, buffers_count, get_attr_count, constants_count, attrs_count, node_args_count, node_meta_count, submodule_count
-
-            # replace all parameters that are on meta device or FakeTensor
-            for name, param in module.named_parameters(recurse=False):
-                if is_meta_or_fake(param):
-                    full_name = f"{module_name}.{name}" if module_name else name
-                    logger.log(VERBOSE, f"Replacing parameter '{full_name}' from meta/fake to {device} (shape={param.shape}, dtype={param.dtype}, requires_grad={param.requires_grad})")
-                    new_param = torch.empty_like(param, device=device)
-                    new_param.requires_grad_(param.requires_grad)
-                    setattr(module, name, torch.nn.Parameter(new_param, requires_grad=param.requires_grad))
-                    params_count += 1
-                    replaced_count += 1
-
-            # replace all buffers that are on meta device or FakeTensor
-            for name, buffer in module.named_buffers(recurse=False):
-                if is_meta_or_fake(buffer):
-                    full_name = f"{module_name}.{name}" if module_name else name
-                    logger.log(VERBOSE, f"Replacing buffer '{full_name}' from meta/fake to {device} (shape={buffer.shape}, dtype={buffer.dtype})")
-                    new_buffer = torch.empty_like(buffer, device=device)
-                    setattr(module, name, new_buffer)
-                    buffers_count += 1
-                    replaced_count += 1
-
-            # check gm
-            if isinstance(module, fx.GraphModule):
-                # replace in get_attr nodes
-                for node in module.graph.nodes:
-                    if node.op == 'get_attr':
-                        try:
-                            attr_value = getattr(module, node.target)
-                            if is_meta_or_fake(attr_value):
-                                full_name = f"{module_name}.{node.target}" if module_name else node.target
-                                logger.log(VERBOSE, f"Replacing get_attr '{full_name}' from meta/fake to {device} (shape={attr_value.shape}, dtype={attr_value.dtype})")
-                                new_tensor = torch.empty_like(attr_value, device=device)
-                                setattr(module, node.target, new_tensor)
-                                get_attr_count += 1
-                                replaced_count += 1
-                        except AttributeError:
-                            pass
-
-                    # check node.meta for tensor values
-                    if hasattr(node, 'meta') and isinstance(node.meta, dict):
-                        for key, value in node.meta.items():
-                            if is_meta_or_fake(value):
-                                full_name = f"{module_name}.{node.name}.meta['{key}']" if module_name else f"{node.name}.meta['{key}']"
-                                logger.log(VERBOSE, f"Replacing node.meta['{key}'] for node '{node.name}' from meta/fake to {device} (shape={value.shape}, dtype={value.dtype})")
-                                node.meta[key] = torch.empty_like(value, device=device)
-                                node_meta_count += 1
-                                replaced_count += 1
-
-                # replace in constants dict if it exists
-                if hasattr(module, '_constants'):
-                    for key, value in module._constants.items():
-                        if is_meta_or_fake(value):
-                            full_name = f"{module_name}._constants['{key}']" if module_name else f"_constants['{key}']"
-                            logger.log(VERBOSE, f"Replacing constant '{full_name}' from meta/fake to {device} (shape={value.shape}, dtype={value.dtype})")
-                            module._constants[key] = torch.empty_like(value, device=device)
-                            constants_count += 1
-                            replaced_count += 1
-
-                # traverse all nodes and replace meta tensors in args/kwargs
-                for node in module.graph.nodes:
-                    # replace in args
-                    new_args = []
-                    for arg in node.args:
-                        if is_meta_or_fake(arg):
-                            full_name = f"{module_name}.{node.name}" if module_name else node.name
-                            logger.log(VERBOSE, f"Replacing meta/fake tensor in node '{full_name}' args (shape={arg.shape}, dtype={arg.dtype})")
-                            new_args.append(torch.empty_like(arg, device=device))
-                            node_args_count += 1
-                            replaced_count += 1
-                        elif isinstance(arg, torch.device) and arg.type == 'meta':
-                            # replace meta device objects with the actual device
-                            full_name = f"{module_name}.{node.name}" if module_name else node.name
-                            logger.log(VERBOSE, f"Replacing meta device object in node '{full_name}' args with {device}")
-                            new_args.append(torch.device(device))
-                            node_args_count += 1
-                            replaced_count += 1
-                        elif isinstance(arg, (list, tuple)):
-                            new_args.append(type(arg)(replace_meta_tensor(x) for x in arg))
-                        else:
-                            new_args.append(arg)
-                    node.args = tuple(new_args)
-
-                    # replace in kwargs
-                    new_kwargs = {}
-                    for key, value in node.kwargs.items():
-                        if is_meta_or_fake(value):
-                            full_name = f"{module_name}.{node.name}" if module_name else node.name
-                            logger.log(VERBOSE, f"Replacing meta/fake tensor in node '{full_name}' kwargs['{key}'] (shape={value.shape}, dtype={value.dtype})")
-                            new_kwargs[key] = torch.empty_like(value, device=device)
-                            node_args_count += 1
-                            replaced_count += 1
-                        elif isinstance(value, torch.device) and value.type == 'meta':
-                            # replace meta device objects with the actual device
-                            full_name = f"{module_name}.{node.name}" if module_name else node.name
-                            logger.log(VERBOSE, f"Replacing meta device object in node '{full_name}' kwargs['{key}'] with {device}")
-                            new_kwargs[key] = torch.device(device)
-                            node_args_count += 1
-                            replaced_count += 1
-                        elif isinstance(value, (list, tuple)):
-                            new_kwargs[key] = type(value)(replace_meta_tensor(x) for x in value)
-                        else:
-                            new_kwargs[key] = value
-                    node.kwargs = new_kwargs
 
-                # recompile the GraphModule after modifications
+        device = self.device
+        replaced = 0
+
+        def _fix(value):
+            nonlocal replaced
+            if isinstance(value, torch.device):
+                if value.type == "meta":
+                    replaced += 1
+                    return torch.device(device)
+                return value
+            if isinstance(value, tuple):
+                return tuple(_fix(v) for v in value)
+            if isinstance(value, list):
+                return [_fix(v) for v in value]
+            if isinstance(value, dict):
+                return {k: _fix(v) for k, v in value.items()}
+            return value
+
+        for module in gm.modules():
+            if not isinstance(module, fx.GraphModule):
+                continue
+            before = replaced
+            for node in module.graph.nodes:
+                node.args = _fix(node.args)
+                node.kwargs = _fix(node.kwargs)
+            if replaced != before:
                 module.recompile()
 
-            # recursively process all submodules
-            for name, submodule in module.named_children():
-                submodule_name = f"{module_name}.{name}" if module_name else name
-                replace_in_module(submodule, submodule_name)
-                submodule_count += 1
-
-        replace_in_module(gm)
-
-        if replaced_count > 0:
-            logger.log(VERBOSE,
-                f"_replace_meta_constants: Replaced {replaced_count} meta device tensors "
-                f"(params: {params_count}, buffers: {buffers_count}, get_attr: {get_attr_count}, "
-                f"constants: {constants_count}, attrs: {attrs_count}, node_args: {node_args_count}, "
-                f"node_meta: {node_meta_count}, checked {submodule_count} submodules)"
-            )
-        else:
-            logger.log(VERBOSE, f"_replace_meta_constants: No meta device tensors found to replace! (checked {submodule_count} submodules)")
-
-        return gm
-
-    def _load_stage_kernels(self):
-        try:
-            try:
-                import torchtitan.models.common.moe.kernels
-                import torchtitan.models.common.moe.utils
-                from torchtitan.models.common.moe.kernels import generate_permute_indices
-                from torchtitan.models.common.moe.kernels import fill_indices_wrapper
-            except ModuleNotFoundError:
-                import torchtitan.models.moe.kernels
-                import torchtitan.models.moe.utils
-                from torchtitan.models.moe.kernels import generate_permute_indices
-                from torchtitan.models.moe.kernels import fill_indices_wrapper
-            import torch._higher_order_ops.triton_kernel_wrap
-            self.logger.log(VERBOSE, f"Actor {self.global_rank} imported and initialized Triton kernel modules")
-        except ImportError as e:
-            self.logger.warning(
-                f"Actor {self.global_rank} failed to import Triton kernel modules: {e}. "
-                f"This may cause issues with Triton kernels in the graph."
-            )
-
-        # HACK: manually register Triton kernel in the side table
-        # the graph expects kernel_idx=0, so we need to register it at that index
-        self.logger.log(VERBOSE, f"Manually registering Triton kernel at index 0 on actor {self.global_rank}...")
-        try:
-            # Import the kernel wrapper
-            try:
-                from torchtitan.models.common.moe.kernels import fill_indices_wrapper
-            except ModuleNotFoundError:
-                from torchtitan.models.moe.kernels import fill_indices_wrapper
-
-            # create a dummy function that uses the Triton kernel
-            # This needs to be compiled with torch.compile to register the kernel
-            def dummy_kernel_user(tokens, starts, offsets):
-                return fill_indices_wrapper(
-                    tokens, starts, offsets,
-                    experts_per_rank=1, num_ranks=1, max_len=8, block_size=128
-                )
-
-            # Compile the function to trigger kernel registration
-            device_idx = int(self.device.split(":")[1]) if ":" in self.device else 0
-            with torch.cuda.device(device_idx):
-                # Create dummy inputs
-                dummy_tokens = torch.zeros(8, dtype=torch.int32, device=self.device)
-                dummy_start = torch.zeros(8, dtype=torch.int64, device=self.device)
-                dummy_offsets = torch.zeros(1, dtype=torch.int64, device=self.device)
-
-                # Compile and run to register the kernel
-                compiled_dummy = torch.compile(dummy_kernel_user, mode="reduce-overhead")
-                _ = compiled_dummy(dummy_tokens, dummy_start, dummy_offsets)
-
-            # Register the kernel at index 0 in id_to_kernel (kernel_idx=0 is baked into
-            # compiled graphs; constant_args are restored separately in _load_stage).
-            from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
-            if hasattr(kernel_side_table, 'id_to_kernel'):
-                registered_kernels = list(kernel_side_table.id_to_kernel.items())
-                if registered_kernels:
-                    max_idx, kernel = max(registered_kernels, key=lambda x: x[0])
-                    kernel_side_table.id_to_kernel[0] = kernel
-                    self.logger.log(VERBOSE,
-                        f"Actor {self.global_rank} registered kernel at id_to_kernel[0] "
-                        f"(copied from index {max_idx})"
-                    )
-                else:
-                    self.logger.warning(
-                        f"Actor {self.global_rank} no kernels found in side table after dummy compile"
-                    )
-        except Exception as e:
-            self.logger.warning(
-                f"Actor {self.global_rank} failed to manually register kernel: {e}. "
-                f"This may cause Triton kernel errors."
-            )
-            import traceback
-            self.logger.info(f"Traceback: {traceback.format_exc()}")
-
     def _load_stage(
         self,
         stage_id: int,
@@ -720,12 +438,11 @@ def _load_stage(
         """Load a (possibly bucketed) stage.
 
         *modules_data* is a list of dicts, one per module/bucket, each with keys:
-        ``gm_data``, ``graphargs``, ``input_idxs``, ``param_idxs``, ``unique_bucket_id``.
+        ``gm_data``, ``graphargs``, ``input_idxs``, ``param_idxs``, ``bucket_key``.
 
         A non-bucketed stage is represented as a single-element list.
-        All per-bucket data structures are keyed by ``unique_bucket_id`` — the
-        globally unique bucket index assigned by piper.py — so that run_dag can
-        look up bucket data without knowing which stage owns a bucket.
+        All per-bucket data structures are keyed by ``bucket_key`` so run_dag
+        can look up bucket data without knowing which stage owns a bucket.
         """
         self.logger.debug(
             f"Loading stage {stage_id} ({len(modules_data)} module(s)) on actor {self.global_rank}"
@@ -734,55 +451,26 @@ def _load_stage(
         g = torch.Generator(device=self.device)
         g.manual_seed(1000 * self.global_rank + stage_id)
 
-        self._load_stage_kernels()
-
-        # Restore triton kernel constant_args that were captured at compile time.
-        try:
-            from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
-            for bd in modules_data:
-                if "triton_constant_args_list" in bd:
-                    const_arg_sets = bd.get("triton_constant_args_list", [])
-                else:
-                    const_arg_sets = [bd.get("triton_constant_args", {})]
-                for arg_set in const_arg_sets:
-                    for idx, args in arg_set.items():
-                        kernel_side_table.constant_args[int(idx)] = args
-        except Exception as e:
-            self.logger.warning(f"Actor {self.global_rank} failed to restore triton constant_args: {e}")
-
         first_gm = None
 
         for b_idx, bd in enumerate(modules_data):
-            ubid: int = bd["unique_bucket_id"]
+            ubid: Any = bd["bucket_key"]
             ac_num_subgraphs = int(bd.get("ac_num_subgraphs", 1))
             ac_requested_subgraphs = int(bd.get("ac_requested_subgraphs", ac_num_subgraphs))
             gms = [_deserialize_graphmodule(gm_data) for gm_data in bd["gm_data_list"]] if "gm_data_list" in bd else [_deserialize_graphmodule(bd["gm_data"])]
-            gms = [self._replace_meta_constants(gm, self.device) for gm in gms]
+            for gm in gms:
+                self._relocate_meta_devices(gm)
             if self.use_inductor:
                 compiled_gms = []
                 for subgraph_idx, gm in enumerate(gms):
-                    try:
-                        compiled_gm = torch.compile(gm)
-                        compiled_gms.append(compiled_gm)
-                        self.logger.debug(
-                            f"[load_stage_compile] rank={self.global_rank} stage={stage_id} "
-                            f"ubid={ubid} subgraph={subgraph_idx} compiled=True"
-                        )
-                    except Exception as exc:
-                        self.logger.warning(
-                            f"[load_stage_compile] rank={self.global_rank} stage={stage_id} "
-                            f"ubid={ubid} subgraph={subgraph_idx} compiled=False error={exc}"
-                        )
-                        compiled_gms.append(gm)
+                    compiled_gm = torch.compile(gm)
+                    compiled_gms.append(compiled_gm)
+                    self.logger.debug(
+                        f"[load_stage_compile] rank={self.global_rank} stage={stage_id} "
+                        f"ubid={ubid} subgraph={subgraph_idx} compiled=True"
+                    )
                 gms = compiled_gms
-            # if "gm_data_list" in bd:
-            #     gms = [
-            #         self._replace_meta_constants(_deserialize_graphmodule(gm_data), self.device)
-            #         for gm_data in bd["gm_data_list"]
-            #     ]
-            # else:
-            #     gms = [self._replace_meta_constants(_deserialize_graphmodule(bd["gm_data"]), self.device)]
-            
+
             if b_idx == 0:
                 first_gm = gms[0]
 
@@ -793,31 +481,12 @@ def _load_stage(
             shared_placeholder_names = list(
                 bd.get("shared_placeholder_names", bd.get("placeholder_names", []))
             )
-            input_placeholder_names = [
-                shared_placeholder_names[i] if 0 <= i < len(shared_placeholder_names) else f"<missing:{i}>"
-                for i in b_input_idxs
-            ]
-            param_placeholder_names = [
-                shared_placeholder_names[i] if 0 <= i < len(shared_placeholder_names) else f"<missing:{i}>"
-                for i in b_param_idxs
-            ]
-            self.logger.log(VERBOSE,
-                f"[load_stage_bucket] rank={self.global_rank} stage={stage_id} "
-                f"ubid={ubid} shared_placeholders={shared_placeholder_names} "
-                f"input_idxs={b_input_idxs} input_placeholders={input_placeholder_names} "
-                f"param_idxs={b_param_idxs} param_placeholders={param_placeholder_names}"
-            )
-
             # Extract FX placeholder names for each param index.
             self.bucket_param_names[ubid] = [
                 shared_placeholder_names[i] if i < len(shared_placeholder_names) else f"ubid{ubid}_p{i}"
                 for i in b_param_idxs
             ]
 
-            self.logger.log(VERBOSE,
-                f"Stage {stage_id} bucket {b_idx} (ubid={ubid}) input indices: {b_input_idxs}"
-            )
-
             # Save input tensor metadata for pre-allocating FWD recv buffers.
             # Stored as a list of (shape, dtype, requires_grad) in input-slot order.
             recv_meta = []
@@ -910,33 +579,6 @@ def _bucket_forward_runner(
                             call_args.append(dynamic_name_to_value[name])
                         else:
                             call_args.append(shared_args[_name_to_idx[name]])
-                    try:
-                        self.logger.debug(
-                            f"[bucket_fwd_call] rank={self.global_rank} stage={_stage_id} "
-                            f"ubid={_ubid} subgraph={subgraph_idx} "
-                            f"shared_placeholders={list(_shared_placeholder_names)} "
-                            f"placeholders={placeholder_names} "
-                            f"dynamic_names={dynamic_names}"
-                        )
-                        zero_storage = []
-                        for name, arg in zip(placeholder_names, call_args):
-                            if not isinstance(arg, torch.Tensor):
-                                continue
-                            storage = arg.untyped_storage()
-                            storage_size = storage.size()
-                            if storage_size == 0:
-                                zero_storage.append(
-                                    f"{name}: shape={tuple(arg.shape)} stride={tuple(arg.stride())} "
-                                    f"requires_grad={arg.requires_grad} dtype={arg.dtype} "
-                                    f"device={arg.device} storage_offset={arg.storage_offset()}"
-                                )
-                        if zero_storage:
-                            self.logger.error(
-                                f"[bucket_fwd_zero_storage] rank={self.global_rank} stage={_stage_id} "
-                                f"ubid={_ubid} subgraph={subgraph_idx}: " + " | ".join(zero_storage)
-                            )
-                    except Exception:
-                        self.logger.debug("bucket forward pre-call logging failed", exc_info=True)
                     if _use_ac:
                         out = torch.utils.checkpoint.checkpoint(
                             forward_impl,
@@ -944,32 +586,7 @@ def _bucket_forward_runner(
                             use_reentrant=False,
                         )
                     else:
-                        try:
-                            out = forward_impl(*call_args)
-                        except Exception as exc:
-                            tensor_summaries = []
-                            for name, arg in zip(placeholder_names, call_args):
-                                if isinstance(arg, torch.Tensor):
-                                    storage = arg.untyped_storage()
-                                    tensor_summaries.append(
-                                        f"{name}: shape={tuple(arg.shape)} stride={tuple(arg.stride())} "
-                                        f"requires_grad={arg.requires_grad} dtype={arg.dtype} "
-                                        f"device={arg.device} storage_size={storage.size()} "
-                                        f"storage_offset={arg.storage_offset()}"
-                                    )
-                                else:
-                                    tensor_summaries.append(f"{name}: type={type(arg).__name__}")
-                            self.logger.error(
-                                f"[bucket_fwd_exception] rank={self.global_rank} stage={_stage_id} "
-                                f"ubid={_ubid} subgraph={subgraph_idx} "
-                                f"shared_placeholders={list(_shared_placeholder_names)} "
-                                f"placeholders={placeholder_names} "
-                                f"dynamic_names={dynamic_names} "
-                                f"exc_type={type(exc).__name__} exc={exc} "
-                                f"args={' | '.join(tensor_summaries)}",
-                                exc_info=True,
-                            )
-                            raise
+                        out = forward_impl(*call_args)
                 return out
 
             self.bucket_fwd_fns[ubid] = _bucket_forward_runner
@@ -982,7 +599,16 @@ def _bucket_forward_runner(
             ]
             self.bucket_trainable_param_idxs[ubid] = trainable_idxs
 
-            if self.zero_stage > 0 and self.dp_degree > 1 and apply_zero and trainable_idxs:
+            zero_managed = (
+                self.dp_degree > 1
+                and apply_zero
+                and bool(trainable_idxs)
+                and ubid in self.zero_managed_ubids
+            )
+            params_sharded = ubid in self.param_sharded_ubids
+            grads_sharded = ubid in self.grad_sharded_ubids
+
+            if zero_managed:
                 trainable = [realized[i] for i in trainable_idxs]
                 flat_params = torch.cat([p.detach().view(-1) for p in trainable]).contiguous()
                 flat_params.requires_grad_(False)
@@ -1002,12 +628,12 @@ def _bucket_forward_runner(
                     realized[idx] = realized[idx].detach()
                     realized[idx].data = flat_params[offset:offset + numel].view(p.shape)
                     realized[idx].requires_grad_(True)
-                    realized[idx].grad_dtype = self.zero_grad_dtype
+                    realized[idx].grad_dtype = self.grad_buffer_dtype
                     view_specs.append((realized[idx], offset, numel, tuple(p.shape)))
                     offset += numel
 
                 shard_start = self.dp_rank * shard_size
-                if self.zero_stage == 3:
+                if params_sharded:
                     shard_param = flat_params[shard_start:shard_start + shard_size].detach().clone()
                 else:
                     shard_param = flat_params[shard_start:shard_start + shard_size]
@@ -1019,21 +645,21 @@ def _bucket_forward_runner(
                 self.bucket_flat_params[ubid] = flat_params
                 self.bucket_flat_grads[ubid] = (
                     None
-                    if self.zero_stage in (2, 3)
-                    else torch.zeros(padded_numel, dtype=self.zero_grad_dtype, device=self.device)
+                    if grads_sharded
+                    else torch.zeros(padded_numel, dtype=self.grad_buffer_dtype, device=self.device)
                 )
                 self.bucket_shard_params[ubid] = shard_param
                 self.bucket_shard_optims[ubid] = self.optim_class([shard_param])
-                self.bucket_rs_grads[ubid] = torch.zeros(
-                    shard_size, dtype=self.zero_grad_dtype, device=self.device
+                self.bucket_rs_grads[ubid] = (
+                    torch.zeros(shard_size, dtype=self.grad_buffer_dtype, device=self.device)
+                    if grads_sharded
+                    else None
                 )
                 self.param_shard_info[ubid] = (shard_start, shard_size, orig_numel)
                 self.bucket_param_view_specs[ubid] = view_specs
-                self.zero3_full_params_fresh[ubid] = False
-                # self.bucket_param_locks[ubid] = threading.Lock()
-                # self.bucket_grad_locks[ubid] = threading.Lock()
+                self.full_params_fresh[ubid] = False
 
-                if self.zero_stage == 3:
+                if params_sharded:
                     storage = flat_params.untyped_storage()
                     if storage.size() != 0:
                         storage.resize_(0)
@@ -1044,113 +670,184 @@ def _bucket_forward_runner(
                 self.bucket_param_view_specs[ubid] = []
                 self.bucket_flat_params[ubid] = None
                 self.bucket_flat_grads[ubid] = None
-                self.bucket_param_locks.pop(ubid, None)
-                self.bucket_grad_locks.pop(ubid, None)
                 trainable_for_optim = [realized[i] for i in trainable_idxs]
                 optim = self.optim_class(trainable_for_optim, fused=True) if trainable_for_optim else None
             self.bucket_optims[ubid] = optim
 
-            self.logger.log(VERBOSE,
-                f"[ac_bucket] rank={self.global_rank} stage={stage_id} bucket={b_idx} "
-                f"ubid={ubid}: ac_enabled={use_activation_checkpointing} "
-                f"requested_subgraphs={ac_requested_subgraphs} "
-                f"actual_subgraphs={ac_num_subgraphs} inputs={len(b_input_idxs)} "
-                f"params={len(b_param_idxs)} trainable_params={len(trainable_idxs)} "
-                f"apply_zero={apply_zero}"
-            )
-
-
         # Keep first GraphModule for compatibility with external inspection tools.
         self.graph_modules[stage_id] = first_gm
 
-        # self._log_mem(f"post_load_stage_{stage_id}")
-
     # -----------------------------------------------------------------------
     # DAG-based execution
     # -----------------------------------------------------------------------
 
-    def load_dag(self, dag: TaskDAG) -> None:
-        """Store the per-rank TaskDAG for subsequent run_dag() calls."""
-        self.dag = dag
-        self.sorted_dag_nodes = sorted(dag.nodes, key=runtime_sort_key)
+    def _stream_id(self, node_or_stream: Any) -> str:
+        if isinstance(node_or_stream, str):
+            return node_or_stream
+        return str(getattr(node_or_stream, "stream", "default_stream"))
 
-    def get_all_params(self) -> dict:
-        """Return {unique_bucket_id: flat_cpu_tensor} for every trainable bucket."""
-        result: dict = {}
-        for ubid, trainable_idxs in self.bucket_trainable_param_idxs.items():
-            params = []
-            bucket_args = self.bucket_fwd_args.get(ubid, [])
-            for idx in trainable_idxs:
-                tensor = bucket_args[idx]
-                if tensor is not None:
-                    params.append(tensor.detach().cpu().view(-1))
-            if params:
-                result[ubid] = torch.cat(params).clone()
-        return result
-
-    def get_named_params(self) -> dict:
-        """Return {fx_placeholder_name: cpu_float32_tensor} for all trainable params."""
-        result: dict = {}
-        for ubid, b_fwd_args in self.bucket_fwd_args.items():
-            b_param_idxs = self.bucket_param_idxs[ubid]
-            names = self.bucket_param_names.get(ubid, [])
-            for i, idx in enumerate(b_param_idxs):
-                p = b_fwd_args[idx]
-                if p is not None and p.requires_grad:
-                    name = names[i] if i < len(names) else f"ubid{ubid}_p{i}"
-                    result[name] = p.detach().cpu().clone().float()
-        return result
-
-    def set_named_params(self, named_params: dict) -> None:
-        """Load parameter values from ``{fx_placeholder_name: tensor}`` dict."""
-        for ubid, b_fwd_args in self.bucket_fwd_args.items():
-            b_param_idxs = self.bucket_param_idxs[ubid]
-            names = self.bucket_param_names.get(ubid, [])
-            for i, idx in enumerate(b_param_idxs):
-                p = b_fwd_args[idx]
-                if p is not None and p.requires_grad:
-                    name = names[i] if i < len(names) else None
-                    if name and name in named_params:
-                        with torch.no_grad():
-                            p.data.copy_(named_params[name].to(p.device, p.dtype))
-
-    def get_bucket_fwd_counts(self) -> dict:
-        """Return the set of unique_bucket_ids loaded on this actor."""
-        return list(self.bucket_fwd_fns.keys())
-
-    def _compute_stream_for_id(self, compute_stream_id: str) -> "torch.cuda.Stream":
-        if compute_stream_id == "overlapped_comp_stream":
-            return self.overlapped_comp_stream
-        return self.comp_stream
-
-    def _compute_stream_for_task(self, task: Task) -> "torch.cuda.Stream":
-        return self._compute_stream_for_id(task.compute_stream_id)
-
-    def _timing_stream(self, task) -> "torch.cuda.Stream":
-        """Return the CUDA stream that drives work for the given task type."""
-        task_type = task.task_type
-        if task_type == TaskType.SEND:
-            return self.p2p_send_stream
-        if task_type == TaskType.RECV:
-            return self.p2p_recv_stream
-        if task_type in (TaskType.FWD_A2A, TaskType.BWD_A2A):
-            return self.a2a_stream
-        if task_type in (TaskType.ALL_REDUCE, TaskType.REDUCE_SCATTER, TaskType.ALL_GATHER):
-            return self.ar_stream
-        return self._compute_stream_for_task(task)
-
-    def get_node_runtimes(self) -> dict:
-        """Return ``{uid: profiling_measurements}`` for every profiled DAG node.
-
-        Only nodes that have at least one measurement are included.  Call this
-        after one or more ``run_dag(profiling=True)`` iterations.
-        """
-        return {
-            node.uid: list(node.profiling_measurements)
-            for node in self.dag.nodes
-            if node.profiling_measurements
+    def _initialize_streams_for_training_dag(self, training_dag: Any) -> None:
+        stream_ids = {
+            self._stream_id(n)
+            for n in training_dag.nodes.values()
+            if getattr(n, "stream", None) is not None
+        }
+        stream_ids.add("default_stream")
+        self.streams = {
+            stream_id: torch.cuda.Stream(device=self.device)
+            for stream_id in sorted(stream_ids)
         }
 
+        # Force cuBLAS context initialization on every logical stream used by
+        # this DAG so the first backward pass does not hit lazy CUDA warnings.
+        for stream in self.streams.values():
+            with torch.cuda.stream(stream):
+                _w = torch.zeros(4, 4, device=self.device)
+                torch.mm(_w, _w)
+
+    def _stream_for_id(self, stream_id: str) -> "torch.cuda.Stream":
+        assert stream_id in self.streams, (
+            f"TrainingDAG referenced stream={stream_id!r}, but load_training_dag "
+            f"initialized only {sorted(self.streams)}"
+        )
+        return self.streams[stream_id]
+
+    def _stream_for_task(self, task: Any) -> "torch.cuda.Stream":
+        return self._stream_for_id(self._stream_id(task))
+
+    def _default_stream(self) -> "torch.cuda.Stream":
+        return self._stream_for_id("default_stream")
+
+    def load_training_dag(self, training_dag: Any) -> None:
+        """Load per-PP-rank TrainingDAG compute nodes using existing _load_stage logic.
+
+        This is an adapter for the new TrainingDAG representation where each
+        COMPUTE/FWD node with ``node_meta['gm']`` is equivalent to one bucket/module.
+        The runtime executes TrainingDAG nodes via ``run_dag``; this method
+        only populates actor-side bucket/module state.
+        """
+        if training_dag is None:
+            raise ValueError("load_training_dag requires non-None training_dag")
+        if not hasattr(training_dag, "nodes"):
+            raise TypeError("load_training_dag expected object with 'nodes' field")
+
+        self._derive_dag_bucket_modes(training_dag)
+        self._initialize_streams_for_training_dag(training_dag)
+
+        # Clear any prior module state before loading a new training DAG.
+        self.graph_modules.clear()
+        self.forward_args.clear()
+        self.forward_input_meta.clear()
+
+        stage_to_modules: dict[int, list[dict]] = defaultdict(list)
+
+        # Deterministic order by stage/segment then uid.
+        compute_nodes = [
+            n for n in training_dag.nodes.values()
+            if getattr(n, "node_kind", None) == "COMPUTE"
+            and getattr(n, "compute_subkind", None) == "FWD"
+        ]
+        compute_nodes.sort(
+            key=lambda n: (
+                int(getattr(n, "node_meta", {}).get("stage_id", 10**9)),
+                int(getattr(n, "node_meta", {}).get("segment_id", 10**9)),
+                str(getattr(n, "uid", "")),
+            )
+        )
+
+        seen_bucket_keys: set[Any] = set()
+        skipped_dupe_fwd_nodes = 0
+        for node in compute_nodes:
+            meta = getattr(node, "node_meta", {}) or {}
+            bucket_key = meta.get("bucket_key", getattr(node, "uid", None))
+            if bucket_key in seen_bucket_keys:
+                skipped_dupe_fwd_nodes += 1
+                continue
+            seen_bucket_keys.add(bucket_key)
+            gm = meta.get("gm")
+            gm_data = meta.get("gm_data")
+            stage_id = meta.get("stage_id")
+            input_idxs = meta.get("input_idxs")
+            param_idxs = meta.get("param_idxs")
+            graphargs = meta.get("graphargs")
+            input_names = meta.get("input_names", [])
+            output_names = meta.get("output_names", [])
+
+            if gm_data is None and gm is None:
+                raise ValueError(
+                    f"load_training_dag: compute node {getattr(node, 'uid', '<unknown>')} "
+                    f"is missing required metadata field 'gm_data' (or fallback 'gm')"
+                )
+            if stage_id is None or input_idxs is None or param_idxs is None or graphargs is None:
+                raise ValueError(
+                    f"load_training_dag: compute node {getattr(node, 'uid', '<unknown>')} "
+                    f"is missing required metadata fields"
+                )
+
+            module_data = {
+                "gm_data": gm_data if gm_data is not None else _serialize_graphmodule(gm),
+                "graphargs": list(graphargs),
+                "input_idxs": list(input_idxs),
+                "param_idxs": list(param_idxs),
+                "placeholder_names": list(input_names),
+                "output_names": list(output_names),
+                "shared_placeholder_names": list(input_names),
+                "bucket_key": bucket_key,
+                "ac_num_subgraphs": 1,
+                "ac_requested_subgraphs": 1,
+                "apply_zero": bool(meta.get("apply_zero", True)),
+                "training_dag_uid": getattr(node, "uid", None),
+                "triton_constant_args": dict(meta.get("triton_constant_args", {})),
+            }
+            stage_to_modules[int(stage_id)].append(module_data)
+
+        assert stage_to_modules, (
+            "load_training_dag: no FWD compute nodes with gm metadata found"
+        )
+
+        for stage_id in sorted(stage_to_modules.keys()):
+            self._load_stage(
+                stage_id=stage_id,
+                modules_data=stage_to_modules[stage_id],
+                a2a_boundaries={},
+                use_activation_checkpointing=False,
+            )
+
+        # Build runtime adjacency on TrainingDAG nodes so run_dag can execute directly.
+        for n in training_dag.nodes.values():
+            n.data_preds = []
+            n.data_succs = []
+            n.temporal_preds = []
+            n.temporal_succs = []
+            if n.node_kind == "SEND_COMM":
+                n.peer_pp_rank = n.node_meta.get("peer_pp_rank")
+            elif n.node_kind == "RECV_COMM":
+                n.peer_pp_rank = n.node_meta.get("peer_pp_rank")
+            else:
+                n.peer_pp_rank = None
+        for e in training_dag.edges:
+            if e.src_uid not in training_dag.nodes or e.dst_uid not in training_dag.nodes:
+                continue
+            src = training_dag.nodes[e.src_uid]
+            dst = training_dag.nodes[e.dst_uid]
+            if e.dep_kind == "temporal":
+                src.temporal_succs.append(dst)
+                dst.temporal_preds.append(src)
+            else:
+                src.data_succs.append(dst)
+                dst.data_preds.append(src)
+
+        for n in training_dag.nodes.values():
+            n.task_type = _training_dag_task_type(n)
+            mb = n.tag.get("MB", 0)
+            st = n.tag.get("PP", 0)
+            n.batches = [type("RuntimeBatch", (), {"stage_id": st, "mb_idx": mb})()]
+
+        self.dag = training_dag
+        self.sorted_dag_nodes = [
+            training_dag.nodes[uid] for uid in _serial_topological_order(training_dag)
+        ]
+
     def _clear_param_grads(self) -> None:
         """Restore standard PyTorch semantics: inactive params keep grad=None."""
         for ubid, trainable_idxs in self.bucket_trainable_param_idxs.items():
@@ -1159,237 +856,102 @@ def _clear_param_grads(self) -> None:
                 t = args[idx]
                 if t is not None:
                     t.grad = None
-    def _param_has_effective_grad(self, param: torch.Tensor | None) -> bool:
-        """Return True when a param carries a materialized gradient."""
-        return param is not None and param.grad is not None
 
-    def _accumulate_zero_param_grads_to_flat(self, ubid: int | None) -> None:
+    def _accumulate_zero_param_grads_to_flat(
+        self, ubid: Any | None, stream: "torch.cuda.Stream"
+    ) -> None:
         """Accumulate ZeRO-managed param grads into the fp32 flat grad buffer."""
-        if ubid is None or self.zero_stage <= 0 or self.dp_degree <= 1:
+        if ubid is None or ubid not in self.zero_managed_ubids or self.dp_degree <= 1:
             return
+        if ubid in self.grad_sharded_ubids:
+            self._wait_pending_free(self.pending_grad_frees, ubid)
         specs = self.bucket_param_view_specs.get(ubid, [])
         if not specs:
             return
-        flat_grads = self.bucket_flat_grads.get(ubid)
-        if flat_grads is None:
-            shard_info = self.param_shard_info.get(ubid)
-            if shard_info is None:
-                return
-            _shard_start, shard_size, _orig_numel = shard_info
-            flat_grads = torch.zeros(
-                shard_size * self.dp_degree,
-                dtype=self.zero_grad_dtype,
-                device=self.device,
-            )
-            self.bucket_flat_grads[ubid] = flat_grads
-        for param, offset, numel, _shape in specs:
-            grad = param.grad
-            if grad is None:
-                continue
-            flat_grads[offset:offset + numel].add_(grad.detach().reshape(-1).to(flat_grads.dtype))
-            param.grad = None
+        with torch.cuda.stream(stream):
+            flat_grads = self.bucket_flat_grads.get(ubid)
+            if flat_grads is None:
+                shard_info = self.param_shard_info.get(ubid)
+                if shard_info is None:
+                    return
+                _shard_start, shard_size, _orig_numel = shard_info
+                flat_grads = torch.zeros(
+                    shard_size * self.dp_degree,
+                    dtype=self.grad_buffer_dtype,
+                    device=self.device,
+                )
+                self.bucket_flat_grads[ubid] = flat_grads
+            for param, offset, numel, _shape in specs:
+                grad = param.grad
+                if grad is None:
+                    continue
+                flat_grads[offset:offset + numel].add_(grad.detach().reshape(-1).to(flat_grads.dtype))
+                param.grad = None
 
-    def _sync_payload_ubid(self, node: Task) -> int | None:
-        if node.unique_bucket_id is not None:
-            return node.unique_bucket_id
+    def _sync_payload_ubid(self, node: Any) -> Any | None:
+        bk = self._node_bucket_key(node)
+        if bk is not None:
+            return bk
         if node.data_preds:
-            return node.data_preds[0].unique_bucket_id
+            pred_bk = self._node_bucket_key(node.data_preds[0])
+            if pred_bk is not None:
+                return pred_bk
         return None
 
-    def _sync_payload_ubids(self, node: Task) -> list[int]:
-        sync_ubids = node.custom_metadata.get("sync_ubids")
+    def _sync_payload_ubids(self, node: Any) -> list[Any]:
+        sync_ubids = self._node_meta(node).get("sync_ubids")
         if sync_ubids:
             return list(sync_ubids)
         ubid = self._sync_payload_ubid(node)
         return [ubid] if ubid is not None else []
 
-    def _collect_saved_tensors(
-        self,
-        obj: Any,
-        seen_objs: set[int],
-        seen_tensors: set[int],
-        tensors: list[torch.Tensor],
-    ) -> None:
-        obj_id = id(obj)
-        if obj_id in seen_objs:
-            return
-        seen_objs.add(obj_id)
-
-        if isinstance(obj, torch.Tensor):
-            tensor_id = id(obj)
-            if tensor_id not in seen_tensors:
-                seen_tensors.add(tensor_id)
-                tensors.append(obj)
+    def _wait_pending_free(self, pending: dict[Any, Future], ubid: Any | None) -> None:
+        if ubid is None:
             return
-
-        if isinstance(obj, dict):
-            for v in obj.values():
-                self._collect_saved_tensors(v, seen_objs, seen_tensors, tensors)
+        fut = pending.pop(ubid, None)
+        if fut is not None:
+            fut.result()
+
+    def _drain_pending_frees(self) -> None:
+        for pending in (self.pending_param_frees, self.pending_grad_frees):
+            futures = list(pending.values())
+            pending.clear()
+            for fut in futures:
+                fut.result()
+
+    def _defer_free_full_params(self, ubid: Any | None, evt: torch.cuda.Event) -> None:
+        if ubid is None or ubid not in self.param_sharded_ubids:
             return
+        self._wait_pending_free(self.pending_param_frees, ubid)
+        self.pending_param_frees[ubid] = self.cleanup_executor.submit(
+            self._wait_then_free_full_params,
+            ubid,
+            evt,
+        )
 
-        if isinstance(obj, (list, tuple, set)):
-            for v in obj:
-                self._collect_saved_tensors(v, seen_objs, seen_tensors, tensors)
+    def _defer_free_full_grads(self, ubid: Any | None, evt: torch.cuda.Event) -> None:
+        if ubid is None or ubid not in self.grad_sharded_ubids:
             return
+        self._wait_pending_free(self.pending_grad_frees, ubid)
+        self.pending_grad_frees[ubid] = self.cleanup_executor.submit(
+            self._wait_then_free_full_grads,
+            ubid,
+            evt,
+        )
 
-        if isinstance(obj, Node):
-            for name in dir(obj):
-                if not name.startswith("_saved_"):
-                    continue
-                try:
-                    value = getattr(obj, name)
-                except Exception:
-                    continue
-                self._collect_saved_tensors(value, seen_objs, seen_tensors, tensors)
-
-    def _summarize_param_groups(self, param_groups: list[dict[str, Any]]) -> dict[str, int]:
-        grad_tensor_count = 0
-        grad_numel = 0
-        grad_bytes = 0
-        intermediate_count = 0
-        param_count = 0
-        saved_tensors: list[torch.Tensor] = []
-        seen_objs: set[int] = set()
-        seen_tensors: set[int] = set()
-
-        for param_group in param_groups:
-            intermediates = param_group.get("intermediates", [])
-            grads = param_group.get("grads", [])
-            params = param_group.get("params", [])
-            intermediate_count += len(intermediates)
-            param_count += len(params)
-
-            for grads_tuple in grads:
-                if grads_tuple is None:
-                    continue
-                for grad in grads_tuple:
-                    if grad is None:
-                        continue
-                    grad_tensor_count += 1
-                    grad_numel += grad.numel()
-                    grad_bytes += grad.numel() * grad.element_size()
-
-            for intermediate in intermediates:
-                self._collect_saved_tensors(intermediate, seen_objs, seen_tensors, saved_tensors)
-
-        saved_numel = sum(t.numel() for t in saved_tensors)
-        saved_bytes = sum(t.numel() * t.element_size() for t in saved_tensors)
-
-        return {
-            "groups": len(param_groups),
-            "intermediates": intermediate_count,
-            "params": param_count,
-            "grad_tensors": grad_tensor_count,
-            "grad_numel": grad_numel,
-            "grad_bytes": grad_bytes,
-            "saved_tensors": len(saved_tensors),
-            "saved_numel": saved_numel,
-            "saved_bytes": saved_bytes,
-        }
+    def _wait_then_free_full_params(self, ubid: Any, evt: torch.cuda.Event) -> None:
+        evt.synchronize()
+        self._free_full_params(ubid)
 
-    def _summarize_outstanding_bwdi(self) -> dict[str, int]:
-        buffers = 0
-        groups = 0
-        params = 0
-        intermediates = 0
-        grad_tensors = 0
-        grad_bytes = 0
-        saved_tensors = 0
-        saved_bytes = 0
-
-        for value in self.task_buffer.values():
-            if not isinstance(value, dict):
-                continue
-            param_groups = value.get("param_groups")
-            if param_groups is None:
-                continue
-            buffers += 1
-            summary = self._summarize_param_groups(param_groups)
-            groups += summary["groups"]
-            params += summary["params"]
-            intermediates += summary["intermediates"]
-            grad_tensors += summary["grad_tensors"]
-            grad_bytes += summary["grad_bytes"]
-            saved_tensors += summary["saved_tensors"]
-            saved_bytes += summary["saved_bytes"]
+    def _wait_then_free_full_grads(self, ubid: Any, evt: torch.cuda.Event) -> None:
+        evt.synchronize()
+        self._free_full_grads(ubid)
 
-        return {
-            "buffers": buffers,
-            "groups": groups,
-            "params": params,
-            "intermediates": intermediates,
-            "grad_tensors": grad_tensors,
-            "grad_bytes": grad_bytes,
-            "saved_tensors": saved_tensors,
-            "saved_bytes": saved_bytes,
-        }
-
-    def _accumulate_tensor_bytes(self, obj: Any, seen: set[int]) -> int:
-        if isinstance(obj, torch.Tensor):
-            obj_id = id(obj)
-            if obj_id in seen:
-                return 0
-            seen.add(obj_id)
-            return obj.numel() * obj.element_size()
-
-        if isinstance(obj, dict):
-            return sum(self._accumulate_tensor_bytes(v, seen) for v in obj.values())
-
-        if isinstance(obj, (list, tuple, set)):
-            return sum(self._accumulate_tensor_bytes(v, seen) for v in obj)
-
-        return 0
-
-    def _summarize_task_buffer(self) -> dict[str, float]:
-        categories = {
-            "fwd_mb": 0,
-            "shape_ref": 0,
-            "recv": 0,
-            "bwd_i": 0,
-            "bwd": 0,
-            "a2a": 0,
-            "other": 0,
-        }
-        counts = {k: 0 for k in categories}
-
-        for key, value in self.task_buffer.items():
-            seen: set[int] = set()
-            nbytes = self._accumulate_tensor_bytes(value, seen)
-
-            category = "other"
-            if isinstance(key, tuple) and len(key) == 2 and key[0] == "shape_ref":
-                category = "shape_ref"
-            elif isinstance(key, tuple) and len(key) == 2 and isinstance(key[0], int):
-                category = "fwd_mb"
-            elif isinstance(value, list):
-                category = "recv"
-            elif isinstance(value, dict):
-                if "param_groups" in value:
-                    category = "bwd_i"
-                elif "inp_grads" in value:
-                    category = "bwd"
-                elif "detached_outs" in value:
-                    category = "a2a" if "pre_detach_outs" not in value else "other"
-
-            categories[category] += nbytes
-            counts[category] += 1
-
-        summary = {}
-        total_bytes = 0
-        total_entries = 0
-        for key, nbytes in categories.items():
-            summary[f"{key}_bytes"] = nbytes
-            summary[f"{key}_count"] = counts[key]
-            total_bytes += nbytes
-            total_entries += counts[key]
-        summary["total_bytes"] = total_bytes
-        summary["total_entries"] = total_entries
-        return summary
-
-    def _init_task_buffer_refcounts(self, dag: TaskDAG) -> None:
+    def _init_task_buffer_refcounts(self, dag: Any) -> None:
+        nodes_iter = dag.nodes.values() if isinstance(dag.nodes, dict) else dag.nodes
         self.task_buffer_refcounts = {
             node.uid: len(node.data_succs)
-            for node in dag.nodes
+            for node in nodes_iter
             if node.data_succs
         }
 
@@ -1405,50 +967,16 @@ def _release_task_buffer_uid(self, uid: int) -> None:
         else:
             self.task_buffer_refcounts[uid] = remaining
 
-    def run_dag(self, loss_fn=None, profiling: bool = False):
-        """Thin wrapper around ``_run_dag_body`` that dumps a CUDA memory
-        snapshot if the body raises ``torch.cuda.OutOfMemoryError``.
-
-        The dump is written to ``self._oom_snapshot_dir`` (set via
-        ``set_oom_snapshot_dir``) or ``/tmp/piper_oom_snapshots`` as a fallback.
-        The original exception is re-raised after the snapshot attempt so the
-        caller (driver) still sees the OOM.
-        """
-        # Mark the entire iteration boundary for the memory-viz timeline.
+    def run_dag(self, loss_fn=None):
+        # Mark the entire iteration boundary for the NVTX timeline.
         iter_idx = getattr(self, "_iter_counter", 0)
         self._iter_counter = iter_idx + 1
         self._nvtx_push(f"iter_{iter_idx}_rank_{self.global_rank}")
-        try:
-            # self._log_mem(f"iter_{iter_idx}_start")
-            return self._run_dag_body(loss_fn=loss_fn, profiling=profiling)
-        except torch.cuda.OutOfMemoryError as oom_exc:
-            oom_dir = getattr(self, "_oom_snapshot_dir", None) or "/tmp/piper_oom_snapshots"
-            try:
-                os.makedirs(oom_dir, exist_ok=True)
-                filepath = os.path.join(
-                    oom_dir, f"oom_rank{self.global_rank}_memory.pickle"
-                )
-                torch.cuda.memory._dump_snapshot(filepath)
-                self.logger.error(
-                    f"[OOM] rank={self.global_rank} iter={iter_idx} "
-                    f"dumped snapshot to {filepath}: {oom_exc}"
-                )
-            except Exception as dump_exc:
-                self.logger.error(
-                    f"[OOM] rank={self.global_rank} iter={iter_idx} "
-                    f"snapshot dump FAILED: {dump_exc} (original OOM: {oom_exc})"
-                )
-            try:
-                torch.cuda.memory._record_memory_history(enabled=None)
-            except Exception:
-                pass
-            # self._log_mem(f"iter_{iter_idx}_oom")
-            raise
-        finally:
-            self._nvtx_pop()
+        self._run_dag_body(loss_fn=loss_fn)
+        self._nvtx_pop()
 
-    def _run_dag_body(self, loss_fn=None, profiling: bool = False):
-        """Execute the loaded TaskDAG in topological order.
+    def _run_dag_body(self, loss_fn=None):
+        """Execute the loaded TrainingDAG in topological order.
 
         All inter-task data is routed through ``task_buffer``, keyed by the
         producing task's ``uid``.  Each task method receives its inputs as
@@ -1456,16 +984,16 @@ def _run_dag_body(self, loss_fn=None, profiling: bool = False):
         reads from and writes to ``task_buffer``.
 
         Synchronisation contract:
-        - Before a SEND node: p2p_send_stream waits on the compute event so the
+        - Before a SEND node: the node's stream waits on the compute event so the
           activation / gradient tensor is fully computed before the send begins.
-        - Before a compute node that has a RECV predecessor: comp_stream waits
-          on the recv_event so the recv buffer is populated before compute reads it.
+        - Before a compute node that has a RECV predecessor: the compute stream
+          waits on the recv_event so the recv buffer is populated before compute reads it.
         - RECV records a recv_event after the dist.recv completes.
         """
-        assert self.dag is not None, "load_dag() must be called before run_dag()"
-        assert self.sorted_dag_nodes is not None, "load_dag() must initialize sorted node order"
+        assert self.dag is not None, "load_training_dag() must be called before run_dag()"
+        assert self.sorted_dag_nodes is not None, "load_training_dag() must initialize sorted node order"
+        self._drain_pending_frees()
         debug_enabled = self.logger.isEnabledFor(logging.DEBUG)
-        verbose_enabled = self.logger.isEnabledFor(VERBOSE)
         # Reset per-iteration state
         self.task_buffer = {}
         self.task_buffer_refcounts = {}
@@ -1473,130 +1001,70 @@ def _run_dag_body(self, loss_fn=None, profiling: bool = False):
         self.a2a_events = {}
         self.ar_events = {}
         self.rs_events = {}
-        if self.zero_stage == 3:
-            self.ag_events = {}
+        self.ag_events = {}
         self.bwd_events = {}
         self._clear_param_grads()
-        if self.zero_stage >= 1:
-            with torch.cuda.stream(self.comp_stream):
-                for buf in self.bucket_flat_grads.values():
-                    if buf is not None:
-                        buf.zero_()
-        if self.zero_stage >= 2:
-            with torch.cuda.stream(self.comp_stream):
-                for buf in self.bucket_rs_grads.values():
-                    if buf is not None:
-                        buf.zero_()
+        default_stream = self._default_stream()
+        with torch.cuda.stream(default_stream):
+            for buf in self.bucket_flat_grads.values():
+                if buf is not None:
+                    buf.zero_()
+            for buf in self.bucket_rs_grads.values():
+                if buf is not None:
+                    buf.zero_()
+        zero_evt = torch.cuda.Event()
+        zero_evt.record(default_stream)
+        for stream in self.streams.values():
+            if stream is not default_stream:
+                stream.wait_event(zero_evt)
         comp_events: dict = {}  # task_uid -> cuda.Event for compute tasks
-        # profiling: list of (node, start_evt, end_evt, mem_before_bytes)
-        # populated during the loop; elapsed_time() is only called after the
-        # final synchronize() so we never block the CPU mid-dispatch.
-        self._prof_records: list = []
 
         self._init_task_buffer_refcounts(self.dag)
         last_comp_event_by_stream: dict[str, torch.cuda.Event] = {}
-
-        def _wait_for_all_gather(compute_node: Task) -> None:
-            compute_stream = self._compute_stream_for_task(compute_node)
+        def _node_tag_str(node: Any) -> str:
+            tag = getattr(node, "tag", None)
+            if not isinstance(tag, dict) or not tag:
+                return "{}"
+            items = sorted(tag.items(), key=lambda kv: kv[0])
+            return "{" + ",".join(f"{k}={v}" for k, v in items) + "}"
+
+        def _wait_for_all_gather(compute_node: Any) -> None:
+            compute_stream = self._stream_for_task(compute_node)
             for pred in compute_node.data_preds:
                 if pred.task_type == TaskType.ALL_GATHER:
                     ag_evt = self.ag_events.get(pred.uid)
                     if ag_evt is not None:
                         compute_stream.wait_event(ag_evt)
 
-        def _metadata_ubids(node: Task, key: str) -> list[int]:
-            value = node.custom_metadata.get(key)
-            if value is True:
-                sync_ubids = self._sync_payload_ubids(node)
-                if sync_ubids:
-                    return sync_ubids
-                return [node.unique_bucket_id] if node.unique_bucket_id is not None else []
-            if isinstance(value, (list, tuple)):
-                return [int(ubid) for ubid in value]
-            return []
-
-        def _run_free_full_grads(free_node: Task) -> None:
-            self._nvtx_push(f"free_full_grads_ubid{free_node.unique_bucket_id}")
-            try:
-                for pred in free_node.data_preds:
-                    if pred.task_type == TaskType.REDUCE_SCATTER:
-                        rs_evt = self.rs_events.get(pred.uid)
-                        if rs_evt is not None:
-                            self.comp_stream.wait_event(rs_evt)
-                            # self.logger.info("synchronizing free_full_grads on thread_id=%s", threading.get_ident())
-                            rs_evt.synchronize()
-                    elif pred.task_type == TaskType.ALL_REDUCE:
-                        ar_evt = self.ar_events.get(pred.uid)
-                        if ar_evt is not None:
-                            self.comp_stream.wait_event(ar_evt)
-                            # self.logger.info("synchronizing free_full_grads on thread_id=%s", threading.get_ident())
-                            ar_evt.synchronize()
-                self._free_full_grads(free_node.unique_bucket_id)
-            finally:
-                self._nvtx_pop()
-
-        def _run_free_full_params(free_node: Task) -> None:
-            self._nvtx_push(f"free_full_params_ubid{free_node.unique_bucket_id}")
-            try:
-                for pred in free_node.data_preds:
-                    if pred.uid in comp_events:
-                        # _free_full_params resizes CUDA storage from the host.
-                        # Stream waits are not enough here: the CPU must not
-                        # release the full ZeRO-3 buffer while a compute kernel
-                        # launched on comp_stream may still be reading it.
-                        comp_events[pred.uid].synchronize()
-                self._free_full_params(free_node.unique_bucket_id)
-            finally:
-                self._nvtx_pop()
-
         for node in self.sorted_dag_nodes:
 
             task_type = node.task_type
             batch = node.batches[0]
             mb_idx = batch.mb_idx
-            ubid = node.unique_bucket_id  # None for non-compute nodes
-            node_comp_stream = self._compute_stream_for_task(node)
+            ubid = self._node_bucket_key(node)  # None for non-compute nodes
+            node_stream = self._stream_for_task(node)
+            node_stream_id = self._stream_id(node)
+            node_tag = _node_tag_str(node)
 
             if debug_enabled:
                 self.logger.debug(
-                    f"run_dag dispatch (time {node.time_step}): {task_type.value} "
-                    f"mb{mb_idx} ubid={ubid}"
+                    f"run_dag dispatch: {task_type.value} "
+                    f"tag={node_tag}"
                 )
 
-            # Timeline marker visible in NVTX traces & PyTorch memory-viz.
-            _task_label = f"{task_type.value}_mb{mb_idx}_ubid{ubid}_t{node.time_step}"
+            # Timeline marker visible in NVTX traces / PyTorch profiler.
+            _task_label = f"{task_type.value}:{node_tag}:uid{node.uid}"
             self._nvtx_push(_task_label)
-            _log_heavy_task = task_type in (
-                TaskType.FWD,
-                TaskType.BWD,
-                TaskType.BWD_I,
-                TaskType.BWD_W,
-                TaskType.FWD_BWD,
-                TaskType.UPD,
-                TaskType.ALLOC_FULL_PARAMS,
-                TaskType.FREE_FULL_PARAMS,
-                TaskType.ALLOC_FULL_GRADS,
-                TaskType.FREE_FULL_GRADS,
-            )
-            # if _log_heavy_task:
-                # self._log_mem(f"before_{_task_label}")
-
-            if profiling:
-                _stream = self._timing_stream(node)
-                _start_evt = torch.cuda.Event(enable_timing=True)
-                _start_evt.record(_stream)
-                _mem_before = torch.cuda.memory_allocated()
+            _rf = self._rf_enter(_task_label)
 
             match task_type:
 
                 case TaskType.SEND:
                     # data_preds[0] is the compute task whose output we send.
                     compute_node = node.data_preds[0]
-                    self.p2p_send_stream.wait_event(comp_events[compute_node.uid])
+                    node_stream.wait_event(comp_events[compute_node.uid])
                     send_data = self.task_buffer[compute_node.uid]["send_output"]
-                    self._start_timing(self.p2p_send_stream, "p2p_send")
-                    self._exec_send(send_data, node.peer_pp_rank)
-                    self._stop_timing(self.p2p_send_stream, "p2p_send")
+                    self._exec_send(send_data, node.peer_pp_rank, stream=node_stream)
                     # Free the send buffer.  For FWD/FWD_A2A the whole entry is stale
                     # after the send (task_buffer[(ubid,mb)] is the BWD consumer).
                     # For BWD/BWD_I/BWD_A2A keep the dict but drop the send tensor —
@@ -1610,141 +1078,112 @@ def _run_free_full_params(free_node: Task) -> None:
                 case TaskType.RECV:
                     # data_succs[0] is the downstream compute task.
                     compute_node = node.data_succs[0]
-                    comp_evt = last_comp_event_by_stream.get(node.compute_stream_id)
+                    comp_evt = last_comp_event_by_stream.get(self._stream_id(compute_node))
                     if comp_evt is not None:
-                        self.p2p_recv_stream.wait_event(comp_evt)
+                        node_stream.wait_event(comp_evt)
                     if compute_node.task_type == TaskType.FWD:
                         # FWD recv: pre-allocate based on target bucket's input metadata.
-                        recv_ubid = compute_node.unique_bucket_id
-                        self._start_timing(self.p2p_recv_stream, "p2p_recv")
-                        recv_tensors = self._exec_recv_fwd(recv_ubid, node.peer_pp_rank)
-                        self._stop_timing(self.p2p_recv_stream, "p2p_recv")
+                        recv_ubid = self._node_bucket_key(compute_node)
+                        recv_tensors = self._exec_recv_fwd(
+                            recv_ubid, node.peer_pp_rank, stream=node_stream
+                        )
                     else:
                         # BWD recv: pre-allocate based on FWD output shapes.
-                        fwd_ubid = node.custom_metadata["fwd_ubid"]
-                        shape_meta = self.task_buffer[("shape_ref", fwd_ubid)]
-                        self._start_timing(self.p2p_recv_stream, "p2p_recv")
-                        recv_tensors = self._exec_recv_bwd(shape_meta, node.peer_pp_rank)
-                        self._stop_timing(self.p2p_recv_stream, "p2p_recv")
+                        fwd_uid = compute_node.node_meta.get("fwd_uid")
+                        fwd_key = (compute_node.node_meta.get("bucket_key"), fwd_uid)
+                        shape_meta = self.task_buffer[("shape_ref",) + fwd_key]
+                        recv_tensors = self._exec_recv_bwd(
+                            shape_meta, node.peer_pp_rank, stream=node_stream
+                        )
                     self.task_buffer[node.uid] = recv_tensors
                     recv_evt = torch.cuda.Event()
-                    recv_evt.record(self.p2p_recv_stream)
+                    recv_evt.record(node_stream)
                     self.recv_events[node.uid] = recv_evt
 
                 case TaskType.FWD_A2A:
                     fwd_pred = next(p for p in node.data_preds if p.task_type == TaskType.FWD)
-                    self.a2a_stream.wait_event(comp_events[fwd_pred.uid])
-                    tensor_idx = node.custom_metadata["a2a_tensor_idx"]
+                    node_stream.wait_event(comp_events[fwd_pred.uid])
+                    tensor_idx = self._node_meta(node)["a2a_tensor_idx"]
                     # Pass-through: copy upstream FWD task_buffer, apply A2A only at tensor_idx.
                     fwd_buf = dict(self.task_buffer[fwd_pred.uid])
                     # FWD_A2A now owns the copy; release predecessor after consuming it.
                     self._release_task_buffer_uid(fwd_pred.uid)
                     detached_outs = list(fwd_buf["detached_outs"])
-                    self._nvtx_push(f"fwd_a2a_ubid{fwd_pred.unique_bucket_id}_mb{mb_idx}")
-                    self._start_timing(self.a2a_stream, "fwd_a2a")
-                    detached_outs[tensor_idx] = self._exec_a2a(detached_outs[tensor_idx]).requires_grad_(True)
-                    self._stop_timing(self.a2a_stream, "fwd_a2a")
-                    self._nvtx_pop()
+                    detached_outs[tensor_idx] = self._exec_a2a(
+                        detached_outs[tensor_idx], stream=node_stream
+                    ).requires_grad_(True)
                     fwd_buf["detached_outs"] = detached_outs
                     self.task_buffer[node.uid] = fwd_buf
                     a2a_evt = torch.cuda.Event()
-                    a2a_evt.record(self.a2a_stream)
+                    a2a_evt.record(node_stream)
                     self.a2a_events[node.uid] = a2a_evt
-                    node_comp_stream.wait_event(a2a_evt)
+                    # node_stream.wait_event(a2a_evt)
 
                 case TaskType.BWD_A2A:
                     bwd_pred = next(
                         p for p in node.data_preds
                         if p.task_type in (TaskType.BWD, TaskType.BWD_I)
                     )
-                    self.a2a_stream.wait_event(comp_events[bwd_pred.uid])
-                    tensor_idx = node.custom_metadata["a2a_tensor_idx"]
+                    node_stream.wait_event(comp_events[bwd_pred.uid])
+                    tensor_idx = self._node_meta(node)["a2a_tensor_idx"]
                     # Pass-through: copy upstream BWD/BWD_I task_buffer, apply reversed A2A only at tensor_idx.
                     bwd_buf = dict(self.task_buffer[bwd_pred.uid])
                     self._release_task_buffer_uid(bwd_pred.uid)
                     inp_grads = list(bwd_buf["inp_grads"])
                     grad_a2a_out = inp_grads[tensor_idx]
                     assert grad_a2a_out is not None, (
-                        f"BWD_A2A mb={mb_idx}: grad at a2a_tensor_idx={tensor_idx} is None"
+                        f"BWD_A2A tag={node_tag}: grad at a2a_tensor_idx={tensor_idx} is None"
                     )
                     if not grad_a2a_out.is_contiguous():
                         grad_a2a_out = grad_a2a_out.contiguous()
-                    self._nvtx_push(f"bwd_a2a_ubid{bwd_pred.unique_bucket_id}_mb{mb_idx}")
-                    self._start_timing(self.a2a_stream, "bwd_a2a")
-                    inp_grads[tensor_idx] = self._exec_a2a(grad_a2a_out)
-                    self._stop_timing(self.a2a_stream, "bwd_a2a")
-                    self._nvtx_pop()
+                    inp_grads[tensor_idx] = self._exec_a2a(grad_a2a_out, stream=node_stream)
                     bwd_buf["inp_grads"] = inp_grads
                     self.task_buffer[node.uid] = bwd_buf
                     a2a_evt = torch.cuda.Event()
-                    a2a_evt.record(self.a2a_stream)
+                    a2a_evt.record(node_stream)
                     self.a2a_events[node.uid] = a2a_evt
-                    node_comp_stream.wait_event(a2a_evt)
+                    # node_stream.wait_event(a2a_evt)
 
                 case TaskType.ALL_REDUCE:
                     bwd_node = node.data_preds[0]
                     ar_ubids = self._sync_payload_ubids(node)
-                    ar_ubid = ar_ubids[0] if ar_ubids else bwd_node.unique_bucket_id
-                    self._nvtx_push(f"all_reduce_ubid{ar_ubid}")
-                    self.ar_stream.wait_event(comp_events[bwd_node.uid])
-                    self._start_timing(self.ar_stream, "all_reduce")
-                    runtime_bytes = sum(self._exec_all_reduce_grads(ubid) for ubid in ar_ubids)
-                    self._stop_timing(self.ar_stream, "all_reduce")
-                    if runtime_bytes > 0:
-                        ar_evt = torch.cuda.Event()
-                        ar_evt.record(self.ar_stream)
-                        self.ar_events[node.uid] = ar_evt
-                        for free_ubid in _metadata_ubids(node, "zero_free_full_grads_after_ubids"):
-                            ar_evt.synchronize()
-                            self._free_full_grads(free_ubid)
-                    self._nvtx_pop()
+                    assert ar_ubids, (
+                        f"ALL_REDUCE node uid={node.uid} has no sync_payload_ubids"
+                    )
+                    node_stream.wait_event(comp_events[bwd_node.uid])
+                    for ubid in ar_ubids:
+                        self._exec_all_reduce_grads(ubid, stream=node_stream)
+                    ar_evt = torch.cuda.Event()
+                    ar_evt.record(node_stream)
+                    self.ar_events[node.uid] = ar_evt
                     self._release_task_buffer_uid(bwd_node.uid)
 
                 case TaskType.REDUCE_SCATTER:
                     bwd_node = node.data_preds[0]
-                    rs_ubid = node.unique_bucket_id if node.unique_bucket_id is not None else bwd_node.unique_bucket_id
-                    self._nvtx_push(f"reduce_scatter_ubid{rs_ubid}")
-                    self.ar_stream.wait_event(comp_events[bwd_node.uid])
-                    self._start_timing(self.ar_stream, "reduce_scatter")
-                    self._exec_reduce_scatter(rs_ubid)
-                    self._stop_timing(self.ar_stream, "reduce_scatter")
+                    rs_ubid = self._node_bucket_key(node)
+                    assert rs_ubid is not None, (
+                        f"REDUCE_SCATTER node uid={node.uid} has no bucket_key"
+                    )
+                    node_stream.wait_event(comp_events[bwd_node.uid])
+                    rs_bytes = self._exec_reduce_scatter(rs_ubid, stream=node_stream)
                     rs_evt = torch.cuda.Event()
-                    rs_evt.record(self.ar_stream)
+                    rs_evt.record(node_stream)
                     self.rs_events[node.uid] = rs_evt
-                    for free_ubid in _metadata_ubids(node, "zero_free_full_grads_after_ubids"):
-                        rs_evt.synchronize()
-                        self._free_full_grads(free_ubid)
-                    self._nvtx_pop()
+                    if rs_bytes:
+                        self._defer_free_full_grads(rs_ubid, rs_evt)
+                    if self._node_meta(node).get("zero_free_full_params_after"):
+                        assert False and "Param free should happen after a compute node"
                     self._release_task_buffer_uid(bwd_node.uid)
 
                 case TaskType.ALL_GATHER:
-                    for alloc_ubid in _metadata_ubids(node, "zero_alloc_full_params_before"):
-                        self._alloc_full_params(alloc_ubid)
-                    self._nvtx_push(f"all_gather_s{batch.stage_id}_b{node.bucket_id}")
-                    self._start_timing(self.ar_stream, "all_gather")
-                    ag_launched = self._exec_all_gather(node.unique_bucket_id)
-                    self._stop_timing(self.ar_stream, "all_gather")
-                    if ag_launched:
-                        ag_evt = torch.cuda.Event()
-                        ag_evt.record(self.ar_stream)
-                        self.ag_events[node.uid] = ag_evt
-                    self._nvtx_pop()
-
-                case TaskType.ALLOC_FULL_GRADS:
-                    self._nvtx_push(f"alloc_full_grads_ubid{node.unique_bucket_id}")
-                    self._alloc_full_grads(node.unique_bucket_id)
-                    self._nvtx_pop()
-
-                case TaskType.FREE_FULL_GRADS:
-                    _run_free_full_grads(node)
-
-                case TaskType.ALLOC_FULL_PARAMS:
-                    self._nvtx_push(f"alloc_full_params_ubid{node.unique_bucket_id}")
-                    self._alloc_full_params(node.unique_bucket_id)
-                    self._nvtx_pop()
-
-                case TaskType.FREE_FULL_PARAMS:
-                    _run_free_full_params(node)
+                    ag_ubid = self._node_bucket_key(node)
+                    assert ag_ubid is not None, (
+                        f"ALL_GATHER node uid={node.uid} has no bucket_key"
+                    )
+                    self._exec_all_gather(ag_ubid, stream=node_stream)
+                    ag_evt = torch.cuda.Event()
+                    ag_evt.record(node_stream)
+                    self.ag_events[node.uid] = ag_evt
 
                 case TaskType.FWD:
                     # Wait on any RECV predecessor.
@@ -1752,7 +1191,14 @@ def _run_free_full_params(free_node: Task) -> None:
                         (p for p in node.data_preds if p.task_type == TaskType.RECV), None
                     )
                     if recv_pred is not None and recv_pred.uid in self.recv_events:
-                        node_comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
+                        node_stream.wait_event(self.recv_events.pop(recv_pred.uid))
+
+                    # Wait on any FWD_A2A predecessor.
+                    a2a_pred = next(
+                        (p for p in node.data_preds if p.task_type == TaskType.FWD_A2A), None
+                    )
+                    if a2a_pred is not None and a2a_pred.uid in self.a2a_events:
+                        node_stream.wait_event(self.a2a_events.pop(a2a_pred.uid))
 
                     # Gather input tensors from task_buffer.
                     # FWD(bkt=0 of first stage): inputs come from self.inputs.
@@ -1774,23 +1220,20 @@ def _run_free_full_params(free_node: Task) -> None:
 
                     _wait_for_all_gather(node)
 
-                    self._nvtx_push(f"forward_ubid{ubid}_mb{mb_idx}")
-                    self._start_timing(node_comp_stream, "forward")
-                    fwd_out = self._forward_dag(ubid, mb_idx, input_tensors, node_comp_stream)
-                    self._stop_timing(node_comp_stream, "forward")
-                    self._nvtx_pop()
+                    fwd_out = self._forward_dag(ubid, mb_idx, input_tensors, node_stream)
                     self.task_buffer[node.uid] = fwd_out
                     # Shape ref for BWD RECV: store lightweight metadata (shape, dtype) so
                     # _exec_recv_bwd can pre-allocate receive buffers without holding live tensors.
-                    self.task_buffer[("shape_ref", ubid)] = [(t.shape, t.dtype) for t in fwd_out["out_with_grad"]]
-                    self.task_buffer[(ubid, mb_idx)] = fwd_out  # per-mb FWD data for BWD
+                    bkey = node.node_meta.get("bucket_key")
+                    fwd_key = (bkey, node.uid)
+                    self.task_buffer[("shape_ref",) + fwd_key] = [(t.shape, t.dtype) for t in fwd_out["out_with_grad"]]
+                    self.task_buffer[fwd_key] = fwd_out  # per-node FWD data for BWD
                     evt = torch.cuda.Event()
-                    evt.record(node_comp_stream)
+                    evt.record(node_stream)
                     comp_events[node.uid] = evt
-                    last_comp_event_by_stream[node.compute_stream_id] = evt
-                    if node.custom_metadata.get("zero_free_full_params_after"):
-                        evt.synchronize()
-                        self._free_full_params(ubid)
+                    last_comp_event_by_stream[node_stream_id] = evt
+                    if self._node_meta(node).get("zero_free_full_params_after"):
+                        self._defer_free_full_params(ubid, evt)
 
                 case TaskType.BWD:
                     # Wait on upstream grad RECV if present.
@@ -1798,15 +1241,26 @@ def _run_free_full_params(free_node: Task) -> None:
                         (p for p in node.data_preds if p.task_type == TaskType.RECV), None
                     )
                     if recv_pred is not None and recv_pred.uid in self.recv_events:
-                        node_comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
+                        node_stream.wait_event(self.recv_events.pop(recv_pred.uid))
                     _wait_for_all_gather(node)
 
+                    # Wait on any BWD_A2A predecessor.
+                    a2a_pred = next(
+                        (p for p in node.data_preds if p.task_type == TaskType.BWD_A2A), None
+                    )
+                    if a2a_pred is not None and a2a_pred.uid in self.a2a_events:
+                        node_stream.wait_event(self.a2a_events.pop(a2a_pred.uid))
+
                     # Resolve outputs and upstream grads via per-mb ubid lookup.
-                    fwd_out = self.task_buffer[(ubid, mb_idx)]
+                    fwd_uid = node.node_meta.get("fwd_uid")
+                    fwd_key = (node.node_meta.get("bucket_key"), fwd_uid)
+                    fwd_out = self.task_buffer[fwd_key]
 
-                    if node.compute_loss:
+                    if self._node_meta(node).get("compute_loss", False):
                         assert loss_fn is not None
-                        with torch.cuda.stream(node_comp_stream):
+                        if self.logger.isEnabledFor(logging.DEBUG):
+                            self._log_compute_loss_inputs(node, fwd_key, fwd_out)
+                        with torch.cuda.stream(node_stream):
                             outputs_or_loss = [loss_fn(fwd_out["out_with_grad"][0], self.labels)]
                         upstream_grads = None
                     elif recv_pred is not None:
@@ -1820,11 +1274,8 @@ def _run_free_full_params(free_node: Task) -> None:
                         outputs_or_loss = fwd_out["out_with_grad"]
                         upstream_grads = None  # last stage, no recv
 
-                    bwd_a2a_pred = next(
-                        (p for p in node.data_preds if p.task_type == TaskType.BWD_A2A), None
-                    )
-                    if bwd_a2a_pred is not None:
-                        a2a_buf = self.task_buffer[bwd_a2a_pred.uid]
+                    if a2a_pred is not None:
+                        a2a_buf = self.task_buffer[a2a_pred.uid]
                         pre_detach_outs = fwd_out["pre_detach_outs"]
                         detached_outs = fwd_out["detached_outs"]
                         # Set .grad for ALL requires_grad detached boundary tensors from
@@ -1835,7 +1286,7 @@ def _run_free_full_params(free_node: Task) -> None:
                         for i, (d, g) in enumerate(zip(detached_outs, a2a_buf["inp_grads"])):
                             if d is not None and isinstance(d, torch.Tensor) and d.requires_grad and g is not None:
                                 d.grad = g
-                        self._release_task_buffer_uid(bwd_a2a_pred.uid)
+                        self._release_task_buffer_uid(a2a_pred.uid)
                     else:
                         bwd_pred = next(
                             (p for p in node.data_preds
@@ -1863,19 +1314,15 @@ def _run_free_full_params(free_node: Task) -> None:
                             detached_outs = None
 
                     inp_with_grad = fwd_out.get("inp_with_grad")
-                    if node.custom_metadata.get("zero_alloc_full_grads_before"):
-                        self._alloc_full_grads(ubid)
+                    if self._node_meta(node).get("zero_alloc_full_grads_before"):
+                        self._alloc_full_grads(ubid, node_stream)
 
-                    self._nvtx_push(f"backward_ubid{ubid}_mb{mb_idx}")
-                    self._start_timing(node_comp_stream, "backward")
                     bwd_out = self._backward_dag(
                         ubid, mb_idx, outputs_or_loss, upstream_grads,
                         pre_detach_outs, detached_outs, inp_with_grad,
                         fwd_out.get("out_with_grad"),
-                        node_comp_stream,
+                        node_stream,
                     )
-                    self._stop_timing(node_comp_stream, "backward")
-                    self._nvtx_pop()
                     buf = bwd_out if bwd_out is not None else {}
                     # Full-length inp_grads parallel to fwd_inputs / detached_outs so
                     # BWD_A2A can index with a2a_tensor_idx without filtering offset.
@@ -1886,21 +1333,20 @@ def _run_free_full_params(free_node: Task) -> None:
                         if fwd_inputs_full is not None
                         else [t.grad for t in (inp_with_grad or [])]
                     )
-                    self._accumulate_zero_param_grads_to_flat(ubid)
+                    self._accumulate_zero_param_grads_to_flat(ubid, node_stream)
                     self.task_buffer[node.uid] = buf
                     # FWD stored the same dict under both node.uid and (ubid, mb_idx).
                     # Clear the dict in-place so any remaining alias stops keeping
                     # activations and autograd edges alive through the optimizer step.
                     fwd_out.clear()
-                    del self.task_buffer[(ubid, mb_idx)]
+                    del self.task_buffer[fwd_key]
                     evt = torch.cuda.Event()
-                    evt.record(node_comp_stream)
+                    evt.record(node_stream)
                     comp_events[node.uid] = evt
                     self.bwd_events[ubid] = evt
-                    last_comp_event_by_stream[node.compute_stream_id] = evt
-                    if node.custom_metadata.get("zero_free_full_params_after"):
-                        evt.synchronize()
-                        self._free_full_params(ubid)
+                    last_comp_event_by_stream[node_stream_id] = evt
+                    if self._node_meta(node).get("zero_free_full_params_after"):
+                        self._defer_free_full_params(ubid, evt)
 
                 case TaskType.BWD_I:
                     # Wait on upstream grad RECV if present.
@@ -1908,15 +1354,19 @@ def _run_free_full_params(free_node: Task) -> None:
                         (p for p in node.data_preds if p.task_type == TaskType.RECV), None
                     )
                     if recv_pred is not None and recv_pred.uid in self.recv_events:
-                        node_comp_stream.wait_event(self.recv_events.pop(recv_pred.uid))
+                        node_stream.wait_event(self.recv_events.pop(recv_pred.uid))
                     _wait_for_all_gather(node)
 
                     # Resolve stage_outputs_or_loss and output_grads.
-                    fwd_out = self.task_buffer[(ubid, mb_idx)]
+                    fwd_uid = node.node_meta.get("fwd_uid")
+                    fwd_key = (node.node_meta.get("bucket_key"), fwd_uid)
+                    fwd_out = self.task_buffer[fwd_key]
 
-                    if node.compute_loss:
+                    if self._node_meta(node).get("compute_loss", False):
                         assert loss_fn is not None
-                        with torch.cuda.stream(node_comp_stream):
+                        if self.logger.isEnabledFor(logging.DEBUG):
+                            self._log_compute_loss_inputs(node, fwd_key, fwd_out)
+                        with torch.cuda.stream(node_stream):
                             stage_outputs_or_loss = [loss_fn(fwd_out["out_with_grad"][0], self.labels)]
                         output_grads = None
                     elif recv_pred is not None:
@@ -1935,7 +1385,11 @@ def _run_free_full_params(free_node: Task) -> None:
                     bwd_a2a_pred = next(
                         (p for p in node.data_preds if p.task_type == TaskType.BWD_A2A), None
                     )
-                    if bwd_a2a_pred is not None and not node.compute_loss and recv_pred is None:
+                    if (
+                        bwd_a2a_pred is not None
+                        and not self._node_meta(node).get("compute_loss", False)
+                        and recv_pred is None
+                    ):
                         a2a_buf = self.task_buffer[bwd_a2a_pred.uid]
                         # BWD_A2A passes through BWD_I(bkt+1)'s task_buffer with
                         # inp_grads[tensor_idx] replaced by the reversed A2A gradient.
@@ -1962,14 +1416,10 @@ def _run_free_full_params(free_node: Task) -> None:
                     weights = [b_fwd_args[i] for i in self.bucket_param_idxs[ubid]
                                if b_fwd_args[i] is not None]
 
-                    self._nvtx_push(f"backward_input_ubid{ubid}_mb{mb_idx}")
-                    self._start_timing(node_comp_stream, "backward_input")
-                    with torch.cuda.stream(node_comp_stream):
+                    with torch.cuda.stream(node_stream):
                         dinputs, param_groups = self._bucket_backward_input(
                             stage_outputs_or_loss, output_grads, input_values, iter(weights)
                         )
-                    self._stop_timing(node_comp_stream, "backward_input")
-                    self._nvtx_pop()
                     # Full-length inp_grads parallel to fwd_inputs / detached_outs.
                     fwd_inputs_full = fwd_out.get("fwd_inputs")
                     if fwd_inputs_full is not None:
@@ -1984,24 +1434,6 @@ def _run_free_full_params(free_node: Task) -> None:
                         "inp_grads": inp_grads_full,
                         "param_groups": param_groups,
                     }
-                    if verbose_enabled:
-                        pg_summary = self._summarize_param_groups(param_groups)
-                        outstanding = self._summarize_outstanding_bwdi()
-                        self.logger.log(VERBOSE,
-                            f"[bwd_i_retain] rank={self.global_rank} ubid={ubid} mb={mb_idx}: "
-                            f"groups={pg_summary['groups']} "
-                            f"params={pg_summary['params']} "
-                            f"intermediates={pg_summary['intermediates']} "
-                            f"grad_tensors={pg_summary['grad_tensors']} "
-                            f"grad_bytes_gb={pg_summary['grad_bytes'] / (1024 ** 3):.2f} "
-                            f"saved_tensors={pg_summary['saved_tensors']} "
-                            f"saved_bytes_gb={pg_summary['saved_bytes'] / (1024 ** 3):.2f} "
-                            f"outstanding_buffers={outstanding['buffers']} "
-                            f"outstanding_grad_bytes_gb={outstanding['grad_bytes'] / (1024 ** 3):.2f} "
-                            f"outstanding_saved_bytes_gb={outstanding['saved_bytes'] / (1024 ** 3):.2f} "
-                            f"alloc_gb={torch.cuda.memory_allocated(self.device) / (1024 ** 3):.2f} "
-                            f"reserved_gb={torch.cuda.memory_reserved(self.device) / (1024 ** 3):.2f}"
-                        )
                     # Propagate input grad to SEND (if this is the first bucket and has predecessors).
                     if dinputs:
                         self.task_buffer[node.uid]["send_output"] = list(dinputs)
@@ -2010,148 +1442,117 @@ def _run_free_full_params(free_node: Task) -> None:
                     # activations and autograd edges alive through the optimizer step.
                     fwd_out.clear()
                     del stage_outputs_or_loss, fwd_out
-                    del self.task_buffer[(ubid, mb_idx)]
+                    del self.task_buffer[fwd_key]
                     evt = torch.cuda.Event()
-                    evt.record(node_comp_stream)
+                    evt.record(node_stream)
                     comp_events[node.uid] = evt
                     self.bwd_events[ubid] = evt
-                    last_comp_event_by_stream[node.compute_stream_id] = evt
-                    if node.custom_metadata.get("zero_free_full_params_after"):
-                        evt.synchronize()
-                        self._free_full_params(ubid)
+                    last_comp_event_by_stream[node_stream_id] = evt
+                    if self._node_meta(node).get("zero_free_full_params_after"):
+                        self._defer_free_full_params(ubid, evt)
 
                 case TaskType.BWD_W:
                     _wait_for_all_gather(node)
-                    if node.custom_metadata.get("zero_alloc_full_grads_before"):
-                        self._alloc_full_grads(ubid)
+                    if self._node_meta(node).get("zero_alloc_full_grads_before"):
+                        self._alloc_full_grads(ubid, node_stream)
                     # data_preds[0] is the BWD_I task for bkt=K-1 (head of chain).
                     # For bkt<K-1 the chain-link edge (BWD_W predecessor) is prepended
                     # before Pass 2 appends the BWD_I edge, so data_preds[0] would be
                     # the wrong task.  Find BWD_I explicitly by type to be safe.
                     bwdi_node = next(p for p in node.data_preds if p.task_type == TaskType.BWD_I)
                     param_groups = self.task_buffer[bwdi_node.uid]["param_groups"]
-                    if verbose_enabled:
-                        pg_summary = self._summarize_param_groups(param_groups)
-                        outstanding = self._summarize_outstanding_bwdi()
-                        self.logger.log(VERBOSE,
-                            f"[bwd_w_begin] rank={self.global_rank} ubid={ubid} mb={mb_idx}: "
-                            f"groups={pg_summary['groups']} "
-                            f"params={pg_summary['params']} "
-                            f"intermediates={pg_summary['intermediates']} "
-                            f"grad_tensors={pg_summary['grad_tensors']} "
-                            f"grad_bytes_gb={pg_summary['grad_bytes'] / (1024 ** 3):.2f} "
-                            f"saved_tensors={pg_summary['saved_tensors']} "
-                            f"saved_bytes_gb={pg_summary['saved_bytes'] / (1024 ** 3):.2f} "
-                            f"outstanding_buffers={outstanding['buffers']} "
-                            f"outstanding_grad_bytes_gb={outstanding['grad_bytes'] / (1024 ** 3):.2f} "
-                            f"outstanding_saved_bytes_gb={outstanding['saved_bytes'] / (1024 ** 3):.2f} "
-                            f"alloc_gb={torch.cuda.memory_allocated(self.device) / (1024 ** 3):.2f} "
-                            f"reserved_gb={torch.cuda.memory_reserved(self.device) / (1024 ** 3):.2f}"
-                        )
                     b_fwd_args = self.bucket_fwd_args[ubid]
                     weights = [b_fwd_args[i] for i in self.bucket_param_idxs[ubid]
                                if b_fwd_args[i] is not None]
-                    self._nvtx_push(f"backward_weight_ubid{ubid}_mb{mb_idx}")
-                    self._start_timing(node_comp_stream, "backward_weight")
-                    with torch.cuda.stream(node_comp_stream):
+                    with torch.cuda.stream(node_stream):
                         self._bucket_backward_weight(iter(weights), param_groups, ubid=ubid, mb_idx=mb_idx)
-                    self._stop_timing(node_comp_stream, "backward_weight")
-                    self._nvtx_pop()
-                    self._accumulate_zero_param_grads_to_flat(ubid)
+                    self._accumulate_zero_param_grads_to_flat(ubid, node_stream)
                     self.task_buffer[node.uid] = {}
                     # BWD_I buffer is no longer needed: _bucket_backward_weight already
                     # deleted param_groups["intermediates"] and param_groups["grads"] and
                     # the autograd graph was freed by retain_graph=False.  Drop the shell.
                     self._release_task_buffer_uid(bwdi_node.uid)
                     evt = torch.cuda.Event()
-                    evt.record(node_comp_stream)
+                    evt.record(node_stream)
                     comp_events[node.uid] = evt
                     self.bwd_events[ubid] = evt
-                    last_comp_event_by_stream[node.compute_stream_id] = evt
-                    if node.custom_metadata.get("zero_free_full_params_after"):
-                        evt.synchronize()
-                        self._free_full_params(ubid)
+                    last_comp_event_by_stream[node_stream_id] = evt
+                    if self._node_meta(node).get("zero_free_full_params_after"):
+                        self._defer_free_full_params(ubid, evt)
 
                 case TaskType.UPD:
-                    self._nvtx_push("update")
-                    self._start_timing(self.comp_stream, "optim_step")
-                    self._update()
-                    self._stop_timing(self.comp_stream, "optim_step")
-                    self._nvtx_pop()
-
-            if profiling:
-                _end_evt = torch.cuda.Event(enable_timing=True)
-                _end_evt.record(_stream)
-                _mem_after = torch.cuda.memory_allocated()
-                self._prof_records.append((node, mb_idx, ubid, _start_evt, _end_evt, _mem_before))
-                self.logger.info(f"Rank {self.global_rank} task {node.task_type.value} (time {node.time_step}) mem before {_mem_before / 1024 ** 3:.2f} GB after {_mem_after / 1024 ** 3:.2f} GB")
-
-            # if _log_heavy_task:
-                # self._log_mem(f"after_{_task_label}")
+                    self._update(node_stream)
+
+                case TaskType.ORDER_DUMMY:
+                    pass
+
+            self._rf_exit(_rf)
             self._nvtx_pop()
 
-            if verbose_enabled:
-                tb_summary = self._summarize_task_buffer()
-                self.logger.log(VERBOSE,
-                    f"[task_mem] rank={self.global_rank} time={node.time_step} "
-                    f"task={node.task_type.value} mb={mb_idx} ubid={ubid}: "
-                    f"alloc_gb={torch.cuda.memory_allocated(self.device) / (1024 ** 3):.2f} "
-                    f"reserved_gb={torch.cuda.memory_reserved(self.device) / (1024 ** 3):.2f} "
-                    f"tb_entries={tb_summary['total_entries']} "
-                    f"tb_total_gb={tb_summary['total_bytes'] / (1024 ** 3):.2f} "
-                    f"fwd_mb_count={tb_summary['fwd_mb_count']} "
-                    f"fwd_mb_gb={tb_summary['fwd_mb_bytes'] / (1024 ** 3):.2f} "
-                    f"recv_count={tb_summary['recv_count']} "
-                    f"recv_gb={tb_summary['recv_bytes'] / (1024 ** 3):.2f} "
-                    f"bwd_i_count={tb_summary['bwd_i_count']} "
-                    f"bwd_i_gb={tb_summary['bwd_i_bytes'] / (1024 ** 3):.2f} "
-                    f"bwd_count={tb_summary['bwd_count']} "
-                    f"bwd_gb={tb_summary['bwd_bytes'] / (1024 ** 3):.2f} "
-                    f"shape_ref_count={tb_summary['shape_ref_count']} "
-                    f"shape_ref_gb={tb_summary['shape_ref_bytes'] / (1024 ** 3):.2f} "
-                    f"a2a_count={tb_summary['a2a_count']} "
-                    f"a2a_gb={tb_summary['a2a_bytes'] / (1024 ** 3):.2f} "
-                    f"other_count={tb_summary['other_count']} "
-                    f"other_gb={tb_summary['other_bytes'] / (1024 ** 3):.2f}"
+    def _log_compute_loss_inputs(self, node: Any, fwd_key: Any, fwd_out: dict) -> None:
+        def _summarize_value(value: Any) -> str:
+            if isinstance(value, torch.Tensor):
+                return (
+                    f"Tensor(shape={tuple(value.shape)}, dtype={value.dtype}, "
+                    f"requires_grad={value.requires_grad}, device={value.device})"
                 )
+            if isinstance(value, (list, tuple)):
+                return "[" + ", ".join(_summarize_value(v) for v in value) + "]"
+            if value is None:
+                return "None"
+            return type(value).__name__
 
-    def _exec_send(self, send_data, peer_pp_rank: int) -> None:
-        """Send tensors to peer_pp_rank on p2p_send_stream.
+        self.logger.debug(
+            "compute_loss inputs: rank=%s node_uid=%s node_type=%s tag=%s "
+            "fwd_key=%s labels=%s out_with_grad=%s pre_detach_outs=%s "
+            "detached_outs=%s send_output=%s",
+            self.global_rank,
+            getattr(node, "uid", None),
+            getattr(getattr(node, "task_type", None), "value", None),
+            getattr(node, "tag", None),
+            fwd_key,
+            _summarize_value(self.labels),
+            _summarize_value(fwd_out.get("out_with_grad")),
+            _summarize_value(fwd_out.get("pre_detach_outs")),
+            _summarize_value(fwd_out.get("detached_outs")),
+            _summarize_value(fwd_out.get("send_output")),
+        )
 
-        The caller (run_dag) must have already made p2p_send_stream wait on the
+    def _exec_send(self, send_data, peer_pp_rank: int, stream) -> None:
+        """Send tensors to peer_pp_rank on the stream assigned to the SEND node.
+
+        The caller (run_dag) must have already made the node stream wait on the
         compute event before calling this method.
         """
         global_dst_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
-        if self.logger.isEnabledFor(VERBOSE):
-            self.logger.log(VERBOSE, f"exec_send to global rank {global_dst_rank}")
 
-        with torch.cuda.stream(self.p2p_send_stream):
+        with torch.cuda.stream(stream):
             tensors = send_data if isinstance(send_data, (list, tuple)) else [send_data]
-            pp_group = self.pp_lo_hi if global_dst_rank > self.global_rank else self.pp_hi_lo
+            use_lo_hi = global_dst_rank > self.global_rank
+            pp_group = self.pp_lo_hi if use_lo_hi else self.pp_hi_lo
             for tensor in tensors:
                 dist.send(tensor, dst=global_dst_rank, group=pp_group)
 
-    def _exec_recv_fwd(self, recv_ubid: int, peer_pp_rank: int) -> list:
+    def _exec_recv_fwd(self, recv_ubid: Any, peer_pp_rank: int, stream) -> list:
         """Receive FWD activations from peer_pp_rank into pre-allocated buffers.
 
         Buffer shapes are derived from the target bucket's stored input metadata.
         Returns the received tensor list (stored in task_buffer by run_dag).
         """
         global_src_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
-        if self.logger.isEnabledFor(VERBOSE):
-            self.logger.log(VERBOSE, f"exec_recv_fwd ubid={recv_ubid} from global rank {global_src_rank}")
 
         buf = [
             torch.empty(shape, dtype=dtype, requires_grad=requires_grad, device=self.device)
             for shape, dtype, requires_grad in self.forward_input_meta[recv_ubid]
         ]
-        with torch.cuda.stream(self.p2p_recv_stream):
-            pp_group = self.pp_hi_lo if global_src_rank > self.global_rank else self.pp_lo_hi
+        with torch.cuda.stream(stream):
+            use_hi_lo = global_src_rank > self.global_rank
+            pp_group = self.pp_hi_lo if use_hi_lo else self.pp_lo_hi
             for tensor in buf:
                 dist.recv(tensor, src=global_src_rank, group=pp_group)
         return buf
 
-    def _exec_recv_bwd(self, shape_meta: list, peer_pp_rank: int) -> list:
+    def _exec_recv_bwd(self, shape_meta: list, peer_pp_rank: int, stream) -> list:
         """Receive BWD upstream gradients from peer_pp_rank.
 
         Buffer shapes are derived from shape_meta, a list of (shape, dtype) tuples
@@ -2159,28 +1560,27 @@ def _exec_recv_bwd(self, shape_meta: list, peer_pp_rank: int) -> list:
         Returns the received gradient list (stored in task_buffer by run_dag).
         """
         global_src_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
-        if self.logger.isEnabledFor(VERBOSE):
-            self.logger.log(VERBOSE, f"exec_recv_bwd from global rank {global_src_rank}")
 
         buf = [torch.empty(shape, dtype=dtype, device=self.device) for shape, dtype in shape_meta]
-        with torch.cuda.stream(self.p2p_recv_stream):
-            pp_group = self.pp_hi_lo if global_src_rank > self.global_rank else self.pp_lo_hi
+        with torch.cuda.stream(stream):
+            use_hi_lo = global_src_rank > self.global_rank
+            pp_group = self.pp_hi_lo if use_hi_lo else self.pp_lo_hi
             for tensor in buf:
                 dist.recv(tensor, src=global_src_rank, group=pp_group)
         return buf
 
-    def _exec_a2a(self, input_tensor: torch.Tensor) -> torch.Tensor:
-        """Apply dist.all_to_all_single on a2a_stream and return the output tensor.
+    def _exec_a2a(self, input_tensor: torch.Tensor, stream) -> torch.Tensor:
+        """Apply dist.all_to_all_single on the node stream and return the output tensor.
 
         Used for both FWD_A2A and BWD_A2A — the operation is symmetric.
         Stream waits and event recording are handled by run_dag.
         """
         output_buf = torch.empty_like(input_tensor, device=self.device)
-        with torch.cuda.stream(self.a2a_stream):
+        with torch.cuda.stream(stream):
             dist.all_to_all_single(output_buf, input_tensor, group=self.ep_group)
         return output_buf
 
-    def _forward_dag(self, ubid: int, mb_idx: int, input_tensors, compute_stream) -> dict:
+    def _forward_dag(self, ubid: Any, mb_idx: int, input_tensors, compute_stream) -> dict:
         """Run the forward function for bucket *ubid* and return a result dict.
 
         The result dict always contains:
@@ -2219,14 +1619,6 @@ def _forward_dag(self, ubid: int, mb_idx: int, input_tensors, compute_stream) ->
 
         inp_with_grad = [t for t in fwd_inputs if t is not None and t.requires_grad]
 
-        if ubid not in self._logged_bucket_forward_info:
-            self._logged_bucket_forward_info.add(ubid)
-            self.logger.log(VERBOSE,
-                f"[ac_forward] rank={self.global_rank} ubid={ubid}: "
-                f"ac_subgraphs={self.bucket_ac_subgraph_counts.get(ubid, 1)} "
-                f"inputs={len(input_idxs)}"
-            )
-
         # Run the forward function.
         with torch.cuda.stream(compute_stream):
             output = fwd_fn(fwd_args)
@@ -2259,7 +1651,7 @@ def _forward_dag(self, ubid: int, mb_idx: int, input_tensors, compute_stream) ->
 
     def _backward_dag(
         self,
-        ubid: int,
+        ubid: Any,
         mb_idx: int,
         outputs_or_loss: list,
         upstream_grads,
@@ -2311,6 +1703,62 @@ def _backward_dag(
                 return {"send_output": output_grads}
         return None
 
+    @staticmethod
+    def _grad_with_param_layout(weight: Parameter, grad: torch.Tensor) -> torch.Tensor:
+        if (
+            grad.dtype == weight.dtype
+            and grad.device == weight.device
+            and grad.layout == weight.layout
+            and tuple(grad.stride()) == tuple(weight.stride())
+        ):
+            return grad
+        if weight.layout == torch.strided and grad.layout != torch.strided:
+            grad = grad.to_dense()
+        out = torch.empty_strided(
+            tuple(weight.shape),
+            tuple(weight.stride()),
+            dtype=weight.dtype,
+            device=weight.device,
+        )
+        out.copy_(grad)
+        return out
+
+    def _fused_backward(self, stage_outputs_or_loss: list, output_grads, weights: list) -> None:
+        """Fused backward writing weight grads directly into ``weight.grad``.
+
+        Used for the first pipeline stage, whose inputs (token ids) do not
+        require grad. The split B/W path relies on input-grad prehooks firing to
+        capture intermediate grads, but with no input grads to compute those
+        prehooks never run and stage 0 would never accumulate weight gradients.
+        Computing dW in one pass here (and no-opping the paired BWD_W) keeps the
+        first stage training without redundant work.
+        """
+        wts = [w for w in weights if w.requires_grad]
+        if not wts:
+            return
+        dweights = torch.autograd.grad(
+            stage_outputs_or_loss,
+            inputs=wts,
+            grad_outputs=output_grads,
+            retain_graph=False,
+            allow_unused=True,
+        )
+        for w, dw in zip(wts, dweights):
+            if dw is None:
+                continue
+            dw = self._grad_with_param_layout(w, dw)
+            if w.grad is None:
+                w.grad = dw
+            else:
+                if (
+                    w.grad.dtype != w.dtype
+                    or w.grad.device != w.device
+                    or w.grad.layout != w.layout
+                    or tuple(w.grad.stride()) != tuple(w.stride())
+                ):
+                    w.grad = self._grad_with_param_layout(w, w.grad)
+                w.grad += dw
+
     def _bucket_backward_input(
         self,
         stage_outputs_or_loss: list,
@@ -2325,6 +1773,24 @@ def _bucket_backward_input(
         """
         from .backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
 
+        weights = list(weights)
+
+        if output_grads is None:
+            output_grads = [torch.ones_like(o) for o in stage_outputs_or_loss]
+
+        input_values = [inp for inp in input_values if inp.requires_grad]
+        if not input_values:
+            # First pipeline stage: no input gradient to compute or send. The
+            # split B/W mechanism would never fire its prehooks here, so compute
+            # weight grads in a single fused backward and return empty
+            # param_groups so the paired BWD_W is a no-op.
+            self._fused_backward(stage_outputs_or_loss, output_grads, weights)
+            for i, t in enumerate(stage_outputs_or_loss):
+                if not isinstance(t, torch.Tensor):
+                    continue
+                stage_outputs_or_loss[i] = t.detach()
+            return (), []
+
         stage_output_grad_fns = list(filter(None, map(_get_grad_fn_or_grad_acc, stage_outputs_or_loss)))
         stage_input_grad_fns = list(filter(None, map(_get_grad_fn_or_grad_acc, input_values)))
         weight_grad_fns = list(filter(None, map(_get_grad_fn_or_grad_acc, weights)))
@@ -2343,25 +1809,18 @@ def hook(grad_inputs):
                     return hook
                 handles.append(intermediate.register_prehook(get_hook(param_group, i)))
 
-        if output_grads is None:
-            output_grads = [torch.ones_like(o) for o in stage_outputs_or_loss]
-
-        input_values = [inp for inp in input_values if inp.requires_grad]
-        if input_values:
-            dinputs = torch.autograd.grad(
-                stage_outputs_or_loss,
-                inputs=input_values,
-                grad_outputs=output_grads,
-                retain_graph=True,
-                allow_unused=True,
-            )
-            for inp, dinput in zip(input_values, dinputs):
-                if inp.grad is None:
-                    inp.grad = dinput
-                else:
-                    inp.grad += dinput
-        else:
-            dinputs = ()
+        dinputs = torch.autograd.grad(
+            stage_outputs_or_loss,
+            inputs=input_values,
+            grad_outputs=output_grads,
+            retain_graph=True,
+            allow_unused=True,
+        )
+        for inp, dinput in zip(input_values, dinputs):
+            if inp.grad is None:
+                inp.grad = dinput
+            else:
+                inp.grad += dinput
 
         # Match torch.distributed.pipelining.stage_backward_input(): once dinputs
         # are computed, these outputs are no longer needed by the later weight
@@ -2369,25 +1828,28 @@ def hook(grad_inputs):
         for i, t in enumerate(stage_outputs_or_loss):
             if not isinstance(t, torch.Tensor):
                 continue
-            try:
-                t.detach_()
-            except RuntimeError:
-                # Some tensors may be views; replace the list entry so the original
-                # graph-carrying tensor can be released once local refs are dropped.
-                stage_outputs_or_loss[i] = t.detach()
+            stage_outputs_or_loss[i] = t.detach()
 
         for handle in handles:
             handle.remove()
 
         return dinputs, param_groups
 
-    def _bucket_backward_weight(self, weights, param_groups: list, ubid: int | None = None, mb_idx: int | None = None) -> None:
+    def _bucket_backward_weight(self, weights, param_groups: list, ubid: Any | None = None, mb_idx: int | None = None) -> None:
         """Compute weight gradients for a single bucket (BWD_W).
 
         Matches ``torch.distributed.pipelining.stage_backward_weight`` exactly,
         with "stage" replaced by "bucket".  Accumulates gradients into param.grad.
         """
+        if not param_groups:
+            # First-stage BWD_W is a no-op: BWD_I already ran the fused backward and
+            # wrote weight.grad directly, so there are no param_groups to consume.
+            # Skip the grad-accumulator preamble (one throwaway view per weight).
+            return
+
         grad_acc_to_weight: Dict[Node, Parameter] = {}
+        _grad_with_param_layout = self._grad_with_param_layout
+
         for weight in weights:
             grad_acc = _get_grad_fn_or_grad_acc(weight)
             grad_acc_to_weight[grad_acc] = weight
@@ -2415,23 +1877,12 @@ def _bucket_backward_weight(self, weights, param_groups: list, ubid: int | None
             if not weight_edges:
                 continue
 
-            try:
-                dweights = torch.autograd.grad(
-                    valid_edges,
-                    weight_edges,
-                    grad_outputs=valid_grad_outputs,
-                    retain_graph=False,
-                )
-            except torch.OutOfMemoryError:
-                self.logger.error(
-                    f"[bwd_w_oom] rank={self.global_rank} ubid={ubid} mb={mb_idx}: "
-                    f"group_params={len(param_group['params'])} "
-                    f"group_valid_edges={len(valid_edges)} "
-                    f"group_grad_outputs={len(valid_grad_outputs)} "
-                    f"alloc_gb={torch.cuda.memory_allocated(self.device) / (1024 ** 3):.2f} "
-                    f"reserved_gb={torch.cuda.memory_reserved(self.device) / (1024 ** 3):.2f}"
-                )
-                raise
+            dweights = torch.autograd.grad(
+                valid_edges,
+                weight_edges,
+                grad_outputs=valid_grad_outputs,
+                retain_graph=False,
+            )
 
             del param_group["grads"]
 
@@ -2439,405 +1890,262 @@ def _bucket_backward_weight(self, weights, param_groups: list, ubid: int | None
                 if dw is None or grad_acc not in grad_acc_to_weight:
                     continue
                 weight = grad_acc_to_weight[grad_acc]
+                dw = _grad_with_param_layout(weight, dw)
                 if weight.grad is None:
                     weight.grad = dw
                 else:
+                    if (
+                        weight.grad.dtype != weight.dtype
+                        or weight.grad.device != weight.device
+                        or weight.grad.layout != weight.layout
+                        or tuple(weight.grad.stride()) != tuple(weight.stride())
+                    ):
+                        weight.grad = _grad_with_param_layout(weight, weight.grad)
                     weight.grad += dw
 
-    def _exec_all_reduce_grads(self, ubid: int) -> int:
-        """All-reduce each materialized parameter grad for *ubid* on ar_stream.
+    def _exec_all_reduce_grads(self, ubid: Any, stream) -> int:
+        """All-reduce each materialized parameter grad for *ubid* on *stream*.
 
         Returns the total payload bytes actually dispatched at runtime.
         """
-        if self.zero_stage == 1 and ubid in self.bucket_flat_grads:
-            flat_grads = self.bucket_flat_grads.get(ubid)
-            if flat_grads is None:
-                self.logger.log(VERBOSE,
-                    f"[all_reduce_bucket] rank={self.global_rank} ubid={ubid}: "
-                    "skipped no ZeRO flat grad buffer"
-                )
-                return 0
-            total_bytes = flat_grads.numel() * flat_grads.element_size()
-            self.logger.log(VERBOSE,
-                f"[collective_payload] op=all_reduce rank={self.global_rank} "
-                f"ubid={ubid} mode=flat numel={flat_grads.numel()} "
-                f"element_size={flat_grads.element_size()} dtype={flat_grads.dtype} "
-                f"payload_bytes={total_bytes}"
-            )
-            with torch.cuda.stream(self.ar_stream):
-                dist.all_reduce(flat_grads, group=self.dp_group)
-            return total_bytes
-
+        assert ubid is not None, "_exec_all_reduce_grads requires a non-None ubid"
+        if not self._has_trainable_params_for_collective(ubid, "_exec_all_reduce_grads"):
+            return 0
         args = self.bucket_fwd_args.get(ubid, [])
         trainable_idxs = self.bucket_trainable_param_idxs.get(ubid, [])
         grad_tensors = []
         for idx in trainable_idxs:
             param = args[idx]
-            if param is not None and param.grad is not None:
-                grad_tensors.append((idx, param.grad))
-
-        if not grad_tensors:
-            self.logger.log(VERBOSE,
-                f"[all_reduce_bucket] rank={self.global_rank} ubid={ubid}: "
-                "skipped no materialized grads"
+            assert param is not None, (
+                f"_exec_all_reduce_grads: ubid={ubid} idx={idx} param is None"
             )
-            return 0
+            assert param.grad is not None, (
+                f"_exec_all_reduce_grads: ubid={ubid} idx={idx} param.grad is None"
+            )
+            grad_tensors.append((idx, param.grad))
 
-        total_numel = sum(grad.numel() for _, grad in grad_tensors)
         total_bytes = sum(grad.numel() * grad.element_size() for _, grad in grad_tensors)
-        largest_idx, largest_grad = max(grad_tensors, key=lambda item: item[1].numel())
-        largest_bytes = largest_grad.numel() * largest_grad.element_size()
-        dtype_summary = ",".join(sorted({str(grad.dtype) for _, grad in grad_tensors}))
-        self.logger.log(VERBOSE,
-            f"[collective_payload] op=all_reduce rank={self.global_rank} "
-            f"ubid={ubid} mode=per_param grad_tensors={len(grad_tensors)} "
-            f"numel={total_numel} payload_bytes={total_bytes} dtypes={dtype_summary}"
-        )
-        self.logger.log(VERBOSE,
-            f"[all_reduce_bucket] rank={self.global_rank} ubid={ubid}: "
-            f"mode=per_param grad_tensors={len(grad_tensors)} "
-            f"grad_numel={total_numel} "
-            f"grad_gb={total_bytes / (1024 ** 3):.4f} "
-            f"largest_idx={largest_idx} "
-            f"largest_numel={largest_grad.numel()} "
-            f"largest_gb={largest_bytes / (1024 ** 3):.4f} "
-            f"grad_dtype={largest_grad.dtype} "
-            f"dp_degree={self.dp_degree}"
-        )
-        for grad_order, (idx, grad) in enumerate(grad_tensors):
-            grad_bytes = grad.numel() * grad.element_size()
-            data_ptr_mod_256 = grad.data_ptr() % 256 if grad.is_cuda else -1
-            self.logger.log(VERBOSE,
-                f"[all_reduce_grad] rank={self.global_rank} ubid={ubid} "
-                f"order={grad_order} idx={idx}: "
-                f"shape={tuple(grad.shape)} stride={tuple(grad.stride())} "
-                f"numel={grad.numel()} gb={grad_bytes / (1024 ** 3):.4f} "
-                f"dtype={grad.dtype} device={grad.device} "
-                f"contiguous={grad.is_contiguous()} "
-                f"storage_offset={grad.storage_offset()} "
-                f"data_ptr_mod_256={data_ptr_mod_256}"
-            )
-        with torch.cuda.stream(self.ar_stream):
+        with torch.cuda.stream(stream):
             for _, grad in grad_tensors:
                 dist.all_reduce(grad, group=self.dp_group)
         return total_bytes
 
-    def _alloc_full_params(self, ubid: int | None) -> None:
-        if self.zero_stage != 3:
-            return
-        if ubid is None:
-            return
-        shard_info = self.param_shard_info.get(ubid)
-        if shard_info is None:
-            return
-        _shard_start, shard_size, _orig_numel = shard_info
+    def _has_trainable_params_for_collective(self, ubid: Any, op_name: str) -> bool:
+        trainable_idxs = self.bucket_trainable_param_idxs.get(ubid, [])
+        if trainable_idxs:
+            return True
+        self.logger.warning(
+            "%s: skipping collective for ubid=%s because it has no trainable param indices",
+            op_name,
+            ubid,
+        )
+        return False
+
+    def _alloc_full_params(self, ubid: Any) -> None:
+        assert ubid is not None, "_alloc_full_params requires a non-None ubid"
+        assert ubid in self.param_sharded_ubids, (
+            f"_alloc_full_params: ubid={ubid} is not in param_sharded_ubids={self.param_sharded_ubids}"
+        )
+        self._wait_pending_free(self.pending_param_frees, ubid)
+        assert self.param_shard_info.get(ubid) is not None, (
+            f"_alloc_full_params: missing param_shard_info for ubid={ubid}"
+        )
         full = self.bucket_flat_params.get(ubid)
-        if full is None:
-            full = self.bucket_shard_params[ubid].new_empty(shard_size * self.dp_degree)
-            self.bucket_flat_params[ubid] = full
+        assert full is not None, (
+            f"_alloc_full_params: missing bucket_flat_params buffer for ubid={ubid}"
+        )
+        specs = self.bucket_param_view_specs.get(ubid, [])
+        assert specs, f"_alloc_full_params: missing bucket_param_view_specs for ubid={ubid}"
         storage = full.untyped_storage()
         required_bytes = full.numel() * full.element_size()
-        if storage.size() != required_bytes:
-            storage.resize_(required_bytes)
+        storage.resize_(required_bytes)
         self.logger.debug(
-            f"[zero3_alloc_full_params] rank={self.global_rank} ubid={ubid}: "
+            f"[alloc_full_params] rank={self.global_rank} ubid={ubid}: "
             f"numel={full.numel()} required_bytes={required_bytes} "
-            f"storage_size={storage.size()} fresh={self.zero3_full_params_fresh.get(ubid, False)}"
+            f"storage_size={storage.size()} fresh={self.full_params_fresh.get(ubid, False)}"
         )
-        for param, offset, numel, shape in self.bucket_param_view_specs.get(ubid, []):
+        for param, offset, numel, shape in specs:
             param.data = full[offset:offset + numel].view(shape)
             param.requires_grad_(True)
         param_names = self.bucket_param_names.get(ubid, [])
         zero_storage = []
-        for i, (param, offset, numel, shape) in enumerate(self.bucket_param_view_specs.get(ubid, [])):
+        for i, (param, offset, numel, shape) in enumerate(specs):
             p_storage = param.untyped_storage()
             if p_storage.size() == 0:
                 name = param_names[i] if i < len(param_names) else f"param{i}"
                 zero_storage.append(
                     f"{name}: offset={offset} numel={numel} shape={shape} stride={tuple(param.stride())}"
                 )
-        if zero_storage:
-            self.logger.error(
-                f"[zero3_alloc_full_params_zero_storage] rank={self.global_rank} ubid={ubid}: "
-                + " | ".join(zero_storage)
-            )
+        assert not zero_storage, (
+            f"[alloc_full_params_zero_storage] rank={self.global_rank} ubid={ubid}: "
+            + " | ".join(zero_storage)
+        )
 
-    def _free_full_params(self, ubid: int | None) -> None:
-        if self.zero_stage != 3:
-            return
+    def _free_full_params(self, ubid: Any) -> None:
+        assert ubid is not None, "_free_full_params requires a non-None ubid"
+        assert ubid in self.param_sharded_ubids, (
+            f"_free_full_params: ubid={ubid} is not in param_sharded_ubids={self.param_sharded_ubids}"
+        )
         full = self.bucket_flat_params.get(ubid)
-        if full is None:
-            return
+        assert full is not None, (
+            f"_free_full_params: missing bucket_flat_params buffer for ubid={ubid}"
+        )
         storage = full.untyped_storage()
         self.logger.debug(
-            f"[zero3_free_full_params] rank={self.global_rank} ubid={ubid}: "
-            f"storage_size_before={storage.size()} fresh_before={self.zero3_full_params_fresh.get(ubid, False)}"
+            f"[free_full_params] rank={self.global_rank} ubid={ubid}: "
+            f"storage_size_before={storage.size()} fresh_before={self.full_params_fresh.get(ubid, False)}"
         )
-        if storage.size() != 0:
-            storage.resize_(0)
-        self.zero3_full_params_fresh[ubid] = False
+        storage.resize_(0)
+        self.full_params_fresh[ubid] = False
 
-    def _alloc_full_grads(self, ubid: int | None) -> None:
+    def _alloc_full_grads(self, ubid: Any, stream: "torch.cuda.Stream") -> None:
+        assert ubid is not None, "_alloc_full_grads requires a non-None ubid"
+        assert ubid in self.grad_sharded_ubids, (
+            f"_alloc_full_grads: ubid={ubid} is not in grad_sharded_ubids={self.grad_sharded_ubids}"
+        )
+        self._wait_pending_free(self.pending_grad_frees, ubid)
         specs = self.bucket_param_view_specs.get(ubid, [])
-        assert specs, f"Expected param view specs for ubid={ubid}"
-        flat_grads = self.bucket_flat_grads.get(ubid)
-        if flat_grads is None:
-            shard_info = self.param_shard_info.get(ubid)
-            if shard_info is None:
-                return
-            _shard_start, shard_size, _orig_numel = shard_info
-            flat_grads = torch.zeros(
-                shard_size * self.dp_degree,
-                dtype=self.zero_grad_dtype,
-                device=self.device,
-            )
-            self.bucket_flat_grads[ubid] = flat_grads
-        else:
-            flat_grads.zero_()
-        if self.zero_stage in (2, 3) and self.bucket_rs_grads.get(ubid) is None:
-            shard_info = self.param_shard_info.get(ubid)
-            if shard_info is None:
-                return
-            _shard_start, shard_size, _orig_numel = shard_info
-            self.bucket_rs_grads[ubid] = torch.zeros(
-                shard_size, dtype=self.zero_grad_dtype, device=self.device
-            )
+        assert specs, f"_alloc_full_grads: missing bucket_param_view_specs for ubid={ubid}"
+        shard_info = self.param_shard_info.get(ubid)
+        assert shard_info is not None, (
+            f"_alloc_full_grads: missing param_shard_info for ubid={ubid}"
+        )
+        shard_size = shard_info[1]
+        with torch.cuda.stream(stream):
+            if self.bucket_flat_grads.get(ubid) is None:
+                self.bucket_flat_grads[ubid] = torch.zeros(
+                    shard_size * self.dp_degree,
+                    dtype=self.grad_buffer_dtype,
+                    device=self.device,
+                )
+            if self.bucket_rs_grads.get(ubid) is None:
+                self.bucket_rs_grads[ubid] = torch.zeros(
+                    shard_size, dtype=self.grad_buffer_dtype, device=self.device
+                )
 
-    def _free_full_grads(self, ubid: int | None) -> None:
-        for param, *_ in self.bucket_param_view_specs.get(ubid, []):
+    def _free_full_grads(self, ubid: Any) -> None:
+        assert ubid is not None, "_free_full_grads requires a non-None ubid"
+        assert ubid in self.grad_sharded_ubids, (
+            f"_free_full_grads: ubid={ubid} is not in grad_sharded_ubids={self.grad_sharded_ubids}"
+        )
+        specs = self.bucket_param_view_specs.get(ubid, [])
+        assert specs, f"_free_full_grads: missing bucket_param_view_specs for ubid={ubid}"
+        for param, *_ in specs:
             param.grad = None
-        if self.zero_stage in (2, 3):
-            self.bucket_flat_grads[ubid] = None
+        self.bucket_flat_grads[ubid] = None
 
-    def _exec_reduce_scatter(self, ubid: int) -> int:
-        with torch.cuda.stream(self.ar_stream):
-            flat_grads = self.bucket_flat_grads.get(ubid)
-            _shard_start, shard_size, _orig_numel = self.param_shard_info.get(ubid)
-            # add logging here
-            rs_out = self.bucket_rs_grads.get(ubid)
-            if flat_grads is None or rs_out is None:
-                if flat_grads is None:
-                    self.logger.warning(f"[reduce_scatter_alloc] rank={self.global_rank} ubid={ubid}: Found no flat grad buffer")
-                    flat_grads = torch.zeros(
-                        shard_size * self.dp_degree,
-                        dtype=self.zero_grad_dtype,
-                        device=self.device,
-                    )
-                    self.bucket_flat_grads[ubid] = flat_grads
-                if rs_out is None:
-                    rs_out = torch.zeros(
-                        shard_size, dtype=self.zero_grad_dtype, device=self.device
-                    )
-                    self.bucket_rs_grads[ubid] = rs_out
-            assert flat_grads is not None and rs_out is not None, (
-                f"REDUCE_SCATTER dispatched for ubid={ubid} without allocated gradient buffers"
-            )
+    def _exec_reduce_scatter(self, ubid: Any, stream) -> int:
+        assert ubid is not None, "_exec_reduce_scatter requires a non-None ubid"
+        if not self._has_trainable_params_for_collective(ubid, "_exec_reduce_scatter"):
+            return 0
+        assert ubid in self.grad_sharded_ubids, (
+            f"_exec_reduce_scatter: ubid={ubid} is not in grad_sharded_ubids={self.grad_sharded_ubids}"
+        )
+        assert self.param_shard_info.get(ubid) is not None, (
+            f"_exec_reduce_scatter: missing param_shard_info for ubid={ubid}"
+        )
+        flat_grads = self.bucket_flat_grads.get(ubid)
+        rs_out = self.bucket_rs_grads.get(ubid)
+        assert flat_grads is not None, (
+            f"_exec_reduce_scatter: missing bucket_flat_grads buffer for ubid={ubid}"
+        )
+        assert rs_out is not None, (
+            f"_exec_reduce_scatter: missing bucket_rs_grads buffer for ubid={ubid}"
+        )
+        with torch.cuda.stream(stream):
             total_bytes = flat_grads.numel() * flat_grads.element_size()
-            self.logger.log(VERBOSE,
-                f"[collective_payload] op=reduce_scatter rank={self.global_rank} "
-                f"ubid={ubid} input_numel={flat_grads.numel()} "
-                f"input_element_size={flat_grads.element_size()} input_dtype={flat_grads.dtype} "
-                f"payload_bytes={total_bytes} output_numel={rs_out.numel()} "
-                f"output_element_size={rs_out.element_size()} output_dtype={rs_out.dtype}"
-            )
             tmp = torch.empty_like(rs_out)
-            flat_grads[_shard_start:_shard_start + shard_size].add_(rs_out)
-            dist.reduce_scatter_tensor(rs_out, flat_grads, group=self.dp_group)
+            dist.reduce_scatter_tensor(tmp, flat_grads, group=self.dp_group)
             rs_out.add_(tmp)
             return total_bytes
 
     def _exec_all_gather(
         self,
-        ubid: int | None,
-    ):
-        if ubid is None:
-            return False
-        with torch.cuda.stream(self.ar_stream):
-            flat_params = self.bucket_flat_params.get(ubid)
-            shard_in = self.bucket_shard_params.get(ubid)
-            if flat_params is None or shard_in is None:
-                return False
-            if self.zero_stage == 3 and self.zero3_full_params_fresh.get(ubid, False):
-                self.logger.debug(
-                    f"[zero3_all_gather_skip] rank={self.global_rank} ubid={ubid}: "
-                    f"storage_size={flat_params.untyped_storage().size()} fresh=True"
-                )
-                return False
+        ubid: Any,
+        stream,
+    ) -> int:
+        assert ubid is not None, "_exec_all_gather requires a non-None ubid"
+        if not self._has_trainable_params_for_collective(ubid, "_exec_all_gather"):
+            return 0
+        assert ubid in self.param_sharded_ubids, (
+            f"_exec_all_gather: ubid={ubid} is not in param_sharded_ubids={self.param_sharded_ubids}"
+        )
+        self._alloc_full_params(ubid)
+        flat_params = self.bucket_flat_params.get(ubid)
+        shard_in = self.bucket_shard_params.get(ubid)
+        assert flat_params is not None, (
+            f"_exec_all_gather: missing bucket_flat_params buffer for ubid={ubid}"
+        )
+        assert shard_in is not None, (
+            f"_exec_all_gather: missing bucket_shard_params buffer for ubid={ubid}"
+        )
+        assert not self.full_params_fresh.get(ubid, False), (
+            f"_exec_all_gather: ubid={ubid} dispatched but full params are already fresh; "
+            "DAG is constructing a redundant ALL_GATHER"
+        )
+        with torch.cuda.stream(stream):
             self.logger.debug(
-                f"[zero3_all_gather_begin] rank={self.global_rank} ubid={ubid}: "
+                f"[all_gather_begin] rank={self.global_rank} ubid={ubid}: "
                 f"flat_numel={flat_params.numel()} flat_storage={flat_params.untyped_storage().size()} "
-                f"shard_numel={shard_in.numel()} shard_storage={shard_in.untyped_storage().size()} "
-                f"fresh={self.zero3_full_params_fresh.get(ubid, False)}"
+                f"shard_numel={shard_in.numel()} shard_storage={shard_in.untyped_storage().size()}"
             )
             dist.all_gather_into_tensor(flat_params, shard_in, group=self.dp_group)
-            if self.zero_stage == 3:
-                self.zero3_full_params_fresh[ubid] = True
+            self.full_params_fresh[ubid] = True
             self.logger.debug(
-                f"[zero3_all_gather_end] rank={self.global_rank} ubid={ubid}: "
-                f"flat_storage={flat_params.untyped_storage().size()} fresh={self.zero3_full_params_fresh.get(ubid, False)}"
+                f"[all_gather_end] rank={self.global_rank} ubid={ubid}: "
+                f"flat_storage={flat_params.untyped_storage().size()}"
             )
-        return True
+            return flat_params.numel() * flat_params.element_size()
 
-    def _update(self, *deps):
-        if self.zero_stage > 0:
-            sync_events = self.ar_events if self.zero_stage == 1 else self.rs_events
-            for evt in sync_events.values():
-                self.comp_stream.wait_event(evt)
+    def _update(self, stream, *deps):
+        self._drain_pending_frees()
+        if self.bucket_shard_optims:
+            for evt in self.rs_events.values():
+                stream.wait_event(evt)
 
             for ubid, shard_optim in self.bucket_shard_optims.items():
                 shard_param = self.bucket_shard_params[ubid]
-                shard_start, shard_size, _orig_numel = self.param_shard_info[ubid]
-                if self.zero_stage == 1:
-                    flat_grads = self.bucket_flat_grads.get(ubid)
-                    if flat_grads is None:
-                        continue
-                    shard_param.grad = flat_grads[shard_start:shard_start + shard_size].to(shard_param.dtype)
-                else:
-                    rs_grad = self.bucket_rs_grads.get(ubid)
-                    if rs_grad is None:
-                        continue
+                rs_grad = self.bucket_rs_grads.get(ubid)
+                if rs_grad is None:
+                    continue
+                with torch.cuda.stream(stream):
                     shard_param.grad = rs_grad.to(shard_param.dtype)
-                with torch.cuda.stream(self.comp_stream):
                     shard_optim.step()
                 shard_param.grad = None
 
-            if self.zero_stage == 3:
-                for ubid, specs in self.bucket_param_view_specs.items():
-                    for param, *_ in specs:
-                        param.grad = None
-                    full = self.bucket_flat_params.get(ubid)
-                    if full is not None:
-                        storage = full.untyped_storage()
-                        if storage.size() != 0:
-                            storage.resize_(0)
-                    self.zero3_full_params_fresh[ubid] = False
+            for ubid in self.param_sharded_ubids:
+                for param, *_ in self.bucket_param_view_specs.get(ubid, []):
+                    param.grad = None
+                full = self.bucket_flat_params.get(ubid)
+                if full is not None:
+                    storage = full.untyped_storage()
+                    if storage.size() != 0:
+                        storage.resize_(0)
+                self.full_params_fresh[ubid] = False
 
             losses = self.loss
             self.loss.clear()
             torch.cuda.synchronize()
             return losses
 
-        pre_step_mem: dict = {}
-        grad_debug: dict = {}
-
         for ar_evt in self.ar_events.values():
-            self.comp_stream.wait_event(ar_evt)
+            stream.wait_event(ar_evt)
 
         for ubid, optim in self.bucket_optims.items():
             if optim is None:
                 continue
             bwd_evt = self.bwd_events.get(ubid)
             if bwd_evt is not None:
-                self.comp_stream.wait_event(bwd_evt)
+                stream.wait_event(bwd_evt)
 
-            params = self.bucket_fwd_args.get(ubid, [])
-            trainable_idxs = self.bucket_trainable_param_idxs.get(ubid, [])
-
-            params_with_grad = 0
-            total_trainable_params = 0
-            total_grad_elems = 0
-            for idx in trainable_idxs:
-                p = params[idx]
-                if p is None:
-                    continue
-                total_trainable_params += p.numel()
-                if p.grad is not None:
-                    params_with_grad += 1
-                    total_grad_elems += p.grad.numel()
-
-            # Exact zero-gradient detection requires scanning each grad tensor and
-            # can allocate a large temporary near peak memory. Treat materialized
-            # grads as active here; structural sparse expert metadata would be
-            # needed to preserve skip-zero semantics without a dense value scan.
-            nonzero_grad_params = params_with_grad
-            nonzero_grad_elems = total_grad_elems
-            zero_grad_params = 0
-
-            state_entries_before = sum(len(optim.state[p]) > 0 for p in optim.param_groups[0]["params"])
-            pre_step_mem[ubid] = (
-                torch.cuda.memory_allocated(self.device),
-                torch.cuda.memory_reserved(self.device),
-            )
-            grad_debug[ubid] = {
-                "num_params": len(trainable_idxs),
-                "params_with_grad": params_with_grad,
-                "nonzero_grad_params": nonzero_grad_params,
-                "total_trainable_params": total_trainable_params,
-                "total_grad_elems": total_grad_elems,
-                "nonzero_grad_elems": nonzero_grad_elems,
-                "zero_grad_params": zero_grad_params,
-                "state_entries_before": state_entries_before,
-            }
-            self.logger.log(VERBOSE,
-                f"[optim_debug_pre] rank={self.global_rank} ubid={ubid}: "
-                f"params={len(trainable_idxs)} "
-                f"params_with_grad={params_with_grad} "
-                f"nonzero_grad_params={nonzero_grad_params} "
-                f"zero_grad_params={zero_grad_params} "
-                f"trainable_elems={total_trainable_params} "
-                f"grad_elems={total_grad_elems} "
-                f"nonzero_grad_elems={nonzero_grad_elems} "
-                f"state_entries_before={state_entries_before} "
-                f"alloc_before_gb={pre_step_mem[ubid][0] / (1024 ** 3):.2f} "
-                f"reserved_before_gb={pre_step_mem[ubid][1] / (1024 ** 3):.2f}"
-            )
-            try:
-                with torch.cuda.stream(self.comp_stream):
-                    optim.step()
-            except torch.OutOfMemoryError:
-                alloc_now = torch.cuda.memory_allocated(self.device)
-                reserv_now = torch.cuda.memory_reserved(self.device)
-                self.logger.error(
-                    f"[optim_debug_oom] rank={self.global_rank} ubid={ubid}: "
-                    f"state_entries_before={state_entries_before} "
-                    f"alloc_now_gb={alloc_now / (1024 ** 3):.2f} "
-                    f"reserved_now_gb={reserv_now / (1024 ** 3):.2f}"
-                )
-                raise
+            with torch.cuda.stream(stream):
+                optim.step()
 
         losses = self.loss
         self.loss.clear()
 
         torch.cuda.synchronize()
 
-        for ubid, optim in self.bucket_optims.items():
-            if optim is None or ubid not in grad_debug:
-                continue
-            alloc_before, reserv_before = pre_step_mem[ubid]
-            alloc_after = torch.cuda.memory_allocated(self.device)
-            reserv_after = torch.cuda.memory_reserved(self.device)
-            state_entries_after = sum(len(optim.state[p]) > 0 for p in optim.param_groups[0]["params"])
-            dbg = grad_debug[ubid]
-            self.logger.log(VERBOSE,
-                f"[optim_debug] rank={self.global_rank} ubid={ubid}: "
-                f"params={dbg['num_params']} "
-                f"params_with_grad={dbg['params_with_grad']} "
-                f"nonzero_grad_params={dbg['nonzero_grad_params']} "
-                f"zero_grad_params={dbg['zero_grad_params']} "
-                f"trainable_elems={dbg['total_trainable_params']} "
-                f"grad_elems={dbg['total_grad_elems']} "
-                f"nonzero_grad_elems={dbg['nonzero_grad_elems']} "
-                f"state_entries_before={dbg['state_entries_before']} "
-                f"state_entries_after={state_entries_after} "
-                f"alloc_before_gb={alloc_before / (1024 ** 3):.2f} "
-                f"alloc_after_gb={alloc_after / (1024 ** 3):.2f} "
-                f"reserved_before_gb={reserv_before / (1024 ** 3):.2f} "
-                f"reserved_after_gb={reserv_after / (1024 ** 3):.2f}"
-            )
-
-        # Process deferred profiling records now that all GPU work is complete.
-        # elapsed_time() is safe to call after synchronize().
-        if self._prof_records:
-            for node, mb_idx, ubid, start_evt, end_evt, mem_before in self._prof_records:
-                t_ms = start_evt.elapsed_time(end_evt)
-                node.profiling_measurements.append(t_ms)
-                self.logger.info(
-                    f"[profiling] rank {self.global_rank} "
-                    f"{node.task_type.value} mb{mb_idx} ubid{ubid} "
-                    f"(time step {node.time_step}): time={t_ms:.3f}ms "
-                    f"mem_before={mem_before/1e9:.3f}GB"
-                )
-
         return {
             "losses": losses,
         }
diff --git a/src/piper_compile.py b/src/piper_compile.py
index ccf704e..2a58326 100644
--- a/src/piper_compile.py
+++ b/src/piper_compile.py
@@ -1,23 +1,21 @@
 import ray
 import torch
-from torch._dynamo.backends.debugging import eager
-import torch.distributed as dist
-from torch._dynamo.backends.debugging import eager
-import torch.distributed as dist
-import threading
-import os
-import gc
 import copy
-import itertools
-
-from torch._dynamo.backends.debugging import eager
+import os
+import time
 
 from .piper_actor import _create_actors
-from .piper_utils import piper_metadata, create_logger, LOG_LEVEL, VERBOSE
-from .piper_exec import DAGEdge, PipelineSchedule, CompType, Chunk, BatchMeta, _validate_schedule
-from .piper import piper, piper_exec_dag
-from .piper_graph_transform import compute_critical_path, visualize_dag
-
+from .piper_utils import (
+    piper_metadata,
+    create_logger,
+    LOG_LEVEL,
+    _serialize_graphmodule,
+)
+from .piper import piper
+from .piper_schedule import (
+    derive_schedule_info,
+    load_schedule_directives,
+)
 logger = create_logger("piper_compile", LOG_LEVEL)
 
 _RANK0_ADDR_ACTOR = "piper_rank0_addr"
@@ -35,65 +33,78 @@ def get(self):
 
 @ray.remote
 class _CompiledDataStore:
-    """Named actor used to share compiled stage/DAG data from dp_rank=0 to dp_rank>0.
-
-    Stage data is published as soon as it is serialized so nonzero DP ranks can
-    start _load_stage while dp_rank=0 continues DAG construction.
-    """
+    """Named actor used to share compiled training-DAG data from dp_rank=0 to dp_rank>0."""
     def __init__(self):
-        self._stage_data = None
-        self._dag_data = None
-        self._stage_ready = False
-        self._dag_ready = False
-
-    def publish(self, data: dict):
-        self._stage_data = {
-            "stages": data["stages"],
-            "stage_bucket_counts": data["stage_bucket_counts"],
-            "trainable_bucket_keys": data["trainable_bucket_keys"],
-            "zero_bucket_keys": data.get("zero_bucket_keys", set()),
-        }
-        self._dag_data = {
-            "per_rank_dags": data["per_rank_dags"],
-            "full_dag_no_overlap": data.get("full_dag_no_overlap"),
-        }
-        self._stage_ready = True
-        self._dag_ready = True
-
-    def publish_stage_data(self, data: dict):
-        self._stage_data = data
-        self._stage_ready = True
-
-    def publish_dag_data(self, data: dict):
-        self._dag_data = data
-        self._dag_ready = True
+        self._training_dag_data = None
+        self._training_dag_ready = False
 
-    def is_ready(self) -> bool:
-        return self._stage_ready and self._dag_ready
+    def publish_training_dag_data(self, data: dict):
+        self._training_dag_data = data
+        self._training_dag_ready = True
 
-    def is_stage_ready(self) -> bool:
-        return self._stage_ready
+    def is_ready(self) -> bool:
+        return self._training_dag_ready
 
-    def is_dag_ready(self) -> bool:
-        return self._dag_ready
+    def is_training_dag_ready(self) -> bool:
+        return self._training_dag_ready
 
     def get(self) -> dict:
         if not self.is_ready():
             return None
-        result = dict(self._stage_data)
-        result.update(self._dag_data)
-        return result
+        return dict(self._training_dag_data)
+
+    def get_training_dag_data(self) -> dict:
+        return self._training_dag_data
+
 
-    def get_stage_data(self) -> dict:
-        return self._stage_data
 
-    def get_dag_data(self) -> dict:
-        return self._dag_data
 
 
+torch._dynamo.config.capture_scalar_outputs = True
 
 
+def _prepare_training_dags_for_rpc(per_pp_training_dags: list) -> list:
+    """Return a Ray-safe copy of per-PP training DAGs.
 
+    Raw fx.GraphModule objects in node_meta are replaced by serialized strings
+    under ``gm_data`` to avoid Ray deserialization invoking FX tracing on HOPs.
+    """
+    rpc_dags = copy.deepcopy(per_pp_training_dags)
+    for dag in rpc_dags:
+        for node in dag.nodes.values():
+            if getattr(node, "node_kind", None) != "COMPUTE":
+                continue
+            if getattr(node, "compute_subkind", None) != "FWD":
+                continue
+            meta = getattr(node, "node_meta", None)
+            if not isinstance(meta, dict):
+                raise ValueError(f"Malformed COMPUTE node metadata for uid={getattr(node, 'uid', '<unknown>')}")
+            gm = meta.get("gm")
+            if gm is None:
+                raise ValueError(
+                    f"Malformed COMPUTE/FWD node {getattr(node, 'uid', '<unknown>')}: missing node_meta['gm']"
+                )
+            meta["gm_data"] = _serialize_graphmodule(gm)
+            del meta["gm"]
+    return rpc_dags
+
+
+def _compute_loss_pp_ranks(per_pp_training_dags: list) -> list[int]:
+    ranks: list[int] = []
+    for pp_rank, dag in enumerate(per_pp_training_dags):
+        if any(
+            isinstance(getattr(node, "node_meta", None), dict)
+            and bool(node.node_meta.get("compute_loss", False))
+            for node in dag.nodes.values()
+        ):
+            ranks.append(pp_rank)
+    return ranks
+
+
+def _resolve_model_constructor_value(value, schedule_info: dict):
+    if callable(value):
+        return value(schedule_info)
+    return value
 
 
 def piper_setup(
@@ -104,61 +115,35 @@ def piper_setup(
     optim_fn=None,
     example_inputs=None,
     example_outputs=None,
-    loss_fn=None,
-    schedule: PipelineSchedule=None,
-    naive_gradient_sync=False,
     activation_checkpointing=False,
     num_checkpoints=1,
-    bucketing=False,
-    bucket_size: int = 25 * 1024 * 1024,
-    zero_stage: int = 0,
-    gradient_accumulation: bool = True,
     use_inductor: bool = False,
-    enable_ep: bool = False,
-    ar_a2a_same_stream: bool = False,
-    overlap_zero_ops: bool = False,
-    overlap_chunks: bool = False,
-    schedule_name: str = "",
-    visualize_dag_render: bool = True,
     no_nvtx: bool = False,
-    a2a_ar_no_overlap=False,
     pg=None,
     nsight=False,
     temp_dir: str = None,
-    model_flops_per_token: float = None,
     visualize_dag: bool = True,
     output_dir: str = "out",
     const_attrs: dict = None,
-    memory_profile: bool = False,
     pp_outer: bool = False,
+    schedule_directives_file: str | None = None,
 ):
     """
-    Compile a model with the piper backend.
+    Compile a model with the TrainingDAG backend.
+
+    The model and example inputs are always traced on the meta device so the
+    full model is never materialized on the driver GPU at compile time.
 
     Args:
         model: The model to compile.
         optim_fn: Callable ``(params) -> Optimizer`` used to create optimizers.
         example_inputs: Example inputs for tracing.
         example_outputs: Example outputs (labels) for tracing.
-        schedule: 2D schedule grid (rank x time_step).
-        naive_gradient_sync: Use a simple blocking all-reduce instead of
-            pipelined per-param hooks.
-        a2a_ar_no_overlap: When true, schedule gradient all-reduces after
-            same-rank A2A operations to avoid NCCL interference.
-        zero_stage: ZeRO stage: 0 disabled, 1 optimizer-state sharding,
-            2 gradient sharding, 3 parameter and gradient sharding.
-        gradient_accumulation: When true, delay bucket gradient reductions
-            until the last occurrence of each bucket.
         use_inductor: When true, actors torch.compile stage GraphModules
             during _load_stage with the default inductor backend.
-        enable_ep: When true, honor expert-parallel A2A annotations by
-            splitting stage graphs and inserting DAG-level A2A tasks.
-        ar_a2a_same_stream: When true, actors run A2A ops on the same CUDA
-            stream as AR ops.
-        overlap_zero_ops: When true, run overlap_zero_ops on per-rank DAGs
-            after ZeRO collective insertion.
-        overlap_chunks: When true, apply the chunk-overlap transform to
-            per-rank DAGs before assigning time steps.
+        schedule_directives_file: JSON schedule description consumed by the
+            TrainingDAG backend. When provided, Piper derives pp/dp/mbs
+            schedule metadata internally.
     """
 
     # Clear Dynamo's global compilation cache so that a previous piper_setup
@@ -166,64 +151,37 @@ def piper_setup(
     # worker process cannot contaminate this compilation via a stale cache hit.
     torch._dynamo.reset()
 
-    stage_to_device = schedule.stage_to_device()
-    assert len(stage_to_device) > 0
-    piper_metadata.stage_to_device = stage_to_device
-    piper_metadata.use_activation_checkpointing = activation_checkpointing
-    piper_metadata.activation_num_checkpoints = max(1, int(num_checkpoints))
-    piper_metadata.bucket_size = bucket_size
-    piper_metadata.zero_stage = int(zero_stage)
-    piper_metadata.gradient_accumulation = bool(gradient_accumulation)
-    piper_metadata.use_inductor = bool(use_inductor)
-    piper_metadata.enable_ep = bool(enable_ep)
-    piper_metadata.ar_a2a_same_stream = bool(ar_a2a_same_stream)
-    piper_metadata.overlap_zero_ops = bool(overlap_zero_ops)
-    piper_metadata.overlap_chunks = bool(overlap_chunks)
-    piper_metadata.schedule_name = schedule_name
-    piper_metadata.visualize_dag_render = visualize_dag_render
-    piper_metadata.a2a_ar_no_overlap = a2a_ar_no_overlap
-    piper_metadata.schedule = schedule
+    schedule_info = {}
+    if schedule_directives_file is not None:
+        schedule_directives = load_schedule_directives(schedule_directives_file)
+        schedule_info = derive_schedule_info(schedule_directives, schedule_directives_file)
+    else:
+        raise ValueError("piper_setup requires schedule_directives_file")
+
     piper_metadata.visualize_dag = visualize_dag
     piper_metadata.output_dir = output_dir
+    piper_metadata.schedule_directives = list(schedule_directives or [])
+    piper_metadata.schedule_directives_file = schedule_directives_file
+    piper_metadata.schedule_info = dict(schedule_info)
 
     # Reset DAG/compile fields so stale data from a prior run never leaks into
-    # this run if the piper backend is somehow not re-invoked.
-    piper_metadata.per_rank_dags = None
-    piper_metadata.full_dag_no_overlap = None
-    piper_metadata.stage_bucket_counts = {}
-    piper_metadata.trainable_bucket_keys = set()
-    piper_metadata.compiled_stage_data = None
+    # this run if the backend is somehow not re-invoked.
+    piper_metadata.training_dag = None
+    piper_metadata.per_pp_training_dags = None
     piper_metadata.compiled_data_store = None
 
-    # MFU tracking
-    piper_metadata.model_flops_per_token = model_flops_per_token
-    if model_flops_per_token is not None and example_inputs is not None:
-        dp_degree = int(os.environ.get("PIPER_DP_DEGREE", "1"))
-        # example_inputs[0] is the token index tensor of shape (batch_size, seq_len)
-        piper_metadata.tokens_per_step = example_inputs[0].numel() * dp_degree
-
-    dag_edges = []
-    # TODO: build dag by analyzing stage dependencies rather than assuming a linear chain
-    for stage_id in piper_metadata.stage_to_device.keys():
-        if stage_id < len(piper_metadata.stage_to_device.keys()) - 1:
-            dag_edges.append(DAGEdge(stage_id, stage_id+1))
-    _validate_schedule(schedule, dag_edges=dag_edges, num_mbs=schedule.num_mbs())
-
-    num_mbs = schedule.num_mbs()
-    num_stages = schedule.num_stages()
-    num_devices = schedule.num_ranks()
+    pp_degree = int(schedule_info["pp_degree"])
 
     # All dp_ranks must agree on a single master_addr: the IP of the actor with
     # global_rank=0 (pp_rank=0, dp_rank=0).  dp_rank=0 publishes it via a named
     # Ray actor; other dp_ranks wait until it's available.
-    import time
     dp_rank = int(os.environ["PIPER_DP_RANK"])
     _create_actors(
-        num_devices, optim_fn, num_mbs, num_stages,
-        naive_gradient_sync, profile=nsight, stage_to_device=stage_to_device,
-        zero_stage=zero_stage, no_nvtx=no_nvtx, pg=pg, temp_dir=temp_dir,
-        use_inductor=use_inductor, ar_a2a_same_stream=ar_a2a_same_stream,
-        memory_profile=memory_profile, pp_outer=pp_outer,
+        pp_degree, optim_fn,
+        profile=nsight,
+        no_nvtx=no_nvtx, pg=pg, temp_dir=temp_dir,
+        use_inductor=use_inductor,
+        pp_outer=pp_outer,
     )
 
     if dp_rank == 0:
@@ -255,7 +213,7 @@ def piper_setup(
 
     logger.debug(f"DP rank {dp_rank}: Master address for process groups: {master_addr}:{master_port}")
 
-    handles = [actor._join_process_groups.remote(master_addr, master_port) for actor in piper_metadata.actors.values()]
+    ray.get([actor._join_process_groups.remote(master_addr, master_port) for actor in piper_metadata.actors.values()])
 
     if dp_rank == 0:
         try:
@@ -275,14 +233,15 @@ def piper_setup(
             actor.load_const_attrs.remote(_const_attrs)
             for actor in piper_metadata.actors.values()
         ])
-        logger.log(VERBOSE, f"Pushed {len(_const_attrs)} const attrs to actors: {list(_const_attrs.keys())}")
 
     if dp_rank == 0:
         # --- dp_rank=0: run torch.compile, build DAGs, publish compiled data ---
 
-        # Build the model directly on meta device
+        # Build the model directly on meta device.
+        resolved_model_args = _resolve_model_constructor_value(model_args, piper_metadata.schedule_info)
+        resolved_model_kwargs = _resolve_model_constructor_value(model_kwargs, piper_metadata.schedule_info)
         with torch.device("meta"):
-            model = model_class(*model_args, **model_kwargs)
+            model = model_class(*tuple(resolved_model_args or ()), **dict(resolved_model_kwargs or {}))
             if model_dtype is not None:
                 model = model.to(model_dtype)
 
@@ -296,69 +255,67 @@ def piper_setup(
         logger.info(f"Model size: {num_params/(1e6):.0f} M parameters ({param_size_gb:.2f} GB), dtype: {model_dtype}")
 
         compiled = torch.compile(model, backend=piper, fullgraph=True)
-        meta_inputs = [x.to(device="meta") for x in example_inputs]
-        _ = compiled(*meta_inputs)
+        compile_inputs = [x.to(device="meta") for x in example_inputs]
+        _ = compiled(*compile_inputs)
+
+        per_pp_training_dags = getattr(piper_metadata, "per_pp_training_dags", None)
+        if not per_pp_training_dags:
+            raise RuntimeError(
+                "piper backend did not produce per_pp_training_dags; backend transition incomplete"
+            )
+        if len(per_pp_training_dags) != pp_degree:
+            raise RuntimeError(
+                f"Expected {pp_degree} per-PP DAGs, got {len(per_pp_training_dags)}"
+            )
+        per_pp_training_dags_rpc = _prepare_training_dags_for_rpc(per_pp_training_dags)
+        piper_metadata.per_pp_training_dags = per_pp_training_dags_rpc
+
+        ray.get([
+            piper_metadata.actors[pp_rank].load_training_dag.remote(pp_dag)
+            for pp_rank, pp_dag in enumerate(per_pp_training_dags_rpc)
+        ])
+        ray.get(
+            piper_metadata.compiled_data_store.publish_training_dag_data.remote(
+                {"per_pp_training_dags": per_pp_training_dags_rpc}
+            )
+        )
 
     else:
-        # --- dp_rank>0: wait for stage data first, then load while dp_rank=0 builds DAGs. ---
-        logger.debug(f"DP rank {dp_rank} waiting for dp_rank=0 stage data...")
-        stage_data = None
-        while stage_data is None:
+        logger.debug(f"DP rank {dp_rank} waiting for dp_rank=0 training-DAG data...")
+        training_dag_data = None
+        while training_dag_data is None:
             try:
                 store = ray.get_actor(_COMPILED_DATA_ACTOR)
-                if ray.get(store.is_stage_ready.remote()):
-                    stage_data = ray.get(store.get_stage_data.remote())
+                if ray.get(store.is_training_dag_ready.remote()):
+                    training_dag_data = ray.get(store.get_training_dag_data.remote())
             except Exception:
                 pass
-            if stage_data is None:
-                time.sleep(0.2)
-        logger.debug(f"DP rank {dp_rank} received compiled stage data, loading onto actors...")
-
-        # Load stages onto this dp_rank's actors (same serialized graphs, different actor refs).
-        refs = []
-        for stage_id, (modules_data, a2a_boundaries) in stage_data["stages"].items():
-            actor_id = piper_metadata.stage_to_device[stage_id]
-            actor = piper_metadata.actors[actor_id]
-            refs.append(actor._load_stage.remote(
-                stage_id,
-                modules_data,
-                a2a_boundaries,
-                use_activation_checkpointing=activation_checkpointing,
-            ))
-        ray.get(refs)
-
-        # Restore stage metadata that the piper backend sets on dp_rank=0.
-        piper_metadata.stage_bucket_counts = stage_data["stage_bucket_counts"]
-        piper_metadata.trainable_bucket_keys = stage_data["trainable_bucket_keys"]
-        piper_metadata.zero_bucket_keys = stage_data.get("zero_bucket_keys", set())
-
-        logger.debug(f"DP rank {dp_rank} waiting for dp_rank=0 DAG data...")
-        dag_data = None
-        while dag_data is None:
-            try:
-                store = ray.get_actor(_COMPILED_DATA_ACTOR)
-                if ray.get(store.is_dag_ready.remote()):
-                    dag_data = ray.get(store.get_dag_data.remote())
-            except Exception:
-                pass
-            if dag_data is None:
+            if training_dag_data is None:
                 time.sleep(0.2)
 
-        piper_metadata.per_rank_dags = dag_data["per_rank_dags"]
-        piper_metadata.full_dag_no_overlap = dag_data.get("full_dag_no_overlap")
+        per_pp_training_dags = training_dag_data["per_pp_training_dags"]
+        if len(per_pp_training_dags) != pp_degree:
+            raise RuntimeError(
+                f"Expected {pp_degree} per-PP DAGs from dp_rank=0, got {len(per_pp_training_dags)}"
+            )
+        piper_metadata.per_pp_training_dags = per_pp_training_dags
 
-        # Load the same per-rank DAGs onto this dp_rank's actors.
         ray.get([
-            piper_metadata.actors[pp_rank].load_dag.remote(per_rank_dag)
-            for pp_rank, per_rank_dag in enumerate(piper_metadata.per_rank_dags)
+            piper_metadata.actors[pp_rank].load_training_dag.remote(pp_dag)
+            for pp_rank, pp_dag in enumerate(per_pp_training_dags)
         ])
 
-    last_stage_rank = stage_to_device[num_stages - 1]
+    loss_pp_ranks = _compute_loss_pp_ranks(per_pp_training_dags)
+    if not loss_pp_ranks:
+        loss_pp_ranks = [pp_degree - 1]
+        logger.warning(
+            "No compute_loss node found in per-PP DAGs; falling back to labels on pp_rank=%s",
+            loss_pp_ranks[0],
+        )
     ray.get(piper_metadata.actors[0].load_input.remote(example_inputs))
-    ray.get(piper_metadata.actors[last_stage_rank].load_labels.remote(example_outputs))
+    ray.get([
+        piper_metadata.actors[pp_rank].load_labels.remote(example_outputs)
+        for pp_rank in loss_pp_ranks
+    ])
 
     logger.info(f"DP rank {dp_rank} done.")
-
-
-def piper_shutdown():
-    ray.get([actor.shutdown.remote() for actor in piper_metadata.actors.values()])
diff --git a/src/piper_coordinator.py b/src/piper_coordinator.py
index ee3f391..c49734d 100644
--- a/src/piper_coordinator.py
+++ b/src/piper_coordinator.py
@@ -2,9 +2,11 @@
 from typing import Callable
 import os
 
+from ray.util.placement_group import placement_group
 from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
 
 from .piper_utils import create_logger, LOG_LEVEL
+from .piper_schedule import load_schedule_info
 
 
 # Coordinator needs GPUs when using profiling to infer stage boundaries
@@ -28,10 +30,17 @@ def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, training_func: Callab
 class PiperProgramCoordinator:
     """Central Actor that Coordinates all the DP replicas of a single pipeline"""
 
-    def __init__(self, dp_degree, pp_degree, pp_outer: bool = False):
-        self.dp_degree = dp_degree
-        self.pp_degree = pp_degree
-        self.world_size = dp_degree * pp_degree
+    def __init__(
+        self,
+        pp_outer: bool = False,
+        schedule_directives_file: str | None = None,
+    ):
+        if schedule_directives_file is None:
+            raise ValueError("PiperProgramCoordinator requires schedule_directives_file")
+        info = load_schedule_info(schedule_directives_file)
+        self.dp_degree = info["dp_degree"]
+        self.pp_degree = info["pp_degree"]
+        self.world_size = self.dp_degree * self.pp_degree
         # pp_outer=True means one PP stage per node (placement bundles keyed by
         # pp_rank). In that mode DP drivers are spread across the pp bundles.
         self.pp_outer = pp_outer
@@ -70,3 +79,21 @@ def run_program(self, training_func: Callable, pg, *args, **kwargs):
                 for dp_rank in range(self.dp_degree)
             ]
         )
+
+
+def create_piper_placement_group(schedule_directives_file: str, pp_outer: bool = False):
+    info = load_schedule_info(schedule_directives_file)
+    pp_degree = info["pp_degree"]
+    dp_degree = info["dp_degree"]
+
+    if pp_outer:
+        drivers_per_bundle = (dp_degree + pp_degree - 1) // pp_degree
+        return placement_group(
+            [{"CPU": dp_degree + drivers_per_bundle, "GPU": dp_degree}] * pp_degree,
+            strategy="STRICT_SPREAD",
+        )
+
+    return placement_group(
+        [{"CPU": pp_degree, "GPU": pp_degree}] * dp_degree,
+        strategy="SPREAD",
+    )
diff --git a/src/piper_exec.py b/src/piper_exec.py
index e6e0d78..e35b5a6 100644
--- a/src/piper_exec.py
+++ b/src/piper_exec.py
@@ -1,22 +1,4 @@
-import os
-import torch
-import threading
-import time
-import ray
-import torch.distributed as dist
-from typing import NamedTuple, Optional
 from enum import Enum
-from dataclasses import dataclass, field
-import threading
-import time
-from collections import defaultdict
-import itertools
-
-from .piper_utils import piper_metadata, create_logger, LOG_LEVEL
-
-logger = create_logger("piper_exec", LOG_LEVEL)
-
-_uid_counter = itertools.count()
 
 
 class TaskType(Enum):
@@ -31,648 +13,6 @@ class TaskType(Enum):
     ALL_REDUCE = "all_reduce"
     REDUCE_SCATTER = "reduce_scatter"
     ALL_GATHER = "all_gather"
-    ALLOC_FULL_GRADS = "alloc_full_grads"
-    FREE_FULL_GRADS = "free_full_grads"
-    ALLOC_FULL_PARAMS = "alloc_full_params"
-    FREE_FULL_PARAMS = "free_full_params"
     FWD_A2A = "forward_a2a"
     BWD_A2A = "backward_a2a"
-
-# Backwards-compatible alias
-CompType = TaskType
-
-
-class BatchMeta(NamedTuple):
-    """Metadata for one microbatch executed as part of a task."""
-    stage_id: int
-    mb_idx: int
-
-# Chunk for a schedule: one unit of work on one rank.
-class Chunk(NamedTuple):
-    pp_rank: int
-    batches: list[BatchMeta]
-    type: TaskType
-
-    def __repr__(self) -> str:
-        return f"Chunk(pp_rank={self.pp_rank}, batches={[(batch.stage_id, batch.mb_idx) for batch in self.batches]}, type={self.type})"
-
-
-def logical_time(chunk: "Chunk") -> int:
-    """Logical duration of a chunk: BWD = 2 (fused I+W), all others = 1."""
-    time = 1
-    match chunk.type:
-        case TaskType.BWD:
-            time = 2
-        case TaskType.FWD_BWD:
-            time = 3
-    return time
-
-
-@dataclass
-class PipelineSchedule:
-    """Per-rank ordered chunk lists.
-
-    ``grid[rank]`` is the ordered sequence of :class:`Chunk` objects that rank
-    executes.  There are no ``None`` sentinels — bubbles are implied by data
-    dependencies and are made explicit only when rendering via :meth:`_compute_start_times`.
-    """
-    grid: list[list["Chunk"]]
-
-    def stage_to_device(self) -> dict[int, int]:
-        stage_to_device = {}
-        for row in self.grid:
-            for chunk in row:
-                for batch in chunk.batches:
-                    stage_to_device[batch.stage_id] = chunk.pp_rank
-        return stage_to_device
-
-    def num_mbs(self) -> int:
-        mbs = set()
-        for row in self.grid:
-            for chunk in row:
-                for batch in chunk.batches:
-                    mbs.add(batch.mb_idx)
-        return len(mbs)
-
-    def num_stages(self) -> int:
-        stages = set()
-        for row in self.grid:
-            for chunk in row:
-                for batch in chunk.batches:
-                    stages.add(batch.stage_id)
-        return len(stages)
-
-    def num_ranks(self) -> int:
-        return len(self.grid)
-
-    def _compute_start_times(self) -> list[list[int]]:
-        """Return start_times[rank][i] = logical start time of chunk i on rank.
-
-        Uses a repeated-pass list-scheduling simulation that respects:
-        * within-rank sequentiality (each chunk starts after the previous finishes)
-        * cross-rank data deps: FWD(s,m) needs FWD(s-1,m); BWD/BWD_I(s,m) needs
-          BWD(s+1,m); BWD_W(s,m) needs BWD_I(s,m) on the same rank.
-        """
-        n_ranks = len(self.grid)
-        num_stages = self.num_stages()
-        rank_free_at = [0] * n_ranks
-        next_idx = [0] * n_ranks
-        start_times: list[list[Optional[int]]] = [[None] * len(row) for row in self.grid]
-        finish: dict = {}   # keyed by ('FWD', s, m), ('BWD', s, m), ('BWDI', rank, s, m)
-        total = sum(len(row) for row in self.grid)
-        scheduled = 0
-
-        while scheduled < total:
-            progress = False
-            for rank in range(n_ranks):
-                if next_idx[rank] >= len(self.grid[rank]):
-                    continue
-                i = next_idx[rank]
-                chunk = self.grid[rank][i]
-                earliest = rank_free_at[rank]
-                ready = True
-
-                for batch in chunk.batches:
-                    s, m = batch.stage_id, batch.mb_idx
-                    t = chunk.type
-                    if t == TaskType.FWD:
-                        if s > 0:
-                            k = ('FWD', s - 1, m)
-                            if k not in finish:
-                                ready = False; break
-                            earliest = max(earliest, finish[k])
-                    elif t in (TaskType.BWD, TaskType.BWD_I):
-                        if s < num_stages - 1:
-                            k = ('BWD', s + 1, m)
-                            if k not in finish:
-                                ready = False; break
-                            earliest = max(earliest, finish[k])
-                    elif t == TaskType.BWD_W:
-                        k = ('BWDI', rank, s, m)
-                        if k not in finish:
-                            ready = False; break
-                        earliest = max(earliest, finish[k])
-                    elif t == TaskType.FWD_BWD:
-                        s0, m0 = chunk.batches[0].stage_id, chunk.batches[0].mb_idx
-                        s1, m1 = chunk.batches[1].stage_id, chunk.batches[1].mb_idx
-                        if s0 > 0:
-                            k = ('FWD', s0 - 1, m0)
-                            if k not in finish:
-                                ready = False; break
-                            earliest = max(earliest, finish[k])
-                        if s1 < num_stages - 1:
-                            k = ('BWD', s1 + 1, m1)
-                            if k not in finish:
-                                ready = False; break
-                            earliest = max(earliest, finish[k])
-                        break  # FWD_BWD has exactly 2 batches, handled above
-
-                if not ready:
-                    continue
-
-                lt = logical_time(chunk)
-                start_times[rank][i] = earliest
-                fin = earliest + lt
-                rank_free_at[rank] = fin
-
-                for batch in chunk.batches:
-                    s, m = batch.stage_id, batch.mb_idx
-                    t = chunk.type
-                    if t == TaskType.FWD:
-                        finish[('FWD', s, m)] = fin
-                    elif t == TaskType.BWD:
-                        finish[('BWD', s, m)] = fin
-                    elif t == TaskType.BWD_I:
-                        finish[('BWD', s, m)] = fin
-                        finish[('BWDI', rank, s, m)] = fin
-                    elif t == TaskType.FWD_BWD:
-                        s0, m0 = chunk.batches[0].stage_id, chunk.batches[0].mb_idx
-                        s1, m1 = chunk.batches[1].stage_id, chunk.batches[1].mb_idx
-                        finish[('FWD', s0, m0)] = earliest + 1
-                        finish[('BWD', s1, m1)] = fin
-                        break
-
-                next_idx[rank] += 1
-                scheduled += 1
-                progress = True
-
-            if not progress:
-                raise ValueError("PipelineSchedule simulation deadlock — check data dependencies")
-
-        return start_times  # type: ignore[return-value]
-
-
-@dataclass
-class Task:
-    """A single node in the task DAG, corresponding to one non-None cell of a
-    PipelineSchedule grid.
-
-    Edges are of two kinds:
-
-    * **Data-dependency** (``data_preds`` / ``data_succs``): drawn between tasks
-      that share a ``mb_idx`` and are *adjacent* in time – i.e. no other task with
-      the same microbatch index sits between them in the time-step ordering.  These
-      edges may cross rows (actors).
-
-    * **Temporal** (``temporal_preds`` / ``temporal_succs``): drawn between
-      consecutive non-None tasks within the same row.  Normally each task has
-      at most one temporal predecessor and one successor (a linear chain), but
-      :func:`overlap_a2a_tasks` can introduce forks and joins so the fields
-      are lists, mirroring ``data_preds`` / ``data_succs``.
-    """
-    task_type: TaskType
-    batches: list[BatchMeta]
-    task_pp_rank: int
-    pp_rank: int    # row index in PipelineSchedule.grid
-    time_step: int  # column index in PipelineSchedule.grid
-
-    # Filled in by expand_chunks_to_dags()
-    data_preds: list["Task"] = field(default_factory=list)
-    data_succs: list["Task"] = field(default_factory=list)
-    temporal_preds: list["Task"] = field(default_factory=list)
-    temporal_succs: list["Task"] = field(default_factory=list)
-
-    # For SEND/RECV nodes: the peer pipeline rank
-    peer_pp_rank: Optional[int] = None
-
-    # Set by graph-transform passes (not meaningful for raw schedule tasks)
-    bucket_id: int = 0           # stage-local bucket index (stage_bucket_id); actor must not use this
-    resource: str = "compute_stream"
-    compute_stream_id: str = "comp_stream"
-    # Task-type-specific metadata (e.g. {"a2a_tensor_idx": int} for A2A tasks).
-    custom_metadata: dict = field(default_factory=dict)
-
-    # The original Chunk from the PipelineSchedule that this task was derived from.
-    # Set by expand_chunks_to_dags; preserved through all subsequent transforms.
-    source_chunk: Optional[Chunk] = None
-
-    # The specific logical chunk that this task belongs to. For FWD_BWD cells,
-    # this distinguishes the derived FWD half from the derived BWD half.
-    associated_chunk: Optional[Chunk] = None
-
-    # Unique integer ID assigned at construction; survives pickle round-trips.
-    uid: int = field(default_factory=lambda: next(_uid_counter))
-
-    # Profiling data: raw per-iteration GPU times (ms) accumulated by run_dag
-    # when profiling=True.  Set by piper_compile after profiling.
-    profiling_measurements: list = field(default_factory=list)
-
-    # Average of profiling_measurements (ms).  Set by piper_compile.
-    runtime: Optional[float] = None
-
-    # Globally unique bucket index assigned by expand_chunks_to_dags.
-    # The actor uses this to look up bucket_fwd_fns, bucket_fwd_args, etc.
-    # unique_bucket_id is None for non-compute tasks (SEND, RECV, ALL_REDUCE, UPD).
-    unique_bucket_id: Optional[int] = None
-
-    # True on the first BWD/BWD_I bucket of the last pipeline stage.
-    # When set, run_dag applies loss_fn before dispatching the backward method.
-    compute_loss: bool = False
-
-    def node_id(self) -> str:
-        """Unique string identifier for use as a graph node key."""
-        ttype = self.task_type.value if self.task_type is not None else "none"
-        mb = self.batches[0].mb_idx if self.batches else "x"
-        return f"r{self.pp_rank}_t{self.time_step}_{ttype}_mb{mb}"
-
-
-def runtime_sort_key(node: Task) -> tuple:
-    """Return the actor dispatch ordering key for a task.
-
-    Runtime dispatch order is driven by ``Task.time_step`` first. For tasks that
-    intentionally share a time step, dispatch uses a fixed priority:
-    SEND, A2A, alloc, AG, compute, RS/AR, free, RECV.
-    """
-    priority = {
-        TaskType.SEND: 0,
-        TaskType.FWD_A2A: 1,
-        TaskType.BWD_A2A: 1,
-        TaskType.REDUCE_SCATTER: 2,
-        TaskType.ALL_REDUCE: 2,
-        TaskType.ALL_GATHER: 3,
-        TaskType.FWD: 4,
-        TaskType.BWD: 4,
-        TaskType.BWD_I: 4,
-        TaskType.BWD_W: 4,
-        TaskType.FWD_BWD: 4,
-        TaskType.RECV: 5,
-    }.get(node.task_type, 4)
-    return (node.time_step, priority, node.uid)
-
-
-def _rebuild_task_dag(node_data):
-    """Reconstruct a TaskDAG from the flat index-based representation produced
-    by TaskDAG.__reduce__.  Must be a module-level function so pickle can find
-    it by name."""
-    nodes = [
-        Task(task_type=d[0], batches=d[1], task_pp_rank=d[2], pp_rank=d[3], time_step=d[4], peer_pp_rank=d[5],
-                 bucket_id=d[10], resource=d[11], compute_stream_id=d[12], custom_metadata=d[13], source_chunk=d[15],
-                 associated_chunk=d[16], unique_bucket_id=d[17], compute_loss=d[18])
-        for d in node_data
-    ]
-    for node, d in zip(nodes, node_data):
-        node.data_preds     = [nodes[j] for j in d[6]]
-        node.data_succs     = [nodes[j] for j in d[7]]
-        node.temporal_preds = [nodes[j] for j in d[8]]
-        node.temporal_succs = [nodes[j] for j in d[9]]
-        node.uid            = d[14]
-    return TaskDAG(nodes=nodes)
-
-
-@dataclass
-class TaskDAG:
-    """DAG of :class:`Task` objects for one training iteration.
-
-    Produced by :func:`~src.piper_graph_transform.expand_chunks_to_dags`.
-    """
-    nodes: list[Task]
-
-    def roots(self) -> list[Task]:
-        """Return nodes that have no predecessors of any kind."""
-        return [n for n in self.nodes if not n.data_preds and not n.temporal_preds]
-
-    def __reduce__(self):
-        """Serialize as a flat list of index-referenced tuples to avoid the
-        deep recursion that cloudpickle would need when following temporal_succ
-        chains through the full node graph."""
-        idx = {id(n): i for i, n in enumerate(self.nodes)}
-        node_data = [
-            (
-                n.task_type, n.batches, n.task_pp_rank, n.pp_rank, n.time_step, n.peer_pp_rank,
-                [idx[id(p)] for p in n.data_preds],
-                [idx[id(s)] for s in n.data_succs],
-                [idx[id(p)] for p in n.temporal_preds],
-                [idx[id(s)] for s in n.temporal_succs],
-                n.bucket_id, n.resource, n.compute_stream_id, n.custom_metadata, n.uid, n.source_chunk,
-                n.associated_chunk, n.unique_bucket_id, n.compute_loss,
-            )
-            for n in self.nodes
-        ]
-        return (_rebuild_task_dag, (node_data,))
-
-
-class DAGEdge(NamedTuple):
-    from_stage: int
-    to_stage: int
-
-
-def _get_backward_targets(stage_id: int, dag_edges: list[DAGEdge]):
-    return [edge for edge in dag_edges if edge.to_stage == stage_id]
-
-def _validate_schedule(schedule: "PipelineSchedule", dag_edges: list[DAGEdge], num_mbs: int) -> None:
-    """
-    Validate that the schedule respects well-formedness rules and DAG dependencies.
-
-    Args:
-        schedule: PipelineSchedule (list[list[Chunk]], no Nones)
-        dag_edges: List of DAG edges defining stage dependencies
-        num_mbs: Number of microbatches in the schedule
-
-    Raises:
-        ValueError: If the schedule violates any validation rules
-    """
-    start_times_per_rank = schedule._compute_start_times()
-
-    # chunk id -> logical start time for ordering comparisons
-    chunk_start: dict[int, int] = {}
-    for row, starts in zip(schedule.grid, start_times_per_rank):
-        for chunk, start in zip(row, starts):
-            chunk_start[id(chunk)] = start
-
-    # Check well-formedness: no duplicates, pp_rank matches row, all stages present
-    all_tasks: set = set()
-    microbatch_tasks: dict[int, set] = {}
-
-    for pp_rank, row in enumerate(schedule.grid):
-        for chunk in row:
-            if chunk.pp_rank != pp_rank:
-                raise ValueError(
-                    f"Chunk pp_rank {chunk.pp_rank} does not match row {pp_rank}"
-                )
-            for batch in chunk.batches:
-                comp_type = (
-                    chunk.type
-                    if chunk.type != CompType.FWD_BWD
-                    else (CompType.FWD if batch is chunk.batches[0] else CompType.BWD)
-                )
-                task_key = (batch.stage_id, batch.mb_idx, comp_type)
-                if task_key in all_tasks:
-                    raise ValueError(
-                        f"Duplicate chunk found: stage_id={batch.stage_id}, "
-                        f"mb_idx={batch.mb_idx}, type={comp_type}"
-                    )
-                all_tasks.add(task_key)
-                if batch.mb_idx not in microbatch_tasks:
-                    microbatch_tasks[batch.mb_idx] = set()
-                microbatch_tasks[batch.mb_idx].add((batch.stage_id, comp_type))
-
-    # Get all required stages from DAG edges
-    all_required_stages = set()
-    for edge in dag_edges:
-        all_required_stages.add(edge.from_stage)
-        all_required_stages.add(edge.to_stage)
-
-    # Check that each microbatch has all required forward and backward stages
-    for mb_idx, tasks in microbatch_tasks.items():
-        fwd_stages = {stage_id for stage_id, task_type in tasks if task_type == CompType.FWD}
-        bwd_stages = {
-            stage_id
-            for stage_id, task_type in tasks
-            if task_type in (CompType.BWD, CompType.BWD_I)
-        }
-        missing_fwd = all_required_stages - fwd_stages
-        if missing_fwd:
-            raise ValueError(f"Microbatch {mb_idx} missing forward stages: {missing_fwd}")
-        missing_bwd = all_required_stages - bwd_stages
-        if missing_bwd:
-            raise ValueError(
-                f"Microbatch {mb_idx} missing backward stages: {missing_bwd} "
-                f"(need BWD, BWD_I, or FWD_BWD backward for each stage)"
-            )
-
-    # Check pipeline stage dependencies using logical start times
-    for mb_idx in range(num_mbs):
-        fwd_times: dict[int, int] = {}   # stage_id -> logical start
-        bwd_times: dict[int, int] = {}   # stage_id -> logical start (BWD or BWD_I)
-        bwd_w_times: dict[int, int] = {} # stage_id -> logical start (BWD_W)
-
-        for row in schedule.grid:
-            for chunk in row:
-                for batch in chunk.batches:
-                    if batch.mb_idx != mb_idx:
-                        continue
-                    comp_type = (
-                        chunk.type
-                        if chunk.type != CompType.FWD_BWD
-                        else (CompType.FWD if batch is chunk.batches[0] else CompType.BWD)
-                    )
-                    t = chunk_start[id(chunk)]
-                    if comp_type == CompType.FWD:
-                        fwd_times[batch.stage_id] = t
-                    elif comp_type in (CompType.BWD, CompType.BWD_I):
-                        bwd_times[batch.stage_id] = t
-                    elif comp_type == CompType.BWD_W:
-                        bwd_w_times[batch.stage_id] = t
-
-        # Forward ordering: if A -> B, then fwd(A) < fwd(B)
-        for edge in dag_edges:
-            from_stage, to_stage = edge.from_stage, edge.to_stage
-            if from_stage in fwd_times and to_stage in fwd_times:
-                if fwd_times[from_stage] >= fwd_times[to_stage]:
-                    raise ValueError(
-                        f"Forward stage ordering violation for microbatch {mb_idx}: "
-                        f"forward stage {from_stage} (time {fwd_times[from_stage]}) must come "
-                        f"before forward stage {to_stage} (time {fwd_times[to_stage]})"
-                    )
-
-        # Forward-backward ordering: fwd(A) < bwd(A)
-        for stage_id in fwd_times:
-            if stage_id in bwd_times:
-                if fwd_times[stage_id] >= bwd_times[stage_id]:
-                    raise ValueError(
-                        f"Forward-backward ordering violation for microbatch {mb_idx}, "
-                        f"stage {stage_id}: forward (time {fwd_times[stage_id]}) must come "
-                        f"before backward (time {bwd_times[stage_id]})"
-                    )
-
-        # Backward ordering: if A -> B, then bwd(B) < bwd(A)
-        for edge in dag_edges:
-            from_stage, to_stage = edge.from_stage, edge.to_stage
-            if from_stage in bwd_times and to_stage in bwd_times:
-                if bwd_times[to_stage] >= bwd_times[from_stage]:
-                    raise ValueError(
-                        f"Backward stage ordering violation for microbatch {mb_idx}: "
-                        f"backward stage {to_stage} (time {bwd_times[to_stage]}) must come "
-                        f"before backward stage {from_stage} (time {bwd_times[from_stage]})"
-                    )
-
-        # BWD_W must come after BWD_I for same (stage_id, mb_idx)
-        for stage_id in bwd_w_times:
-            if stage_id not in bwd_times:
-                raise ValueError(
-                    f"BWD_W for microbatch {mb_idx} stage {stage_id} has no corresponding "
-                    f"BWD_I or BWD task"
-                )
-            if bwd_w_times[stage_id] <= bwd_times[stage_id]:
-                raise ValueError(
-                    f"BWD_W ordering violation for microbatch {mb_idx}, stage {stage_id}: "
-                    f"BWD_W (time {bwd_w_times[stage_id]}) must come after "
-                    f"BWD_I/BWD (time {bwd_times[stage_id]})"
-                )
-
-
-def _build_p2p_schedule(schedule: "PipelineSchedule", num_stages, num_devices, stage_to_device):
-    """
-    Build a global p2p schedule by iterating the compute schedule in logical execution order.
-
-    Returns:
-        rank_p2p: dict mapping pp_rank -> list of (op_type, stage_from, stage_to, mb_idx, is_fwd)
-        p2p_idx_map: dict mapping (pp_rank, stage_from, stage_to, mb_idx, is_fwd, op_type) -> rank_local_idx
-    """
-    start_times_per_rank = schedule._compute_start_times()
-
-    # Sort events by (logical_start_time, reverse_rank) to match the original
-    # time-step-first, reverse-rank-second iteration order.
-    events: list[tuple[int, int, int, "Chunk"]] = []
-    for rank, (row, starts) in enumerate(zip(schedule.grid, start_times_per_rank)):
-        for chunk, start in zip(row, starts):
-            events.append((start, -rank, rank, chunk))
-    events.sort(key=lambda e: (e[0], e[1]))
-
-    # Global p2p ops in execution order. Each communication is added exactly once,
-    # from the SENDER's compute task, to avoid duplicates.
-    p2p_ops = []
-    for _, _, pp_rank, chunk in events:
-        task_type = chunk.type
-        batches = chunk.batches
-
-        # Forward send
-        if task_type in (CompType.FWD, CompType.FWD_BWD):
-            stage_id, mb_idx = batches[0].stage_id, batches[0].mb_idx
-            if stage_id < num_stages - 1 and stage_to_device[stage_id] != stage_to_device[stage_id + 1]:
-                p2p_ops.append((stage_id, stage_id + 1, mb_idx, True))
-
-        # Backward send
-        if task_type in (CompType.BWD, CompType.BWD_I, CompType.FWD_BWD):
-            b = batches[1] if task_type == CompType.FWD_BWD else batches[0]
-            stage_id, mb_idx = b.stage_id, b.mb_idx
-            if stage_id > 0 and stage_to_device[stage_id] != stage_to_device[stage_id - 1]:
-                p2p_ops.append((stage_id, stage_id - 1, mb_idx, False))
-
-    # Build per-rank schedules and index map
-    rank_p2p = defaultdict(list)
-    p2p_idx_map = {}
-
-    for stage_from, stage_to, mb_idx, is_fwd in p2p_ops:
-        sender_pp = stage_to_device[stage_from]
-        receiver_pp = stage_to_device[stage_to]
-
-        send_idx = len(rank_p2p[sender_pp])
-        rank_p2p[sender_pp].append(("send", stage_from, stage_to, mb_idx, is_fwd))
-        p2p_idx_map[(sender_pp, stage_from, stage_to, mb_idx, is_fwd, "send")] = send_idx
-
-        recv_idx = len(rank_p2p[receiver_pp])
-        rank_p2p[receiver_pp].append(("recv", stage_from, stage_to, mb_idx, is_fwd))
-        p2p_idx_map[(receiver_pp, stage_from, stage_to, mb_idx, is_fwd, "recv")] = recv_idx
-
-    return rank_p2p, p2p_idx_map
-
-
-def piper_exec(
-    schedule: PipelineSchedule,
-    loss_fn,
-    dp_degree=1,
-    naive_gradient_sync=False,
-):
-    """
-    Execute one step of the pipeline schedule on the distributed model.
-
-    Args:
-        schedule: 2D schedule grid (rank x time_step).
-        inputs: Inputs to the model.
-        truth: Ground-truth labels or targets.
-        loss_fn: Loss function to be used for training.
-
-    Returns:
-        List of losses per microbatch (from UPD tasks).
-    """
-    num_mbs = schedule.num_mbs()
-    num_devices = schedule.num_ranks()
-    num_stages = schedule.num_stages()
-
-    dag_edges = list(map(lambda e: DAGEdge(e[0], e[1]), list(piper_metadata.dag)))
-    _validate_schedule(schedule, dag_edges, num_mbs)
-
-    actors = piper_metadata.actors
-    stage_to_device = piper_metadata.stage_to_device
-
-    # Build p2p schedule and send to actors
-    rank_p2p, p2p_idx_map = _build_p2p_schedule(schedule, num_stages, num_devices, stage_to_device)
-
-    def _p2p_recv(actor, pp_rank, stage_from, stage_to, mb_idx, is_fwd, dep):
-        """Dispatch a recv: _exec_p2p_op for cross-rank, old method for local."""
-        key = (pp_rank, stage_from, stage_to, mb_idx, is_fwd, "recv")
-        if key in p2p_idx_map:
-            return actor._exec_p2p_op.remote(p2p_idx_map[key], dep)
-        if is_fwd:
-            return actor._exec_fwd_recv.remote(stage_to, mb_idx, dep)
-        return actor._exec_bwd_recv.remote(stage_to, mb_idx, dep)
-
-    def _p2p_send(actor, pp_rank, stage_from, stage_to, mb_idx, is_fwd, dep):
-        """Dispatch a send: _exec_p2p_op for cross-rank, old method for local."""
-        key = (pp_rank, stage_from, stage_to, mb_idx, is_fwd, "send")
-        if key in p2p_idx_map:
-            return actor._exec_p2p_op.remote(p2p_idx_map[key], dep)
-        if is_fwd:
-            return actor._exec_fwd_send.remote(stage_from, mb_idx, dep)
-        return actor._exec_bwd_send.remote(stage_from, mb_idx, dep)
-
-    ret = []
-    deps = {}
-
-    # Send p2p schedules to actors before dispatch
-    for pp_rank in range(num_devices):
-        deps[pp_rank] = actors[pp_rank].set_p2p_schedule.remote(rank_p2p.get(pp_rank, []))
-
-    # Iterate chunks in logical execution order: sorted by (start_time, reverse_rank)
-    start_times_per_rank = schedule._compute_start_times()
-    events: list[tuple[int, int, int, Chunk]] = []
-    for rank, (row, starts) in enumerate(zip(schedule.grid, start_times_per_rank)):
-        for chunk, start in zip(row, starts):
-            events.append((start, -rank, rank, chunk))
-    events.sort(key=lambda e: (e[0], e[1]))
-
-    for _, _, j, chunk in events:
-        pp_rank, batches, task_type, *_ = chunk
-        actor = actors[j]
-        match task_type:
-            case CompType.UPD:
-                loss = actor._update.remote(deps[pp_rank])
-                ret.append(loss)
-            case CompType.FWD:
-                stage_id, mb_idx = batches[0].stage_id, batches[0].mb_idx
-                dep = deps[pp_rank]
-                if stage_id > 0:
-                    dep = _p2p_recv(actor, pp_rank, stage_id - 1, stage_id, mb_idx, True, dep)
-                dep = actor._forward.remote(stage_id, mb_idx, dep)
-                if stage_id < num_stages - 1:
-                    dep = _p2p_send(actor, pp_rank, stage_id, stage_id + 1, mb_idx, True, dep)
-                deps[pp_rank] = dep
-            case CompType.BWD:
-                stage_id, mb_idx = batches[0].stage_id, batches[0].mb_idx
-                dep = deps[pp_rank]
-                if stage_id < num_stages - 1:
-                    dep = _p2p_recv(actor, pp_rank, stage_id + 1, stage_id, mb_idx, False, dep)
-                dep = actor._backward.remote(stage_id, mb_idx, dep, loss_fn=loss_fn)
-                if stage_id > 0:
-                    dep = _p2p_send(actor, pp_rank, stage_id, stage_id - 1, mb_idx, False, dep)
-                deps[pp_rank] = dep
-            case CompType.BWD_I:
-                stage_id, mb_idx = batches[0].stage_id, batches[0].mb_idx
-                dep = deps[pp_rank]
-                if stage_id < num_stages - 1:
-                    dep = _p2p_recv(actor, pp_rank, stage_id + 1, stage_id, mb_idx, False, dep)
-                dep = actor._backward_input.remote(stage_id, mb_idx, dep, loss_fn=loss_fn)
-                if stage_id > 0:
-                    dep = _p2p_send(actor, pp_rank, stage_id, stage_id - 1, mb_idx, False, dep)
-                deps[pp_rank] = dep
-            case CompType.BWD_W:
-                stage_id, mb_idx = batches[0].stage_id, batches[0].mb_idx
-                dep = deps[pp_rank]
-                dep = actor._backward_weight.remote(stage_id, mb_idx, dep)
-                deps[pp_rank] = dep
-            case CompType.FWD_BWD:
-                fwd_stage_id, fwd_mb_idx = batches[0].stage_id, batches[0].mb_idx
-                bwd_stage_id, bwd_mb_idx = batches[1].stage_id, batches[1].mb_idx
-                dep = deps[pp_rank]
-                if fwd_stage_id > 0:
-                    dep = _p2p_recv(actor, pp_rank, fwd_stage_id - 1, fwd_stage_id, fwd_mb_idx, True, dep)
-                if bwd_stage_id < num_stages - 1:
-                    dep = _p2p_recv(actor, pp_rank, bwd_stage_id + 1, bwd_stage_id, bwd_mb_idx, False, dep)
-                dep = actor._forward_backward.remote(fwd_stage_id, fwd_mb_idx, bwd_stage_id, bwd_mb_idx, dep, loss_fn=loss_fn)
-                if fwd_stage_id < num_stages - 1:
-                    dep = _p2p_send(actor, pp_rank, fwd_stage_id, fwd_stage_id + 1, fwd_mb_idx, True, dep)
-                if bwd_stage_id > 0:
-                    dep = _p2p_send(actor, pp_rank, bwd_stage_id, bwd_stage_id - 1, bwd_mb_idx, False, dep)
-                deps[pp_rank] = dep
-    return ray.get(ret)
+    ORDER_DUMMY = "order_dummy"
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
index c81ac94..122e699 100644
--- a/src/piper_graph_transform.py
+++ b/src/piper_graph_transform.py
@@ -1,19 +1,12 @@
 import hashlib
 import json
-import ray
 import torch
 import torch.fx as fx
-import torch.distributed as dist
-from collections import defaultdict, deque
+from collections import defaultdict
 from pathlib import Path
-from typing import Optional
 import operator
-import os
-import time
-from torch.autograd import Function
 
-from .piper_utils import create_logger, LOG_LEVEL, VERBOSE, piper_metadata
-from .piper_exec import TaskType, PipelineSchedule, Chunk, Task, TaskDAG, runtime_sort_key
+from .piper_utils import create_logger, LOG_LEVEL
 
 logger = create_logger("piper_graph_transform", LOG_LEVEL)
 
@@ -179,34 +172,13 @@ def _exec_node(node, args, kwargs, fn):
         else:
             return fn(*args, **kwargs)
 
-    def _log_env_memory():
-        """Log all active tensors in env and their total memory usage."""
-        active = []
-        total_bytes = 0
-        for name, val in env.items():
-            if isinstance(val, torch.Tensor) and val.device.type != "meta":
-                nbytes = val.nelement() * val.element_size()
-                total_bytes += nbytes
-                active.append((name, tuple(val.shape), val.dtype, nbytes))
-        total_mb = total_bytes / (1024 * 1024)
-        cuda_allocated_mb = torch.cuda.memory_allocated() / (1024 * 1024) if device == "cuda" else 0
-
-        # Log the top 10 largest tensors
-        active.sort(key=lambda x: x[3], reverse=True)
-        for name, shape, dtype, nbytes in active[:10]:
-            logger.log(VERBOSE, f"  {name}: {shape} {dtype} ({nbytes / (1024*1024):.1f} MB)")
-
     # Step 3: Profile each compute node with eager memory freeing
     # Use no_grad to prevent autograd graph accumulation — we only need forward timing.
     node_times = {}  # node -> time in ms
 
     num_warmup = 3
     num_profile = 5
-    log_interval = max(1, len(compute_nodes) // 20)  # Log ~20 times during profiling
-    for node_idx, node in enumerate(compute_nodes):
-        if node_idx % log_interval == 0:
-            _log_env_memory()
-
+    for node in compute_nodes:
         args = _fetch_arg(node.args)
         kwargs = _fetch_arg(node.kwargs)
 
@@ -218,7 +190,6 @@ def _log_env_memory():
                 break
 
         if has_none_input:
-            logger.log(VERBOSE, f"Skipping node {node.name}: predecessor produced None")
             node_times[node.name] = 0.0
             env[node.name] = None
             for inp in node.all_input_nodes:
@@ -399,9 +370,26 @@ def _find_best_split(ideal_idx, stage_start_idx):
     return _split_gm_by_stages(gm)
 
 
-def _meta_tensor_like(example: torch.Tensor, *, requires_grad: bool, as_parameter: bool):
-    t = torch.empty(example.shape, dtype=example.dtype, device="meta")
-    t.requires_grad_(requires_grad)
+def _meta_tensor_like(example, *, requires_grad: bool, as_parameter: bool):
+    sym_int_type = getattr(torch, "SymInt", ())
+    sym_float_type = getattr(torch, "SymFloat", ())
+    sym_bool_type = getattr(torch, "SymBool", ())
+
+    if isinstance(example, torch.Tensor):
+        t = torch.empty(example.shape, dtype=example.dtype, device="meta")
+        t.requires_grad_(requires_grad)
+    elif isinstance(example, (sym_int_type, int)):
+        # Symbolic/python integers do not have .shape/.dtype; represent them as
+        # singleton meta tensors so downstream graph-arg handling remains uniform.
+        t = torch.empty((1,), dtype=torch.int64, device="meta")
+    elif isinstance(example, (sym_float_type, float)):
+        t = torch.empty((1,), dtype=torch.float32, device="meta")
+    elif isinstance(example, (sym_bool_type, bool)):
+        t = torch.empty((1,), dtype=torch.bool, device="meta")
+    else:
+        # Fallback for unknown scalar-like values.
+        t = torch.empty((1,), dtype=torch.float32, device="meta")
+
     if as_parameter:
         return torch.nn.Parameter(t, requires_grad=requires_grad)
     return t
@@ -429,11 +417,8 @@ def _split_gm_by_stages(gm) -> tuple[fx.GraphModule, list[tuple[int, fx.GraphMod
                     nodes_with_metadata.append((node, stage_annotation_id))
     
     if not nodes_with_metadata:
-        logger.log(VERBOSE, "No stage nodes found in graph")
         return gm, []
-    else:
-        logger.log(VERBOSE, f"Found stage nodes in graph")
-    
+
     # Group nodes by stage ID for creating stage modules
     stage_code = defaultdict(list)  # stage_id -> list of all nodes for this stage
     
@@ -772,7 +757,10 @@ def collect_dependencies(node):
                 param_idxs.append(i)
             # For the first stage, the input indices are everything that's not an attribute
             if stage_annotation_id == 0:
-                if 'self' not in placeholder.name:
+                # Only runtime tensor activations should be stage inputs.
+                # Symbolic placeholders (e.g. SymInt shape env entries) are not
+                # fed by run_dag and must stay out of input_idxs.
+                if isinstance(ex, torch.Tensor) and ('self' not in placeholder.name):
                     input_idxs.append(i)
             # For subsequent stages, check if this placeholder corresponds to an output from the previous stage
             else:
@@ -940,2166 +928,248 @@ def collect_dependencies(node):
 
 
 
-def _insert_a2a_ops(gm: fx.GraphModule) -> tuple[fx.GraphModule, int]:
-    """
-    Transform a graph module by inserting communication operations on annotated nodes.
-    
-    This function finds nodes annotated with torch.fx.traceback.annotate containing
-    "collective": "all_to_all_single" and inserts the corresponding communication
-    operations into the graph.
-    
-    For each annotated node, it inserts:
-    1. (Optional) Reshape input if "reshape": ("input", shape) is specified
-    2. buf = torch.empty_like(annotated_tensor)
-    3. torch.distributed.all_to_all_single(buf, annotated_tensor)  # Uses default process group
-    4. (Optional) Reshape output if "reshape": ("output", shape) is specified
-    5. Replaces all uses of annotated_tensor with the result
-    
-    The communication uses the default distributed process group (no group parameter specified).
-    
-    The reshape annotation format is: ("input"|"output", shape_tuple)
-    where shape_tuple is a tuple of dimensions (can include -1 for inferred dimension).
-    
-    Args:
-        gm: The graph module to transform
-        
-    Returns:
-        A new graph module with communication operations inserted
-    """
-    # Find all nodes with collective communication annotations
-    # We need to identify contiguous blocks of annotations, not just group by metadata
-    # Each contiguous block should get its own communication operation
-    
-    # First, collect all annotated nodes with their metadata
-    annotated_nodes = []
-    node_list = list(gm.graph.nodes)
-    for idx, node in enumerate(node_list):
-        if 'custom' in node.meta:
-            custom_meta = node.meta['custom']
-            if isinstance(custom_meta, dict):
-                collective = custom_meta.get('collective')
-                if collective == 'all_to_all_single':
-                    reshape = custom_meta.get('reshape')
-                    annotation_key = tuple(sorted(custom_meta.items()))
-                    annotated_nodes.append((idx, node, annotation_key, reshape))
-    
-    if not annotated_nodes:
-        return gm, 0
-    
-    # Group annotated nodes into contiguous blocks
-    # A contiguous block is a sequence of nodes with the same annotation_key
-    # that appear sequentially in the graph. Each block gets its own communication operation.
-    # We split blocks when:
-    # 1. The annotation_key changes (different annotation pattern)
-    # 2. The same annotation_key appears again after being interrupted (new instance of same pattern)
-    #    This handles the case where the same annotation appears in different layers
-    # Group *all* nodes with the same annotation_key into a single block,
-    # regardless of how far apart they are in the FX graph.
-    # This ensures that every logically-related annotated region (e.g., the
-    # "input" or "output" side of a given MoE layer) is treated as one block.
-    blocks_by_key = {}
-    for idx, node, annotation_key, reshape in annotated_nodes:
-        if annotation_key not in blocks_by_key:
-            blocks_by_key[annotation_key] = {
-                'annotation_key': annotation_key,
-                'nodes': [],
-                'last_idx': idx,
-            }
-        block = blocks_by_key[annotation_key]
-        block['nodes'].append((node, reshape))
-        block['last_idx'] = idx
-
-    annotation_blocks = list(blocks_by_key.values())
-    
-    n_a2a_ops = len(annotation_blocks)
-    logger.log(VERBOSE, f"Found {len(annotation_blocks)} contiguous annotation blocks")
-    
-    # For each contiguous block, find the output node (the one used outside the annotation)
-    # This is the node that should have communication applied to it
-    annotated_output_nodes = []
-    for block_idx, block in enumerate(annotation_blocks):
-        nodes_in_block = block['nodes']
-        nodes_set = {node for node, _ in nodes_in_block}
-        
-        if len(nodes_in_block) == 1:
-            # Single node, use it directly
-            annotated_output_nodes.append(nodes_in_block[0])
-        else:
-            # Multiple nodes in the same block
-            # Find the one that is used by nodes outside the annotation block
-            # This is the output of the annotated block
-            output_candidates = []
-            for node, reshape in nodes_in_block:
-                # Check if this node is used by any node not in the annotation block
-                for user in node.users:
-                    if user not in nodes_set:
-                        output_candidates.append((node, reshape))
-                        break
-            
-            if output_candidates:
-                # Use the first output candidate (should be the output of the annotated block)
-                annotated_output_nodes.append(output_candidates[0])
-            else:
-                # No external users found, use the last node in topological order within the block
-                # This happens if the annotated block's output isn't used yet
-                last_node = max(nodes_in_block, key=lambda x: node_list.index(x[0]))
-                annotated_output_nodes.append(last_node)
-    
-    logger.log(VERBOSE, f"Found {len(annotated_output_nodes)} annotation blocks requiring communication operations")
-    
-    # Create a new graph to build the transformed version
-    new_graph = fx.Graph()
-    node_mapping = {}
-    
-    # Copy placeholders first
-    for node in gm.graph.nodes:
-        if node.op == "placeholder":
-            new_node = new_graph.node_copy(node, lambda n: node_mapping.get(n, n))
-            node_mapping[node] = new_node
-    
-    # Copy all get_attr nodes (parameters) so they're available for use
-    for node in gm.graph.nodes:
-        if node.op == "get_attr":
-            if node not in node_mapping:
-                new_node = new_graph.node_copy(node, lambda n: node_mapping.get(n, n))
-                node_mapping[node] = new_node
-    
-    # Process nodes in topological order
-    for node in gm.graph.nodes:
-        if node.op == "placeholder":
-            # Already handled
-            continue
-        
-        if node.op == "output":
-            # Handle output separately at the end
-            continue
-        
-        if node.op == "get_attr":
-            # Already handled above
-            continue
-        
-        # Check if this node needs communication inserted
-        # This should be the output node of an annotated block
-        needs_comm = False
-        reshape_info = None
-        for annotated_node, reshape in annotated_output_nodes:
-            if node == annotated_node:
-                needs_comm = True
-                reshape_info = reshape
-                break
-        
-        if needs_comm:
-            # First, copy the node to get its output
-            new_node = new_graph.node_copy(
-                node,
-                lambda n: node_mapping.get(n, n)
-            )
-            
-            # Handle input reshape if specified
-            # reshape_info format: ("input"|"output", shape_tuple)
-            if reshape_info is not None:
-                reshape_type, reshape_shape = reshape_info
-                if reshape_type == "input":
-                    # DEBUG: Log reshape information
-                    logger.log(VERBOSE, f"Applying input reshape {reshape_shape} to node {node.name}")
-                    if isinstance(reshape_shape, (list, tuple)) and -1 in reshape_shape:
-                        logger.warning(f"Input reshape shape contains -1: {reshape_shape}, this may cause shape inference issues")
-                    # Reshape before communication
-                    new_node = new_graph.call_function(
-                        torch.reshape,
-                        (new_node, reshape_shape)
-                    )
-            
-            # Insert communication operations after this node
-            # Following the exact pattern from the comments in mixtral.py:
-            # buf = torch.empty_like(expert_inputs)
-            # torch.distributed.all_to_all_single(buf, expert_inputs, group=device_mesh.get_group())
-            # buf = buf.reshape(...)  # if output reshape
-            # expert_inputs = buf
-            
-            # 1. Create buffer: buf = torch.empty_like(new_node)
-            buf_node = new_graph.call_function(
-                torch.empty_like,
-                (new_node,)
-            )
-            
-            # 2. Call all_to_all_single using custom autograd function
-            # Use AllToAllSingleFunction to ensure gradients flow correctly
-            # This wraps all_to_all_single with proper backward pass
-            buf_node = new_graph.call_function(
-                _dispatch_a2a_single,
-                (buf_node, new_node)
-            )
-            # After all_to_all_single, buf_node contains the result
-            # and maintains gradient connection from new_node through the custom Function
-            
-            # Handle output reshape if specified
-            if reshape_info is not None:
-                reshape_type, reshape_shape = reshape_info
-                if reshape_type == "output":
-                    # Reshape after communication
-                    # DEBUG: Log reshape information
-                    logger.log(VERBOSE, f"Applying output reshape {reshape_shape} to node {node.name}, buf_node shape inference needed")
-                    # Log the reshape shape for debugging
-                    if isinstance(reshape_shape, (list, tuple)) and -1 in reshape_shape:
-                        logger.warning(f"Reshape shape contains -1: {reshape_shape}, this may cause shape inference issues")
-                    buf_node = new_graph.call_function(
-                        torch.reshape,
-                        (buf_node, reshape_shape)
-                    )
-            
-            # 3. Replace all uses of the original node with the buffer
-            # The buffer now contains the result after communication (and optional reshape)
-            node_mapping[node] = buf_node
-        else:
-            # Regular node, just copy it
-            new_node = new_graph.node_copy(
-                node,
-                lambda n: node_mapping.get(n, n)
-            )
-            node_mapping[node] = new_node
-    
-    # Handle output node
-    output_node = None
-    for node in gm.graph.nodes:
-        if node.op == "output":
-            output_node = node
-            break
-    
-    if output_node:
-        output_args = fx.graph.map_arg(output_node.args, lambda n: node_mapping.get(n, n))
-        if isinstance(output_args, tuple) and len(output_args) == 1:
-            new_graph.output(output_args[0])
-        else:
-            new_graph.output(output_args)
-    
-    # Create new graph module with a fresh root module
-    root_module = torch.nn.Module()
-    
-    # Copy parameters, buffers, and modules from original module
-    for name, param in gm.named_parameters(recurse=False):
-        root_module.register_parameter(name, param)
-    
-    for name, buffer in gm.named_buffers(recurse=False):
-        root_module.register_buffer(name, buffer)
-    
-    for name, module in gm.named_children():
-        root_module.add_module(name, module)
-    
-    new_gm = fx.GraphModule(root_module, new_graph)
-    
-    new_gm.recompile()
-    
-    return new_gm, n_a2a_ops
+# ---------------------------------------------------------------------------
+# bucket_stage - stage-level parameter bucketing (placeholder params)
+# ---------------------------------------------------------------------------
 
-def _insert_p2p_ops(
-    gm: fx.GraphModule,
-    input_idxs: list[int],
-    prev_stage_id: int,
-    next_stage_id: int,
-) -> fx.GraphModule:
-    """
-    Insert point-to-point recv/send ops into a stage graph.
 
-    For each placeholder index in input_idxs, this will:
-      1. Create an empty_like buffer from the corresponding placeholder.
-      2. Replace all uses of that placeholder with a tensor received from
-         prev_stage_id via torch.distributed.recv.
+def bucket_stage(
+    stage_gm: fx.GraphModule,
+    graphargs: list,
+    input_idxs: list[int],
+    param_idxs: list[int],
+    bucket_size_bytes: int = 25 * 1024 * 1024,
+    debug_name: str | None = None,
+) -> list[tuple[fx.GraphModule, list[int], list[int], list]]:
+    """Split a stage GraphModule into per-parameter-bucket sub-modules.
 
-    For each output tensor, this will:
-      1. Wrap the tensor in a call to torch.distributed.send with dst set
-         to next_stage_id.
+    This function handles stages produced by :func:`_split_gm_by_stages`
+    where trainable parameters are passed as **placeholder inputs** identified
+    by *param_idxs*.
 
     Args:
-        gm: GraphModule to transform.
-        input_idxs: Indices of placeholders that should receive tensors
-            from the previous stage.
-        prev_stage_id: Rank used as src for recv operations.
-        next_stage_id: Rank used as dst for send operations.
+        stage_gm: The stage ``fx.GraphModule`` to split.
+        graphargs: Flat arg list for ``stage_gm.forward``; entries at
+            *param_idxs* are meta tensors, entries at *input_idxs* are None.
+        input_idxs: Positions of activation-input placeholders.
+        param_idxs: Positions of parameter placeholders.
+        bucket_size_bytes: Target bucket size (default 25 MB).
+        debug_name: Optional label included in bucket planning logs.
 
     Returns:
-        A new GraphModule with inserted P2P recv/send operations.
+        A list of ``(bucket_gm, bucket_input_idxs, bucket_param_idxs,
+        bucket_graphargs)`` tuples in execution order.  Returns a
+        single-element list containing the original stage when no split is
+        needed.
     """
-    new_graph = fx.Graph()
-    node_mapping: dict[fx.Node, fx.Node] = {}
-
-    placeholders = [n for n in gm.graph.nodes if n.op == "placeholder"]
-
-    # Copy placeholders first so we preserve the original signature.
-    for idx, node in enumerate(placeholders):
-        new_ph = new_graph.node_copy(node, lambda n: node_mapping.get(n, n))
-        node_mapping[node] = new_ph
-
-        if idx in input_idxs:
-            # Directly recv into the placeholder tensor and use that value
-            # in place of the original placeholder.
-            recv_node = new_graph.call_function(
-                _dispatch_p2p_recv,
-                (new_ph, prev_stage_id),
-            )
-            node_mapping[node] = recv_node
-
-    # Copy get_attr nodes (parameters/buffers).
-    for node in gm.graph.nodes:
-        if node.op == "get_attr" and node not in node_mapping:
-            new_attr = new_graph.node_copy(node, lambda n: node_mapping.get(n, n))
-            node_mapping[node] = new_attr
-
-    # Copy all other nodes except output, rewiring to use P2P-recv inputs.
-    output_node: fx.Node | None = None
-    for node in gm.graph.nodes:
-        if node.op in ("placeholder", "get_attr"):
-            continue
-        if node.op == "output":
-            output_node = node
-            continue
+    nodes = list(stage_gm.graph.nodes)
+    node_idx: dict[fx.Node, int] = {nd: i for i, nd in enumerate(nodes)}
+    ph_nodes = [nd for nd in nodes if nd.op == "placeholder"]
 
-        new_node = new_graph.node_copy(
-            node,
-            lambda n: node_mapping.get(n, n),
-        )
-        node_mapping[node] = new_node
+    param_ph_set = {ph_nodes[i] for i in param_idxs if i < len(ph_nodes)}
+    param_ph_list = [ph_nodes[i] for i in param_idxs if i < len(ph_nodes)]
+    input_ph_set  = {ph_nodes[i] for i in input_idxs  if i < len(ph_nodes)}
 
-    if output_node is None:
-        raise RuntimeError("GraphModule has no output node")
+    if not param_ph_list:
+        return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))]
 
-    # Map original outputs into the new graph.
-    raw_outputs = fx.graph.map_arg(
-        output_node.args,
-        lambda n: node_mapping.get(n, n),
+    compute_set: frozenset[int] = frozenset(
+        i for i, nd in enumerate(nodes)
+        if nd.op not in ("placeholder", "get_attr", "output")
     )
 
-    # For each tensor-valued node in the outputs, add a send op and
-    # return the result of the send so it is kept in the dataflow.
-    def _wrap_with_send(arg):
-        if isinstance(arg, fx.Node):
-            return new_graph.call_function(
-                _dispatch_p2p_send,
-                (arg, next_stage_id),
-            )
-        return arg
+    def _use_range(pnd: fx.Node) -> tuple[int, int]:
+        idxs = [node_idx[u] for u in pnd.users if node_idx[u] in compute_set]
+        return (min(idxs), max(idxs)) if idxs else (node_idx[pnd], node_idx[pnd])
 
-    wrapped_outputs = fx.graph.map_arg(raw_outputs, _wrap_with_send)
+    def _size(pnd: fx.Node) -> int:
+        i = ph_nodes.index(pnd)
+        if i < len(graphargs) and graphargs[i] is not None and hasattr(graphargs[i], "numel"):
+            return int(graphargs[i].numel() * graphargs[i].element_size())
+        ev = pnd.meta.get("example_value")
+        if ev is not None and hasattr(ev, "numel"):
+            return int(ev.numel() * ev.element_size())
+        return 0
 
-    if isinstance(wrapped_outputs, tuple) and len(wrapped_outputs) == 1:
-        new_graph.output(wrapped_outputs[0])
-    else:
-        new_graph.output(wrapped_outputs)
+    debug_name = debug_name or getattr(stage_gm, "__class__", type(stage_gm)).__name__
+    param_ranges = {pn: _use_range(pn) for pn in param_ph_list}
+    param_sizes = {pn: _size(pn) for pn in param_ph_list}
 
-    root_module = torch.nn.Module()
+    # Greedy bucket assignment
+    bucket_id: dict[fx.Node, int] = {}
+    cur_b, cur_sz = 0, 0
+    for pn in param_ph_list:
+        sz = param_sizes[pn]
+        if cur_sz + sz > bucket_size_bytes and cur_sz > 0:
+            cur_b += 1
+            cur_sz = 0
+        bucket_id[pn] = cur_b
+        cur_sz += sz
+    n_init = cur_b + 1
 
-    # Copy parameters, buffers, and submodules from the original module.
-    for name, param in gm.named_parameters(recurse=False):
-        root_module.register_parameter(name, param)
+    # Singleton promotion
+    def _init_range(b: int) -> tuple[int, int]:
+        ms = [pn for pn in param_ph_list if bucket_id[pn] == b]
+        if not ms:
+            return (0, 0)
+        return (min(param_ranges[pn][0] for pn in ms), max(param_ranges[pn][1] for pn in ms))
 
-    for name, buffer in gm.named_buffers(recurse=False):
-        root_module.register_buffer(name, buffer)
+    init_ranges = [_init_range(b) for b in range(n_init)]
 
-    for name, module in gm.named_children():
-        root_module.add_module(name, module)
+    def _bucket_at(idx: int) -> int:
+        for b, (lo, hi) in enumerate(init_ranges):
+            if lo <= idx <= hi:
+                return b
+        return 0
 
-    new_gm = fx.GraphModule(root_module, new_graph)
-    new_gm.recompile()
-    return new_gm
+    sn = n_init
+    for pn in param_ph_list:
+        f, l = param_ranges[pn]
+        if _bucket_at(f) != _bucket_at(l):
+            bucket_id[pn] = sn
+            sn += 1
 
+    # Build bucket list, sort, merge overlapping ranges
+    bm: dict[int, list[fx.Node]] = defaultdict(list)
+    for pn in param_ph_list:
+        bm[bucket_id[pn]].append(pn)
 
-# ---------------------------------------------------------------------------
-# Per-chunk task-chain builder (internal helper)
-# ---------------------------------------------------------------------------
+    blist: list[tuple[int, int, list[fx.Node]]] = []
+    for members in bm.values():
+        f = min(param_ranges[pn][0] for pn in members)
+        l = max(param_ranges[pn][1] for pn in members)
+        blist.append((f, l, members))
+    blist.sort(key=lambda t: t[0])
 
-def _splice_a2a_nodes(
-    chain: list,
-    boundaries: dict,
-    task_type,
-    pp_rank: int,
-    source_chunk,
-) -> list:
-    """Splice A2A nodes into a bucket chain at all-to-all boundaries.
-
-    For FWD: inserts FWD_A2A after FWD(bkt_id) when bkt_id is a boundary.
-    For BWD/BWD_I: inserts BWD_A2A before BWD(bkt_id) in execution order
-    (i.e. between BWD(bkt_id+1) and BWD(bkt_id)).
-
-    Updates data and temporal edges in-place; returns the new chain list
-    with A2A nodes interleaved.
-    """
-    a2a_type = TaskType.FWD_A2A if task_type == TaskType.FWD else TaskType.BWD_A2A
-    new_chain: list = []
-
-    for i, task in enumerate(chain):
-        bkt_id = task.bucket_id
-        is_last = (i == len(chain) - 1)
-
-        if task_type == TaskType.FWD:
-            new_chain.append(task)
-            if bkt_id in boundaries and not is_last:
-                next_task = chain[i + 1]
-                a2a_node = Task(
-                    task_type=a2a_type,
-                    batches=list(task.batches),
-                    task_pp_rank=pp_rank,
-                    pp_rank=pp_rank,
-                    time_step=0,
-                    bucket_id=bkt_id,
-                    resource="ep_stream",
-                    custom_metadata={"a2a_tensor_idx": boundaries[bkt_id]},
-                    source_chunk=source_chunk,
-                    associated_chunk=source_chunk,
-                    compute_stream_id=task.compute_stream_id,
-                )
-                # Rewire data: task → next_task  ⟹  task → a2a → next_task
-                task.data_succs.remove(next_task)
-                next_task.data_preds.remove(task)
-                task.data_succs.append(a2a_node)
-                a2a_node.data_preds.append(task)
-                a2a_node.data_succs.append(next_task)
-                next_task.data_preds.append(a2a_node)
-                # Rewire temporal
-                task.temporal_succs.remove(next_task)
-                next_task.temporal_preds.remove(task)
-                task.temporal_succs.append(a2a_node)
-                a2a_node.temporal_preds.append(task)
-                a2a_node.temporal_succs.append(next_task)
-                next_task.temporal_preds.append(a2a_node)
-                new_chain.append(a2a_node)
+    merged: list[tuple[int, int, list[fx.Node]]] = []
+    for f, l, ms in blist:
+        if merged and f <= merged[-1][1]:
+            pf, pl, pm = merged[-1]
+            merged[-1] = (pf, max(pl, l), pm + ms)
         else:
-            # BWD / BWD_I: insert A2A before task (i.e. between chain[i-1] and chain[i])
-            if bkt_id in boundaries and i > 0:
-                prev_task = chain[i - 1]
-                a2a_node = Task(
-                    task_type=a2a_type,
-                    batches=list(task.batches),
-                    task_pp_rank=pp_rank,
-                    pp_rank=pp_rank,
-                    time_step=0,
-                    bucket_id=bkt_id,
-                    resource="ep_stream",
-                    custom_metadata={"a2a_tensor_idx": boundaries[bkt_id]},
-                    source_chunk=source_chunk,
-                    associated_chunk=source_chunk,
-                    compute_stream_id=task.compute_stream_id,
-                )
-                # Rewire data: prev_task → task  ⟹  prev_task → a2a → task
-                prev_task.data_succs.remove(task)
-                task.data_preds.remove(prev_task)
-                prev_task.data_succs.append(a2a_node)
-                a2a_node.data_preds.append(prev_task)
-                a2a_node.data_succs.append(task)
-                task.data_preds.append(a2a_node)
-                # Rewire temporal
-                prev_task.temporal_succs.remove(task)
-                task.temporal_preds.remove(prev_task)
-                prev_task.temporal_succs.append(a2a_node)
-                a2a_node.temporal_preds.append(prev_task)
-                a2a_node.temporal_succs.append(task)
-                task.temporal_preds.append(a2a_node)
-                new_chain.append(a2a_node)
-            new_chain.append(task)
-
-    return new_chain
-
-
-def _build_chunk_task_chain(
-    chunk,
-    pp_rank: int,
-    col_idx: int,
-    bucket_counts: dict,
-    a2a_info: dict,
-) -> tuple:
-    """Build the expanded task chain for a single schedule chunk.
-
-    For FWD / BWD / BWD_I chunks with bucketing: returns a chain of K bucket
-    tasks with internal data + temporal edges.  A2A nodes are spliced in at
-    boundaries.  For all other chunk types a single task is returned.
-
-    All created tasks have ``source_chunk`` set to *chunk*.
+            merged.append((f, l, ms))
 
-    Returns:
-        ``(all_tasks, head, tail)`` where *head* is the first task in
-        execution order and *tail* is the last.
-    """
-    TIME_SCALE = 1000
-
-    task_type = chunk.type
-    stage_id = chunk.batches[0].stage_id if chunk.batches else None
-    K = bucket_counts.get(stage_id, 1) if stage_id is not None else 1
-
-    # FWD, BWD, BWD_I, BWD_W all support per-bucket tasks
-    if task_type not in (TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W):
-        K = 1
-
-    if K == 1:
-        task = Task(
-            task_type=chunk.type,
-            batches=list(chunk.batches),
-            task_pp_rank=chunk.pp_rank,
-            pp_rank=pp_rank,
-            time_step=col_idx,
-            source_chunk=chunk,
-            associated_chunk=chunk,
-            compute_stream_id="comp_stream",
-        )
-        return [task], task, task
-
-    # --- Bucketed chain ---
-    if task_type == TaskType.FWD:
-        # Execution order: bucket 0, 1, …, K-1
-        chain = [
-            Task(
-                task_type=TaskType.FWD,
-                batches=list(chunk.batches),
-                task_pp_rank=pp_rank,
-                pp_rank=pp_rank,
-                time_step=col_idx * TIME_SCALE + i,
-                bucket_id=b,
-                source_chunk=chunk,
-                associated_chunk=chunk,
-                compute_stream_id="comp_stream",
-            )
-            for i, b in enumerate(range(K))
+    def _bucket_bytes(members: list[fx.Node]) -> int:
+        return sum(param_sizes[pn] for pn in members)
+
+    def _format_members(members: list[fx.Node], *, limit: int = 8) -> str:
+        def _one(pn: fx.Node) -> str:
+            return f"{pn.name}:{param_sizes[pn]}"
+
+        if len(members) <= limit:
+            return "[" + ", ".join(_one(pn) for pn in members) + "]"
+        head = ", ".join(_one(pn) for pn in members[:4])
+        tail = ", ".join(_one(pn) for pn in members[-2:])
+        return f"[{head}, ... ({len(members) - 6} more), {tail}]"
+
+    def _log_bucket_plan(phase: str, seg_ranges: list[tuple[int, int, list[fx.Node]]]) -> None:
+        ownership: dict[fx.Node, list[int]] = defaultdict(list)
+        for seg_idx, (_f, _l, members) in enumerate(seg_ranges):
+            for pn in members:
+                ownership[pn].append(seg_idx)
+        missing = [pn.name for pn in param_ph_list if pn not in ownership]
+        duplicates = [
+            (pn.name, buckets)
+            for pn, buckets in ownership.items()
+            if len(buckets) != 1
         ]
-    else:
-        # BWD / BWD_I: execution order K-1, K-2, …, 0
-        chain = [
-            Task(
-                task_type=task_type,
-                batches=list(chunk.batches),
-                task_pp_rank=pp_rank,
-                pp_rank=pp_rank,
-                time_step=col_idx * TIME_SCALE + i,
-                bucket_id=K - 1 - i,
-                source_chunk=chunk,
-                associated_chunk=chunk,
-                compute_stream_id="comp_stream",
+        total_bytes = sum(param_sizes[pn] for pn in param_ph_list)
+        max_bucket_bytes = max((_bucket_bytes(ms) for _f, _l, ms in seg_ranges), default=0)
+        logger.info(
+            "bucket_stage plan phase=%s node=%s bucket_size_bytes=%d bucket_size_mib=%.6f "
+            "params=%d total_param_bytes=%d initial_buckets=%d final_buckets=%d "
+            "owned_params=%d unique_owned_params=%d missing_params=%d duplicate_params=%d max_bucket_bytes=%d",
+            phase,
+            debug_name,
+            bucket_size_bytes,
+            bucket_size_bytes / (1024 * 1024),
+            len(param_ph_list),
+            total_bytes,
+            n_init,
+            len(seg_ranges),
+            sum(len(ms) for _f, _l, ms in seg_ranges),
+            len(ownership),
+            len(missing),
+            len(duplicates),
+            max_bucket_bytes,
+        )
+        if missing or duplicates:
+            logger.warning(
+                "bucket_stage ownership-invalid phase=%s node=%s missing=%s duplicates=%s",
+                phase,
+                debug_name,
+                missing[:16],
+                duplicates[:16],
             )
-            for i in range(K)
-        ]
 
-    # Connect consecutive bucket tasks with data + temporal edges
-    for i in range(K - 1):
-        chain[i].data_succs.append(chain[i + 1])
-        chain[i + 1].data_preds.append(chain[i])
-        chain[i].temporal_succs.append(chain[i + 1])
-        chain[i + 1].temporal_preds.append(chain[i])
+        merge_candidates: list[tuple[int, int, int]] = []
+        for left_idx in range(len(seg_ranges) - 1):
+            left_bytes = _bucket_bytes(seg_ranges[left_idx][2])
+            right_bytes = _bucket_bytes(seg_ranges[left_idx + 1][2])
+            combined = left_bytes + right_bytes
+            if combined <= bucket_size_bytes:
+                merge_candidates.append((left_idx, left_idx + 1, combined))
+
+        for seg_idx, (first, last, members) in enumerate(seg_ranges):
+            bytes_ = _bucket_bytes(members)
+            if bytes_ <= bucket_size_bytes:
+                status = "within_limit"
+            elif len(members) == 1:
+                status = "single_param_over_limit"
+            else:
+                status = "forced_multi_param_over_limit"
+            logger.info(
+                "bucket_stage bucket-plan phase=%s node=%s bucket=%d/%d param_bytes=%d "
+                "params=%d range=%d:%d status=%s members=%s",
+                phase,
+                debug_name,
+                seg_idx,
+                len(seg_ranges),
+                bytes_,
+                len(members),
+                first,
+                last,
+                status,
+                _format_members(members),
+            )
+        if merge_candidates:
+            logger.warning(
+                "bucket_stage fullness-warning phase=%s node=%s mergeable_adjacent_pairs=%s "
+                "bucket_size_bytes=%d",
+                phase,
+                debug_name,
+                merge_candidates[:16],
+                bucket_size_bytes,
+            )
+        else:
+            logger.info(
+                "bucket_stage fullness-ok phase=%s node=%s adjacent_pairs=%d bucket_size_bytes=%d",
+                phase,
+                debug_name,
+                max(0, len(seg_ranges) - 1),
+                bucket_size_bytes,
+            )
 
-    # Insert A2A nodes at boundaries if applicable.
-    # BWD_W (weight gradients) are computed locally and don't need A2A separation.
-    if task_type != TaskType.BWD_W:
-        boundaries = a2a_info.get(stage_id, {}) if stage_id is not None else {}
-        if boundaries:
-            chain = _splice_a2a_nodes(chain, boundaries, task_type, pp_rank, chunk)
+    _log_bucket_plan("range_merge", merged)
 
-    return chain, chain[0], chain[-1]
+    alias_methods = {
+        "view", "_unsafe_view", "reshape", "transpose", "permute", "t",
+        "movedim", "moveaxis", "swapdims", "swapaxes",
+        "select", "narrow", "slice", "split", "chunk", "unbind",
+        "unsqueeze", "squeeze", "flatten", "expand", "diagonal",
+        "detach", "alias", "as_strided",
+    }
+    multi_output_alias_methods = {"split", "chunk", "unbind"}
 
-
-# ---------------------------------------------------------------------------
-# expand_chunks_to_dags: build the full task DAG chunk-by-chunk
-# ---------------------------------------------------------------------------
-
-def expand_chunks_to_dags(
-    schedule,
-    bucket_counts: dict,
-    a2a_info: dict,
-) -> "TaskDAG":
-    """Build a :class:`TaskDAG` by expanding every schedule chunk in-place.
-
-    Replaces the former pipeline of ``schedule_to_dag`` →
-    ``expand_bucket_tasks`` → ``expand_a2a_tasks`` →
-    ``insert_p2p_ops`` → ``insert_ar_ops``.
-
-    For each non-``None`` cell of *schedule*, :func:`_build_chunk_task_chain`
-    produces the expanded task chain (with bucketing and A2A nodes already
-    wired).  Cross-chunk data edges, SEND/RECV nodes, and ALL_REDUCE nodes
-    are then added in subsequent passes.
-
-    Every task created here has its ``source_chunk`` field set to the
-    originating :class:`Chunk` from the pipeline schedule.
-
-    The returned :class:`TaskDAG` has data edges and within-chain temporal
-    edges only.  Between-chunk temporal edges are inserted by the separate
-    :func:`add_temporal_dependencies` pass.
-
-    Args:
-        schedule: The 2-D pipeline pipeline schedule.
-        bucket_counts: Mapping ``stage_id -> K`` for stages with ``K > 1``
-            buckets.  Stages absent from this dict use a single bucket.
-        a2a_info: Mapping ``stage_id -> {boundary_bucket_id -> tensor_idx}``
-            for expert-parallel stages.  May be empty.
-        dp_degree: Number of data-parallel replicas.  When ``> 1``,
-            ALL_REDUCE nodes are inserted for every BWD/BWD_W bucket.
-            Whether a bucket actually has trainable parameters is checked
-            at runtime in run_dag, keeping the schedule model-agnostic.
-        a2a_ar_no_overlap: When true, adds same-rank temporal dependencies
-            from the last backward compute task to ALL_REDUCE nodes so
-            gradient all-reduces run after backward compute on that actor.
-
-    Returns:
-        A new :class:`TaskDAG` containing all compute, A2A, SEND/RECV, and
-        (if *dp_degree* > 1) ALL_REDUCE task nodes.
-    """
-    # mb_to_entries[mb_idx] = list of (col_idx, sub_idx, head, tail, pp_rank)
-    # Used to build cross-chunk data edges in time order.
-    mb_to_entries: dict = defaultdict(list)
-
-    # (pp_rank, stage_id, mb_idx, bucket_id) -> BWD_I task (for per-bucket BWD_I → BWD_W edges)
-    bwdi_tasks: dict = {}
-
-    all_tasks: list = []
-
-    # ------------------------------------------------------------------
-    # Pass 1: build per-chunk task chains
-    # ------------------------------------------------------------------
-    # Precompute logical start times for cross-rank ordering keys.
-    start_times = schedule._compute_start_times()
-
-    for pp_rank, row in enumerate(schedule.grid):
-        for col_idx, chunk in enumerate(row):
-            if chunk.type == TaskType.FWD_BWD:
-                fwd_batch, bwd_batch = chunk.batches[0], chunk.batches[1]
-
-                fwd_sub = Chunk(pp_rank=pp_rank, batches=[fwd_batch], type=TaskType.FWD)
-                fwd_chain, fwd_head, fwd_tail = _build_chunk_task_chain(
-                    fwd_sub, pp_rank, col_idx, bucket_counts, a2a_info
-                )
-                for t in fwd_chain:
-                    t.source_chunk = chunk  # override to point at original FWD_BWD cell
-
-                bwd_sub = Chunk(pp_rank=pp_rank, batches=[bwd_batch], type=TaskType.BWD)
-                bwd_chain, bwd_head, bwd_tail = _build_chunk_task_chain(
-                    bwd_sub, pp_rank, col_idx, bucket_counts, a2a_info
-                )
-                for t in bwd_chain:
-                    t.source_chunk = chunk
-
-                # Within-cell ordering: FWD executes before BWD
-                fwd_tail.temporal_succs.append(bwd_head)
-                bwd_head.temporal_preds.append(fwd_tail)
-
-                all_tasks.extend(fwd_chain + bwd_chain)
-                logical_t = start_times[pp_rank][col_idx]
-                mb_to_entries[fwd_batch.mb_idx].append((logical_t, 0, fwd_head, fwd_tail, pp_rank))
-                mb_to_entries[bwd_batch.mb_idx].append((logical_t, 1, bwd_head, bwd_tail, pp_rank))
-
-            else:
-                tasks, head, tail = _build_chunk_task_chain(
-                    chunk, pp_rank, col_idx, bucket_counts, a2a_info
-                )
-                all_tasks.extend(tasks)
-
-                # UPD and BWD_W are excluded from the cross-stage mb chain
-                if chunk.type not in (TaskType.UPD, TaskType.BWD_W):
-                    logical_t = start_times[pp_rank][col_idx]
-                    for batch in chunk.batches:
-                        mb_to_entries[batch.mb_idx].append(
-                            (logical_t, 0, head, tail, pp_rank)
-                        )
-
-                # Track per-bucket BWD_I tasks for BWD_I → BWD_W data edges
-                if chunk.type == TaskType.BWD_I and chunk.batches:
-                    b = chunk.batches[0]
-                    for t in tasks:
-                        if t.task_type == TaskType.BWD_I:
-                            bwdi_tasks[(pp_rank, b.stage_id, b.mb_idx, t.bucket_id)] = t
-
-    # ------------------------------------------------------------------
-    # Pass 1.5: assign unique_bucket_id and compute_loss
-    # ------------------------------------------------------------------
-    # Build a sorted (stage_id, stage_bucket_id) → unique_bucket_id mapping.
-    # Sorting by stage_id ensures the mapping is deterministic and consistent
-    # with the offset computation in piper.py that passes unique_bucket_ids
-    # to _load_stage.
-    _compute_types = (TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W)
-    _stage_bucket_pairs = sorted(
-        {(t.batches[0].stage_id, t.bucket_id)
-         for t in all_tasks
-         if t.task_type in _compute_types and t.batches},
-        key=lambda x: (x[0], x[1]),
-    )
-    _sb_to_ubid: dict = {sb: i for i, sb in enumerate(_stage_bucket_pairs)}
-
-    _last_stage_id = max(
-        (t.batches[0].stage_id
-         for t in all_tasks
-         if t.task_type in _compute_types and t.batches),
-        default=None,
-    )
-
-    for task in all_tasks:
-        if task.task_type in _compute_types and task.batches:
-            s = task.batches[0].stage_id
-            task.unique_bucket_id = _sb_to_ubid.get((s, task.bucket_id))
-
-    # Set compute_loss=True on the first-to-run BWD/BWD_I task of the last stage.
-    # "First to run" = highest bucket_id (BWD executes in reverse bucket order).
-    if _last_stage_id is not None:
-        _last_stage_K = bucket_counts.get(_last_stage_id, 1)
-        _last_bkt = _last_stage_K - 1
-        for task in all_tasks:
-            if (task.task_type in (TaskType.BWD, TaskType.BWD_I)
-                    and task.batches
-                    and task.batches[0].stage_id == _last_stage_id
-                    and task.bucket_id == _last_bkt):
-                task.compute_loss = True
-
-    # ------------------------------------------------------------------
-    # Pass 2: per-bucket BWD_I → BWD_W data edges
-    # ------------------------------------------------------------------
-    bwd_w_tasks: dict = {}  # (pp_rank, stage_id, mb_idx, bucket_id) -> Task
-    for task in all_tasks:
-        if task.task_type == TaskType.BWD_W and task.batches:
-            b = task.batches[0]
-            bwd_w_tasks[(task.pp_rank, b.stage_id, b.mb_idx, task.bucket_id)] = task
-
-    for key, bwdi_task in bwdi_tasks.items():
-        bwdw_task = bwd_w_tasks.get(key)
-        if bwdw_task is not None:
-            bwdi_task.data_succs.append(bwdw_task)
-            bwdw_task.data_preds.append(bwdi_task)
-
-    # ------------------------------------------------------------------
-    # Pass 3: cross-chunk data edges
-    # ------------------------------------------------------------------
-    for mb_idx, entries in mb_to_entries.items():
-        entries.sort(key=lambda e: (e[0], e[1], e[4]))  # col_idx, sub_idx, pp_rank
-        for i in range(len(entries) - 1):
-            _ci, _si, _head_i, tail_i, _ri = entries[i]
-            _cn, _sn, head_n, _tn, _rn = entries[i + 1]
-            tail_i.data_succs.append(head_n)
-            head_n.data_preds.append(tail_i)
-
-
-    # ------------------------------------------------------------------
-    # Pass 4: SEND / RECV nodes for cross-rank data edges
-    # ------------------------------------------------------------------
-    cross_edges = [
-        (src, dst)
-        for src in list(all_tasks)
-        for dst in list(src.data_succs)
-        if src.pp_rank != dst.pp_rank
-    ]
-    for src, dst in cross_edges:
-        src.data_succs.remove(dst)
-        dst.data_preds.remove(src)
-
-        send_node = Task(
-            task_type=TaskType.SEND,
-            batches=list(src.batches),
-            task_pp_rank=src.pp_rank,
-            pp_rank=src.pp_rank,
-            time_step=0,  # assigned later by assign_time_steps
-            peer_pp_rank=dst.pp_rank,
-            resource="pp_stream",
-            source_chunk=src.source_chunk,
-            associated_chunk=src.associated_chunk,
-            compute_stream_id=src.compute_stream_id,
-        )
-        src.data_succs.append(send_node)
-        send_node.data_preds.append(src)
-
-        recv_metadata = {}
-        if dst.task_type in (TaskType.BWD, TaskType.BWD_I):
-            recv_metadata["fwd_ubid"] = dst.unique_bucket_id
-        recv_node = Task(
-            task_type=TaskType.RECV,
-            batches=list(dst.batches),
-            task_pp_rank=dst.pp_rank,
-            pp_rank=dst.pp_rank,
-            time_step=0,  # assigned later by assign_time_steps
-            peer_pp_rank=src.pp_rank,
-            resource="pp_stream",
-            source_chunk=dst.source_chunk,
-            associated_chunk=dst.associated_chunk,
-            compute_stream_id=dst.compute_stream_id,
-            custom_metadata=recv_metadata,
-        )
-        recv_node.data_succs.append(dst)
-        dst.data_preds.append(recv_node)
-
-        all_tasks.append(send_node)
-        all_tasks.append(recv_node)
-
-    return TaskDAG(nodes=all_tasks)
-
-
-# ---------------------------------------------------------------------------
-# add_temporal_dependencies: insert temporal edges between adjacent chunks
-# ---------------------------------------------------------------------------
-
-_CRITICAL_PATH_TYPES = frozenset({
-    TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W,
-    TaskType.FWD_BWD, TaskType.FWD_A2A, TaskType.BWD_A2A,
-})
-
-
-def add_temporal_dependencies(dag: "TaskDAG", schedule) -> None:
-    """Insert temporal edges that enforce execution order between adjacent chunks.
-
-    For each row of *schedule*, consecutive non-``None`` chunk pairs ``(c1,
-    c2)`` receive a temporal edge from the last critical-path task of *c1* to
-    the first critical-path task of *c2*.
-
-    The **critical-path chain** for a chunk is the sub-DAG of tasks associated
-    with that chunk whose type is in :data:`_CRITICAL_PATH_TYPES` (i.e.
-    compute and A2A tasks; SEND, RECV, and ALL_REDUCE are excluded).  Head and
-    tail are found by following both data-dependency *and* within-chunk
-    temporal edges.
-
-    A :data:`~TaskType.UPD` node is also synthesised for each pipeline rank
-    and appended to ``dag.nodes`` as the terminal temporal task for that rank.
-
-    Args:
-        dag: :class:`TaskDAG` produced by :func:`expand_chunks_to_dags`.
-            Must contain only data edges and within-chain temporal edges —
-            no between-chunk temporal edges should exist yet.
-        schedule: The :class:`PipelineSchedule` whose grid determines
-            adjacency and row membership.
-    """
-    # Group all tasks by id(source_chunk) for fast lookup.
-    sc_to_tasks: dict = defaultdict(list)
-    for task in dag.nodes:
-        if task.source_chunk is not None:
-            sc_to_tasks[id(task.source_chunk)].append(task)
-
-    def _cp_endpoints(chunk_id: int):
-        """Return (head, tail) of the critical-path sub-DAG for *chunk_id*.
-
-        Uses both data and temporal edges within the filtered task set so
-        that FWD_BWD cells (where FWD and BWD sub-chains are joined only by
-        a temporal edge) are handled correctly.
-        """
-        tasks = [t for t in sc_to_tasks.get(chunk_id, [])
-                 if t.task_type in _CRITICAL_PATH_TYPES]
-        if not tasks:
-            return None, None
-        task_ids = {id(t) for t in tasks}
-        head = next(
-            t for t in tasks
-            if not any(id(p) in task_ids
-                       for p in list(t.data_preds) + list(t.temporal_preds))
-        )
-        tail = next(
-            t for t in tasks
-            if not any(id(s) in task_ids
-                       for s in list(t.data_succs) + list(t.temporal_succs))
-        )
-        return head, tail
-
-    new_nodes: list = []
-
-    for pp_rank, row in enumerate(schedule.grid):
-        non_none = list(enumerate(row))
-        if not non_none:
-            continue
-
-        # Between-chunk temporal edges for consecutive pairs
-        for i in range(len(non_none) - 1):
-            _, c1 = non_none[i]
-            _, c2 = non_none[i + 1]
-            _, tail_c1 = _cp_endpoints(id(c1))
-            head_c2, _ = _cp_endpoints(id(c2))
-            if tail_c1 is not None and head_c2 is not None:
-                tail_c1.temporal_succs.append(head_c2)
-                head_c2.temporal_preds.append(tail_c1)
-
-        # UPD node: final optimizer step for this rank.
-        # In the no-DP case, every BWD-type chunk's critical-path tail feeds
-        # into UPD so that UPD correctly waits for ALL backward passes, not
-        # just the last one in the temporal chain.  In the DP/ZeRO case,
-        # _wire_sync_to_upd (called inside insert_zero_ops) additionally
-        # connects each AR/RS → UPD temporal edge.
-        _BWD_CHUNK_TYPES = frozenset({
-            TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W, TaskType.FWD_BWD,
-        })
-
-        # Use the last BWD-type chunk's tail for UPD's pp_rank / batches.
-        ref_tail = None
-        for _, chunk in reversed(non_none):
-            if chunk.type in _BWD_CHUNK_TYPES:
-                _, ref_tail = _cp_endpoints(id(chunk))
-                if ref_tail is not None:
-                    break
-        # Fall back to the very last chunk if no BWD chunk exists.
-        if ref_tail is None:
-            _, ref_tail = _cp_endpoints(id(non_none[-1][1]))
-        if ref_tail is None:
-            continue
-
-        upd_node = Task(
-            task_type=TaskType.UPD,
-            batches=list(ref_tail.batches),
-            task_pp_rank=ref_tail.pp_rank,
-            pp_rank=ref_tail.pp_rank,
-            time_step=0,
-            source_chunk=non_none[-1][1],
-            compute_stream_id="comp_stream",
-        )
-
-        # Wire every BWD chunk tail → UPD.
-        seen_tails: set[int] = set()
-        for _, chunk in non_none:
-            if chunk.type not in _BWD_CHUNK_TYPES:
-                continue
-            _, tail = _cp_endpoints(id(chunk))
-            if tail is not None and id(tail) not in seen_tails:
-                seen_tails.add(id(tail))
-                tail.temporal_succs.append(upd_node)
-                upd_node.temporal_preds.append(tail)
-
-        # If no BWD chunks (FWD-only schedule), fall back to last chunk.
-        if not upd_node.temporal_preds:
-            ref_tail.temporal_succs.append(upd_node)
-            upd_node.temporal_preds.append(ref_tail)
-
-        new_nodes.append(upd_node)
-
-    dag.nodes.extend(new_nodes)
-
-
-# ---------------------------------------------------------------------------
-# split_dag_by_rank: partition unified DAG into per-rank DAGs
-# ---------------------------------------------------------------------------
-
-def split_dag_by_rank(dag: "TaskDAG") -> list:
-    """Split a unified :class:`TaskDAG` into one :class:`TaskDAG` per PP rank.
-
-    Asserts that no cross-rank data or temporal edges remain (they should
-    have been replaced by SEND/RECV nodes in :func:`expand_chunks_to_dags`).
-
-    Returns:
-        List of :class:`TaskDAG` objects ordered by ascending PP rank.
-    """
-    for node in dag.nodes:
-        for succ in node.data_succs:
-            assert node.pp_rank == succ.pp_rank, (
-                f"Cross-rank data edge still present: "
-                f"{node.node_id()} (rank {node.pp_rank}) → "
-                f"{succ.node_id()} (rank {succ.pp_rank})"
-            )
-        for ts in node.temporal_succs:
-            assert node.pp_rank == ts.pp_rank, (
-                f"Cross-rank temporal edge present: "
-                f"{node.node_id()} (rank {node.pp_rank}) → "
-                f"{ts.node_id()} (rank {ts.pp_rank})"
-            )
-
-    rank_nodes: dict = defaultdict(list)
-    for node in dag.nodes:
-        rank_nodes[node.pp_rank].append(node)
-
-    return [TaskDAG(nodes=rank_nodes[r]) for r in sorted(rank_nodes)]
-
-
-def _remove_ar_nodes(rank_dag: TaskDAG) -> None:
-    ar_set = {id(n) for n in rank_dag.nodes if n.task_type == TaskType.ALL_REDUCE}
-    if not ar_set:
-        return
-    for n in rank_dag.nodes:
-        if id(n) not in ar_set:
-            n.data_preds = [p for p in n.data_preds if id(p) not in ar_set]
-            n.data_succs = [s for s in n.data_succs if id(s) not in ar_set]
-            n.temporal_preds = [p for p in n.temporal_preds if id(p) not in ar_set]
-            n.temporal_succs = [s for s in n.temporal_succs if id(s) not in ar_set]
-    rank_dag.nodes = [n for n in rank_dag.nodes if id(n) not in ar_set]
-
-
-def _zero_task(
-    ref: Task,
-    bucket_id: int,
-    task_type: TaskType,
-    time_step: int = 0,
-    *,
-    ubid: int | None = None,
-    source_chunk: Chunk | None = None,
-) -> Task:
-    resource = "compute_stream"
-    if task_type in {
-        TaskType.ALL_REDUCE,
-        TaskType.REDUCE_SCATTER,
-        TaskType.ALL_GATHER,
-        TaskType.ALLOC_FULL_GRADS,
-        TaskType.FREE_FULL_GRADS,
-        TaskType.ALLOC_FULL_PARAMS,
-        TaskType.FREE_FULL_PARAMS,
-    }:
-        resource = "ep_stream" if piper_metadata.ar_a2a_same_stream else "dp_stream"
-    return Task(
-        task_type=task_type,
-        batches=list(ref.batches),
-        task_pp_rank=ref.pp_rank,
-        pp_rank=ref.pp_rank,
-        time_step=time_step,
-        bucket_id=bucket_id,
-        resource=resource,
-        compute_stream_id=ref.compute_stream_id,
-        unique_bucket_id=ubid,
-        source_chunk=source_chunk or ref.source_chunk,
-        associated_chunk=ref.associated_chunk,
-    )
-
-
-def _bucket_compute_tasks(
-    rank_dag: TaskDAG,
-    bucket_filter_keys: set | None = None,
-) -> dict[int, list[Task]]:
-    compute_types = {TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W}
-    by_ubid: dict[int, list[Task]] = defaultdict(list)
-    for node in rank_dag.nodes:
-        if node.task_type not in compute_types or node.unique_bucket_id is None:
-            continue
-        if bucket_filter_keys is not None:
-            if not node.batches:
-                continue
-            stage_id = node.batches[0].stage_id
-            if (stage_id, node.bucket_id) not in bucket_filter_keys:
-                continue
-        by_ubid[node.unique_bucket_id].append(node)
-    for nodes in by_ubid.values():
-        nodes.sort(key=lambda n: n.time_step)
-    return by_ubid
-
-
-def _add_data_edge(src: Task, dst: Task) -> None:
-    if dst not in src.data_succs:
-        src.data_succs.append(dst)
-    if src not in dst.data_preds:
-        dst.data_preds.append(src)
-
-
-def _add_temporal_edge(src: Task, dst: Task) -> None:
-    if dst not in src.temporal_succs:
-        src.temporal_succs.append(dst)
-    if src not in dst.temporal_preds:
-        dst.temporal_preds.append(src)
-
-
-def _remove_data_edge(src: Task, dst: Task) -> None:
-    if dst in src.data_succs:
-        src.data_succs.remove(dst)
-    if src in dst.data_preds:
-        dst.data_preds.remove(src)
-
-
-def _remove_temporal_edge(src: Task, dst: Task) -> None:
-    if dst in src.temporal_succs:
-        src.temporal_succs.remove(dst)
-    if src in dst.temporal_preds:
-        dst.temporal_preds.remove(src)
-
-
-def _append_metadata_ubid(node: Task, key: str, ubid: int | None) -> None:
-    if ubid is None:
-        return
-    values = node.custom_metadata.setdefault(key, [])
-    if ubid not in values:
-        values.append(ubid)
-
-
-def _insert_zero_grad_ops(
-    rank_dag: TaskDAG,
-    *,
-    sync_type: TaskType,
-    use_grad_lifetime: bool = False,
-    zero_bucket_keys: set | None = None,
-    gradient_accumulation: bool = True,
-) -> None:
-    """Insert gradient-sync nodes for ZeRO-0/1/2.
-
-    With ``gradient_accumulation=True``, a bucket's sync is attached only to the
-    last BWD/BWD_W occurrence of that bucket. For ZeRO-2, the full-grad
-    lifetime widens to first occurrence → last occurrence.
-    """
-    _remove_ar_nodes(rank_dag)
-
-    grad_sync_types = {TaskType.BWD, TaskType.BWD_W}
-    new_nodes: list[Task] = []
-
-    for ubid, bucket_nodes in _bucket_compute_tasks(rank_dag, zero_bucket_keys).items():
-        grad_nodes = [node for node in bucket_nodes if node.task_type in grad_sync_types]
-        if not grad_nodes:
-            continue
-        first_compute = min(grad_nodes, key=lambda node: (node.time_step, node.uid))
-        last_compute = max(grad_nodes, key=lambda node: (node.time_step, node.uid))
-
-        if gradient_accumulation:
-            sync_sources = [last_compute]
-        else:
-            sync_sources = grad_nodes
-
-        for compute_node in sync_sources:
-            sync = _zero_task(compute_node, compute_node.bucket_id, sync_type, ubid=ubid)
-            _add_data_edge(compute_node, sync)
-            new_nodes.append(sync)
-
-        if use_grad_lifetime:
-            if gradient_accumulation:
-                first_compute.custom_metadata["zero_alloc_full_grads_before"] = True
-                _append_metadata_ubid(new_nodes[-1], "zero_free_full_grads_after_ubids", ubid)
-            else:
-                for compute_node in grad_nodes:
-                    sync = next(
-                        node for node in new_nodes
-                        if node.task_type == sync_type and node.unique_bucket_id == ubid and compute_node in node.data_preds
-                    )
-                    compute_node.custom_metadata["zero_alloc_full_grads_before"] = True
-                    _append_metadata_ubid(sync, "zero_free_full_grads_after_ubids", ubid)
-
-    rank_dag.nodes.extend(new_nodes)
-
-
-def _insert_zero1_ops(
-    rank_dag: TaskDAG,
-    zero_bucket_keys: set | None = None,
-    gradient_accumulation: bool = True,
-) -> None:
-    """ZeRO-1: all-reduce after each bucket or after its last occurrence."""
-    _insert_zero_grad_ops(
-        rank_dag,
-        sync_type=TaskType.ALL_REDUCE,
-        zero_bucket_keys=zero_bucket_keys,
-        gradient_accumulation=gradient_accumulation,
-    )
-
-
-def _insert_zero2_ops(
-    rank_dag: TaskDAG,
-    zero_bucket_keys: set | None = None,
-    gradient_accumulation: bool = True,
-) -> None:
-    """ZeRO-2: reduce-scatter with widened grad lifetime under accumulation."""
-    _insert_zero_grad_ops(
-        rank_dag,
-        sync_type=TaskType.REDUCE_SCATTER,
-        use_grad_lifetime=True,
-        zero_bucket_keys=zero_bucket_keys,
-        gradient_accumulation=gradient_accumulation,
-    )
-
-
-def _insert_zero3_ops(
-    rank_dag: TaskDAG,
-    zero_bucket_keys: set | None = None,
-    gradient_accumulation: bool = True,
-) -> None:
-    """ZeRO-3: param gather + per-grad-node reduce-scatter lifetimes.
-
-    * F / B_I  →  P+ → AG → compute → P-
-    * B / B_W  →  (G+ →) and (P+ → AG →) compute (→ RS → G-) and (→ P-)
-    """
-    _remove_ar_nodes(rank_dag)
-
-    # Types that only consume params (no weight-grad accumulation).
-    param_only_types = frozenset({TaskType.FWD, TaskType.BWD_I})
-    # Types that consume params AND accumulate weight gradients.
-    grad_types = frozenset({TaskType.BWD, TaskType.BWD_W})
-
-    new_nodes: list[Task] = []
-
-    for ubid, bucket_nodes in _bucket_compute_tasks(rank_dag, zero_bucket_keys).items():
-        for compute_node in bucket_nodes:
-            t = compute_node.task_type
-            bid = compute_node.bucket_id
-
-            if t in param_only_types:
-                ag      = _zero_task(compute_node, bid, TaskType.ALL_GATHER,        ubid=ubid)
-                _add_data_edge(ag, compute_node)
-                ag.custom_metadata["zero_alloc_full_params_before"] = True
-                compute_node.custom_metadata["zero_free_full_params_after"] = True
-                new_nodes.append(ag)
-
-            elif t in grad_types:
-                ag      = _zero_task(compute_node, bid, TaskType.ALL_GATHER,        ubid=ubid)
-                _add_data_edge(ag, compute_node)
-                ag.custom_metadata["zero_alloc_full_params_before"] = True
-                compute_node.custom_metadata["zero_free_full_params_after"] = True
-                new_nodes.append(ag)
-
-    # Insert grad alloc/reduce/free for grad-producing ZeRO-3 nodes.
-    for ubid, bucket_nodes in _bucket_compute_tasks(rank_dag, zero_bucket_keys).items():
-        grad_nodes = [node for node in bucket_nodes if node.task_type in grad_types]
-        if not grad_nodes:
-            continue
-        first_compute = min(grad_nodes, key=lambda node: (node.time_step, node.uid))
-        last_compute = max(grad_nodes, key=lambda node: (node.time_step, node.uid))
-
-        if gradient_accumulation:
-            rs = _zero_task(last_compute, last_compute.bucket_id, TaskType.REDUCE_SCATTER, ubid=ubid)
-            _add_data_edge(last_compute, rs)
-            first_compute.custom_metadata["zero_alloc_full_grads_before"] = True
-            _append_metadata_ubid(rs, "zero_free_full_grads_after_ubids", ubid)
-            new_nodes.append(rs)
-        else:
-            for compute_node in grad_nodes:
-                rs = _zero_task(compute_node, compute_node.bucket_id, TaskType.REDUCE_SCATTER, ubid=ubid)
-                _add_data_edge(compute_node, rs)
-                compute_node.custom_metadata["zero_alloc_full_grads_before"] = True
-                _append_metadata_ubid(rs, "zero_free_full_grads_after_ubids", ubid)
-                new_nodes.append(rs)
-
-    rank_dag.nodes.extend(new_nodes)
-
-
-def _fuse_consecutive_zero3_param_sequences(
-    rank_dag: TaskDAG,
-    *,
-    zero_bucket_keys: set | None = None,
-) -> None:
-    """Fuse ZeRO runtime metadata across consecutive same-ubid compute runs."""
-
-    def _ordered_compute_nodes() -> list[Task]:
-        compute_types = {TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W}
-        selected: list[Task] = []
-        for node in rank_dag.nodes:
-            if node.task_type not in compute_types or node.unique_bucket_id is None:
-                continue
-            if zero_bucket_keys is not None:
-                if not node.batches:
-                    continue
-                stage_id = node.batches[0].stage_id
-                if (stage_id, node.bucket_id) not in zero_bucket_keys:
-                    continue
-            selected.append(node)
-
-        if not selected:
-            return []
-
-        selected_ids = {id(node) for node in selected}
-        in_degree: dict[int, int] = {id(node): 0 for node in selected}
-        succs: dict[int, list[Task]] = {id(node): [] for node in selected}
-        for node in selected:
-            for succ in node.temporal_succs:
-                if id(succ) not in selected_ids:
-                    continue
-                succs[id(node)].append(succ)
-                in_degree[id(succ)] += 1
-
-        ready = sorted(
-            [node for node in selected if in_degree[id(node)] == 0],
-            key=lambda node: (node.time_step, node.uid),
-        )
-        ordered: list[Task] = []
-        while ready:
-            node = ready.pop(0)
-            ordered.append(node)
-            newly_ready: list[Task] = []
-            for succ in sorted(succs[id(node)], key=lambda cur: (cur.time_step, cur.uid)):
-                succ_id = id(succ)
-                in_degree[succ_id] -= 1
-                if in_degree[succ_id] == 0:
-                    newly_ready.append(succ)
-            if newly_ready:
-                ready.extend(newly_ready)
-                ready.sort(key=lambda cur: (cur.time_step, cur.uid))
-
-        if len(ordered) != len(selected):
-            return sorted(selected, key=lambda node: (node.time_step, node.uid))
-        return ordered
-
-    ordered = _ordered_compute_nodes()
-    runs: list[list[Task]] = []
-    cur_run: list[Task] = []
-    for node in ordered:
-        if cur_run and node.unique_bucket_id == cur_run[-1].unique_bucket_id:
-            cur_run.append(node)
-            continue
-        if len(cur_run) > 1:
-            runs.append(cur_run)
-        cur_run = [node]
-    if len(cur_run) > 1:
-        runs.append(cur_run)
-
-    for run in runs:
-        ubid = run[0].unique_bucket_id
-        ag_nodes: list[Task] = []
-        for compute_node in run:
-            ag = _find_zero_neighbor(compute_node, TaskType.ALL_GATHER, preds=True, ubid=ubid)
-            if ag is None:
-                ag_nodes = []
-                break
-            ag_nodes.append(ag)
-        if ag_nodes:
-            for ag in ag_nodes[1:]:
-                ag.custom_metadata.pop("zero_alloc_full_params_before", None)
-            for compute_node in run[:-1]:
-                compute_node.custom_metadata.pop("zero_free_full_params_after", None)
-
-        grad_run = [node for node in run if node.task_type in {TaskType.BWD, TaskType.BWD_W}]
-        if not grad_run:
-            continue
-        rs_nodes: list[Task] = []
-        for compute_node in grad_run:
-            rs = _find_zero_neighbor(compute_node, TaskType.REDUCE_SCATTER, preds=False, ubid=ubid)
-            if rs is None:
-                rs_nodes = []
-                break
-            rs_nodes.append(rs)
-        if not rs_nodes:
-            continue
-        for compute_node in grad_run[1:]:
-            compute_node.custom_metadata.pop("zero_alloc_full_grads_before", None)
-        for rs in rs_nodes[:-1]:
-            ubids = rs.custom_metadata.get("zero_free_full_grads_after_ubids", [])
-            rs.custom_metadata["zero_free_full_grads_after_ubids"] = [
-                cur for cur in ubids if cur != ubid
-            ]
-            if not rs.custom_metadata["zero_free_full_grads_after_ubids"]:
-                rs.custom_metadata.pop("zero_free_full_grads_after_ubids", None)
-
-
-def _zero_sequence_compute_nodes(rank_dag: TaskDAG, zero_stage: int) -> list[Task]:
-    if zero_stage == 2:
-        compute_types = {TaskType.BWD, TaskType.BWD_W}
-    elif zero_stage == 3:
-        compute_types = {TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W}
-    else:
-        return []
-    return sorted(
-        [node for node in rank_dag.nodes if node.task_type in compute_types],
-        key=lambda node: (node.time_step, node.uid),
-    )
-
-
-def _find_zero_neighbor(node: Task, task_type: TaskType, *, preds: bool, ubid: int | None) -> Task | None:
-    neighbors = node.data_preds if preds else node.data_succs
-    for neighbor in neighbors:
-        if neighbor.task_type != task_type:
-            continue
-        if ubid is not None and neighbor.unique_bucket_id != ubid:
-            continue
-        return neighbor
-    return None
-
-
-def _collect_zero_sequence_nodes(compute_node: Task, zero_stage: int) -> dict[TaskType, Task]:
-    ubid = compute_node.unique_bucket_id
-    seq: dict[TaskType, Task] = {compute_node.task_type: compute_node}
-    if zero_stage == 2:
-        alloc_g = _find_zero_neighbor(compute_node, TaskType.ALLOC_FULL_GRADS, preds=True, ubid=ubid)
-        rs = _find_zero_neighbor(compute_node, TaskType.REDUCE_SCATTER, preds=False, ubid=ubid)
-        free_g = _find_zero_neighbor(rs, TaskType.FREE_FULL_GRADS, preds=False, ubid=ubid) if rs else None
-        if alloc_g is not None:
-            seq[TaskType.ALLOC_FULL_GRADS] = alloc_g
-        if rs is not None:
-            seq[TaskType.REDUCE_SCATTER] = rs
-        if free_g is not None:
-            seq[TaskType.FREE_FULL_GRADS] = free_g
-        return seq
-
-    alloc_p = _find_zero_neighbor(compute_node, TaskType.ALL_GATHER, preds=True, ubid=ubid)
-    if alloc_p is not None:
-        ag = alloc_p
-        alloc_p = _find_zero_neighbor(ag, TaskType.ALLOC_FULL_PARAMS, preds=True, ubid=ubid)
-        if alloc_p is not None:
-            seq[TaskType.ALLOC_FULL_PARAMS] = alloc_p
-        seq[TaskType.ALL_GATHER] = ag
-    free_p = _find_zero_neighbor(compute_node, TaskType.FREE_FULL_PARAMS, preds=False, ubid=ubid)
-    if free_p is not None:
-        seq[TaskType.FREE_FULL_PARAMS] = free_p
-    if compute_node.task_type in {TaskType.BWD, TaskType.BWD_W}:
-        alloc_g = _find_zero_neighbor(compute_node, TaskType.ALLOC_FULL_GRADS, preds=True, ubid=ubid)
-        rs = _find_zero_neighbor(compute_node, TaskType.REDUCE_SCATTER, preds=False, ubid=ubid)
-        free_g = _find_zero_neighbor(rs, TaskType.FREE_FULL_GRADS, preds=False, ubid=ubid) if rs else None
-        if alloc_g is not None:
-            seq[TaskType.ALLOC_FULL_GRADS] = alloc_g
-        if rs is not None:
-            seq[TaskType.REDUCE_SCATTER] = rs
-        if free_g is not None:
-            seq[TaskType.FREE_FULL_GRADS] = free_g
-    return seq
-
-
-def _reachable_downstream(node: Task) -> set[int]:
-    seen: set[int] = set()
-    stack = list(node.data_succs) + list(node.temporal_succs)
-    while stack:
-        cur = stack.pop()
-        cur_id = id(cur)
-        if cur_id in seen:
-            continue
-        seen.add(cur_id)
-        stack.extend(cur.data_succs)
-        stack.extend(cur.temporal_succs)
-    return seen
-
-
-def _wire_sync_to_upd(rank_dag: TaskDAG) -> None:
-    """Add temporal edges from every AR/RS node → UPD on this rank.
-
-    In the DP/ZeRO case the optimizer update must not start until all
-    gradient-synchronisation collectives have completed.  This pass wires
-    that constraint so that ``assign_time_steps`` Pass 3 can push UPD's
-    time_step past the last AR/RS.
-    """
-    upd_nodes  = [n for n in rank_dag.nodes if n.task_type == TaskType.UPD]
-    sync_nodes = [n for n in rank_dag.nodes
-                  if n.task_type in (TaskType.ALL_REDUCE, TaskType.REDUCE_SCATTER)]
-    for upd in upd_nodes:
-        for sync in sync_nodes:
-            if upd not in sync.temporal_succs:
-                sync.temporal_succs.append(upd)
-            if sync not in upd.temporal_preds:
-                upd.temporal_preds.append(sync)
-
-
-def insert_zero_ops(
-    per_rank_dags: list,
-    zero_stage: int,
-    zero_bucket_keys: set | None = None,
-    gradient_accumulation: bool = True,
-) -> list:
-    for rank_dag in per_rank_dags:
-        if zero_stage == 0:
-            _insert_zero1_ops(
-                rank_dag,
-                zero_bucket_keys=zero_bucket_keys,
-                gradient_accumulation=gradient_accumulation,
-            )
-        elif zero_stage == 1:
-            _insert_zero1_ops(
-                rank_dag,
-                zero_bucket_keys=zero_bucket_keys,
-                gradient_accumulation=gradient_accumulation,
-            )
-        elif zero_stage == 2:
-            _insert_zero2_ops(
-                rank_dag,
-                zero_bucket_keys=zero_bucket_keys,
-                gradient_accumulation=gradient_accumulation,
-            )
-        elif zero_stage == 3:
-            _insert_zero3_ops(
-                rank_dag,
-                zero_bucket_keys=zero_bucket_keys,
-                gradient_accumulation=gradient_accumulation,
-            )
-        else:
-            raise ValueError(f"Unsupported ZeRO stage: {zero_stage}")
-        _wire_sync_to_upd(rank_dag)
-    return per_rank_dags
-
-
-
-
-# ---------------------------------------------------------------------------
-# Graphviz visualisation
-# ---------------------------------------------------------------------------
-
-def _task_node_label(node: Task) -> str:
-    """Short human-readable label for a Task."""
-    task_type = node.task_type
-    batches = node.batches
-
-    if task_type in (TaskType.SEND, TaskType.RECV):
-        op = "SEND" if task_type == TaskType.SEND else "RECV"
-        return op
-
-    type_abbrev = {
-        TaskType.FWD:     "F",
-        TaskType.BWD:     "B",
-        TaskType.UPD:     "U",
-        TaskType.BWD_I:   "BI",
-        TaskType.BWD_W:   "Bw",
-        TaskType.FWD_BWD: "FB",
-        TaskType.ALL_REDUCE: "AR",
-        TaskType.REDUCE_SCATTER: "RS",
-        TaskType.ALL_GATHER: "AG",
-        TaskType.ALLOC_FULL_GRADS: "G+",
-        TaskType.FREE_FULL_GRADS: "G-",
-        TaskType.ALLOC_FULL_PARAMS: "P+",
-        TaskType.FREE_FULL_PARAMS: "P-",
-        TaskType.FWD_A2A: "F_A2A",
-        TaskType.BWD_A2A: "B_A2A",
-    }.get(task_type, "?")
-
-    if task_type == TaskType.UPD:
-        return type_abbrev
-
-    if task_type in (
-        TaskType.ALL_REDUCE,
-        TaskType.REDUCE_SCATTER,
-        TaskType.ALL_GATHER,
-        TaskType.ALLOC_FULL_GRADS,
-        TaskType.FREE_FULL_GRADS,
-        TaskType.ALLOC_FULL_PARAMS,
-        TaskType.FREE_FULL_PARAMS,
-    ):
-        return type_abbrev
-
-    if task_type in (TaskType.FWD_A2A, TaskType.BWD_A2A):
-        return type_abbrev
-
-    parts = " + ".join(f"S{b.stage_id} M{b.mb_idx}" for b in batches)
-    return f"{type_abbrev} {parts}"
-
-
-def compute_critical_path(dag: TaskDAG) -> set:
-    """Return the set of UIDs of nodes on the critical path of *dag*.
-
-    Uses both data-dependency and temporal edges.  Nodes whose ``runtime``
-    field is ``None`` are treated as having zero duration (so they appear on
-    the critical path only when they sit between two critical nodes).
-
-    Returns:
-        A ``set[int]`` of ``node.uid`` values for every critical node.
-    """
-    from collections import deque as _deque2
-
-    # Topological sort (Kahn's algorithm over both edge kinds).
-    in_deg = {n.uid: len(n.data_preds) + len(n.temporal_preds) for n in dag.nodes}
-    queue = _deque2(n for n in dag.nodes if in_deg[n.uid] == 0)
-    topo: list = []
-    while queue:
-        node = queue.popleft()
-        topo.append(node)
-        for s in list(node.data_succs) + list(node.temporal_succs):
-            in_deg[s.uid] -= 1
-            if in_deg[s.uid] == 0:
-                queue.append(s)
-
-    # Forward pass: EST[uid] = earliest start time.
-    est: dict = {}
-    for node in topo:
-        preds = list(node.data_preds) + list(node.temporal_preds)
-        est[node.uid] = max(
-            (est[p.uid] + (p.runtime or 0.0) for p in preds),
-            default=0.0,
-        )
-
-    makespan = max(est[n.uid] + (n.runtime or 0.0) for n in dag.nodes)
-
-    # Backward pass: LST[uid] = latest start time that doesn't extend makespan.
-    lst: dict = {}
-    for node in reversed(topo):
-        w = node.runtime or 0.0
-        succs = list(node.data_succs) + list(node.temporal_succs)
-        lst[node.uid] = (
-            min(lst[s.uid] for s in succs) - w if succs else makespan - w
-        )
-
-    _EPS = 1e-6
-    return {n.uid for n in dag.nodes if abs(lst[n.uid] - est[n.uid]) < _EPS}
-
-
-def visualize_dag(
-    dag: TaskDAG,
-    output_path: str = "dag",
-    fmt: str = "png",
-    critical_path_nodes: "set | None" = None,
-) -> None:
-    """Render a :class:`TaskDAG` as a labelled image using *graphviz*.
-
-    Nodes are coloured by pipeline rank.  Two edge styles:
-
-    * **Dashed grey** – temporal edges (serialisation within one actor).
-    * **Solid black** – data-dependency edges.
-
-    Args:
-        dag: The task DAG to render.
-        output_path: Path prefix for the output file (no extension).
-        fmt: Output format passed to graphviz (``"png"``, ``"svg"``, …).
-        critical_path_nodes: Optional set of ``node.uid`` integers.  Nodes
-            whose uid is in this set are outlined in red with a thick border.
-            If ``None`` no critical-path highlighting is applied.
-
-    The image is saved to ``{output_path}.{fmt}``.
-    If *graphviz* is not installed the function logs a warning and returns.
-    """
-    try:
-        import graphviz
-    except ImportError:
-        logger.warning("graphviz Python package not installed; skipping DAG visualisation")
-        return
-
-    if not dag.nodes:
-        logger.warning("Empty task DAG; nothing to visualise")
-        return
-
-    # Build a mapping from pp_rank -> sorted list of stage_ids on that rank.
-    pp_rank_stages: dict[int, list[int]] = {}
-    for n in dag.nodes:
-        if n.batches:
-            rank = n.task_pp_rank
-            sid = n.batches[0].stage_id
-            if rank not in pp_rank_stages:
-                pp_rank_stages[rank] = []
-            if sid not in pp_rank_stages[rank]:
-                pp_rank_stages[rank].append(sid)
-    for rank in pp_rank_stages:
-        pp_rank_stages[rank].sort()
-
-    _COMPUTE_TYPES = (
-        TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W, TaskType.FWD_BWD,
-    )
-
-    def _node_fill(node: Task) -> str:
-        t = node.task_type
-        if t in (
-            TaskType.SEND, TaskType.RECV, TaskType.FWD_A2A, TaskType.BWD_A2A,
-            TaskType.ALL_REDUCE, TaskType.REDUCE_SCATTER, TaskType.ALL_GATHER,
-            TaskType.ALLOC_FULL_GRADS, TaskType.FREE_FULL_GRADS,
-            TaskType.ALLOC_FULL_PARAMS, TaskType.FREE_FULL_PARAMS,
-        ):
-            return "#FFFFFF"
-        if t == TaskType.FWD:
-            return "#FFA500"  # orange
-        if t in (TaskType.BWD, TaskType.BWD_I):
-            return "#27AE60"  # green
-        if t == TaskType.BWD_W:
-            return "#2E86C1"  # blue
-        return "#D5D8DC"
-
-    def _node_fontcolor(node: Task) -> str:
-        t = node.task_type
-        if t not in _COMPUTE_TYPES or not node.batches:
-            return "black"
-        rank = node.task_pp_rank
-        stages = pp_rank_stages.get(rank, [])
-        if len(stages) < 2:
-            return "black"
-        sid = node.batches[0].stage_id
-        # First stage -> black text, second (and beyond) stage -> white text
-        return "black" if sid == stages[0] else "white"
-
-    # Compute topological order using Kahn's algorithm (data + temporal edges).
-    from collections import deque as _deque
-    _in_degree: dict[str, int] = {
-        n.node_id(): len(n.data_preds) + len(n.temporal_preds)
-        for n in dag.nodes
-    }
-    _topo_order: dict[str, int] = {}
-    _queue: _deque = _deque(n for n in dag.nodes if _in_degree[n.node_id()] == 0)
-    _step = 0
-    while _queue:
-        _n = _queue.popleft()
-        _topo_order[_n.node_id()] = _step
-        _step += 1
-        for _s in list(_n.data_succs) + list(_n.temporal_succs):
-            _in_degree[_s.node_id()] -= 1
-            if _in_degree[_s.node_id()] == 0:
-                _queue.append(_s)
-
-    dot = graphviz.Digraph("PiperDAG", comment="Piper Chunk DAG")
-    dot.attr(rankdir="LR", splines="ortho", nodesep="0.4", ranksep="0.6", fontname="Helvetica")
-    dot.attr("node", shape="box", style="filled", fontsize="9", fontname="Helvetica")
-    dot.attr("edge", fontsize="8", fontname="Helvetica")
-
-    for node in dag.nodes:
-        label = f"{node.time_step}\n{_task_node_label(node)}"
-        if node.runtime is not None:
-            label += f"\n{node.runtime:.1f}ms"
-        on_critical = critical_path_nodes is not None and node.uid in critical_path_nodes
-        dot.node(
-            node.node_id(),
-            label=label,
-            fillcolor=_node_fill(node),
-            fontcolor=_node_fontcolor(node),
-            tooltip=repr(node.source_chunk) if node.source_chunk is not None else node.task_type.value,
-            color="red" if on_critical else "black",
-            penwidth="3.0" if on_critical else "1.0",
-        )
-
-    # Group nodes by time_step so that tasks scheduled at the same step share
-    # a column (graphviz rank="same").
-    depth_to_nodes: dict[int, list[Task]] = defaultdict(list)
-    for node in dag.nodes:
-        depth_to_nodes[node.time_step].append(node)
-
-    for col_nodes in depth_to_nodes.values():
-        with dot.subgraph() as sub:
-            sub.attr(rank="same")
-            for node in col_nodes:
-                sub.node(node.node_id())
-
-    for node in dag.nodes:
-        for ts in node.temporal_succs:
-            key = (node.node_id(), ts.node_id())
-            dot.edge(
-                key[0], key[1],
-                style="dashed", color="grey60",
-                penwidth="1.0", arrowsize="0.6", constraint="true",
-            )
-
-    seen_edges: set[tuple[str, str]] = set()
-    for node in dag.nodes:
-        for succ in node.data_succs:
-            key = (node.node_id(), succ.node_id())
-            if key in seen_edges:
-                continue
-            seen_edges.add(key)
-            cross_rank = node.pp_rank != succ.pp_rank
-            dot.edge(
-                key[0], key[1],
-                color="black",
-                penwidth="2.0", arrowsize="0.8",
-                constraint="false" if cross_rank else "true",
-            )
-
-    out = dot.render(output_path, format=fmt, cleanup=False)
-    logger.info(f"DAG visualisation saved to {out}")
-
-
-
-def print_dag_order(
-    dag: TaskDAG,
-    label: str = "",
-    rank: int = 0,
-    out_dir: str = "out",
-) -> None:
-    """Write the execution order of nodes in a :class:`TaskDAG` to a file.
-
-    Nodes are sorted by :func:`runtime_sort_key`, exactly as in ``run_dag``,
-    so the output reflects exactly what the actor will execute.
-    Useful for debugging schedule issues.
-
-    Output is written to ``{out_dir}/dag_order_rank{rank}``.
-    """
-    import os
-
-    sorted_nodes = sorted(dag.nodes, key=runtime_sort_key)
-
-    header = f"--- DAG execution order{': ' + label if label else ''} ---"
-    lines = [header]
-    for step, node in enumerate(sorted_nodes):
-        ttype = node.task_type.value if node.task_type is not None else "?"
-        batches_str = ", ".join(
-            f"s{b.stage_id} mb{b.mb_idx}" for b in node.batches
-        ) if node.batches else ""
-        bkt = f" bkt={node.bucket_id}" if node.bucket_id else ""
-        ubid = f" ubid={node.unique_bucket_id}" if node.unique_bucket_id is not None else ""
-        line = f"  {step:3d}  ts={node.time_step:3d}  rank={node.pp_rank}  {ttype:<14s}  {batches_str}{bkt}{ubid}"
-        lines.append(line)
-        logger.info(line)
-    lines.append("-" * len(header))
-
-    for line in lines:
-        logger.debug(line)
-
-    os.makedirs(out_dir, exist_ok=True)
-    out_path = os.path.join(out_dir, f"dag_order_rank{rank}")
-    with open(out_path, "w") as f:
-        f.write("\n".join(lines) + "\n")
-
-
-# ---------------------------------------------------------------------------
-# bucket_parameters – FX graph transformation
-# ---------------------------------------------------------------------------
-
-def _iter_node_args(node: fx.Node):
-    """Yield every :class:`fx.Node` appearing in *node*'s args and kwargs."""
-    def _collect(obj):
-        if isinstance(obj, fx.Node):
-            yield obj
-        elif isinstance(obj, (list, tuple)):
-            for item in obj:
-                yield from _collect(item)
-        elif isinstance(obj, dict):
-            for v in obj.values():
-                yield from _collect(v)
-    yield from _collect(node.args)
-    yield from _collect(node.kwargs)
-
-
-def bucket_parameters(
-    gm: fx.GraphModule,
-    bucket_size_bytes: int = 25 * 1024 * 1024,
-) -> list[fx.GraphModule]:
-    """Split an FX GraphModule into sequential sub-modules based on parameter buckets.
-
-    Adjacent parameters (in graph order) are greedily grouped into buckets of
-    approximately *bucket_size_bytes* bytes.  If a parameter is used by compute
-    nodes that fall into more than one bucket's node-range, it is promoted to
-    its own singleton bucket so the graph can be cut cleanly.
-
-    The graph is then cut at bucket boundaries and each segment is wrapped in a
-    new :class:`fx.GraphModule`.  All original model inputs (``placeholder``
-    nodes) are re-created in every sub-module.  Intermediate activation tensors
-    that flow between segments become additional placeholder inputs on the
-    receiving module.
-
-    The modules are ordered so that the *i*-th module's outputs are the
-    additional inputs of the *(i+1)*-th module (appended after the original
-    model inputs), producing a pipeline that replicates the original graph.
-
-    Args:
-        gm: Source ``fx.GraphModule``.  ``node.meta["example_value"]`` is used
-            for parameter size if available; falls back to direct attribute
-            inspection.
-        bucket_size_bytes: Target bucket size in bytes (default 25 MB).
-
-    Returns:
-        A list of ``fx.GraphModule`` objects in execution order.  If there is
-        only one bucket (or no parameters), the list contains *gm* unchanged.
-    """
-    nodes = list(gm.graph.nodes)
-    node_idx: dict[fx.Node, int] = {nd: i for i, nd in enumerate(nodes)}
-
-    # ------------------------------------------------------------------ #
-    # Helpers                                                             #
-    # ------------------------------------------------------------------ #
-
-    def _is_param(nd: fx.Node) -> bool:
-        if nd.op != "get_attr":
-            return False
-        obj = gm
-        for part in nd.target.split("."):
-            obj = getattr(obj, part, None)
-            if obj is None:
-                return False
-        return isinstance(obj, torch.nn.Parameter)
-
-    def _attr_size(nd: fx.Node) -> int:
-        ev = nd.meta.get("example_value")
-        if ev is not None and hasattr(ev, "numel"):
-            return int(ev.numel() * ev.element_size())
-        obj = gm
-        for part in nd.target.split("."):
-            obj = getattr(obj, part, None)
-            if obj is None:
-                return 0
-        return int(obj.numel() * obj.element_size()) if isinstance(obj, torch.Tensor) else 0
-
-    # ------------------------------------------------------------------ #
-    # Step 1 – Collect parameter nodes                                   #
-    # ------------------------------------------------------------------ #
-    param_nodes: list[fx.Node] = [nd for nd in nodes if _is_param(nd)]
-    if not param_nodes:
-        return [gm]
-
-    # ------------------------------------------------------------------ #
-    # Step 2 – Per-parameter compute-node use range                      #
-    # ------------------------------------------------------------------ #
-    compute_set: frozenset[int] = frozenset(
-        i for i, nd in enumerate(nodes)
-        if nd.op not in ("placeholder", "get_attr", "output")
-    )
-
-    def _use_range(pnd: fx.Node) -> tuple[int, int]:
-        idxs = [node_idx[u] for u in pnd.users if node_idx[u] in compute_set]
-        if not idxs:
-            return (node_idx[pnd], node_idx[pnd])
-        return (min(idxs), max(idxs))
-
-    param_ranges: dict[fx.Node, tuple[int, int]] = {pn: _use_range(pn) for pn in param_nodes}
-
-    # ------------------------------------------------------------------ #
-    # Step 3 – Greedy bucket assignment                                  #
-    # ------------------------------------------------------------------ #
-    bucket_id: dict[fx.Node, int] = {}
-    cur_bucket = 0
-    cur_size = 0
-    for pn in param_nodes:
-        sz = _attr_size(pn)
-        if cur_size + sz > bucket_size_bytes and cur_size > 0:
-            cur_bucket += 1
-            cur_size = 0
-        bucket_id[pn] = cur_bucket
-        cur_size += sz
-    n_initial = cur_bucket + 1
-
-    # ------------------------------------------------------------------ #
-    # Step 4 – Singleton promotion for cross-bucket parameters           #
-    # ------------------------------------------------------------------ #
-    def _initial_bucket_range(bid: int) -> tuple[int, int]:
-        members = [pn for pn in param_nodes if bucket_id[pn] == bid]
-        if not members:
-            return (0, 0)
-        return (
-            min(param_ranges[pn][0] for pn in members),
-            max(param_ranges[pn][1] for pn in members),
-        )
-
-    init_ranges = [_initial_bucket_range(b) for b in range(n_initial)]
-
-    def _initial_bucket_at(idx: int) -> int:
-        for bid, (lo, hi) in enumerate(init_ranges):
-            if lo <= idx <= hi:
-                return bid
-        return 0
-
-    singleton_next = n_initial
-    for pn in param_nodes:
-        first, last = param_ranges[pn]
-        if _initial_bucket_at(first) != _initial_bucket_at(last):
-            bucket_id[pn] = singleton_next
-            singleton_next += 1
-
-    # ------------------------------------------------------------------ #
-    # Step 5 – Build final bucket list, sort, merge overlapping ranges  #
-    # ------------------------------------------------------------------ #
-    bucket_members: dict[int, list[fx.Node]] = defaultdict(list)
-    for pn in param_nodes:
-        bucket_members[bucket_id[pn]].append(pn)
-
-    def _bucket_use_range(members: list[fx.Node]) -> tuple[int, int]:
-        return (
-            min(param_ranges[pn][0] for pn in members),
-            max(param_ranges[pn][1] for pn in members),
-        )
-
-    bucket_list: list[tuple[int, int, list[fx.Node]]] = []
-    for bid, members in bucket_members.items():
-        f, l = _bucket_use_range(members)
-        bucket_list.append((f, l, members))
-    bucket_list.sort(key=lambda t: t[0])
-
-    merged: list[tuple[int, int, list[fx.Node]]] = []
-    for f, l, members in bucket_list:
-        if merged and f <= merged[-1][1]:
-            pf, pl, pm = merged[-1]
-            merged[-1] = (pf, max(pl, l), pm + members)
-        else:
-            merged.append((f, l, members))
-
-    n_segs = len(merged)
-    if n_segs == 1:
-        return [gm]
-
-    # ------------------------------------------------------------------ #
-    # Step 6 – Cut points (max last-use per bucket except the final one) #
-    # ------------------------------------------------------------------ #
-    # cut_after[i] is the node index after which we cut between seg i and i+1.
-    cut_after: list[int] = [merged[i][1] for i in range(n_segs - 1)]
-
-    # ------------------------------------------------------------------ #
-    # Step 7 – Assign each node to a segment                            #
-    # ------------------------------------------------------------------ #
-    def _seg_of(idx: int) -> int:
-        return sum(1 for c in cut_after if c < idx)
-
-    node_seg: dict[fx.Node, int] = {}
-    for nd in nodes:
-        if nd.op == "placeholder":
-            node_seg[nd] = 0
-        elif nd.op == "output":
-            node_seg[nd] = n_segs - 1
-        elif nd.op == "get_attr":
-            user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
-            node_seg[nd] = _seg_of(min(user_idxs)) if user_idxs else 0
-        else:
-            node_seg[nd] = _seg_of(node_idx[nd])
-
-    # ------------------------------------------------------------------ #
-    # Step 8 – Compute cross-segment activation sets                    #
-    # ------------------------------------------------------------------ #
-    # seg_cross_in[seg] = ordered list of compute nodes produced in segment
-    # < seg that are consumed by any segment >= seg.  Placeholders and
-    # get_attr nodes are excluded: placeholder (model input) tensors are
-    # assumed to be available in every segment directly; parameters never
-    # cross segments after singleton promotion and can be re-issued.
-    node_max_user_seg: dict[fx.Node, int] = {}
-    for nd in nodes:
-        if nd.op == "output":
-            continue
-        node_max_user_seg[nd] = (
-            max(node_seg[u] for u in nd.users) if nd.users else node_seg[nd]
-        )
-
-    seg_cross_in: list[list[fx.Node]] = [[] for _ in range(n_segs)]
-    for nd in nodes:
-        if nd.op in ("placeholder", "get_attr", "output"):
-            continue
-        s = node_seg[nd]
-        mu = node_max_user_seg[nd]
-        for seg in range(s + 1, mu + 1):
-            seg_cross_in[seg].append(nd)
-    # Lists are already in graph order since we iterate nodes in graph order.
-
-    # For each segment, determine which original placeholder nodes it uses
-    # directly (so we only add the placeholders each segment actually needs).
-    seg_placeholders: list[list[fx.Node]] = [[] for _ in range(n_segs)]
-    for nd in nodes:
-        if nd.op != "placeholder":
-            continue
-        for seg in range(n_segs):
-            if any(node_seg[u] == seg for u in nd.users):
-                seg_placeholders[seg].append(nd)
-
-    # ------------------------------------------------------------------ #
-    # Step 9 – Build sub-graphs                                          #
-    # ------------------------------------------------------------------ #
-    sub_graphs: list[fx.GraphModule] = []
-
-    for seg in range(n_segs):
-        sub_g = fx.Graph()
-        remap: dict[fx.Node, fx.Node] = {}
-
-        # Original model inputs used directly by this segment.
-        for nd in seg_placeholders[seg]:
-            new_ph = sub_g.placeholder(nd.name)
-            new_ph.type = nd.type
-            remap[nd] = new_ph
-
-        # Cross-segment activation inputs from earlier segments.
-        for orig in seg_cross_in[seg]:
-            new_ph = sub_g.placeholder(f"_xseg_{orig.name}")
-            new_ph.type = orig.type
-            remap[orig] = new_ph
-
-        # get_attr nodes whose attributes belong to this segment.
-        for nd in nodes:
-            if nd.op == "get_attr" and node_seg[nd] == seg:
-                new_ga = sub_g.get_attr(nd.target)
-                new_ga.type = nd.type
-                remap[nd] = new_ga
-
-        # Compute nodes for this segment (graph order preserved).
-        for nd in nodes:
-            if nd.op in ("placeholder", "get_attr", "output"):
-                continue
-            if node_seg[nd] != seg:
-                continue
-            new_nd = sub_g.node_copy(nd, arg_transform=lambda x, r=remap: r[x])
-            remap[nd] = new_nd
-
-        # Output node.
-        if seg == n_segs - 1:
-            orig_out = next(nd for nd in nodes if nd.op == "output")
-            # orig_out.args[0] is the actual return value (node, tuple of nodes, etc.)
-            sub_g.output(fx.map_arg(orig_out.args[0], lambda x: remap[x]))
-        else:
-            out_nodes = [remap[orig] for orig in seg_cross_in[seg + 1]]
-            sub_g.output(tuple(out_nodes) if len(out_nodes) != 1 else out_nodes[0])
-
-        sub_g.lint()
-        sub_graphs.append(fx.GraphModule(gm, sub_g))
-
-    return sub_graphs
-
-
-# ---------------------------------------------------------------------------
-# bucket_stage – stage-level parameter bucketing (placeholder params)
-# ---------------------------------------------------------------------------
-
-def bucket_stage(
-    stage_gm: fx.GraphModule,
-    graphargs: list,
-    input_idxs: list[int],
-    param_idxs: list[int],
-    bucket_size_bytes: int = 25 * 1024 * 1024,
-) -> list[tuple[fx.GraphModule, list[int], list[int], list]]:
-    """Split a stage GraphModule into per-parameter-bucket sub-modules.
-
-    Unlike :func:`bucket_parameters`, which handles ``get_attr`` parameter
-    nodes, this function handles stages produced by :func:`_split_gm_by_stages`
-    where trainable parameters are passed as **placeholder inputs** identified
-    by *param_idxs*.
-
-    Args:
-        stage_gm: The stage ``fx.GraphModule`` to split.
-        graphargs: Flat arg list for ``stage_gm.forward``; entries at
-            *param_idxs* are meta tensors, entries at *input_idxs* are None.
-        input_idxs: Positions of activation-input placeholders.
-        param_idxs: Positions of parameter placeholders.
-        bucket_size_bytes: Target bucket size (default 25 MB).
-
-    Returns:
-        A list of ``(bucket_gm, bucket_input_idxs, bucket_param_idxs,
-        bucket_graphargs)`` tuples in execution order.  Returns a
-        single-element list containing the original stage when no split is
-        needed.
-    """
-    nodes = list(stage_gm.graph.nodes)
-    node_idx: dict[fx.Node, int] = {nd: i for i, nd in enumerate(nodes)}
-    ph_nodes = [nd for nd in nodes if nd.op == "placeholder"]
-
-    param_ph_set = {ph_nodes[i] for i in param_idxs if i < len(ph_nodes)}
-    param_ph_list = [ph_nodes[i] for i in param_idxs if i < len(ph_nodes)]
-    input_ph_set  = {ph_nodes[i] for i in input_idxs  if i < len(ph_nodes)}
-
-    if not param_ph_list:
-        return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))]
-
-    compute_set: frozenset[int] = frozenset(
-        i for i, nd in enumerate(nodes)
-        if nd.op not in ("placeholder", "get_attr", "output")
-    )
-
-    def _use_range(pnd: fx.Node) -> tuple[int, int]:
-        idxs = [node_idx[u] for u in pnd.users if node_idx[u] in compute_set]
-        return (min(idxs), max(idxs)) if idxs else (node_idx[pnd], node_idx[pnd])
-
-    def _size(pnd: fx.Node) -> int:
-        i = ph_nodes.index(pnd)
-        if i < len(graphargs) and graphargs[i] is not None and hasattr(graphargs[i], "numel"):
-            return int(graphargs[i].numel() * graphargs[i].element_size())
-        ev = pnd.meta.get("example_value")
-        if ev is not None and hasattr(ev, "numel"):
-            return int(ev.numel() * ev.element_size())
-        return 0
-
-    param_ranges = {pn: _use_range(pn) for pn in param_ph_list}
-
-    # Greedy bucket assignment
-    bucket_id: dict[fx.Node, int] = {}
-    cur_b, cur_sz = 0, 0
-    for pn in param_ph_list:
-        sz = _size(pn)
-        if cur_sz + sz > bucket_size_bytes and cur_sz > 0:
-            cur_b += 1
-            cur_sz = 0
-        bucket_id[pn] = cur_b
-        cur_sz += sz
-    n_init = cur_b + 1
-
-    # Singleton promotion
-    def _init_range(b: int) -> tuple[int, int]:
-        ms = [pn for pn in param_ph_list if bucket_id[pn] == b]
-        if not ms:
-            return (0, 0)
-        return (min(param_ranges[pn][0] for pn in ms), max(param_ranges[pn][1] for pn in ms))
-
-    init_ranges = [_init_range(b) for b in range(n_init)]
-
-    def _bucket_at(idx: int) -> int:
-        for b, (lo, hi) in enumerate(init_ranges):
-            if lo <= idx <= hi:
-                return b
-        return 0
-
-    sn = n_init
-    for pn in param_ph_list:
-        f, l = param_ranges[pn]
-        if _bucket_at(f) != _bucket_at(l):
-            bucket_id[pn] = sn
-            sn += 1
-
-    # Build bucket list, sort, merge overlapping ranges
-    bm: dict[int, list[fx.Node]] = defaultdict(list)
-    for pn in param_ph_list:
-        bm[bucket_id[pn]].append(pn)
-
-    blist: list[tuple[int, int, list[fx.Node]]] = []
-    for members in bm.values():
-        f = min(param_ranges[pn][0] for pn in members)
-        l = max(param_ranges[pn][1] for pn in members)
-        blist.append((f, l, members))
-    blist.sort(key=lambda t: t[0])
-
-    merged: list[tuple[int, int, list[fx.Node]]] = []
-    for f, l, ms in blist:
-        if merged and f <= merged[-1][1]:
-            pf, pl, pm = merged[-1]
-            merged[-1] = (pf, max(pl, l), pm + ms)
-        else:
-            merged.append((f, l, ms))
-
-    alias_methods = {
-        "view", "_unsafe_view", "reshape", "transpose", "permute", "t",
-        "movedim", "moveaxis", "swapdims", "swapaxes",
-        "select", "narrow", "slice", "split", "chunk", "unbind",
-        "unsqueeze", "squeeze", "flatten", "expand", "diagonal",
-        "detach", "alias", "as_strided",
-    }
-    multi_output_alias_methods = {"split", "chunk", "unbind"}
-
-    alias_function_names = {
-        "view", "_unsafe_view", "reshape", "transpose", "permute", "t",
-        "movedim", "moveaxis", "swapdims", "swapaxes",
-        "select", "narrow", "slice", "split", "chunk", "unbind",
-        "unsqueeze", "squeeze", "flatten", "expand", "diagonal",
-        "detach", "alias", "as_strided",
-    }
-    multi_output_alias_function_names = {"split", "chunk", "unbind"}
-    hard_forbid_cross_bucket_prefixes = ("bfloat16_",)
+    alias_function_names = {
+        "view", "_unsafe_view", "reshape", "transpose", "permute", "t",
+        "movedim", "moveaxis", "swapdims", "swapaxes",
+        "select", "narrow", "slice", "split", "chunk", "unbind",
+        "unsqueeze", "squeeze", "flatten", "expand", "diagonal",
+        "detach", "alias", "as_strided",
+    }
+    multi_output_alias_function_names = {"split", "chunk", "unbind"}
+    hard_forbid_cross_bucket_prefixes = ("bfloat16_",)
 
     def _target_name(target: object) -> str | None:
         if isinstance(target, str):
@@ -3173,12 +1243,13 @@ def _seg_of(idx: int) -> int:
 
         seg_cross_in: list[list[fx.Node]] = [[] for _ in range(len(seg_ranges))]
         for nd in nodes:
-            if nd.op in ("get_attr", "output"):
-                continue
-            if nd.op == "placeholder" and nd in param_ph_set:
+            if nd.op == "output":
                 continue
             s = node_seg[nd]
             mu = node_max_user_seg[nd]
+            # Only values that cross a segment boundary must be forwarded.
+            if mu <= s:
+                continue
             for seg in range(s + 1, mu + 1):
                 seg_cross_in[seg].append(nd)
 
@@ -3209,6 +1280,8 @@ def _seg_of(idx: int) -> int:
         if not merged_boundary:
             break
 
+    _log_bucket_plan("final", merged)
+
     n_segs = len(merged)
     if n_segs == 1:
         return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))]
@@ -3299,231 +1372,6 @@ def _add_ph(nd: fx.Node, name: str, is_input: bool) -> None:
     return results
 
 
-def apply_activation_checkpointing(
-    stage_gm: fx.GraphModule,
-    graphargs: list,
-    input_idxs: list[int],
-    param_idxs: list[int],
-    num_checkpoints: int,
-) -> list[fx.GraphModule]:
-    """Split a bucket into sequential activation-checkpoint regions.
-
-    The regions are chosen to be roughly balanced by parameter bytes. Live
-    intermediate tensors can flow between adjacent regions as needed, so a
-    later region may receive and forward multiple carried tensors.
-    """
-    num_checkpoints = max(1, int(num_checkpoints))
-    if num_checkpoints <= 1:
-        return [stage_gm]
-    gm_name = getattr(stage_gm, "_get_name", lambda: type(stage_gm).__name__)()
-
-    nodes = list(stage_gm.graph.nodes)
-    node_idx: dict[fx.Node, int] = {nd: i for i, nd in enumerate(nodes)}
-    ph_nodes = [nd for nd in nodes if nd.op == "placeholder"]
-    param_ph_set = {ph_nodes[i] for i in param_idxs if i < len(ph_nodes)}
-    param_ph_list = [ph_nodes[i] for i in param_idxs if i < len(ph_nodes)]
-    input_ph_set = {ph_nodes[i] for i in input_idxs if i < len(ph_nodes)}
-    compute_nodes = [nd for nd in nodes if nd.op not in ("placeholder", "get_attr", "output")]
-
-    if not param_ph_list or len(compute_nodes) < 2:
-        logger.warning(
-            f"[ac_split_skip] gm={gm_name} requested_subgraphs={num_checkpoints} "
-            f"params={len(param_ph_list)} compute_nodes={len(compute_nodes)} "
-            "reason=insufficient_params_or_compute"
-        )
-        return [stage_gm]
-
-    compute_set: frozenset[int] = frozenset(node_idx[nd] for nd in compute_nodes)
-
-    def _tensor_meta(nd: fx.Node):
-        ev = nd.meta.get("example_value")
-        if ev is None:
-            ev = nd.meta.get("val")
-        if isinstance(ev, torch.Tensor):
-            return ev
-        return None
-
-    def _param_size(pnd: fx.Node) -> int:
-        i = ph_nodes.index(pnd)
-        if i < len(graphargs) and graphargs[i] is not None and hasattr(graphargs[i], "numel"):
-            return int(graphargs[i].numel() * graphargs[i].element_size())
-        ev = _tensor_meta(pnd)
-        if ev is not None:
-            return int(ev.numel() * ev.element_size())
-        return 0
-
-    def _use_indices(pnd: fx.Node) -> list[int]:
-        return [node_idx[u] for u in pnd.users if node_idx[u] in compute_set]
-
-    param_first_use: dict[fx.Node, int] = {}
-    for pn in param_ph_list:
-        uses = _use_indices(pn)
-        param_first_use[pn] = min(uses) if uses else node_idx[pn]
-
-    param_sizes = {pn: _param_size(pn) for pn in param_ph_list}
-    total_param_bytes = sum(param_sizes.values())
-    if total_param_bytes <= 0:
-        logger.warning(
-            f"[ac_split_skip] gm={gm_name} requested_subgraphs={num_checkpoints} "
-            f"params={len(param_ph_list)} compute_nodes={len(compute_nodes)} "
-            "reason=nonpositive_param_bytes"
-        )
-        return [stage_gm]
-
-    sorted_params = sorted(param_ph_list, key=lambda pn: param_first_use[pn])
-    running_bytes = 0
-    next_param = 0
-
-    candidate_cuts: list[tuple[int, int]] = []
-    for cut_node in compute_nodes[:-1]:
-        cut_idx = node_idx[cut_node]
-        while next_param < len(sorted_params) and param_first_use[sorted_params[next_param]] <= cut_idx:
-            running_bytes += param_sizes[sorted_params[next_param]]
-            next_param += 1
-        candidate_cuts.append((cut_idx, running_bytes))
-
-    if len(candidate_cuts) < num_checkpoints - 1:
-        logger.warning(
-            f"[ac_split_reject] gm={gm_name} requested_subgraphs={num_checkpoints} "
-            f"params={len(param_ph_list)} compute_nodes={len(compute_nodes)} "
-            f"candidate_cuts={len(candidate_cuts)} required_cuts={num_checkpoints - 1} "
-            "reason=insufficient_compute_cuts"
-        )
-        return [stage_gm]
-
-    selected_cuts: list[int] = []
-    start = 0
-    for split_idx in range(1, num_checkpoints):
-        target = total_param_bytes * split_idx / num_checkpoints
-        remaining = (num_checkpoints - 1) - split_idx
-        eligible = candidate_cuts[start: len(candidate_cuts) - remaining]
-        if not eligible:
-            logger.warning(
-                f"[ac_split_reject] gm={gm_name} requested_subgraphs={num_checkpoints} "
-                f"candidate_cuts={len(candidate_cuts)} chosen_cuts={len(selected_cuts)} "
-                f"failed_pick={split_idx}/{num_checkpoints - 1} reason=no_eligible_cut"
-            )
-            return [stage_gm]
-        best_rel = min(
-            range(len(eligible)),
-            key=lambda rel: abs(eligible[rel][1] - target),
-        )
-        chosen = eligible[best_rel]
-        selected_cuts.append(chosen[0])
-        start += best_rel + 1
-
-    n_segs = num_checkpoints
-
-    def _seg_of(idx: int) -> int:
-        return sum(1 for cut in selected_cuts if cut < idx)
-
-    node_seg: dict[fx.Node, int] = {}
-    for nd in nodes:
-        if nd.op == "output":
-            node_seg[nd] = n_segs - 1
-        elif nd.op == "placeholder":
-            if nd in param_ph_set:
-                uses = _use_indices(nd)
-                node_seg[nd] = _seg_of(min(uses)) if uses else 0
-            else:
-                user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
-                node_seg[nd] = _seg_of(min(user_idxs)) if user_idxs else 0
-        elif nd.op == "get_attr":
-            user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
-            node_seg[nd] = _seg_of(min(user_idxs)) if user_idxs else 0
-        else:
-            node_seg[nd] = _seg_of(node_idx[nd])
-
-    def _consumer_seg(user: fx.Node) -> int:
-        if user.op == "output":
-            return n_segs - 1
-        return node_seg[user]
-
-    seg_live_inputs: list[list[fx.Node]] = [[] for _ in range(n_segs)]
-    for seg in range(1, n_segs):
-        live_nodes: list[fx.Node] = []
-        seen: set[fx.Node] = set()
-        for nd in compute_nodes:
-            if node_seg[nd] >= seg:
-                continue
-            if any(_consumer_seg(user) >= seg for user in nd.users):
-                if nd not in seen:
-                    seen.add(nd)
-                    live_nodes.append(nd)
-        seg_live_inputs[seg] = live_nodes
-
-    seg_placeholder_inputs: list[list[fx.Node]] = [[] for _ in range(n_segs)]
-    for nd in nodes:
-        if nd.op != "placeholder" or nd in param_ph_set:
-            continue
-        for seg in range(n_segs):
-            if any(node_seg[u] == seg for u in nd.users):
-                seg_placeholder_inputs[seg].append(nd)
-
-    results: list[fx.GraphModule] = []
-    for seg in range(n_segs):
-        sub_g = fx.Graph()
-        remap: dict[fx.Node, fx.Node] = {}
-
-        if seg > 0:
-            for live_idx, boundary_nd in enumerate(seg_live_inputs[seg]):
-                new_ph = sub_g.placeholder(f"_ac_in_{live_idx}")
-                new_ph.type = boundary_nd.type
-                remap[boundary_nd] = new_ph
-
-        for nd in seg_placeholder_inputs[seg]:
-            new_ph = sub_g.placeholder(nd.name)
-            new_ph.type = nd.type
-            remap[nd] = new_ph
-
-        for nd in nodes:
-            if nd.op == "placeholder" and nd in param_ph_set and node_seg[nd] == seg:
-                new_ph = sub_g.placeholder(nd.name)
-                new_ph.type = nd.type
-                remap[nd] = new_ph
-
-        for nd in nodes:
-            if nd.op == "get_attr" and node_seg[nd] == seg:
-                new_ga = sub_g.get_attr(nd.target)
-                new_ga.type = nd.type
-                remap[nd] = new_ga
-
-        try:
-            for nd in nodes:
-                if nd.op in ("placeholder", "get_attr", "output"):
-                    continue
-                if node_seg[nd] != seg:
-                    continue
-                remap[nd] = sub_g.node_copy(nd, arg_transform=lambda x, r=remap: r[x])
-        except KeyError as exc:
-            logger.warning(
-                f"[ac_split_reject] gm={gm_name} requested_subgraphs={num_checkpoints} "
-                f"segment={seg} missing_node={exc} reason=non_local_tensor_during_lowering"
-            )
-            return [stage_gm]
-
-        if seg == n_segs - 1:
-            orig_out = next(nd for nd in nodes if nd.op == "output")
-            sub_g.output(fx.map_arg(orig_out.args[0], lambda x: remap[x]))
-        else:
-            live_outputs = [remap[nd] for nd in seg_live_inputs[seg + 1]]
-            if len(live_outputs) == 1:
-                sub_g.output(live_outputs[0])
-            else:
-                sub_g.output(tuple(live_outputs))
-
-        sub_g.lint()
-        results.append(fx.GraphModule(stage_gm, sub_g))
-
-    logger.debug(
-        f"[ac_split_accept] gm={gm_name} requested_subgraphs={num_checkpoints} "
-        f"actual_subgraphs={len(results)} candidate_cuts={len(candidate_cuts)} "
-        f"selected_cuts={selected_cuts} "
-        f"live_counts={[len(seg_live_inputs[i]) for i in range(1, n_segs)]}"
-    )
-    return results
-
-
 # ---------------------------------------------------------------------------
 # split_by_a2a: split an FX GraphModule at all-to-all annotation boundaries
 # ---------------------------------------------------------------------------
@@ -3596,15 +1444,6 @@ def split_by_a2a(
     if not annotated_nodes:
         return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))], []
 
-    logger.log(VERBOSE,
-        f"split_by_a2a: found {len(annotated_nodes)} annotated nodes:\n"
-        + "\n".join(
-            f"  [{i}] pos={idx} name={nd.name!r} "
-            f"key={'...' + str(ak)[-60:]}"
-            for i, (idx, nd, ak, _) in enumerate(annotated_nodes)
-        )
-    )
-
     # Group CONSECUTIVE annotated nodes with the same annotation_key into blocks,
     # but only if they are graph-adjacent (no other compute nodes between them).
     # Adjacency is required to handle models where all A2A annotations share the
@@ -3636,16 +1475,6 @@ def split_by_a2a(
         })
     n_boundaries = len(blocks)
 
-    logger.log(VERBOSE,
-        f"split_by_a2a: grouped into {n_boundaries} consecutive blocks:\n"
-        + "\n".join(
-            f"  block[{i}]: {len(b['nodes'])} nodes "
-            f"[{', '.join(nd.name for _, nd in b['nodes'])}] "
-            f"reshape={b['reshape']}"
-            for i, b in enumerate(blocks)
-        )
-    )
-
     if n_boundaries % 2 != 0:
         raise ValueError(
             f"split_by_a2a: expected an even number of A2A annotation blocks "
@@ -3835,300 +1664,20 @@ def _add_ph(nd: fx.Node, name: str, is_input: bool) -> None:
                     f"(segment {seg}) is not in seg_cross_in[{seg + 1}]; "
                     f"seg_cross_in = {[n.name for n in cross_out_list]}"
                 )
+            boundary_custom = boundary_nd.meta.get("custom") if hasattr(boundary_nd, "meta") else None
+            boundary_name = (
+                boundary_custom.get("name")
+                if isinstance(boundary_custom, dict)
+                else None
+            )
             boundary_infos.append({
                 "tensor_idx": tensor_idx,
                 "reshape_input": reshape_input,
                 "reshape_output": reshape_output,
+                "name": boundary_name,
             })
 
         sub_g.lint()
         results.append((fx.GraphModule(stage_gm, sub_g), new_input_idxs, new_param_idxs, new_graphargs))
 
     return results, boundary_infos
-
-
-# ---------------------------------------------------------------------------
-# get_overlappable_tasks / overlap_chunks
-# ---------------------------------------------------------------------------
-
-def get_overlappable_tasks(schedule: PipelineSchedule) -> list[tuple[Chunk, Chunk]]:
-    """Return one ``(FWD, BWD)`` pair for each ``FWD_BWD`` schedule cell."""
-    result: list[tuple[Chunk, Chunk]] = []
-    for row in schedule.grid:
-        for chunk in row:
-            if chunk.type != TaskType.FWD_BWD or len(chunk.batches) != 2:
-                continue
-            fwd_batch, bwd_batch = chunk.batches
-            result.append((
-                Chunk(pp_rank=chunk.pp_rank, batches=[fwd_batch], type=TaskType.FWD),
-                Chunk(pp_rank=chunk.pp_rank, batches=[bwd_batch], type=TaskType.BWD),
-            ))
-    return result
-
-
-def _task_matches_chunk(task: Task, chunk: Chunk) -> bool:
-    return (
-        task.task_type != TaskType.UPD
-        and task.associated_chunk == chunk
-    )
-
-
-def _tasks_for_chunk(rank_dag: TaskDAG, chunk: Chunk) -> list[Task]:
-    tasks = [node for node in rank_dag.nodes if _task_matches_chunk(node, chunk)]
-    if not tasks:
-        raise ValueError(f"No DAG tasks found for chunk descriptor {chunk}")
-    return tasks
-
-
-def _chunk_head_tasks(tasks: list[Task]) -> list[Task]:
-    task_ids = {id(task) for task in tasks}
-    return [
-        task for task in tasks
-        if not any(id(pred) in task_ids for pred in list(task.data_preds) + list(task.temporal_preds))
-    ]
-
-
-def _local_descendant_counts(tasks: list[Task]) -> dict[int, set[int]]:
-    task_ids = {id(task) for task in tasks}
-    descendants: dict[int, set[int]] = {}
-    for task in tasks:
-        seen: set[int] = set()
-        stack = [
-            succ for succ in list(task.data_succs) + list(task.temporal_succs)
-            if id(succ) in task_ids
-        ]
-        while stack:
-            cur = stack.pop()
-            cur_id = id(cur)
-            if cur_id in seen:
-                continue
-            seen.add(cur_id)
-            stack.extend(
-                succ for succ in list(cur.data_succs) + list(cur.temporal_succs)
-                if id(succ) in task_ids
-            )
-        descendants[id(task)] = seen
-    return descendants
-
-
-def _overlap_ready_priority(task: Task) -> tuple[int, int, int]:
-    """Tie-break ready tasks during overlap scheduling.
-
-    Lower tuples win. SEND/RECV take priority over collective comm tasks when
-    downstream counts tie, then fall back to stable task construction order.
-    """
-    op_priority = {
-        TaskType.SEND: 0,
-        TaskType.RECV: 0,
-        TaskType.ALL_GATHER: 1,
-        TaskType.REDUCE_SCATTER: 1,
-        TaskType.ALL_REDUCE: 1,
-    }.get(task.task_type, 2)
-    return (op_priority, task.uid)
-
-
-def overlap_chunks(rank_dag: TaskDAG, chunk_pairs: list[tuple[Chunk, Chunk]]) -> None:
-    """Overlap the tasks belonging to each ``(FWD, BWD)`` chunk pair in-place."""
-    for first_chunk, second_chunk in chunk_pairs:
-        first_tasks = _tasks_for_chunk(rank_dag, first_chunk)
-        second_tasks = _tasks_for_chunk(rank_dag, second_chunk)
-        for task in first_tasks:
-            task.compute_stream_id = "comp_stream"
-        for task in second_tasks:
-            task.compute_stream_id = "overlapped_comp_stream"
-        first_ids = {id(task) for task in first_tasks}
-        second_ids = {id(task) for task in second_tasks}
-        pair_ids = first_ids | second_ids
-
-        cross_temporal = [
-            (src, dst)
-            for src in first_tasks
-            for dst in src.temporal_succs
-            if id(dst) in second_ids
-        ]
-        if len(cross_temporal) != 1:
-            raise ValueError(
-                f"Expected exactly one temporal edge between overlapped chunks "
-                f"{first_chunk} and {second_chunk}, found {len(cross_temporal)}"
-            )
-        _remove_temporal_edge(*cross_temporal[0])
-
-        all_tasks = first_tasks + second_tasks
-        descendants = {
-            0: _local_descendant_counts(first_tasks),
-            1: _local_descendant_counts(second_tasks),
-        }
-        chunk_index = {id(task): 0 for task in first_tasks}
-        chunk_index.update({id(task): 1 for task in second_tasks})
-        assigned: set[int] = set()
-        ready = _chunk_head_tasks(first_tasks) + _chunk_head_tasks(second_tasks)
-        ready_ids = {id(task) for task in ready}
-        resource_bins: dict[str, list[Task]] = defaultdict(list)
-        next_chunk_idx = 0
-
-        def _is_locally_ready(task: Task) -> bool:
-            preds = [
-                pred for pred in list(task.data_preds) + list(task.temporal_preds)
-                if id(pred) in pair_ids
-            ]
-            return all(id(pred) in assigned for pred in preds)
-
-        def _remaining_downstream(task: Task) -> int:
-            task_chunk = chunk_index[id(task)]
-            return sum(1 for desc_id in descendants[task_chunk][id(task)] if desc_id not in assigned)
-
-        while len(assigned) < len(all_tasks):
-            ready = [task for task in ready if id(task) not in assigned and _is_locally_ready(task)]
-            if not ready:
-                raise ValueError(
-                    f"No ready tasks remain while overlapping chunks {first_chunk} and {second_chunk}"
-                )
-
-            best_remaining = max(_remaining_downstream(task) for task in ready)
-            candidates = [task for task in ready if _remaining_downstream(task) == best_remaining]
-            preferred = [task for task in candidates if chunk_index[id(task)] == next_chunk_idx]
-            current = min(preferred or candidates, key=_overlap_ready_priority)
-
-            assigned.add(id(current))
-            ready_ids.discard(id(current))
-            resource_bins[current.resource].append(current)
-            next_chunk_idx = 1 - chunk_index[id(current)]
-
-            for succ in list(current.data_succs) + list(current.temporal_succs):
-                succ_id = id(succ)
-                if succ_id not in pair_ids or succ_id in assigned or succ_id in ready_ids:
-                    continue
-                if _is_locally_ready(succ):
-                    ready.append(succ)
-                    ready_ids.add(succ_id)
-
-        for resource_tasks in resource_bins.values():
-            for src, dst in zip(resource_tasks, resource_tasks[1:]):
-                _add_temporal_edge(src, dst)
-
-        compute_tasks = resource_bins.get("compute_stream", [])
-        if not compute_tasks:
-            continue
-
-        first_compute = compute_tasks[0]
-        last_compute = compute_tasks[-1]
-
-        for task in compute_tasks:
-            if task is not first_compute:
-                external_preds = [pred for pred in list(task.temporal_preds) if id(pred) not in pair_ids]
-                for pred in external_preds:
-                    _remove_temporal_edge(pred, task)
-                    _add_temporal_edge(pred, first_compute)
-
-            if task is not last_compute:
-                external_succs = [succ for succ in list(task.temporal_succs) if id(succ) not in pair_ids]
-                for succ in external_succs:
-                    _remove_temporal_edge(task, succ)
-                    _add_temporal_edge(last_compute, succ)
-
-# ---------------------------------------------------------------------------
-# Assign time_step from temporal chain position
-# ---------------------------------------------------------------------------
-
-def assign_time_steps(dag: TaskDAG) -> None:
-    """Assign ``time_step`` to every node in two passes.
-
-    **Pass 1** – Kahn's toposort over *temporal* edges, restricted to
-    compute / A2A task types (F / B / B_I / B_W / FWD_BWD / F_A2A /
-    B_A2A / UPD).  Non-compute nodes do not contribute to the temporal
-    ordering and are skipped.
-
-    **Pass 2** – assign time steps to the remaining nodes via their
-    direct data-edge neighbours, using BFS propagation from the compute
-    nodes so that chains of non-compute nodes (e.g. P+ → AG → compute
-    or compute → RS → G- → P-) are resolved correctly:
-
-    * RECV / P+ (ALLOC_FULL_PARAMS) / G+ (ALLOC_FULL_GRADS) /
-      AG (ALL_GATHER) → ``direct_data_succ.time_step - 1``
-    * SEND / RS (REDUCE_SCATTER) / AR (ALL_REDUCE) /
-      G- (FREE_FULL_GRADS) / P- (FREE_FULL_PARAMS)
-      → ``direct_data_pred.time_step + 1``
-
-    This function is idempotent and safe to call both on the full DAG
-    (before :func:`split_dag_by_rank`) and on individual per-rank DAGs
-    after :func:`insert_zero_ops` has added the ZeRO collective nodes.
-    """
-    _COMPUTE_TYPES = frozenset({
-        TaskType.FWD, TaskType.BWD, TaskType.BWD_I, TaskType.BWD_W,
-        TaskType.FWD_BWD, TaskType.FWD_A2A, TaskType.BWD_A2A, TaskType.UPD,
-    })
-    # Non-compute nodes that sit *before* their data successor: get ts - 1.
-    _DOWNSTREAM_MINUS_ONE = frozenset({
-        TaskType.RECV,
-        TaskType.ALLOC_FULL_PARAMS,
-        TaskType.ALLOC_FULL_GRADS,
-        TaskType.ALL_GATHER,
-    })
-    # Non-compute nodes that sit *after* their data predecessor: get ts + 1.
-    _UPSTREAM_PLUS_ONE = frozenset({
-        TaskType.SEND,
-        TaskType.REDUCE_SCATTER,
-        TaskType.ALL_REDUCE,
-        TaskType.FREE_FULL_GRADS,
-        TaskType.FREE_FULL_PARAMS,
-    })
-
-    # ---- Pass 1: toposort compute nodes via temporal edges ----
-    compute_nodes = [n for n in dag.nodes if n.task_type in _COMPUTE_TYPES]
-
-    in_degree: dict[int, int] = {
-        id(n): sum(1 for p in n.temporal_preds if p.task_type in _COMPUTE_TYPES)
-        for n in compute_nodes
-    }
-    ts_map: dict[int, int] = {}
-    queue: deque[Task] = deque()
-    for node in compute_nodes:
-        if in_degree[id(node)] == 0:
-            ts_map[id(node)] = 0
-            queue.append(node)
-
-    while queue:
-        node = queue.popleft()
-        node.time_step = ts_map[id(node)]
-        for succ in node.temporal_succs:
-            if succ.task_type not in _COMPUTE_TYPES:
-                continue
-            in_degree[id(succ)] -= 1
-            ts_map[id(succ)] = max(ts_map.get(id(succ), 0), node.time_step + 1)
-            if in_degree[id(succ)] == 0:
-                queue.append(succ)
-
-    # ---- Pass 2: BFS from compute nodes to assign non-compute nodes ----
-    # Forward BFS: compute → _UPSTREAM_PLUS_ONE successors (time_step + 1 per hop).
-    visited: set[int] = {id(n) for n in compute_nodes}
-    bfs: deque[Task] = deque(compute_nodes)
-    while bfs:
-        node = bfs.popleft()
-        for succ in node.data_succs:
-            if succ.task_type not in _UPSTREAM_PLUS_ONE or id(succ) in visited:
-                continue
-            visited.add(id(succ))
-            succ.time_step = node.time_step + 1
-            bfs.append(succ)
-
-    # Backward BFS: compute → _DOWNSTREAM_MINUS_ONE predecessors (time_step - 1 per hop).
-    visited2: set[int] = {id(n) for n in compute_nodes}
-    bfs2: deque[Task] = deque(compute_nodes)
-    while bfs2:
-        node = bfs2.popleft()
-        for pred in node.data_preds:
-            if pred.task_type not in _DOWNSTREAM_MINUS_ONE or id(pred) in visited2:
-                continue
-            visited2.add(id(pred))
-            pred.time_step = node.time_step - 1
-            bfs2.append(pred)
-
-    # ---- Pass 3: push UPD past any AR/RS temporal predecessors ----
-    # AR/RS nodes are in _UPSTREAM_PLUS_ONE (assigned in Pass 2, not Pass 1).
-    # If _wire_sync_to_upd added AR/RS → UPD temporal edges, UPD must land
-    # at least one step after the latest AR/RS.
-    for node in dag.nodes:
-        if node.task_type == TaskType.UPD:
-            for pred in node.temporal_preds:
-                if pred.task_type in _UPSTREAM_PLUS_ONE:
-                    node.time_step = max(node.time_step, pred.time_step + 1)
diff --git a/src/piper_schedule.py b/src/piper_schedule.py
new file mode 100644
index 0000000..cad00e6
--- /dev/null
+++ b/src/piper_schedule.py
@@ -0,0 +1,127 @@
+import json
+import os
+from typing import Any
+
+
+def load_schedule_directives(path: str) -> list[dict]:
+    with open(path, "r", encoding="utf-8") as f:
+        directives = json.load(f)
+    if not isinstance(directives, list):
+        raise ValueError(f"schedule directives file must contain a list, got: {type(directives)}")
+
+    for directive in directives:
+        if not isinstance(directive, dict):
+            continue
+        if "filter" in directive:
+            directive["filter"] = _normalize_filter_list(directive["filter"])
+        if "filters" in directive and isinstance(directive["filters"], list):
+            directive["filters"] = _normalize_order_filters(directive["filters"])
+        if directive.get("op") == "split" and directive.get("num_microbatches") == "__MBS__":
+            raise ValueError(
+                "split.num_microbatches must be encoded in the schedule JSON; "
+                "'__MBS__' is no longer supported"
+            )
+    return directives
+
+
+def load_schedule_info(path: str) -> dict[str, Any]:
+    return derive_schedule_info(load_schedule_directives(path), path)
+
+
+def derive_schedule_info(directives: list[dict], schedule_path: str) -> dict[str, Any]:
+    pp_to_devices: dict[int, list[int]] = {}
+    num_microbatches = None
+
+    for directive in directives:
+        if not isinstance(directive, dict):
+            continue
+        op = directive.get("op")
+        if op == "place":
+            pp_idx = _filter_value(directive.get("filter"), "PP")
+            devices = directive.get("devices", directive.get("device"))
+            if pp_idx is None or not isinstance(devices, list) or not devices:
+                raise ValueError(f"place directive must include PP filter and non-empty devices: {directive}")
+            pp_to_devices[int(pp_idx)] = [int(d) for d in devices]
+        elif op == "split":
+            n = int(directive.get("num_microbatches", 0))
+            if n <= 0:
+                raise ValueError(f"split directive requires num_microbatches > 0: {directive}")
+            if num_microbatches is not None and num_microbatches != n:
+                raise ValueError(
+                    f"multiple split directives disagree on num_microbatches: "
+                    f"{num_microbatches} vs {n}"
+                )
+            num_microbatches = n
+
+    if not pp_to_devices:
+        raise ValueError("schedule JSON must include place directives with PP filters")
+    pp_indices = sorted(pp_to_devices)
+    expected_pp = list(range(len(pp_indices)))
+    if pp_indices != expected_pp:
+        raise ValueError(f"PP indices must be contiguous from 0, got {pp_indices}")
+    device_counts = {len(devices) for devices in pp_to_devices.values()}
+    if len(device_counts) != 1:
+        raise ValueError(f"all PP place directives must use the same device count, got {pp_to_devices}")
+    device_keys = sorted({tuple(devices) for devices in pp_to_devices.values()})
+    if num_microbatches is None:
+        raise ValueError("schedule JSON must include a split directive with num_microbatches")
+
+    return {
+        "name": os.path.splitext(os.path.basename(schedule_path))[0],
+        "path": schedule_path,
+        "num_stages": len(pp_indices),
+        "pp_degree": len(device_keys),
+        "dp_degree": next(iter(device_counts)),
+        "num_microbatches": num_microbatches,
+    }
+
+
+def _normalize_filter_list(spec):
+    # JSON encodes tuples as lists; the schedule normalizer expects list[tuple[tag, value]].
+    if isinstance(spec, list):
+        out = []
+        for item in spec:
+            if (
+                isinstance(item, list)
+                and len(item) == 2
+                and isinstance(item[0], str)
+            ):
+                out.append((item[0], item[1]))
+            else:
+                out.append(item)
+        return out
+    return spec
+
+
+def _is_filter_list(spec):
+    return isinstance(spec, list) and all(
+        isinstance(item, list)
+        and len(item) == 2
+        and isinstance(item[0], str)
+        for item in spec
+    )
+
+
+def _normalize_order_filters(filters):
+    normalized = []
+    for item in filters:
+        if _is_filter_list(item):
+            normalized.append(_normalize_filter_list(item))
+        elif isinstance(item, list):
+            normalized.append([
+                _normalize_filter_list(f) if _is_filter_list(f) else f
+                for f in item
+            ])
+        else:
+            normalized.append(item)
+    return normalized
+
+
+def _filter_value(filter_spec, key: str):
+    if isinstance(filter_spec, dict):
+        return filter_spec.get(key)
+    if isinstance(filter_spec, list):
+        for item in filter_spec:
+            if isinstance(item, tuple) and len(item) == 2 and item[0] == key:
+                return item[1]
+    return None
diff --git a/src/piper_utils.py b/src/piper_utils.py
index bce9759..d410a0b 100644
--- a/src/piper_utils.py
+++ b/src/piper_utils.py
@@ -1,49 +1,17 @@
 import sys
-import queue
-import ray
 import torch
 import inspect
 import logging
 import json, importlib, operator
 import torch.fx as fx
-from collections import defaultdict
-from dataclasses import dataclass
-from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
-from typing import Any, Optional
+from typing import Optional
 
 LOG_LEVEL = "INFO"
 
-""" 
-Print the backward graph of a tensor
-"""
-
-def print_backward_graph(printer, tensor, prefix=""):
-    seen = set()
-    def _print(t, indent=0):
-        fn = t.grad_fn if hasattr(t, 'grad_fn') and t.grad_fn is not None else None
-        if fn is None:
-            printer(" " * indent + f"{prefix}Tensor: no grad_fn")
-            return
-        if fn in seen:
-            printer(" " * indent + f"{prefix}{type(fn).__name__} (recursive/ref)")
-            return
-        seen.add(fn)
-        printer(" " * indent + f"{prefix}{type(fn).__name__}")
-        for next_fn, _ in fn.next_functions:
-            if next_fn is not None and hasattr(next_fn, 'variable'):
-                printer(" " * (indent + 2) + f"{prefix}Variable: {type(next_fn.variable).__name__}")
-            elif next_fn is not None:
-                _print(type('Dummy', (), {'grad_fn': next_fn})(), indent + 2)
-            else:
-                printer(" " * (indent + 2) + f"{prefix}None")
-    _print(tensor, 0)
-
-
 """
 Logger utility
 """
 
-VERBOSE = 5
 def create_logger(name: str, log_level: str):
     match log_level:
         case "DEBUG":
@@ -54,8 +22,6 @@ def create_logger(name: str, log_level: str):
             log_level = logging.WARNING
         case "ERROR":
             log_level = logging.ERROR
-        case "VERBOSE":
-            log_level = VERBOSE
 
     logger = logging.getLogger(name)
     logger.setLevel(log_level)
@@ -70,264 +36,6 @@ def create_logger(name: str, log_level: str):
     return logger
 
 
-_CRITICAL_OPS     = frozenset({"fwd_p2p_recv", "fwd_p2p_send", "bwd_p2p_recv", "bwd_p2p_send", "fwd_a2a", "bwd_a2a"})
-_NON_CRITICAL_OPS = frozenset({"grad_allreduce", "grad_allreduce_naive"})
-
-_nccl_log = logging.getLogger("piper_nccl")
-
-
-class _DepGate:
-    """One-shot handoff of a CUDA end-event from a critical op to a non-critical op.
-
-    signal() is called by the critical op's after_kernel.
-    wait()   is called by the non-critical op's before_kernel.
-
-    wait() is intentionally non-blocking: if the critical op hasn't fired yet
-    (e.g. both are dispatched from the same CPU thread and the critical op comes
-    later in the schedule), enforcement is skipped rather than deadlocking.
-    Unmatched signals are drained by NcclOverlapDetector.reset_iteration().
-    """
-    __slots__ = ("_q",)
-
-    def __init__(self):
-        self._q: queue.Queue = queue.Queue()
-
-    def signal(self, end_event: "torch.cuda.Event") -> None:
-        self._q.put(end_event)
-
-    def wait(self, stream: "torch.cuda.Stream") -> None:
-        try:
-            event = self._q.get_nowait()
-            stream.wait_event(event)    # GPU-side dependency
-        except queue.Empty:
-            pass  # critical op not yet dispatched; skip enforcement for this call
-
-    def drain(self) -> None:
-        """Discard any unmatched signals left over from the previous iteration."""
-        while True:
-            try:
-                self._q.get_nowait()
-            except queue.Empty:
-                break
-
-
-@dataclass
-class _NcclKernelRecord:
-    kernel_name: str
-    stream_label: str
-    start_event: "torch.cuda.Event"
-    end_event: Optional["torch.cuda.Event"] = None
-    # GPU-side timestamps (ms from reference event); populated by find_overlaps()
-    t_start_ms: Optional[float] = None
-    t_end_ms: Optional[float] = None
-    # Position of this kernel in dispatch order on its stream; set by build_enforcement
-    stream_instance_idx: Optional[int] = None
-
-
-class NcclOverlapDetector:
-    """
-    Passively records GPU-side start/end events for every NCCL kernel while
-    monitoring is enabled, then performs a single retrospective overlap analysis
-    after the iteration completes.
-
-    No callbacks, no host functions, no GPU stalls.
-
-    Typical usage in a training loop:
-
-        # After warmup, enable monitoring for one iteration:
-        actor.enable_nccl_monitoring.remote()
-        losses = piper.step(batch)
-        actor.print_nccl_overlaps.remote()   # syncs GPU, analyses, prints, then disables
-
-    At each NCCL launch site (AFTER any stream.wait_stream / wait_event):
-        token = detector.before_kernel(cuda_stream, kernel_name, stream_label)
-        # ... launch the NCCL kernel ...
-        detector.after_kernel(cuda_stream, token)
-
-    reset_iteration() is called automatically at the end of every training iteration.
-    It clears records only when monitoring is disabled, so a monitored iteration's
-    records survive until print_nccl_overlaps() consumes them.
-    """
-
-    def __init__(self):
-        self._records: list[_NcclKernelRecord] = []
-        self._ref_event: Optional["torch.cuda.Event"] = None
-        self.enabled: bool = False
-        # Enforcement — keyed by (stream_label, stream_instance_idx)
-        self._signal_gates: dict[tuple[str, int], list[_DepGate]] = {}
-        self._wait_gates:   dict[tuple[str, int], list[_DepGate]] = {}
-        self._per_stream_counter: dict[str, int] = {}  # reset each iteration
-        self._enforcement_active: bool = False
-
-    def enable(self) -> None:
-        """Enable monitoring and reset any previous records."""
-        self._records.clear()
-        self._ref_event = torch.cuda.Event(enable_timing=True)
-        self._ref_event.record()
-        self.enabled = True
-
-    def disable(self) -> None:
-        self.enabled = False
-
-    def before_kernel(
-        self,
-        cuda_stream: "torch.cuda.Stream",
-        kernel_name: str,
-        stream_label: str,
-    ) -> str:
-        """Record start_event on cuda_stream and return an opaque token.
-
-        Must be called AFTER any stream.wait_stream() / stream.wait_event() so the
-        event is enqueued only once GPU-side dependencies have resolved, giving an
-        accurate kernel-start timestamp.
-        """
-        # Enforcement: compute per-stream instance index and wait on any gates for this slot
-        instance_idx = -1
-        if self._enforcement_active:
-            instance_idx = self._per_stream_counter.get(stream_label, 0)
-            for gate in self._wait_gates.get((stream_label, instance_idx), []):
-                gate.wait(cuda_stream)
-            self._per_stream_counter[stream_label] = instance_idx + 1
-
-        if not self.enabled:
-            # Enforcement-only: encode (stream_label, instance_idx) for after_kernel
-            return f"E:{stream_label}:{instance_idx}" if self._enforcement_active else ""
-
-        start_event = torch.cuda.Event(enable_timing=True)
-        start_event.record(cuda_stream)
-        token = str(len(self._records))
-        self._records.append(_NcclKernelRecord(
-            kernel_name=kernel_name,
-            stream_label=stream_label,
-            start_event=start_event,
-            stream_instance_idx=instance_idx if self._enforcement_active else None,
-        ))
-        return token
-
-    def after_kernel(self, cuda_stream: "torch.cuda.Stream", token: str) -> None:
-        """Record end_event on cuda_stream. Call after the NCCL kernel."""
-        if not token:
-            return
-
-        if token.startswith("E:"):
-            # Enforcement-only mode: parse "E:{stream_label}:{instance_idx}"
-            _, stream_label, idx_str = token.split(":")
-            instance_idx = int(idx_str)
-            end_event = torch.cuda.Event(enable_timing=False)
-            end_event.record(cuda_stream)
-            for gate in self._signal_gates.get((stream_label, instance_idx), []):
-                gate.signal(end_event)
-            return
-
-        # Monitoring mode (with or without enforcement): token is a record index
-        record_idx = int(token)
-        record = self._records[record_idx]
-
-        end_event = None
-        if self.enabled:
-            end_event = torch.cuda.Event(enable_timing=True)
-            end_event.record(cuda_stream)
-            record.end_event = end_event
-
-        if self._enforcement_active and record.stream_instance_idx is not None:
-            if end_event is None:
-                end_event = torch.cuda.Event(enable_timing=False)
-                end_event.record(cuda_stream)
-            for gate in self._signal_gates.get(
-                (record.stream_label, record.stream_instance_idx), []
-            ):
-                gate.signal(end_event)
-
-    def build_enforcement(self, overlaps: list, logger) -> None:
-        """Build per-instance gate map from detected overlaps. Call after find_overlaps().
-
-        Assigns a stable stream_instance_idx to every record (= position in dispatch
-        order on that stream), then creates one _DepGate per detected overlap instance.
-        Gates are permanent — they are not rebuilt each iteration; only
-        _per_stream_counter is reset so the same indices are reproduced.
-        """
-        # Assign stream_instance_idx to every record in monitoring order
-        stream_counts: dict[str, int] = {}
-        for r in self._records:
-            r.stream_instance_idx = stream_counts.get(r.stream_label, 0)
-            stream_counts[r.stream_label] = r.stream_instance_idx + 1
-
-        # Build one gate per detected overlap instance
-        signal: dict[tuple[str, int], list[_DepGate]] = {}
-        wait:   dict[tuple[str, int], list[_DepGate]] = {}
-        n_pairs = 0
-        for a, b, _ in overlaps:
-            if a.kernel_name in _CRITICAL_OPS and b.kernel_name in _NON_CRITICAL_OPS:
-                crit, noncrit = a, b
-            elif b.kernel_name in _CRITICAL_OPS and a.kernel_name in _NON_CRITICAL_OPS:
-                crit, noncrit = b, a
-            else:
-                continue
-            logger.debug(f"Enforcing dependency between {crit} -> {noncrit} NCCL kernels")
-            gate = _DepGate()
-            signal.setdefault((crit.stream_label,    crit.stream_instance_idx),    []).append(gate)
-            wait.setdefault(  (noncrit.stream_label, noncrit.stream_instance_idx), []).append(gate)
-            n_pairs += 1
-
-        self._signal_gates = signal
-        self._wait_gates   = wait
-        self._enforcement_active = bool(n_pairs)
-        if self._enforcement_active:
-            _nccl_log.info(
-                "NcclOverlapDetector: enforcement active — %d instance-level gate(s)", n_pairs
-            )
-
-    def find_overlaps(self, logger) -> list[tuple["_NcclKernelRecord", "_NcclKernelRecord", float]]:
-        """Compute GPU-time intervals and return all overlapping cross-stream pairs.
-
-        Must be called after torch.cuda.synchronize() so all events are complete.
-        Returns a list of (record_a, record_b, overlap_ms) tuples.
-        """
-        if not self._records or self._ref_event is None:
-            return []
-
-        ref = self._ref_event
-        for r in self._records:
-            if r.end_event is None or r.t_start_ms is not None:
-                continue
-            try:
-                r.t_start_ms = ref.elapsed_time(r.start_event)
-                r.t_end_ms   = ref.elapsed_time(r.end_event)
-            except Exception:
-                pass
-
-        valid = [r for r in self._records if r.t_start_ms is not None and r.t_end_ms is not None]
-        overlaps = []
-        for i, a in enumerate(valid):
-            # logger.info(f"NCCL kernel '{a.kernel_name}' on stream '{a.stream_label}': {a.t_start_ms:.3f}-{a.t_end_ms:.3f} ms")
-            for b in valid[i + 1:]:
-                if a.stream_label == b.stream_label:
-                    continue
-                if a.t_start_ms < b.t_end_ms and b.t_start_ms < a.t_end_ms:
-                    overlap_ms = (min(a.t_end_ms, b.t_end_ms)
-                                  - max(a.t_start_ms, b.t_start_ms))
-                    overlaps.append((a, b, overlap_ms))
-        return overlaps
-
-    def reset_iteration(self) -> None:
-        """Called at the end of every training iteration (after streams are synced).
-
-        Clears records only when monitoring is disabled.  When monitoring is enabled,
-        records are preserved so that print_nccl_overlaps() can analyse them after
-        the iteration returns.  Gates are permanent and not rebuilt; only
-        _per_stream_counter is reset so each iteration reproduces the same
-        (stream_label, index) assignments as during monitoring.
-        """
-        if not self.enabled:
-            self._records.clear()
-            self._ref_event = None
-        if self._enforcement_active:
-            # Drain any unmatched signals (cases where the critical op fired after
-            # the non-critical op's non-blocking wait already returned empty).
-            for gates in self._signal_gates.values():
-                for gate in gates:
-                    gate.drain()
-            self._per_stream_counter.clear()
 
 
 """
@@ -336,116 +44,18 @@ def reset_iteration(self) -> None:
 
 class PiperMetadata:
     actors = dict()
-    dag = set()
-    stage_to_device = dict()
-    naive_gradient_sync = False
-    use_activation_checkpointing = False
-    activation_num_checkpoints = 1
-    bucketing = False  # Whether to split stages into per-param-bucket sub-modules
-    bucket_size: int = 25 * 1024 * 1024  # Target bucket size in bytes
-    a2a_ar_no_overlap = False  # Whether ALL_REDUCE tasks must wait for all same-rank A2A tasks
-    schedule = None   # PipelineSchedule set by piper_setup; used by the piper backend
-    task_dag = None   # TaskDAG built from schedule by the piper backend
-    full_dag_no_overlap = None  # Deep copy of the full DAG (pre-P2P-split, no overlap_a2a_tasks); used for profiling and critical-path analysis
-    per_rank_dags = None  # Per-rank TaskDAGs built by the piper backend
-    zero_stage: int = 0  # ZeRO stage: 0=disabled, 1=optim states, 2=+gradients, 3=+parameters
-    gradient_accumulation: bool = True  # Delay gradient sync to the last occurrence of each bucket
-    use_inductor: bool = False  # Whether actors should torch.compile stage GraphModules in _load_stage
-    enable_ep: bool = False  # Whether expert-parallel A2A graph splitting/tasks are enabled
-    ar_a2a_same_stream: bool = False  # Whether A2A ops should share the AR stream on actors
-    overlap_zero_ops: bool = False  # Whether to apply overlap_zero_ops to per-rank DAGs
-    overlap_chunks: bool = False  # Whether to apply the chunk-overlap transform to per-rank DAGs
-    schedule_name: str = ""  # Human-readable schedule name for debug artifacts
-    visualize_dag_render: bool = True  # If False, save Graphviz source only
-    stage_bucket_counts: dict = {}   # stage_id -> number of buckets (set by piper backend)
-    trainable_bucket_keys: set = set()  # (stage_id, bucket_id) pairs with trainable params
-    zero_bucket_keys: set = set()  # (stage_id, bucket_id) pairs that should receive ZeRO transforms
-    # Populated by the piper backend on dp_rank=0; broadcast to dp_rank>0 by piper_setup
-    # so they can skip torch.compile entirely.  Contains serialized graph + param metadata
-    # (no actual weight values) for all stages, plus the built per-rank TaskDAGs.
-    compiled_stage_data: dict = None
-    # MFU tracking: set by piper_setup when model_flops_per_token is provided
-    model_flops_per_token: Optional[float] = None  # FLOPs per token for forward+backward pass
-    tokens_per_step: Optional[int] = None           # Global batch tokens per training step
     visualize_dag: bool = True  # Whether to render per-rank DAG PNGs after compilation
     output_dir: str = "out"  # Base directory for debug artifacts emitted during runs
+    training_dag = None  # DAG of annotated model segments and transform-inserted nodes
+    per_pp_training_dags = None  # Per-PP-rank DAGs built by the TrainingDAG backend
+    compiled_data_store = None  # Ray actor used to share compiled DAGs across DP ranks
+    schedule_directives: list = []  # Program of DAG transform directives (e.g., place(...))
+    schedule_directives_file: Optional[str] = None  # JSON source for schedule_directives
+    schedule_info: dict = {}  # Derived schedule facts such as pp/dp/mbs
 
 piper_metadata = PiperMetadata()
 
 
-def should_enable_ep(dp_degree: int) -> bool:
-    return bool(piper_metadata.enable_ep) and dp_degree > 1
-
-
-# ---------------------------------------------------------------------------
-# GPU peak FLOPs lookup for MFU computation
-# ---------------------------------------------------------------------------
-
-# Theoretical peak BF16 tensor-core FLOPs/s for common GPUs.
-# Values are from official NVIDIA spec sheets.
-_GPU_PEAK_FLOPS_BF16: list[tuple[str, float]] = [
-    ("nvidia h200",      1979e12),
-    ("h100 sxm",   989e12),
-    ("h100 pcie",  756e12),
-    ("h100",       989e12),
-    ("a100",       312e12),
-    ("a6000 ada",  362e12),
-    ("rtx 4090",   165e12),
-    ("rtx 3090",   142e12),
-]
-
-
-def get_gpu_peak_flops_bf16() -> Optional[float]:
-    """Return theoretical peak BF16 FLOPs/s for the current CUDA device, or None if unknown."""
-    if not torch.cuda.is_available():
-        return None
-    name = torch.cuda.get_device_properties(torch.cuda.current_device()).name.lower()
-    for key, flops in _GPU_PEAK_FLOPS_BF16:
-        if key in name:
-            return flops
-    return 1979e12  # Default to H200-level FLOPs for unknown devices, since it's better to slightly overestimate than underestimate
-
-
-def compute_transformer_flops_per_token(
-    hidden_dim: int,
-    n_layers: int,
-    ffn_dim: int,
-    n_heads: int,
-    n_kv_heads: int,
-    head_dim: int,
-    activation_checkpointing: bool = False,
-    moe_top_k: int = 1,
-) -> float:
-    """Estimate FLOPs per token for a forward+backward pass of a transformer.
-
-    Uses the standard approximation (sequence-length-independent terms only):
-    - Attention: Q/K/V projections + output projection per layer
-    - FFN (SwiGLU): gate + up + down projections × moe_top_k active experts
-    - Multiply by 3× for fwd+bwd (or 4× with full activation-checkpointing recompute)
-
-    Args:
-        hidden_dim: Model hidden dimension (H).
-        n_layers: Number of transformer layers.
-        ffn_dim: FFN intermediate dimension (per expert for MoE).
-        n_heads: Number of attention heads.
-        n_kv_heads: Number of key/value heads (GQA).
-        head_dim: Dimension per attention head.
-        activation_checkpointing: If True, use 4× multiplier (full recompute).
-        moe_top_k: Number of active experts per token (1 for dense).
-
-    Returns:
-        Estimated FLOPs per token (float).
-    """
-    kv_hidden = n_kv_heads * head_dim
-    # Q proj (H→H) + K proj (H→kv_hidden) + V proj (H→kv_hidden) + out proj (H→H)
-    attn_flops = 2 * hidden_dim * (2 * hidden_dim + 2 * kv_hidden)
-    # SwiGLU FFN: gate_proj + up_proj + down_proj (each H×ffn_dim), scaled by active experts
-    ffn_flops = 6 * hidden_dim * ffn_dim * moe_top_k
-    fwd_total = n_layers * (attn_flops + ffn_flops)
-    multiplier = 4 if activation_checkpointing else 3
-    return fwd_total * multiplier
-
-
 """
 Serialize/deserialize an fx.GraphModule
 """
diff --git a/test/all2all_bench.py b/test/all2all_bench.py
deleted file mode 100644
index 076bf71..0000000
--- a/test/all2all_bench.py
+++ /dev/null
@@ -1,126 +0,0 @@
-#!/usr/bin/env python
-import argparse
-import time
-from typing import List
-
-import torch
-import torch.distributed as dist
-import numpy as np
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser("all_to_all_single benchmark")
-    parser.add_argument(
-        "--backend", type=str, default="nccl", choices=["nccl", "gloo"],
-        help="Process group backend (default: nccl)",
-    )
-    parser.add_argument(
-        "--dtype", type=str, default="float16",
-        choices=["float16", "float32", "bfloat16"],
-        help="Tensor dtype (default: float16)",
-    )
-    parser.add_argument(
-        "--warmup", type=int, default=10,
-        help="Warmup iterations (default: 10)",
-    )
-    parser.add_argument(
-        "--iters", type=int, default=10,
-        help="Timed iterations (default: 50)",
-    )
-    return parser.parse_args()
-
-
-def get_dtype(name: str) -> torch.dtype:
-    if name == "float16":
-        return torch.float16
-    if name == "float32":
-        return torch.float32
-    if name == "bfloat16":
-        return torch.bfloat16
-    raise ValueError(f"Unsupported dtype: {name}")
-
-
-def main() -> None:
-    args = parse_args()
-
-    dist.init_process_group(backend=args.backend)
-    world_size = dist.get_world_size()
-    rank = dist.get_rank()
-
-    torch.cuda.set_device(rank % torch.cuda.device_count())
-    device = torch.device("cuda", rank % torch.cuda.device_count())
-
-    dtype = get_dtype(args.dtype)
-    bytes_per_elem = torch.tensor([], dtype=dtype).element_size()
-
-    # Tensor sizes per rank (bytes). Adjust as desired.
-    tensor_shapes = [
-        (2, 4, 16, 2048),
-        (2, 4, 16, 4096),
-        (2, 4, 64, 14336),
-        (4, 8, 64, 2048),
-        (4, 8, 64, 4096),
-        (4, 8, 64, 14336),
-        (8, 8, 64, 2048),
-        (8, 8, 64, 4096),
-        (8, 8, 64, 14336),
-    ]
-
-    if rank == 0:
-        print(
-            f"Running all_to_all_single benchmark with world_size={world_size}, "
-            f"dtype={dtype}, warmup={args.warmup}, iters={args.iters}"
-        )
-
-    for shape in tensor_shapes:
-        # Number of elements per rank
-        numel = np.prod(shape)
-
-        # all_to_all_single with equal splits requires numel divisible by world_size
-        if numel % world_size != 0:
-            if rank == 0:
-                print(
-                    f"Skipping shape {shape}: "
-                    f"numel={numel} not divisible by world_size={world_size}"
-                )
-            continue
-
-        in_tensor = torch.empty(shape, device=device, dtype=dtype)
-        out_tensor = torch.empty_like(in_tensor)
-
-        # Warmup
-        dist.barrier()
-        for _ in range(args.warmup):
-            dist.all_to_all_single(out_tensor, in_tensor)
-        dist.barrier()
-
-        # Timed iterations
-        dist.barrier()
-        start_event = torch.cuda.Event(enable_timing=True)
-        end_event = torch.cuda.Event(enable_timing=True)
-
-        torch.cuda.synchronize(device)
-        start_event.record()
-        for _ in range(args.iters):
-            dist.all_to_all_single(out_tensor, in_tensor)
-        end_event.record()
-        torch.cuda.synchronize(device)
-
-        total_ms = start_event.elapsed_time(end_event)  # milliseconds
-        avg_ms = total_ms / args.iters
-        avg_sec = avg_ms / 1e3
-        gb = np.prod(shape) * bytes_per_elem / (1 << 30)
-        gbps = gb / avg_sec
-
-        if rank == 0:
-            print(
-                f"shape={shape}, "
-                f"size={gb:.3f} GB per rank, "
-                f"time={avg_ms:.3f} ms, "
-                f"eff_bw={gbps:.2f} GB/s (per rank, one direction)"
-            )
-
-    dist.destroy_process_group()
-
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/test/models/qwen3.py b/test/models/qwen3.py
index 15e6cb5..e6995d8 100644
--- a/test/models/qwen3.py
+++ b/test/models/qwen3.py
@@ -1,5 +1,7 @@
 from typing import Optional
 import torch
+import torch.nn.functional as F
+from torch import nn
 from torch.nn.attention import sdpa_kernel, SDPBackend
 
 from torchtitan.models.qwen3 import Qwen3Model, Qwen3ModelArgs
@@ -7,6 +9,8 @@
 from torchtitan.models.moe import MoE, MoEArgs
 from torchtitan.protocols.model import AttentionMasksType
 
+PP_TAG = "PP"
+EP_TAG = "EP"
 
 def create_qwen3_config(name: str) -> Qwen3ModelArgs:
     """Create Qwen3 model config based on name."""
@@ -15,7 +19,7 @@ def create_qwen3_config(name: str) -> Qwen3ModelArgs:
             return Qwen3ModelArgs(
                 vocab_size=2048,
                 dim=256,
-                n_layers=8,
+                n_layers=4,
                 n_heads=8,
                 n_kv_heads=4,
                 head_dim=32,
@@ -81,7 +85,7 @@ def create_qwen3_config(name: str) -> Qwen3ModelArgs:
             return Qwen3ModelArgs(
                 vocab_size=151936,
                 dim=2048,
-                n_layers=24,
+                n_layers=4, #24,
                 n_heads=32,
                 n_kv_heads=8,
                 head_dim=64,
@@ -114,7 +118,7 @@ def create_qwen3_config(name: str) -> Qwen3ModelArgs:
             return Qwen3ModelArgs(
                 vocab_size=151936,
                 dim=4096,
-                n_layers=32,
+                n_layers=4, #32,
                 n_heads=32,
                 n_kv_heads=8,
                 head_dim=128,
@@ -201,7 +205,7 @@ def create_qwen3_config(name: str) -> Qwen3ModelArgs:
             return Qwen3ModelArgs(
                 vocab_size=152064,
                 dim=8192,
-                n_layers=80,
+                n_layers=4, #80,
                 n_heads=64,
                 n_kv_heads=8,
                 head_dim=128,
@@ -218,12 +222,40 @@ def create_qwen3_config(name: str) -> Qwen3ModelArgs:
             raise ValueError(f"Unknown model config: {name}")
 
 
+class BmmExperts(nn.Module):
+    """Triton-free expert GEMM.
+
+    Mirrors torchtitan ``GroupedExperts`` parameter shapes/names so the param
+    registry is unchanged, but runs the per-expert SwiGLU via batched matmul
+    instead of ``torch._grouped_mm`` (which pulls in Triton permute-index
+    kernels). Tokens are assumed evenly distributed across experts, matching the
+    fixed-capacity reshape the caller performs before dispatch.
+    """
+
+    def __init__(self, dim: int, hidden_dim: int, num_experts: int):
+        super().__init__()
+        self.num_experts = num_experts
+        self.w1 = nn.Parameter(torch.empty(num_experts, hidden_dim, dim))
+        self.w2 = nn.Parameter(torch.empty(num_experts, dim, hidden_dim))
+        self.w3 = nn.Parameter(torch.empty(num_experts, hidden_dim, dim))
+
+    def forward(self, x: torch.Tensor, num_tokens_per_expert: torch.Tensor) -> torch.Tensor:
+        dim = x.shape[-1]
+        # (num_experts * capacity, dim) -> (num_experts, capacity, dim)
+        x = x.reshape(self.num_experts, -1, dim)
+        h = F.silu(torch.bmm(x, self.w1.transpose(-2, -1)))
+        h = h * torch.bmm(x, self.w3.transpose(-2, -1))
+        out = torch.bmm(h, self.w2.transpose(-2, -1))
+        return out.reshape(-1, dim)
+
+
 class AnnotatedMoE(MoE):
-    def flush_tokens_per_expert(self) -> None:
-        if hasattr(self, '_tokens_per_expert_acc'):
-            with torch.no_grad():
-                self.tokens_per_expert.add_(self._tokens_per_expert_acc)
-                self._tokens_per_expert_acc.zero_()
+    def __init__(self, moe_args: MoEArgs, dim: int, hidden_dim: int):
+        super().__init__(moe_args, dim=dim, hidden_dim=hidden_dim)
+        # Replace the grouped-mm (Triton) experts with a batched-matmul variant.
+        self.experts = BmmExperts(
+            dim=dim, hidden_dim=hidden_dim, num_experts=moe_args.num_experts
+        )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         bs, slen, dim = x.shape
@@ -251,10 +283,11 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             ).to(x.dtype)
 
         routed_input = routed_input.reshape(
-            2, self.experts.num_experts, -1, dim)
+            self.experts.num_experts, -1, dim)
 
         # dispatch tokens to experts via all_to_all
         with torch.fx.traceback.annotate({
+            "name": EP_TAG,
             "collective": "all_to_all_single",
             "group": "ep",
         }):
@@ -266,6 +299,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
         # gather expert outputs back via all_to_all
         with torch.fx.traceback.annotate({
+            "name": EP_TAG,
             "collective": "all_to_all_single",
             "group": "ep",
         }):
@@ -320,8 +354,13 @@ class PiperQwen3Model(Qwen3Model):
     annotations in the MoE layers.
     """
 
-    def __init__(self, config: Qwen3ModelArgs, num_stages: int = 2):
+    def __init__(self, config: Qwen3ModelArgs, num_stages: int):
         super().__init__(config)
+        if num_stages is None:
+            from src.piper_utils import piper_metadata
+
+            schedule_info = piper_metadata.schedule_info or {}
+            num_stages = int(schedule_info.get("num_stages", schedule_info.get("pp_degree", 2)))
         self.num_stages = num_stages
 
         # Replace TransformerBlock layers with AnnotatedQwen3TransformerBlock
@@ -365,7 +404,7 @@ def forward(
         layers_per_stage = num_layers // self.num_stages
 
         for stage_id in range(self.num_stages):
-            with torch.fx.traceback.annotate({"stage": stage_id}):
+            with torch.fx.traceback.annotate({"name": PP_TAG, "stage": stage_id}):
                 if stage_id == 0:
                     h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
 
diff --git a/test/print_schedules.py b/test/print_schedules.py
deleted file mode 100644
index 508e0a5..0000000
--- a/test/print_schedules.py
+++ /dev/null
@@ -1,38 +0,0 @@
-"""
-Step-by-step test of the PipelineSchedule → TaskDAG → per-rank DAG pipeline.
-
-Run with:
-    python3 -m test.print_schedules
-"""
-
-from .schedule_helpers import build_1f1b_schedule, build_interleaved_1f1b_schedule, build_zerobubble_schedule, visualize_pipeline_schedule, build_dualpipev_schedule, build_interleaved_zero_bubble
-from src.piper_exec import DAGEdge, TaskType, _validate_schedule
-from src.piper_graph_transform import expand_chunks_to_dags, add_temporal_dependencies, split_dag_by_rank, assign_time_steps, visualize_dag
-
-
-mbs = 16
-pp = 8
-
-schedule = build_1f1b_schedule(n_mbs=mbs, n_stages=pp)
-_validate_schedule(schedule, dag_edges=(DAGEdge(0, 1), DAGEdge(1, 2), DAGEdge(2, 3)), num_mbs=mbs)
-visualize_pipeline_schedule(schedule, "out/1f1b-template")
-
-schedule = build_interleaved_1f1b_schedule(n_mbs=mbs, pp=pp)
-_validate_schedule(schedule, dag_edges=(DAGEdge(0, 1), DAGEdge(1, 2), DAGEdge(2, 3), DAGEdge(3, 4), DAGEdge(4, 5), DAGEdge(5, 6), DAGEdge(6, 7)), num_mbs=mbs)
-visualize_pipeline_schedule(schedule, "out/interleaved-1f1b-template")
-
-schedule = build_zerobubble_schedule(n_mbs=mbs, pp=pp)
-_validate_schedule(schedule, dag_edges=(DAGEdge(0, 1), DAGEdge(1, 2), DAGEdge(2, 3)), num_mbs=mbs)
-visualize_pipeline_schedule(schedule, "out/zero-bubble-template")
-
-schedule = build_interleaved_zero_bubble(n_mbs=mbs, pp=pp)
-_validate_schedule(schedule, dag_edges=(DAGEdge(0, 1), DAGEdge(1, 2), DAGEdge(2, 3), DAGEdge(3, 4), DAGEdge(4, 5), DAGEdge(5, 6), DAGEdge(6, 7)), num_mbs=mbs)
-visualize_pipeline_schedule(schedule, "out/interleaved-zero-bubble-template")
-
-schedule = build_dualpipev_schedule(n_mbs=10, pp=4)
-_validate_schedule(schedule, dag_edges=(DAGEdge(0, 1), DAGEdge(1, 2), DAGEdge(2, 3), DAGEdge(3, 4), DAGEdge(4, 5), DAGEdge(5, 6), DAGEdge(6, 7)), num_mbs=8)
-visualize_pipeline_schedule(schedule, "out/dualpipev-template")
-
-schedule = build_dualpipev_schedule(n_mbs=10, pp=4, seq=True)
-_validate_schedule(schedule, dag_edges=(DAGEdge(0, 1), DAGEdge(1, 2), DAGEdge(2, 3), DAGEdge(3, 4), DAGEdge(4, 5), DAGEdge(5, 6), DAGEdge(6, 7)), num_mbs=8)
-visualize_pipeline_schedule(schedule, "out/dualpipev-sequential-template")
diff --git a/test/schedule_helpers.py b/test/schedule_helpers.py
deleted file mode 100644
index 12accfe..0000000
--- a/test/schedule_helpers.py
+++ /dev/null
@@ -1,1952 +0,0 @@
-
-from src.piper_exec import (
-    Chunk,
-    BatchMeta,
-    TaskType,
-    CompType,  # backwards-compatible alias for TaskType
-    PipelineSchedule,
-    Task,
-    TaskDAG,
-    logical_time,
-)
-from src.piper_graph_transform import visualize_dag
-
-
-def _chunk_label(chunk: Chunk) -> str:
-    """Short label for a chunk: stage:mb:f/b/u, or first+second for dual batches."""
-    c = (
-        "u" if chunk.type == CompType.UPD
-        else "f" if chunk.type == CompType.FWD
-        else "b" if chunk.type == CompType.BWD
-        else "fb" if chunk.type == CompType.FWD_BWD
-        else "bi" if chunk.type == CompType.BWD_I
-        else "bw" if chunk.type == CompType.BWD_W
-        else "?"
-    )
-    if chunk.type == CompType.UPD:
-        label = "   u    "
-    else:
-        first = chunk.batches[0]
-        label = f"{first.stage_id}:{first.mb_idx}:{c}"
-    if len(chunk.batches) > 1:
-        second = chunk.batches[1]
-        label += f"+{second.stage_id}:{second.mb_idx}"
-    else:
-        label += "    "
-    return label
-
-
-def print_schedule(schedule: PipelineSchedule) -> None:
-    """Print the schedule as a 2-D timed grid (rows=ranks, columns=logical time)."""
-    start_times_per_rank = schedule._compute_start_times()
-    max_time = max(
-        start + logical_time(chunk)
-        for row, starts in zip(schedule.grid, start_times_per_rank)
-        for chunk, start in zip(row, starts)
-    )
-    for rank, (row, starts) in enumerate(zip(schedule.grid, start_times_per_rank)):
-        # Build a sparse mapping from start_time -> chunk for this rank
-        time_to_chunk = {start: chunk for chunk, start in zip(row, starts)}
-        for t in range(max_time):
-            chunk = time_to_chunk.get(t)
-            if chunk is None:
-                print(" ------ ", end="\t")
-            else:
-                print(_chunk_label(chunk), end="\t")
-        print()
-
-
-def build_gpipe_schedule(n_mbs: int, n_stages: int) -> PipelineSchedule:
-    rows: list[list[Chunk]] = [[] for _ in range(n_stages)]
-    for stage_id in range(n_stages):
-        for mb_idx in range(n_mbs):
-            rows[stage_id].append(Chunk(
-                pp_rank=stage_id,
-                batches=[BatchMeta(stage_id=stage_id, mb_idx=mb_idx)],
-                type=CompType.FWD,
-            ))
-    for stage_id in reversed(range(n_stages)):
-        for mb_idx in range(n_mbs):
-            rows[stage_id].append(Chunk(
-                pp_rank=stage_id,
-                batches=[BatchMeta(stage_id=stage_id, mb_idx=mb_idx)],
-                type=CompType.BWD,
-            ))
-    return PipelineSchedule(grid=rows)
-
-
-def build_1f1b_schedule(n_mbs: int, n_stages: int) -> PipelineSchedule:
-    """Standard 1F1B schedule (no virtual stages, BWD = fused)."""
-    rows: list[list[Chunk]] = [[] for _ in range(n_stages)]
-    # Warmup: rank r does r+1 forward passes before first backward
-    fwd_mb = [0] * n_stages
-    bwd_mb = [0] * n_stages
-    for stage_id in range(n_stages):
-        warmup = n_stages - 1 - stage_id
-        for _ in range(min(warmup, n_mbs)):
-            rows[stage_id].append(Chunk(
-                pp_rank=stage_id,
-                batches=[BatchMeta(stage_id=stage_id, mb_idx=fwd_mb[stage_id])],
-                type=CompType.FWD,
-            ))
-            fwd_mb[stage_id] += 1
-    # 1F1B steady state + cooldown
-    for stage_id in range(n_stages):
-        while bwd_mb[stage_id] < n_mbs:
-            if fwd_mb[stage_id] < n_mbs:
-                rows[stage_id].append(Chunk(
-                    pp_rank=stage_id,
-                    batches=[BatchMeta(stage_id=stage_id, mb_idx=fwd_mb[stage_id])],
-                    type=CompType.FWD,
-                ))
-                fwd_mb[stage_id] += 1
-            rows[stage_id].append(Chunk(
-                pp_rank=stage_id,
-                batches=[BatchMeta(stage_id=stage_id, mb_idx=bwd_mb[stage_id])],
-                type=CompType.BWD,
-            ))
-            bwd_mb[stage_id] += 1
-    return PipelineSchedule(grid=rows)
-
-
-def build_interleaved_1f1b_schedule(n_mbs: int, pp: int, v: int = 2) -> PipelineSchedule:
-    """Interleaved 1F1B schedule (v virtual stages per rank, BWD = fused).
-
-    Rank r owns stages r, r+pp, r+2*pp, ..., r+(v-1)*pp.
-    Uses PyTorch's warmup formula: warmup(r) = min((v-1)*pp + 2*(pp-1-r), n_mbs*v).
-    """
-    from collections import defaultdict
-    microbatches_per_round = pp
-
-    def warmup_ops(rank):
-        return min((v - 1) * pp + 2 * (pp - 1 - rank), n_mbs * v)
-
-    def forward_stage_index(step, rank, warmup):
-        local_index = (step // microbatches_per_round) % v
-        return local_index * pp + rank
-
-    def backward_stage_index(step, rank, warmup):
-        local_index = (v - 1 - ((step - warmup) // microbatches_per_round) % v)
-        return local_index * pp + rank
-
-    rows: list[list[Chunk]] = []
-    for rank in range(pp):
-        wu = warmup_ops(rank)
-        microbatch_ops = v * n_mbs
-        fwd_bwd_ops = microbatch_ops - wu
-        cooldown_ops = microbatch_ops - fwd_bwd_ops
-
-        fwd_mb: dict[int, int] = defaultdict(int)
-        bwd_mb: dict[int, int] = defaultdict(int)
-        chunks: list[Chunk] = []
-
-        for op in range(wu + fwd_bwd_ops + cooldown_ops):
-            if op < wu:
-                s = forward_stage_index(op, rank, wu)
-                m = fwd_mb[s]; fwd_mb[s] += 1
-                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(s, m)], type=CompType.FWD))
-            elif op < wu + fwd_bwd_ops:
-                s = forward_stage_index(op, rank, wu)
-                m = fwd_mb[s]; fwd_mb[s] += 1
-                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(s, m)], type=CompType.FWD))
-                s = backward_stage_index(op, rank, wu)
-                m = bwd_mb[s]; bwd_mb[s] += 1
-                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(s, m)], type=CompType.BWD))
-            else:
-                s = backward_stage_index(op, rank, wu)
-                m = bwd_mb[s]; bwd_mb[s] += 1
-                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(s, m)], type=CompType.BWD))
-
-        rows.append(chunks)
-    return PipelineSchedule(grid=rows)
-
-
-def build_zerobubble_schedule(n_mbs: int, pp: int) -> PipelineSchedule:
-    """ZeroBubble (ZB-1) schedule: one stage per rank, backward split into BWD_I + BWD_W.
-
-    Uses PyTorch's ZB warmup formula: warmup(r) = min(pp - 1 - r, n_mbs).
-    BWD_W for microbatch m on rank r is deferred by r slots relative to BWD_I.
-    """
-    from collections import defaultdict
-
-    def warmup_ops(rank):
-        return min(pp - 1 - rank, n_mbs)
-
-    rows: list[list[Chunk]] = []
-    for rank in range(pp):
-        wu = warmup_ops(rank)
-        microbatch_ops = n_mbs
-        fwd_bwd_ops = microbatch_ops - wu
-        cooldown_ops = microbatch_ops - fwd_bwd_ops
-        num_1f1b_microbatches = rank  # W is deferred by this many steps
-
-        fwd_mb = 0
-        bwd_mb = 0
-        w_mb = 0
-        chunks: list[Chunk] = []
-        backward_ops: list[int] = []  # track op indices where BWD_I was emitted
-        weight_op_count = 0
-
-        for op in range(wu + fwd_bwd_ops + cooldown_ops):
-            stage = rank  # single stage per rank
-            if op < wu:
-                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, fwd_mb)], type=CompType.FWD))
-                fwd_mb += 1
-            elif op < wu + fwd_bwd_ops:
-                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, fwd_mb)], type=CompType.FWD))
-                fwd_mb += 1
-                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, bwd_mb)], type=CompType.BWD_I))
-                bwd_mb += 1
-                backward_ops.append(op)
-                if op - wu >= num_1f1b_microbatches:
-                    chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, w_mb)], type=CompType.BWD_W))
-                    w_mb += 1
-                    weight_op_count += 1
-            else:
-                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, bwd_mb)], type=CompType.BWD_I))
-                bwd_mb += 1
-                backward_ops.append(op)
-                if op - wu >= num_1f1b_microbatches:
-                    chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, w_mb)], type=CompType.BWD_W))
-                    w_mb += 1
-                    weight_op_count += 1
-
-        while weight_op_count < len(backward_ops):
-            chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(rank, w_mb)], type=CompType.BWD_W))
-            w_mb += 1
-            weight_op_count += 1
-
-        rows.append(chunks)
-    return PipelineSchedule(grid=rows)
-
-
-
-def build_interleaved_zero_bubble(n_mbs: int, pp: int, v: int = 2) -> PipelineSchedule:
-    """Interleaved ZeroBubble schedule: v virtual stages per rank, backward split into BWD_I + BWD_W.
-
-    Matches torch.distributed.pipelining ScheduleInterleavedZeroBubble op-order exactly.
-
-    Rank r owns stages r, r+pp, r+2*pp, ..., r+(v-1)*pp (same as interleaved 1F1B).
-    Warmup formula (multiply_factor=1, matching torch):
-        warmup(r) = min((v-1)*microbatches_per_round + 1*(pp-1-r), n_mbs*v)
-    where microbatches_per_round = n_mbs // max(1, n_mbs // pp).
-
-    Contrast with interleaved 1F1B which uses multiply_factor=2. The ZeroBubble variant
-    uses multiply_factor=1 because W ops fill the bubble time that would otherwise require
-    extra warmup F ops, so fewer warmup forwards are needed.
-
-    BWD_W is deferred by `rank` 1F1B steps relative to BWD_I (emit W once bwdi_count > rank),
-    matching torch's condition `op - warmup_ops >= rank`. A FIFO weight queue tracks
-    outstanding (stage, mb) pairs whose BWD_I has completed but BWD_W has not yet been emitted.
-
-    Bubble placement: torch inserts explicit None slots (rank pre-padding + pp-rank-1
-    post-warmup Nones). Piper omits explicit Nones; _compute_start_times() recovers the
-    same absolute timing via FWD/BWD dependency constraints.
-    """
-    from collections import defaultdict
-
-    microbatches_per_round = pp  # = n_mbs // max(1, n_mbs // pp) for balanced configs
-
-    def warmup_ops(rank: int) -> int:
-        # multiply_factor=1 (ZeroBubble) vs 2 (1F1B); matches torch's ScheduleInterleavedZeroBubble
-        return min((v - 1) * microbatches_per_round + 1 * (pp - 1 - rank), n_mbs * v)
-
-    def forward_stage_index(step: int, rank: int) -> int:
-        local_index = (step // microbatches_per_round) % v
-        return local_index * pp + rank
-
-    def backward_stage_index(step: int, rank: int, warmup: int) -> int:
-        local_index = (v - 1 - ((step - warmup) // microbatches_per_round) % v)
-        return local_index * pp + rank
-
-    rows: list[list[Chunk]] = []
-    for rank in range(pp):
-        wu = warmup_ops(rank)
-        microbatch_ops = v * n_mbs
-        fwd_bwd_ops = microbatch_ops - wu
-        cooldown_ops = microbatch_ops - fwd_bwd_ops
-
-        fwd_mb: dict[int, int] = defaultdict(int)   # stage_id -> next fwd microbatch index
-        bwd_mb: dict[int, int] = defaultdict(int)   # stage_id -> next bwdi microbatch index
-        chunks: list[Chunk] = []
-        weight_queue: list[tuple[int, int]] = []    # FIFO of (stage_id, mb_idx) pending BWD_W
-        bwdi_count = 0
-        # Defer the first `rank` BWD_Ws — same as non-interleaved ZeroBubble.
-        # This fills the cooldown-phase bubble produced by pipeline depth `rank`.
-        num_deferred = rank
-
-        def _maybe_emit_w() -> None:
-            """Emit the oldest queued BWD_W if the deferral window has passed."""
-            if bwdi_count > num_deferred and weight_queue:
-                ws, wm = weight_queue.pop(0)
-                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(ws, wm)], type=CompType.BWD_W))
-
-        for op in range(wu + fwd_bwd_ops + cooldown_ops):
-            if op < wu:
-                # Warmup: forward only
-                s = forward_stage_index(op, rank)
-                m = fwd_mb[s]; fwd_mb[s] += 1
-                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(s, m)], type=CompType.FWD))
-            elif op < wu + fwd_bwd_ops:
-                # Steady state: FWD then BWD_I, then maybe BWD_W
-                s_fwd = forward_stage_index(op, rank)
-                m_fwd = fwd_mb[s_fwd]; fwd_mb[s_fwd] += 1
-                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(s_fwd, m_fwd)], type=CompType.FWD))
-
-                s_bwd = backward_stage_index(op, rank, wu)
-                m_bwd = bwd_mb[s_bwd]; bwd_mb[s_bwd] += 1
-                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(s_bwd, m_bwd)], type=CompType.BWD_I))
-                weight_queue.append((s_bwd, m_bwd))
-                bwdi_count += 1
-                _maybe_emit_w()
-            else:
-                # Cooldown: BWD_I only, then maybe BWD_W
-                s_bwd = backward_stage_index(op, rank, wu)
-                m_bwd = bwd_mb[s_bwd]; bwd_mb[s_bwd] += 1
-                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(s_bwd, m_bwd)], type=CompType.BWD_I))
-                weight_queue.append((s_bwd, m_bwd))
-                bwdi_count += 1
-                _maybe_emit_w()
-
-        # Drain any remaining deferred BWD_Ws
-        while weight_queue:
-            ws, wm = weight_queue.pop(0)
-            chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(ws, wm)], type=CompType.BWD_W))
-
-        rows.append(chunks)
-    return PipelineSchedule(grid=rows)
-
-
-def build_dualpipev_schedule(n_mbs: int, pp: int, seq: bool = False) -> PipelineSchedule:
-    """DualPipeV schedule: V-layout, 2 virtual stages per rank, zero-bubble optimization.
-
-    Stage assignment (V-layout): rank r owns stage r (stage0) and stage 2*pp-1-r (stage1).
-    Directly ports PyTorch's ScheduleDualPipeV._calculate_single_rank_operations.
-    """
-    rows: list[list[Chunk]] = [[] for _ in range(pp)]
-
-    for rank in range(pp):
-        s0 = rank               # first (outer) stage
-        s1 = 2 * pp - 1 - rank  # second (inner) stage
-        chunks = rows[rank]
-        cnt: dict[tuple[int, str], int] = {}
-        weight_queue: list[tuple[int, int]] = []
-
-        def c(stage: int, key: str) -> int:
-            return cnt.get((stage, key), 0)
-
-        def inc(stage: int, key: str) -> None:
-            cnt[(stage, key)] = c(stage, key) + 1
-
-        def inc_bwd(stage: int) -> None:
-            inc(stage, "i"); inc(stage, "w")
-
-        def fwd(stage: int) -> None:
-            m = c(stage, "f")
-            chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, m)], type=CompType.FWD))
-            inc(stage, "f")
-
-        def bwdi(stage: int) -> None:
-            m = c(stage, "i")
-            chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, m)], type=CompType.BWD_I))
-            weight_queue.append((stage, m))
-            inc(stage, "i")
-
-        def drain_w() -> None:
-            if not weight_queue:
-                return
-            stage, m = weight_queue.pop(0)
-            chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, m)], type=CompType.BWD_W))
-            inc(stage, "w")
-
-        def full_bwd(stage: int) -> None:
-            m = c(stage, "i")
-            chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(stage, m)], type=CompType.BWD))
-            inc_bwd(stage)
-
-        def overlap_fb(fwd_stage: int, bwd_stage: int) -> None:
-            fm = c(fwd_stage, "f")
-            bm = c(bwd_stage, "i")
-            if seq:
-                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(fwd_stage, fm)], type=CompType.FWD))
-                chunks.append(Chunk(pp_rank=rank, batches=[BatchMeta(bwd_stage, bm)], type=CompType.BWD))
-            else:
-                chunks.append(Chunk(
-                    pp_rank=rank,
-                    batches=[BatchMeta(fwd_stage, fm), BatchMeta(bwd_stage, bm)],
-                    type=CompType.FWD_BWD,
-                ))
-            inc(fwd_stage, "f")
-            inc_bwd(bwd_stage)
-
-        # Phase 1: F0 warmup
-        for _ in range((pp - rank - 1) * 2):
-            fwd(s0)
-
-        # Phase 2: F0F1
-        for _ in range(rank + 1):
-            fwd(s0); fwd(s1)
-
-        # Phase 3: I1 W1 F1 (zero-bubble)
-        for _ in range(pp - rank - 1):
-            bwdi(s1); drain_w(); fwd(s1)
-
-        # Phase 4: Main — F0B1 + F1B0 (overlapped)
-        for i in range(n_mbs - pp * 2 + rank + 1):
-            if i == 0 and rank == pp - 1:
-                fwd(s0); full_bwd(s1)
-            else:
-                overlap_fb(s0, s1)
-            overlap_fb(s1, s0)
-
-        # Phase 5: B1 + F1B0
-        for _ in range(pp - rank - 1):
-            full_bwd(s1); overlap_fb(s1, s0)
-
-        # Phase 6: B1B0 (second half switches to zero-bubble split)
-        enable_zb = False
-        for i in range(rank + 1):
-            if i == (rank + 1) // 2 and rank % 2 == 1:
-                enable_zb = True
-            (bwdi if enable_zb else full_bwd)(s1)
-            if i == (rank + 1) // 2 and rank % 2 == 0:
-                enable_zb = True
-            (bwdi if enable_zb else full_bwd)(s0)
-
-        # Phase 7: W0 B0
-        for _ in range(pp - rank - 1):
-            drain_w()
-            (bwdi if enable_zb else full_bwd)(s0)
-
-        # Phase 8: W0
-        for _ in range(rank + 1):
-            drain_w()
-
-    return PipelineSchedule(grid=rows)
-
-
-def visualize_pipeline_schedule(
-    schedule: PipelineSchedule,
-    output_path: str = "pipeline_schedule",
-    fmt: str = "png",
-) -> None:
-    """Render a PipelineSchedule as a 2-D colored grid.
-
-    Rows = PP ranks.  Columns = logical time steps.
-    Each chunk occupies ``logical_time(chunk)`` consecutive columns.
-    Colors: FWD = yellow, BWD/BWD_I = green, BWD_W = blue, UPD = orange.
-    """
-    import matplotlib
-    matplotlib.use("Agg")
-    import matplotlib.pyplot as plt
-    import matplotlib.patches as mpatches
-
-    COLORS = {
-        CompType.FWD:     "#FFD700",   # yellow
-        CompType.BWD:     "#90EE90",   # green
-        CompType.BWD_I:   "#90EE90",   # green
-        CompType.BWD_W:   "#87CEEB",   # blue
-        CompType.UPD:     "#FFA500",   # orange
-        CompType.FWD_BWD: "#DDA0DD",   # plum
-    }
-
-    start_times_per_rank = schedule._compute_start_times()
-    n_ranks = len(schedule.grid)
-    max_time = max(
-        start_times_per_rank[r][i] + logical_time(schedule.grid[r][i])
-        for r in range(n_ranks)
-        for i in range(len(schedule.grid[r]))
-    )
-
-    CELL_W = 1.0   # width per logical time unit
-    CELL_H = 1.0   # height per rank
-    PAD = 0.04     # gap between cells
-
-    fig_w = max_time * CELL_W + 1.5
-    fig_h = n_ranks * CELL_H + 0.8
-    fig, ax = plt.subplots(figsize=(fig_w, fig_h))
-    ax.set_xlim(0, max_time * CELL_W)
-    ax.set_ylim(0, n_ranks * CELL_H)
-    ax.axis("off")
-
-    # Per-rank sorted unique stages, to determine virtual stage index (0=black, 1=white)
-    rank_stages: list[list[int]] = []
-    for row in schedule.grid:
-        seen: list[int] = []
-        for chunk in row:
-            for b in chunk.batches:
-                if b.stage_id not in seen:
-                    seen.append(b.stage_id)
-        rank_stages.append(sorted(seen))
-
-    for rank, (row, starts) in enumerate(zip(schedule.grid, start_times_per_rank)):
-        # rank 0 at top → y increases downward, so flip
-        y = (n_ranks - 1 - rank) * CELL_H
-        stages = rank_stages[rank]
-        for chunk, t in zip(row, starts):
-            lt = logical_time(chunk)
-            color = COLORS.get(chunk.type, "#FFFFFF")
-            x = t * CELL_W
-
-            rect = mpatches.FancyBboxPatch(
-                (x + PAD, y + PAD),
-                lt * CELL_W - 2 * PAD,
-                CELL_H - 2 * PAD,
-                boxstyle="round,pad=0.02",
-                facecolor=color,
-                edgecolor="#555555",
-                linewidth=0.6,
-            )
-            ax.add_patch(rect)
-
-            if chunk.batches:
-                b = chunk.batches[0]
-                label = f"{chunk.type.name}\ns{b.stage_id} m{b.mb_idx}"
-                vstage_idx = stages.index(b.stage_id) if b.stage_id in stages else 0
-            else:
-                label = chunk.type.name
-                vstage_idx = 0
-            font_color = "white" if vstage_idx % 2 == 1 else "black"
-            ax.text(
-                x + lt * CELL_W / 2,
-                y + CELL_H / 2,
-                label,
-                ha="center", va="center",
-                fontsize=7,
-                fontweight="bold",
-                color=font_color,
-            )
-
-    # Rank labels on the left
-    for rank in range(n_ranks):
-        y = (n_ranks - 1 - rank) * CELL_H + CELL_H / 2
-        ax.text(-0.15, y, f"rank {rank}", ha="right", va="center", fontsize=8)
-
-    plt.tight_layout(pad=0.3)
-    out_file = f"{output_path}.{fmt}"
-    plt.savefig(out_file, dpi=150, bbox_inches="tight")
-    plt.close(fig)
-    print(f"Saved schedule visualization to {out_file}")
-
-
-ZEROBUBBLE_MB4_SCHEDULE = PipelineSchedule(
-    grid=[
-        [
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),          # t0:  F0
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),          # t2:  F1
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD_I),        # t3:  I0
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD_W),        # t4:  W0
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),          # t5:  F2
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD_I),        # t6:  I1
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD_W),        # t7:  W1
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),          # t8:  F3
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD_I),        # t9:  I2
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD_W),        # t10: W2
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD_I),        # t11: I3
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD_W),        # t12: W3
-        ],
-        [
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),          # t1:  F0
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD_I),        # t2:  I0
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),          # t3:  F1
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD_I),        # t4:  I1
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD_W),        # t5:  W0
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),          # t6:  F2
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD_I),        # t7:  I2
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD_W),        # t8:  W1
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),          # t9:  F3
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD_I),        # t10: I3
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD_W),        # t11: W2
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD_W),        # t12: W3
-        ],
-    ])
-
-NO_PP_SCHEDULE = PipelineSchedule(
-    grid=[
-        [
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
-        ]
-    ],
-)
-
-
-DUALPIPEV_MB6_SCHEDULE = PipelineSchedule(
-    grid=[
-        [
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2), BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4), BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3), BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5), BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4), BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5), BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
-        ],
-        [
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2), BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2), BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3), BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3), BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4), BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4), BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5), BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5), BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_W),
-        ]
-    ])
-
-DUALPIPEV_SEQUENTIAL_MB6_SCHEDULE = PipelineSchedule(
-    grid=[
-        [
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
-        ],
-        [
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_W),
-        ]
-    ])
-
-DUALPIPEV_NOZB_MB6_SCHEDULE = PipelineSchedule(
-    grid=[
-        [
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=2), BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4), BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=3), BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5), BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4), BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5), BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[], type=CompType.UPD),
-        ],
-        [
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2), BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=2), BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3), BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=3), BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4), BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4), BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5), BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5), BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD_BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[], type=CompType.UPD),
-        ]
-    ])
-
-INTERLEAVED_1F1B_PP2_MB6_SCHEDULE = PipelineSchedule(
-    grid=[
-        [
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[], type=CompType.UPD),
-        ],
-        [
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[], type=CompType.UPD),
-        ],
-    ])
-
-INTERLEAVED_1F1B_PP2_MB4_SCHEDULE = PipelineSchedule(
-    grid=[
-        [
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
-        ],
-        [
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
-        ],
-    ])
-
-
-INTERLEAVED_GPIPE_PP2_MB4_SCHEDULE = PipelineSchedule(
-    grid=[
-        [
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[], type=CompType.UPD),
-        ],
-        [
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[], type=CompType.UPD),
-        ],
-    ])
-
-INTERLEAVED_1F1B_PP4_MB8_SCHEDULE = PipelineSchedule(
-    grid=[
-        [
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[], type=CompType.UPD),
-        ],
-        [
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[], type=CompType.UPD),
-        ],
-        [
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[], type=CompType.UPD),
-        ],
-        [
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[], type=CompType.UPD),
-        ],
-    ])
-
-
-
-INTERLEAVED_1F1B_PP8_MB16_SCHEDULE = PipelineSchedule(
-    grid=[
-        [  # rank 0, stages 0 and 8
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=8)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=9)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=10)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=11)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=12)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=13)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=14)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=8, mb_idx=15)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=8)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=9)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=10)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=11)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=12)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=13)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=14)], type=CompType.BWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=15)], type=CompType.BWD),
-        ],
-        [  # rank 1, stages 1 and 9
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=8)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=9)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=10)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=11)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=12)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=13)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=14)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=9, mb_idx=15)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=8)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=9)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=10)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=11)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=12)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=13)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=14)], type=CompType.BWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=15)], type=CompType.BWD),
-        ],
-        [  # rank 2, stages 2 and 10
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=8)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=9)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=10)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=11)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=12)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=13)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=14)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=10, mb_idx=15)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=8)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=9)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=10)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=11)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=12)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=13)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=14)], type=CompType.BWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=15)], type=CompType.BWD),
-        ],
-        [  # rank 3, stages 3 and 11
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=8)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=9)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=10)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=11)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=12)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=13)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=14)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=11, mb_idx=15)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=8)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=9)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=10)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=11)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=12)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=13)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=14)], type=CompType.BWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=15)], type=CompType.BWD),
-        ],
-        [  # rank 4, stages 4 and 12
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=8)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=9)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=10)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=11)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=12)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=13)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=14)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=12, mb_idx=15)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=8)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=9)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=10)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=11)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=12)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=13)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=14)], type=CompType.BWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=15)], type=CompType.BWD),
-        ],
-        [  # rank 5, stages 5 and 13
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=8)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=9)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=10)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=11)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=12)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=13)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=14)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=13, mb_idx=15)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=8)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=9)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=10)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=11)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=12)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=13)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=14)], type=CompType.BWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=15)], type=CompType.BWD),
-        ],
-        [  # rank 6, stages 6 and 14
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=8)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=9)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=10)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=11)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=12)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=13)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=14)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=14, mb_idx=15)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=8)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=9)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=10)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=11)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=12)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=13)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=14)], type=CompType.BWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=15)], type=CompType.BWD),
-        ],
-        [  # rank 7, stages 7 and 15
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=8)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=9)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=10)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=11)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=12)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=13)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=14)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=15, mb_idx=15)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=8)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=9)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=10)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=11)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=12)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=13)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=14)], type=CompType.BWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=15)], type=CompType.BWD),
-        ],
-    ])
-
-
-ZEROBUBBLE_PP8_MB16_SCHEDULE = PipelineSchedule(
-    grid=[
-        [  # rank 0, stage 0   warmup=7 1f1b=9 cooldown=7
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=0)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=1)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=2)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=3)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=4)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=5)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=6)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=7)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=8)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=8)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=9)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=9)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=10)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=10)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=11)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=11)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=12)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=12)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=13)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=13)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=14)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=14)], type=CompType.BWD_W),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=15)], type=CompType.BWD_I),
-            Chunk(pp_rank=0, batches=[BatchMeta(stage_id=0, mb_idx=15)], type=CompType.BWD_W),
-        ],
-        [  # rank 1, stage 1   warmup=6 1f1b=10 cooldown=6
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=0)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=1)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=2)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=3)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=4)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=5)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=6)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=8)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=7)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=9)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=8)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=10)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=9)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=11)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=10)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=12)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=11)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=13)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=12)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=14)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=13)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=15)], type=CompType.BWD_I),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=14)], type=CompType.BWD_W),
-            Chunk(pp_rank=1, batches=[BatchMeta(stage_id=1, mb_idx=15)], type=CompType.BWD_W),
-        ],
-        [  # rank 2, stage 2   warmup=5 1f1b=11 cooldown=5
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD_I),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD_I),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD_I),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=0)], type=CompType.BWD_W),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD_I),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=1)], type=CompType.BWD_W),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD_I),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=2)], type=CompType.BWD_W),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_I),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=3)], type=CompType.BWD_W),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.BWD_I),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=4)], type=CompType.BWD_W),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.BWD_I),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=5)], type=CompType.BWD_W),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=8)], type=CompType.BWD_I),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=6)], type=CompType.BWD_W),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=9)], type=CompType.BWD_I),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=7)], type=CompType.BWD_W),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=10)], type=CompType.BWD_I),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=8)], type=CompType.BWD_W),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=11)], type=CompType.BWD_I),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=9)], type=CompType.BWD_W),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=12)], type=CompType.BWD_I),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=10)], type=CompType.BWD_W),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=13)], type=CompType.BWD_I),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=11)], type=CompType.BWD_W),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=14)], type=CompType.BWD_I),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=12)], type=CompType.BWD_W),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=15)], type=CompType.BWD_I),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=13)], type=CompType.BWD_W),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=14)], type=CompType.BWD_W),
-            Chunk(pp_rank=2, batches=[BatchMeta(stage_id=2, mb_idx=15)], type=CompType.BWD_W),
-        ],
-        [  # rank 3, stage 3   warmup=4 1f1b=12 cooldown=4
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_I),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_I),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD_I),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD_I),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=0)], type=CompType.BWD_W),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD_I),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=1)], type=CompType.BWD_W),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_I),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=2)], type=CompType.BWD_W),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.BWD_I),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=3)], type=CompType.BWD_W),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.BWD_I),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=4)], type=CompType.BWD_W),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=8)], type=CompType.BWD_I),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=5)], type=CompType.BWD_W),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=9)], type=CompType.BWD_I),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=6)], type=CompType.BWD_W),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=10)], type=CompType.BWD_I),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=7)], type=CompType.BWD_W),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=11)], type=CompType.BWD_I),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=8)], type=CompType.BWD_W),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=12)], type=CompType.BWD_I),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=9)], type=CompType.BWD_W),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=13)], type=CompType.BWD_I),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=10)], type=CompType.BWD_W),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=14)], type=CompType.BWD_I),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=11)], type=CompType.BWD_W),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=15)], type=CompType.BWD_I),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=12)], type=CompType.BWD_W),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=13)], type=CompType.BWD_W),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=14)], type=CompType.BWD_W),
-            Chunk(pp_rank=3, batches=[BatchMeta(stage_id=3, mb_idx=15)], type=CompType.BWD_W),
-        ],
-        [  # rank 4, stage 4   warmup=3 1f1b=13 cooldown=3
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.BWD_I),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.BWD_I),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.BWD_I),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.BWD_I),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.BWD_I),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=0)], type=CompType.BWD_W),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.BWD_I),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=1)], type=CompType.BWD_W),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.BWD_I),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=2)], type=CompType.BWD_W),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.BWD_I),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=3)], type=CompType.BWD_W),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=8)], type=CompType.BWD_I),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=4)], type=CompType.BWD_W),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=9)], type=CompType.BWD_I),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=5)], type=CompType.BWD_W),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=10)], type=CompType.BWD_I),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=6)], type=CompType.BWD_W),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=11)], type=CompType.BWD_I),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=7)], type=CompType.BWD_W),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=12)], type=CompType.BWD_I),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=8)], type=CompType.BWD_W),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=13)], type=CompType.BWD_I),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=9)], type=CompType.BWD_W),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=14)], type=CompType.BWD_I),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=10)], type=CompType.BWD_W),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=15)], type=CompType.BWD_I),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=11)], type=CompType.BWD_W),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=12)], type=CompType.BWD_W),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=13)], type=CompType.BWD_W),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=14)], type=CompType.BWD_W),
-            Chunk(pp_rank=4, batches=[BatchMeta(stage_id=4, mb_idx=15)], type=CompType.BWD_W),
-        ],
-        [  # rank 5, stage 5   warmup=2 1f1b=14 cooldown=2
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.BWD_I),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.BWD_I),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.BWD_I),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.BWD_I),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.BWD_I),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.BWD_I),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=0)], type=CompType.BWD_W),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.BWD_I),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=1)], type=CompType.BWD_W),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.BWD_I),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=2)], type=CompType.BWD_W),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=8)], type=CompType.BWD_I),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=3)], type=CompType.BWD_W),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=9)], type=CompType.BWD_I),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=4)], type=CompType.BWD_W),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=10)], type=CompType.BWD_I),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=5)], type=CompType.BWD_W),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=11)], type=CompType.BWD_I),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=6)], type=CompType.BWD_W),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=12)], type=CompType.BWD_I),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=7)], type=CompType.BWD_W),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=13)], type=CompType.BWD_I),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=8)], type=CompType.BWD_W),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=14)], type=CompType.BWD_I),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=9)], type=CompType.BWD_W),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=15)], type=CompType.BWD_I),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=10)], type=CompType.BWD_W),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=11)], type=CompType.BWD_W),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=12)], type=CompType.BWD_W),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=13)], type=CompType.BWD_W),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=14)], type=CompType.BWD_W),
-            Chunk(pp_rank=5, batches=[BatchMeta(stage_id=5, mb_idx=15)], type=CompType.BWD_W),
-        ],
-        [  # rank 6, stage 6   warmup=1 1f1b=15 cooldown=1
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.BWD_I),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.BWD_I),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.BWD_I),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.BWD_I),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.BWD_I),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.BWD_I),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.BWD_I),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=0)], type=CompType.BWD_W),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.BWD_I),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=1)], type=CompType.BWD_W),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=8)], type=CompType.BWD_I),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=2)], type=CompType.BWD_W),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=9)], type=CompType.BWD_I),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=3)], type=CompType.BWD_W),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=10)], type=CompType.BWD_I),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=4)], type=CompType.BWD_W),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=11)], type=CompType.BWD_I),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=5)], type=CompType.BWD_W),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=12)], type=CompType.BWD_I),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=6)], type=CompType.BWD_W),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=13)], type=CompType.BWD_I),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=7)], type=CompType.BWD_W),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=14)], type=CompType.BWD_I),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=8)], type=CompType.BWD_W),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=15)], type=CompType.BWD_I),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=9)], type=CompType.BWD_W),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=10)], type=CompType.BWD_W),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=11)], type=CompType.BWD_W),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=12)], type=CompType.BWD_W),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=13)], type=CompType.BWD_W),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=14)], type=CompType.BWD_W),
-            Chunk(pp_rank=6, batches=[BatchMeta(stage_id=6, mb_idx=15)], type=CompType.BWD_W),
-        ],
-        [  # rank 7, stage 7   warmup=0 1f1b=16 cooldown=0
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.BWD_I),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.BWD_I),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.BWD_I),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.BWD_I),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.BWD_I),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.BWD_I),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.BWD_I),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.BWD_I),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=0)], type=CompType.BWD_W),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=8)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=8)], type=CompType.BWD_I),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=1)], type=CompType.BWD_W),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=9)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=9)], type=CompType.BWD_I),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=2)], type=CompType.BWD_W),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=10)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=10)], type=CompType.BWD_I),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=3)], type=CompType.BWD_W),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=11)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=11)], type=CompType.BWD_I),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=4)], type=CompType.BWD_W),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=12)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=12)], type=CompType.BWD_I),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=5)], type=CompType.BWD_W),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=13)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=13)], type=CompType.BWD_I),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=6)], type=CompType.BWD_W),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=14)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=14)], type=CompType.BWD_I),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=7)], type=CompType.BWD_W),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=15)], type=CompType.FWD),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=15)], type=CompType.BWD_I),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=8)], type=CompType.BWD_W),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=9)], type=CompType.BWD_W),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=10)], type=CompType.BWD_W),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=11)], type=CompType.BWD_W),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=12)], type=CompType.BWD_W),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=13)], type=CompType.BWD_W),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=14)], type=CompType.BWD_W),
-            Chunk(pp_rank=7, batches=[BatchMeta(stage_id=7, mb_idx=15)], type=CompType.BWD_W),
-        ],
-    ])
diff --git a/test/schedules/build_order_directives.py b/test/schedules/build_order_directives.py
new file mode 100644
index 0000000..52e74de
--- /dev/null
+++ b/test/schedules/build_order_directives.py
@@ -0,0 +1,638 @@
+"""Build JSON order directives for common pipeline schedules."""
+
+from __future__ import annotations
+
+from collections import defaultdict
+from contextlib import suppress
+from dataclasses import dataclass
+from html import escape
+from pathlib import Path
+import subprocess
+from typing import Literal
+
+Pass = Literal["F", "B", "BI", "BW"]
+_VALID_PASSES = frozenset({"F", "B", "BI", "BW"})
+
+
+@dataclass(frozen=True)
+class _Op:
+    pp: int
+    mb: int
+    pass_: Pass
+
+
+# A "slot" is one timestep in a rank's row. A slot holds one op normally; when
+# multiple ops are nested in the same order-directive filter group they share a
+# slot and visualize as a vertically-stacked cell, indicating that they run
+# interleaved within the same scheduling block.
+_Slot = list[_Op]
+
+
+_PASS_COLOR: dict[str, str] = {
+    "F": "#FFE08A",
+    "B": "#A8E6A1",
+    "BI": "#A9D6FF",
+    "BW": "#C6B6FF",
+}
+
+
+def _filter(pp: int, mb: int, pass_: Pass) -> list[list[int | str]]:
+    return [["PP", pp], ["MB", mb], ["PASS", pass_]]
+
+
+def _order_directive(ops: list[_Op]) -> dict:
+    return _order_directive_from_slots([[op] for op in ops])
+
+
+def _order_directive_from_slots(slots: list[_Slot]) -> dict:
+    return {
+        "op": "order",
+        "filters": [
+            [_filter(op.pp, op.mb, op.pass_) for op in slot]
+            for slot in slots
+        ],
+    }
+
+
+def _op_width(op: _Op) -> int:
+    if op.pass_ == "B":
+        return 2
+    return 1
+
+
+def _slot_width(slot: _Slot) -> int:
+    return sum(_op_width(op) for op in slot)
+
+
+def _cleanup_visualizer_sidecars(
+    output_base: Path,
+    keep_suffix: str | None = None,
+) -> None:
+    for suffix in (".dot", ".txt"):
+        if suffix == keep_suffix:
+            continue
+        with suppress(FileNotFoundError):
+            output_base.with_suffix(suffix).unlink()
+
+
+def visualize_order_directives(
+    order_directives: list[dict],
+    output_path: str | Path = "out/schedule",
+    fmt: str = "png",
+) -> str:
+    """Render a 2-D schedule grid, one row per physical rank.
+
+    The order directives do not carry explicit ``None`` slots. Columns are
+    reconstructed as earliest feasible logical times from per-rank temporal
+    order and FWD/BWD pipeline data dependencies.
+    """
+    rows = _rows_from_order_directives(order_directives)
+    placements = _compute_columns(rows)
+    max_col = max(
+        (col + _slot_width(rows[rank][slot_idx]) - 1 for (rank, slot_idx), col in placements.items()),
+        default=-1,
+    )
+
+    table: list[list[_Slot | None]] = [
+        [None for _ in range(max_col + 1)]
+        for _ in rows
+    ]
+    for key, col in placements.items():
+        rank, slot_idx = key
+        table[rank][col] = rows[rank][slot_idx]
+
+    output = Path(output_path)
+    if output.suffix:
+        fmt = output.suffix.lstrip(".")
+        output = output.with_suffix("")
+
+    output.parent.mkdir(parents=True, exist_ok=True)
+    rendered_suffix = f".{fmt}"
+    _cleanup_visualizer_sidecars(output, keep_suffix=rendered_suffix)
+    try:
+        import graphviz
+
+        dot = graphviz.Digraph("PipelineSchedule")
+        dot.attr(rankdir="LR")
+        dot.node("schedule", label=_html_table(table), shape="plain")
+        try:
+            return dot.render(str(output), format=fmt, cleanup=True)
+        finally:
+            _cleanup_visualizer_sidecars(output, keep_suffix=rendered_suffix)
+    except ImportError:
+        dot_path = output.with_suffix(".dot")
+        rendered_path = output.with_suffix(f".{fmt}")
+        dot_path.write_text(_dot_source(table), encoding="utf-8")
+        rendered_ok = False
+        try:
+            subprocess.run(
+                ["dot", f"-T{fmt}", str(dot_path), "-o", str(rendered_path)],
+                check=True,
+            )
+            rendered_ok = True
+            return str(rendered_path)
+        except (FileNotFoundError, subprocess.CalledProcessError):
+            fallback = output.with_suffix(".txt")
+            fallback.write_text(_format_text_table(table), encoding="utf-8")
+            return str(fallback)
+        finally:
+            if rendered_ok:
+                _cleanup_visualizer_sidecars(output, keep_suffix=rendered_suffix)
+
+
+def build_1f1b_schedule(n_ranks: int, n_mbs: int) -> list[dict]:
+    """Return one 1F1B order directive per rank.
+
+    This preserves the old PipelineSchedule builder's non-None per-rank op order:
+    rank r warms up with ``n_ranks - 1 - r`` forwards, then alternates forward
+    and fused backward until all microbatches have completed.
+    """
+    _validate_positive("n_ranks", n_ranks)
+    _validate_positive("n_mbs", n_mbs)
+
+    rows: list[list[_Op]] = [[] for _ in range(n_ranks)]
+    fwd_mb = [0] * n_ranks
+    bwd_mb = [0] * n_ranks
+
+    for rank in range(n_ranks):
+        warmup = n_ranks - 1 - rank
+        for _ in range(min(warmup, n_mbs)):
+            rows[rank].append(_Op(pp=rank, mb=fwd_mb[rank], pass_="F"))
+            fwd_mb[rank] += 1
+
+    for rank in range(n_ranks):
+        while bwd_mb[rank] < n_mbs:
+            if fwd_mb[rank] < n_mbs:
+                rows[rank].append(_Op(pp=rank, mb=fwd_mb[rank], pass_="F"))
+                fwd_mb[rank] += 1
+            rows[rank].append(_Op(pp=rank, mb=bwd_mb[rank], pass_="B"))
+            bwd_mb[rank] += 1
+
+    return [_order_directive(row) for row in rows]
+
+
+def build_zerobubble_schedule(n_ranks: int, n_mbs: int) -> list[dict]:
+    """Return one ZeroBubble order directive per rank.
+
+    This is the old ZB-1 schedule builder expressed as order directives. Each
+    rank owns one pipeline stage, fused backward is split into BI/BW, and BW is
+    deferred by ``rank`` backward-input steps.
+    """
+    _validate_positive("n_ranks", n_ranks)
+    _validate_positive("n_mbs", n_mbs)
+
+    rows: list[list[_Op]] = []
+    for rank in range(n_ranks):
+        warmup = min(n_ranks - 1 - rank, n_mbs)
+        fwd_bwd_ops = n_mbs - warmup
+        cooldown_ops = n_mbs - fwd_bwd_ops
+        deferred_bw_count = rank
+
+        fwd_mb = 0
+        bwd_mb = 0
+        bw_mb = 0
+        bwdi_count = 0
+        emitted_bw_count = 0
+        ops: list[_Op] = []
+
+        for op_idx in range(warmup + fwd_bwd_ops + cooldown_ops):
+            if op_idx < warmup:
+                ops.append(_Op(pp=rank, mb=fwd_mb, pass_="F"))
+                fwd_mb += 1
+                continue
+
+            if op_idx < warmup + fwd_bwd_ops:
+                ops.append(_Op(pp=rank, mb=fwd_mb, pass_="F"))
+                fwd_mb += 1
+
+            ops.append(_Op(pp=rank, mb=bwd_mb, pass_="BI"))
+            bwd_mb += 1
+            bwdi_count += 1
+
+            if bwdi_count > deferred_bw_count:
+                ops.append(_Op(pp=rank, mb=bw_mb, pass_="BW"))
+                bw_mb += 1
+                emitted_bw_count += 1
+
+        while emitted_bw_count < bwdi_count:
+            ops.append(_Op(pp=rank, mb=bw_mb, pass_="BW"))
+            bw_mb += 1
+            emitted_bw_count += 1
+
+        rows.append(ops)
+
+    return [_order_directive(row) for row in rows]
+
+
+def build_interleaved_1f1b_schedule(
+    n_ranks: int,
+    n_mbs: int,
+    n_virtual_stages: int,
+) -> list[dict]:
+    """Return one interleaved 1F1B order directive per physical rank.
+
+    Rank ``r`` owns virtual pipeline stages ``r + k * n_ranks``. The op order is
+    the old interleaved PipelineSchedule order with the placeholder ``None`` slots
+    omitted.
+    """
+    _validate_positive("n_ranks", n_ranks)
+    _validate_positive("n_mbs", n_mbs)
+    _validate_positive("n_virtual_stages", n_virtual_stages)
+
+    microbatches_per_round = n_ranks
+
+    def warmup_ops(rank: int) -> int:
+        return min(
+            (n_virtual_stages - 1) * n_ranks + 2 * (n_ranks - 1 - rank),
+            n_mbs * n_virtual_stages,
+        )
+
+    def forward_stage_index(step: int, rank: int) -> int:
+        local_index = (step // microbatches_per_round) % n_virtual_stages
+        return local_index * n_ranks + rank
+
+    def backward_stage_index(step: int, rank: int, warmup: int) -> int:
+        local_index = (
+            n_virtual_stages
+            - 1
+            - ((step - warmup) // microbatches_per_round) % n_virtual_stages
+        )
+        return local_index * n_ranks + rank
+
+    rows: list[list[_Op]] = []
+    for rank in range(n_ranks):
+        warmup = warmup_ops(rank)
+        microbatch_ops = n_virtual_stages * n_mbs
+        fwd_bwd_ops = microbatch_ops - warmup
+        cooldown_ops = microbatch_ops - fwd_bwd_ops
+
+        fwd_mb: dict[int, int] = defaultdict(int)
+        bwd_mb: dict[int, int] = defaultdict(int)
+        ops: list[_Op] = []
+
+        for op_idx in range(warmup + fwd_bwd_ops + cooldown_ops):
+            if op_idx < warmup:
+                stage = forward_stage_index(op_idx, rank)
+                mb = fwd_mb[stage]
+                fwd_mb[stage] += 1
+                ops.append(_Op(pp=stage, mb=mb, pass_="F"))
+            elif op_idx < warmup + fwd_bwd_ops:
+                stage = forward_stage_index(op_idx, rank)
+                mb = fwd_mb[stage]
+                fwd_mb[stage] += 1
+                ops.append(_Op(pp=stage, mb=mb, pass_="F"))
+
+                stage = backward_stage_index(op_idx, rank, warmup)
+                mb = bwd_mb[stage]
+                bwd_mb[stage] += 1
+                ops.append(_Op(pp=stage, mb=mb, pass_="B"))
+            else:
+                stage = backward_stage_index(op_idx, rank, warmup)
+                mb = bwd_mb[stage]
+                bwd_mb[stage] += 1
+                ops.append(_Op(pp=stage, mb=mb, pass_="B"))
+
+        rows.append(ops)
+
+    return [_order_directive(row) for row in rows]
+
+
+def build_dualpipev_schedule(n_ranks: int, n_mbs: int) -> list[dict]:
+    """Return DualPipeV order directives with overlapped FWD/BWD slots.
+
+    Rank ``r`` owns V-layout stages ``r`` and ``2 * n_ranks - 1 - r``. The
+    old ``FWD_BWD`` chunk is represented as one nested order filter group
+    containing the corresponding FWD filter and fused BWD filter.
+    """
+    _validate_positive("n_ranks", n_ranks)
+    _validate_positive("n_mbs", n_mbs)
+    if n_mbs < 2 * n_ranks:
+        raise ValueError(
+            f"dualpipev requires n_mbs >= 2 * n_ranks, got n_mbs={n_mbs}, "
+            f"n_ranks={n_ranks}"
+        )
+
+    rows: list[list[_Slot]] = [[] for _ in range(n_ranks)]
+
+    for rank in range(n_ranks):
+        s0 = rank
+        s1 = 2 * n_ranks - 1 - rank
+        slots = rows[rank]
+        counts: dict[tuple[int, str], int] = {}
+        weight_queue: list[tuple[int, int]] = []
+
+        def count(stage: int, key: str) -> int:
+            return counts.get((stage, key), 0)
+
+        def inc(stage: int, key: str) -> None:
+            counts[(stage, key)] = count(stage, key) + 1
+
+        def inc_bwd(stage: int) -> None:
+            inc(stage, "i")
+            inc(stage, "w")
+
+        def append_op(stage: int, mb: int, pass_: Pass) -> None:
+            slots.append([_Op(pp=stage, mb=mb, pass_=pass_)])
+
+        def fwd(stage: int) -> None:
+            mb = count(stage, "f")
+            append_op(stage, mb, "F")
+            inc(stage, "f")
+
+        def bwdi(stage: int) -> None:
+            mb = count(stage, "i")
+            append_op(stage, mb, "BI")
+            weight_queue.append((stage, mb))
+            inc(stage, "i")
+
+        def drain_w() -> None:
+            if not weight_queue:
+                return
+            stage, mb = weight_queue.pop(0)
+            append_op(stage, mb, "BW")
+            inc(stage, "w")
+
+        def full_bwd(stage: int) -> None:
+            mb = count(stage, "i")
+            append_op(stage, mb, "B")
+            inc_bwd(stage)
+
+        def overlap_fb(fwd_stage: int, bwd_stage: int) -> None:
+            fwd_mb = count(fwd_stage, "f")
+            bwd_mb = count(bwd_stage, "i")
+            slots.append([
+                _Op(pp=fwd_stage, mb=fwd_mb, pass_="F"),
+                _Op(pp=bwd_stage, mb=bwd_mb, pass_="B"),
+            ])
+            inc(fwd_stage, "f")
+            inc_bwd(bwd_stage)
+
+        # Phase 1: F0 warmup.
+        for _ in range((n_ranks - rank - 1) * 2):
+            fwd(s0)
+
+        # Phase 2: F0F1.
+        for _ in range(rank + 1):
+            fwd(s0)
+            fwd(s1)
+
+        # Phase 3: I1 W1 F1.
+        for _ in range(n_ranks - rank - 1):
+            bwdi(s1)
+            drain_w()
+            fwd(s1)
+
+        # Phase 4: Main overlapped F0B1 + F1B0.
+        for i in range(n_mbs - n_ranks * 2 + rank + 1):
+            if i == 0 and rank == n_ranks - 1:
+                fwd(s0)
+                full_bwd(s1)
+            else:
+                overlap_fb(s0, s1)
+            overlap_fb(s1, s0)
+
+        # Phase 5: B1 + F1B0.
+        for _ in range(n_ranks - rank - 1):
+            full_bwd(s1)
+            overlap_fb(s1, s0)
+
+        # Phase 6: B1B0, switching the second half to split backward.
+        enable_zb = False
+        for i in range(rank + 1):
+            if i == (rank + 1) // 2 and rank % 2 == 1:
+                enable_zb = True
+            (bwdi if enable_zb else full_bwd)(s1)
+            if i == (rank + 1) // 2 and rank % 2 == 0:
+                enable_zb = True
+            (bwdi if enable_zb else full_bwd)(s0)
+
+        # Phase 7: W0 B0.
+        for _ in range(n_ranks - rank - 1):
+            drain_w()
+            (bwdi if enable_zb else full_bwd)(s0)
+
+        # Phase 8: W0.
+        for _ in range(rank + 1):
+            drain_w()
+
+    return [_order_directive_from_slots(row) for row in rows]
+
+
+def _validate_positive(name: str, value: int) -> None:
+    if not isinstance(value, int) or value <= 0:
+        raise ValueError(f"{name} must be a positive integer, got {value!r}")
+
+
+def _rows_from_order_directives(order_directives: list[dict]) -> list[list[_Slot]]:
+    rows = []
+    for directive in order_directives:
+        if directive.get("op") != "order":
+            raise ValueError(f"expected order directive, got {directive}")
+        filters = directive.get("filters")
+        if not isinstance(filters, list):
+            raise ValueError(f"order directive requires filters list: {directive}")
+        row: list[_Slot] = []
+        for filter_slot in _parse_filter_slots(filters):
+            slot: _Slot = []
+            for flt in filter_slot:
+                spec = _filter_to_dict(flt)
+                pass_value = spec["PASS"]
+                if pass_value not in _VALID_PASSES:
+                    raise ValueError(
+                        f"unsupported PASS={pass_value!r}; expected one of {sorted(_VALID_PASSES)}"
+                    )
+                slot.append(_Op(pp=int(spec["PP"]), mb=int(spec["MB"]), pass_=pass_value))
+            row.append(slot)
+        rows.append(row)
+    return rows
+
+
+def _is_filter_spec(flt: object) -> bool:
+    if isinstance(flt, dict):
+        return True
+    if isinstance(flt, list):
+        return all(
+            isinstance(item, (list, tuple))
+            and len(item) == 2
+            and isinstance(item[0], str)
+            for item in flt
+        )
+    return False
+
+
+def _parse_filter_slots(filters: list) -> list[list]:
+    """Group the directive's filter entries into slots.
+
+    Each top-level entry in ``filters`` corresponds to one slot (one timestep
+    on the owning rank). A bare filter spec becomes a single-op slot. A list of
+    filter specs becomes a multi-op slot whose ops run interleaved at the same
+    scheduling step and visualize stacked vertically in one block.
+    """
+    slots: list[list] = []
+    for item in filters:
+        if _is_filter_spec(item):
+            slots.append([item])
+        elif isinstance(item, list):
+            slots.append(list(item))
+        else:
+            raise ValueError(f"invalid order filter group: {item}")
+    return slots
+
+
+def _filter_to_dict(flt: object) -> dict:
+    if isinstance(flt, dict):
+        return dict(flt)
+    if isinstance(flt, list):
+        out = {}
+        for item in flt:
+            if not isinstance(item, (list, tuple)) or len(item) != 2:
+                raise ValueError(f"invalid order filter item: {item}")
+            out[item[0]] = item[1]
+        return out
+    raise ValueError(f"invalid order filter: {flt}")
+
+
+def _compute_columns(rows: list[list[_Slot]]) -> dict[tuple[int, int], int]:
+    # Each (rank, slot_idx) is one timeslot. Multiple ops within a slot share
+    # that slot's column; the slot's width is max(op width) across its ops.
+    key_by_op: dict[tuple[int, int, str], tuple[int, int]] = {}
+    for rank, row in enumerate(rows):
+        for slot_idx, slot in enumerate(row):
+            for op in slot:
+                op_key = (op.pp, op.mb, op.pass_)
+                if op_key in key_by_op:
+                    raise ValueError(f"duplicate scheduled op: {op_key}")
+                key_by_op[op_key] = (rank, slot_idx)
+
+    preds: dict[tuple[int, int], set[tuple[int, int]]] = {
+        (rank, slot_idx): set()
+        for rank, row in enumerate(rows)
+        for slot_idx, _slot in enumerate(row)
+    }
+    succs: dict[tuple[int, int], set[tuple[int, int]]] = {
+        key: set() for key in preds
+    }
+
+    def add_edge(src: tuple[int, int] | None, dst: tuple[int, int] | None) -> None:
+        if src is None or dst is None or src == dst:
+            return
+        preds[dst].add(src)
+        succs[src].add(dst)
+
+    for rank, row in enumerate(rows):
+        for slot_idx in range(1, len(row)):
+            add_edge((rank, slot_idx - 1), (rank, slot_idx))
+
+    for rank, row in enumerate(rows):
+        for slot_idx, slot in enumerate(row):
+            dst = (rank, slot_idx)
+            for op in slot:
+                if op.pass_ == "F":
+                    add_edge(key_by_op.get((op.pp - 1, op.mb, "F")), dst)
+                elif op.pass_ == "B":
+                    add_edge(key_by_op.get((op.pp, op.mb, "F")), dst)
+                    add_edge(key_by_op.get((op.pp + 1, op.mb, "B")), dst)
+                elif op.pass_ == "BI":
+                    add_edge(key_by_op.get((op.pp, op.mb, "F")), dst)
+                    add_edge(key_by_op.get((op.pp + 1, op.mb, "BI")), dst)
+                elif op.pass_ == "BW":
+                    add_edge(key_by_op.get((op.pp, op.mb, "BI")), dst)
+                else:
+                    raise ValueError(f"unsupported pass={op.pass_!r}")
+
+    columns = {key: 0 for key in preds}
+    pending = {key: set(value) for key, value in preds.items()}
+    ready = [key for key, value in pending.items() if not value]
+    visited = 0
+
+    while ready:
+        key = ready.pop(0)
+        visited += 1
+        for succ in sorted(succs[key]):
+            rank, slot_idx = key
+            columns[succ] = max(columns[succ], columns[key] + _slot_width(rows[rank][slot_idx]))
+            pending[succ].discard(key)
+            if not pending[succ]:
+                ready.append(succ)
+
+    if visited != len(preds):
+        blocked = [key for key, value in pending.items() if value]
+        raise ValueError(f"schedule dependencies contain a cycle; blocked={blocked[:8]}")
+    return columns
+
+
+def _html_table(table: list[list[_Slot | None]]) -> str:
+    max_cols = max((len(row) for row in table), default=0)
+    lines = ['<<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="6">']
+    lines.append('<TR><TD BGCOLOR="#eeeeee">rank</TD>')
+    for col in range(max_cols):
+        lines.append(f'<TD BGCOLOR="#eeeeee">t{col}</TD>')
+    lines.append("</TR>")
+    for rank, row in enumerate(table):
+        lines.append(f'<TR><TD BGCOLOR="#eeeeee">r{rank}</TD>')
+        col = 0
+        while col < len(row):
+            slot = row[col]
+            if slot is None:
+                lines.append('<TD WIDTH="72" HEIGHT="36"></TD>')
+                col += 1
+            else:
+                colspan = _slot_width(slot)
+                colspan_attr = f' COLSPAN="{colspan}"' if colspan > 1 else ""
+                width = 72 * colspan
+                if len(slot) == 1:
+                    op = slot[0]
+                    color = _PASS_COLOR[op.pass_]
+                    label = f"{escape(op.pass_)}<BR/>PP{op.pp} MB{op.mb}"
+                    lines.append(
+                        f'<TD BGCOLOR="{color}" WIDTH="{width}" HEIGHT="36"{colspan_attr}>{label}</TD>'
+                    )
+                else:
+                    # Multi-op slot: ops run interleaved and stack vertically in
+                    # one block. Use a nested table so each op gets its own
+                    # colored row spanning the full slot width.
+                    inner = ['<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="6">']
+                    for op in slot:
+                        color = _PASS_COLOR[op.pass_]
+                        label = f"{escape(op.pass_)}<BR/>PP{op.pp} MB{op.mb}"
+                        inner.append(
+                            f'<TR><TD BGCOLOR="{color}" WIDTH="{width}" HEIGHT="36">{label}</TD></TR>'
+                        )
+                    inner.append("</TABLE>")
+                    lines.append(
+                        f'<TD WIDTH="{width}"{colspan_attr} CELLPADDING="0">{"".join(inner)}</TD>'
+                    )
+                col += colspan
+        lines.append("</TR>")
+    lines.append("</TABLE>>")
+    return "".join(lines)
+
+
+def _dot_source(table: list[list[_Op | None]]) -> str:
+    return "\n".join([
+        "digraph PipelineSchedule {",
+        "  rankdir=LR;",
+        f"  schedule [shape=plain label={_html_table(table)}];",
+        "}",
+        "",
+    ])
+
+
+def _format_text_table(table: list[list[_Slot | None]]) -> str:
+    lines = []
+    for rank, row in enumerate(table):
+        cells = []
+        col = 0
+        while col < len(row):
+            slot = row[col]
+            if slot is None:
+                cells.append("--------")
+                col += 1
+            else:
+                width = _slot_width(slot)
+                # Stacked ops in one slot join with "/" to flag interleaving.
+                label = "/".join(f"{op.pass_}:s{op.pp}:m{op.mb}" for op in slot)
+                cells.append(label.ljust(8 * width, "="))
+                col += width
+        lines.append(f"rank {rank}: " + " | ".join(cells))
+    return "\n".join(lines) + "\n"
diff --git a/test/schedules/build_schedule.py b/test/schedules/build_schedule.py
new file mode 100644
index 0000000..ebfb7c1
--- /dev/null
+++ b/test/schedules/build_schedule.py
@@ -0,0 +1,16 @@
+"""Compatibility entry point for the schedule-building test harness."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+_REPO_ROOT = Path(__file__).resolve().parents[2]
+if str(_REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(_REPO_ROOT))
+
+from test.test_harness import main
+
+
+if __name__ == "__main__":
+    main()
diff --git a/test/schedules/llama_pp.json b/test/schedules/llama_pp.json
new file mode 100644
index 0000000..a93ee90
--- /dev/null
+++ b/test/schedules/llama_pp.json
@@ -0,0 +1,14 @@
+[
+  {
+    "op": "place",
+    "filter": [["PP", 0]],
+    "devices": [0],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 1]],
+    "devices": [1],
+    "stream": "pp_stream"
+  }
+]
diff --git a/test/schedules/qwen_pp2.json b/test/schedules/qwen_pp2.json
new file mode 100644
index 0000000..a93ee90
--- /dev/null
+++ b/test/schedules/qwen_pp2.json
@@ -0,0 +1,14 @@
+[
+  {
+    "op": "place",
+    "filter": [["PP", 0]],
+    "devices": [0],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 1]],
+    "devices": [1],
+    "stream": "pp_stream"
+  }
+]
diff --git a/test/schedules/qwen_pp2_dp2_ep2.json b/test/schedules/qwen_pp2_dp2_ep2.json
new file mode 100644
index 0000000..c183ee1
--- /dev/null
+++ b/test/schedules/qwen_pp2_dp2_ep2.json
@@ -0,0 +1,38 @@
+[
+  {
+    "op": "place",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "shard",
+    "filter": [["PP", 0], ["EP", "*"]],
+    "devices": [0, 2],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "shard",
+    "filter": [["PP", 1], ["EP", "*"]],
+    "devices": [1, 3],
+    "stream": "ep_stream"
+  }
+]
diff --git a/test/schedules/qwen_pp2_dp2_ep2_dualpipev.json b/test/schedules/qwen_pp2_dp2_ep2_dualpipev.json
new file mode 100644
index 0000000..a97f416
--- /dev/null
+++ b/test/schedules/qwen_pp2_dp2_ep2_dualpipev.json
@@ -0,0 +1,74 @@
+[
+  {
+    "op": "place",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 2]],
+    "devices": [1, 3],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 3]],
+    "devices": [0, 2],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 2]],
+    "devices": [1, 3],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 3]],
+    "devices": [0, 2],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "shard",
+    "filter": [["PP", 0], ["EP", "*"]],
+    "devices": [0, 2],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "shard",
+    "filter": [["PP", 1], ["EP", "*"]],
+    "devices": [1, 3],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "shard",
+    "filter": [["PP", 2], ["EP", "*"]],
+    "devices": [1, 3],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "shard",
+    "filter": [["PP", 3], ["EP", "*"]],
+    "devices": [0, 2],
+    "stream": "ep_stream"
+  }
+]
diff --git a/test/schedules/qwen_pp2_dp2_ep2_interleaved.json b/test/schedules/qwen_pp2_dp2_ep2_interleaved.json
new file mode 100644
index 0000000..0929510
--- /dev/null
+++ b/test/schedules/qwen_pp2_dp2_ep2_interleaved.json
@@ -0,0 +1,50 @@
+[
+  {
+    "op": "place",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 2]],
+    "devices": [0, 2],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 3]],
+    "devices": [1, 3],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "shard",
+    "filter": [["PP", 0], ["EP", "*"]],
+    "devices": [0, 2],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "shard",
+    "filter": [["PP", 1], ["EP", "*"]],
+    "devices": [1, 3],
+    "stream": "ep_stream"
+  }
+]
diff --git a/test/schedules/qwen_pp2_interleaved.json b/test/schedules/qwen_pp2_interleaved.json
new file mode 100644
index 0000000..3702849
--- /dev/null
+++ b/test/schedules/qwen_pp2_interleaved.json
@@ -0,0 +1,26 @@
+[
+  {
+    "op": "place",
+    "filter": [["PP", 0]],
+    "devices": [0],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 1]],
+    "devices": [1],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 2]],
+    "devices": [0],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 3]],
+    "devices": [1],
+    "stream": "pp_stream"
+  }
+]
diff --git a/test/schedules/qwen_pp4.json b/test/schedules/qwen_pp4.json
new file mode 100644
index 0000000..3702849
--- /dev/null
+++ b/test/schedules/qwen_pp4.json
@@ -0,0 +1,26 @@
+[
+  {
+    "op": "place",
+    "filter": [["PP", 0]],
+    "devices": [0],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 1]],
+    "devices": [1],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 2]],
+    "devices": [0],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 3]],
+    "devices": [1],
+    "stream": "pp_stream"
+  }
+]
diff --git a/test/schedules/qwen_pp_dp.json b/test/schedules/qwen_pp_dp.json
new file mode 100644
index 0000000..20286c1
--- /dev/null
+++ b/test/schedules/qwen_pp_dp.json
@@ -0,0 +1,26 @@
+[
+  {
+    "op": "place",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "stream": "dp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "stream": "dp_stream"
+  }
+]
diff --git a/test/schedules/qwen_pp_dp_ep_overlap.json b/test/schedules/qwen_pp_dp_ep_overlap.json
new file mode 100644
index 0000000..d47c2b5
--- /dev/null
+++ b/test/schedules/qwen_pp_dp_ep_overlap.json
@@ -0,0 +1,52 @@
+[
+  {
+    "op": "place",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "shard",
+    "filter": [["PP", 0], ["EP", "*"]],
+    "devices": [0, 2],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "shard",
+    "filter": [["PP", 1], ["EP", "*"]],
+    "devices": [1, 3],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "split",
+    "filter": [],
+    "dim_name": "MB",
+    "num_microbatches": 2
+  },
+  {
+    "op": "order",
+    "filters": [[["PP", 0], ["MB", 0], ["PASS", "F"]], [["PP", 0], ["MB", 1], ["PASS", "F"]], [["PP", 0], ["MB", 0], ["PASS", "B"]], [["PP", 0], ["MB", 1], ["PASS", "B"]]]
+  },
+  {
+    "op": "order",
+    "filters": [[["PP", 1], ["MB", 0], ["PASS", "F"]], [[["PP", 1], ["MB", 0], ["PASS", "B"]], [["PP", 1], ["MB", 1], ["PASS", "F"]]], [["PP", 1], ["MB", 1], ["PASS", "B"]]]
+  }
+]
diff --git a/test/schedules/qwen_pp_dp_ep_zero2.json b/test/schedules/qwen_pp_dp_ep_zero2.json
new file mode 100644
index 0000000..2a79c82
--- /dev/null
+++ b/test/schedules/qwen_pp_dp_ep_zero2.json
@@ -0,0 +1,40 @@
+[
+  {
+    "op": "place",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "stream": "dp_stream",
+    "shard_grads": true
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "stream": "dp_stream",
+    "shard_grads": true
+  },
+  {
+    "op": "shard",
+    "filter": [["PP", 0], ["EP", "*"]],
+    "devices": [0, 2],
+    "stream": "dp_stream"
+  },
+  {
+    "op": "shard",
+    "filter": [["PP", 1], ["EP", "*"]],
+    "devices": [1, 3],
+    "stream": "dp_stream"
+  }
+]
diff --git a/test/schedules/qwen_pp_dp_ep_zero3.json b/test/schedules/qwen_pp_dp_ep_zero3.json
new file mode 100644
index 0000000..7d24f20
--- /dev/null
+++ b/test/schedules/qwen_pp_dp_ep_zero3.json
@@ -0,0 +1,42 @@
+[
+  {
+    "op": "place",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "stream": "dp_stream",
+    "shard_grads": true,
+    "shard_params": true
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "stream": "dp_stream",
+    "shard_grads": true,
+    "shard_params": true
+  },
+  {
+    "op": "shard",
+    "filter": [["PP", 0], ["EP", "*"]],
+    "devices": [0, 2],
+    "stream": "dp_stream"
+  },
+  {
+    "op": "shard",
+    "filter": [["PP", 1], ["EP", "*"]],
+    "devices": [1, 3],
+    "stream": "dp_stream"
+  }
+]
diff --git a/test/schedules/qwen_pp_dp_zero2.json b/test/schedules/qwen_pp_dp_zero2.json
new file mode 100644
index 0000000..c1c11cb
--- /dev/null
+++ b/test/schedules/qwen_pp_dp_zero2.json
@@ -0,0 +1,28 @@
+[
+  {
+    "op": "place",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "stream": "dp_stream",
+    "shard_grads": true
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "stream": "dp_stream",
+    "shard_grads": true
+  }
+]
diff --git a/test/schedules/qwen_pp_dp_zero3.json b/test/schedules/qwen_pp_dp_zero3.json
new file mode 100644
index 0000000..1ac6495
--- /dev/null
+++ b/test/schedules/qwen_pp_dp_zero3.json
@@ -0,0 +1,34 @@
+[
+  {
+    "op": "place",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 0]],
+    "devices": [0, 2],
+    "gather_stream": "gather_stream",
+    "reduce_stream": "reduce_stream",
+    "shard_grads": true,
+    "shard_params": true,
+    "bucket_size": 1000
+  },
+  {
+    "op": "replicate",
+    "filter": [["PP", 1]],
+    "devices": [1, 3],
+    "gather_stream": "gather_stream",
+    "reduce_stream": "reduce_stream",
+    "shard_grads": true,
+    "shard_params": true,
+    "bucket_size": 1000
+  }
+]
diff --git a/test/test_backward_split.py b/test/test_backward_split.py
deleted file mode 100644
index edc0890..0000000
--- a/test/test_backward_split.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import ray
-
-from piper.piper import piper, distributed_stage
-from piper.exec import piper_exec, Task, TaskType
-from piper.utils import piper_metadata
-from piper.compile import piper_setup 
-from .schedule_helpers import build_zb1p_schedule, print_schedule
-
-
-class MyModelVanilla(nn.Module):
-    """
-    Simple 2-layer MLP: 1024 -> 512 -> 512
-    Used as the baseline (non-pipelined) model.
-    """
-    def __init__(self, in_dim=1024, hidden_dim=512, out_dim=512):
-        super().__init__()
-        self.fc1 = nn.Linear(in_dim, hidden_dim)
-        self.fc2 = nn.Linear(hidden_dim, out_dim)
-
-    def forward(self, x):
-        x = F.relu(self.fc1(x))
-        x = F.relu(self.fc2(x))
-        return x
-
-
-class MyModelPipelined(nn.Module):
-    def __init__(self, in_dim=1024, hidden_dim=512, out_dim=512):
-        super().__init__()
-        self.fc1 = nn.Linear(in_dim, hidden_dim)
-        self.fc2 = nn.Linear(hidden_dim, out_dim)
-
-    def forward(self, x, dynamo_mb: int = 0):
-        distributed_stage(0, actor_id=0, mb=dynamo_mb, optim=torch.optim.Adam)
-        x = F.relu(self.fc1(x))
-
-        distributed_stage(1, actor_id=1, mb=dynamo_mb, optim=torch.optim.Adam)
-        x = F.relu(self.fc2(x))
-
-        return x
-
-
-def main():
-    torch.manual_seed(0)
-    ray.init(ignore_reinit_error=True)
-
-    device = "cuda"
-
-    init_model = MyModelVanilla().to(device)
-    init_state = init_model.state_dict()
-
-    model_base = MyModelVanilla().to(device)
-    model_base.load_state_dict(init_state)
-    opt_base = torch.optim.Adam(model_base.parameters(), lr=1e-3)
-
-    model_pipe = MyModelPipelined().to(device)
-    model_pipe.load_state_dict(init_state)
-
-    batch_size = 8
-    in_dim = 1024
-    out_dim = 512
-    num_mbs = 4
-
-    x = torch.randn(batch_size, in_dim, device=device)
-    y = torch.randn(batch_size, out_dim, device=device)
-    loss_fn = nn.MSELoss(reduction="mean")
-
-    compiled = piper_setup(
-        model_pipe,
-        example_inputs=[x],
-        backend=piper,
-    )
-
-    actors = piper_metadata["actors"]
-    num_actors = len(actors)
-    assert num_actors == 2, f"Expected 2 actors, got {num_actors}"
-
-    ray.get(actors[0].send_input.remote(x))
-    ray.get(actors[num_actors - 1].send_truth.remote(y))
-
-    schedule = build_zb1p_schedule(n_mbs=num_mbs, n_stages=2)
-    print("Schedule:")
-    print_schedule(schedule)
-
-    def iter_schedule():
-        out = piper_exec(compiled, schedule, [x], y, loss_fn, num_mbs)
-        return ray.get(out)
-
-    iter_schedule()
-
-    piper_grads = []
-    for stage_id in range(num_actors):
-        stage_grads = ray.get(actors[stage_id].get_param_grads.remote(stage_id))
-        piper_grads.extend(stage_grads)
-
-    print(piper_grads)
-
-    piper_params_after = []
-    for stage_id in range(num_actors):
-        stage_params = ray.get(actors[stage_id].get_params.remote(stage_id))
-        piper_params_after.extend(stage_params)
-
-    opt_base.zero_grad(set_to_none=True)
-
-    for _ in range(num_mbs):
-        out_base = model_base(x)
-        loss_base = loss_fn(out_base, y)
-        loss_base.backward()
-
-    print(opt_base)
-    opt_base.step()
-
-    base_params_after = [
-        p.detach().clone().cpu() for p in model_base.parameters()
-    ]
-    base_grads = [p.grad.detach().clone().cpu() for p in model_base.parameters()]
-
-    assert len(piper_params_after) == len(base_params_after), \
-        "Mismatch in # of parameters between pipeline and baseline models"
-
-    # for i, (g_pipe, g_base) in enumerate(zip(piper_grads, base_grads)):
-    #     if g_pipe is None and g_base is None:
-    #         print(f"Grad {i}: both None")
-    #         continue
-    #     if g_pipe is None or g_base is None:
-    #         print(f"Grad {i}: one is None, one not")
-    #         continue
-    #     diff = (g_pipe - g_base).abs().max().item()
-    #     print(f"Grad {i}: max |Δ| = {diff:.6e}")
-
-
-    max_abs_diff = 0.0
-    for i, (p_pipe, p_base) in enumerate(zip(piper_params_after, base_params_after)):
-        diff = (p_pipe - p_base).abs().max().item()
-        max_abs_diff = max(max_abs_diff, diff)
-        print(f"Param {i}: max |Δ| = {diff:.6e}")
-
-    print(f"\n[RESULT] Max param diff after one step: {max_abs_diff:.6e}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/test_graph_transform.py b/test/test_graph_transform.py
similarity index 98%
rename from src/test_graph_transform.py
rename to test/test_graph_transform.py
index e6f4ec3..8752fc3 100644
--- a/src/test_graph_transform.py
+++ b/test/test_graph_transform.py
@@ -11,7 +11,7 @@
 in that same way via a custom torch.compile backend.
 
 Usage:
-    python -m src.test_graph_transform
+    python -m test.test_graph_transform
 """
 import argparse
 import copy
@@ -20,7 +20,7 @@
 import torch.nn as nn
 import torch.fx as fx
 
-from .piper_graph_transform import _split_gm_by_stages, _profile_and_split_gm
+from src.piper_graph_transform import _split_gm_by_stages, _profile_and_split_gm
 
 
 # ---------------------------------------------------------------------------
diff --git a/test/test_harness.py b/test/test_harness.py
new file mode 100644
index 0000000..df71e56
--- /dev/null
+++ b/test/test_harness.py
@@ -0,0 +1,634 @@
+"""Build a schedule JSON and run a model test with it.
+
+This is a small harness around ``test.test_qwen`` / ``test.test_llama``. It
+consumes only schedule-building arguments; all remaining arguments are forwarded
+to the selected test module unchanged.
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import re
+import statistics
+import subprocess
+import sys
+import tempfile
+from datetime import datetime
+from pathlib import Path
+
+try:
+    from .schedules.build_order_directives import (
+        build_1f1b_schedule,
+        build_dualpipev_schedule,
+        build_interleaved_1f1b_schedule,
+        build_zerobubble_schedule,
+        visualize_order_directives,
+    )
+except ImportError:
+    from schedules.build_order_directives import (
+        build_1f1b_schedule,
+        build_dualpipev_schedule,
+        build_interleaved_1f1b_schedule,
+        build_zerobubble_schedule,
+        visualize_order_directives,
+    )
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description=(
+            "Build a full schedule JSON from a base schedule and generated order "
+            "directives, then run test_qwen or test_llama with it."
+        )
+    )
+    parser.add_argument(
+        "--test-file",
+        "--test",
+        required=True,
+        help="Test module/file to run: test_qwen, test_llama, test.test_qwen, or a .py path.",
+    )
+    parser.add_argument(
+        "--base-schedule",
+        required=True,
+        type=Path,
+        help="Input JSON schedule without order directives.",
+    )
+    parser.add_argument(
+        "--schedule",
+        "--schedule-name",
+        dest="schedule",
+        required=True,
+        choices=("1f1b", "interleaved_1f1b", "zerobubble", "dualpipev", "custom"),
+        help=(
+            "Order directive schedule variant to append. Use 'custom' to keep the "
+            "split/order directives already in --base-schedule untouched."
+        ),
+    )
+    parser.add_argument(
+        "--ranks",
+        type=int,
+        default=None,
+        help="Number of PP ranks. Required unless --schedule custom.",
+    )
+    parser.add_argument(
+        "--mbs",
+        type=int,
+        default=None,
+        help="Number of microbatches. Required unless --schedule custom.",
+    )
+    parser.add_argument(
+        "--virtual-stages",
+        type=int,
+        default=None,
+        help=argparse.SUPPRESS,
+    )
+    parser.add_argument(
+        "--generated-schedule-file",
+        "--output",
+        type=Path,
+        default=None,
+        help="Where to write the generated full schedule JSON.",
+    )
+    parser.add_argument(
+        "--keep-existing-order",
+        action="store_true",
+        help="Keep any order directives already present in the base schedule.",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Write the generated schedule and print the test command without running it.",
+    )
+    parser.add_argument(
+        "--schedule-viz-file",
+        type=Path,
+        default=None,
+        help="Where to render the generated schedule visualization (default: out/<schedule>.png).",
+    )
+    parser.add_argument(
+        "--no-schedule-viz",
+        action="store_true",
+        help="Do not render a Graphviz schedule visualization.",
+    )
+    parser.add_argument(
+        "--pytorch-profiler",
+        action="store_true",
+        help="Run extra iterations under torch.profiler and combine the per-actor "
+             "chrome traces into one trace per dp-rank under out/.",
+    )
+    parser.add_argument(
+        "--pytorch-profile-dir",
+        type=Path,
+        default=None,
+        help="Shared directory where actors write per-actor chrome traces "
+             "(default: <run_dir>/pytorch_profiles). Removed after combining.",
+    )
+    args, test_args = parser.parse_known_args()
+
+    if args.schedule != "custom":
+        missing = [
+            flag for flag, value in (("--ranks", args.ranks), ("--mbs", args.mbs))
+            if value is None
+        ]
+        if missing:
+            parser.error(
+                f"the following arguments are required unless --schedule custom: {', '.join(missing)}"
+            )
+        if args.virtual_stages is not None:
+            parser.error(
+                "--virtual-stages is internal; it is 2 for interleaved_1f1b/dualpipev "
+                "and 1 otherwise"
+            )
+        args.virtual_stages = _virtual_stages_for_schedule(args.schedule)
+
+    # Each run gets its own timestamped output directory under out/, holding its
+    # results.csv (one row per DP rank), schedule viz, and combined profiles.
+    run_dir = Path("out") / datetime.now().strftime("%Y%m%d_%H%M%S")
+    run_dir.mkdir(parents=True, exist_ok=True)
+
+    generated_schedule, viz_path = build_schedule_file(args, run_dir)
+    forwarded_args = _replace_schedule_arg(test_args, generated_schedule)
+    forwarded_args = _replace_arg(forwarded_args, "--output-dir", str(run_dir))
+
+    profile_args: list[str] = []
+    profile_dir: Path | None = None
+    if args.pytorch_profiler:
+        # Absolute path on the shared filesystem so remote actors (possibly on
+        # other nodes) write to the same place the harness later reads.
+        profile_dir = (args.pytorch_profile_dir or (run_dir / "pytorch_profiles")).resolve()
+        if profile_dir.exists():
+            for stale in profile_dir.glob("dp*_pp*.json"):
+                stale.unlink()
+        profile_dir.mkdir(parents=True, exist_ok=True)
+        profile_args = ["--pytorch-profiler", "--pytorch-profile-dir", str(profile_dir)]
+
+    if args.dry_run:
+        cmd = [sys.executable, "-m", _test_module(args.test_file), *forwarded_args, *profile_args]
+        print(" ".join(cmd))
+        print(f"generated_schedule={generated_schedule}")
+        print(f"order_directives={generated_schedule.with_name(generated_schedule.stem + '_order_directives.json')}")
+        if viz_path is not None:
+            print(f"schedule_viz={viz_path}")
+        return
+
+    with tempfile.TemporaryDirectory() as tmp:
+        metrics_json = Path(tmp) / "metrics.json"
+        cmd = [
+            sys.executable,
+            "-m",
+            _test_module(args.test_file),
+            *forwarded_args,
+            *profile_args,
+            "--metrics-json-out",
+            str(metrics_json),
+        ]
+        returncode = subprocess.run(cmd, check=False).returncode
+        if returncode == 0 and metrics_json.exists():
+            with metrics_json.open(encoding="utf-8") as f:
+                dp_metrics = json.load(f)
+            if dp_metrics:
+                rows = _metrics_rows(dp_metrics)
+                results_csv = run_dir / "results.csv"
+                _write_results_csv(results_csv, rows)
+                print(f"metrics written to {results_csv}")
+        if returncode == 0 and profile_dir is not None:
+            _combine_pytorch_profiles(
+                profile_dir,
+                run_dir,
+                schedule=args.schedule,
+                pp=args.ranks if args.ranks is not None else "X",
+                mbs=args.mbs if args.mbs is not None else "X",
+            )
+    raise SystemExit(returncode)
+
+
+def _combine_pytorch_profiles(
+    profile_dir: Path,
+    out_dir: Path,
+    schedule: str,
+    pp: int | str,
+    mbs: int | str,
+) -> None:
+    """Group per-actor chrome traces (dp{dp}_pp{pp}.json) by dp-rank and merge
+    each group into one trace per dp-rank under out_dir. The combined filename
+    encodes the run configuration: schedule, pp degree, dp degree, mbs, dp-rank.
+
+    Each pp-rank's events keep their own pid namespace via a collision-free
+    remap, and process_name metadata is prefixed with the pp rank so the merged
+    timeline groups work by stage. The intermediate per-actor traces and the
+    (now-empty) profile_dir are removed once combining succeeds.
+    """
+    name_re = re.compile(r"^dp(\d+)_pp(\d+)\.json$")
+    groups: dict[int, dict[int, Path]] = {}
+    for path in sorted(profile_dir.glob("dp*_pp*.json")):
+        m = name_re.match(path.name)
+        if not m:
+            continue
+        dp_rank, pp_rank = int(m.group(1)), int(m.group(2))
+        groups.setdefault(dp_rank, {})[pp_rank] = path
+
+    if not groups:
+        print(f"pytorch profiler: no trace files found in {profile_dir}")
+        return
+
+    dp_degree = len(groups)
+    out_dir.mkdir(parents=True, exist_ok=True)
+    consumed: list[Path] = []
+    for dp_rank, pp_paths in sorted(groups.items()):
+        combined_events: list = []
+        next_pid = 0
+        for pp_rank, path in sorted(pp_paths.items()):
+            with path.open(encoding="utf-8") as f:
+                trace = json.load(f)
+            events = trace.get("traceEvents", [])
+            pid_map: dict = {}
+            for ev in events:
+                opid = ev.get("pid")
+                if opid is not None and opid not in pid_map:
+                    pid_map[opid] = next_pid
+                    next_pid += 1
+            for ev in events:
+                if "pid" in ev and ev["pid"] in pid_map:
+                    ev["pid"] = pid_map[ev["pid"]]
+                if ev.get("name") == "process_name":
+                    pname = ev.get("args", {}).get("name", "")
+                    ev["args"]["name"] = f"dp{dp_rank} pp{pp_rank}: {pname}"
+                combined_events.append(ev)
+            consumed.append(path)
+        out_name = (
+            f"pytorch_profile_{schedule}_pp{pp}_dp{dp_degree}"
+            f"_mbs{mbs}_dprank{dp_rank}.json"
+        )
+        out_path = out_dir / out_name
+        with out_path.open("w", encoding="utf-8") as f:
+            json.dump({"traceEvents": combined_events}, f)
+        print(
+            f"pytorch profiler: combined {len(pp_paths)} pp-rank trace(s) "
+            f"-> {out_path}"
+        )
+
+    for path in consumed:
+        path.unlink(missing_ok=True)
+    try:
+        profile_dir.rmdir()
+    except OSError:
+        pass
+
+
+def _metrics_rows(dp_metrics: list[dict]) -> list[dict]:
+    """Summarize the raw metrics into one CSV row per DP rank (no averaging
+    across DP ranks).
+
+    Each row's timing scalars come from that DP rank's own iter times; peak
+    memory is taken from that DP rank's per-(global=pp)-rank values and emitted
+    as one peak_memory_pp{i}_gb column per pp rank.
+    """
+    rows: list[dict] = []
+    for m in sorted(dp_metrics, key=lambda d: int(d.get("dp_rank", 0))):
+        iter_times = m.get("iter_times_s") or []
+        mean_iter = statistics.fmean(iter_times) if iter_times else float("nan")
+        std_iter = statistics.pstdev(iter_times) if len(iter_times) > 1 else 0.0
+        tokens = (
+            (m.get("batch_size") or 0)
+            * (m.get("num_microbatches") or 1)
+            * (m.get("seq_len") or 0)
+        )
+        throughput = tokens / mean_iter if iter_times and mean_iter else float("nan")
+
+        row: dict = {
+            "dp_rank": m.get("dp_rank"),
+            "model": m.get("model"),
+            "schedule": m.get("schedule"),
+            "pp": m.get("pp"),
+            "dp": m.get("dp"),
+            "batch_size": m.get("batch_size"),
+            "num_microbatches": m.get("num_microbatches"),
+            "seq_len": m.get("seq_len"),
+            "samples": len(iter_times),
+            "iter_time_mean_s": mean_iter,
+            "iter_time_std_s": std_iter,
+            "throughput_tokens_per_s": throughput,
+        }
+        # peak_memory_by_rank is keyed by global rank; within a DP replica the
+        # global ranks ascend with pp stage (both pp-inner and pp-outer layouts),
+        # so enumerate them as local stage indices for columns that align across
+        # DP-rank rows.
+        peak = m.get("peak_memory_by_rank") or {}
+        for stage, rank in enumerate(sorted(peak, key=lambda r: int(r))):
+            row[f"peak_memory_pp{stage}_gb"] = float(peak[rank]) / (1024 ** 3)
+        rows.append(row)
+    return rows
+
+
+def _write_results_csv(csv_path: Path, rows: list[dict]) -> None:
+    """Write ``rows`` (one per DP rank) to a fresh per-run results CSV."""
+    csv_path.parent.mkdir(parents=True, exist_ok=True)
+    fieldnames: list[str] = []
+    for row in rows:
+        for key in row:
+            if key not in fieldnames:
+                fieldnames.append(key)
+    with csv_path.open("w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=fieldnames, restval="")
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def build_schedule_file(args: argparse.Namespace, run_dir: Path) -> tuple[Path, str | None]:
+    base = _load_schedule(args.base_schedule)
+    if args.schedule == "custom":
+        # Pass the base schedule through untouched: trust its existing split and
+        # order directives. Only used for visualization below.
+        schedule = list(base)
+        order_directives = [
+            d for d in schedule
+            if isinstance(d, dict) and d.get("op") == "order"
+        ]
+    else:
+        if args.keep_existing_order:
+            schedule = list(base)
+        else:
+            schedule = [
+                directive for directive in base
+                if not (isinstance(directive, dict) and directive.get("op") == "order")
+            ]
+        _set_num_microbatches(schedule, args.mbs)
+
+        if args.schedule == "1f1b":
+            order_directives = build_1f1b_schedule(args.ranks, args.mbs)
+        elif args.schedule == "zerobubble":
+            order_directives = build_zerobubble_schedule(args.ranks, args.mbs)
+        elif args.schedule == "dualpipev":
+            order_directives = build_dualpipev_schedule(args.ranks, args.mbs)
+        else:
+            order_directives = build_interleaved_1f1b_schedule(
+                args.ranks,
+                args.mbs,
+                args.virtual_stages,
+            )
+
+        _validate_generated_order_placement(
+            schedule,
+            order_directives,
+            schedule_name=args.schedule,
+            ranks=args.ranks,
+        )
+        schedule.extend(order_directives)
+    schedule_stem = _schedule_attr_stem(
+        args.schedule,
+        args.ranks,
+        args.mbs,
+        args.virtual_stages,
+        base_schedule=args.base_schedule,
+    )
+    output = args.generated_schedule_file or _default_output_path(schedule_stem)
+    output.parent.mkdir(parents=True, exist_ok=True)
+    with output.open("w", encoding="utf-8") as f:
+        json.dump(schedule, f, indent=2)
+        f.write("\n")
+    order_output = output.with_name(f"{output.stem}_order_directives.json")
+    with order_output.open("w", encoding="utf-8") as f:
+        json.dump(order_directives, f, indent=2)
+        f.write("\n")
+
+    # Drop a copy of the complete schedule into the run directory so the exact
+    # directives used (base + appended order) are visible alongside results.csv,
+    # profile traces, and the schedule viz.
+    run_schedule = run_dir / f"{schedule_stem}.json"
+    if run_schedule.resolve() != output.resolve():
+        with run_schedule.open("w", encoding="utf-8") as f:
+            json.dump(schedule, f, indent=2)
+            f.write("\n")
+
+    viz_path = None
+    if not args.no_schedule_viz:
+        viz_output = args.schedule_viz_file or run_dir / f"{schedule_stem}.png"
+        viz_path = visualize_order_directives(order_directives, viz_output)
+    return output, viz_path
+
+
+def _schedule_attr_stem(
+    schedule_name: str,
+    ranks: int | None,
+    mbs: int | None,
+    virtual_stages: int | None,
+    base_schedule: Path | None = None,
+) -> str:
+    if schedule_name == "custom":
+        # ranks/mbs/virtual_stages aren't required for custom; derive a stable
+        # name from the base schedule filename instead.
+        if base_schedule is not None:
+            return f"custom_{Path(base_schedule).stem}"
+        return "custom"
+    suffix = f"{schedule_name}_pp{ranks}_mbs{mbs}"
+    if schedule_name == "interleaved_1f1b":
+        suffix += f"_v{virtual_stages}"
+    return suffix
+
+
+def _virtual_stages_for_schedule(schedule_name: str) -> int:
+    if schedule_name in ("interleaved_1f1b", "dualpipev"):
+        return 2
+    return 1
+
+
+def _default_output_path(schedule_stem: str) -> Path:
+    return Path("test/schedules/generated") / f"{schedule_stem}.json"
+
+
+def _load_schedule(path: Path) -> list[dict]:
+    with path.open("r", encoding="utf-8") as f:
+        data = json.load(f)
+    if not isinstance(data, list):
+        raise ValueError(f"schedule JSON must contain a list, got {type(data)}")
+    for i, directive in enumerate(data):
+        if not isinstance(directive, dict):
+            raise ValueError(f"schedule directive[{i}] must be an object, got {type(directive)}")
+    return data
+
+
+def _set_num_microbatches(schedule: list[dict], n_mbs: int) -> None:
+    """Ensure the schedule has exactly one split directive with num_microbatches=n_mbs.
+
+    If a split directive already exists, its num_microbatches is overwritten.
+    Otherwise a default MB-split directive is appended.
+    """
+    found = False
+    for directive in schedule:
+        if directive.get("op") == "split":
+            directive["num_microbatches"] = n_mbs
+            found = True
+    if not found:
+        schedule.append({
+            "op": "split",
+            "filter": [],
+            "dim_name": "MB",
+            "num_microbatches": n_mbs,
+        })
+
+
+def _validate_generated_order_placement(
+    base_schedule: list[dict],
+    order_directives: list[dict],
+    *,
+    schedule_name: str,
+    ranks: int | None,
+) -> None:
+    """Generated order rows are per physical rank and must be device-local."""
+    stage_devices = _stage_devices_from_place_directives(base_schedule)
+    if not stage_devices:
+        return
+
+    errors: list[str] = []
+    for row_idx, directive in enumerate(order_directives):
+        row_devices: set[tuple[int, ...]] = set()
+        for slot_idx, slot in enumerate(_order_filter_slots(directive.get("filters", []))):
+            slot_devices: set[tuple[int, ...]] = set()
+            slot_pps: list[int] = []
+            for flt in slot:
+                spec = _filter_to_dict(flt)
+                pp = spec.get("PP")
+                if pp is None or pp == "*":
+                    continue
+                pp = int(pp)
+                slot_pps.append(pp)
+                dev = stage_devices.get(pp)
+                if dev is None:
+                    errors.append(
+                        f"row {row_idx} slot {slot_idx} references PP{pp}, "
+                        "but no matching place directive exists"
+                    )
+                    continue
+                slot_devices.add(dev)
+            if len(slot_devices) > 1:
+                errors.append(
+                    f"row {row_idx} slot {slot_idx} groups stages {slot_pps} "
+                    f"across devices {sorted(slot_devices)}"
+                )
+            row_devices.update(slot_devices)
+        if len(row_devices) > 1:
+            errors.append(
+                f"row {row_idx} orders stages across devices {sorted(row_devices)}"
+            )
+
+    if not errors:
+        return
+
+    hint = ""
+    if schedule_name == "dualpipev" and ranks is not None:
+        expected = [
+            (rank, 2 * ranks - 1 - rank)
+            for rank in range(ranks)
+        ]
+        hint = (
+            " DualPipeV uses V-layout virtual stages; each pair "
+            f"{expected} must be placed on the same physical device set."
+        )
+    raise ValueError(
+        "generated order directives are not compatible with the base placement: "
+        + "; ".join(errors[:4])
+        + hint
+    )
+
+
+def _stage_devices_from_place_directives(schedule: list[dict]) -> dict[int, tuple[int, ...]]:
+    stage_devices: dict[int, tuple[int, ...]] = {}
+    for directive in schedule:
+        if directive.get("op") != "place":
+            continue
+        devices = directive.get("devices", directive.get("device"))
+        if not isinstance(devices, list):
+            continue
+        spec = _filter_to_dict(directive.get("filter", []))
+        pp = spec.get("PP")
+        if pp is None or pp == "*":
+            continue
+        stage_devices[int(pp)] = tuple(sorted(int(d) for d in devices))
+    return stage_devices
+
+
+def _order_filter_slots(filters: object) -> list[list[object]]:
+    if not isinstance(filters, list):
+        raise ValueError(f"order directive requires filters list, got {type(filters)}")
+    slots: list[list[object]] = []
+    for item in filters:
+        if _is_filter_spec(item):
+            slots.append([item])
+        elif isinstance(item, list):
+            slots.append(list(item))
+        else:
+            raise ValueError(f"invalid order filter group: {item}")
+    return slots
+
+
+def _is_filter_spec(flt: object) -> bool:
+    if isinstance(flt, dict):
+        return True
+    if isinstance(flt, list):
+        return all(
+            isinstance(item, (list, tuple))
+            and len(item) == 2
+            and isinstance(item[0], str)
+            for item in flt
+        )
+    return False
+
+
+def _filter_to_dict(flt: object) -> dict:
+    if isinstance(flt, dict):
+        return dict(flt)
+    if isinstance(flt, list):
+        out = {}
+        for item in flt:
+            if not isinstance(item, (list, tuple)) or len(item) != 2:
+                raise ValueError(f"invalid filter item: {item}")
+            out[item[0]] = item[1]
+        return out
+    raise ValueError(f"invalid filter: {flt}")
+
+
+def _replace_schedule_arg(test_args: list[str], schedule_path: Path) -> list[str]:
+    return _replace_arg(test_args, "--schedule-directives-file", str(schedule_path))
+
+
+def _replace_arg(test_args: list[str], flag: str, value: str) -> list[str]:
+    """Drop any existing occurrence of ``flag`` (and its value) from forwarded
+    test args, then append ``flag value`` so the harness setting wins."""
+    out: list[str] = []
+    skip_next = False
+    for arg in test_args:
+        if skip_next:
+            skip_next = False
+            continue
+        if arg == flag:
+            skip_next = True
+            continue
+        if arg.startswith(f"{flag}="):
+            continue
+        out.append(arg)
+    out.extend([flag, value])
+    return out
+
+
+def _test_module(test_file: str) -> str:
+    if test_file in ("test_qwen", "test_llama"):
+        return f"test.{test_file}"
+    if test_file.endswith(".py"):
+        path = Path(test_file)
+        if len(path.parts) == 1 and path.stem in ("test_qwen", "test_llama"):
+            return f"test.{path.stem}"
+        if path.is_absolute():
+            try:
+                path = path.relative_to(Path.cwd())
+            except ValueError as exc:
+                raise ValueError(
+                    f"absolute test file must be under the current repo: {test_file}"
+                ) from exc
+        return ".".join(path.with_suffix("").parts)
+    return test_file
+
+
+if __name__ == "__main__":
+    main()
diff --git a/test/test_llama.py b/test/test_llama.py
index 2bcead1..5376c24 100644
--- a/test/test_llama.py
+++ b/test/test_llama.py
@@ -1,161 +1,95 @@
-import logging
-import ray
-import torch
-import time
+"""End-to-end LLaMA test for the JSON-driven TrainingDAG backend."""
 import argparse
 import json
+import logging
 import os
-import numpy as np
-
-logger = logging.getLogger(__name__)
+import time
 
-from ray.util.placement_group import (
-    placement_group,
-    placement_group_table,
-)
+import ray
+import torch
+from ray.util.placement_group import placement_group_table
 
-from src.piper_coordinator import PiperProgramCoordinator
-from src.piper_compile import piper_setup
 from src.piper import piper_exec_dag
-from src.piper_utils import piper_metadata, compute_transformer_flops_per_token
+from src.piper_compile import piper_setup
+from src.piper_coordinator import PiperProgramCoordinator, create_piper_placement_group
+from src.piper_utils import (
+    LOG_LEVEL,
+    create_logger,
+    piper_metadata,
+)
 
-from .models.llama import Transformer, precompute_freqs_cis, LLAMA_DEBUG, LLAMA_1B, LLAMA_3B, LLAMA_8B, LLAMA_70B
-from .schedule_helpers import (
-    visualize_pipeline_schedule,
-    build_1f1b_schedule,
-    build_gpipe_schedule,
-    build_interleaved_1f1b_schedule,
-    build_zerobubble_schedule,
-    build_interleaved_zero_bubble,
-    build_dualpipev_schedule,
-    NO_PP_SCHEDULE,
+from .models.llama import (
+    LLAMA_1B,
+    LLAMA_3B,
+    LLAMA_8B,
+    LLAMA_70B,
+    LLAMA_DEBUG,
+    Transformer,
+    precompute_freqs_cis,
 )
 
+logger = create_logger("test_llama", LOG_LEVEL)
 
-def _metrics_output_path(args):
-    return os.path.join(
-        args.output_dir,
-        f"llama{args.model}-pp{args.pp}-dp{args.dp}-{args.schedule}",
-    )
+
+def _model_config(name: str):
+    match name:
+        case "debug":
+            return LLAMA_DEBUG
+        case "1b":
+            return LLAMA_1B
+        case "3b":
+            return LLAMA_3B
+        case "8b":
+            return LLAMA_8B
+        case "70b":
+            return LLAMA_70B
+    raise ValueError(f"Unknown LLaMA model: {name}")
 
 
-def _build_benchmark_metrics(args, dp_rank, iter_times):
-    mean_iter_time = float(np.mean(iter_times))
-    std_iter_time = float(np.std(iter_times))
-    throughput = float((args.batch_size * args.mbs * args.seq_len) / mean_iter_time)
+def _raw_metrics(args, iter_times, peak_memory_stats):
+    """Assemble raw per-dp-rank measurements + run config for the harness.
 
+    No derived statistics are computed here; the harness summarizes the metrics
+    and writes the CSV. ``peak_memory_by_rank`` maps global rank -> peak bytes.
+    """
+    info = dict(getattr(piper_metadata, "schedule_info", {}) or {})
     return {
-        "dp_rank": dp_rank,
+        "dp_rank": int(os.environ["PIPER_DP_RANK"]),
         "model": args.model,
-        "schedule": args.schedule,
-        "pp": args.pp,
-        "dp": args.dp,
+        "schedule": info.get(
+            "name", os.path.splitext(os.path.basename(args.schedule_directives_file))[0]
+        ),
+        "schedule_directives_file": args.schedule_directives_file,
+        "pp": info.get("pp_degree"),
+        "dp": info.get("dp_degree"),
         "batch_size": args.batch_size,
-        "mbs": args.mbs,
+        "num_microbatches": int(info.get("num_microbatches", 1)),
         "seq_len": args.seq_len,
-        "samples": len(iter_times),
-        "iter_time_mean_s": mean_iter_time,
-        "iter_time_std_s": std_iter_time,
-        "throughput_tokens_per_s": throughput,
-        "iter_times_s": [float(iter_time) for iter_time in iter_times],
+        "iter_times_s": [float(t) for t in iter_times],
+        "peak_memory_by_rank": {
+            int(rank): int(max_alloc) for rank, max_alloc in peak_memory_stats
+        },
     }
 
 
-def _format_benchmark_log_lines(metrics):
-    rank = metrics["dp_rank"]
-    lines = [
-        (
-            f"rank {rank} iter time= {metrics['iter_time_mean_s']:.5f} +/- "
-            f"{metrics['iter_time_std_s']:.5f} s ({metrics['samples']} samples)"
-        ),
-        f"rank {rank} throughput= {metrics['throughput_tokens_per_s']:.3f} tokens/s",
-    ]
-    for trace in metrics.get("trace_times", []):
-        lines.append(
-            f"rank {trace['rank']} {trace['key']} time= {trace['mean_ms']:.3f} +/- "
-            f"{trace['std_ms']:.3f} ms ({trace['samples']} samples)"
-        )
-    lines.append(f"rank {rank} metrics_json= {json.dumps(metrics, sort_keys=True)}")
-    return lines
-
-
-def _write_benchmark_metrics(args, metrics):
-    metrics_path = _metrics_output_path(args)
-    os.makedirs(os.path.dirname(metrics_path), exist_ok=True)
-    with open(metrics_path, "a", encoding="utf-8") as output_file:
-        for line in _format_benchmark_log_lines(metrics):
-            output_file.write(line + "\n")
-        output_file.write("\n")
-
-    return metrics_path
-
-
 def main(args, pg):
-    match args.model:
-        case 'debug':
-            llama_config = LLAMA_DEBUG
-        case '1b':
-            llama_config = LLAMA_1B
-        case '3b':
-            llama_config = LLAMA_3B
-        case '8b':
-            llama_config = LLAMA_8B
-        case '70b':
-            llama_config = LLAMA_70B
+    llama_config = _model_config(args.model)
     logger.info(args)
 
-    _ce = torch.nn.CrossEntropyLoss()
-    loss_fn = lambda output, labels: _ce(output.view(-1, output.size(-1)), labels.view(-1))
+    loss_mod = torch.nn.CrossEntropyLoss()
+    loss_fn = lambda output, labels: loss_mod(output.view(-1, output.size(-1)), labels.view(-1))
 
     x = torch.randint(0, llama_config.vocab_size, (args.batch_size, args.seq_len))
     y = torch.randint(0, llama_config.vocab_size, (args.batch_size, args.seq_len))
 
-    match args.schedule:
-        case "no-pp":
-            schedule = NO_PP_SCHEDULE
-        case "interleaved-1f1b":
-            schedule = build_interleaved_1f1b_schedule(args.mbs, args.pp, v=2)
-        case "1f1b":
-            schedule = build_1f1b_schedule(args.mbs, args.pp)
-        case "gpipe":
-            schedule = build_gpipe_schedule(args.mbs, args.pp)
-        case "interleaved-gpipe":
-            assert False
-        case "dualpipev":
-            schedule = build_dualpipev_schedule(args.mbs, args.pp)
-        case "dualpipev-nozb":
-            assert False
-        case "zerobubble":
-            schedule = build_zerobubble_schedule(args.mbs, args.pp)
-        case "interleaved-zerobubble":
-            schedule = build_interleaved_zero_bubble(args.mbs, args.pp, v=2)
-    
-    visualize_pipeline_schedule(schedule, f"out/{args.schedule}-pp{args.pp}-mb{args.mbs}")
-
-    # Llama FFN hidden dim (SwiGLU): 2/3 * 4 * dim, rounded up to multiple_of
-    _ffn_base = int(2 * 4 * llama_config.dim / 3)
-    if llama_config.ffn_dim_multiplier is not None:
-        _ffn_base = int(llama_config.ffn_dim_multiplier * _ffn_base)
-    _ffn_dim = llama_config.multiple_of * ((_ffn_base + llama_config.multiple_of - 1) // llama_config.multiple_of)
-    n_kv_heads = llama_config.n_kv_heads if llama_config.n_kv_heads is not None else llama_config.n_heads
-    flops_per_token = compute_transformer_flops_per_token(
-        hidden_dim=llama_config.dim,
-        n_layers=llama_config.n_layers,
-        ffn_dim=_ffn_dim,
-        n_heads=llama_config.n_heads,
-        n_kv_heads=n_kv_heads,
-        head_dim=llama_config.dim // llama_config.n_heads,
-        activation_checkpointing=args.activation_checkpointing,
-    )
-
     freqs_cis = precompute_freqs_cis(
         llama_config.dim // llama_config.n_heads,
         args.seq_len,
         llama_config.rope_theta,
     )
-    _mask = torch.full((args.seq_len, args.seq_len), float("-inf"))
-    _mask = torch.triu(_mask, diagonal=1)
-    mask = torch.hstack([torch.zeros((args.seq_len, 0)), _mask])
+    causal = torch.full((args.seq_len, args.seq_len), float("-inf"))
+    causal = torch.triu(causal, diagonal=1)
+    mask = torch.hstack([torch.zeros((args.seq_len, 0)), causal])
 
     piper_setup(
         Transformer,
@@ -163,75 +97,53 @@ def main(args, pg):
         optim_fn=torch.optim.Adam,
         example_inputs=[x],
         example_outputs=y,
-        schedule=schedule,
-        naive_gradient_sync=args.naive_grad_sync,
+        loss_fn=loss_fn,
         activation_checkpointing=args.activation_checkpointing,
         num_checkpoints=args.num_checkpoints,
-        bucket_size=int(args.bucket_size * 1024 * 1024),
         model_dtype=torch.bfloat16,
         pg=pg,
         nsight=args.nsight,
         temp_dir=args.temp_dir,
-        model_flops_per_token=flops_per_token,
         visualize_dag=args.save_viz,
+        output_dir=args.output_dir,
         const_attrs={"freqs_cis": freqs_cis, "mask": mask},
+        use_inductor=args.use_inductor,
+        pp_outer=args.pp_outer,
+        schedule_directives_file=args.schedule_directives_file,
     )
 
-    logger.info(f"Running {args.warmup} warmup iterations...")
+    actors = piper_metadata.actors
+    logger.info(f"Running {args.warmup} warmup iterations")
     for _ in range(args.warmup):
-        piper_exec_dag(loss_fn, log_stats=True, profiling=args.profiling)
+        piper_exec_dag(loss_fn, log_stats=True)
         time.sleep(1)
 
-    actors = piper_metadata.actors
-
-    logger.info(f"Running {args.iters} timed iterations...")
+    ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
+    logger.info(f"Running {args.iters} timed iterations")
     iter_times = []
     for _ in range(args.iters):
         start = time.perf_counter()
-        piper_exec_dag(loss_fn, log_stats=True, profiling=args.profiling)
+        piper_exec_dag(loss_fn, log_stats=True)
         end = time.perf_counter()
         iter_times.append(end - start)
         time.sleep(1)
 
-    dp_rank = int(os.environ['PIPER_DP_RANK'])
-    metrics = _build_benchmark_metrics(args, dp_rank, iter_times)
-    iter_time_msg, throughput_msg, _ = _format_benchmark_log_lines(metrics)
-    metrics_msg = f"rank {dp_rank} benchmark metrics will be saved by the driver to {_metrics_output_path(args)}"
-    logger.info(iter_time_msg)
-    logger.info(throughput_msg)
-    logger.info(metrics_msg)
-    print(iter_time_msg, flush=True)
-    print(throughput_msg, flush=True)
-    print(metrics_msg, flush=True)
+    peak_memory_stats = ray.get(
+        [actor.get_and_reset_peak_memory_stats.remote() for actor in actors.values()]
+    )
+
+    metrics = _raw_metrics(args, iter_times, peak_memory_stats)
 
-    if args.tracing:
-        ray.get([actor.set_tracing.remote(True) for actor in actors.values()])
-        logger.info(f"Running {args.trace_iters} tracing iterations...")
-        for _ in range(args.trace_iters):
+    if args.pytorch_profiler:
+        logger.info(f"Running {args.pytorch_profiler_iters} PyTorch-profiled iterations")
+        ray.get([actor.start_pytorch_profiler.remote() for actor in actors.values()])
+        for _ in range(args.pytorch_profiler_iters):
             piper_exec_dag(loss_fn)
-            ray.get([actor.flush_timing_events.remote() for actor in actors.values()])
             time.sleep(1)
-        trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
-        trace_times = []
-        for rank, trace_data in trace_data_ret:
-            for key in trace_data:
-                all_times = trace_data[key]
-                trace = {
-                    "rank": rank,
-                    "key": key,
-                    "mean_ms": float(np.mean(all_times)),
-                    "std_ms": float(np.std(all_times)),
-                    "samples": len(all_times),
-                    "times_ms": [float(trace_time) for trace_time in all_times],
-                }
-                trace_times.append(trace)
-                trace_msg = (
-                    f"rank {trace['rank']} {trace['key']} time= {trace['mean_ms']:.3f} +/- "
-                    f"{trace['std_ms']:.3f} ms ({trace['samples']} samples)"
-                )
-                logger.info(trace_msg)
-                print(trace_msg, flush=True)
-        metrics["trace_times"] = trace_times
+        ray.get([
+            actor.stop_pytorch_profiler.remote(args.pytorch_profile_dir)
+            for actor in actors.values()
+        ])
 
     if args.nsight:
         logger.info("Stopping Piper actors so Nsight Systems reports are flushed")
@@ -243,83 +155,87 @@ def main(args, pg):
 
 
 def parse_args():
-    parser = argparse.ArgumentParser(description='Run LLaMA model with pipeline parallelism')
-    parser.add_argument('--model', choices=['debug', '1b', '3b', '8b', '70b'], default='debug')
+    parser = argparse.ArgumentParser(description="Run LLaMA with JSON-driven TrainingDAG execution")
+    parser.add_argument("--model", choices=["debug", "1b", "3b", "8b", "70b"], default="debug")
+    parser.add_argument("--batch-size", type=int, default=16)
+    parser.add_argument("--seq-len", type=int, default=256)
+    parser.add_argument("--warmup", type=int, default=3)
+    parser.add_argument("--iters", type=int, default=5)
+    parser.add_argument("--activation-checkpointing", action="store_true", default=False)
+    parser.add_argument(
+        "--num-checkpoints",
+        type=int,
+        default=1,
+        help="Number of sequential activation-checkpoint regions per Piper bucket",
+    )
+    parser.add_argument("--nsight", action="store_true", default=False)
+    parser.add_argument("--save-viz", action="store_true", default=False)
+    parser.add_argument("--address", default="")
+    parser.add_argument("--port", type=int, default=4567)
+    parser.add_argument("--temp-dir", default="/tmp/piper/ray_tmp")
+    parser.add_argument("--output-dir", default="out")
+    parser.add_argument("--use-inductor", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--pp-outer", action=argparse.BooleanOptionalAction, default=False)
+    parser.add_argument(
+        "--schedule-directives-file",
+        type=str,
+        default="test/schedules/llama_pp_only.json",
+        help="JSON file containing TrainingDAG schedule directives",
+    )
+    parser.add_argument(
+        "--metrics-json-out",
+        type=str,
+        default="",
+        help="If set, write the raw per-dp-rank metrics list as JSON to this path "
+             "(consumed by test_harness to build the metrics CSV).",
+    )
     parser.add_argument(
-        '--schedule',
-        choices=['gpipe', '1f1b', 'interleaved-1f1b', 'interleaved-gpipe',
-                 'dualpipev-nozb', 'dualpipev', 'zerobubble', 'interleaved-zerobubble', 'no-pp'],
-        default='1f1b',
+        "--pytorch-profiler",
+        action="store_true",
+        default=False,
+        help="Run extra iterations under torch.profiler on every actor and write "
+             "per-actor chrome traces (combined per dp-rank by test_harness).",
+    )
+    parser.add_argument(
+        "--pytorch-profile-dir",
+        type=str,
+        default="",
+        help="Directory where actors write per-actor chrome traces "
+             "(dp{dp}_pp{pp}.json). Set by test_harness.",
+    )
+    parser.add_argument(
+        "--pytorch-profiler-iters",
+        type=int,
+        default=3,
+        help="Number of iterations to run under the PyTorch profiler.",
     )
-    parser.add_argument('--dp', type=int, default=1)
-    parser.add_argument('--pp', type=int, default=2)
-    parser.add_argument('--batch-size', type=int, default=16)
-    parser.add_argument('--mbs', type=int, default=4)
-    parser.add_argument('--seq-len', type=int, default=256)
-    parser.add_argument('--warmup', type=int, default=3)
-    parser.add_argument('--iters', type=int, default=5)
-    parser.add_argument('--trace-iters', type=int, default=3)
-    parser.add_argument('--tracing', action='store_true', default=False)
-    parser.add_argument('--naive-grad-sync', action='store_true', default=False)
-    parser.add_argument('--activation-checkpointing', action='store_true', default=False)
-    parser.add_argument('--num-checkpoints', type=int, default=1,
-                        help='Number of sequential activation-checkpoint regions per Piper bucket')
-    parser.add_argument('--bucket-size', type=float, default=25,
-                        help='Bucket size in MB (default: 25)')
-    parser.add_argument('--nsight', action='store_true', default=False,
-                        help='Whether to use Nsight Systems for tracing')
-    parser.add_argument('--profiling', action='store_true', default=False,
-                        help='Profile each DAG task: log time and memory delta per task')
-    parser.add_argument('--save-viz', action='store_true', default=False,
-                        help='Save per-rank DAG visualization (slow for models > 1B)')
-    parser.add_argument('--address', default='',
-                        help='Ray head address to connect to')
-    parser.add_argument('--port', type=int, default=4567,
-                        help='Ray head port to connect to (default: 4567)')
-    parser.add_argument('--temp-dir', default='/tmp/piper/ray_tmp',
-                        help='Ray temp directory (default: /tmp/piper/ray_tmp)')
-    parser.add_argument('--output-dir', default='out',
-                        help='Directory for benchmark metrics output (default: out)')
     return parser.parse_args()
 
-"""
-/m-coriander/coriander/mfris/miniconda3/envs/piper/bin/python \
-    /m-coriander/coriander/mfris/miniconda3/envs/piper/lib/python3.10/site-packages/ray/scripts/scripts.py \
-        start --head \
-        --port 3456 \
-        --temp-dir=/m-coriander/coriander/mfris/piper/ray_tmp \
-        --include-dashboard=false
-"""
+
 if __name__ == "__main__":
     logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
     args = parse_args()
-    metrics_path = _metrics_output_path(args)
-    os.makedirs(os.path.dirname(metrics_path), exist_ok=True)
-    open(metrics_path, "w", encoding="utf-8").close()
     logger.info(args)
-    if args.address != "":
-        ray.init(
-            address=f"{args.address}:{args.port}",
-            namespace="llama",
-            log_to_driver=True,
-            include_dashboard=False,
-            _temp_dir=args.temp_dir,
-        )
-    else:
-        ray.init(
-            namespace="llama",
-            log_to_driver=True,
-            include_dashboard=False,
-            _temp_dir=args.temp_dir,
-        )
-    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="SPREAD")
+
+    ray.init(
+        address=f"{args.address}:{args.port}" if args.address else None,
+        namespace="llama",
+        log_to_driver=True,
+        include_dashboard=False,
+        _temp_dir=args.temp_dir,
+    )
+    pg = create_piper_placement_group(args.schedule_directives_file, pp_outer=args.pp_outer)
     ray.get(pg.ready(), timeout=600)
     logger.info(placement_group_table(pg))
-    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
+    piper_coordinator = PiperProgramCoordinator.remote(
+        schedule_directives_file=args.schedule_directives_file,
+        pp_outer=args.pp_outer,
+    )
     handles = piper_coordinator.run_program.remote(main, pg, args, pg)
     dp_metrics = ray.get(handles)
-    open(metrics_path, "w", encoding="utf-8").close()
-    for metrics in sorted(dp_metrics, key=lambda item: item["dp_rank"]):
-        _write_benchmark_metrics(args, metrics)
-    logger.info(f"Benchmark metrics saved to {metrics_path}")
+    if args.metrics_json_out:
+        os.makedirs(os.path.dirname(args.metrics_json_out) or ".", exist_ok=True)
+        with open(args.metrics_json_out, "w", encoding="utf-8") as f:
+            json.dump(dp_metrics, f)
+        logger.info(f"Raw metrics written to {args.metrics_json_out}")
     ray.shutdown()
diff --git a/test/test_order_directive.py b/test/test_order_directive.py
new file mode 100644
index 0000000..f140e16
--- /dev/null
+++ b/test/test_order_directive.py
@@ -0,0 +1,174 @@
+from src.piper import (
+    TrainingDAG,
+    TrainingDAGEdge,
+    TrainingDAGNode,
+    _apply_order_directive,
+    _apply_split_backward_stencil,
+    _apply_split_directive,
+    _normalize_order_directive,
+    _serial_topological_order,
+    _validate_split_backward_order_stencil,
+)
+
+
+def _compute(uid: str, tag: dict) -> TrainingDAGNode:
+    return TrainingDAGNode(
+        uid=uid,
+        node_kind="COMPUTE",
+        compute_subkind="FWD",
+        tag=tag,
+        device=[0],
+        stream="default_stream",
+        node_meta={},
+    )
+
+
+def _bwd(uid: str, fwd_uid: str, tag: dict) -> TrainingDAGNode:
+    return TrainingDAGNode(
+        uid=uid,
+        node_kind="COMPUTE",
+        compute_subkind="BWD",
+        tag=tag,
+        device=[0],
+        stream="default_stream",
+        node_meta={"fwd_uid": fwd_uid, "bucket_key": fwd_uid},
+    )
+
+
+def test_normalize_order_directive_accepts_nested_filter_groups() -> None:
+    directive = {
+        "op": "order",
+        "filters": [
+            [
+                [["PP", 0], ["MB", 0], ["PASS", "F"]],
+                [["PP", 0], ["MB", 1], ["PASS", "F"]],
+            ],
+            [
+                [["PP", 0], ["MB", 0], ["PASS", "B"]],
+            ],
+        ],
+    }
+
+    groups = _normalize_order_directive(directive)
+
+    assert groups == [
+        [
+            {"PP": 0, "MB": 0, "PASS": "F"},
+            {"PP": 0, "MB": 1, "PASS": "F"},
+        ],
+        [
+            {"PP": 0, "MB": 0, "PASS": "B"},
+        ],
+    ]
+
+
+def test_normalize_order_directive_wraps_legacy_flat_filters() -> None:
+    directive = {
+        "op": "order",
+        "filters": [
+            [["PP", 0], ["MB", 0], ["PASS", "F"]],
+            [["PP", 0], ["MB", 0], ["PASS", "B"]],
+        ],
+    }
+
+    groups = _normalize_order_directive(directive)
+
+    assert groups == [
+        [{"PP": 0, "MB": 0, "PASS": "F"}],
+        [{"PP": 0, "MB": 0, "PASS": "B"}],
+    ]
+
+
+def test_split_backward_stencil_handles_nested_filter_groups() -> None:
+    directive = {
+        "op": "order",
+        "filters": [
+            [
+                [["PP", 0], ["MB", 0], ["PASS", "BI"]],
+                [["PP", 0], ["MB", 0], ["PASS", "BW"]],
+            ],
+            [
+                [["PP", 0], ["MB", 1], ["PASS", "F"]],
+            ],
+        ],
+    }
+
+    split_keys = _validate_split_backward_order_stencil([directive])
+
+    assert split_keys == {(("MB", 0), ("PP", 0))}
+
+
+def test_split_backward_stencil_allows_mixed_fused_and_split_microbatches() -> None:
+    directive = {
+        "op": "order",
+        "filters": [
+            [[["PP", 0], ["MB", 0], ["PASS", "B"]]],
+            [[["PP", 0], ["MB", 1], ["PASS", "BI"]]],
+            [[["PP", 0], ["MB", 1], ["PASS", "BW"]]],
+        ],
+    }
+    dag = TrainingDAG()
+    dag.add_node(_compute("fwd", {"PP": 0, "PASS": "F"}))
+    dag.add_node(_bwd("bwd", "fwd", {"PP": 0, "PASS": "B"}))
+    dag.add_edge(TrainingDAGEdge("fwd", "bwd", "data"))
+
+    split_keys = _validate_split_backward_order_stencil([directive])
+    _apply_split_directive(dag, {}, "MB", 2)
+    _apply_split_backward_stencil(dag, split_keys)
+
+    assert dag.nodes["bwd"].compute_subkind == "BWD"
+    assert dag.nodes["bwd"].tag["PASS"] == "B"
+    split_bwd_uid = "bwd.splitMB1"
+    assert dag.nodes[split_bwd_uid].compute_subkind == "BWD_I"
+    assert dag.nodes[split_bwd_uid].tag["PASS"] == "BI"
+    assert dag.nodes[f"{split_bwd_uid}.bw"].compute_subkind == "BWD_W"
+
+
+def test_apply_order_directive_groups_nested_subdags_with_dummy_source_sink() -> None:
+    dag = TrainingDAG()
+    for node in [
+        _compute("prev", {"slot": 0}),
+        _compute("lane0.first", {"slot": 1, "lane": 0}),
+        _compute("lane0.last", {"slot": 1, "lane": 0}),
+        _compute("lane1", {"slot": 1, "lane": 1}),
+        _compute("next", {"slot": 2}),
+    ]:
+        dag.add_node(node)
+    dag.add_edge(TrainingDAGEdge("lane0.first", "lane0.last", "data"))
+
+    _apply_order_directive(
+        dag,
+        [
+            [{"slot": 0}],
+            [{"slot": 1, "lane": 0}, {"slot": 1, "lane": 1}],
+            [{"slot": 2}],
+        ],
+        directive_idx=7,
+    )
+
+    source_uid = "order.7.group1.source"
+    sink_uid = "order.7.group1.sink"
+    assert dag.nodes[source_uid].node_kind == "ORDER_DUMMY"
+    assert dag.nodes[sink_uid].node_kind == "ORDER_DUMMY"
+
+    temporal_edges = {
+        (edge.src_uid, edge.dst_uid)
+        for edge in dag.edges
+        if edge.dep_kind == "temporal"
+    }
+    assert temporal_edges == {
+        ("prev", source_uid),
+        (source_uid, "lane0.first"),
+        (source_uid, "lane1"),
+        ("lane0.last", sink_uid),
+        ("lane1", sink_uid),
+        (sink_uid, "next"),
+    }
+
+    topo = _serial_topological_order(dag)
+    assert topo.index("prev") < topo.index(source_uid)
+    assert topo.index(source_uid) < topo.index("lane0.first")
+    assert topo.index(source_uid) < topo.index("lane1")
+    assert topo.index("lane0.last") < topo.index(sink_uid)
+    assert topo.index("lane1") < topo.index(sink_uid)
+    assert topo.index(sink_uid) < topo.index("next")
diff --git a/test/test_qwen.py b/test/test_qwen.py
index 3ec1e61..9823d22 100644
--- a/test/test_qwen.py
+++ b/test/test_qwen.py
@@ -1,13 +1,4 @@
-"""
-test_dag.py – end-to-end test for the DAG-based execution path (piper_exec_dag).
-
-Run exactly like test_mixtral.py:
-
-    python -m test.test_dag [--model tiny] [--pp 2] [--dp 1] ...
-
-The main difference is that each training step is dispatched through
-piper_exec_dag() rather than the legacy piper_exec() scheduler.
-"""
+"""End-to-end Qwen test for the JSON-driven TrainingDAG backend."""
 import logging
 import ray
 import torch
@@ -15,162 +6,57 @@
 import json
 import time
 import os
-import numpy as np
 
 from ray.util.placement_group import (
-    placement_group,
     placement_group_table,
 )
 
-from src.piper_coordinator import PiperProgramCoordinator
+from src.piper_coordinator import PiperProgramCoordinator, create_piper_placement_group
 from src.piper_compile import piper_setup
 from src.piper import piper_exec_dag
-from src.piper_utils import piper_metadata, compute_transformer_flops_per_token, create_logger, LOG_LEVEL
+from src.piper_schedule import load_schedule_directives
+from src.piper_utils import piper_metadata, create_logger, LOG_LEVEL
 
 from .models.qwen3 import PiperQwen3Model, create_qwen3_config
 from torchtitan.models.qwen3.model.model import precompute_rope_cache
-from .schedule_helpers import (
-    build_1f1b_schedule,
-    build_gpipe_schedule,
-    build_interleaved_1f1b_schedule,
-    build_zerobubble_schedule,
-    build_interleaved_zero_bubble,
-    build_dualpipev_schedule,
-    visualize_pipeline_schedule,
-    NO_PP_SCHEDULE,
-)
-
 
 logger = create_logger("test_qwen", LOG_LEVEL)
 
-# from triton.backends.amd.driver import HIPDriver
-# HIPDriver.is_active = staticmethod(lambda: False)
-
 
-def _metrics_output_path(args):
-    return os.path.join(
-        args.output_dir,
-        (
-            f"qwen{args.model}-pp{args.pp}-dp{args.dp}-zero{args.zero_stage}-"
-            f"ep{int(args.ep)}-"
-            f"bs{args.batch_size}-sl{args.seq_len}-ga{int(args.gradient_accumulation)}-"
-            f"aras{int(args.ar_a2a_same_stream)}-ozo{int(args.overlap_zero_ops)}-"
-            f"och{int(args.overlap_chunks)}-"
-            f"{args.schedule}"
-        ),
-    )
+def _raw_metrics(args, iter_times, peak_memory_stats):
+    """Assemble raw per-dp-rank measurements + run config for the harness.
 
-
-def _build_benchmark_metrics(args, dp_rank, iter_times, peak_memory_stats=None):
-    mean_iter_time = float(np.mean(iter_times))
-    std_iter_time = float(np.std(iter_times))
-    throughput = float((args.batch_size * args.mbs * args.seq_len) / mean_iter_time)
-
-    metrics = {
-        "dp_rank": dp_rank,
+    No derived statistics are computed here; the harness summarizes the metrics
+    and writes the CSV. ``peak_memory_by_rank`` maps global rank -> peak bytes.
+    """
+    info = dict(getattr(piper_metadata, "schedule_info", {}) or {})
+    return {
+        "dp_rank": int(os.environ["PIPER_DP_RANK"]),
         "model": args.model,
-        "schedule": args.schedule,
-        "pp": args.pp,
-        "dp": args.dp,
-        "ep": bool(args.ep),
+        "schedule": info.get(
+            "name", os.path.splitext(os.path.basename(args.schedule_directives_file))[0]
+        ),
+        "schedule_directives_file": args.schedule_directives_file,
+        "pp": info.get("pp_degree"),
+        "dp": info.get("dp_degree"),
         "batch_size": args.batch_size,
-        "mbs": args.mbs,
+        "num_microbatches": int(info.get("num_microbatches", 1)),
         "seq_len": args.seq_len,
-        "gradient_accumulation": bool(args.gradient_accumulation),
-        "ar_a2a_same_stream": bool(args.ar_a2a_same_stream),
-        "overlap_zero_ops": bool(args.overlap_zero_ops),
-        "overlap_chunks": bool(args.overlap_chunks),
-        "samples": len(iter_times),
-        "iter_time_mean_s": mean_iter_time,
-        "iter_time_std_s": std_iter_time,
-        "throughput_tokens_per_s": throughput,
-        "iter_times_s": [float(iter_time) for iter_time in iter_times],
+        "iter_times_s": [float(t) for t in iter_times],
+        "peak_memory_by_rank": {
+            int(rank): int(max_alloc) for rank, max_alloc in peak_memory_stats
+        },
     }
-    if peak_memory_stats is not None:
-        peak_memory_by_rank = {
-            str(rank): {
-                "peak_memory_bytes": int(max_alloc_bytes),
-                "peak_memory_gb": float(max_alloc_bytes / (1024 ** 3)),
-            }
-            for rank, max_alloc_bytes in sorted(peak_memory_stats)
-        }
-        metrics["peak_memory_by_rank"] = peak_memory_by_rank
-    return metrics
-
-
-def _format_benchmark_log_lines(metrics):
-    rank = metrics["dp_rank"]
-    peak_memory_summary = "/".join(
-        f"{mem_stats['peak_memory_gb']:.3f}"
-        for mem_rank, mem_stats in metrics.get("peak_memory_by_rank", {}).items()
-    )
-    lines = [
-        (
-            f"{metrics['iter_time_mean_s']:.5f},"
-            f"{metrics['iter_time_std_s']:.5f},"
-            f"{peak_memory_summary}"
-        ),
-        (
-            f"rank {rank} iter time= {metrics['iter_time_mean_s']:.5f} +/- "
-            f"{metrics['iter_time_std_s']:.5f} s ({metrics['samples']} samples)"
-        ),
-        f"rank {rank} throughput= {metrics['throughput_tokens_per_s']:.3f} tokens/s",
-    ]
-    for mem_rank, mem_stats in metrics.get("peak_memory_by_rank", {}).items():
-        lines.append(
-            f"rank {mem_rank} peak memory= {mem_stats['peak_memory_gb']:.3f} GB"
-        )
-    for trace in metrics.get("trace_times", []):
-        lines.append(
-            f"rank {trace['rank']} {trace['key']} time= {trace['mean_ms']:.3f} +/- "
-            f"{trace['std_ms']:.3f} ms ({trace['samples']} samples)"
-        )
-    lines.append(f"rank {rank} metrics_json= {json.dumps(metrics, sort_keys=True)}")
-    return lines
-
-
-def _write_benchmark_metrics(args, metrics):
-    metrics_path = _metrics_output_path(args)
-    os.makedirs(os.path.dirname(metrics_path), exist_ok=True)
-    with open(metrics_path, "a", encoding="utf-8") as output_file:
-        for line in _format_benchmark_log_lines(metrics):
-            output_file.write(line + "\n")
-        output_file.write("\n")
-
-    return metrics_path
 
 
 def main(args, pg):
     batch_size = args.batch_size
 
     config = create_qwen3_config(args.model)
-
-    num_stages = args.pp
-    v = 2
-    match args.schedule:
-        case "no-pp":
-            schedule = NO_PP_SCHEDULE
-        case "interleaved-1f1b":
-            schedule = build_interleaved_1f1b_schedule(args.mbs, args.pp, v=v)
-            num_stages *= v
-        case "1f1b":
-            schedule = build_1f1b_schedule(args.mbs, args.pp)
-        case "gpipe":
-            schedule = build_gpipe_schedule(args.mbs, args.pp)
-        case "interleaved-gpipe":
-            assert False
-        case "dualpipev":
-            schedule = build_dualpipev_schedule(args.mbs, args.pp)
-            num_stages *= v
-        case "dualpipev-nozb":
-            assert False
-        case "zerobubble":
-            schedule = build_zerobubble_schedule(args.mbs, args.pp)
-        case "interleaved-zerobubble":
-            schedule = build_interleaved_zero_bubble(args.mbs, args.pp, v=v)
-            num_stages *= v
-
-    # visualize_pipeline_schedule(schedule, f"out/{args.schedule}-pp{args.pp}-mb{args.mbs}")
+    num_stages = int(
+        getattr(args, "num_stages", 0)
+        or _derive_num_stages(args.schedule_directives_file)
+    )
 
     x = torch.randint(0, config.vocab_size, (batch_size, args.seq_len))
     y = torch.randint(0, config.vocab_size, (batch_size, args.seq_len))
@@ -178,113 +64,45 @@ def main(args, pg):
     _ce = torch.nn.CrossEntropyLoss()
     loss_fn = lambda output, labels: _ce(output.view(-1, output.size(-1)), labels.view(-1))
 
-    flops_per_token = compute_transformer_flops_per_token(
-        hidden_dim=config.dim,
-        n_layers=config.n_layers,
-        ffn_dim=config.moe_inter_dim if config.moe_enabled else config.hidden_dim,
-        n_heads=config.n_heads,
-        n_kv_heads=config.n_kv_heads,
-        head_dim=config.head_dim,
-        activation_checkpointing=False,
-        moe_top_k=config.moe_args.top_k if config.moe_enabled else 1,
-    )
-
     rope_cache = precompute_rope_cache(
         config.head_dim,
         config.max_seq_len,
         config.rope_theta,
     )
-
     piper_setup(
         PiperQwen3Model,
         model_args=(config, num_stages),
         optim_fn=torch.optim.Adam,
         example_inputs=[x],
         example_outputs=y,
-        loss_fn=loss_fn,
-        schedule=schedule,
-        naive_gradient_sync=args.naive_grad_sync,
         activation_checkpointing=args.activation_checkpointing,
-        a2a_ar_no_overlap=True,
-        bucket_size=int(args.bucket_size * 1024 * 1024) if args.bucket_size is not None else None,
-        zero_stage=args.zero_stage,
-        gradient_accumulation=args.gradient_accumulation,
         model_dtype=torch.bfloat16,
         pg=pg,
         nsight=args.nsight,
         temp_dir=args.temp_dir,
-        model_flops_per_token=flops_per_token,
         visualize_dag=args.save_viz,
         output_dir=args.output_dir,
-        memory_profile=args.memory_profile,
         const_attrs={"rope_cache": rope_cache},
         use_inductor=args.use_inductor,
-        enable_ep=args.ep,
-        ar_a2a_same_stream=args.ar_a2a_same_stream,
-        overlap_zero_ops=args.overlap_zero_ops,
-        overlap_chunks=args.overlap_chunks,
         pp_outer=args.pp_outer,
+        schedule_directives_file=args.schedule_directives_file,
     )
 
     del x, y
 
     actors = piper_metadata.actors
 
-    # Configure OOM snapshot destination on every actor so a CUDA OOM inside
-    # run_dag still produces a snapshot for pytorch.org/memory_viz.
-    mem_dir = args.memory_profile_dir or os.path.join(args.output_dir, "memory_snapshots")
-    os.makedirs(mem_dir, exist_ok=True)
-    ray.get([actor.set_oom_snapshot_dir.remote(mem_dir) for actor in actors.values()])
-
-    def _safe_dump_snapshots(reason: str, timeout: float = 30.0) -> None:
-        if not args.memory_profile:
-            return
-        refs_by_rank = {
-            rank: actor.dump_memory_snapshot.remote(mem_dir)
-            for rank, actor in actors.items()
-        }
-        # Use ray.wait with a timeout so a single NCCL-watchdog-hung actor
-        # cannot block the driver for the full NCCL timeout (~10 min).
-        ready, pending = ray.wait(
-            list(refs_by_rank.values()),
-            num_returns=len(refs_by_rank),
-            timeout=timeout,
-        )
-        for ref in ready:
-            try:
-                path = ray.get(ref)
-                logger.info(f"Memory snapshot saved ({reason}): {path}")
-            except Exception as exc:
-                logger.error(f"Snapshot dump failed on an actor ({reason}): {exc}")
-        if pending:
-            stuck_ranks = [
-                rank for rank, ref in refs_by_rank.items() if ref in pending
-            ]
-            logger.error(
-                f"Snapshot dump timed out after {timeout}s on ranks={stuck_ranks} "
-                f"(likely NCCL watchdog hang); skipping."
-            )
-
     logger.info(f"Running {args.warmup} warmup iterations")
     for _ in range(args.warmup):
         piper_exec_dag(loss_fn)
         time.sleep(1)
 
-    # if args.memory_profile:
-    #     logger.info(f"Memory profiling enabled — snapshots will be saved under {mem_dir}")
-    #     try:
-    #         piper_exec_dag(loss_fn, log_stats=True)
-    #     finally:
-    #         _safe_dump_snapshots("profiling_iter")
-
     logger.info(f"Running {args.iters} timed iterations")
     ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
-    # Record post-reset / pre-training memory state on every actor so the log
-    # shows the baseline right before timed iterations begin.
     iter_times = []
     for _ in range(args.iters):
         start = time.perf_counter()
-        losses = piper_exec_dag(loss_fn, log_stats=True, profiling=args.profiling)
+        piper_exec_dag(loss_fn, log_stats=True)
         end = time.perf_counter()
         iter_times.append(end - start)
         time.sleep(1)
@@ -293,55 +111,19 @@ def _safe_dump_snapshots(reason: str, timeout: float = 30.0) -> None:
         [actor.get_and_reset_peak_memory_stats.remote() for actor in actors.values()]
     )
 
-    dp_rank = int(os.environ["PIPER_DP_RANK"])
-    metrics = _build_benchmark_metrics(
-        args,
-        dp_rank,
-        iter_times,
-        peak_memory_stats=peak_memory_stats,
-    )
-    metric_lines = _format_benchmark_log_lines(metrics)
-    metrics_msg = f"rank {dp_rank} benchmark metrics will be saved by the driver to {_metrics_output_path(args)}"
-    for line in metric_lines:
-        logger.info(line)
-    logger.info(metrics_msg)
-    for line in metric_lines:
-        print(line, flush=True)
-    print(metrics_msg, flush=True)
+    metrics = _raw_metrics(args, iter_times, peak_memory_stats)
 
-    if args.tracing:
-        ray.get([actor.set_tracing.remote(True) for actor in actors.values()])
-        logger.info(f"Running {args.trace_iters} tracing iterations...")
-        for _ in range(args.trace_iters):
+    if args.pytorch_profiler:
+        logger.info(f"Running {args.pytorch_profiler_iters} PyTorch-profiled iterations")
+        ray.get([actor.start_pytorch_profiler.remote() for actor in actors.values()])
+        for _ in range(args.pytorch_profiler_iters):
             piper_exec_dag(loss_fn)
-            ray.get([actor.flush_timing_events.remote() for actor in actors.values()])
             time.sleep(1)
-        trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
-        trace_times = []
-        for rank, trace_data in trace_data_ret:
-            for key in trace_data:
-                all_times = trace_data[key]
-                trace = {
-                    "rank": rank,
-                    "key": key,
-                    "mean_ms": float(np.mean(all_times)),
-                    "std_ms": float(np.std(all_times)),
-                    "samples": len(all_times),
-                    "times_ms": [float(trace_time) for trace_time in all_times],
-                }
-                trace_times.append(trace)
-                trace_msg = (
-                    f"rank {trace['rank']} {trace['key']} time= {trace['mean_ms']:.3f} +/- "
-                    f"{trace['std_ms']:.3f} ms ({trace['samples']} samples)"
-                )
-                logger.info(trace_msg)
-                print(trace_msg, flush=True)
-        metrics["trace_times"] = trace_times
+        ray.get([
+            actor.stop_pytorch_profiler.remote(args.pytorch_profile_dir)
+            for actor in actors.values()
+        ])
 
-    # os.makedirs("out", exist_ok=True)
-    # timeline_filename = f"out/qwen-dag-pp{args.pp}-dp{args.dp}-{args.schedule}"
-    # ray.timeline(timeline_filename)
-    # print(f"Ray timeline saved to: {timeline_filename}")
     if args.nsight:
         logger.info("Stopping Piper actors so Nsight Systems reports are flushed")
         try:
@@ -351,35 +133,32 @@ def _safe_dump_snapshots(reason: str, timeout: float = 30.0) -> None:
     return metrics
 
 
+def _derive_num_stages(schedule_directives_file: str) -> int:
+    schedule_directives = load_schedule_directives(schedule_directives_file)
+    num_stages = sum(
+        1
+        for directive in schedule_directives
+        if isinstance(directive, dict) and directive.get("op") == "place"
+    )
+    if num_stages <= 0:
+        raise ValueError(
+            f"schedule directives file must contain at least one place directive: "
+            f"{schedule_directives_file}"
+        )
+    return num_stages
+
+
 def parse_args():
     parser = argparse.ArgumentParser(
-        description="Test DAG-based piper execution with the Mixtral model"
-    )
-    parser.add_argument('--model', choices=['9M', '1B', '9B', '48B', '30B-A3B', '30-A3B-half', '72B'], default='1B',
-                        help='Model configuration: 9M, 1B, 9B, 48B, 30B-A3B, 30-A3B-half, or 72B (default: 1B)')
-    parser.add_argument("--schedule", choices=["gpipe", "1f1b", "no-pp", "interleaved-1f1b", "dualpipev", "dualpipev-seq", "zerobubble", "interleaved-zerobubble"], default="1f1b",)
-    parser.add_argument("--dp", type=int, default=1)
-    parser.add_argument(
-        "--ep",
-        action="store_true",
-        default=False,
-        help="Enable expert-parallel handling of all_to_all annotations",
+        description="Test JSON-driven Qwen TrainingDAG execution"
     )
-    parser.add_argument("--pp", type=int, default=2)
+    parser.add_argument('--model', choices=['9M', '1B', '9B', '48B', '30B-A3B', '30-A3B-half', '72B'], default='9M',
+                        help='Model configuration: 9M, 1B, 9B, 48B, 30B-A3B, 30-A3B-half, or 72B (default: 9M)')
     parser.add_argument("--batch-size", type=int, default=4)
     parser.add_argument("--seq-len", type=int, default=1024)
-    parser.add_argument("--mbs", type=int, default=4)
     parser.add_argument("--warmup", type=int, default=3)
-    parser.add_argument("--iters", type=int, default=5)
-    parser.add_argument("--trace-iters", type=int, default=3)
-    parser.add_argument("--tracing", action="store_true", default=False)
-    parser.add_argument("--naive-grad-sync", action="store_true", default=False)
+    parser.add_argument("--iters", type=int, default=3)
     parser.add_argument('--activation-checkpointing', action='store_true', default=False)
-    parser.add_argument("--zero-stage", type=int, default=0, choices=[0, 1, 2, 3],
-                        help="Apply ZeRO memory optimizations")
-    parser.add_argument("--bucket-size", type=float, help="Bucket size in MB")
-    parser.add_argument("--profiling", action="store_true", default=False,
-                        help="Profile each DAG task: log time and memory delta per task")
     parser.add_argument("--nsight", action="store_true", default=False,
                         help="Whether to use Nsight Systems for tracing")
     parser.add_argument("--save-viz", action="store_true", default=False,
@@ -398,30 +177,6 @@ def parse_args():
         default=True,
         help="Whether actors torch.compile stage GraphModules in _load_stage (default: true)",
     )
-    parser.add_argument(
-        "--gradient-accumulation",
-        action=argparse.BooleanOptionalAction,
-        default=True,
-        help="Whether to delay bucket gradient reductions to the last occurrence (default: true)",
-    )
-    parser.add_argument(
-        "--ar-a2a-same-stream",
-        action=argparse.BooleanOptionalAction,
-        default=False,
-        help="Whether A2A ops should share the AR CUDA stream on actors (default: false)",
-    )
-    parser.add_argument(
-        "--overlap-zero-ops",
-        action=argparse.BooleanOptionalAction,
-        default=False,
-        help="Whether to run overlap_zero_ops on the per-rank DAGs (default: false)",
-    )
-    parser.add_argument(
-        "--overlap-chunks",
-        action=argparse.BooleanOptionalAction,
-        default=False,
-        help="Whether to run overlap_chunks on the per-rank DAGs (default: false)",
-    )
     parser.add_argument(
         "--pp-outer",
         action=argparse.BooleanOptionalAction,
@@ -434,16 +189,37 @@ def parse_args():
         ),
     )
     parser.add_argument(
-        "--memory-profile",
+        "--schedule-directives-file",
+        type=str,
+        default="test/schedules/qwen_pp_only.json",
+        help="JSON file containing schedule directives for the piper backend",
+    )
+    parser.add_argument(
+        "--metrics-json-out",
+        type=str,
+        default="",
+        help="If set, write the raw per-dp-rank metrics list as JSON to this path "
+             "(consumed by test_harness to build the metrics CSV).",
+    )
+    parser.add_argument(
+        "--pytorch-profiler",
         action="store_true",
         default=False,
-        help="Record a CUDA memory snapshot for one iteration after warmup (PyTorch memory viz)",
+        help="Run extra iterations under torch.profiler on every actor and write "
+             "per-actor chrome traces (combined per dp-rank by test_harness).",
     )
     parser.add_argument(
-        "--memory-profile-dir",
+        "--pytorch-profile-dir",
         type=str,
         default="",
-        help="Directory for rank*_memory.pickle (default: <output-dir>/memory_snapshots)",
+        help="Directory where actors write per-actor chrome traces "
+             "(dp{dp}_pp{pp}.json). Set by test_harness.",
+    )
+    parser.add_argument(
+        "--pytorch-profiler-iters",
+        type=int,
+        default=3,
+        help="Number of iterations to run under the PyTorch profiler.",
     )
     return parser.parse_args()
 
@@ -458,9 +234,7 @@ def parse_args():
 if __name__ == "__main__":
     logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
     args = parse_args()
-    metrics_path = _metrics_output_path(args)
-    os.makedirs(os.path.dirname(metrics_path), exist_ok=True)
-    open(metrics_path, "w", encoding="utf-8").close()
+    args.num_stages = _derive_num_stages(args.schedule_directives_file)
     logger.info(args)
     if args.address != "":
         ray.init(
@@ -477,34 +251,21 @@ def parse_args():
             include_dashboard=False,
             _temp_dir=args.temp_dir,
         )
-    if args.pp_outer:
-        # PP is the outer placement dim: one bundle per pipeline stage, pinned
-        # to its own node via STRICT_SPREAD. Each bundle reserves dp GPUs so
-        # all DP replicas at a given stage colocate on one node, making
-        # per-stage EP AllToAll and DP AllReduce intra-node.
-        # CPU per bundle = dp (one per actor; Ray's actor scheduling default is
-        # num_cpus=1) + ceil(dp/pp) slack for the run_dp_rank driver tasks that
-        # land in this bundle (see PiperProgramCoordinator.run_program).
-        drivers_per_bundle = (args.dp + args.pp - 1) // args.pp
-        pg = placement_group(
-            [{"CPU": args.dp + drivers_per_bundle, "GPU": args.dp}] * args.pp,
-            strategy="STRICT_SPREAD",
-        )
-    else:
-        # Legacy layout: one bundle per DP replica holding all PP ranks.
-        pg = placement_group(
-            [{"CPU": args.pp, "GPU": args.pp}] * args.dp,
-            strategy="SPREAD",
-        )
+    pg = create_piper_placement_group(
+        args.schedule_directives_file,
+        pp_outer=args.pp_outer,
+    )
     ray.get(pg.ready(), timeout=600)
     logger.info(placement_group_table(pg))
     piper_coordinator = PiperProgramCoordinator.remote(
-        pp_degree=args.pp, dp_degree=args.dp, pp_outer=args.pp_outer,
+        schedule_directives_file=args.schedule_directives_file,
+        pp_outer=args.pp_outer,
     )
     handles = piper_coordinator.run_program.remote(main, pg, args, pg)
     dp_metrics = ray.get(handles)
-    open(metrics_path, "w", encoding="utf-8").close()
-    for metrics in sorted(dp_metrics, key=lambda item: item["dp_rank"]):
-        _write_benchmark_metrics(args, metrics)
-    logger.info(f"Benchmark metrics saved to {metrics_path}")
+    if args.metrics_json_out:
+        os.makedirs(os.path.dirname(args.metrics_json_out) or ".", exist_ok=True)
+        with open(args.metrics_json_out, "w", encoding="utf-8") as f:
+            json.dump(dp_metrics, f)
+        logger.info(f"Raw metrics written to {args.metrics_json_out}")
     ray.shutdown()
diff --git a/test/test_qwen_shubham.py b/test/test_qwen_shubham.py
deleted file mode 100644
index e397a32..0000000
--- a/test/test_qwen_shubham.py
+++ /dev/null
@@ -1,262 +0,0 @@
-import ray
-import torch
-import torch.nn as nn
-import argparse
-import time
-import os
-import numpy as np
-import json
-
-from src.piper_coordinator import PiperProgramCoordinator
-from src.piper_compile import piper_setup, piper_shutdown
-from src.piper_exec import piper_exec
-from src.piper_utils import piper_metadata
-from ray.util.placement_group import (
-    placement_group,
-    placement_group_table,
-    remove_placement_group,
-)
-
-from .models.qwen3 import PiperQwen3Model, create_qwen3_config
-from torchtitan.models.qwen3 import Qwen3Model, Qwen3TransformerBlock
-from torchtitan.models.common import GQAttention, RoPE, FeedForward
-from torchtitan.models.common.moe import MoE
-
-from .schedule_helpers import (
-    build_1f1b_schedule, 
-    build_gpipe_schedule, 
-    print_schedule,
-    INTERLEAVED_1F1B_PP2_MB4_SCHEDULE,
-    INTERLEAVED_1F1B_PP2_MB6_SCHEDULE,
-    INTERLEAVED_1F1B_PP4_MB8_SCHEDULE,
-    INTERLEAVED_GPIPE_PP2_MB4_SCHEDULE,
-    NO_PP_SCHEDULE,
-    DUALPIPEV_MB6_SCHEDULE,
-    DUALPIPEV_NOZB_MB6_SCHEDULE,
-    ZEROBUBBLE_MB4_SCHEDULE,
-)
-
-
-def main(args, pg):
-    world_size = args.dp * args.pp
-    mbs = args.mbs
-    batch_size = args.batch_size
-    seq_len = args.seq_len
-
-    # Create model config
-    config = create_qwen3_config(args.model)
-
-    # Select schedule
-    schedule = None
-    match args.schedule:
-        case "no-pp":
-            schedule = NO_PP_SCHEDULE
-        case "interleaved-1f1b":
-            schedule = INTERLEAVED_1F1B_PP2_MB4_SCHEDULE if args.pp == 2 else INTERLEAVED_1F1B_PP4_MB8_SCHEDULE
-        case "1f1b":
-            schedule = build_1f1b_schedule(args.mbs, args.pp)
-        case "gpipe":
-            schedule = build_gpipe_schedule(args.mbs, args.pp)
-    
-    print("Schedule:")
-    print_schedule(schedule)
-
-    # Create model with pipeline stages
-    model = PiperQwen3Model(config, num_stages=args.pp)
-    model.to('cuda')
-    
-
-    # # Initialize weights using TorchTitan's method
-    # with torch.no_grad():
-    #     model.init_weights(buffer_device=torch.device("cuda:0"))
-    
-    # Create input tensors
-    # Qwen3 forward signature: forward(tokens, attention_masks=None, positions=None)
-    x = torch.randint(0, config.vocab_size, (batch_size, seq_len)).to('cuda')
-    # For Qwen3, we don't need input_pos like Mixtral - positions can be None
-    # attention_masks can also be None for causal attention
-    
-    # Create labels for loss (vocab_size)
-    y = torch.randn(batch_size, seq_len, config.vocab_size).to('cuda')
-
-    # Setup Piper
-    piper_setup(
-        PiperQwen3Model,
-        model_args=(config, args.pp),
-        optim_fn=torch.optim.Adam,
-        example_inputs=[x],
-        example_outputs=y,
-        schedule=schedule,
-        naive_gradient_sync=args.naive_gradient_sync,
-        activation_checkpointing=args.activation_checkpointing,
-        pg=pg,
-    )
-
-    # Send data to actors ahead of time
-    actors = piper_metadata.actors
-    ray.get(actors[0].load_input.remote([x]))
-    ray.get(actors[len(actors)-1].load_labels.remote(y))
-
-    loss_fn = torch.nn.CrossEntropyLoss()
-
-    print(f"Running {args.warmup} warmup iterations...")
-    for _ in range(args.warmup):
-        piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
-        # piper_exec(schedule, [x], y, loss_fn, args.dp, args.naive_gradient_sync)
-    
-    print(f"Running {args.iters} timed iterations...")
-    iter_times = []
-    profile_iter = args.profile_iter
-    
-
-    # if profile_iter is not None:
-    #     os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/memory_snapshots", exist_ok=True)
-    #     os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/overlapped", exist_ok=True)
-    #     os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/tensorboard", exist_ok=True)
-    #     os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/chrome_traces", exist_ok=True)
-    
-    for iter_idx in range(args.iters):
-        # Enable profiling for the specified iteration
-        if profile_iter is not None and iter_idx == profile_iter:
-            print(f"\n=== Enabling profiling for iteration {iter_idx} ===")
-            # ray.get([actor.enable_profiling.remote(True) for actor in actors.values()])
-            # ray.get([actor.set_tracing.remote(True) for actor in actors.values()])
-            # ray.get([actor.enable_memory_tracing.remote(True) for actor in actors.values()])
-            # ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
-            # ray.get([actor.clear_trace_data.remote() for actor in actors.values()])
-        
-        start = time.perf_counter()
-        if profile_iter is not None and iter_idx == profile_iter:
-            with torch.profiler.record_function("piper_exec_iteration"):
-                piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
-        else:
-            piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
-
-        end = time.perf_counter()
-        iter_times.append(end - start)
-
-        # Collect profiling data and disable profiling after the specified iteration
-        # if profile_iter is not None and iter_idx == profile_iter:
-        #     print(f"\n=== Collecting profiling data for iteration {iter_idx} ===")
-            
-            # # Memory profiling
-            # snapshot_paths = ray.get([actor.dump_memory_snapshot.remote() for actor in actors.values()])
-            # print("\nMemory snapshots:")
-            # for rank, path in enumerate(snapshot_paths):
-            #     if path:
-            #         print(f"Rank {rank}: {path}")
-            
-            # # Peak memory
-            # mem_data_ret = ray.get([actor.get_peak_memory.remote() for actor in actors.values()])
-            # print("\nPeak memory usage:")
-            # for rank, peak_mem in mem_data_ret:
-            #     print(f"  Rank {rank}: {peak_mem:.3f} GB")
-            
-            # # Timing data
-            # trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
-            # print("\nTiming data:")
-            # for rank, trace_data in trace_data_ret:
-            #     print(f"\n  Rank {rank}:")
-            #     for key in trace_data:
-            #         all_times = trace_data[key]
-            #         if all_times:
-            #             print(f"    {key}: {np.mean(all_times):.3f} ± {np.std(all_times):.3f} ms ({len(all_times)} samples)")
-            
-            # # Disable profiling
-            # print("\n=== Disabling profiling ===")
-            # ray.get([actor.enable_profiling.remote(False) for actor in actors.values()])
-            # ray.get([actor.set_tracing.remote(False) for actor in actors.values()])
-            # ray.get([actor.enable_memory_tracing.remote(False) for actor in actors.values()])
-    
-    dp_rank = int(os.environ['PIPER_DP_RANK'])
-    print(
-        f"rank {dp_rank} iter time= {np.mean(iter_times):.2f} ± {np.std(iter_times):.2f} s ({len(iter_times)} samples)\n"
-        f"rank {dp_rank} throughput= {(args.batch_size * args.mbs * seq_len)/np.mean(iter_times):.2f} tokens/s ({len(iter_times)} samples)"
-    )
-
-    if args.tracing:
-        ray.get([actor.set_tracing.remote(args.tracing) for actor in actors.values()])
-
-        print(f"Running {args.warmup} tracing iterations...")
-        for _ in range(args.warmup):
-            piper_exec(schedule, loss_fn, args.dp, args.naive_gradient_sync)
-
-        trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
-        for rank, trace_data in trace_data_ret:
-            for key in trace_data:
-                all_times = trace_data[key]
-                print(f"rank {rank} {key} time= {np.mean(all_times):.3f} ± {np.std(all_times):.3f} ms ({len(all_times)} samples)")
-
-
-
-    if args.naive_gradient_sync:
-        suffix = "-naive-sync"
-    else:
-        suffix = ""
-    # Collect task_id -> label mappings from all actors
-    all_task_labels = {}
-    actors = piper_metadata.actors
-    for labels in ray.get([actor.get_task_labels.remote() for actor in actors.values()]):
-        all_task_labels.update(labels)
-
-
-    os.makedirs("/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/timeline", exist_ok=True)
-    timeline_filename = f"/m-coriander/coriander/shubham/moe-scheduling/piper_profiling/timeline/qwen3-pp{args.pp}-dp{args.dp}-{args.schedule}{suffix}.json"
-    ray.timeline(timeline_filename)
-    labels_filename = timeline_filename.replace(".json", "-labels.json")
-    with open(labels_filename, "w") as f:
-        import json
-        json.dump(all_task_labels, f)
-    print(f"Ray timeline saved to: {timeline_filename}")
-    print(f"Task labels saved to: {labels_filename}")
-
-    piper_shutdown()
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='Run Qwen3 model with pipeline parallelism')
-    parser.add_argument('--model', choices=['tiny', 'small', 'medium', 'large', '30B-A3B', '72B'], default='small',
-                        help='Model configuration: tiny, small, medium, large, 30B-A3B, or 72B (default: tiny)')
-    parser.add_argument('--schedule', choices=['gpipe', '1f1b', 'interleaved-1f1b', 'no-pp'], default='1f1b',
-                        help='Schedule type: gpipe, 1f1b, or interleaved-1f1b (default: 1f1b)')
-    parser.add_argument('--dp', type=int, default=1,
-                        help='Number of data parallel degrees (default: 1)')
-    parser.add_argument('--pp', type=int, default=2,
-                        help='Number of pipeline parallel degrees (default: 2)')
-    parser.add_argument('--batch_size', type=int, default=16,
-                        help='Batch size (default: 16)')
-    parser.add_argument('--seq_len', type=int, default=1024,
-                        help='Sequence length (default: 2048)')
-    parser.add_argument('--mbs', type=int, default=4,
-                        help='Number of microbatches (default: 4)')
-    parser.add_argument('--warmup', type=int, default=5,
-                        help='Number of warmup iterations (default: 5)')
-    parser.add_argument('--iters', type=int, default=10,
-                        help='Number of timing iterations (default: 10)')
-    parser.add_argument('--tracing', action='store_true', default=False,
-                        help='Enable tracing')
-    parser.add_argument('--naive_gradient_sync', action='store_true', default=False,
-                        help='Enable naive gradient sync')
-    parser.add_argument('--profile_iter', type=int, default=5,
-                        help='Iteration number to profile (0-indexed). If None, no profiling (default: None)')
-    parser.add_argument('--activation_checkpointing', action='store_true', default=False,
-                        help='Enable activation checkpointing')
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    ray.init(
-        include_dashboard=False,
-        log_to_driver=True,
-        namespace="qwen3",
-        _temp_dir="/dev/shm/ray"
-    )
-    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp)
-    ray.get(pg.ready(), timeout=10)
-    print(placement_group_table(pg))
-    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
-    handles = piper_coordinator.run_program.remote(main, args, pg)
-    ray.get(handles)
-    time.sleep(3)
-    ray.shutdown()
\ No newline at end of file
diff --git a/test/test_tracing.py b/test/test_tracing.py
deleted file mode 100644
index bd1731a..0000000
--- a/test/test_tracing.py
+++ /dev/null
@@ -1,290 +0,0 @@
-import torch
-import torch.fx as fx
-import time
-import logging
-from .models.llama import Transformer, LLAMA_DEBUG, LLAMA_1B, LLAMA_8B, LLAMA_70B
-from src.piper_compile import piper_setup
-from src import piper as piper_backend
-from src.piper_utils import piper_metadata
-import ray
-
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-def test_meta_device_compile():
-    """Test torch.compile on Llama model using meta device."""
-    logger.info("Testing torch.compile on Llama model with meta device...")
-    
-    config = LLAMA_70B
-    seq_len = 256
-    batch_size = 16
-    
-    try:
-        # Create model on meta device to avoid allocating memory
-        with torch.device("meta"):
-            start = time.perf_counter()
-            model = Transformer(config, seq_len)
-            end = time.perf_counter()
-            logger.info("✓ Model created on meta device")
-            logger.info(f"Model creation time: {(end - start)*1e3:.0f} ms")
-            
-        # Create dummy inputs on meta device
-        tokens = torch.randint(0, config.vocab_size, (batch_size, seq_len), device="meta")
-        logger.info(f"✓ Dummy tokens created on meta device: shape {tokens.shape}")
-        
-        # Compile the model on meta device
-        start = time.perf_counter()
-        compiled_model = torch.compile(model)
-        end = time.perf_counter()
-        logger.info("✓ torch.compile succeeded on meta device model")
-        logger.info(f"Compilation time: {(end - start)*1e3:.0f} ms")
-                
-        try:
-            output = compiled_model(tokens)
-            logger.info(f"✓ Inference successful on meta device, output shape: {output.shape}")
-        except Exception as e:
-            import traceback
-            tb = traceback.format_exc()
-            err_type = type(e).__name__
-            err_msg = str(e)
-            # Log detailed information for debugging
-            logger.warning(
-                f"⚠ Forward pass on meta device produced error (expected): {err_type}: {err_msg}\n{tb}"
-            )
-            # Add short hints for common error types
-            if isinstance(e, KeyError):
-                logger.warning(f"  Missing key in mapping: {e.args}")
-            elif isinstance(e, TypeError):
-                logger.warning(f"  TypeError details: args={e.args}")
-            elif isinstance(e, RuntimeError):
-                logger.warning(f"  RuntimeError details: args={e.args}")
-            
-        return True
-    except Exception as e:
-        logger.error(f"✗ Meta device compile test failed: {e}")
-        import traceback
-        traceback.print_exc()
-        return False
-
-def test_meta_device_fake_tensor():
-    """Test torch.compile on Llama model using meta params + fake inputs."""
-    logger.info("Testing torch.compile on Llama model with meta device + fake inputs...")
-
-    config = LLAMA_70B
-    seq_len = 256
-    batch_size = 16
-
-    try:
-        # Create model on meta device to avoid allocating memory
-        with torch.device("meta"):
-            start = time.perf_counter()
-            model = Transformer(config, seq_len)
-            end = time.perf_counter()
-            logger.info("✓ Model created on meta device")
-            logger.info(f"Model creation time: {(end - start)*1e3:.0f} ms")
-
-        from torch._subclasses.fake_tensor import FakeTensorMode
-        fake_mode = FakeTensorMode(allow_non_fake_inputs=False)
-
-        trace_device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-
-        # Create fake tokens (no storage) that pretend to be on trace_device
-        with fake_mode:
-            tokens = torch.empty((batch_size, seq_len), dtype=torch.long, device=trace_device)
-            logger.info(f"✓ Fake tokens created: shape={tuple(tokens.shape)} device={tokens.device} type={type(tokens)}")
-
-            start = time.perf_counter()
-            compiled_model = torch.compile(model)
-            end = time.perf_counter()
-            logger.info("✓ torch.compile succeeded (meta model + fake inputs)")
-            logger.info(f"Compilation time: {(end - start)*1e3:.0f} ms")
-
-            try:
-                output = compiled_model(tokens)
-                # output may also be fake
-                logger.info(f"✓ Forward pass succeeded, output shape: {tuple(output.shape)}")
-            except Exception as e:
-                import traceback
-                tb = traceback.format_exc()
-                logger.warning(f"⚠ Forward pass produced error (still useful): {type(e).__name__}: {e}\n{tb}")
-
-        return True
-
-    except Exception as e:
-        logger.error(f"✗ Meta+fake compile test failed: {e}")
-        import traceback
-        traceback.print_exc()
-        return False
-
-def test_meta_device_fx_trace():
-    """Test torch.fx.symbolic_trace on Llama model using meta device."""
-    logger.info("\nTesting torch.fx.symbolic_trace on Llama model with meta device...")
-    
-    config = LLAMA_DEBUG
-    seq_len = 128
-    batch_size = 1
-    
-    try:
-        # Create model on meta device
-        model = Transformer(config, seq_len).to("meta")
-        logger.info("✓ Model created on meta device")
-        model.eval()
-        
-        # Create dummy inputs on meta device
-        tokens = torch.randint(0, config.vocab_size, (batch_size, seq_len), device="meta")
-        logger.info(f"✓ Dummy tokens created on meta device: shape {tokens.shape}")
-        
-        # Attempt symbolic tracing with meta device
-        traced_model = fx.symbolic_trace(model)
-        logger.info("✓ torch.fx.symbolic_trace succeeded")
-        logger.info(f"✓ Graph has {len(list(traced_model.graph.nodes))} nodes")
-        
-        return True
-    except Exception as e:
-        logger.error(f"✗ torch.fx.symbolic_trace failed: {e}")
-        import traceback
-        traceback.print_exc()
-        return False
-
-
-def test_meta_device_graph_module():
-    """Test creating a GraphModule from meta device model."""
-    logger.info("\nTesting GraphModule creation with meta device...")
-    
-    config = LLAMA_8B
-    seq_len = 128
-    batch_size = 1
-    
-    try:
-        # Create model on meta device
-        model = Transformer(config, seq_len).to("meta")
-        logger.info("✓ Model created on meta device")
-        model.eval()
-        
-        # Try to trace and examine the computation graph
-        tokens = torch.randint(0, config.vocab_size, (batch_size, seq_len), device="meta")
-        
-        # Use torch._dynamo to trace the model
-        traced = torch.compile(model, backend="eager", fullgraph=False)
-        logger.info("✓ Model compiled with torch.compile on meta device")
-        
-        # Get model info
-        num_params = sum(p.numel() for p in model.parameters())
-        logger.info(f"✓ Model has {num_params:,} parameters")
-        
-        return True
-    except Exception as e:
-        logger.error(f"✗ GraphModule test failed: {e}")
-        import traceback
-        traceback.print_exc()
-        return False
-
-
-if __name__ == "__main__":
-    logger.info("Starting Llama symbolic tracing with meta device...\n")
-    
-    results = {
-        "torch.compile (meta)": test_meta_device_compile(),
-        # "torch.compile + fake tensors (meta)": test_meta_device_fake_tensor(),
-        # "torch.fx.symbolic_trace (meta)": test_meta_device_fx_trace(),
-        # "GraphModule (meta)": test_meta_device_graph_module(),
-        # "piper_backend (meta)": False,
-    }
-
-    def test_piper_backend_meta():
-        """Quick smoke test that calls the piper backend setup (no schedule execution)."""
-        logger.info("\nTesting piper backend setup with meta device...")
-        config = LLAMA_DEBUG
-        seq_len = 128
-        batch_size = 1
-        try:
-            # dummy input shapes (on cpu) are sufficient for piper_setup
-            x = torch.randint(0, config.vocab_size, (batch_size, seq_len))
-            # call piper setup which prepares the pipeline/actors
-            compiled = piper_setup(Transformer, (config, seq_len), torch.optim.Adam, [x], 2, 2)
-            logger.info("✓ piper_setup completed (no schedule run)")
-            return True
-        except Exception as e:
-            logger.error(f"✗ piper backend setup failed: {e}")
-            import traceback
-            traceback.print_exc()
-            return False
-
-    # run piper backend smoke test last (it may start actors)
-    # results["piper_backend (meta)"] = test_piper_backend_meta()
-
-    def test_piper_backend_meta_graph():
-        """Compile the model with the `piper` torch.compile backend on meta device.
-
-        This attempts to invoke the backend compilation path to produce serialized
-        graphmodules without requiring real Ray actors or GPUs by monkeypatching
-        a minimal actor handle and `ray.get`.
-        """
-        logger.info("\nTesting piper backend as torch.compile backend with meta device...")
-        config = LLAMA_DEBUG
-        seq_len = 128
-
-        # Build model on meta device
-        model = Transformer(config, seq_len).to("meta")
-
-        # Prepare example input on meta device
-        tokens = torch.randint(0, config.vocab_size, (1, seq_len), device="meta")
-
-        # Save original ray.get to restore later
-        orig_ray_get = ray.get
-        try:
-            # Set minimal piper metadata to allow backend to run
-            piper_metadata.currently_compiling = True
-            piper_metadata.current_stage = 0
-            piper_metadata.current_actor = 0
-            piper_metadata.first_graph_of_stage = True
-
-            # Create a minimal dummy actor handle with a .load_graph.remote
-            class _DummyLoad:
-                def remote(self, *a, **k):
-                    return None
-
-            class _DummyActor:
-                def __init__(self):
-                    self.load_graph = _DummyLoad()
-
-            piper_metadata.actors = {0: _DummyActor()}
-
-            # Monkeypatch ray.get so it accepts our dummy return value
-            ray.get = lambda x: x
-
-            # Compile with piper backend (this will call into src.piper.piper)
-            compiled = torch.compile(model, backend=piper_backend.piper)
-            logger.info("✓ torch.compile returned a compiled function (piper backend)")
-
-            # Call compiled to trigger backend handling (serialization/loading)
-            try:
-                _ = compiled(tokens)
-                logger.info("✓ Invoked compiled function (backend path exercised)")
-            except Exception:
-                # It's acceptable for the runtime invocation to raise when using meta tensors;
-                # the important part is that the backend path executed during compilation.
-                logger.info("⚠ Invocation raised during meta-device run (expected) — compilation path exercised")
-
-            return True
-        except Exception as e:
-            logger.error(f"✗ piper backend meta compile failed: {e}")
-            import traceback
-            traceback.print_exc()
-            return False
-        finally:
-            # restore
-            ray.get = orig_ray_get
-            piper_metadata.currently_compiling = False
-            piper_metadata.actors = {}
-
-    # results["piper_backend_graph (meta)"] = test_piper_backend_meta_graph()
-    
-    logger.info("\n" + "="*60)
-    logger.info("Test Results Summary:")
-    for test_name, result in results.items():
-        status = "✓ PASS" if result else "✗ FAIL"
-        logger.info(f"  {test_name}: {status}")
-    logger.info("="*60)
\ No newline at end of file

From 8d851b3f82228a705447fc0ff24da08816456a20 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Mon, 1 Jun 2026 22:53:38 +0000
Subject: [PATCH 098/100] refactor

---
 README.md                                     |   70 +-
 REFACTOR_PLAN.md                              |  470 +++
 examples/__init__.py                          |    1 +
 examples/base-schedules/pp2.json              |   22 +
 examples/base-schedules/pp2_dp2.json          |   46 +
 examples/base-schedules/pp2_dp2_ep2.json      |   70 +
 .../base-schedules/pp2_dp2_ep2_bucket25.json  |   72 +
 .../pp2_dp2_ep2_custom_order.json             |  140 +
 .../base-schedules/pp2_dp2_ep2_zero2.json     |   72 +
 .../base-schedules/pp2_dp2_ep2_zero3.json     |   74 +
 examples/base-schedules/pp2_dp2_zero2.json    |   48 +
 .../base-schedules/pp2_dp2_zero3.json         |   36 +-
 .../pp4_dp2_ep2_interleaved.json              |  138 +
 .../pp4_dp2_ep2_v_placement.json              |  138 +
 examples/base-schedules/pp4_interleaved.json  |   42 +
 examples/base-schedules/pp4_v_placement.json  |   42 +
 examples/build_schedule.py                    |  334 ++
 examples/models/__init__.py                   |    1 +
 .../llama.py => examples/models/llama3.py     |  122 +-
 {test => examples}/models/qwen3.py            |   76 +-
 {test => examples}/test_harness.py            |  239 +-
 {test => examples}/test_llama.py              |   52 +-
 {test => examples}/test_qwen.py               |   78 +-
 pyproject.toml                                |   33 +
 requirements.txt                              |    2 +
 scripts/test_env.py                           |    6 +-
 src/actor.py                                  |  738 +++++
 src/{backward_utils.py => backward.py}        |    0
 src/bucket.py                                 |  450 +++
 src/{piper_compile.py => compile.py}          |   39 +-
 src/{piper_coordinator.py => coordinator.py}  |   19 +-
 src/dag.py                                    |  340 ++
 src/directives.py                             | 1626 ++++++++++
 src/executors.py                              |  959 ++++++
 src/fx.py                                     |  694 ++++
 src/ordering.py                               |  178 ++
 src/piper.py                                  | 2801 +----------------
 src/piper_actor.py                            | 2151 -------------
 src/piper_exec.py                             |   18 -
 src/piper_graph_transform.py                  | 1683 ----------
 src/piper_utils.py                            |  399 ---
 src/runtime.py                                |  495 +++
 src/{piper_schedule.py => schedule.py}        |   76 +-
 src/state.py                                  |   52 +
 src/tasks.py                                  |   51 +
 src/visualization.py                          |  412 +++
 src/zero.py                                   |  369 +++
 test/models/mixtral-dtensor-a2a.py            |  354 ---
 test/models/mixtral-dtensor.py                |  343 --
 test/models/mixtral-orig.py                   |  288 --
 test/models/mixtral.py                        |  356 ---
 test/models/mixtral_a2a.py                    |  314 --
 test/pippy_profiling/1f1b_event_profile.py    |  300 --
 .../1f1b_expected_schedule.dot                |  118 -
 .../1f1b_expected_schedule.png                |  Bin 60730 -> 0 bytes
 .../zb1p_expected_schedule.dot                |  122 -
 .../zb1p_expected_schedule.png                |  Bin 100276 -> 0 bytes
 .../zb_1f1b_memory_profiling.py               |  206 --
 .../zerobubble_event_profile.py               |  318 --
 test/pippy_profiling/zerobubble_example.py    |  270 --
 test/schedules/build_order_directives.py      |  638 ----
 test/schedules/build_schedule.py              |   16 -
 test/schedules/llama_pp.json                  |   14 -
 test/schedules/qwen_pp2.json                  |   14 -
 test/schedules/qwen_pp2_dp2_ep2.json          |   38 -
 .../schedules/qwen_pp2_dp2_ep2_dualpipev.json |   74 -
 .../qwen_pp2_dp2_ep2_interleaved.json         |   50 -
 test/schedules/qwen_pp2_interleaved.json      |   26 -
 test/schedules/qwen_pp4.json                  |   26 -
 test/schedules/qwen_pp_dp.json                |   26 -
 test/schedules/qwen_pp_dp_ep_overlap.json     |   52 -
 test/schedules/qwen_pp_dp_ep_zero2.json       |   40 -
 test/schedules/qwen_pp_dp_ep_zero3.json       |   42 -
 test/schedules/qwen_pp_dp_zero2.json          |   28 -
 test/test_graph_transform.py                  |  278 +-
 test/test_mixtral.py                          |  191 --
 test/test_order_directive.py                  |  122 +-
 test/test_piper_schedule.py                   |   72 +
 78 files changed, 8771 insertions(+), 11939 deletions(-)
 create mode 100644 REFACTOR_PLAN.md
 create mode 100644 examples/__init__.py
 create mode 100644 examples/base-schedules/pp2.json
 create mode 100644 examples/base-schedules/pp2_dp2.json
 create mode 100644 examples/base-schedules/pp2_dp2_ep2.json
 create mode 100644 examples/base-schedules/pp2_dp2_ep2_bucket25.json
 create mode 100644 examples/base-schedules/pp2_dp2_ep2_custom_order.json
 create mode 100644 examples/base-schedules/pp2_dp2_ep2_zero2.json
 create mode 100644 examples/base-schedules/pp2_dp2_ep2_zero3.json
 create mode 100644 examples/base-schedules/pp2_dp2_zero2.json
 rename test/schedules/qwen_pp_dp_zero3.json => examples/base-schedules/pp2_dp2_zero3.json (60%)
 create mode 100644 examples/base-schedules/pp4_dp2_ep2_interleaved.json
 create mode 100644 examples/base-schedules/pp4_dp2_ep2_v_placement.json
 create mode 100644 examples/base-schedules/pp4_interleaved.json
 create mode 100644 examples/base-schedules/pp4_v_placement.json
 create mode 100644 examples/build_schedule.py
 create mode 100644 examples/models/__init__.py
 rename test/models/llama.py => examples/models/llama3.py (74%)
 rename {test => examples}/models/qwen3.py (86%)
 rename {test => examples}/test_harness.py (77%)
 rename {test => examples}/test_llama.py (79%)
 rename {test => examples}/test_qwen.py (73%)
 create mode 100644 pyproject.toml
 create mode 100644 src/actor.py
 rename src/{backward_utils.py => backward.py} (100%)
 create mode 100644 src/bucket.py
 rename src/{piper_compile.py => compile.py} (90%)
 rename src/{piper_coordinator.py => coordinator.py} (82%)
 create mode 100644 src/dag.py
 create mode 100644 src/directives.py
 create mode 100644 src/executors.py
 create mode 100644 src/fx.py
 create mode 100644 src/ordering.py
 delete mode 100644 src/piper_actor.py
 delete mode 100644 src/piper_exec.py
 delete mode 100644 src/piper_graph_transform.py
 delete mode 100644 src/piper_utils.py
 create mode 100644 src/runtime.py
 rename src/{piper_schedule.py => schedule.py} (66%)
 create mode 100644 src/state.py
 create mode 100644 src/tasks.py
 create mode 100644 src/visualization.py
 create mode 100644 src/zero.py
 delete mode 100644 test/models/mixtral-dtensor-a2a.py
 delete mode 100644 test/models/mixtral-dtensor.py
 delete mode 100644 test/models/mixtral-orig.py
 delete mode 100644 test/models/mixtral.py
 delete mode 100644 test/models/mixtral_a2a.py
 delete mode 100644 test/pippy_profiling/1f1b_event_profile.py
 delete mode 100644 test/pippy_profiling/1f1b_expected_schedule.dot
 delete mode 100644 test/pippy_profiling/1f1b_expected_schedule.png
 delete mode 100644 test/pippy_profiling/zb1p_expected_schedule.dot
 delete mode 100644 test/pippy_profiling/zb1p_expected_schedule.png
 delete mode 100644 test/pippy_profiling/zb_1f1b_memory_profiling.py
 delete mode 100644 test/pippy_profiling/zerobubble_event_profile.py
 delete mode 100644 test/pippy_profiling/zerobubble_example.py
 delete mode 100644 test/schedules/build_order_directives.py
 delete mode 100644 test/schedules/build_schedule.py
 delete mode 100644 test/schedules/llama_pp.json
 delete mode 100644 test/schedules/qwen_pp2.json
 delete mode 100644 test/schedules/qwen_pp2_dp2_ep2.json
 delete mode 100644 test/schedules/qwen_pp2_dp2_ep2_dualpipev.json
 delete mode 100644 test/schedules/qwen_pp2_dp2_ep2_interleaved.json
 delete mode 100644 test/schedules/qwen_pp2_interleaved.json
 delete mode 100644 test/schedules/qwen_pp4.json
 delete mode 100644 test/schedules/qwen_pp_dp.json
 delete mode 100644 test/schedules/qwen_pp_dp_ep_overlap.json
 delete mode 100644 test/schedules/qwen_pp_dp_ep_zero2.json
 delete mode 100644 test/schedules/qwen_pp_dp_ep_zero3.json
 delete mode 100644 test/schedules/qwen_pp_dp_zero2.json
 delete mode 100644 test/test_mixtral.py
 create mode 100644 test/test_piper_schedule.py

diff --git a/README.md b/README.md
index 653920b..28369c7 100644
--- a/README.md
+++ b/README.md
@@ -1,49 +1,39 @@
 # Piper
 
-Piper is a PyTorch library for training large models with flexible pipeline parallel schedules.
+Piper is a user-controllable distributed training system that decouples distributed training strategy from runtime implementation. Piper lets users express high-level placement and low-level scheduling intent through lightweight model annotations and scheduling directives. The compiler lowers this intent into a unified global training DAG that explicitly represents computation, communication, data dependencies, temporal dependencies, device placement, and GPU stream assignment. A centralized scheduler then decomposes the DAG into per-device execution plans, and a Ray-based distributed runtime executes those plans on GPU workers while managing streams, communicators, and memory. This design allows Piper to match existing general-purpose training systems on common strategies while making it easier to express and optimize composed strategies such as PP x EP/DP DualPipe-style schedules.
 
-## Environment setup: conda
-We assume a Linux-based environment
+![Piper architecture](figs/piper-architecture.png)
 
-1. Create a conda environment with `python==3.10`
-2. Install the requirements in `requirements.txt`
-3. Modify PyTorch and Ray dependencies according to the instructions below
+## Install
 
-## Modifying Ray dependency
+The following installation has been tested with Python 3.10 on Linux with CUDA 12.8 drivers. 
 
-**Ray**
-
-Tensor transport backends currently only support 1 return value per task.
-- WIP: Upstream this into Ray.
-- Modifications (2): Comment out the [assertion in ActorMethod._remote()](https://github.com/ray-project/ray/blob/b70d990db786a1f2259dec0504acccf2590353f3/python/ray/actor.py#L824-L828) and add logic for [handling multiple return values with a GPU object manager](https://github.com/ray-project/ray/blob/b70d990db786a1f2259dec0504acccf2590353f3/python/ray/actor.py#L880-L887).
-```
-####### PIPER MODIFICATION START #######
-# if num_returns != 1:
-#     raise ValueError(
-#         f"Currently, methods with tensor_transport={tensor_transport.name} only support 1 return value. "
-#         "Please make sure the actor method is decorated with `@ray.method(num_returns=1)` (the default)."
-#     )
-####### PIPER MODIFICATION END #######
-```
-```
-####### PIPER MODIFICATION START #######
-gpu_object_manager = ray._private.worker.global_worker.gpu_object_manager
-if isinstance(object_refs, ObjectRef):
-    object_ref = object_refs
-    gpu_object_manager.add_gpu_object_ref(
-        object_ref, self._actor, tensor_transport
-    )
-else:
-    for object_ref in object_refs:
-        assert isinstance(object_ref, ObjectRef)
-        gpu_object_manager.add_gpu_object_ref(
-            object_ref, self._actor, tensor_transport
-        )
-####### PIPER MODIFICATION END #######
+```bash
+python3.10 -m venv .venv
+source .venv/bin/activate
+python -m pip install --upgrade pip
+python -m pip install -e .
 ```
 
-## Training Llama in Piper
-The llama test program `test/test_llama.py` uses JSON schedule directives.
-```
-python3 -m test.test_llama --model debug --schedule-directives-file test/schedules/llama_pp_only.json
+## Quickstart
+
+Run the test harness with the `pp2` base schedule and a generated 1F1B order schedule:
+
+```bash
+python examples/test_harness.py \
+  --test-file examples/test_qwen.py \
+  --base-schedule examples/base-schedules/pp2.json \
+  --schedule 1f1b \
+  --ranks 2 \
+  --mbs 4
 ```
+
+Results and the generated full schedule are written under `out/<timestamp>/`. Add `--viz` to also render the generated schedule and per-rank TrainingDAGs.
+
+## Citation
+
+TBD
+
+## Contact
+
+TBD
diff --git a/REFACTOR_PLAN.md b/REFACTOR_PLAN.md
new file mode 100644
index 0000000..1331e3b
--- /dev/null
+++ b/REFACTOR_PLAN.md
@@ -0,0 +1,470 @@
+# Piper Refactor Plan
+
+This document records the agreed refactor direction for the Piper implementation
+under `src/`. It is a planning document, not an implementation checklist that
+must be completed in one change.
+
+## Goals
+
+- Improve module boundaries and make each file responsible for one coherent
+  part of the system.
+- Remove `piper_` from implementation filenames. Only the main backend module
+  should keep the name `piper.py`.
+- Keep `src.piper` as the public user-facing API surface for `annotate`, backend
+  registration, and execution entry points.
+- Preserve the DAG as the single source of scheduling policy, including
+  communication, ZeRO allocation/free behavior, and task order.
+- Reduce `PiperActor` complexity by grouping state and extracting execution
+  helpers while keeping the top-level DAG execution loop easy to read.
+- Make illegal states fail early through schedule/API validation rather than
+  through indirect runtime failures.
+
+## Refactoring Principles
+
+- Separate data structures, parsing, graph transforms, runtime orchestration,
+  and distributed execution.
+- Prefer mechanical moves before behavioral rewrites so diffs remain reviewable.
+- Avoid compatibility shims unless explicitly required.
+- Avoid parallel dictionaries where one typed state object can express ownership.
+- Keep the schedule execution semantics visible: the top-level executor should
+  still iterate over the sorted DAG and dispatch by task type.
+- Keep ZeRO policy out of parameter storage. The DAG decides when to allocate,
+  free, gather, scatter, and sync.
+
+## Target Module Layout
+
+### `piper.py`
+
+Public API and backend registration only:
+
+- `annotate`
+- annotation state reset
+- `piper` backend callback
+- `piper_exec_dag`
+- minimal public glue
+
+`piper.py` should delegate DAG construction, schedule directive application,
+ordering, visualization, and runtime setup to focused modules.
+
+### `actor.py`
+
+Ray actor API shell and lifecycle:
+
+- actor construction
+- process group setup
+- loading inputs, labels, constants, stages, and DAGs
+- profiler/NVTX public methods
+- delegating task execution to `DagExecutor`
+
+The actor should own composed state objects, not a large collection of unrelated
+parallel dictionaries.
+
+### `coordinator.py`
+
+Distributed program coordination:
+
+- `PiperProgramCoordinator`
+- `run_dp_rank`
+- placement group construction
+
+### `compile.py`
+
+Compile-time orchestration:
+
+- `piper_setup`
+- compile-time Ray stores
+- model construction/tracing setup
+- pushing compiled DAG data to actors
+
+### `schedule.py`
+
+Schedule JSON loading and schema-level validation:
+
+- load schedule directives
+- validate current JSON API shape
+- derive schedule info such as PP/DP degrees and microbatch count
+
+### `tasks.py`
+
+Task type enum and task-level constants.
+
+### `state.py`
+
+Shared process-local state and logging:
+
+- `PiperMetadata`
+- global metadata instance
+- `create_logger`
+
+### `fx.py`
+
+FX graph infrastructure:
+
+- GraphModule serialization/deserialization
+- annotation stack validation
+- annotation-based FX splitting into segments
+
+This combines the current FX serialization helpers and FX splitting logic,
+because both are infrastructure around `torch.fx.GraphModule` representation.
+
+### `bucket.py`
+
+Parameter bucket planning and bucket GraphModule construction.
+
+This remains separate from `fx.py` because bucketing has separate invariants and
+is tied to optimizer/parameter ownership rather than generic FX transport.
+
+### `dag.py`
+
+Training DAG data structures, construction, and core graph utilities:
+
+- `TrainingDAG`
+- `TrainingDAGNode`
+- `TrainingDAGEdge`
+- construction of the initial TrainingDAG from annotated FX segments
+- graph mutation helpers
+- basic topological utilities that are not specifically ordering directives
+
+### `directives.py`
+
+Schedule directive parsing, matching, validation, and application.
+
+This file should include communication insertion because communication nodes are
+created as the effect of applying schedule directives:
+
+- `place`
+- `replicate`
+- `shard`
+- `split`
+- `order` parsing handoff
+- send/recv insertion
+- reduce/all-reduce insertion
+- all-gather insertion
+- reduce-scatter insertion
+- A2A insertion
+
+There should not be a separate `comm_transforms.py`; communication transforms
+are part of directive application.
+
+### `ordering.py`
+
+Ordering-specific graph logic:
+
+- serial and per-stream topological ordering
+- order directive sub-DAG matching
+- temporal edge insertion for order directives
+- stream total order resolution
+
+### `zero.py`
+
+ZeRO-specific DAG metadata and graph rewrites:
+
+- ZeRO lifetime metadata pruning
+- inter-chain temporal edge insertion around ZeRO lifetimes
+- helper logic for full-param/full-grad lifetime correctness
+
+This module should operate on the DAG and metadata. It should not own runtime
+parameter storage policy.
+
+### `visualization.py`
+
+Debug/render output:
+
+- TrainingDAG order dumps
+- Graphviz rendering
+- schedule visualization currently produced by `examples/build_schedule.py`
+- dependency logging if it remains debug-output oriented
+
+The example harness should expose a single `--viz` flag. That flag should guard
+both schedule visualization and TrainingDAG visualization; there should not be
+separate schedule-only or DAG-only example flags unless a later use case needs
+that split.
+
+### `backward.py`
+
+Current `backward_utils.py` helpers for split backward/autograd graph traversal.
+
+## Actor Design
+
+`PiperActor` should become a thin Ray shell around typed runtime components.
+
+### `DagExecutor`
+
+Owns the top-level sorted-DAG execution loop.
+
+This should remain the readable control plane:
+
+```python
+for node in sorted_nodes:
+    match task_type:
+        case TaskType.FWD:
+            compute.forward(node)
+        case TaskType.BWD:
+            compute.backward(node)
+        case TaskType.SEND:
+            communication.send(node)
+        case TaskType.RECV:
+            communication.recv(node)
+        case TaskType.ALL_GATHER:
+            communication.all_gather(node)
+        case TaskType.REDUCE_SCATTER:
+            communication.reduce_scatter(node)
+        case TaskType.A2A:
+            communication.a2a(node)
+```
+
+`DagExecutor` also handles metadata-driven lifecycle hooks, because the DAG is
+the source of policy:
+
+```python
+if node.meta.get("zero_alloc_full_grads_before"):
+    stage_store.params.alloc_full_grads(...)
+
+execute_task(node)
+
+if node.meta.get("zero_free_full_params_after"):
+    stage_store.params.free_full_params(...)
+```
+
+### `ComputeExecutor`
+
+Executes compute tasks:
+
+- forward
+- fused backward
+- backward input
+- backward weight
+- loss application
+- optimizer/update execution where appropriate
+
+It should use `StageStore`, `BufferStore`, and `ParamStorage` rather than owning
+parameter or buffer dictionaries itself.
+
+### `CommunicationExecutor`
+
+Executes communication tasks:
+
+- send
+- recv
+- all-reduce
+- reduce-scatter
+- all-gather
+- A2A
+
+Send and recv are generic communication tasks. There are no direction-specific
+send/recv task types in the current implementation and the refactor should not
+introduce them.
+
+### `StageStore`
+
+Owns loaded executable stage/bucket state:
+
+- bucket modules
+- graph args
+- input and parameter indices
+- optimizers
+- bucket metadata
+- parameter storage
+
+### `BucketState`
+
+One executable bucket:
+
+- bucket id
+- GraphModule/submodules
+- placeholder metadata
+- optimizer
+- parameter metadata
+- task/bucket mode metadata
+
+### `ParamStorage`
+
+Storage and tensor lookup only. It should not encode ZeRO scheduling policy.
+
+Responsibilities:
+
+- own parameter tensors or local shards
+- expose args for forward/compute
+- map placeholder names/indices to tensors
+- store and clear full parameter materializations
+- store and clear full gradient buffers
+- expose local shards and grad shards
+- provide trainable parameter views/layout metadata
+
+The DAG and `DagExecutor` decide when to allocate/free/gather/scatter. This
+avoids separate `Zero2ParamState` or `Zero3ParamState` policy classes, because
+ZeRO behavior is already encoded by DAG nodes and metadata.
+
+### `BufferStore`
+
+Owns runtime buffers:
+
+- forward outputs
+- backward outputs
+- recv payloads
+- send payloads
+- task buffers and refcounts
+
+### `RuntimeState`
+
+Owns runtime environment:
+
+- ranks and degrees
+- device
+- process groups
+- streams
+- stream lookup
+- profiler/NVTX helpers
+
+## Staged Implementation Plan
+
+1. Rename files mechanically:
+   - `piper_actor.py` -> `actor.py`
+   - `piper_compile.py` -> `compile.py`
+   - `piper_coordinator.py` -> `coordinator.py`
+   - `piper_exec.py` -> `tasks.py`
+   - `piper_graph_transform.py` -> temporary `fx.py` or split later
+   - `piper_schedule.py` -> `schedule.py`
+   - `piper_utils.py` -> temporary `state.py` or split later
+   - `backward_utils.py` -> `backward.py`
+
+2. Update all imports, tests, and examples. Run unit tests and the Qwen
+   verification matrix before doing semantic moves.
+
+3. Split state and FX helpers:
+   - move metadata/logging into `state.py`
+   - move GraphModule serialization into `fx.py`
+   - move annotation splitting into `fx.py`
+
+4. Move bucket planning into `bucket.py`.
+
+5. Move TrainingDAG dataclasses, initial DAG construction from annotated FX
+   segments, and core graph utilities into `dag.py`.
+
+6. Move schedule directive parsing/application and communication insertion into
+   `directives.py`.
+
+7. Move ordering-specific logic into `ordering.py`.
+
+8. Move ZeRO DAG lifetime/metadata transforms into `zero.py`.
+
+9. Move debug rendering, schedule visualization, and DAG order output into
+   `visualization.py`. Rename the example visualization flag from `--save-viz`
+   to `--viz` and use it to guard both schedule and DAG visualization.
+
+10. Refactor `PiperActor` internally:
+    - introduce `RuntimeState`
+    - introduce `StageStore`
+    - introduce `BucketState`
+    - introduce `ParamStorage`
+    - introduce `BufferStore`
+    - introduce `ComputeExecutor`
+    - introduce `CommunicationExecutor`
+    - introduce `DagExecutor`
+
+11. Keep the top-level DAG loop visible in `DagExecutor`; avoid replacing it
+    with a registry of opaque callbacks unless there is a clear benefit.
+
+12. After each stage:
+    - run `python3 -m py_compile` on touched modules
+    - run `.venv/bin/python -m pytest test`
+    - run direct-file PP-only Qwen smoke tests with the current requested GPU
+      mask (`CUDA_VISIBLE_DEVICES=0,2`) on:
+      - PP2 topology with 1F1B
+      - V-placement PP topology with DualPipeV
+
+DualPipeV verification should use the corresponding V-placement base schedule,
+because the generated DualPipeV order references virtual pipeline stages.
+
+## Current Progress
+
+- Stages 1-9 are implemented:
+  - implementation modules have been renamed away from `piper_*`
+  - state/logging, FX serialization/splitting, bucket planning, DAG
+    construction, directive application, ordering, ZeRO DAG transforms, and
+    visualization have been moved into focused modules
+  - `examples/test_harness.py` uses `--viz` as the single visualization flag,
+    and `piper_setup(..., visualize_dag=False)` is the default
+- Stage 10 is implemented:
+  - `RuntimeState` owns ranks, device selection, process groups, CUDA streams,
+    and profiler/NVTX helpers
+  - `BufferStore` owns per-iteration task outputs and refcounts
+  - `EventStore` owns per-iteration non-compute CUDA events
+  - `StageStore` owns loaded stage/bucket dictionaries and ZeRO bucket-mode sets
+  - `BucketState` owns per-bucket forward args/functions, optimizer state, and
+    ZeRO storage buffers, replacing the previous parallel `bucket_*` maps
+  - `CommunicationExecutor` owns P2P send/recv, A2A, all-reduce, and
+    reduce-scatter execution
+  - `ComputeExecutor` owns bucket forward, fused backward, split backward-input,
+    split backward-weight, and compute-loss debug logging
+  - `ParamStorage` owns ZeRO full-param/full-grad storage, pending async frees,
+    flat gradient accumulation, all-gather for sharded params, and ZeRO shard
+    optimizer cleanup
+  - `DagExecutor` owns the top-level sorted-DAG `match task_type` loop, as
+    intended
+- Stage 10 actor cleanup is implemented.
+
+Latest verification after introducing `RuntimeState`:
+
+- `.venv/bin/python -m py_compile src/*.py examples/*.py examples/models/*.py test/*.py scripts/test_env.py`
+- `.venv/bin/python -m pytest test` (`15 passed`)
+- `CUDA_VISIBLE_DEVICES=2,3` PP2 + 1F1B Qwen run:
+  `out/20260601_203107/results.csv`
+- `CUDA_VISIBLE_DEVICES=2,3` V-placement PP + DualPipeV Qwen run:
+  `out/20260601_203147/results.csv`
+
+Latest verification after introducing `BucketState`:
+
+- `.venv/bin/python -m py_compile src/*.py examples/*.py examples/models/*.py test/*.py scripts/test_env.py`
+- `.venv/bin/python -m pytest test` (`15 passed`)
+- `CUDA_VISIBLE_DEVICES=2,3` PP2 + 1F1B Qwen run:
+  `out/20260601_203620/results.csv`
+- `CUDA_VISIBLE_DEVICES=2,3` V-placement PP + DualPipeV Qwen run:
+  `out/20260601_203700/results.csv`
+
+Latest verification after introducing `CommunicationExecutor`:
+
+- `.venv/bin/python -m py_compile src/*.py examples/*.py examples/models/*.py test/*.py scripts/test_env.py`
+- `.venv/bin/python -m pytest test` (`15 passed`)
+- `CUDA_VISIBLE_DEVICES=0,2` PP2 + 1F1B Qwen run:
+  `out/20260601_204331/results.csv`
+- `CUDA_VISIBLE_DEVICES=0,2` V-placement PP + DualPipeV Qwen run:
+  `out/20260601_204412/results.csv`
+
+Latest verification after introducing `ComputeExecutor`:
+
+- `.venv/bin/python -m py_compile src/*.py examples/*.py examples/models/*.py test/*.py scripts/test_env.py`
+- `.venv/bin/python -m pytest test` (`15 passed`)
+- `CUDA_VISIBLE_DEVICES=0,2` PP2 + 1F1B Qwen run:
+  `out/20260601_204816/results.csv`
+- `CUDA_VISIBLE_DEVICES=0,2` V-placement PP + DualPipeV Qwen run:
+  `out/20260601_204855/results.csv`
+
+Latest verification after introducing `ParamStorage`:
+
+- `.venv/bin/python -m py_compile src/*.py examples/*.py examples/models/*.py test/*.py scripts/test_env.py`
+- `.venv/bin/python -m pytest test` (`15 passed`)
+- `CUDA_VISIBLE_DEVICES=0,2` PP2 + 1F1B Qwen run:
+  `out/20260601_205514/results.csv`
+- `CUDA_VISIBLE_DEVICES=0,2` V-placement PP + DualPipeV Qwen run:
+  `out/20260601_205554/results.csv`
+
+Latest verification after introducing `DagExecutor`:
+
+- `.venv/bin/python -m py_compile src/*.py examples/*.py examples/models/*.py test/*.py scripts/test_env.py`
+- `.venv/bin/python -m pytest test` (`15 passed`)
+- Initial `CUDA_VISIBLE_DEVICES=0,2` PP2 + 1F1B Qwen run exposed an eager
+  actor input-state bug after the loop move; fixed by initializing
+  `PiperActor.inputs`
+- `CUDA_VISIBLE_DEVICES=0,2` PP2 + 1F1B Qwen run after the fix:
+  `out/20260601_210222/results.csv`
+- `CUDA_VISIBLE_DEVICES=0,2` V-placement PP + DualPipeV Qwen run:
+  `out/20260601_210306/results.csv`
+
+## Resolved Decisions
+
+- `visualization.py` should own schedule visualization as well as DAG
+  visualization. The example `--viz` flag should guard both outputs.
+- Send/recv task types remain generic; the refactor should not add directional
+  send/recv variants.
+- `piper_exec_dag` stays in `piper.py`.
diff --git a/examples/__init__.py b/examples/__init__.py
new file mode 100644
index 0000000..11e4542
--- /dev/null
+++ b/examples/__init__.py
@@ -0,0 +1 @@
+"""Runnable Piper examples and benchmark harnesses."""
diff --git a/examples/base-schedules/pp2.json b/examples/base-schedules/pp2.json
new file mode 100644
index 0000000..69c2aa0
--- /dev/null
+++ b/examples/base-schedules/pp2.json
@@ -0,0 +1,22 @@
+[
+  {
+    "op": "place",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1
+    ],
+    "stream": "pp_stream"
+  }
+]
diff --git a/examples/base-schedules/pp2_dp2.json b/examples/base-schedules/pp2_dp2.json
new file mode 100644
index 0000000..2a764ec
--- /dev/null
+++ b/examples/base-schedules/pp2_dp2.json
@@ -0,0 +1,46 @@
+[
+  {
+    "op": "place",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "dp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "dp_stream"
+  }
+]
diff --git a/examples/base-schedules/pp2_dp2_ep2.json b/examples/base-schedules/pp2_dp2_ep2.json
new file mode 100644
index 0000000..6baf5f8
--- /dev/null
+++ b/examples/base-schedules/pp2_dp2_ep2.json
@@ -0,0 +1,70 @@
+[
+  {
+    "op": "place",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 0,
+      "EP": "*"
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 1,
+      "EP": "*"
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "ep_stream"
+  }
+]
diff --git a/examples/base-schedules/pp2_dp2_ep2_bucket25.json b/examples/base-schedules/pp2_dp2_ep2_bucket25.json
new file mode 100644
index 0000000..ed3d335
--- /dev/null
+++ b/examples/base-schedules/pp2_dp2_ep2_bucket25.json
@@ -0,0 +1,72 @@
+[
+  {
+    "op": "place",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "reduce_stream": "dp_stream",
+    "bucket_size": 25
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "reduce_stream": "dp_stream",
+    "bucket_size": 25
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 0,
+      "EP": "*"
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 1,
+      "EP": "*"
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "ep_stream"
+  }
+]
diff --git a/examples/base-schedules/pp2_dp2_ep2_custom_order.json b/examples/base-schedules/pp2_dp2_ep2_custom_order.json
new file mode 100644
index 0000000..51cdb0e
--- /dev/null
+++ b/examples/base-schedules/pp2_dp2_ep2_custom_order.json
@@ -0,0 +1,140 @@
+[
+  {
+    "op": "place",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 0,
+      "EP": "*"
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 1,
+      "EP": "*"
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "split",
+    "filter": {},
+    "dim_name": "MB",
+    "num_microbatches": 2
+  },
+  {
+    "op": "order",
+    "filters": [
+      [
+        {
+          "PP": 0,
+          "MB": 0,
+          "PASS": "F"
+        }
+      ],
+      [
+        {
+          "PP": 0,
+          "MB": 1,
+          "PASS": "F"
+        }
+      ],
+      [
+        {
+          "PP": 0,
+          "MB": 0,
+          "PASS": "B"
+        }
+      ],
+      [
+        {
+          "PP": 0,
+          "MB": 1,
+          "PASS": "B"
+        }
+      ]
+    ]
+  },
+  {
+    "op": "order",
+    "filters": [
+      [
+        {
+          "PP": 1,
+          "MB": 0,
+          "PASS": "F"
+        }
+      ],
+      [
+        {
+          "PP": 1,
+          "MB": 0,
+          "PASS": "B"
+        },
+        {
+          "PP": 1,
+          "MB": 1,
+          "PASS": "F"
+        }
+      ],
+      [
+        {
+          "PP": 1,
+          "MB": 1,
+          "PASS": "B"
+        }
+      ]
+    ]
+  }
+]
diff --git a/examples/base-schedules/pp2_dp2_ep2_zero2.json b/examples/base-schedules/pp2_dp2_ep2_zero2.json
new file mode 100644
index 0000000..19f9430
--- /dev/null
+++ b/examples/base-schedules/pp2_dp2_ep2_zero2.json
@@ -0,0 +1,72 @@
+[
+  {
+    "op": "place",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "dp_stream",
+    "shard_grads": true
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "dp_stream",
+    "shard_grads": true
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 0,
+      "EP": "*"
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "dp_stream"
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 1,
+      "EP": "*"
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "dp_stream"
+  }
+]
diff --git a/examples/base-schedules/pp2_dp2_ep2_zero3.json b/examples/base-schedules/pp2_dp2_ep2_zero3.json
new file mode 100644
index 0000000..3b9a217
--- /dev/null
+++ b/examples/base-schedules/pp2_dp2_ep2_zero3.json
@@ -0,0 +1,74 @@
+[
+  {
+    "op": "place",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "dp_stream",
+    "shard_grads": true,
+    "shard_params": true
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "dp_stream",
+    "shard_grads": true,
+    "shard_params": true
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 0,
+      "EP": "*"
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "dp_stream"
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 1,
+      "EP": "*"
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "dp_stream"
+  }
+]
diff --git a/examples/base-schedules/pp2_dp2_zero2.json b/examples/base-schedules/pp2_dp2_zero2.json
new file mode 100644
index 0000000..9b5d715
--- /dev/null
+++ b/examples/base-schedules/pp2_dp2_zero2.json
@@ -0,0 +1,48 @@
+[
+  {
+    "op": "place",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "dp_stream",
+    "shard_grads": true
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "dp_stream",
+    "shard_grads": true
+  }
+]
diff --git a/test/schedules/qwen_pp_dp_zero3.json b/examples/base-schedules/pp2_dp2_zero3.json
similarity index 60%
rename from test/schedules/qwen_pp_dp_zero3.json
rename to examples/base-schedules/pp2_dp2_zero3.json
index 1ac6495..4304f04 100644
--- a/test/schedules/qwen_pp_dp_zero3.json
+++ b/examples/base-schedules/pp2_dp2_zero3.json
@@ -1,20 +1,35 @@
 [
   {
     "op": "place",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
     "stream": "pp_stream"
   },
   {
     "op": "place",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
     "stream": "pp_stream"
   },
   {
     "op": "replicate",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
     "gather_stream": "gather_stream",
     "reduce_stream": "reduce_stream",
     "shard_grads": true,
@@ -23,8 +38,13 @@
   },
   {
     "op": "replicate",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
     "gather_stream": "gather_stream",
     "reduce_stream": "reduce_stream",
     "shard_grads": true,
diff --git a/examples/base-schedules/pp4_dp2_ep2_interleaved.json b/examples/base-schedules/pp4_dp2_ep2_interleaved.json
new file mode 100644
index 0000000..11e335c
--- /dev/null
+++ b/examples/base-schedules/pp4_dp2_ep2_interleaved.json
@@ -0,0 +1,138 @@
+[
+  {
+    "op": "place",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 2
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 3
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 2
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 3
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 0,
+      "EP": "*"
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 1,
+      "EP": "*"
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 2,
+      "EP": "*"
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 3,
+      "EP": "*"
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "ep_stream"
+  }
+]
diff --git a/examples/base-schedules/pp4_dp2_ep2_v_placement.json b/examples/base-schedules/pp4_dp2_ep2_v_placement.json
new file mode 100644
index 0000000..7e2fae2
--- /dev/null
+++ b/examples/base-schedules/pp4_dp2_ep2_v_placement.json
@@ -0,0 +1,138 @@
+[
+  {
+    "op": "place",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 2
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 3
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 2
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "replicate",
+    "filter": {
+      "PP": 3
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "reduce_stream": "dp_stream"
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 0,
+      "EP": "*"
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 1,
+      "EP": "*"
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 2,
+      "EP": "*"
+    },
+    "devices": [
+      1,
+      3
+    ],
+    "stream": "ep_stream"
+  },
+  {
+    "op": "shard",
+    "filter": {
+      "PP": 3,
+      "EP": "*"
+    },
+    "devices": [
+      0,
+      2
+    ],
+    "stream": "ep_stream"
+  }
+]
diff --git a/examples/base-schedules/pp4_interleaved.json b/examples/base-schedules/pp4_interleaved.json
new file mode 100644
index 0000000..01e30c5
--- /dev/null
+++ b/examples/base-schedules/pp4_interleaved.json
@@ -0,0 +1,42 @@
+[
+  {
+    "op": "place",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 2
+    },
+    "devices": [
+      0
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 3
+    },
+    "devices": [
+      1
+    ],
+    "stream": "pp_stream"
+  }
+]
diff --git a/examples/base-schedules/pp4_v_placement.json b/examples/base-schedules/pp4_v_placement.json
new file mode 100644
index 0000000..100e73d
--- /dev/null
+++ b/examples/base-schedules/pp4_v_placement.json
@@ -0,0 +1,42 @@
+[
+  {
+    "op": "place",
+    "filter": {
+      "PP": 0
+    },
+    "devices": [
+      0
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 1
+    },
+    "devices": [
+      1
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 2
+    },
+    "devices": [
+      1
+    ],
+    "stream": "pp_stream"
+  },
+  {
+    "op": "place",
+    "filter": {
+      "PP": 3
+    },
+    "devices": [
+      0
+    ],
+    "stream": "pp_stream"
+  }
+]
diff --git a/examples/build_schedule.py b/examples/build_schedule.py
new file mode 100644
index 0000000..b25439c
--- /dev/null
+++ b/examples/build_schedule.py
@@ -0,0 +1,334 @@
+"""Build JSON order directives for common pipeline schedules."""
+
+from __future__ import annotations
+
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Literal
+
+Pass = Literal["F", "B", "BI", "BW"]
+
+
+@dataclass(frozen=True)
+class _Op:
+    pp: int
+    mb: int
+    pass_: Pass
+
+
+# A "slot" is one timestep in a rank's row. A slot holds one op normally; when
+# multiple ops are nested in the same order-directive filter group they share a
+# slot and visualize as a vertically-stacked cell, indicating that they run
+# interleaved within the same scheduling block.
+_Slot = list[_Op]
+
+
+def _filter(pp: int, mb: int, pass_: Pass) -> dict[str, int | str]:
+    return {"PP": pp, "MB": mb, "PASS": pass_}
+
+
+def _order_directive(ops: list[_Op]) -> dict:
+    return _order_directive_from_slots([[op] for op in ops])
+
+
+def _order_directive_from_slots(slots: list[_Slot]) -> dict:
+    return {
+        "op": "order",
+        "filters": [
+            [_filter(op.pp, op.mb, op.pass_) for op in slot]
+            for slot in slots
+        ],
+    }
+
+
+def build_1f1b_schedule(n_ranks: int, n_mbs: int) -> list[dict]:
+    """Return one 1F1B order directive per rank.
+
+    This preserves the old PipelineSchedule builder's non-None per-rank op order:
+    rank r warms up with ``n_ranks - 1 - r`` forwards, then alternates forward
+    and fused backward until all microbatches have completed.
+    """
+    _validate_positive("n_ranks", n_ranks)
+    _validate_positive("n_mbs", n_mbs)
+
+    rows: list[list[_Op]] = [[] for _ in range(n_ranks)]
+    fwd_mb = [0] * n_ranks
+    bwd_mb = [0] * n_ranks
+
+    for rank in range(n_ranks):
+        warmup = n_ranks - 1 - rank
+        for _ in range(min(warmup, n_mbs)):
+            rows[rank].append(_Op(pp=rank, mb=fwd_mb[rank], pass_="F"))
+            fwd_mb[rank] += 1
+
+    for rank in range(n_ranks):
+        while bwd_mb[rank] < n_mbs:
+            if fwd_mb[rank] < n_mbs:
+                rows[rank].append(_Op(pp=rank, mb=fwd_mb[rank], pass_="F"))
+                fwd_mb[rank] += 1
+            rows[rank].append(_Op(pp=rank, mb=bwd_mb[rank], pass_="B"))
+            bwd_mb[rank] += 1
+
+    return [_order_directive(row) for row in rows]
+
+
+def build_zerobubble_schedule(n_ranks: int, n_mbs: int) -> list[dict]:
+    """Return one ZeroBubble order directive per rank.
+
+    This is the old ZB-1 schedule builder expressed as order directives. Each
+    rank owns one pipeline stage, fused backward is split into BI/BW, and BW is
+    deferred by ``rank`` backward-input steps.
+    """
+    _validate_positive("n_ranks", n_ranks)
+    _validate_positive("n_mbs", n_mbs)
+
+    rows: list[list[_Op]] = []
+    for rank in range(n_ranks):
+        warmup = min(n_ranks - 1 - rank, n_mbs)
+        fwd_bwd_ops = n_mbs - warmup
+        cooldown_ops = n_mbs - fwd_bwd_ops
+        deferred_bw_count = rank
+
+        fwd_mb = 0
+        bwd_mb = 0
+        bw_mb = 0
+        bwdi_count = 0
+        emitted_bw_count = 0
+        ops: list[_Op] = []
+
+        for op_idx in range(warmup + fwd_bwd_ops + cooldown_ops):
+            if op_idx < warmup:
+                ops.append(_Op(pp=rank, mb=fwd_mb, pass_="F"))
+                fwd_mb += 1
+                continue
+
+            if op_idx < warmup + fwd_bwd_ops:
+                ops.append(_Op(pp=rank, mb=fwd_mb, pass_="F"))
+                fwd_mb += 1
+
+            ops.append(_Op(pp=rank, mb=bwd_mb, pass_="BI"))
+            bwd_mb += 1
+            bwdi_count += 1
+
+            if bwdi_count > deferred_bw_count:
+                ops.append(_Op(pp=rank, mb=bw_mb, pass_="BW"))
+                bw_mb += 1
+                emitted_bw_count += 1
+
+        while emitted_bw_count < bwdi_count:
+            ops.append(_Op(pp=rank, mb=bw_mb, pass_="BW"))
+            bw_mb += 1
+            emitted_bw_count += 1
+
+        rows.append(ops)
+
+    return [_order_directive(row) for row in rows]
+
+
+def build_interleaved_1f1b_schedule(
+    n_ranks: int,
+    n_mbs: int,
+    n_virtual_stages: int,
+) -> list[dict]:
+    """Return one interleaved 1F1B order directive per physical rank.
+
+    Rank ``r`` owns virtual pipeline stages ``r + k * n_ranks``. The op order is
+    the old interleaved PipelineSchedule order with the placeholder ``None`` slots
+    omitted.
+    """
+    _validate_positive("n_ranks", n_ranks)
+    _validate_positive("n_mbs", n_mbs)
+    _validate_positive("n_virtual_stages", n_virtual_stages)
+
+    microbatches_per_round = n_ranks
+
+    def warmup_ops(rank: int) -> int:
+        return min(
+            (n_virtual_stages - 1) * n_ranks + 2 * (n_ranks - 1 - rank),
+            n_mbs * n_virtual_stages,
+        )
+
+    def forward_stage_index(step: int, rank: int) -> int:
+        local_index = (step // microbatches_per_round) % n_virtual_stages
+        return local_index * n_ranks + rank
+
+    def backward_stage_index(step: int, rank: int, warmup: int) -> int:
+        local_index = (
+            n_virtual_stages
+            - 1
+            - ((step - warmup) // microbatches_per_round) % n_virtual_stages
+        )
+        return local_index * n_ranks + rank
+
+    rows: list[list[_Op]] = []
+    for rank in range(n_ranks):
+        warmup = warmup_ops(rank)
+        microbatch_ops = n_virtual_stages * n_mbs
+        fwd_bwd_ops = microbatch_ops - warmup
+        cooldown_ops = microbatch_ops - fwd_bwd_ops
+
+        fwd_mb: dict[int, int] = defaultdict(int)
+        bwd_mb: dict[int, int] = defaultdict(int)
+        ops: list[_Op] = []
+
+        for op_idx in range(warmup + fwd_bwd_ops + cooldown_ops):
+            if op_idx < warmup:
+                stage = forward_stage_index(op_idx, rank)
+                mb = fwd_mb[stage]
+                fwd_mb[stage] += 1
+                ops.append(_Op(pp=stage, mb=mb, pass_="F"))
+            elif op_idx < warmup + fwd_bwd_ops:
+                stage = forward_stage_index(op_idx, rank)
+                mb = fwd_mb[stage]
+                fwd_mb[stage] += 1
+                ops.append(_Op(pp=stage, mb=mb, pass_="F"))
+
+                stage = backward_stage_index(op_idx, rank, warmup)
+                mb = bwd_mb[stage]
+                bwd_mb[stage] += 1
+                ops.append(_Op(pp=stage, mb=mb, pass_="B"))
+            else:
+                stage = backward_stage_index(op_idx, rank, warmup)
+                mb = bwd_mb[stage]
+                bwd_mb[stage] += 1
+                ops.append(_Op(pp=stage, mb=mb, pass_="B"))
+
+        rows.append(ops)
+
+    return [_order_directive(row) for row in rows]
+
+
+def build_dualpipev_schedule(n_ranks: int, n_mbs: int) -> list[dict]:
+    """Return DualPipeV order directives with overlapped FWD/BWD slots.
+
+    Rank ``r`` owns V-layout stages ``r`` and ``2 * n_ranks - 1 - r``. The
+    old ``FWD_BWD`` chunk is represented as one nested order filter group
+    containing the corresponding FWD filter and fused BWD filter.
+    """
+    _validate_positive("n_ranks", n_ranks)
+    _validate_positive("n_mbs", n_mbs)
+    if n_mbs < 2 * n_ranks:
+        raise ValueError(
+            f"dualpipev requires n_mbs >= 2 * n_ranks, got n_mbs={n_mbs}, "
+            f"n_ranks={n_ranks}"
+        )
+
+    rows: list[list[_Slot]] = [[] for _ in range(n_ranks)]
+
+    for rank in range(n_ranks):
+        s0 = rank
+        s1 = 2 * n_ranks - 1 - rank
+        slots = rows[rank]
+        counts: dict[tuple[int, str], int] = {}
+        weight_queue: list[tuple[int, int]] = []
+
+        def count(stage: int, key: str) -> int:
+            return counts.get((stage, key), 0)
+
+        def inc(stage: int, key: str) -> None:
+            counts[(stage, key)] = count(stage, key) + 1
+
+        def inc_bwd(stage: int) -> None:
+            inc(stage, "i")
+            inc(stage, "w")
+
+        def append_op(stage: int, mb: int, pass_: Pass) -> None:
+            slots.append([_Op(pp=stage, mb=mb, pass_=pass_)])
+
+        def fwd(stage: int) -> None:
+            mb = count(stage, "f")
+            append_op(stage, mb, "F")
+            inc(stage, "f")
+
+        def bwdi(stage: int) -> None:
+            mb = count(stage, "i")
+            append_op(stage, mb, "BI")
+            weight_queue.append((stage, mb))
+            inc(stage, "i")
+
+        def drain_w() -> None:
+            if not weight_queue:
+                return
+            stage, mb = weight_queue.pop(0)
+            append_op(stage, mb, "BW")
+            inc(stage, "w")
+
+        def full_bwd(stage: int) -> None:
+            mb = count(stage, "i")
+            append_op(stage, mb, "B")
+            inc_bwd(stage)
+
+        def overlap_fb(fwd_stage: int, bwd_stage: int) -> None:
+            fwd_mb = count(fwd_stage, "f")
+            bwd_mb = count(bwd_stage, "i")
+            slots.append([
+                _Op(pp=fwd_stage, mb=fwd_mb, pass_="F"),
+                _Op(pp=bwd_stage, mb=bwd_mb, pass_="B"),
+            ])
+            inc(fwd_stage, "f")
+            inc_bwd(bwd_stage)
+
+        # Phase 1: F0 warmup.
+        for _ in range((n_ranks - rank - 1) * 2):
+            fwd(s0)
+
+        # Phase 2: F0F1.
+        for _ in range(rank + 1):
+            fwd(s0)
+            fwd(s1)
+
+        # Phase 3: I1 W1 F1.
+        for _ in range(n_ranks - rank - 1):
+            bwdi(s1)
+            drain_w()
+            fwd(s1)
+
+        # Phase 4: Main overlapped F0B1 + F1B0.
+        for i in range(n_mbs - n_ranks * 2 + rank + 1):
+            if i == 0 and rank == n_ranks - 1:
+                fwd(s0)
+                full_bwd(s1)
+            else:
+                overlap_fb(s0, s1)
+            overlap_fb(s1, s0)
+
+        # Phase 5: B1 + F1B0.
+        for _ in range(n_ranks - rank - 1):
+            full_bwd(s1)
+            overlap_fb(s1, s0)
+
+        # Phase 6: B1B0, switching the second half to split backward.
+        enable_zb = False
+        for i in range(rank + 1):
+            if i == (rank + 1) // 2 and rank % 2 == 1:
+                enable_zb = True
+            (bwdi if enable_zb else full_bwd)(s1)
+            if i == (rank + 1) // 2 and rank % 2 == 0:
+                enable_zb = True
+            (bwdi if enable_zb else full_bwd)(s0)
+
+        # Phase 7: W0 B0.
+        for _ in range(n_ranks - rank - 1):
+            drain_w()
+            (bwdi if enable_zb else full_bwd)(s0)
+
+        # Phase 8: W0.
+        for _ in range(rank + 1):
+            drain_w()
+
+    return [_order_directive_from_slots(row) for row in rows]
+
+
+def _validate_positive(name: str, value: int) -> None:
+    if not isinstance(value, int) or value <= 0:
+        raise ValueError(f"{name} must be a positive integer, got {value!r}")
+
+
+def main() -> None:
+    from test_harness import main as harness_main
+
+    harness_main()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/models/__init__.py b/examples/models/__init__.py
new file mode 100644
index 0000000..88ecaf4
--- /dev/null
+++ b/examples/models/__init__.py
@@ -0,0 +1 @@
+"""Example model definitions used by Piper harness programs."""
diff --git a/test/models/llama.py b/examples/models/llama3.py
similarity index 74%
rename from test/models/llama.py
rename to examples/models/llama3.py
index 288b03f..78c3062 100644
--- a/test/models/llama.py
+++ b/examples/models/llama3.py
@@ -13,6 +13,7 @@
 import torch.nn.functional as F
 from torch import Tensor, nn
 from torch.nn.utils import parameters_to_vector
+from src.piper import annotate
 
 # import fairscale.nn.model_parallel.initialize as fs_init
 # from fairscale.nn.model_parallel.layers import (
@@ -23,6 +24,7 @@
 
 
 logger = logging.getLogger(__name__)
+PP_TAG = "PP"
 
 
 @dataclass
@@ -357,17 +359,19 @@ def forward(
         return out
 
 
-def partition(*args):
-    pass
-
-
 class Transformer(nn.Module):
-    def __init__(self, params: ModelArgs, seq_len: int):
+    def __init__(self, params: ModelArgs, seq_len: int, num_stages: int | None = None):
         super().__init__()
         self.params = params
         self.vocab_size = params.vocab_size
         self.n_layers = params.n_layers
         self.seq_len = seq_len
+        if num_stages is None:
+            from src.state import piper_metadata
+
+            schedule_info = piper_metadata.schedule_info or {}
+            num_stages = int(schedule_info.get("num_stages", schedule_info.get("pp_degree", 2)))
+        self.num_stages = num_stages
 
         def log_size(layer, indent=0):
             num_params = sum(p.numel() for p in layer.parameters())
@@ -412,101 +416,17 @@ def log_size(layer, indent=0):
         mask = torch.triu(mask, diagonal=1)
         self.mask = torch.hstack([torch.zeros((self.seq_len, 0)), mask])
 
-    # """
-    # 8 STAGES
-    # """
-    # def forward(self, tokens: torch.Tensor):
-
-    #     with torch.fx.traceback.annotate({"stage": 0}):
-    #         h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-    #         start_pos = 0
-    #         for layer in self.layers[:self.n_layers//8]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[self.n_layers//8:2*self.n_layers//8]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 2}):
-    #         for layer in self.layers[2*self.n_layers//8:3*self.n_layers//8]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 3}):
-    #         for layer in self.layers[3*self.n_layers//8:4*self.n_layers//8]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 4}):
-    #         for layer in self.layers[4*self.n_layers//8:5*self.n_layers//8]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 5}):
-    #         for layer in self.layers[5*self.n_layers//8:6*self.n_layers//8]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 6}):
-    #         for layer in self.layers[6*self.n_layers//8:7*self.n_layers//8]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 7}):
-    #         for layer in self.layers[7*self.n_layers//8:]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-    #         h = self.norm(h) if self.norm else h
-    #         output = self.output(h).float() if self.output else h
-
-    #     return output
-
-    # """
-    # 4 STAGES
-    # """
-    # def forward(self, tokens: torch.Tensor):
-
-    #     with torch.fx.traceback.annotate({"stage": 0}):
-    #         h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
-    #         start_pos = 0
-    #         for layer in self.layers[:self.n_layers//4]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[self.n_layers//4:self.n_layers//2]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 2}):
-    #         for layer in self.layers[self.n_layers//2:3*self.n_layers//4]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 3}):
-    #         for layer in self.layers[3*self.n_layers//4:]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-    #         h = self.norm(h) if self.norm else h
-    #         output = self.output(h).float() if self.output else h
-
-    #     return output
-
-    # """
-    # 2 STAGES
-    # """
-    # def forward(self, tokens: torch.Tensor):
-    #     with torch.fx.traceback.annotate({"stage": 0}):
-    #         h = self.tok_embeddings(tokens)
-    #         start_pos = 0
-    #         for layer in self.layers[:self.n_layers//2]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-    #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[self.n_layers//2:]:
-    #             h = layer(h, start_pos, self.freqs_cis, self.mask)
-    #         h = self.norm(h)
-    #         output = self.output(h).float()
-
-    #     return output
-
-    """
-    No annotations: auto infer stages
-    """
     def forward(self, tokens: torch.Tensor):
-        h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
         start_pos = 0
-        for layer in self.layers:
-            h = layer(h, start_pos, self.freqs_cis, self.mask)
-        h = self.norm(h) if self.norm else h
-        output = self.output(h).float() if self.output else h
-        return output
\ No newline at end of file
+        for stage_id in range(self.num_stages):
+            layer_start = stage_id * self.n_layers // self.num_stages
+            layer_end = (stage_id + 1) * self.n_layers // self.num_stages
+            with annotate(PP_TAG):
+                if stage_id == 0:
+                    h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
+                for layer in self.layers[layer_start:layer_end]:
+                    h = layer(h, start_pos, self.freqs_cis, self.mask)
+                if stage_id == self.num_stages - 1:
+                    h = self.norm(h) if self.norm else h
+                    output = self.output(h).float() if self.output else h
+        return output
diff --git a/test/models/qwen3.py b/examples/models/qwen3.py
similarity index 86%
rename from test/models/qwen3.py
rename to examples/models/qwen3.py
index e6995d8..95832ef 100644
--- a/test/models/qwen3.py
+++ b/examples/models/qwen3.py
@@ -8,6 +8,7 @@
 from torchtitan.models.qwen3.model.model import TransformerBlock
 from torchtitan.models.moe import MoE, MoEArgs
 from torchtitan.protocols.model import AttentionMasksType
+from src.piper import annotate
 
 PP_TAG = "PP"
 EP_TAG = "EP"
@@ -285,24 +286,12 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         routed_input = routed_input.reshape(
             self.experts.num_experts, -1, dim)
 
-        # dispatch tokens to experts via all_to_all
-        with torch.fx.traceback.annotate({
-            "name": EP_TAG,
-            "collective": "all_to_all_single",
-            "group": "ep",
-        }):
+        # expert component
+        with annotate(EP_TAG):
             dispatched_input = routed_input.contiguous()
-
-        dispatched_input = dispatched_input.reshape(-1, dim)
-        routed_output = self.experts(dispatched_input, num_tokens_per_expert)
-        routed_output = routed_output.reshape(self.experts.num_experts, -1, dim)
-
-        # gather expert outputs back via all_to_all
-        with torch.fx.traceback.annotate({
-            "name": EP_TAG,
-            "collective": "all_to_all_single",
-            "group": "ep",
-        }):
+            dispatched_input = dispatched_input.reshape(-1, dim)
+            routed_output = self.experts(dispatched_input, num_tokens_per_expert)
+            routed_output = routed_output.reshape(self.experts.num_experts, -1, dim)
             gathered_output = routed_output.contiguous()
 
         gathered_output = gathered_output.reshape(-1, dim)
@@ -339,11 +328,28 @@ class AnnotatedQwen3TransformerBlock(TransformerBlock):
     def __init__(self, layer_id: int, model_args: Qwen3ModelArgs):
         super().__init__(layer_id, model_args)
 
+        self.layer_id = layer_id
         if self.moe_enabled:
             self.moe = AnnotatedMoE(
                 model_args.moe_args, dim=model_args.dim, hidden_dim=model_args.moe_inter_dim
             )
 
+    def forward(
+        self,
+        x: torch.Tensor,
+        freqs_cis: torch.Tensor,
+        attention_masks: AttentionMasksType | None,
+        positions: torch.Tensor | None = None,
+    ):
+        x = x + self.attention(
+            self.attention_norm(x), freqs_cis, attention_masks, positions
+        )
+
+        if self.moe_enabled:
+            x = x + self.moe(self.ffn_norm(x))
+        else:
+            x = x + self.feed_forward(self.ffn_norm(x))
+        return x
 
 class PiperQwen3Model(Qwen3Model):
     """
@@ -357,7 +363,7 @@ class PiperQwen3Model(Qwen3Model):
     def __init__(self, config: Qwen3ModelArgs, num_stages: int):
         super().__init__(config)
         if num_stages is None:
-            from src.piper_utils import piper_metadata
+            from src.state import piper_metadata
 
             schedule_info = piper_metadata.schedule_info or {}
             num_stages = int(schedule_info.get("num_stages", schedule_info.get("pp_degree", 2)))
@@ -367,33 +373,6 @@ def __init__(self, config: Qwen3ModelArgs, num_stages: int):
         for layer_id in range(config.n_layers):
             self.layers[str(layer_id)] = AnnotatedQwen3TransformerBlock(layer_id, config)
 
-    # def forward(
-    #     self,
-    #     tokens: torch.Tensor,
-    #     attention_masks: Optional[AttentionMasksType] = None,
-    #     positions: Optional[torch.Tensor] = None,
-    # ) -> torch.Tensor:
-
-    #     assert len(self.layers) == 5
-        
-    #     with torch.fx.traceback.annotate({"stage": 0}):
-    #         h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
-    #         h = self.layers["0"](h, self.rope_cache, attention_masks, positions)
-    #         h = self.layers["1"](h, self.rope_cache, attention_masks, positions)
-
-    #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         h = self.layers["2"](h, self.rope_cache, attention_masks, positions)
-
-    #     with torch.fx.traceback.annotate({"stage": 2}):
-    #         h = self.layers["3"](h, self.rope_cache, attention_masks, positions)
-
-    #     with torch.fx.traceback.annotate({"stage": 3}):
-    #         h = self.layers["4"](h, self.rope_cache, attention_masks, positions)
-    #         h = self.norm(h) if self.norm is not None else h
-    #         output = self.output(h) if self.output is not None else h
-
-    #     return output
-    
     def forward(
         self,
         tokens: torch.Tensor,
@@ -401,14 +380,15 @@ def forward(
         positions: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         num_layers = len(self.layers)
-        layers_per_stage = num_layers // self.num_stages
 
         for stage_id in range(self.num_stages):
-            with torch.fx.traceback.annotate({"name": PP_TAG, "stage": stage_id}):
+            layer_start = stage_id * num_layers // self.num_stages
+            layer_end = (stage_id + 1) * num_layers // self.num_stages
+            with annotate(PP_TAG):
                 if stage_id == 0:
                     h = self.tok_embeddings(tokens) if self.tok_embeddings is not None else tokens
 
-                for i in range(stage_id * layers_per_stage, (stage_id + 1) * layers_per_stage):
+                for i in range(layer_start, layer_end):
                     layer = self.layers[str(i)]
                     h = layer(h, self.rope_cache, attention_masks, positions)
 
diff --git a/test/test_harness.py b/examples/test_harness.py
similarity index 77%
rename from test/test_harness.py
rename to examples/test_harness.py
index df71e56..6c60cf4 100644
--- a/test/test_harness.py
+++ b/examples/test_harness.py
@@ -1,53 +1,54 @@
 """Build a schedule JSON and run a model test with it.
 
-This is a small harness around ``test.test_qwen`` / ``test.test_llama``. It
-consumes only schedule-building arguments; all remaining arguments are forwarded
-to the selected test module unchanged.
+This is a small harness around model example modules such as
+``examples.test_qwen`` and ``examples.test_llama``. It consumes schedule/runtime
+arguments, parses the selected test module's arguments, starts Ray, creates the
+Piper placement group, and runs the model through ``PiperProgramCoordinator``.
 """
 
 from __future__ import annotations
 
 import argparse
 import csv
+import importlib.util
 import json
+import logging
 import re
 import statistics
-import subprocess
 import sys
 import tempfile
 from datetime import datetime
 from pathlib import Path
 
-try:
-    from .schedules.build_order_directives import (
-        build_1f1b_schedule,
-        build_dualpipev_schedule,
-        build_interleaved_1f1b_schedule,
-        build_zerobubble_schedule,
-        visualize_order_directives,
-    )
-except ImportError:
-    from schedules.build_order_directives import (
-        build_1f1b_schedule,
-        build_dualpipev_schedule,
-        build_interleaved_1f1b_schedule,
-        build_zerobubble_schedule,
-        visualize_order_directives,
-    )
+import ray
+from ray.util.placement_group import placement_group_table
+
+from src.coordinator import PiperProgramCoordinator, create_piper_placement_group
+from src.schedule import load_schedule_directives
+from src.visualization import visualize_order_directives
+
+from build_schedule import (
+    build_1f1b_schedule,
+    build_dualpipev_schedule,
+    build_interleaved_1f1b_schedule,
+    build_zerobubble_schedule,
+)
 
 
 def main() -> None:
     parser = argparse.ArgumentParser(
         description=(
             "Build a full schedule JSON from a base schedule and generated order "
-            "directives, then run test_qwen or test_llama with it."
+            "directives, then run a model test with it."
         )
     )
     parser.add_argument(
         "--test-file",
         "--test",
         required=True,
-        help="Test module/file to run: test_qwen, test_llama, test.test_qwen, or a .py path.",
+        help=(
+            "Example file to run directly, e.g. examples/test_qwen.py."
+        ),
     )
     parser.add_argument(
         "--base-schedule",
@@ -108,9 +109,9 @@ def main() -> None:
         help="Where to render the generated schedule visualization (default: out/<schedule>.png).",
     )
     parser.add_argument(
-        "--no-schedule-viz",
+        "--viz",
         action="store_true",
-        help="Do not render a Graphviz schedule visualization.",
+        help="Render the generated schedule and per-rank TrainingDAG visualizations.",
     )
     parser.add_argument(
         "--pytorch-profiler",
@@ -125,6 +126,27 @@ def main() -> None:
         help="Shared directory where actors write per-actor chrome traces "
              "(default: <run_dir>/pytorch_profiles). Removed after combining.",
     )
+    parser.add_argument(
+        "--address",
+        default="",
+        help="Ray head address to connect to. Omit to start a local Ray session.",
+    )
+    parser.add_argument(
+        "--port",
+        type=int,
+        default=4567,
+        help="Ray head port to connect to when --address is set.",
+    )
+    parser.add_argument(
+        "--temp-dir",
+        default="/tmp/piper/ray_tmp",
+        help="Ray temp directory.",
+    )
+    parser.add_argument(
+        "--ray-namespace",
+        default=None,
+        help="Ray namespace. Defaults to the selected test module name.",
+    )
     args, test_args = parser.parse_known_args()
 
     if args.schedule != "custom":
@@ -151,6 +173,8 @@ def main() -> None:
     generated_schedule, viz_path = build_schedule_file(args, run_dir)
     forwarded_args = _replace_schedule_arg(test_args, generated_schedule)
     forwarded_args = _replace_arg(forwarded_args, "--output-dir", str(run_dir))
+    if args.viz:
+        forwarded_args.append("--viz")
 
     profile_args: list[str] = []
     profile_dir: Path | None = None
@@ -165,35 +189,45 @@ def main() -> None:
         profile_args = ["--pytorch-profiler", "--pytorch-profile-dir", str(profile_dir)]
 
     if args.dry_run:
-        cmd = [sys.executable, "-m", _test_module(args.test_file), *forwarded_args, *profile_args]
+        cmd = [
+            sys.executable,
+            str(Path(__file__).resolve()),
+            "--test-file",
+            args.test_file,
+            "--base-schedule",
+            str(args.base_schedule),
+            "--schedule",
+            args.schedule,
+        ]
+        if args.ranks is not None:
+            cmd.extend(["--ranks", str(args.ranks)])
+        if args.mbs is not None:
+            cmd.extend(["--mbs", str(args.mbs)])
+        cmd.extend([*forwarded_args, *profile_args])
         print(" ".join(cmd))
         print(f"generated_schedule={generated_schedule}")
-        print(f"order_directives={generated_schedule.with_name(generated_schedule.stem + '_order_directives.json')}")
         if viz_path is not None:
             print(f"schedule_viz={viz_path}")
         return
 
     with tempfile.TemporaryDirectory() as tmp:
         metrics_json = Path(tmp) / "metrics.json"
-        cmd = [
-            sys.executable,
-            "-m",
-            _test_module(args.test_file),
+        forwarded_args = [
             *forwarded_args,
             *profile_args,
             "--metrics-json-out",
             str(metrics_json),
         ]
-        returncode = subprocess.run(cmd, check=False).returncode
-        if returncode == 0 and metrics_json.exists():
+        dp_metrics = _run_test_module(args, forwarded_args)
+        if metrics_json.exists():
             with metrics_json.open(encoding="utf-8") as f:
                 dp_metrics = json.load(f)
-            if dp_metrics:
-                rows = _metrics_rows(dp_metrics)
-                results_csv = run_dir / "results.csv"
-                _write_results_csv(results_csv, rows)
-                print(f"metrics written to {results_csv}")
-        if returncode == 0 and profile_dir is not None:
+        if dp_metrics:
+            rows = _metrics_rows(dp_metrics)
+            results_csv = run_dir / "results.csv"
+            _write_results_csv(results_csv, rows)
+            print(f"metrics written to {results_csv}")
+        if profile_dir is not None:
             _combine_pytorch_profiles(
                 profile_dir,
                 run_dir,
@@ -201,7 +235,49 @@ def main() -> None:
                 pp=args.ranks if args.ranks is not None else "X",
                 mbs=args.mbs if args.mbs is not None else "X",
             )
-    raise SystemExit(returncode)
+
+
+def _run_test_module(args: argparse.Namespace, test_args: list[str]) -> list[dict]:
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    )
+    module_name, test_module = _load_test_file(args.test_file)
+    test_ns = test_module.parse_args(test_args)
+    if hasattr(test_ns, "temp_dir"):
+        test_ns.temp_dir = args.temp_dir
+    if hasattr(test_module, "_derive_num_stages"):
+        test_ns.num_stages = test_module._derive_num_stages(test_ns.schedule_directives_file)
+
+    namespace = args.ray_namespace or module_name.rsplit(".", 1)[-1].replace("test_", "")
+    ray.init(
+        address=f"{args.address}:{args.port}" if args.address else None,
+        namespace=namespace,
+        log_to_driver=True,
+        include_dashboard=False,
+        _temp_dir=args.temp_dir,
+    )
+    try:
+        pp_outer = getattr(test_ns, "pp_outer", False)
+        pg = create_piper_placement_group(test_ns.schedule_directives_file, pp_outer=pp_outer)
+        ray.get(pg.ready(), timeout=600)
+        logging.getLogger(module_name).info(placement_group_table(pg))
+        coordinator = PiperProgramCoordinator.remote(
+            schedule_directives_file=test_ns.schedule_directives_file,
+            pp_outer=pp_outer,
+        )
+        handles = coordinator.run_program.remote(test_module.main, pg, test_ns, pg)
+        dp_metrics = ray.get(handles)
+        metrics_json_out = getattr(test_ns, "metrics_json_out", "")
+        if metrics_json_out:
+            metrics_path = Path(metrics_json_out)
+            metrics_path.parent.mkdir(parents=True, exist_ok=True)
+            with metrics_path.open("w", encoding="utf-8") as f:
+                json.dump(dp_metrics, f)
+            logging.getLogger(module_name).info("Raw metrics written to %s", metrics_path)
+        return dp_metrics
+    finally:
+        ray.shutdown()
 
 
 def _combine_pytorch_profiles(
@@ -383,27 +459,23 @@ def build_schedule_file(args: argparse.Namespace, run_dir: Path) -> tuple[Path,
         args.virtual_stages,
         base_schedule=args.base_schedule,
     )
-    output = args.generated_schedule_file or _default_output_path(schedule_stem)
+    run_schedule = run_dir / f"{schedule_stem}.json"
+    output = args.generated_schedule_file or run_schedule
     output.parent.mkdir(parents=True, exist_ok=True)
     with output.open("w", encoding="utf-8") as f:
         json.dump(schedule, f, indent=2)
         f.write("\n")
-    order_output = output.with_name(f"{output.stem}_order_directives.json")
-    with order_output.open("w", encoding="utf-8") as f:
-        json.dump(order_directives, f, indent=2)
-        f.write("\n")
 
     # Drop a copy of the complete schedule into the run directory so the exact
     # directives used (base + appended order) are visible alongside results.csv,
     # profile traces, and the schedule viz.
-    run_schedule = run_dir / f"{schedule_stem}.json"
     if run_schedule.resolve() != output.resolve():
         with run_schedule.open("w", encoding="utf-8") as f:
             json.dump(schedule, f, indent=2)
             f.write("\n")
 
     viz_path = None
-    if not args.no_schedule_viz:
+    if args.viz:
         viz_output = args.schedule_viz_file or run_dir / f"{schedule_stem}.png"
         viz_path = visualize_order_directives(order_directives, viz_output)
     return output, viz_path
@@ -434,19 +506,8 @@ def _virtual_stages_for_schedule(schedule_name: str) -> int:
     return 1
 
 
-def _default_output_path(schedule_stem: str) -> Path:
-    return Path("test/schedules/generated") / f"{schedule_stem}.json"
-
-
 def _load_schedule(path: Path) -> list[dict]:
-    with path.open("r", encoding="utf-8") as f:
-        data = json.load(f)
-    if not isinstance(data, list):
-        raise ValueError(f"schedule JSON must contain a list, got {type(data)}")
-    for i, directive in enumerate(data):
-        if not isinstance(directive, dict):
-            raise ValueError(f"schedule directive[{i}] must be an object, got {type(directive)}")
-    return data
+    return load_schedule_directives(str(path))
 
 
 def _set_num_microbatches(schedule: list[dict], n_mbs: int) -> None:
@@ -463,7 +524,7 @@ def _set_num_microbatches(schedule: list[dict], n_mbs: int) -> None:
     if not found:
         schedule.append({
             "op": "split",
-            "filter": [],
+            "filter": {},
             "dim_name": "MB",
             "num_microbatches": n_mbs,
         })
@@ -554,38 +615,18 @@ def _order_filter_slots(filters: object) -> list[list[object]]:
         raise ValueError(f"order directive requires filters list, got {type(filters)}")
     slots: list[list[object]] = []
     for item in filters:
-        if _is_filter_spec(item):
-            slots.append([item])
-        elif isinstance(item, list):
+        if isinstance(item, list) and item:
+            if not all(isinstance(flt, dict) for flt in item):
+                raise ValueError(f"invalid order filter group: {item}")
             slots.append(list(item))
         else:
             raise ValueError(f"invalid order filter group: {item}")
     return slots
 
 
-def _is_filter_spec(flt: object) -> bool:
-    if isinstance(flt, dict):
-        return True
-    if isinstance(flt, list):
-        return all(
-            isinstance(item, (list, tuple))
-            and len(item) == 2
-            and isinstance(item[0], str)
-            for item in flt
-        )
-    return False
-
-
 def _filter_to_dict(flt: object) -> dict:
     if isinstance(flt, dict):
         return dict(flt)
-    if isinstance(flt, list):
-        out = {}
-        for item in flt:
-            if not isinstance(item, (list, tuple)) or len(item) != 2:
-                raise ValueError(f"invalid filter item: {item}")
-            out[item[0]] = item[1]
-        return out
     raise ValueError(f"invalid filter: {flt}")
 
 
@@ -612,22 +653,24 @@ def _replace_arg(test_args: list[str], flag: str, value: str) -> list[str]:
     return out
 
 
-def _test_module(test_file: str) -> str:
-    if test_file in ("test_qwen", "test_llama"):
-        return f"test.{test_file}"
-    if test_file.endswith(".py"):
-        path = Path(test_file)
-        if len(path.parts) == 1 and path.stem in ("test_qwen", "test_llama"):
-            return f"test.{path.stem}"
-        if path.is_absolute():
-            try:
-                path = path.relative_to(Path.cwd())
-            except ValueError as exc:
-                raise ValueError(
-                    f"absolute test file must be under the current repo: {test_file}"
-                ) from exc
-        return ".".join(path.with_suffix("").parts)
-    return test_file
+def _load_test_file(test_file: str):
+    path = Path(test_file)
+    if path.suffix != ".py":
+        raise ValueError(f"--test-file must be a Python file path, got: {test_file}")
+    if not path.is_absolute():
+        path = Path.cwd() / path
+    if not path.exists():
+        raise FileNotFoundError(f"test file does not exist: {path}")
+
+    sys.path.insert(0, str(path.parent))
+    module_name = path.stem
+    spec = importlib.util.spec_from_file_location(module_name, path)
+    if spec is None or spec.loader is None:
+        raise ImportError(f"could not load Python file: {path}")
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)
+    return module_name, module
 
 
 if __name__ == "__main__":
diff --git a/test/test_llama.py b/examples/test_llama.py
similarity index 79%
rename from test/test_llama.py
rename to examples/test_llama.py
index 5376c24..f764952 100644
--- a/test/test_llama.py
+++ b/examples/test_llama.py
@@ -1,24 +1,20 @@
 """End-to-end LLaMA test for the JSON-driven TrainingDAG backend."""
 import argparse
-import json
-import logging
 import os
 import time
 
 import ray
 import torch
-from ray.util.placement_group import placement_group_table
 
 from src.piper import piper_exec_dag
-from src.piper_compile import piper_setup
-from src.piper_coordinator import PiperProgramCoordinator, create_piper_placement_group
-from src.piper_utils import (
+from src.compile import piper_setup
+from src.state import (
     LOG_LEVEL,
     create_logger,
     piper_metadata,
 )
 
-from .models.llama import (
+from models.llama3 import (
     LLAMA_1B,
     LLAMA_3B,
     LLAMA_8B,
@@ -97,14 +93,13 @@ def main(args, pg):
         optim_fn=torch.optim.Adam,
         example_inputs=[x],
         example_outputs=y,
-        loss_fn=loss_fn,
         activation_checkpointing=args.activation_checkpointing,
         num_checkpoints=args.num_checkpoints,
         model_dtype=torch.bfloat16,
         pg=pg,
         nsight=args.nsight,
         temp_dir=args.temp_dir,
-        visualize_dag=args.save_viz,
+        visualize_dag=args.viz,
         output_dir=args.output_dir,
         const_attrs={"freqs_cis": freqs_cis, "mask": mask},
         use_inductor=args.use_inductor,
@@ -154,7 +149,7 @@ def main(args, pg):
     return metrics
 
 
-def parse_args():
+def parse_args(argv=None):
     parser = argparse.ArgumentParser(description="Run LLaMA with JSON-driven TrainingDAG execution")
     parser.add_argument("--model", choices=["debug", "1b", "3b", "8b", "70b"], default="debug")
     parser.add_argument("--batch-size", type=int, default=16)
@@ -169,9 +164,7 @@ def parse_args():
         help="Number of sequential activation-checkpoint regions per Piper bucket",
     )
     parser.add_argument("--nsight", action="store_true", default=False)
-    parser.add_argument("--save-viz", action="store_true", default=False)
-    parser.add_argument("--address", default="")
-    parser.add_argument("--port", type=int, default=4567)
+    parser.add_argument("--viz", action="store_true", default=False)
     parser.add_argument("--temp-dir", default="/tmp/piper/ray_tmp")
     parser.add_argument("--output-dir", default="out")
     parser.add_argument("--use-inductor", action=argparse.BooleanOptionalAction, default=True)
@@ -179,7 +172,7 @@ def parse_args():
     parser.add_argument(
         "--schedule-directives-file",
         type=str,
-        default="test/schedules/llama_pp_only.json",
+        default="examples/base-schedules/pp2.json",
         help="JSON file containing TrainingDAG schedule directives",
     )
     parser.add_argument(
@@ -209,33 +202,4 @@ def parse_args():
         default=3,
         help="Number of iterations to run under the PyTorch profiler.",
     )
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
-    args = parse_args()
-    logger.info(args)
-
-    ray.init(
-        address=f"{args.address}:{args.port}" if args.address else None,
-        namespace="llama",
-        log_to_driver=True,
-        include_dashboard=False,
-        _temp_dir=args.temp_dir,
-    )
-    pg = create_piper_placement_group(args.schedule_directives_file, pp_outer=args.pp_outer)
-    ray.get(pg.ready(), timeout=600)
-    logger.info(placement_group_table(pg))
-    piper_coordinator = PiperProgramCoordinator.remote(
-        schedule_directives_file=args.schedule_directives_file,
-        pp_outer=args.pp_outer,
-    )
-    handles = piper_coordinator.run_program.remote(main, pg, args, pg)
-    dp_metrics = ray.get(handles)
-    if args.metrics_json_out:
-        os.makedirs(os.path.dirname(args.metrics_json_out) or ".", exist_ok=True)
-        with open(args.metrics_json_out, "w", encoding="utf-8") as f:
-            json.dump(dp_metrics, f)
-        logger.info(f"Raw metrics written to {args.metrics_json_out}")
-    ray.shutdown()
+    return parser.parse_args(argv)
diff --git a/test/test_qwen.py b/examples/test_qwen.py
similarity index 73%
rename from test/test_qwen.py
rename to examples/test_qwen.py
index 9823d22..b087c86 100644
--- a/test/test_qwen.py
+++ b/examples/test_qwen.py
@@ -1,23 +1,16 @@
 """End-to-end Qwen test for the JSON-driven TrainingDAG backend."""
-import logging
 import ray
 import torch
 import argparse
-import json
 import time
 import os
 
-from ray.util.placement_group import (
-    placement_group_table,
-)
-
-from src.piper_coordinator import PiperProgramCoordinator, create_piper_placement_group
-from src.piper_compile import piper_setup
+from src.compile import piper_setup
 from src.piper import piper_exec_dag
-from src.piper_schedule import load_schedule_directives
-from src.piper_utils import piper_metadata, create_logger, LOG_LEVEL
+from src.schedule import load_schedule_directives
+from src.state import piper_metadata, create_logger, LOG_LEVEL
 
-from .models.qwen3 import PiperQwen3Model, create_qwen3_config
+from models.qwen3 import PiperQwen3Model, create_qwen3_config
 from torchtitan.models.qwen3.model.model import precompute_rope_cache
 
 logger = create_logger("test_qwen", LOG_LEVEL)
@@ -80,7 +73,7 @@ def main(args, pg):
         pg=pg,
         nsight=args.nsight,
         temp_dir=args.temp_dir,
-        visualize_dag=args.save_viz,
+        visualize_dag=args.viz,
         output_dir=args.output_dir,
         const_attrs={"rope_cache": rope_cache},
         use_inductor=args.use_inductor,
@@ -148,7 +141,7 @@ def _derive_num_stages(schedule_directives_file: str) -> int:
     return num_stages
 
 
-def parse_args():
+def parse_args(argv=None):
     parser = argparse.ArgumentParser(
         description="Test JSON-driven Qwen TrainingDAG execution"
     )
@@ -161,12 +154,8 @@ def parse_args():
     parser.add_argument('--activation-checkpointing', action='store_true', default=False)
     parser.add_argument("--nsight", action="store_true", default=False,
                         help="Whether to use Nsight Systems for tracing")
-    parser.add_argument("--save-viz", action="store_true", default=False,
-                        help="Save per-rank DAG visualization (slow for models > 1B)")
-    parser.add_argument("--address", default="",
-                        help="Ray head address to connect to")
-    parser.add_argument("--port", type=int, default=4567,
-                        help="Ray head port to connect to (default: 4567)")
+    parser.add_argument("--viz", action="store_true", default=False,
+                        help="Save schedule and per-rank DAG visualizations")
     parser.add_argument("--temp-dir", default="/tmp/piper/ray_tmp",
                         help="Ray temp directory (default: /tmp/piper/ray_tmp)")
     parser.add_argument("--output-dir", default="out",
@@ -191,7 +180,7 @@ def parse_args():
     parser.add_argument(
         "--schedule-directives-file",
         type=str,
-        default="test/schedules/qwen_pp_only.json",
+        default="examples/base-schedules/pp2.json",
         help="JSON file containing schedule directives for the piper backend",
     )
     parser.add_argument(
@@ -221,51 +210,4 @@ def parse_args():
         default=3,
         help="Number of iterations to run under the PyTorch profiler.",
     )
-    return parser.parse_args()
-
-
-"""
-python /m-coriander/coriander/mfris/miniconda3/envs/piper/lib/python3.10/site-packages/ray/scripts/scripts.py \
-        start --head \
-        --port 4567 \
-        --temp-dir=/m-coriander/coriander/mfris/piper/ray_tmp \
-        --include-dashboard=false
-"""
-if __name__ == "__main__":
-    logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
-    args = parse_args()
-    args.num_stages = _derive_num_stages(args.schedule_directives_file)
-    logger.info(args)
-    if args.address != "":
-        ray.init(
-            address=f"{args.address}:{args.port}",
-            namespace="qwen",
-            log_to_driver=True,
-            include_dashboard=False,
-            _temp_dir=args.temp_dir,
-        )
-    else:
-        ray.init(
-            namespace="qwen",
-            log_to_driver=True,
-            include_dashboard=False,
-            _temp_dir=args.temp_dir,
-        )
-    pg = create_piper_placement_group(
-        args.schedule_directives_file,
-        pp_outer=args.pp_outer,
-    )
-    ray.get(pg.ready(), timeout=600)
-    logger.info(placement_group_table(pg))
-    piper_coordinator = PiperProgramCoordinator.remote(
-        schedule_directives_file=args.schedule_directives_file,
-        pp_outer=args.pp_outer,
-    )
-    handles = piper_coordinator.run_program.remote(main, pg, args, pg)
-    dp_metrics = ray.get(handles)
-    if args.metrics_json_out:
-        os.makedirs(os.path.dirname(args.metrics_json_out) or ".", exist_ok=True)
-        with open(args.metrics_json_out, "w", encoding="utf-8") as f:
-            json.dump(dp_metrics, f)
-        logger.info(f"Raw metrics written to {args.metrics_json_out}")
-    ray.shutdown()
+    return parser.parse_args(argv)
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..859cf31
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,33 @@
+[build-system]
+requires = ["setuptools>=68", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "piper"
+version = "0.1.0"
+description = "User-controllable distributed training schedules for large-scale ML models."
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+  "torchtitan",
+  "numpy",
+  "torch==2.10.0",
+  "ray==2.44.1",
+  "click<8.1",
+  "cupy-cuda12x",
+  "bitsandbytes",
+  "graphviz",
+  "matplotlib",
+]
+
+[project.optional-dependencies]
+dev = [
+  "pytest",
+]
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["src", "src.*", "test", "test.*", "examples", "examples.*"]
+
+[tool.pytest.ini_options]
+testpaths = ["test"]
diff --git a/requirements.txt b/requirements.txt
index 2d96a59..376ad5c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,8 +1,10 @@
 torchtitan
 numpy
 torch==2.10.0
+ray==2.44.1
 click<8.1
 cupy-cuda12x
 bitsandbytes
 graphviz
 matplotlib
+pytest
diff --git a/scripts/test_env.py b/scripts/test_env.py
index 85ebd1a..9a5d1bb 100644
--- a/scripts/test_env.py
+++ b/scripts/test_env.py
@@ -69,9 +69,9 @@ def check_fi_info():
     print("         'NET/OFI Initializing aws-ofi-nccl' and 'Using EFA' in the output.", flush=True)
 
     print("Piper imports", flush=True)
-    ok &= check("piper_compile", lambda: (__import__("src.piper_compile"), "ok")[1])
-    ok &= check("piper_exec", lambda: (__import__("src.piper_exec"), "ok")[1])
-    ok &= check("piper_actor", lambda: (__import__("src.piper_actor"), "ok")[1])
+    ok &= check("compile", lambda: (__import__("src.compile"), "ok")[1])
+    ok &= check("tasks", lambda: (__import__("src.tasks"), "ok")[1])
+    ok &= check("actor", lambda: (__import__("src.actor"), "ok")[1])
 
     print("Ray", flush=True)
     import ray
diff --git a/src/actor.py b/src/actor.py
new file mode 100644
index 0000000..5e1a1df
--- /dev/null
+++ b/src/actor.py
@@ -0,0 +1,738 @@
+import ray
+import torch
+import os
+import re
+from typing import Any
+import gc
+from torch.autograd.graph import set_warn_on_accumulate_grad_stream_mismatch
+import torch.distributed as dist
+from collections import defaultdict
+
+from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
+
+from .state import (
+    create_logger,
+    LOG_LEVEL,
+)
+from .fx import _deserialize_graphmodule, _serialize_graphmodule
+from .executors import CommunicationExecutor, ComputeExecutor, DagExecutor
+from .ordering import _serial_topological_order
+from .runtime import BufferStore, EventStore, ParamStorage, RuntimeState, StageStore
+from .tasks import training_dag_task_type as _training_dag_task_type
+
+CLEANUP_MEMORY = False
+
+logger = create_logger("actor", LOG_LEVEL)
+
+def _get_rank(pp_rank, dp_rank, pp_degree):
+    return pp_rank + dp_rank * pp_degree
+
+
+def _create_actors(
+    num_actors,
+    optim_class,
+    profile=False,
+    no_nvtx: bool = False,
+    pg=None,
+    temp_dir: str = None,
+    use_inductor: bool = False,
+    pp_outer: bool = False,
+):
+    dp_rank = int(os.environ["PIPER_DP_RANK"])
+    world_size = int(os.environ["PIPER_WORLD_SIZE"])
+    dp_degree = int(os.environ["PIPER_DP_DEGREE"])
+    pp_degree = int(os.environ["PIPER_PP_DEGREE"])
+
+    from .state import piper_metadata
+
+    for pp_rank in range(num_actors):
+        global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
+        nsight_env = {"nsight": {
+            "t": "cuda,cudnn,cublas,nvtx",
+            "sample": "process-tree",
+            "backtrace": "dwarf",
+            "cudabacktrace": "sync:0,memory:0",
+            "python-backtrace": "cuda",
+            "stop-on-exit": "true",
+        }} if profile else {}
+        nccl_env = {
+            "env_vars": {
+                # "NCCL_SOCKET_IFNAME": "ens32",
+                # "GLOO_SOCKET_IFNAME": "ens32",
+                # "NCCL_P2P_DISABLE": "1",
+                # "NCCL_DEBUG": "INFO",
+                **({"TMPDIR": temp_dir} if (profile and temp_dir) else {}),
+            }
+        }
+        # When pp_outer=True, one bundle corresponds to one pipeline stage and
+        # holds all DP replicas for that stage (placement group shape is
+        # [{"GPU": dp}] * pp). Otherwise one bundle is one DP replica holding
+        # all PP ranks (shape is [{"GPU": pp}] * dp).
+        bundle_index = pp_rank if pp_outer else dp_rank
+        actor = PiperActor.options(
+            num_gpus=0.6,
+            runtime_env={**nsight_env, **nccl_env},
+            scheduling_strategy=PlacementGroupSchedulingStrategy(
+                placement_group=pg,
+                placement_group_bundle_index=bundle_index,
+            ),
+        ).remote(
+            pp_rank,
+            optim_class,
+            world_size,
+            dp_rank=dp_rank,
+            dp_degree=dp_degree,
+            pp_degree=pp_degree,
+            no_nvtx=no_nvtx,
+            use_inductor=use_inductor,
+        )
+        piper_metadata.actors[pp_rank] = actor
+
+
+@ray.remote
+class PiperActor:
+    def __init__(
+        self,
+        pp_rank,
+        optim_class,
+        world_size,
+        dp_rank=0,
+        dp_degree=1,
+        pp_degree=1,
+        no_nvtx: bool = False,
+        use_inductor: bool = False,
+    ):
+        self.logger = create_logger("actor", LOG_LEVEL)
+
+        # BWD_I uses torch.autograd.grad (not .backward), so AccumulateGrad nodes are
+        # traversed for stream-sync bookkeeping but never accumulate to p.grad.
+        # Suppress the spurious stream-mismatch warning.
+        set_warn_on_accumulate_grad_stream_mismatch(False)
+
+        self.optim_class = optim_class
+        self.use_inductor = bool(use_inductor)
+        self.runtime = RuntimeState(
+            pp_rank=pp_rank,
+            dp_rank=dp_rank,
+            dp_degree=dp_degree,
+            pp_degree=pp_degree,
+            world_size=world_size,
+            no_nvtx=no_nvtx,
+        )
+
+        self.logger.debug(
+            f"Initializing Ray actor {self.runtime.global_rank} GPU {os.environ['CUDA_VISIBLE_DEVICES']}"
+        )
+
+        self.inputs = None
+        self.labels = None
+
+        self.stages = StageStore()
+        # accumuate loss for each microbatch
+        self.loss = []
+
+        # DAG execution state
+        self.dag = None
+        self.sorted_dag_nodes = None
+
+        self.buffers = BufferStore()
+        self.events = EventStore()
+        self.communication = CommunicationExecutor(self.runtime, self.stages, self.logger)
+        self.compute = ComputeExecutor(self.runtime, self.stages, self.logger)
+        self.params = ParamStorage(self.runtime, self.stages, self.logger)
+        self.dag_executor = DagExecutor(
+            self.runtime,
+            self.stages,
+            self.buffers,
+            self.events,
+            self.params,
+            self.communication,
+            self.compute,
+            self.logger,
+        )
+
+        # Non-trainable constant tensor attributes (e.g. freqs_cis, mask) pushed
+        # from the coordinator before compilation so _load_stage can fill them in
+        # instead of zero-initializing.  Keyed by bare attribute name (e.g. "freqs_cis").
+        self.model_const_attrs: dict = {}
+
+    def get_and_reset_peak_memory_stats(self) -> tuple:
+        """Return (global_rank, max_memory_allocated_bytes) and reset peak stats."""
+        max_alloc = torch.cuda.max_memory_allocated()
+        torch.cuda.reset_peak_memory_stats()
+        return self.runtime.global_rank, max_alloc
+
+    def reset_peak_memory(self):
+        torch.cuda.reset_peak_memory_stats()
+
+    def _nvtx_push(self, label: str) -> None:
+        self.runtime.nvtx_push(label)
+
+    def _nvtx_pop(self) -> None:
+        self.runtime.nvtx_pop()
+
+    def start_pytorch_profiler(self) -> None:
+        """Begin a torch.profiler session spanning the next run_dag iterations.
+
+        Each node's execution in DagExecutor is wrapped in a record_function
+        labelled the same as its NVTX range, so the resulting trace identifies
+        per-node work.
+        """
+        self.runtime.torch_profiler = torch.profiler.profile(
+            activities=[
+                torch.profiler.ProfilerActivity.CPU,
+                torch.profiler.ProfilerActivity.CUDA,
+            ],
+        )
+        self.runtime.torch_profiler.__enter__()
+        self.runtime.pytorch_profiler_enabled = True
+        self.logger.info(f"Actor {self.runtime.global_rank}: PyTorch profiler started")
+
+    def stop_pytorch_profiler(self, profile_dir: str) -> str:
+        """End the profiler session and export a chrome trace.
+
+        The file is named ``dp{dp_rank}_pp{pp_rank}.json`` inside *profile_dir*
+        so the harness can group same-dp-rank actors. Returns the written path.
+        """
+        self.runtime.pytorch_profiler_enabled = False
+        self.runtime.torch_profiler.__exit__(None, None, None)
+        os.makedirs(profile_dir, exist_ok=True)
+        filepath = os.path.join(
+            profile_dir, f"dp{self.runtime.dp_rank}_pp{self.runtime.pp_rank}.json"
+        )
+        self.runtime.torch_profiler.export_chrome_trace(filepath)
+        self.runtime.torch_profiler = None
+        self.logger.info(
+            f"Actor {self.runtime.global_rank}: PyTorch profiler trace written to {filepath}"
+        )
+        return filepath
+
+    def load_input(self, inputs):
+        self.inputs = [inp.to(self.runtime.device) for inp in inputs]
+        self.logger.debug(f"Actor {self.runtime.global_rank} loaded inputs {len(self.inputs)}")
+
+    def load_labels(self, labels):
+        self.labels = labels.to(self.runtime.device)
+        self.logger.debug(f"Actor {self.runtime.global_rank} loaded labels {self.labels.shape}")
+
+    def load_const_attrs(self, const_attrs: dict) -> None:
+        """Store non-trainable constant tensor attributes (e.g. freqs_cis, mask).
+
+        *const_attrs* maps bare attribute name → CPU tensor.  Values are moved to
+        the actor's device so ``_load_stage`` can copy them directly.
+        """
+        self.model_const_attrs = {k: v.to(self.runtime.device) for k, v in const_attrs.items()}
+
+    def get_node_ip_and_free_port(self):
+        import socket
+
+        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+            s.bind(("0.0.0.0", 0))
+            port = s.getsockname()[1]
+        return ray.util.get_node_ip_address(), port
+
+    def _join_process_groups(self, master_addr, master_port):
+
+        self.logger.info(f"Actor {self.runtime.global_rank} using GPU {os.environ['CUDA_VISIBLE_DEVICES']}, master addr {master_addr}:{master_port}")
+
+        init_method = f"tcp://{master_addr}:{master_port}"
+
+        self.runtime.device = f"cuda:{self.runtime.global_rank % torch.cuda.device_count()}"
+        torch.cuda.set_device(self.runtime.device)
+
+        if self.runtime.pp_degree > 1 or self.runtime.dp_degree > 1:
+            dist.init_process_group(
+                "nccl",
+                init_method=init_method,
+                rank=self.runtime.global_rank,
+                world_size=self.runtime.world_size,
+            )
+
+            if self.runtime.dp_degree > 1:
+                self._join_dp_process_group()
+            if self.runtime.pp_degree > 1:
+                self._join_pp_process_group()
+
+            self.logger.info(f"Actor {self.runtime.global_rank} joined process groups")
+
+    def _join_dp_process_group(self):
+        num_dp_groups = self.runtime.world_size // self.runtime.dp_degree
+        for dp_group_id in range(num_dp_groups):
+            group_ranks = [
+                (dp_group_id + num_dp_groups * i) for i in range(self.runtime.dp_degree)
+            ]
+            # Two separate NCCL communicators over the same ranks: one for allreduce,
+            # one for all2all.  Sharing a communicator causes both op types to run on
+            # the same internal NCCL proxy stream, which prevents true overlap.
+            process_group = dist.new_group(ranks=group_ranks, backend="nccl")
+            ep_process_group = dist.new_group(ranks=group_ranks, backend="nccl")
+            if self.runtime.global_rank % num_dp_groups == dp_group_id:
+                self.runtime.dp_group = process_group
+                self.runtime.ep_group = ep_process_group
+
+    def _join_pp_process_group(self):
+        num_pp_groups = self.runtime.world_size // self.runtime.pp_degree
+
+        for pp_group_id in range(num_pp_groups):
+            group_ranks = [
+                (pp_group_id * self.runtime.pp_degree + i) for i in range(self.runtime.pp_degree)
+            ]
+            lo_hi_group = dist.new_group(ranks=group_ranks, backend="nccl")
+            hi_lo_group = dist.new_group(ranks=group_ranks, backend="nccl")
+
+            if self.runtime.global_rank in group_ranks:
+                self.runtime.pp_lo_hi = lo_hi_group
+                self.runtime.pp_hi_lo = hi_lo_group
+
+    def _derive_dag_bucket_modes(self, training_dag: Any) -> None:
+        self.stages.param_sharded_ubids = set()
+        self.stages.grad_sharded_ubids = set()
+        for node in training_dag.nodes.values():
+            meta = getattr(node, "node_meta", {}) or {}
+            ubid = meta.get("bucket_key")
+            if ubid is None:
+                continue
+            if node.node_kind == "ALL_GATHER_COMM":
+                self.stages.param_sharded_ubids.add(ubid)
+            elif node.node_kind == "REDUCE_SCATTER_COMM":
+                self.stages.grad_sharded_ubids.add(ubid)
+        self.stages.zero_managed_ubids = self.stages.param_sharded_ubids | self.stages.grad_sharded_ubids
+
+    def _relocate_meta_devices(self, gm) -> None:
+        """Rewrite baked ``torch.device('meta')`` literals to the actor device.
+
+        Meta-device compilation captures whatever device was current at trace
+        time (inside ``type_as`` / ``.to(device=x.device)`` / device-aware
+        factory ops like ``torch.zeros(..., device=...)``). That literal
+        round-trips through serialization as ``meta`` and would otherwise make
+        those ops emit meta tensors at runtime, mismatching the CUDA inputs.
+        Lifted params/buffers/inputs are unaffected (placed on device directly),
+        so this only touches device literals embedded in node args/kwargs.
+        """
+        import torch.fx as fx
+
+        device = self.runtime.device
+        replaced = 0
+
+        def _fix(value):
+            nonlocal replaced
+            if isinstance(value, torch.device):
+                if value.type == "meta":
+                    replaced += 1
+                    return torch.device(device)
+                return value
+            if isinstance(value, tuple):
+                return tuple(_fix(v) for v in value)
+            if isinstance(value, list):
+                return [_fix(v) for v in value]
+            if isinstance(value, dict):
+                return {k: _fix(v) for k, v in value.items()}
+            return value
+
+        for module in gm.modules():
+            if not isinstance(module, fx.GraphModule):
+                continue
+            before = replaced
+            for node in module.graph.nodes:
+                node.args = _fix(node.args)
+                node.kwargs = _fix(node.kwargs)
+            if replaced != before:
+                module.recompile()
+
+    def _load_stage(
+        self,
+        stage_id: int,
+        modules_data: list,
+        a2a_boundaries: dict = None,
+        use_activation_checkpointing: bool = False,
+    ) -> None:
+        """Load a (possibly bucketed) stage.
+
+        *modules_data* is a list of dicts, one per module/bucket, each with keys:
+        ``gm_data``, ``graphargs``, ``input_idxs``, ``param_idxs``, ``bucket_key``.
+
+        A non-bucketed stage is represented as a single-element list.
+        All per-bucket data structures are keyed by ``bucket_key`` so run_dag
+        can look up bucket data without knowing which stage owns a bucket.
+        """
+        self.logger.debug(
+            f"Loading stage {stage_id} ({len(modules_data)} module(s)) on actor {self.runtime.global_rank}"
+        )
+
+        g = torch.Generator(device=self.runtime.device)
+        g.manual_seed(1000 * self.runtime.global_rank + stage_id)
+
+        first_gm = None
+
+        for b_idx, bd in enumerate(modules_data):
+            ubid: Any = bd["bucket_key"]
+            bucket = self.stages.ensure_bucket(ubid)
+            ac_num_subgraphs = int(bd.get("ac_num_subgraphs", 1))
+            ac_requested_subgraphs = int(bd.get("ac_requested_subgraphs", ac_num_subgraphs))
+            gms = [_deserialize_graphmodule(gm_data) for gm_data in bd["gm_data_list"]] if "gm_data_list" in bd else [_deserialize_graphmodule(bd["gm_data"])]
+            for gm in gms:
+                self._relocate_meta_devices(gm)
+            if self.use_inductor:
+                compiled_gms = []
+                for subgraph_idx, gm in enumerate(gms):
+                    compiled_gm = torch.compile(gm)
+                    compiled_gms.append(compiled_gm)
+                    self.logger.debug(
+                        f"[load_stage_compile] rank={self.runtime.global_rank} stage={stage_id} "
+                        f"ubid={ubid} subgraph={subgraph_idx} compiled=True"
+                    )
+                gms = compiled_gms
+
+            if b_idx == 0:
+                first_gm = gms[0]
+
+            forward_args = list(bd["graphargs"])
+            b_input_idxs = list(bd["input_idxs"])
+            b_param_idxs = list(bd["param_idxs"])
+            apply_zero = bool(bd.get("apply_zero", True))
+            shared_placeholder_names = list(
+                bd.get("shared_placeholder_names", bd.get("placeholder_names", []))
+            )
+            # Extract FX placeholder names for each param index.
+            bucket.param_names = [
+                shared_placeholder_names[i] if i < len(shared_placeholder_names) else f"ubid{ubid}_p{i}"
+                for i in b_param_idxs
+            ]
+
+            # Save input tensor metadata for pre-allocating FWD recv buffers.
+            # Stored as a list of (shape, dtype, requires_grad) in input-slot order.
+            recv_meta = []
+            for i in b_input_idxs:
+                meta = forward_args[i]
+                if meta is not None:
+                    recv_meta.append((tuple(meta.shape), meta.dtype,
+                                      bool(getattr(meta, "requires_grad", False))))
+                forward_args[i] = None  # clear slot; run_dag will fill it at execution time
+            bucket.forward_input_meta = recv_meta
+
+            bucket.input_idxs = b_input_idxs
+
+            # Realize parameter tensors.
+            realized = [None] * len(forward_args)
+            for i, arg in enumerate(forward_args):
+                if arg is None:
+                    continue
+                t = torch.empty(arg.shape, dtype=arg.dtype, device=self.runtime.device)
+                if arg.requires_grad:
+                    t.requires_grad_(True)
+                    torch.nn.init.normal_(t, mean=0.0, std=0.02, generator=g)
+                else:
+                    # Non-trainable slot: try to fill from const attrs (freqs_cis, mask, …)
+                    # before falling back to zeros.  Dynamo names direct model attrs as
+                    # "l_self_<attr_name>", so strip that prefix to get the bare name.
+                    ph_name = shared_placeholder_names[i] if i < len(shared_placeholder_names) else ""
+                    attr_name = re.sub(r'^l_self_', '', ph_name)
+                    const_val = self.model_const_attrs.get(attr_name)
+                    if (
+                        const_val is not None
+                        and tuple(const_val.shape) == tuple(arg.shape)
+                        and const_val.dtype == arg.dtype
+                    ):
+                        t.copy_(const_val)
+                    else:
+                        t.zero_()
+                realized[i] = t
+
+            shared_name_to_idx = {
+                name: i for i, name in enumerate(shared_placeholder_names)
+            }
+            subgraph_specs = []
+            for gm in gms:
+                sub_placeholder_names = [
+                    n.name for n in gm.graph.nodes if n.op == "placeholder"
+                ]
+                dynamic_names = [
+                    name for name in sub_placeholder_names
+                    if name not in shared_name_to_idx
+                ]
+                subgraph_specs.append((gm.forward, sub_placeholder_names, dynamic_names))
+
+            def _bucket_forward_runner(
+                shared_args,
+                _specs=subgraph_specs,
+                _name_to_idx=shared_name_to_idx,
+                _use_ac=use_activation_checkpointing,
+                _stage_id=stage_id,
+                _ubid=ubid,
+                _shared_placeholder_names=tuple(shared_placeholder_names),
+            ):
+                out = None
+                for subgraph_idx, (forward_impl, placeholder_names, dynamic_names) in enumerate(_specs):
+                    if len(dynamic_names) == 0:
+                        dynamic_values = []
+                    elif len(dynamic_names) == 1:
+                        dyn_arg = out
+                        if isinstance(dyn_arg, (tuple, list)) and len(dyn_arg) == 1:
+                            dyn_arg = dyn_arg[0]
+                        dynamic_values = [dyn_arg]
+                    else:
+                        if not isinstance(out, (tuple, list)):
+                            raise RuntimeError(
+                                f"Bucket forward expected {len(dynamic_names)} dynamic inputs "
+                                f"but previous subgraph produced {type(out).__name__}"
+                            )
+                        dynamic_values = list(out)
+                        if len(dynamic_values) != len(dynamic_names):
+                            raise RuntimeError(
+                                f"Bucket forward expected {len(dynamic_names)} dynamic inputs "
+                                f"but previous subgraph produced {len(dynamic_values)} values"
+                            )
+                    dynamic_name_to_value = {
+                        name: value for name, value in zip(dynamic_names, dynamic_values)
+                    }
+                    call_args = []
+                    for name in placeholder_names:
+                        if name in dynamic_name_to_value:
+                            call_args.append(dynamic_name_to_value[name])
+                        else:
+                            call_args.append(shared_args[_name_to_idx[name]])
+                    if _use_ac:
+                        out = torch.utils.checkpoint.checkpoint(
+                            forward_impl,
+                            *call_args,
+                            use_reentrant=False,
+                        )
+                    else:
+                        out = forward_impl(*call_args)
+                return out
+
+            bucket.forward_fn = _bucket_forward_runner
+            bucket.forward_args = realized
+            bucket.param_idxs = b_param_idxs
+            bucket.activation_checkpoint_subgraph_count = ac_num_subgraphs
+            trainable_idxs = [
+                i for i in b_param_idxs
+                if realized[i] is not None and realized[i].requires_grad
+            ]
+            bucket.trainable_param_idxs = trainable_idxs
+
+            zero_managed = (
+                self.runtime.dp_degree > 1
+                and apply_zero
+                and bool(trainable_idxs)
+                and ubid in self.stages.zero_managed_ubids
+            )
+            params_sharded = ubid in self.stages.param_sharded_ubids
+            grads_sharded = ubid in self.stages.grad_sharded_ubids
+
+            if zero_managed:
+                trainable = [realized[i] for i in trainable_idxs]
+                flat_params = torch.cat([p.detach().view(-1) for p in trainable]).contiguous()
+                flat_params.requires_grad_(False)
+                orig_numel = flat_params.numel()
+                dp = self.runtime.dp_degree
+                shard_size = (orig_numel + dp - 1) // dp
+                padded_numel = shard_size * dp
+                if padded_numel > orig_numel:
+                    padded = flat_params.new_zeros(padded_numel)
+                    padded[:orig_numel].copy_(flat_params)
+                    flat_params = padded
+
+                offset = 0
+                view_specs = []
+                for idx, p in zip(trainable_idxs, trainable):
+                    numel = p.numel()
+                    realized[idx] = realized[idx].detach()
+                    realized[idx].data = flat_params[offset:offset + numel].view(p.shape)
+                    realized[idx].requires_grad_(True)
+                    realized[idx].grad_dtype = self.params.grad_buffer_dtype
+                    view_specs.append((realized[idx], offset, numel, tuple(p.shape)))
+                    offset += numel
+
+                shard_start = self.runtime.dp_rank * shard_size
+                if params_sharded:
+                    shard_param = flat_params[shard_start:shard_start + shard_size].detach().clone()
+                else:
+                    shard_param = flat_params[shard_start:shard_start + shard_size]
+                shard_param.requires_grad_(True)
+                # Keep ZeRO comm/storage buffers in fp32, but let the optimizer consume
+                # grads in the shard-param dtype to match Adam's bf16 foreach path.
+                shard_param.grad_dtype = None
+
+                bucket.flat_params = flat_params
+                bucket.flat_grads = (
+                    None
+                    if grads_sharded
+                    else torch.zeros(padded_numel, dtype=self.params.grad_buffer_dtype, device=self.runtime.device)
+                )
+                bucket.shard_param = shard_param
+                bucket.shard_optimizer = self.optim_class([shard_param])
+                bucket.reduce_scatter_grads = (
+                    torch.zeros(shard_size, dtype=self.params.grad_buffer_dtype, device=self.runtime.device)
+                    if grads_sharded
+                    else None
+                )
+                bucket.param_shard_info = (shard_start, shard_size, orig_numel)
+                bucket.param_view_specs = view_specs
+                bucket.full_params_fresh = False
+
+                if params_sharded:
+                    storage = flat_params.untyped_storage()
+                    if storage.size() != 0:
+                        storage.resize_(0)
+
+                optim = None
+            else:
+                # Keep trainable params as separate tensors for the non-ZeRO path.
+                bucket.param_view_specs = []
+                bucket.flat_params = None
+                bucket.flat_grads = None
+                bucket.shard_param = None
+                bucket.shard_optimizer = None
+                bucket.reduce_scatter_grads = None
+                bucket.param_shard_info = None
+                bucket.full_params_fresh = False
+                trainable_for_optim = [realized[i] for i in trainable_idxs]
+                optim = self.optim_class(trainable_for_optim, fused=True) if trainable_for_optim else None
+            bucket.optimizer = optim
+
+        # Keep first GraphModule for compatibility with external inspection tools.
+        self.stages.graph_modules[stage_id] = first_gm
+
+    def load_training_dag(self, training_dag: Any) -> None:
+        """Load per-PP-rank TrainingDAG compute nodes using existing _load_stage logic.
+
+        This is an adapter for the new TrainingDAG representation where each
+        COMPUTE/FWD node with ``node_meta['gm']`` is equivalent to one bucket/module.
+        The runtime executes TrainingDAG nodes via ``run_dag``; this method
+        only populates actor-side bucket/module state.
+        """
+        if training_dag is None:
+            raise ValueError("load_training_dag requires non-None training_dag")
+        if not hasattr(training_dag, "nodes"):
+            raise TypeError("load_training_dag expected object with 'nodes' field")
+
+        self._derive_dag_bucket_modes(training_dag)
+        self.runtime.initialize_streams_for_training_dag(training_dag)
+
+        # Clear any prior module state before loading a new training DAG.
+        self.stages.clear_loaded_modules()
+
+        stage_to_modules: dict[int, list[dict]] = defaultdict(list)
+
+        # Deterministic order by stage/segment then uid.
+        compute_nodes = [
+            n for n in training_dag.nodes.values()
+            if getattr(n, "node_kind", None) == "COMPUTE"
+            and getattr(n, "compute_subkind", None) == "FWD"
+        ]
+        compute_nodes.sort(
+            key=lambda n: (
+                int(getattr(n, "node_meta", {}).get("stage_id", 10**9)),
+                int(getattr(n, "node_meta", {}).get("segment_id", 10**9)),
+                str(getattr(n, "uid", "")),
+            )
+        )
+
+        seen_bucket_keys: set[Any] = set()
+        skipped_dupe_fwd_nodes = 0
+        for node in compute_nodes:
+            meta = getattr(node, "node_meta", {}) or {}
+            bucket_key = meta.get("bucket_key", getattr(node, "uid", None))
+            if bucket_key in seen_bucket_keys:
+                skipped_dupe_fwd_nodes += 1
+                continue
+            seen_bucket_keys.add(bucket_key)
+            gm = meta.get("gm")
+            gm_data = meta.get("gm_data")
+            stage_id = meta.get("stage_id")
+            input_idxs = meta.get("input_idxs")
+            param_idxs = meta.get("param_idxs")
+            graphargs = meta.get("graphargs")
+            input_names = meta.get("input_names", [])
+            output_names = meta.get("output_names", [])
+
+            if gm_data is None and gm is None:
+                raise ValueError(
+                    f"load_training_dag: compute node {getattr(node, 'uid', '<unknown>')} "
+                    f"is missing required metadata field 'gm_data' (or fallback 'gm')"
+                )
+            if stage_id is None or input_idxs is None or param_idxs is None or graphargs is None:
+                raise ValueError(
+                    f"load_training_dag: compute node {getattr(node, 'uid', '<unknown>')} "
+                    f"is missing required metadata fields"
+                )
+
+            module_data = {
+                "gm_data": gm_data if gm_data is not None else _serialize_graphmodule(gm),
+                "graphargs": list(graphargs),
+                "input_idxs": list(input_idxs),
+                "param_idxs": list(param_idxs),
+                "placeholder_names": list(input_names),
+                "output_names": list(output_names),
+                "shared_placeholder_names": list(input_names),
+                "bucket_key": bucket_key,
+                "ac_num_subgraphs": 1,
+                "ac_requested_subgraphs": 1,
+                "apply_zero": bool(meta.get("apply_zero", True)),
+                "training_dag_uid": getattr(node, "uid", None),
+                "triton_constant_args": dict(meta.get("triton_constant_args", {})),
+            }
+            stage_to_modules[int(stage_id)].append(module_data)
+
+        assert stage_to_modules, (
+            "load_training_dag: no FWD compute nodes with gm metadata found"
+        )
+
+        for stage_id in sorted(stage_to_modules.keys()):
+            self._load_stage(
+                stage_id=stage_id,
+                modules_data=stage_to_modules[stage_id],
+                a2a_boundaries={},
+                use_activation_checkpointing=False,
+            )
+
+        # Build runtime adjacency on TrainingDAG nodes so run_dag can execute directly.
+        for n in training_dag.nodes.values():
+            n.data_preds = []
+            n.data_succs = []
+            n.temporal_preds = []
+            n.temporal_succs = []
+            if n.node_kind == "SEND_COMM":
+                n.peer_pp_rank = n.node_meta.get("peer_pp_rank")
+            elif n.node_kind == "RECV_COMM":
+                n.peer_pp_rank = n.node_meta.get("peer_pp_rank")
+            else:
+                n.peer_pp_rank = None
+        for e in training_dag.edges:
+            if e.src_uid not in training_dag.nodes or e.dst_uid not in training_dag.nodes:
+                continue
+            src = training_dag.nodes[e.src_uid]
+            dst = training_dag.nodes[e.dst_uid]
+            if e.dep_kind == "temporal":
+                src.temporal_succs.append(dst)
+                dst.temporal_preds.append(src)
+            else:
+                src.data_succs.append(dst)
+                dst.data_preds.append(src)
+
+        for n in training_dag.nodes.values():
+            n.task_type = _training_dag_task_type(n)
+            mb = n.tag.get("MB", 0)
+            st = n.tag.get("PP", 0)
+            n.batches = [type("RuntimeBatch", (), {"stage_id": st, "mb_idx": mb})()]
+
+        self.dag = training_dag
+        self.sorted_dag_nodes = [
+            training_dag.nodes[uid] for uid in _serial_topological_order(training_dag)
+        ]
+
+    def run_dag(self, loss_fn=None):
+        # Mark the entire iteration boundary for the NVTX timeline.
+        iter_idx = getattr(self, "_iter_counter", 0)
+        self._iter_counter = iter_idx + 1
+        self._nvtx_push(f"iter_{iter_idx}_rank_{self.runtime.global_rank}")
+        self.dag_executor.run(
+            self.dag,
+            self.sorted_dag_nodes,
+            self.inputs,
+            self.labels,
+            self.loss,
+            loss_fn=loss_fn,
+        )
+        self._nvtx_pop()
diff --git a/src/backward_utils.py b/src/backward.py
similarity index 100%
rename from src/backward_utils.py
rename to src/backward.py
diff --git a/src/bucket.py b/src/bucket.py
new file mode 100644
index 0000000..6490f63
--- /dev/null
+++ b/src/bucket.py
@@ -0,0 +1,450 @@
+import operator
+from collections import defaultdict
+
+import torch
+import torch.fx as fx
+
+from .state import LOG_LEVEL, create_logger
+
+logger = create_logger("bucket", LOG_LEVEL)
+
+
+# ---------------------------------------------------------------------------
+# bucket_stage - stage-level parameter bucketing (placeholder params)
+# ---------------------------------------------------------------------------
+def bucket_stage(
+    stage_gm: fx.GraphModule,
+    graphargs: list,
+    input_idxs: list[int],
+    param_idxs: list[int],
+    bucket_size_bytes: int = 25 * 1024 * 1024,
+    debug_name: str | None = None,
+) -> list[tuple[fx.GraphModule, list[int], list[int], list]]:
+    """Split a stage GraphModule into per-parameter-bucket sub-modules.
+
+    This function handles annotation segments where trainable parameters are
+    passed as **placeholder inputs** identified by *param_idxs*.
+
+    Args:
+        stage_gm: The stage ``fx.GraphModule`` to split.
+        graphargs: Flat arg list for ``stage_gm.forward``; entries at
+            *param_idxs* are meta tensors, entries at *input_idxs* are None.
+        input_idxs: Positions of activation-input placeholders.
+        param_idxs: Positions of parameter placeholders.
+        bucket_size_bytes: Target bucket size (default 25 MB).
+        debug_name: Optional label included in bucket planning logs.
+
+    Returns:
+        A list of ``(bucket_gm, bucket_input_idxs, bucket_param_idxs,
+        bucket_graphargs)`` tuples in execution order.  Returns a
+        single-element list containing the original stage when no split is
+        needed.
+    """
+    nodes = list(stage_gm.graph.nodes)
+    node_idx: dict[fx.Node, int] = {nd: i for i, nd in enumerate(nodes)}
+    ph_nodes = [nd for nd in nodes if nd.op == "placeholder"]
+
+    param_ph_set = {ph_nodes[i] for i in param_idxs if i < len(ph_nodes)}
+    param_ph_list = [ph_nodes[i] for i in param_idxs if i < len(ph_nodes)]
+    input_ph_set  = {ph_nodes[i] for i in input_idxs  if i < len(ph_nodes)}
+
+    if not param_ph_list:
+        return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))]
+
+    compute_set: frozenset[int] = frozenset(
+        i for i, nd in enumerate(nodes)
+        if nd.op not in ("placeholder", "get_attr", "output")
+    )
+
+    def _use_range(pnd: fx.Node) -> tuple[int, int]:
+        idxs = [node_idx[u] for u in pnd.users if node_idx[u] in compute_set]
+        return (min(idxs), max(idxs)) if idxs else (node_idx[pnd], node_idx[pnd])
+
+    def _size(pnd: fx.Node) -> int:
+        i = ph_nodes.index(pnd)
+        if i < len(graphargs) and graphargs[i] is not None and hasattr(graphargs[i], "numel"):
+            return int(graphargs[i].numel() * graphargs[i].element_size())
+        ev = pnd.meta.get("example_value")
+        if ev is not None and hasattr(ev, "numel"):
+            return int(ev.numel() * ev.element_size())
+        return 0
+
+    debug_name = debug_name or getattr(stage_gm, "__class__", type(stage_gm)).__name__
+    param_ranges = {pn: _use_range(pn) for pn in param_ph_list}
+    param_sizes = {pn: _size(pn) for pn in param_ph_list}
+
+    # Greedy bucket assignment
+    bucket_id: dict[fx.Node, int] = {}
+    cur_b, cur_sz = 0, 0
+    for pn in param_ph_list:
+        sz = param_sizes[pn]
+        if cur_sz + sz > bucket_size_bytes and cur_sz > 0:
+            cur_b += 1
+            cur_sz = 0
+        bucket_id[pn] = cur_b
+        cur_sz += sz
+    n_init = cur_b + 1
+
+    # Singleton promotion
+    def _init_range(b: int) -> tuple[int, int]:
+        ms = [pn for pn in param_ph_list if bucket_id[pn] == b]
+        if not ms:
+            return (0, 0)
+        return (min(param_ranges[pn][0] for pn in ms), max(param_ranges[pn][1] for pn in ms))
+
+    init_ranges = [_init_range(b) for b in range(n_init)]
+
+    def _bucket_at(idx: int) -> int:
+        for b, (lo, hi) in enumerate(init_ranges):
+            if lo <= idx <= hi:
+                return b
+        return 0
+
+    sn = n_init
+    for pn in param_ph_list:
+        f, l = param_ranges[pn]
+        if _bucket_at(f) != _bucket_at(l):
+            bucket_id[pn] = sn
+            sn += 1
+
+    # Build bucket list, sort, merge overlapping ranges
+    bm: dict[int, list[fx.Node]] = defaultdict(list)
+    for pn in param_ph_list:
+        bm[bucket_id[pn]].append(pn)
+
+    blist: list[tuple[int, int, list[fx.Node]]] = []
+    for members in bm.values():
+        f = min(param_ranges[pn][0] for pn in members)
+        l = max(param_ranges[pn][1] for pn in members)
+        blist.append((f, l, members))
+    blist.sort(key=lambda t: t[0])
+
+    merged: list[tuple[int, int, list[fx.Node]]] = []
+    for f, l, ms in blist:
+        if merged and f <= merged[-1][1]:
+            pf, pl, pm = merged[-1]
+            merged[-1] = (pf, max(pl, l), pm + ms)
+        else:
+            merged.append((f, l, ms))
+
+    def _bucket_bytes(members: list[fx.Node]) -> int:
+        return sum(param_sizes[pn] for pn in members)
+
+    def _format_members(members: list[fx.Node], *, limit: int = 8) -> str:
+        def _one(pn: fx.Node) -> str:
+            return f"{pn.name}:{param_sizes[pn]}"
+
+        if len(members) <= limit:
+            return "[" + ", ".join(_one(pn) for pn in members) + "]"
+        head = ", ".join(_one(pn) for pn in members[:4])
+        tail = ", ".join(_one(pn) for pn in members[-2:])
+        return f"[{head}, ... ({len(members) - 6} more), {tail}]"
+
+    def _log_bucket_plan(phase: str, seg_ranges: list[tuple[int, int, list[fx.Node]]]) -> None:
+        ownership: dict[fx.Node, list[int]] = defaultdict(list)
+        for seg_idx, (_f, _l, members) in enumerate(seg_ranges):
+            for pn in members:
+                ownership[pn].append(seg_idx)
+        missing = [pn.name for pn in param_ph_list if pn not in ownership]
+        duplicates = [
+            (pn.name, buckets)
+            for pn, buckets in ownership.items()
+            if len(buckets) != 1
+        ]
+        total_bytes = sum(param_sizes[pn] for pn in param_ph_list)
+        max_bucket_bytes = max((_bucket_bytes(ms) for _f, _l, ms in seg_ranges), default=0)
+        logger.info(
+            "bucket_stage plan phase=%s node=%s bucket_size_bytes=%d bucket_size_mib=%.6f "
+            "params=%d total_param_bytes=%d initial_buckets=%d final_buckets=%d "
+            "owned_params=%d unique_owned_params=%d missing_params=%d duplicate_params=%d max_bucket_bytes=%d",
+            phase,
+            debug_name,
+            bucket_size_bytes,
+            bucket_size_bytes / (1024 * 1024),
+            len(param_ph_list),
+            total_bytes,
+            n_init,
+            len(seg_ranges),
+            sum(len(ms) for _f, _l, ms in seg_ranges),
+            len(ownership),
+            len(missing),
+            len(duplicates),
+            max_bucket_bytes,
+        )
+        if missing or duplicates:
+            logger.warning(
+                "bucket_stage ownership-invalid phase=%s node=%s missing=%s duplicates=%s",
+                phase,
+                debug_name,
+                missing[:16],
+                duplicates[:16],
+            )
+
+        merge_candidates: list[tuple[int, int, int]] = []
+        for left_idx in range(len(seg_ranges) - 1):
+            left_bytes = _bucket_bytes(seg_ranges[left_idx][2])
+            right_bytes = _bucket_bytes(seg_ranges[left_idx + 1][2])
+            combined = left_bytes + right_bytes
+            if combined <= bucket_size_bytes:
+                merge_candidates.append((left_idx, left_idx + 1, combined))
+
+        for seg_idx, (first, last, members) in enumerate(seg_ranges):
+            bytes_ = _bucket_bytes(members)
+            if bytes_ <= bucket_size_bytes:
+                status = "within_limit"
+            elif len(members) == 1:
+                status = "single_param_over_limit"
+            else:
+                status = "forced_multi_param_over_limit"
+            logger.info(
+                "bucket_stage bucket-plan phase=%s node=%s bucket=%d/%d param_bytes=%d "
+                "params=%d range=%d:%d status=%s members=%s",
+                phase,
+                debug_name,
+                seg_idx,
+                len(seg_ranges),
+                bytes_,
+                len(members),
+                first,
+                last,
+                status,
+                _format_members(members),
+            )
+        if merge_candidates:
+            logger.warning(
+                "bucket_stage fullness-warning phase=%s node=%s mergeable_adjacent_pairs=%s "
+                "bucket_size_bytes=%d",
+                phase,
+                debug_name,
+                merge_candidates[:16],
+                bucket_size_bytes,
+            )
+        else:
+            logger.info(
+                "bucket_stage fullness-ok phase=%s node=%s adjacent_pairs=%d bucket_size_bytes=%d",
+                phase,
+                debug_name,
+                max(0, len(seg_ranges) - 1),
+                bucket_size_bytes,
+            )
+
+    _log_bucket_plan("range_merge", merged)
+
+    alias_methods = {
+        "view", "_unsafe_view", "reshape", "transpose", "permute", "t",
+        "movedim", "moveaxis", "swapdims", "swapaxes",
+        "select", "narrow", "slice", "split", "chunk", "unbind",
+        "unsqueeze", "squeeze", "flatten", "expand", "diagonal",
+        "detach", "alias", "as_strided",
+    }
+    multi_output_alias_methods = {"split", "chunk", "unbind"}
+
+    alias_function_names = {
+        "view", "_unsafe_view", "reshape", "transpose", "permute", "t",
+        "movedim", "moveaxis", "swapdims", "swapaxes",
+        "select", "narrow", "slice", "split", "chunk", "unbind",
+        "unsqueeze", "squeeze", "flatten", "expand", "diagonal",
+        "detach", "alias", "as_strided",
+    }
+    multi_output_alias_function_names = {"split", "chunk", "unbind"}
+    hard_forbid_cross_bucket_prefixes = ("bfloat16_",)
+
+    def _target_name(target: object) -> str | None:
+        if isinstance(target, str):
+            return target
+        return getattr(target, "__name__", None)
+
+    def _alias_passthrough_sources(nd: fx.Node) -> set[fx.Node]:
+        if nd.op == "call_method":
+            if nd.target in alias_methods:
+                base = next(iter(nd.all_input_nodes), None)
+                return set(alias_sources.get(base, set())) if base is not None else set()
+            return set()
+
+        if nd.op != "call_function":
+            return set()
+
+        if nd.target == operator.getitem:
+            base = nd.args[0] if nd.args else None
+            if isinstance(base, fx.Node):
+                return set(alias_sources.get(base, set()))
+            return set()
+
+        name = _target_name(nd.target)
+        if name in alias_function_names:
+            base = next(iter(nd.all_input_nodes), None)
+            return set(alias_sources.get(base, set())) if base is not None else set()
+        return set()
+
+    alias_sources: dict[fx.Node, set[fx.Node]] = {}
+    for nd in nodes:
+        if nd.op == "placeholder":
+            alias_sources[nd] = {nd} if nd in param_ph_set else set()
+            continue
+        if nd.op in ("get_attr", "output"):
+            alias_sources[nd] = set()
+            continue
+        alias_sources[nd] = _alias_passthrough_sources(nd)
+
+    def _compute_seg_metadata(
+        seg_ranges: list[tuple[int, int, list[fx.Node]]]
+    ) -> tuple[dict[fx.Node, int], dict[fx.Node, int], list[list[fx.Node]]]:
+        """Return (node_seg, node_max_user_seg, seg_cross_in) for segment ranges."""
+        cut_after = [seg_ranges[i][1] for i in range(len(seg_ranges) - 1)]
+
+        def _seg_of(idx: int) -> int:
+            return sum(1 for c in cut_after if c < idx)
+
+        node_seg: dict[fx.Node, int] = {}
+        for nd in nodes:
+            if nd.op == "output":
+                node_seg[nd] = len(seg_ranges) - 1
+            elif nd.op == "placeholder":
+                if nd in param_ph_set:
+                    user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
+                    node_seg[nd] = _seg_of(min(user_idxs)) if user_idxs else 0
+                else:
+                    node_seg[nd] = 0
+            elif nd.op == "get_attr":
+                user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
+                node_seg[nd] = _seg_of(min(user_idxs)) if user_idxs else 0
+            else:
+                node_seg[nd] = _seg_of(node_idx[nd])
+
+        node_max_user_seg: dict[fx.Node, int] = {}
+        for nd in nodes:
+            if nd.op == "output":
+                continue
+            node_max_user_seg[nd] = (
+                max(node_seg[u] for u in nd.users) if nd.users else node_seg[nd]
+            )
+
+        seg_cross_in: list[list[fx.Node]] = [[] for _ in range(len(seg_ranges))]
+        for nd in nodes:
+            if nd.op == "output":
+                continue
+            s = node_seg[nd]
+            mu = node_max_user_seg[nd]
+            # Only values that cross a segment boundary must be forwarded.
+            if mu <= s:
+                continue
+            for seg in range(s + 1, mu + 1):
+                seg_cross_in[seg].append(nd)
+
+        return node_seg, node_max_user_seg, seg_cross_in
+
+    n_segs = len(merged)
+    if n_segs == 1:
+        return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))]
+
+    while True:
+        node_seg, node_max_user_seg, seg_cross_in = _compute_seg_metadata(merged)
+        merged_boundary = False
+        for seg in range(1, len(merged)):
+            alias_crossers = [
+                nd for nd in seg_cross_in[seg]
+                if alias_sources.get(nd) or any(
+                    nd.name.startswith(prefix) for prefix in hard_forbid_cross_bucket_prefixes
+                )
+            ]
+            if not alias_crossers:
+                continue
+            pf, _pl, pms = merged[seg - 1]
+            _cf, cl, cms = merged[seg]
+            merged[seg - 1] = (pf, cl, pms + cms)
+            del merged[seg]
+            merged_boundary = True
+            break
+        if not merged_boundary:
+            break
+
+    _log_bucket_plan("final", merged)
+
+    n_segs = len(merged)
+    if n_segs == 1:
+        return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))]
+
+    # Build sub-graphs
+    results: list[tuple[fx.GraphModule, list[int], list[int], list]] = []
+
+    for seg in range(n_segs):
+        sub_g = fx.Graph()
+        remap: dict[fx.Node, fx.Node] = {}
+        new_input_idxs: list[int] = []
+        new_param_idxs: list[int] = []
+        new_graphargs: list = []
+        pos = 0
+
+        def _meta_tensor_for(nd: fx.Node):
+            """Return a meta tensor matching nd's shape/dtype, or None if unavailable."""
+            ev = nd.meta.get("example_value")
+            if ev is None:
+                ev = nd.meta.get("val")
+            if ev is not None and hasattr(ev, "shape"):
+                return torch.empty(ev.shape, dtype=ev.dtype, device="meta", requires_grad=ev.requires_grad)
+            return None
+
+        def _add_ph(nd: fx.Node, name: str, is_input: bool) -> None:
+            nonlocal pos
+            new_ph = sub_g.placeholder(name)
+            new_ph.type = nd.type
+            remap[nd] = new_ph
+            if is_input:
+                new_input_idxs.append(pos)
+                meta = _meta_tensor_for(nd)
+                if meta is None:
+                    # Fallback: use the corresponding entry from the incoming
+                    # graphargs for freshly-created placeholder nodes that lack
+                    # example_value metadata.
+                    i_orig = ph_nodes.index(nd) if nd in ph_nodes else -1
+                    if 0 <= i_orig < len(graphargs):
+                        meta = graphargs[i_orig]
+                new_graphargs.append(meta)
+            else:
+                new_param_idxs.append(pos)
+                i_orig = ph_nodes.index(nd) if nd in ph_nodes else -1
+                new_graphargs.append(graphargs[i_orig] if 0 <= i_orig < len(graphargs) else None)
+            pos += 1
+
+        if seg == 0:
+            # Non-param placeholders (activation inputs + other non-param inputs).
+            for nd in nodes:
+                if nd.op == "placeholder" and nd not in param_ph_set:
+                    _add_ph(nd, nd.name, is_input=(nd in input_ph_set))
+        else:
+            # Cross-segment activation inputs from the previous segment.
+            for orig in seg_cross_in[seg]:
+                _add_ph(orig, f"_xseg_{orig.name}", is_input=True)
+
+        # Parameter placeholders belonging to this segment.
+        for nd in nodes:
+            if nd.op == "placeholder" and nd in param_ph_set and node_seg[nd] == seg:
+                _add_ph(nd, nd.name, is_input=False)
+
+        # get_attr nodes for this segment.
+        for nd in nodes:
+            if nd.op == "get_attr" and node_seg[nd] == seg:
+                new_ga = sub_g.get_attr(nd.target)
+                new_ga.type = nd.type
+                remap[nd] = new_ga
+
+        # Compute nodes for this segment (graph order preserved).
+        for nd in nodes:
+            if nd.op in ("placeholder", "get_attr", "output"):
+                continue
+            if node_seg[nd] != seg:
+                continue
+            remap[nd] = sub_g.node_copy(nd, arg_transform=lambda x, r=remap: r[x])
+
+        # Output node.
+        if seg == n_segs - 1:
+            orig_out = next(nd for nd in nodes if nd.op == "output")
+            sub_g.output(fx.map_arg(orig_out.args[0], lambda x: remap[x]))
+        else:
+            out_nodes = [remap[orig] for orig in seg_cross_in[seg + 1]]
+            sub_g.output(tuple(out_nodes) if len(out_nodes) != 1 else out_nodes[0])
+
+        sub_g.lint()
+        results.append((fx.GraphModule(stage_gm, sub_g), new_input_idxs, new_param_idxs, new_graphargs))
+
+    return results
diff --git a/src/piper_compile.py b/src/compile.py
similarity index 90%
rename from src/piper_compile.py
rename to src/compile.py
index 2a58326..22616de 100644
--- a/src/piper_compile.py
+++ b/src/compile.py
@@ -4,19 +4,19 @@
 import os
 import time
 
-from .piper_actor import _create_actors
-from .piper_utils import (
+from .actor import _create_actors
+from .state import (
     piper_metadata,
     create_logger,
     LOG_LEVEL,
-    _serialize_graphmodule,
 )
-from .piper import piper
-from .piper_schedule import (
+from .fx import _serialize_graphmodule
+from .piper import _reset_annotation_state, piper
+from .schedule import (
     derive_schedule_info,
     load_schedule_directives,
 )
-logger = create_logger("piper_compile", LOG_LEVEL)
+logger = create_logger("compile", LOG_LEVEL)
 
 _RANK0_ADDR_ACTOR = "piper_rank0_addr"
 _COMPILED_DATA_ACTOR = "piper_compiled_data"
@@ -122,7 +122,7 @@ def piper_setup(
     pg=None,
     nsight=False,
     temp_dir: str = None,
-    visualize_dag: bool = True,
+    visualize_dag: bool = False,
     output_dir: str = "out",
     const_attrs: dict = None,
     pp_outer: bool = False,
@@ -204,12 +204,20 @@ def piper_setup(
         ray.get(addr_store.get.remote())
     else:
         master_addr = master_port = None
+        deadline = time.monotonic() + 600
         while master_addr is None:
             try:
                 store = ray.get_actor(_RANK0_ADDR_ACTOR)
                 master_addr, master_port = ray.get(store.get.remote())
-            except Exception:
+            except ValueError:
+                if time.monotonic() > deadline:
+                    raise TimeoutError(
+                        f"Timed out waiting for Ray actor {_RANK0_ADDR_ACTOR}"
+                    )
                 time.sleep(0.05)
+            except Exception:
+                logger.exception("Failed while waiting for Ray actor %s", _RANK0_ADDR_ACTOR)
+                raise
 
     logger.debug(f"DP rank {dp_rank}: Master address for process groups: {master_addr}:{master_port}")
 
@@ -218,8 +226,11 @@ def piper_setup(
     if dp_rank == 0:
         try:
             ray.kill(ray.get_actor(_RANK0_ADDR_ACTOR))
+        except ValueError:
+            logger.debug("Ray actor %s already absent during cleanup", _RANK0_ADDR_ACTOR)
         except Exception:
-            pass
+            logger.exception("Failed to clean up Ray actor %s", _RANK0_ADDR_ACTOR)
+            raise
 
     # Push non-trainable constant tensor attributes (e.g. freqs_cis, rope_cache, mask)
     # to all actors so _load_stage can initialize them correctly instead of zero-filling.
@@ -256,6 +267,7 @@ def piper_setup(
 
         compiled = torch.compile(model, backend=piper, fullgraph=True)
         compile_inputs = [x.to(device="meta") for x in example_inputs]
+        _reset_annotation_state()
         _ = compiled(*compile_inputs)
 
         per_pp_training_dags = getattr(piper_metadata, "per_pp_training_dags", None)
@@ -283,13 +295,20 @@ def piper_setup(
     else:
         logger.debug(f"DP rank {dp_rank} waiting for dp_rank=0 training-DAG data...")
         training_dag_data = None
+        deadline = time.monotonic() + 600
         while training_dag_data is None:
             try:
                 store = ray.get_actor(_COMPILED_DATA_ACTOR)
                 if ray.get(store.is_training_dag_ready.remote()):
                     training_dag_data = ray.get(store.get_training_dag_data.remote())
+            except ValueError:
+                if time.monotonic() > deadline:
+                    raise TimeoutError(
+                        f"Timed out waiting for Ray actor {_COMPILED_DATA_ACTOR}"
+                    )
             except Exception:
-                pass
+                logger.exception("Failed while waiting for Ray actor %s", _COMPILED_DATA_ACTOR)
+                raise
             if training_dag_data is None:
                 time.sleep(0.2)
 
diff --git a/src/piper_coordinator.py b/src/coordinator.py
similarity index 82%
rename from src/piper_coordinator.py
rename to src/coordinator.py
index c49734d..ec226ed 100644
--- a/src/piper_coordinator.py
+++ b/src/coordinator.py
@@ -5,8 +5,8 @@
 from ray.util.placement_group import placement_group
 from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
 
-from .piper_utils import create_logger, LOG_LEVEL
-from .piper_schedule import load_schedule_info
+from .state import create_logger, LOG_LEVEL
+from .schedule import load_schedule_info
 
 
 # Coordinator needs GPUs when using profiling to infer stage boundaries
@@ -15,7 +15,7 @@
 # Use manual stage annotations- more stable
 @ray.remote(num_gpus=0.1)
 def run_dp_rank(dp_rank, dp_degree, pp_degree, world_size, training_func: Callable, *args, **kwargs):
-    logger = create_logger("piper_coordinator", LOG_LEVEL)
+    logger = create_logger("coordinator", LOG_LEVEL)
     logger.debug(f"Running DP rank {dp_rank+1} of {dp_degree}")
 
     os.environ["PIPER_DP_RANK"] = str(dp_rank)
@@ -46,17 +46,24 @@ def __init__(
         self.pp_outer = pp_outer
 
     def run_program(self, training_func: Callable, pg, *args, **kwargs):
-        from .piper_compile import _RANK0_ADDR_ACTOR, _COMPILED_DATA_ACTOR
+        from .compile import _RANK0_ADDR_ACTOR, _COMPILED_DATA_ACTOR
+        logger = create_logger("coordinator", LOG_LEVEL)
         try:
             ray.kill(ray.get_actor(_RANK0_ADDR_ACTOR))
+        except ValueError:
+            logger.debug("No stale Ray actor named %s to kill", _RANK0_ADDR_ACTOR)
         except Exception:
-            pass
+            logger.exception("Failed to kill stale Ray actor named %s", _RANK0_ADDR_ACTOR)
+            raise
         # Kill any stale compiled-data store from a previous run so that
         # dp_rank>0 workers cannot read outdated (e.g. wrong-model) stage data.
         try:
             ray.kill(ray.get_actor(_COMPILED_DATA_ACTOR))
+        except ValueError:
+            logger.debug("No stale Ray actor named %s to kill", _COMPILED_DATA_ACTOR)
         except Exception:
-            pass
+            logger.exception("Failed to kill stale Ray actor named %s", _COMPILED_DATA_ACTOR)
+            raise
 
         return ray.get(
             [
diff --git a/src/dag.py b/src/dag.py
new file mode 100644
index 0000000..37e88ac
--- /dev/null
+++ b/src/dag.py
@@ -0,0 +1,340 @@
+from dataclasses import dataclass, field
+from typing import Any
+
+import torch.fx as fx
+
+from .fx import AnnotationSegment
+
+def _collect_triton_constant_args(gm: fx.GraphModule) -> dict[int, Any]:
+    from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
+    out: dict[int, Any] = {}
+    for node in gm.graph.nodes:
+        if node.op != "call_function":
+            continue
+        target_str = getattr(node.target, "__name__", "") or str(node.target)
+        if "triton_kernel_wrapper" not in target_str:
+            continue
+        idx = node.kwargs.get("constant_args_idx")
+        if idx is not None and idx in kernel_side_table.constant_args:
+            out[int(idx)] = kernel_side_table.constant_args[idx]
+    return out
+
+
+@dataclass(frozen=True)
+class TrainingDAGEdge:
+    src_uid: str
+    dst_uid: str
+    dep_kind: str  # "data" | "temporal"
+    tensor_name: str | None = None
+
+
+@dataclass
+class TrainingDAGNode:
+    uid: str
+    node_kind: str  # "COMPUTE" | "SEND_COMM" | "RECV_COMM" | "REDUCE_COMM" | "ALL_GATHER_COMM" | "REDUCE_SCATTER_COMM" | "A2A_COMM" | "ORDER_DUMMY"
+    compute_subkind: str | None  # "FWD" | "BWD" | "BWD_I" | "BWD_W" when node_kind == "COMPUTE"
+    tag: dict[str, int | None]
+    device: list[int] | None
+    stream: str
+    node_meta: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class TrainingDAG:
+    nodes: dict[str, TrainingDAGNode] = field(default_factory=dict)
+    edges: list[TrainingDAGEdge] = field(default_factory=list)
+    succs: dict[str, set[str]] = field(default_factory=dict)
+    preds: dict[str, set[str]] = field(default_factory=dict)
+    edge_keys: set[tuple[str, str, str, str | None]] = field(default_factory=set)
+
+    def add_node(self, node: TrainingDAGNode) -> None:
+        if node.uid in self.nodes:
+            raise ValueError(f"Duplicate node uid: {node.uid}")
+        self.nodes[node.uid] = node
+        self.succs[node.uid] = set()
+        self.preds[node.uid] = set()
+
+    def add_edge(self, edge: TrainingDAGEdge) -> None:
+        if edge.src_uid not in self.nodes or edge.dst_uid not in self.nodes:
+            raise ValueError(f"Edge references unknown node: {edge}")
+        edge_key = (edge.src_uid, edge.dst_uid, edge.dep_kind, edge.tensor_name)
+        if edge_key in self.edge_keys:
+            return
+        self.edge_keys.add(edge_key)
+        self.edges.append(edge)
+        self.succs[edge.src_uid].add(edge.dst_uid)
+        self.preds[edge.dst_uid].add(edge.src_uid)
+
+
+
+def _flatten_output_nodes(out_arg: Any) -> list[fx.Node]:
+    out_nodes: list[fx.Node] = []
+
+    def _walk(v: Any) -> None:
+        if isinstance(v, fx.Node):
+            out_nodes.append(v)
+        elif isinstance(v, (tuple, list)):
+            for x in v:
+                _walk(x)
+        elif isinstance(v, dict):
+            for x in v.values():
+                _walk(x)
+
+    _walk(out_arg)
+    return out_nodes
+
+
+
+def _node_io_names(seg_gm: fx.GraphModule) -> tuple[list[str], list[str]]:
+    inputs = [n.name for n in seg_gm.graph.nodes if n.op == "placeholder"]
+    out_node = next((n for n in seg_gm.graph.nodes if n.op == "output"), None)
+    if out_node is None:
+        return inputs, []
+    outputs = [n.name for n in _flatten_output_nodes(out_node.args[0])]
+    return inputs, outputs
+
+def _with_pass_tag(tag: dict[str, Any], pass_value: str | None) -> dict[str, Any]:
+    t = dict(tag)
+    t["PASS"] = pass_value
+    return t
+
+def build_training_dag(
+    annotation_segments: list[AnnotationSegment],
+) -> TrainingDAG:
+    """Build a TrainingDAG where each node is one annotated GraphModule segment."""
+    dag = TrainingDAG()
+
+    global_prev_uid: str | None = None
+
+    for segment in annotation_segments:
+        uid = f"s{segment.stage_id}.seg{segment.segment_id}"
+        tag = _with_pass_tag(segment.tag, "F")
+        in_names, out_names = _node_io_names(segment.gm)
+        node = TrainingDAGNode(
+            uid=uid,
+            node_kind="COMPUTE",
+            compute_subkind="FWD",
+            tag=tag,
+            device=None,
+            stream="default_stream",
+            node_meta={
+                "bucket_key": uid,
+                "stage_id": segment.stage_id,
+                "segment_id": segment.segment_id,
+                "gm": segment.gm,
+                "input_idxs": list(segment.input_idxs),
+                "param_idxs": list(segment.param_idxs),
+                "graphargs": list(segment.graphargs),
+                "input_names": in_names,
+                "output_names": out_names,
+                "a2a_boundary_after": segment.a2a_boundary_after,
+                "triton_constant_args": _collect_triton_constant_args(segment.gm),
+            },
+        )
+        dag.add_node(node)
+
+        # Explicit activation-flow ordering in annotation segment order.
+        if global_prev_uid is not None:
+            dag.add_edge(TrainingDAGEdge(src_uid=global_prev_uid, dst_uid=uid, dep_kind="data"))
+        global_prev_uid = uid
+
+    # Build backward-pass nodes and dependencies by reversing forward data edges.
+    fwd_compute_uids = [
+        uid for uid, node in dag.nodes.items()
+        if node.node_kind == "COMPUTE" and node.compute_subkind == "FWD"
+    ]
+    fwd_uid_to_bwd_uid: dict[str, str] = {}
+    for fwd_uid in fwd_compute_uids:
+        fwd_node = dag.nodes[fwd_uid]
+        bwd_uid = f"{fwd_uid}.bwd"
+        fwd_uid_to_bwd_uid[fwd_uid] = bwd_uid
+        dag.add_node(
+            TrainingDAGNode(
+                uid=bwd_uid,
+                node_kind="COMPUTE",
+                compute_subkind="BWD",
+                tag=_with_pass_tag(fwd_node.tag, "B"),
+                device=fwd_node.device,
+                stream="default_stream",
+                node_meta={
+                    "fwd_uid": fwd_uid,
+                    "bucket_key": fwd_node.node_meta.get("bucket_key", fwd_uid),
+                    "compute_loss": False,
+                },
+            )
+        )
+
+    fwd_edges = [
+        e for e in dag.edges
+        if e.dep_kind == "data"
+        and dag.nodes[e.src_uid].node_kind == "COMPUTE"
+        and dag.nodes[e.dst_uid].node_kind == "COMPUTE"
+        and dag.nodes[e.src_uid].compute_subkind == "FWD"
+        and dag.nodes[e.dst_uid].compute_subkind == "FWD"
+    ]
+
+    # Reverse every forward dependency for the backward pass:
+    # FWD:  u -> v    ==>    BWD: v' -> u'
+    for e in fwd_edges:
+        bwd_src = fwd_uid_to_bwd_uid[e.dst_uid]
+        bwd_dst = fwd_uid_to_bwd_uid[e.src_uid]
+        dag.add_edge(
+            TrainingDAGEdge(
+                src_uid=bwd_src,
+                dst_uid=bwd_dst,
+                dep_kind="data",
+                tensor_name=e.tensor_name,
+            )
+        )
+
+    # Bridge from forward phase into backward phase:
+    # last_fwd -> first_bwd (where first_bwd is the BWD node paired with last_fwd).
+    if fwd_compute_uids:
+        def _fwd_sort_key(uid: str) -> tuple[int, int]:
+            n = dag.nodes[uid]
+            return (
+                int(n.node_meta.get("stage_id", -1)),
+                int(n.node_meta.get("segment_id", -1)),
+            )
+
+        last_fwd_uid = max(fwd_compute_uids, key=_fwd_sort_key)
+        first_bwd_uid = fwd_uid_to_bwd_uid[last_fwd_uid]
+        dag.add_edge(
+            TrainingDAGEdge(
+                src_uid=last_fwd_uid,
+                dst_uid=first_bwd_uid,
+                dep_kind="data",
+                tensor_name=None,
+            )
+        )
+
+        # Mark loss computation point: first BWD node (earliest in BWD execution).
+        if first_bwd_uid in dag.nodes:
+            dag.nodes[first_bwd_uid].node_meta["compute_loss"] = True
+
+    # Add explicit UPD node with temporal dependency from every BWD compute node.
+    # UPD participates in the compute temporal chain and is not scheduled from
+    # data edges.
+    upd_uid = "upd.0"
+    dag.add_node(
+        TrainingDAGNode(
+            uid=upd_uid,
+            node_kind="UPD",
+            compute_subkind=None,
+            tag={"PASS": None},
+            device=None,
+            stream="default_stream",
+            node_meta={},
+        )
+    )
+    for uid, node in list(dag.nodes.items()):
+        if node.node_kind == "COMPUTE" and node.compute_subkind == "BWD":
+            dag.add_edge(
+                TrainingDAGEdge(
+                    src_uid=uid,
+                    dst_uid=upd_uid,
+                    dep_kind="temporal",
+                    tensor_name=None,
+                )
+            )
+
+    return dag
+
+def _iter_data_edges(dag: TrainingDAG) -> list[TrainingDAGEdge]:
+    return [e for e in dag.edges if e.dep_kind == "data"]
+
+
+def _remove_edge(dag: TrainingDAG, edge: TrainingDAGEdge) -> None:
+    edge_key = (edge.src_uid, edge.dst_uid, edge.dep_kind, edge.tensor_name)
+    if edge_key not in dag.edge_keys:
+        return
+    dag.edge_keys.remove(edge_key)
+    dag.edges = [
+        e for e in dag.edges
+        if not (
+            e.src_uid == edge.src_uid
+            and e.dst_uid == edge.dst_uid
+            and e.dep_kind == edge.dep_kind
+            and e.tensor_name == edge.tensor_name
+        )
+    ]
+    if edge.src_uid in dag.succs:
+        dag.succs[edge.src_uid].discard(edge.dst_uid)
+    if edge.dst_uid in dag.preds:
+        dag.preds[edge.dst_uid].discard(edge.src_uid)
+
+def _remove_node_and_incident_edges(dag: TrainingDAG, uid: str) -> None:
+    incident = [e for e in dag.edges if e.src_uid == uid or e.dst_uid == uid]
+    for e in incident:
+        _remove_edge(dag, e)
+    dag.nodes.pop(uid, None)
+    dag.succs.pop(uid, None)
+    dag.preds.pop(uid, None)
+    for s in dag.succs.values():
+        s.discard(uid)
+    for p in dag.preds.values():
+        p.discard(uid)
+
+def _topological_order(dag: TrainingDAG) -> list[str]:
+    in_deg: dict[str, int] = {uid: 0 for uid in dag.nodes}
+    out_edges_by_src: dict[str, list[TrainingDAGEdge]] = {uid: [] for uid in dag.nodes}
+    for e in dag.edges:
+        if e.dst_uid in in_deg:
+            in_deg[e.dst_uid] += 1
+        if e.src_uid in out_edges_by_src:
+            out_edges_by_src[e.src_uid].append(e)
+    q = sorted(uid for uid, d in in_deg.items() if d == 0)
+    out: list[str] = []
+    while q:
+        cur_level = q
+        q = []
+        for u in cur_level:
+            out.append(u)
+            # Decrement per outgoing edge (not per unique successor), because we
+            # can have parallel edges between the same pair (e.g., data + temporal).
+            for e in out_edges_by_src.get(u, []):
+                v = e.dst_uid
+                if v not in in_deg:
+                    continue
+                in_deg[v] -= 1
+                if in_deg[v] == 0:
+                    q.append(v)
+        q.sort()
+    if len(out) != len(dag.nodes):
+        remaining = [uid for uid, d in in_deg.items() if d > 0]
+        raise ValueError(
+            "TrainingDAG topological sort failed: unresolved incoming edges remain; "
+            f"possible real cycle or malformed parallel-edge bookkeeping. Remaining nodes: {remaining[:8]}"
+        )
+    return out
+
+
+def _topological_levels(dag: TrainingDAG) -> dict[str, int]:
+    """Return each node's topological level.
+
+    Source nodes are level 0. Every other node is one level after its latest
+    predecessor, so independent successors can share the same level.
+    """
+    topo = _topological_order(dag)
+    levels: dict[str, int] = {}
+    for uid in topo:
+        pred_levels = [
+            levels[pred_uid]
+            for pred_uid in dag.preds.get(uid, set())
+            if pred_uid in levels
+        ]
+        levels[uid] = (max(pred_levels) + 1) if pred_levels else 0
+    return levels
+
+def _has_path(dag: TrainingDAG, src_uid: str, dst_uid: str) -> bool:
+    seen: set[str] = set()
+    stack = [src_uid]
+    while stack:
+        uid = stack.pop()
+        if uid == dst_uid:
+            return True
+        if uid in seen:
+            continue
+        seen.add(uid)
+        stack.extend(dag.succs.get(uid, set()))
+    return False
diff --git a/src/directives.py b/src/directives.py
new file mode 100644
index 0000000..cca1cfb
--- /dev/null
+++ b/src/directives.py
@@ -0,0 +1,1626 @@
+from collections import Counter
+from dataclasses import dataclass
+from typing import Any
+
+import torch.fx as fx
+
+from .bucket import bucket_stage
+from .dag import (
+    TrainingDAG,
+    TrainingDAGEdge,
+    TrainingDAGNode,
+    _iter_data_edges,
+    _remove_edge,
+    _remove_node_and_incident_edges,
+    _topological_order,
+    _with_pass_tag,
+)
+from .state import LOG_LEVEL, create_logger
+
+logger = create_logger("directives", LOG_LEVEL)
+_ANY_TAG_INDEX = "__ANY_TAG_INDEX__"
+_FORWARD_PASS = "F"
+_FUSED_BWD_PASS = "B"
+_BWD_INPUT_PASS = "BI"
+_BWD_WEIGHT_PASS = "BW"
+_DEFAULT_STREAM = "default_stream"
+_VALID_ORDER_PASSES = frozenset({
+    _FORWARD_PASS,
+    _FUSED_BWD_PASS,
+    _BWD_INPUT_PASS,
+    _BWD_WEIGHT_PASS,
+})
+
+def _is_backward_activation_subkind(subkind: str | None) -> bool:
+    return subkind in ("BWD", "BWD_I")
+
+
+def _is_backward_weight_subkind(subkind: str | None) -> bool:
+    return subkind in ("BWD", "BWD_W")
+
+
+def _is_backward_compute_subkind(subkind: str | None) -> bool:
+    return subkind in ("BWD", "BWD_I", "BWD_W")
+
+def _match_filter(tag: dict[str, int | None], flt: dict[str, Any]) -> bool:
+    for k, v in flt.items():
+        if k not in tag:
+            return False
+        if v == _ANY_TAG_INDEX:
+            # "*" wildcard matches any concrete index for this tag, but not None.
+            if tag[k] is None:
+                return False
+        elif tag[k] != v:
+            return False
+    return True
+
+
+def _apply_split_backward_stencil(
+    dag: TrainingDAG,
+    split_base_keys: set[tuple[tuple[str, Any], ...]],
+) -> None:
+    if not split_base_keys:
+        return
+    split_filters = [dict(key) for key in split_base_keys]
+    for node in list(dag.nodes.values()):
+        if (
+            node.node_kind != "COMPUTE"
+            or node.compute_subkind != "BWD"
+            or not any(_match_filter(node.tag, flt) for flt in split_filters)
+        ):
+            continue
+        _split_fused_bwd_node(dag, node.uid)
+
+
+def _split_fused_bwd_node(dag: TrainingDAG, bwd_uid: str) -> None:
+    bwd_i = dag.nodes[bwd_uid]
+    if bwd_i.node_kind != "COMPUTE" or bwd_i.compute_subkind != "BWD":
+        raise ValueError(f"expected fused BWD compute node, got {bwd_uid}: {bwd_i}")
+    bwd_w_uid = f"{bwd_uid}.bw"
+    if bwd_w_uid in dag.nodes:
+        raise ValueError(f"duplicate BWD_W uid while splitting {bwd_uid}: {bwd_w_uid}")
+
+    bwd_i.compute_subkind = "BWD_I"
+    bwd_i.tag = _with_pass_tag(bwd_i.tag, _BWD_INPUT_PASS)
+
+    bwd_w_meta = dict(bwd_i.node_meta)
+    bwd_w_meta["compute_loss"] = False
+    dag.add_node(
+        TrainingDAGNode(
+            uid=bwd_w_uid,
+            node_kind="COMPUTE",
+            compute_subkind="BWD_W",
+            tag=_with_pass_tag(bwd_i.tag, _BWD_WEIGHT_PASS),
+            device=list(bwd_i.device) if bwd_i.device is not None else None,
+            stream=bwd_i.stream,
+            node_meta=bwd_w_meta,
+        )
+    )
+
+    for edge in [
+        e for e in list(dag.edges)
+        if e.dep_kind == "temporal"
+        and e.src_uid == bwd_uid
+        and dag.nodes[e.dst_uid].node_kind == "UPD"
+    ]:
+        _remove_edge(dag, edge)
+        dag.add_edge(
+            TrainingDAGEdge(
+                src_uid=bwd_w_uid,
+                dst_uid=edge.dst_uid,
+                dep_kind="temporal",
+                tensor_name=edge.tensor_name,
+            )
+        )
+
+    dag.add_edge(
+        TrainingDAGEdge(
+            src_uid=bwd_uid,
+            dst_uid=bwd_w_uid,
+            dep_kind="data",
+            tensor_name=None,
+        )
+    )
+
+
+def _normalize_filter_devices_directive(
+    directive: Any
+) -> tuple[str, list[dict[str, Any]], list[int], str | None, str | None, str | None, bool, bool, int | None]:
+    if isinstance(directive, dict):
+        op = directive.get("op")
+        if op not in ("place", "replicate", "shard"):
+            raise ValueError(f"Unsupported directive op: {op}")
+        if "filter" not in directive:
+            raise ValueError(f"{op} directive requires current API field 'filter': {directive}")
+        if "filters" in directive:
+            raise ValueError(f"{op} directive does not accept 'filters': {directive}")
+        filter_spec = directive.get("filter")
+        devices = directive.get("devices", directive.get("device"))
+        stream = directive.get("stream")
+        gather_stream = directive.get("gather_stream")
+        reduce_stream = directive.get("reduce_stream")
+        if op == "replicate":
+            if stream is not None:
+                raise ValueError(
+                    f"replicate directive does not accept 'stream'; use 'gather_stream' and/or 'reduce_stream': {directive}"
+                )
+        else:
+            if gather_stream is not None or reduce_stream is not None:
+                raise ValueError(
+                    f"{op} directive does not accept 'gather_stream'/'reduce_stream'; use 'stream': {directive}"
+                )
+        shard_params = bool(directive.get("shard_params", False))
+        shard_grads = bool(directive.get("shard_grads", False))
+        bucket_size_raw = directive.get("bucket_size", None)
+        bucket_size = None if bucket_size_raw is None else int(bucket_size_raw)
+        if not isinstance(devices, list) or not devices:
+            raise ValueError(f"place directive requires non-empty devices list: {directive}")
+        n = _normalize_filter_spec(filter_spec, directive)
+        return (
+            op,
+            [n],
+            [int(d) for d in devices],
+            (None if stream is None else str(stream)),
+            (None if gather_stream is None else str(gather_stream)),
+            (None if reduce_stream is None else str(reduce_stream)),
+            shard_params,
+            shard_grads,
+            bucket_size,
+        )
+
+    raise ValueError(f"Schedule directives must be JSON objects, got: {type(directive)}")
+
+
+def _normalize_filter_spec(filter_spec: Any, directive: Any) -> dict[str, Any]:
+    """Normalize a single filter spec into {tag_name: value}."""
+    def _norm_value(k: str, v: Any) -> Any:
+        if k == "PASS":
+            if not isinstance(v, str):
+                raise ValueError(
+                    f"'PASS' filter value must be a string in {{'F','B','BI','BW'}}: {directive}"
+                )
+            if v not in ("F", "B", "BI", "BW"):
+                raise ValueError(
+                    f"Invalid 'PASS' filter value '{v}'. Allowed: 'F', 'B', 'BI', 'BW': {directive}"
+                )
+            return v
+        if v == "*":
+            return _ANY_TAG_INDEX
+        if v is None:
+            return None
+        if isinstance(v, int):
+            return int(v)
+        if isinstance(v, str):
+            # Keep pass-like symbolic tags (e.g., "FWD"/"BWD") as strings.
+            try:
+                return int(v)
+            except ValueError:
+                return v
+        return v
+
+    if not isinstance(filter_spec, dict):
+        raise ValueError(f"filter must be a JSON object: {directive}")
+    out: dict[str, Any] = {}
+    for k, v in filter_spec.items():
+        if not isinstance(k, str):
+            raise ValueError(f"Filter keys must be strings: {directive}")
+        out[k] = _norm_value(k, v)
+    return out
+
+
+def _iter_filter_specs(spec: Any):
+    if isinstance(spec, dict):
+        yield spec
+        return
+    if isinstance(spec, list):
+        if all(
+            isinstance(item, (list, tuple))
+            and len(item) == 2
+            and isinstance(item[0], str)
+            for item in spec
+        ):
+            yield spec
+            return
+        for item in spec:
+            yield from _iter_filter_specs(item)
+
+
+def _schedule_filter_tag_names(directives: Any) -> set[str]:
+    if not isinstance(directives, list):
+        return set()
+
+    runtime_tags = {"PASS"}
+    for directive in directives:
+        if isinstance(directive, dict) and directive.get("op") == "split":
+            dim_name = directive.get("dim_name")
+            if isinstance(dim_name, str) and dim_name:
+                runtime_tags.add(dim_name)
+
+    tag_names: set[str] = set()
+    for directive in directives:
+        if not isinstance(directive, dict):
+            continue
+        specs = []
+        if "filter" in directive:
+            specs.append(directive["filter"])
+        if isinstance(directive.get("filters"), list):
+            specs.append(directive["filters"])
+        for spec in _iter_filter_specs(specs):
+            if isinstance(spec, dict):
+                tag_names.update(k for k in spec if k not in runtime_tags)
+            elif isinstance(spec, list):
+                for item in spec:
+                    if (
+                        isinstance(item, (list, tuple))
+                        and len(item) == 2
+                        and isinstance(item[0], str)
+                        and item[0] not in runtime_tags
+                    ):
+                        tag_names.add(item[0])
+    return tag_names
+
+
+def _validate_schedule_tags_exist(
+    training_dag: TrainingDAG,
+    directives: Any,
+) -> None:
+    schedule_tags = _schedule_filter_tag_names(directives)
+    if not schedule_tags:
+        return
+    model_tags = {
+        tag_name
+        for node in training_dag.nodes.values()
+        for tag_name in node.tag
+        if tag_name != "PASS"
+    }
+    missing = sorted(schedule_tags - model_tags)
+    if missing:
+        raise ValueError(
+            "Schedule references tag name(s) not found in model annotations: "
+            f"{missing}. Model tag names: {sorted(model_tags)}."
+        )
+
+
+def _normalize_split_directive(directive: Any) -> tuple[dict[str, Any], str, int]:
+    if not isinstance(directive, dict):
+        raise ValueError(f"split directive must be a dict: {directive}")
+    if directive.get("op") != "split":
+        raise ValueError(f"Unsupported split directive op: {directive}")
+    if "filter" not in directive:
+        raise ValueError(f"split directive requires 'filter': {directive}")
+    dim_name = directive.get("dim_name")
+    if not isinstance(dim_name, str) or not dim_name:
+        raise ValueError(f"split directive requires non-empty string dim_name: {directive}")
+    num_microbatches = int(directive.get("num_microbatches", 0))
+    if num_microbatches <= 0:
+        raise ValueError(f"split directive requires num_microbatches > 0: {directive}")
+    flt = _normalize_filter_spec(directive["filter"], directive)
+    return flt, dim_name, num_microbatches
+
+
+def _parse_order_directive(directive: Any) -> list[list[dict[str, Any]]]:
+    if not isinstance(directive, dict):
+        raise ValueError(f"order directive must be a dict: {directive}")
+    if directive.get("op") != "order":
+        raise ValueError(f"Unsupported order directive op: {directive}")
+    raw_filters = directive.get("filters")
+    if not isinstance(raw_filters, list) or len(raw_filters) < 2:
+        raise ValueError(
+            f"order directive requires filters list with at least 2 groups: {directive}"
+        )
+
+    filter_groups: list[list[dict[str, Any]]] = []
+    for group_idx, raw_group in enumerate(raw_filters):
+        if not isinstance(raw_group, list) or not raw_group:
+            raise ValueError(
+                f"order directive filter group[{group_idx}] must be a non-empty list "
+                f"of filters: {directive}"
+            )
+
+        group = []
+        for raw_filter in raw_group:
+            if not isinstance(raw_filter, dict):
+                raise ValueError(
+                    f"order directive filter group[{group_idx}] contains invalid "
+                    f"filter object: {raw_filter}"
+                )
+            flt = _normalize_filter_spec(raw_filter, directive)
+            pass_value = flt.get("PASS")
+            if pass_value is not None and pass_value not in _VALID_ORDER_PASSES:
+                raise ValueError(
+                    f"order directive has unsupported pass={pass_value!r}; "
+                    f"expected one of {sorted(_VALID_ORDER_PASSES)}"
+                )
+            group.append(flt)
+        filter_groups.append(group)
+
+    return filter_groups
+
+
+def _filter_key_without(flt: dict[str, Any], ignored_keys: set[str]) -> tuple[tuple[str, Any], ...]:
+    return tuple(sorted((k, v) for k, v in flt.items() if k not in ignored_keys))
+
+
+def _validate_split_backward_order_stencil(
+    directives: list[Any],
+) -> set[tuple[tuple[str, Any], ...]]:
+    """Validate BI/BW order entries and return base filter keys to split.
+
+    This is intentionally narrow: it validates only the current split-backward
+    stencil until the schedule JSON grows a full schema validation pass.
+    """
+    split_keys: set[tuple[tuple[str, Any], ...]] = set()
+    split_entries: dict[tuple[tuple[str, Any], ...], list[tuple[int, tuple[int, int], str]]] = {}
+
+    for directive_idx, raw in enumerate(directives):
+        if not isinstance(raw, dict) or raw.get("op") != "order":
+            continue
+        filter_groups = _parse_order_directive(raw)
+        seen_in_row: dict[tuple[tuple[str, Any], ...], dict[str, tuple[int, int]]] = {}
+        for group_idx, group in enumerate(filter_groups):
+            for filter_idx, flt in enumerate(group):
+                filter_pos = (group_idx, filter_idx)
+                pass_value = flt.get("PASS")
+                if pass_value is None:
+                    continue
+                if pass_value == _FUSED_BWD_PASS:
+                    continue
+                if pass_value not in (_BWD_INPUT_PASS, _BWD_WEIGHT_PASS):
+                    continue
+                key = _filter_key_without(flt, {"PASS"})
+                by_pass = seen_in_row.setdefault(key, {})
+                if pass_value in by_pass:
+                    raise ValueError(
+                        f"order directive[{directive_idx}] has duplicate split-backward "
+                        f"{pass_value} entry for {dict(key)} at indices "
+                        f"{by_pass[pass_value]} and {filter_pos}"
+                    )
+                by_pass[pass_value] = filter_pos
+                split_keys.add(key)
+                split_entries.setdefault(key, []).append((directive_idx, filter_pos, pass_value))
+
+    for key, entries in sorted(split_entries.items()):
+        bi = [entry for entry in entries if entry[2] == _BWD_INPUT_PASS]
+        bw = [entry for entry in entries if entry[2] == _BWD_WEIGHT_PASS]
+        if len(bi) != 1 or len(bw) != 1:
+            raise ValueError(
+                f"split backward order entries for {dict(key)} must contain exactly "
+                f"one BI and one BW; got BI={len(bi)} BW={len(bw)}"
+            )
+        if bi[0][0] != bw[0][0]:
+            raise ValueError(
+                f"split backward order entries for {dict(key)} must appear in the "
+                "same order directive row"
+            )
+        if bi[0][1] > bw[0][1]:
+            raise ValueError(
+                f"split backward order entries for {dict(key)} must place BI before BW"
+            )
+
+    return split_keys
+
+
+
+def _device_to_physical_pp_rank(dag: TrainingDAG) -> dict[tuple[int, ...], int]:
+    device_keys = sorted({
+        tuple(sorted(node.device))
+        for node in dag.nodes.values()
+        if node.device is not None
+    })
+    return {key: idx for idx, key in enumerate(device_keys)}
+
+
+def _insert_send_recv_comm_nodes(dag: TrainingDAG, comm_stream: str | None = None) -> None:
+    data_edges = _iter_data_edges(dag)
+    device_to_pp_rank = _device_to_physical_pp_rank(dag)
+    send_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "SEND_COMM")
+    recv_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "RECV_COMM")
+    for edge in data_edges:
+        src = dag.nodes[edge.src_uid]
+        dst = dag.nodes[edge.dst_uid]
+        if src.node_kind != "COMPUTE" or dst.node_kind != "COMPUTE":
+            continue
+        if src.device is None or dst.device is None:
+            raise ValueError(f"cannot insert send/recv for unplaced edge {src.uid} -> {dst.uid}")
+        if src.device == dst.device:
+            continue
+        src_device_key = tuple(sorted(src.device))
+        dst_device_key = tuple(sorted(dst.device))
+        send_uid = f"send.{send_idx}"
+        send_idx += 1
+        recv_uid = f"recv.{recv_idx}"
+        recv_idx += 1
+        stream_name = comm_stream if comm_stream is not None else "pp_stream"
+        send_node = TrainingDAGNode(
+            uid=send_uid,
+            node_kind="SEND_COMM",
+            compute_subkind=None,
+            tag=dict(src.tag),
+            device=(None if src.device is None else list(src.device)),
+            stream=stream_name,
+            node_meta={
+                "from_uid": src.uid,
+                "to_uid": dst.uid,
+                "peer_pp_rank": device_to_pp_rank[dst_device_key],
+                "bucket_key": src.node_meta.get("bucket_key"),
+            },
+        )
+        recv_node = TrainingDAGNode(
+            uid=recv_uid,
+            node_kind="RECV_COMM",
+            compute_subkind=None,
+            tag=dict(dst.tag),
+            device=(None if dst.device is None else list(dst.device)),
+            stream=stream_name,
+            node_meta={
+                "from_uid": src.uid,
+                "to_uid": dst.uid,
+                "peer_pp_rank": device_to_pp_rank[src_device_key],
+                "bucket_key": dst.node_meta.get("bucket_key"),
+            },
+        )
+        dag.add_node(send_node)
+        dag.add_node(recv_node)
+        _remove_edge(dag, edge)
+        dag.add_edge(TrainingDAGEdge(src_uid=src.uid, dst_uid=send_uid, dep_kind="data", tensor_name=edge.tensor_name))
+        dag.add_edge(TrainingDAGEdge(src_uid=recv_uid, dst_uid=dst.uid, dep_kind="data", tensor_name=edge.tensor_name))
+
+
+def _replicate_update_nodes_by_device(dag: TrainingDAG) -> None:
+    """Replicate UPD nodes per upstream device set so split components stay homogeneous."""
+    upd_nodes = [n for n in list(dag.nodes.values()) if n.node_kind == "UPD" and ".rep" not in n.uid]
+    for upd in upd_nodes:
+        in_edges = [e for e in list(dag.edges) if e.dep_kind == "temporal" and e.dst_uid == upd.uid]
+        by_dev: dict[tuple[int, ...], list[TrainingDAGEdge]] = {}
+        for e in in_edges:
+            src = dag.nodes[e.src_uid]
+            if src.device is None:
+                continue
+            key = tuple(sorted(src.device))
+            by_dev.setdefault(key, []).append(e)
+        if len(by_dev) <= 1:
+            if len(by_dev) == 1:
+                upd.device = list(next(iter(by_dev.keys())))
+            continue
+        created_uids: list[str] = []
+        for i_upd, (dev_key, dev_edges) in enumerate(sorted(by_dev.items(), key=lambda kv: kv[0])):
+            nu = upd.uid if i_upd == 0 else f"{upd.uid}.rep{i_upd}"
+            if i_upd == 0:
+                upd.device = list(dev_key)
+            else:
+                dag.add_node(
+                    TrainingDAGNode(
+                        uid=nu,
+                        node_kind="UPD",
+                        compute_subkind=None,
+                        tag=dict(upd.tag),
+                        device=list(dev_key),
+                        stream=upd.stream,
+                        node_meta=dict(upd.node_meta),
+                    )
+                )
+            created_uids.append(nu)
+            for e in dev_edges:
+                _remove_edge(dag, e)
+                dag.add_edge(
+                    TrainingDAGEdge(
+                        src_uid=e.src_uid,
+                        dst_uid=nu,
+                        dep_kind=e.dep_kind,
+                        tensor_name=e.tensor_name,
+                    )
+                )
+        # Duplicate outgoing edges of the original UPD to all replicas.
+        out_edges = [e for e in list(dag.edges) if e.dep_kind == "temporal" and e.src_uid == upd.uid]
+        if out_edges:
+            for e in out_edges:
+                for nu in created_uids[1:]:
+                    dag.add_edge(
+                        TrainingDAGEdge(
+                            src_uid=nu,
+                            dst_uid=e.dst_uid,
+                            dep_kind=e.dep_kind,
+                            tensor_name=e.tensor_name,
+                        )
+                    )
+
+
+
+def _rewire_bwd_successors_through_sync(
+    dag: TrainingDAG,
+    bwd_uid: str,
+    sync_uid: str,
+) -> None:
+    """Add update ordering through a gradient sync comm node.
+
+    BWD data successors carry activation gradients, including P2P sends to an
+    upstream pipeline stage.  Gradient synchronization is for parameter grads,
+    so it must not be inserted into those data paths.  UPD dependencies are
+    temporal; add sync -> UPD temporal edges so the
+    update waits for gradient synchronization as well as the BWD compute.
+    """
+    upd_temporal_outs = [
+        e for e in list(dag.edges)
+        if e.dep_kind == "temporal"
+        and e.src_uid == bwd_uid
+        and dag.nodes[e.dst_uid].node_kind == "UPD"
+    ]
+    for e in upd_temporal_outs:
+        dag.add_edge(
+            TrainingDAGEdge(
+                src_uid=sync_uid,
+                dst_uid=e.dst_uid,
+                dep_kind="temporal",
+                tensor_name=None,
+            )
+        )
+
+
+def _bucket_matched_fwd_nodes(
+    dag: TrainingDAG,
+    filters: list[dict[str, Any]],
+    bucket_size_mb: int,
+) -> None:
+    if bucket_size_mb <= 0:
+        raise ValueError(f"bucket_size must be > 0 MB, got {bucket_size_mb}")
+    bucket_size_bytes = int(bucket_size_mb) * 1024 * 1024
+
+    fwd_targets = [
+        uid for uid, node in dag.nodes.items()
+        if node.node_kind == "COMPUTE"
+        and node.compute_subkind == "FWD"
+        and any(_match_filter(node.tag, flt) for flt in filters)
+    ]
+
+    # Deterministic rewrite order.
+    fwd_targets.sort(key=lambda uid: (
+        int(dag.nodes[uid].node_meta.get("stage_id", 10**9)),
+        int(dag.nodes[uid].node_meta.get("segment_id", 10**9)),
+        int(dag.nodes[uid].tag.get("MB", 0) or 0),
+    ))
+
+    for fwd_uid in fwd_targets:
+        if fwd_uid not in dag.nodes:
+            continue
+        fwd_node = dag.nodes[fwd_uid]
+        bwd_candidates = sorted(
+            uid for uid, node in dag.nodes.items()
+            if node.node_kind == "COMPUTE"
+            and node.compute_subkind == "BWD"
+            and node.node_meta.get("fwd_uid") == fwd_uid
+        )
+        if len(bwd_candidates) != 1:
+            raise ValueError(
+                f"Expected exactly one fused BWD node for {fwd_uid}, "
+                f"found {bwd_candidates}"
+            )
+        bwd_uid = bwd_candidates[0]
+        bwd_node = dag.nodes[bwd_uid]
+
+        gm = fwd_node.node_meta.get("gm")
+        graphargs = fwd_node.node_meta.get("graphargs")
+        input_idxs = fwd_node.node_meta.get("input_idxs")
+        param_idxs = fwd_node.node_meta.get("param_idxs")
+        if gm is None or graphargs is None or input_idxs is None or param_idxs is None:
+            continue
+
+        def _param_bytes(gargs: list, pidxs: list[int]) -> int:
+            total = 0
+            for i in pidxs:
+                if i < 0 or i >= len(gargs):
+                    continue
+                t = gargs[i]
+                if t is not None and hasattr(t, "numel") and hasattr(t, "element_size"):
+                    total += int(t.numel()) * int(t.element_size())
+            return total
+
+        def _placeholder_names(module: fx.GraphModule) -> list[str]:
+            return [n.name for n in module.graph.nodes if n.op == "placeholder"]
+
+        def _param_names(module: fx.GraphModule, pidxs: list[int]) -> list[str]:
+            names = _placeholder_names(module)
+            return [
+                names[i] if 0 <= i < len(names) else f"<invalid-param-idx:{i}>"
+                for i in pidxs
+            ]
+
+        pre_bytes = _param_bytes(graphargs, param_idxs)
+        logger.info(
+            "bucket_stage pre-size node=%s stage=%s seg=%s bucket_size_value=%d "
+            "bucket_size_bytes_passed=%d bucket_size_mib_passed=%.6f param_bytes=%d",
+            fwd_uid,
+            fwd_node.node_meta.get("stage_id"),
+            fwd_node.node_meta.get("segment_id"),
+            int(bucket_size_mb),
+            bucket_size_bytes,
+            bucket_size_bytes / (1024 * 1024),
+            pre_bytes,
+        )
+
+        try:
+            buckets = bucket_stage(
+                gm,
+                graphargs,
+                input_idxs,
+                param_idxs,
+                bucket_size_bytes=bucket_size_bytes,
+                debug_name=fwd_uid,
+            )
+        except Exception as exc:
+            logger.warning(
+                "replicate bucketing skipped for node %s (stage=%s seg=%s): %s",
+                fwd_uid,
+                fwd_node.node_meta.get("stage_id"),
+                fwd_node.node_meta.get("segment_id"),
+                exc,
+            )
+            continue
+        if len(buckets) <= 1:
+            continue
+        original_param_names = set(_param_names(gm, param_idxs))
+        lowered_param_counts: Counter[str] = Counter()
+        for bi, (_bgm, _bin, b_param, b_args) in enumerate(buckets):
+            post_bytes = _param_bytes(b_args, b_param)
+            bucket_param_names = _param_names(_bgm, b_param)
+            lowered_param_counts.update(bucket_param_names)
+            logger.info(
+                "bucket_stage bucket-size node=%s bucket=%d/%d param_bytes=%d params=%d names=%s",
+                fwd_uid,
+                bi,
+                len(buckets),
+                post_bytes,
+                len(bucket_param_names),
+                bucket_param_names,
+            )
+        missing_param_names = sorted(original_param_names - set(lowered_param_counts))
+        unknown_param_names = sorted(set(lowered_param_counts) - original_param_names)
+        duplicate_param_names = sorted(
+            name for name, count in lowered_param_counts.items() if count != 1
+        )
+        logger.info(
+            "bucket_stage lowered-ownership node=%s original_params=%d lowered_param_refs=%d "
+            "unique_lowered_params=%d missing_params=%d duplicate_params=%d unknown_params=%d",
+            fwd_uid,
+            len(original_param_names),
+            sum(lowered_param_counts.values()),
+            len(lowered_param_counts),
+            len(missing_param_names),
+            len(duplicate_param_names),
+            len(unknown_param_names),
+        )
+        if missing_param_names or duplicate_param_names or unknown_param_names:
+            logger.warning(
+                "bucket_stage lowered-ownership-invalid node=%s missing=%s duplicates=%s unknown=%s",
+                fwd_uid,
+                missing_param_names[:16],
+                duplicate_param_names[:16],
+                unknown_param_names[:16],
+            )
+
+        # Snapshot old incident edges before rewrite.
+        in_fwd = [e for e in dag.edges if e.dst_uid == fwd_uid]
+        out_fwd = [e for e in dag.edges if e.src_uid == fwd_uid]
+        in_bwd = [e for e in dag.edges if e.dst_uid == bwd_uid]
+        out_bwd = [e for e in dag.edges if e.src_uid == bwd_uid]
+
+        orig_stage = int(fwd_node.node_meta.get("stage_id", -1))
+        orig_seg = int(fwd_node.node_meta.get("segment_id", -1))
+
+        fwd_bucket_uids: list[str] = []
+        bwd_bucket_uids: list[str] = []
+        for bi, (bgm, b_in, b_param, b_args) in enumerate(buckets):
+            f_uid = f"{fwd_uid}.bucket{bi}"
+            b_uid = f"{f_uid}.bwd"
+            fwd_bucket_uids.append(f_uid)
+            bwd_bucket_uids.append(b_uid)
+            dag.add_node(
+                TrainingDAGNode(
+                    uid=f_uid,
+                    node_kind="COMPUTE",
+                    compute_subkind="FWD",
+                    tag=_with_pass_tag(fwd_node.tag, "F"),
+                    device=list(fwd_node.device) if fwd_node.device is not None else None,
+                    stream=fwd_node.stream,
+                    node_meta={
+                        "stage_id": orig_stage,
+                        "segment_id": orig_seg * 1000 + bi,
+                        "gm": bgm,
+                        "input_idxs": list(b_in),
+                        "param_idxs": list(b_param),
+                        "graphargs": list(b_args),
+                        "input_names": [n.name for n in bgm.graph.nodes if n.op == "placeholder"],
+                        "output_names": [],
+                        "a2a_boundary_after": None,
+                        "bucket_idx": bi,
+                        "num_buckets": len(buckets),
+                        "bucket_key": f_uid,
+                    },
+                )
+            )
+            dag.add_node(
+                TrainingDAGNode(
+                    uid=b_uid,
+                    node_kind="COMPUTE",
+                    compute_subkind="BWD",
+                    tag=_with_pass_tag(fwd_node.tag, "B"),
+                    device=list(bwd_node.device) if bwd_node.device is not None else None,
+                    stream=bwd_node.stream,
+                    node_meta={
+                        "fwd_uid": f_uid,
+                        "bucket_idx": bi,
+                        "num_buckets": len(buckets),
+                        "bucket_key": f_uid,
+                        "compute_loss": bool(bwd_node.node_meta.get("compute_loss", False)) and bi == (len(buckets) - 1),
+                    },
+                )
+            )
+
+        first_fwd = fwd_bucket_uids[0]
+        last_fwd = fwd_bucket_uids[-1]
+        # Mirror backward bucket chain in reverse order.
+        first_bwd = bwd_bucket_uids[-1]
+        last_bwd = bwd_bucket_uids[0]
+
+        def _remap(uid: str) -> str:
+            if uid == fwd_uid:
+                return last_fwd
+            if uid == bwd_uid:
+                return first_bwd
+            return uid
+
+        # Remove originals and their incident edges.
+        _remove_node_and_incident_edges(dag, fwd_uid)
+        _remove_node_and_incident_edges(dag, bwd_uid)
+
+        # Re-attach incoming/outgoing edges at bucket chain boundaries.
+        for e in in_fwd:
+            dag.add_edge(TrainingDAGEdge(src_uid=_remap(e.src_uid), dst_uid=first_fwd, dep_kind=e.dep_kind, tensor_name=e.tensor_name))
+        for e in out_fwd:
+            dst = first_bwd if e.dst_uid == bwd_uid else e.dst_uid
+            dag.add_edge(TrainingDAGEdge(src_uid=last_fwd, dst_uid=dst, dep_kind=e.dep_kind, tensor_name=e.tensor_name))
+        for e in in_bwd:
+            dag.add_edge(TrainingDAGEdge(src_uid=_remap(e.src_uid), dst_uid=first_bwd, dep_kind=e.dep_kind, tensor_name=e.tensor_name))
+        for e in out_bwd:
+            dag.add_edge(TrainingDAGEdge(src_uid=last_bwd, dst_uid=_remap(e.dst_uid), dep_kind=e.dep_kind, tensor_name=e.tensor_name))
+
+        # Internal chain edges.
+        for i in range(len(fwd_bucket_uids) - 1):
+            dag.add_edge(TrainingDAGEdge(src_uid=fwd_bucket_uids[i], dst_uid=fwd_bucket_uids[i + 1], dep_kind="data", tensor_name=None))
+        for i in range(len(bwd_bucket_uids) - 1, 0, -1):
+            dag.add_edge(TrainingDAGEdge(src_uid=bwd_bucket_uids[i], dst_uid=bwd_bucket_uids[i - 1], dep_kind="data", tensor_name=None))
+
+
+def _insert_reduce_comm_nodes(
+    dag: TrainingDAG,
+    filters: list[dict[str, Any]],
+    devices: list[int],
+    comm_stream: str | None = None,
+) -> None:
+    reduce_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "REDUCE_COMM")
+    expected = sorted(int(d) for d in devices)
+    for node in list(dag.nodes.values()):
+        if node.node_kind != "COMPUTE" or not _is_backward_weight_subkind(node.compute_subkind):
+            continue
+        if not any(_match_filter(node.tag, flt) for flt in filters):
+            continue
+        if node.device is None:
+            raise ValueError(
+                f"replicate requires placed backward weight-gradient nodes, but node {node.uid} has device=None; "
+                f"expected devices={devices}"
+            )
+        got = sorted(int(d) for d in node.device)
+        if got != expected:
+            raise ValueError(
+                f"replicate device mismatch for node {node.uid}: "
+                f"expected devices={devices}, node_devices={node.device}"
+            )
+        reduce_uid = f"reduce.{reduce_idx}"
+        reduce_idx += 1
+        reduce_node = TrainingDAGNode(
+            uid=reduce_uid,
+            node_kind="REDUCE_COMM",
+            compute_subkind=None,
+            tag=dict(node.tag),
+            device=list(node.device),
+            stream=comm_stream if comm_stream is not None else "default_stream",
+            node_meta={
+                "bwd_uid": node.uid,
+                "bucket_key": node.node_meta.get("bucket_key"),
+            },
+        )
+        dag.add_node(reduce_node)
+        dag.add_edge(
+            TrainingDAGEdge(
+                src_uid=node.uid,
+                dst_uid=reduce_uid,
+                dep_kind="data",
+                tensor_name=None,
+            )
+        )
+        _rewire_bwd_successors_through_sync(dag, node.uid, reduce_uid)
+
+
+def _node_has_trainable_params(dag: TrainingDAG, node: TrainingDAGNode) -> bool:
+    meta = node.node_meta
+    if _is_backward_compute_subkind(node.compute_subkind):
+        fwd_uid = meta.get("fwd_uid")
+        if isinstance(fwd_uid, str) and fwd_uid in dag.nodes:
+            meta = dag.nodes[fwd_uid].node_meta
+    graphargs = meta.get("graphargs")
+    param_idxs = meta.get("param_idxs")
+    if graphargs is None or param_idxs is None:
+        return True
+    return any(
+        0 <= int(i) < len(graphargs)
+        and graphargs[int(i)] is not None
+        and bool(getattr(graphargs[int(i)], "requires_grad", False))
+        for i in param_idxs
+    )
+
+
+def _insert_all_gather_comm_nodes(
+    dag: TrainingDAG,
+    filters: list[dict[str, Any]],
+    devices: list[int],
+    comm_stream: str | None = None,
+) -> None:
+    ag_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "ALL_GATHER_COMM")
+    expected = sorted(int(d) for d in devices)
+    for node in list(dag.nodes.values()):
+        if (
+            node.node_kind != "COMPUTE"
+            or node.compute_subkind not in ("FWD", "BWD", "BWD_I", "BWD_W")
+        ):
+            continue
+        if not any(_match_filter(node.tag, flt) for flt in filters):
+            continue
+        if not _node_has_trainable_params(dag, node):
+            continue
+        if node.device is None:
+            raise ValueError(
+                f"replicate(shard_params=True) requires placed nodes, but node {node.uid} has device=None; "
+                f"expected devices={devices}"
+            )
+        got = sorted(int(d) for d in node.device)
+        if got != expected:
+            raise ValueError(
+                f"replicate device mismatch for node {node.uid}: "
+                f"expected devices={devices}, node_devices={node.device}"
+            )
+        ag_uid = f"all_gather.{ag_idx}"
+        ag_idx += 1
+        ag_tag = dict(node.tag)
+        ag_node = TrainingDAGNode(
+            uid=ag_uid,
+            node_kind="ALL_GATHER_COMM",
+            compute_subkind=None,
+            tag=ag_tag,
+            device=list(node.device),
+            stream=comm_stream if comm_stream is not None else "default_stream",
+            node_meta={
+                "compute_uid": node.uid,
+                "bucket_key": node.node_meta.get("bucket_key"),
+            },
+        )
+        dag.add_node(ag_node)
+        node.node_meta["zero_free_full_params_after"] = True
+        if node.compute_subkind == "BWD_W":
+            for pred_uid in sorted(dag.preds.get(node.uid, set())):
+                pred = dag.nodes[pred_uid]
+                if pred.node_kind == "COMPUTE" and pred.compute_subkind == "BWD_I":
+                    dag.add_edge(
+                        TrainingDAGEdge(
+                            src_uid=pred.uid,
+                            dst_uid=ag_uid,
+                            dep_kind="data",
+                            tensor_name=None,
+                        )
+                    )
+        # ALL_GATHER is a pre-dependency of compute: AG -> COMPUTE
+        dag.add_edge(
+            TrainingDAGEdge(
+                src_uid=ag_uid,
+                dst_uid=node.uid,
+                dep_kind="data",
+                tensor_name=None,
+            )
+        )
+
+
+def _insert_reduce_scatter_comm_nodes(
+    dag: TrainingDAG,
+    filters: list[dict[str, Any]],
+    devices: list[int],
+    comm_stream: str | None = None,
+) -> None:
+    rs_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "REDUCE_SCATTER_COMM")
+    expected = sorted(int(d) for d in devices)
+    for node in list(dag.nodes.values()):
+        if node.node_kind != "COMPUTE" or not _is_backward_weight_subkind(node.compute_subkind):
+            continue
+        if not any(_match_filter(node.tag, flt) for flt in filters):
+            continue
+        if not _node_has_trainable_params(dag, node):
+            continue
+        if node.device is None:
+            raise ValueError(
+                f"replicate(shard_grads/shard_params) requires placed backward weight-gradient nodes, but node {node.uid} has device=None; "
+                f"expected devices={devices}"
+            )
+        got = sorted(int(d) for d in node.device)
+        if got != expected:
+            raise ValueError(
+                f"replicate device mismatch for node {node.uid}: "
+                f"expected devices={devices}, node_devices={node.device}"
+            )
+        rs_uid = f"reduce_scatter.{rs_idx}"
+        rs_idx += 1
+        rs_node = TrainingDAGNode(
+            uid=rs_uid,
+            node_kind="REDUCE_SCATTER_COMM",
+            compute_subkind=None,
+            tag=dict(node.tag),
+            device=list(node.device),
+            stream=comm_stream if comm_stream is not None else "default_stream",
+            node_meta={
+                "bwd_uid": node.uid,
+                "bucket_key": node.node_meta.get("bucket_key"),
+            },
+        )
+        node.node_meta["zero_alloc_full_grads_before"] = True
+        if node.node_meta.pop("zero_free_full_params_after", None) is not None:
+            rs_node.node_meta["zero_free_full_params_after"] = True
+        dag.add_node(rs_node)
+        dag.add_edge(
+            TrainingDAGEdge(
+                src_uid=node.uid,
+                dst_uid=rs_uid,
+                dep_kind="data",
+                tensor_name=None,
+            )
+        )
+        _rewire_bwd_successors_through_sync(dag, node.uid, rs_uid)
+
+
+def _insert_shard_a2a_comm_nodes(
+    dag: TrainingDAG,
+    filters: list[dict[str, Any]],
+    devices: list[int],
+    comm_stream: str | None = None,
+) -> None:
+    expected = sorted(int(d) for d in devices)
+    a2a_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "A2A_COMM")
+
+    matched_uids = [
+        uid for uid, node in dag.nodes.items()
+        if node.node_kind == "COMPUTE" and any(_match_filter(node.tag, flt) for flt in filters)
+    ]
+
+    def _a2a_boundary_for_edge(src_node: TrainingDAGNode, dst_node: TrainingDAGNode) -> dict[str, Any]:
+        # Backward data edges are reversed from the forward graph:
+        #     FWD: B -> A    becomes    BWD: A.bwd -> B.bwd
+        # The A2A tensor position is defined by the original forward producer,
+        # which is the fwd node paired with the backward edge destination.
+        if (
+            _is_backward_activation_subkind(src_node.compute_subkind)
+            and _is_backward_activation_subkind(dst_node.compute_subkind)
+        ):
+            fwd_producer_uid = dst_node.node_meta.get("fwd_uid")
+            if not isinstance(fwd_producer_uid, str) or fwd_producer_uid not in dag.nodes:
+                raise ValueError(
+                    f"A2A_COMM could not resolve forward producer for BWD edge "
+                    f"{src_node.uid}->{dst_node.uid}: fwd_uid={fwd_producer_uid!r}"
+                )
+            producer = dag.nodes[fwd_producer_uid]
+        else:
+            producer = src_node
+
+        binfo = producer.node_meta.get("a2a_boundary_after")
+        if not isinstance(binfo, dict) or binfo.get("tensor_idx") is None:
+            raise ValueError(
+                f"A2A_COMM missing tensor_idx for edge {src_node.uid}->{dst_node.uid}; "
+                f"producer={producer.uid} boundary_info={binfo!r}"
+            )
+        return binfo
+
+    for uid in matched_uids:
+        if uid not in dag.nodes:
+            continue
+        node = dag.nodes[uid]
+        if node.device is None or sorted(int(d) for d in node.device) != expected:
+            raise ValueError(
+                f"shard requires matched node {uid} to have devices={devices}, got node_devices={node.device}"
+            )
+
+        if node.compute_subkind == "FWD":
+            node.node_meta["apply_zero"] = False
+        elif _is_backward_compute_subkind(node.compute_subkind):
+            fwd_uid = node.node_meta.get("fwd_uid")
+            if isinstance(fwd_uid, str) and fwd_uid in dag.nodes:
+                dag.nodes[fwd_uid].node_meta["apply_zero"] = False
+
+        # Shard replaces replicate-style grad/param sync comms around matched
+        # compute nodes; remove existing gather/reduce comm nodes attached to
+        # this compute node before inserting A2A edges.
+        removable_sync_kinds = {"ALL_GATHER_COMM", "REDUCE_COMM", "REDUCE_SCATTER_COMM"}
+        removable_comm_uids: set[str] = set()
+        for e in list(dag.edges):
+            if e.dep_kind != "data":
+                continue
+            if e.src_uid == uid and e.dst_uid in dag.nodes:
+                other = dag.nodes[e.dst_uid]
+                if other.node_kind in removable_sync_kinds:
+                    removable_comm_uids.add(other.uid)
+            elif e.dst_uid == uid and e.src_uid in dag.nodes:
+                other = dag.nodes[e.src_uid]
+                if other.node_kind in removable_sync_kinds:
+                    removable_comm_uids.add(other.uid)
+        for comm_uid in sorted(removable_comm_uids):
+            if comm_uid not in dag.nodes:
+                continue
+            # Bypass removed sync-comm nodes so dataflow remains connected:
+            # pred -> COMM -> succ  ==>  pred -> succ
+            in_comm = [e for e in list(dag.edges) if e.dep_kind == "data" and e.dst_uid == comm_uid]
+            out_comm = [e for e in list(dag.edges) if e.dep_kind == "data" and e.src_uid == comm_uid]
+            for ie in in_comm:
+                for oe in out_comm:
+                    dag.add_edge(
+                        TrainingDAGEdge(
+                            src_uid=ie.src_uid,
+                            dst_uid=oe.dst_uid,
+                            dep_kind="data",
+                            tensor_name=(oe.tensor_name if oe.tensor_name is not None else ie.tensor_name),
+                        )
+                    )
+            _remove_node_and_incident_edges(dag, comm_uid)
+        if removable_comm_uids:
+            node.node_meta.pop("zero_free_full_params_after", None)
+        if node.compute_subkind == "BWD_W":
+            continue
+
+        def _is_a2a_compute_edge(e: TrainingDAGEdge) -> bool:
+            src = dag.nodes[e.src_uid]
+            dst = dag.nodes[e.dst_uid]
+            if src.node_kind != "COMPUTE" or dst.node_kind != "COMPUTE":
+                return False
+            if node.compute_subkind == "FWD":
+                return src.compute_subkind == "FWD" and dst.compute_subkind == "FWD"
+            if _is_backward_activation_subkind(node.compute_subkind):
+                return (
+                    _is_backward_activation_subkind(src.compute_subkind)
+                    and _is_backward_activation_subkind(dst.compute_subkind)
+                )
+            return False
+
+        incoming = [
+            e for e in list(dag.edges)
+            if e.dep_kind == "data"
+            and e.dst_uid == uid
+            and _is_a2a_compute_edge(e)
+        ]
+        outgoing = [
+            e for e in list(dag.edges)
+            if e.dep_kind == "data"
+            and e.src_uid == uid
+            and _is_a2a_compute_edge(e)
+        ]
+
+        for e in incoming:
+            src = dag.nodes[e.src_uid]
+            if src.device is None or sorted(int(d) for d in src.device) != expected:
+                raise ValueError(
+                    f"shard precondition failed for incoming edge {e.src_uid}->{uid}: "
+                    f"upstream compute must be replicated on devices={devices}, got {src.device}"
+                )
+            if src.tag.get("PASS") != node.tag.get("PASS"):
+                raise ValueError(
+                    f"A2A_COMM requires matching compute pass tags; "
+                    f"incoming edge {e.src_uid}->{uid} has src.PASS={src.tag.get('PASS')} "
+                    f"dst.PASS={node.tag.get('PASS')}"
+                )
+        for e in outgoing:
+            dst = dag.nodes[e.dst_uid]
+            if dst.device is None or sorted(int(d) for d in dst.device) != expected:
+                raise ValueError(
+                    f"shard precondition failed for outgoing edge {uid}->{e.dst_uid}: "
+                    f"downstream compute must be replicated on devices={devices}, got {dst.device}"
+                )
+            if dst.tag.get("PASS") != node.tag.get("PASS"):
+                raise ValueError(
+                    f"A2A_COMM requires matching compute pass tags; "
+                    f"outgoing edge {uid}->{e.dst_uid} has src.PASS={node.tag.get('PASS')} "
+                    f"dst.PASS={dst.tag.get('PASS')}"
+                )
+
+        for e in incoming:
+            src_node = dag.nodes[e.src_uid]
+            binfo = _a2a_boundary_for_edge(src_node, node)
+            a2a_tensor_idx = binfo["tensor_idx"]
+            comm_uid = f"a2a.{a2a_idx}"
+            a2a_idx += 1
+            comm_node = TrainingDAGNode(
+                uid=comm_uid,
+                node_kind="A2A_COMM",
+                compute_subkind=None,
+                tag=dict(node.tag),
+                device=list(node.device) if node.device is not None else None,
+                stream=comm_stream if comm_stream is not None else "default_stream",
+                node_meta={
+                    "direction": "incoming",
+                    "target_uid": uid,
+                    "a2a_tensor_idx": a2a_tensor_idx,
+                    "bucket_key": node.node_meta.get("bucket_key", src_node.node_meta.get("bucket_key")),
+                },
+            )
+            dag.add_node(comm_node)
+            _remove_edge(dag, e)
+            dag.add_edge(TrainingDAGEdge(src_uid=e.src_uid, dst_uid=comm_uid, dep_kind="data", tensor_name=e.tensor_name))
+            dag.add_edge(TrainingDAGEdge(src_uid=comm_uid, dst_uid=uid, dep_kind="data", tensor_name=e.tensor_name))
+
+        for e in outgoing:
+            dst_node = dag.nodes[e.dst_uid]
+            binfo = _a2a_boundary_for_edge(node, dst_node)
+            a2a_tensor_idx = binfo["tensor_idx"]
+            comm_uid = f"a2a.{a2a_idx}"
+            a2a_idx += 1
+            comm_node = TrainingDAGNode(
+                uid=comm_uid,
+                node_kind="A2A_COMM",
+                compute_subkind=None,
+                tag=dict(node.tag),
+                device=list(node.device) if node.device is not None else None,
+                stream=comm_stream if comm_stream is not None else "default_stream",
+                node_meta={
+                    "direction": "outgoing",
+                    "source_uid": uid,
+                    "a2a_tensor_idx": a2a_tensor_idx,
+                    "bucket_key": node.node_meta.get("bucket_key", dst_node.node_meta.get("bucket_key")),
+                },
+            )
+            dag.add_node(comm_node)
+            _remove_edge(dag, e)
+            dag.add_edge(TrainingDAGEdge(src_uid=uid, dst_uid=comm_uid, dep_kind="data", tensor_name=e.tensor_name))
+            dag.add_edge(TrainingDAGEdge(src_uid=comm_uid, dst_uid=e.dst_uid, dep_kind="data", tensor_name=e.tensor_name))
+
+
+def _apply_split_directive(
+    dag: TrainingDAG,
+    flt: dict[str, Any],
+    dim_name: str,
+    num_microbatches: int,
+) -> None:
+    matched = {uid for uid, n in dag.nodes.items() if _match_filter(n.tag, flt)}
+    if not matched:
+        raise ValueError(f"split(filter={flt}) matched zero nodes")
+
+    # UPD nodes are global reduction/update sinks and must not be duplicated
+    # by microbatch split; duplicated predecessors should continue to target
+    # the same UPD node(s).
+    matched = {uid for uid in matched if dag.nodes[uid].node_kind != "UPD"}
+    if not matched:
+        raise ValueError(f"split(filter={flt}) matched no splittable non-UPD nodes")
+
+    topo = _topological_order(dag)
+    idx = {u: i for i, u in enumerate(topo)}
+    mpos = sorted(idx[u] for u in matched)
+    lo, hi = mpos[0], mpos[-1]
+    interleaved = [
+        u for u in topo[lo:hi + 1]
+        if u not in matched and dag.nodes[u].node_kind != "UPD"
+    ]
+    if interleaved:
+        raise ValueError(
+            f"split requires a contiguous sub-DAG; found interleaved non-matching nodes: {interleaved[:8]}"
+        )
+
+    incoming_boundary = [e for e in list(dag.edges) if e.dst_uid in matched and e.src_uid not in matched]
+    outgoing_boundary = [e for e in list(dag.edges) if e.src_uid in matched and e.dst_uid not in matched]
+
+    sources = {
+        u for u in matched
+        if not any(pred in matched for pred in dag.preds.get(u, set()))
+    }
+    sinks = {
+        u for u in matched
+        if not any(succ in matched for succ in dag.succs.get(u, set()))
+    }
+
+    # Ensure outside edges only touch source/sink boundaries.
+    for e in incoming_boundary:
+        if dag.nodes[e.src_uid].node_kind == "UPD" or dag.nodes[e.dst_uid].node_kind == "UPD":
+            continue
+        if e.dst_uid not in sources:
+            raise ValueError(
+                f"split boundary violation: incoming outside edge targets non-source node {e.dst_uid}"
+            )
+    for e in outgoing_boundary:
+        if dag.nodes[e.src_uid].node_kind == "UPD" or dag.nodes[e.dst_uid].node_kind == "UPD":
+            continue
+        if e.src_uid not in sinks:
+            raise ValueError(
+                f"split boundary violation: outgoing outside edge starts at non-sink node {e.src_uid}"
+            )
+
+    inside_edges = [e for e in list(dag.edges) if e.src_uid in matched and e.dst_uid in matched]
+
+    # Tag originals as microbatch 0.
+    for uid in matched:
+        dag.nodes[uid].tag[dim_name] = 0
+
+    # Create copies for microbatches 1..N-1.
+    copy_uid: dict[tuple[str, int], str] = {}
+    for mb in range(1, num_microbatches):
+        for uid in topo:
+            if uid not in matched:
+                continue
+            nu = f"{uid}.split{dim_name}{mb}"
+            copy_uid[(uid, mb)] = nu
+
+    uid_meta_fields = (
+        "fwd_uid",
+        "compute_uid",
+        "bwd_uid",
+        "source_uid",
+        "target_uid",
+        "src_uid",
+        "dst_uid",
+        "from_uid",
+        "to_uid",
+    )
+    for mb in range(1, num_microbatches):
+        for uid in topo:
+            if uid not in matched:
+                continue
+            n = dag.nodes[uid]
+            nu = copy_uid[(uid, mb)]
+            copied_meta = dict(n.node_meta)
+            for k in uid_meta_fields:
+                v = copied_meta.get(k)
+                if isinstance(v, str) and v in matched:
+                    copied_meta[k] = copy_uid[(v, mb)]
+            dag.add_node(
+                TrainingDAGNode(
+                    uid=nu,
+                    node_kind=n.node_kind,
+                    compute_subkind=n.compute_subkind,
+                    tag={**n.tag, dim_name: mb},
+                    device=list(n.device) if n.device is not None else None,
+                    stream=n.stream,
+                    node_meta=copied_meta,
+                )
+            )
+
+    # Duplicate internal sub-DAG edges for each copied microbatch.
+    for mb in range(1, num_microbatches):
+        for e in inside_edges:
+            dag.add_edge(
+                TrainingDAGEdge(
+                    src_uid=copy_uid[(e.src_uid, mb)],
+                    dst_uid=copy_uid[(e.dst_uid, mb)],
+                    dep_kind=e.dep_kind,
+                    tensor_name=e.tensor_name,
+                )
+            )
+
+    # Duplicate incoming edges onto copied sources.
+    for mb in range(1, num_microbatches):
+        for e in incoming_boundary:
+            dag.add_edge(
+                TrainingDAGEdge(
+                    src_uid=e.src_uid,
+                    dst_uid=copy_uid[(e.dst_uid, mb)],
+                    dep_kind=e.dep_kind,
+                    tensor_name=e.tensor_name,
+                )
+            )
+
+    # Duplicate outgoing edges from copied sinks.
+    for mb in range(1, num_microbatches):
+        for e in outgoing_boundary:
+            dag.add_edge(
+                TrainingDAGEdge(
+                    src_uid=copy_uid[(e.src_uid, mb)],
+                    dst_uid=e.dst_uid,
+                    dep_kind=e.dep_kind,
+                    tensor_name=e.tensor_name,
+                )
+            )
+
+
+def _match_set_contiguous_subdag(
+    dag: TrainingDAG,
+    matched: set[str],
+    *,
+    context: str,
+) -> tuple[set[str], set[str], set[str], set[str]]:
+    if not matched:
+        raise ValueError(f"{context}: filter matched zero nodes")
+    # Contiguity should be dependency-based, not tied to one arbitrary
+    # topological ordering (which can interleave independent branches).
+    # A non-matching node is considered interleaving iff it is on some path
+    # between matched nodes: matched -> ... -> node -> ... -> matched.
+    interleaved: list[str] = []
+    for u in dag.nodes.keys():
+        if u in matched:
+            continue
+        # Has matched ancestor?
+        has_matched_ancestor = False
+        seen = {u}
+        stack = list(dag.preds.get(u, set()))
+        while stack:
+            cur = stack.pop()
+            if cur in seen:
+                continue
+            seen.add(cur)
+            if cur in matched:
+                has_matched_ancestor = True
+                break
+            stack.extend(dag.preds.get(cur, set()))
+        if not has_matched_ancestor:
+            continue
+
+        # Has matched descendant?
+        has_matched_descendant = False
+        seen = {u}
+        stack = list(dag.succs.get(u, set()))
+        while stack:
+            cur = stack.pop()
+            if cur in seen:
+                continue
+            seen.add(cur)
+            if cur in matched:
+                has_matched_descendant = True
+                break
+            stack.extend(dag.succs.get(cur, set()))
+        if has_matched_descendant:
+            interleaved.append(u)
+
+    if interleaved:
+        raise ValueError(
+            f"{context}: matched nodes are not contiguous; interleaved non-matching nodes: {interleaved[:8]}"
+        )
+
+    sources = {
+        u for u in matched
+        if not any(pred in matched for pred in dag.preds.get(u, set()))
+    }
+    sinks = {
+        u for u in matched
+        if not any(succ in matched for succ in dag.succs.get(u, set()))
+    }
+    if not sources or not sinks:
+        raise ValueError(f"{context}: failed to identify non-empty source/sink sets")
+    compute_nodes = {u for u in matched if dag.nodes[u].node_kind == "COMPUTE"}
+
+    def _has_upstream_compute(u: str) -> bool:
+        seen = {u}
+        stack = list(dag.preds.get(u, set()))
+        while stack:
+            cur = stack.pop()
+            if cur in seen or cur not in matched:
+                continue
+            seen.add(cur)
+            if dag.nodes[cur].node_kind == "COMPUTE":
+                return True
+            stack.extend(dag.preds.get(cur, set()))
+        return False
+
+    def _has_downstream_compute(u: str) -> bool:
+        seen = {u}
+        stack = list(dag.succs.get(u, set()))
+        while stack:
+            cur = stack.pop()
+            if cur in seen or cur not in matched:
+                continue
+            seen.add(cur)
+            if dag.nodes[cur].node_kind == "COMPUTE":
+                return True
+            stack.extend(dag.succs.get(cur, set()))
+        return False
+
+    compute_sources = {u for u in compute_nodes if not _has_upstream_compute(u)}
+    compute_sinks = {u for u in compute_nodes if not _has_downstream_compute(u)}
+    if not compute_sources or not compute_sinks:
+        raise ValueError(f"{context}: failed to identify non-empty compute source/sink sets")
+
+    return sources, sinks, compute_sources, compute_sinks
+
+
+@dataclass(frozen=True)
+class _OrderSegment:
+    source_uids: set[str]
+    sink_uids: set[str]
+
+
+def _single_device_for_nodes(dag: TrainingDAG, uids: set[str]) -> list[int] | None:
+    device_keys = {
+        tuple(n.device)
+        for uid in uids
+        for n in [dag.nodes[uid]]
+        if n.device is not None
+    }
+    if len(device_keys) == 1:
+        return list(next(iter(device_keys)))
+    return None
+
+
+def _add_order_dummy_node(
+    dag: TrainingDAG,
+    *,
+    uid: str,
+    directive_idx: int,
+    group_idx: int,
+    role: str,
+    grouped_uids: set[str],
+) -> str:
+    dag.add_node(
+        TrainingDAGNode(
+            uid=uid,
+            node_kind="ORDER_DUMMY",
+            compute_subkind=None,
+            tag={"order": directive_idx, "group": group_idx},
+            device=_single_device_for_nodes(dag, grouped_uids),
+            stream=_DEFAULT_STREAM,
+            node_meta={"role": role},
+        )
+    )
+    return uid
+
+
+def _add_temporal_order_edge(dag: TrainingDAG, src_uid: str, dst_uid: str) -> None:
+    logger.debug("Adding temporal dependency edge for order directive: %s -> %s", src_uid, dst_uid)
+    dag.add_edge(
+        TrainingDAGEdge(
+            src_uid=src_uid,
+            dst_uid=dst_uid,
+            dep_kind="temporal",
+            tensor_name=None,
+        )
+    )
+
+
+def _apply_order_directive(
+    dag: TrainingDAG,
+    filter_groups: list[list[dict[str, Any]]],
+    *,
+    directive_idx: int,
+) -> None:
+    segments: list[_OrderSegment] = []
+
+    for group_idx, group in enumerate(filter_groups):
+        group_sources: list[set[str]] = []
+        group_sinks: list[set[str]] = []
+        grouped_uids: set[str] = set()
+        for filter_idx, flt in enumerate(group):
+            matched = {
+                uid for uid, n in dag.nodes.items()
+                if n.node_kind != "ORDER_DUMMY" and _match_filter(n.tag, flt)
+            }
+            _srcs, _snks, csrcs, csnks = _match_set_contiguous_subdag(
+                dag,
+                matched,
+                context=f"order filter[{group_idx}][{filter_idx}]={flt}",
+            )
+            group_sources.append(csrcs)
+            group_sinks.append(csnks)
+            grouped_uids.update(matched)
+
+        if len(group) == 1:
+            segments.append(_OrderSegment(source_uids=group_sources[0], sink_uids=group_sinks[0]))
+            continue
+
+        src_uid = _add_order_dummy_node(
+            dag,
+            uid=f"order.{directive_idx}.group{group_idx}.source",
+            directive_idx=directive_idx,
+            group_idx=group_idx,
+            role="source",
+            grouped_uids=grouped_uids,
+        )
+        sink_uid = _add_order_dummy_node(
+            dag,
+            uid=f"order.{directive_idx}.group{group_idx}.sink",
+            directive_idx=directive_idx,
+            group_idx=group_idx,
+            role="sink",
+            grouped_uids=grouped_uids,
+        )
+        for csrcs in group_sources:
+            for v in csrcs:
+                _add_temporal_order_edge(dag, src_uid, v)
+        for csnks in group_sinks:
+            for u in csnks:
+                _add_temporal_order_edge(dag, u, sink_uid)
+        segments.append(_OrderSegment(source_uids={src_uid}, sink_uids={sink_uid}))
+
+    for i in range(len(segments) - 1):
+        for u in segments[i].sink_uids:
+            for v in segments[i + 1].source_uids:
+                _add_temporal_order_edge(dag, u, v)
+
+
+def apply_schedule_directives(training_dag: TrainingDAG, directives: list[Any] | None) -> None:
+    if not directives:
+        return
+    split_backward_keys = _validate_split_backward_order_stencil(directives)
+
+    # Microbatch expansion must run before split-backward lowering so schedules
+    # can mix fused B and split BI/BW for the same bucket on different MBs.
+    for i, raw in enumerate(directives):
+        if not isinstance(raw, dict) or raw.get("op") != "split":
+            continue
+        flt, dim_name, num_microbatches = _normalize_split_directive(raw)
+        logger.info(
+            "Applying directive[%d]: split(filter=%s, dim_name=%s, num_microbatches=%d)",
+            i, flt, dim_name, num_microbatches
+        )
+        _apply_split_directive(training_dag, flt, dim_name, num_microbatches)
+
+    _apply_split_backward_stencil(training_dag, split_backward_keys)
+    place_streams: set[str] = set()
+    for i, raw in enumerate(directives):
+        if isinstance(raw, dict) and raw.get("op") != "place":
+            continue
+        op, filters, devices, stream, _gather_stream, _reduce_stream, shard_params, shard_grads, bucket_size = _normalize_filter_devices_directive(raw)
+        logger.info(
+            "Applying directive[%d]: %s(filters=%s, devices=%s, stream=%s, shard_params=%s, shard_grads=%s, bucket_size=%s)",
+            i, op, filters, devices, stream, shard_params, shard_grads, bucket_size
+        )
+        matched_nodes = 0
+        for node in training_dag.nodes.values():
+            if node.node_kind not in ("COMPUTE", "UPD"):
+                continue
+            if any(_match_filter(node.tag, flt) for flt in filters):
+                node.device = list(devices)
+                matched_nodes += 1
+        if matched_nodes == 0:
+            raise ValueError(f"place directive[{i}] matched zero nodes: {raw}")
+        if stream is not None:
+            place_streams.add(stream)
+
+    if place_streams:
+        if len(place_streams) != 1:
+            raise ValueError(f"place directives must agree on stream, got {sorted(place_streams)}")
+        place_stream = next(iter(place_streams))
+    else:
+        place_stream = None
+    _replicate_update_nodes_by_device(training_dag)
+    _insert_send_recv_comm_nodes(training_dag, comm_stream=place_stream)
+
+    for i, raw in enumerate(directives):
+        if isinstance(raw, dict) and raw.get("op") in ("place", "split", "order"):
+            continue
+        op, filters, devices, stream, gather_stream, reduce_stream, shard_params, shard_grads, bucket_size = _normalize_filter_devices_directive(raw)
+        logger.info(
+            "Applying directive[%d]: %s(filters=%s, devices=%s, stream=%s, gather_stream=%s, reduce_stream=%s, shard_params=%s, shard_grads=%s, bucket_size=%s)",
+            i, op, filters, devices, stream, gather_stream, reduce_stream, shard_params, shard_grads, bucket_size
+        )
+        matched_nodes = [
+            uid for uid, node in training_dag.nodes.items()
+            if node.node_kind == "COMPUTE"
+            and any(_match_filter(node.tag, flt) for flt in filters)
+        ]
+        if not matched_nodes:
+            raise ValueError(f"{op} directive[{i}] matched zero compute nodes: {raw}")
+        if op == "replicate":
+            if bucket_size is not None:
+                _bucket_matched_fwd_nodes(training_dag, filters, int(bucket_size))
+            if shard_params:
+                _insert_all_gather_comm_nodes(training_dag, filters, devices, comm_stream=gather_stream)
+                _insert_reduce_scatter_comm_nodes(training_dag, filters, devices, comm_stream=reduce_stream)
+            elif shard_grads:
+                _insert_reduce_scatter_comm_nodes(training_dag, filters, devices, comm_stream=reduce_stream)
+            else:
+                _insert_reduce_comm_nodes(training_dag, filters, devices, comm_stream=reduce_stream)
+        elif op == "shard":
+            _insert_shard_a2a_comm_nodes(training_dag, filters, devices, comm_stream=stream)
+        else:
+            raise ValueError(f"Unsupported directive op after normalization: {op}")
+
+    # Final pass for order directives (add temporal dependencies across sub-DAGs).
+    for i, raw in enumerate(directives):
+        if not isinstance(raw, dict) or raw.get("op") != "order":
+            continue
+        filter_groups = _parse_order_directive(raw)
+        logger.info("Applying directive[%d]: order(filters=%s)", i, filter_groups)
+        _apply_order_directive(training_dag, filter_groups, directive_idx=i)
diff --git a/src/executors.py b/src/executors.py
new file mode 100644
index 0000000..c0383f1
--- /dev/null
+++ b/src/executors.py
@@ -0,0 +1,959 @@
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from typing import Any
+
+import torch
+import torch.distributed as dist
+from torch.autograd.graph import GradientEdge, Node
+from torch.nn import Parameter
+
+from .backward import construct_reverse_graph, get_param_groups, _get_grad_fn_or_grad_acc
+from .runtime import BufferStore, EventStore, ParamStorage, RuntimeState, StageStore
+from .tasks import TaskType
+
+
+@dataclass
+class CommunicationExecutor:
+    """Actor-local communication operations used by the DAG dispatcher."""
+
+    runtime: RuntimeState
+    stages: StageStore
+    logger: Any
+
+    def send(self, send_data: Any, peer_pp_rank: int, stream: torch.cuda.Stream) -> None:
+        global_dst_rank = self.runtime.pipeline_peer_global_rank(peer_pp_rank)
+
+        with torch.cuda.stream(stream):
+            tensors = send_data if isinstance(send_data, (list, tuple)) else [send_data]
+            use_lo_hi = global_dst_rank > self.runtime.global_rank
+            pp_group = self.runtime.pp_lo_hi if use_lo_hi else self.runtime.pp_hi_lo
+            for tensor in tensors:
+                dist.send(tensor, dst=global_dst_rank, group=pp_group)
+
+    def recv_fwd(self, recv_ubid: Any, peer_pp_rank: int, stream: torch.cuda.Stream) -> list:
+        global_src_rank = self.runtime.pipeline_peer_global_rank(peer_pp_rank)
+
+        buf = [
+            torch.empty(
+                shape,
+                dtype=dtype,
+                requires_grad=requires_grad,
+                device=self.runtime.device,
+            )
+            for shape, dtype, requires_grad in self.stages.bucket(recv_ubid).forward_input_meta
+        ]
+        with torch.cuda.stream(stream):
+            use_hi_lo = global_src_rank > self.runtime.global_rank
+            pp_group = self.runtime.pp_hi_lo if use_hi_lo else self.runtime.pp_lo_hi
+            for tensor in buf:
+                dist.recv(tensor, src=global_src_rank, group=pp_group)
+        return buf
+
+    def recv_bwd(self, shape_meta: list, peer_pp_rank: int, stream: torch.cuda.Stream) -> list:
+        global_src_rank = self.runtime.pipeline_peer_global_rank(peer_pp_rank)
+
+        buf = [
+            torch.empty(shape, dtype=dtype, device=self.runtime.device)
+            for shape, dtype in shape_meta
+        ]
+        with torch.cuda.stream(stream):
+            use_hi_lo = global_src_rank > self.runtime.global_rank
+            pp_group = self.runtime.pp_hi_lo if use_hi_lo else self.runtime.pp_lo_hi
+            for tensor in buf:
+                dist.recv(tensor, src=global_src_rank, group=pp_group)
+        return buf
+
+    def all_to_all(self, input_tensor: torch.Tensor, stream: torch.cuda.Stream) -> torch.Tensor:
+        output_buf = torch.empty_like(input_tensor, device=self.runtime.device)
+        with torch.cuda.stream(stream):
+            dist.all_to_all_single(output_buf, input_tensor, group=self.runtime.ep_group)
+        return output_buf
+
+    def all_reduce_grads(self, ubid: Any, stream: torch.cuda.Stream) -> int:
+        assert ubid is not None, "all_reduce_grads requires a non-None ubid"
+        if not self.has_trainable_params_for_collective(ubid, "all_reduce_grads"):
+            return 0
+        bucket = self.stages.bucket(ubid)
+        grad_tensors = []
+        for idx in bucket.trainable_param_idxs:
+            param = bucket.forward_args[idx]
+            assert param is not None, (
+                f"all_reduce_grads: ubid={ubid} idx={idx} param is None"
+            )
+            assert param.grad is not None, (
+                f"all_reduce_grads: ubid={ubid} idx={idx} param.grad is None"
+            )
+            grad_tensors.append(param.grad)
+
+        total_bytes = sum(grad.numel() * grad.element_size() for grad in grad_tensors)
+        with torch.cuda.stream(stream):
+            for grad in grad_tensors:
+                dist.all_reduce(grad, group=self.runtime.dp_group)
+        return total_bytes
+
+    def reduce_scatter(self, ubid: Any, stream: torch.cuda.Stream) -> int:
+        assert ubid is not None, "reduce_scatter requires a non-None ubid"
+        if not self.has_trainable_params_for_collective(ubid, "reduce_scatter"):
+            return 0
+        assert ubid in self.stages.grad_sharded_ubids, (
+            f"reduce_scatter: ubid={ubid} is not in grad_sharded_ubids="
+            f"{self.stages.grad_sharded_ubids}"
+        )
+        bucket = self.stages.bucket(ubid)
+        assert bucket.param_shard_info is not None, (
+            f"reduce_scatter: missing param_shard_info for ubid={ubid}"
+        )
+        flat_grads = bucket.flat_grads
+        rs_out = bucket.reduce_scatter_grads
+        assert flat_grads is not None, (
+            f"reduce_scatter: missing flat_grads buffer for ubid={ubid}"
+        )
+        assert rs_out is not None, (
+            f"reduce_scatter: missing reduce_scatter_grads buffer for ubid={ubid}"
+        )
+        with torch.cuda.stream(stream):
+            total_bytes = flat_grads.numel() * flat_grads.element_size()
+            tmp = torch.empty_like(rs_out)
+            dist.reduce_scatter_tensor(tmp, flat_grads, group=self.runtime.dp_group)
+            rs_out.add_(tmp)
+            return total_bytes
+
+    def has_trainable_params_for_collective(self, ubid: Any, op_name: str) -> bool:
+        bucket = self.stages.get_bucket(ubid)
+        if bucket is not None and bucket.trainable_param_idxs:
+            return True
+        self.logger.warning(
+            "%s: skipping collective for ubid=%s because it has no trainable param indices",
+            op_name,
+            ubid,
+        )
+        return False
+
+
+@dataclass
+class ComputeExecutor:
+    """Actor-local bucket forward/backward execution."""
+
+    runtime: RuntimeState
+    stages: StageStore
+    logger: Any
+
+    def log_compute_loss_inputs(
+        self,
+        labels: Any,
+        node: Any,
+        fwd_key: Any,
+        fwd_out: dict,
+    ) -> None:
+        def _summarize_value(value: Any) -> str:
+            if isinstance(value, torch.Tensor):
+                return (
+                    f"Tensor(shape={tuple(value.shape)}, dtype={value.dtype}, "
+                    f"requires_grad={value.requires_grad}, device={value.device})"
+                )
+            if isinstance(value, (list, tuple)):
+                return "[" + ", ".join(_summarize_value(v) for v in value) + "]"
+            if value is None:
+                return "None"
+            return type(value).__name__
+
+        self.logger.debug(
+            "compute_loss inputs: rank=%s node_uid=%s node_type=%s tag=%s "
+            "fwd_key=%s labels=%s out_with_grad=%s pre_detach_outs=%s "
+            "detached_outs=%s send_output=%s",
+            self.runtime.global_rank,
+            getattr(node, "uid", None),
+            getattr(getattr(node, "task_type", None), "value", None),
+            getattr(node, "tag", None),
+            fwd_key,
+            _summarize_value(labels),
+            _summarize_value(fwd_out.get("out_with_grad")),
+            _summarize_value(fwd_out.get("pre_detach_outs")),
+            _summarize_value(fwd_out.get("detached_outs")),
+            _summarize_value(fwd_out.get("send_output")),
+        )
+
+    def forward(self, ubid: Any, input_tensors: Any, compute_stream: torch.cuda.Stream) -> dict:
+        bucket = self.stages.bucket(ubid)
+        fwd_fn = bucket.forward_fn
+        fwd_args = bucket.forward_args
+        input_idxs = bucket.input_idxs
+
+        if not isinstance(input_tensors, (list, tuple)):
+            input_tensors = [input_tensors]
+
+        for i, tensor in zip(input_idxs, input_tensors):
+            if isinstance(tensor, (list, tuple)):
+                tensor = tensor[0]
+            if tensor.requires_grad:
+                tensor = tensor.detach().requires_grad_(True)
+            fwd_args[i] = tensor
+
+        fwd_inputs = [fwd_args[i] for i in input_idxs]
+        inp_with_grad = [t for t in fwd_inputs if t is not None and t.requires_grad]
+
+        with torch.cuda.stream(compute_stream):
+            output = fwd_fn(fwd_args)
+
+        for i in input_idxs:
+            fwd_args[i] = None
+
+        out_list = list(output) if isinstance(output, tuple) else [output]
+        possibly_detached = [
+            t.detach().requires_grad_(True)
+            if isinstance(t, torch.Tensor) and t.requires_grad
+            else t
+            for t in out_list
+        ]
+        out_with_grad = [
+            t for t in out_list if isinstance(t, torch.Tensor) and t.requires_grad
+        ]
+
+        return {
+            "pre_detach_outs": out_list,
+            "detached_outs": possibly_detached,
+            "out_with_grad": out_with_grad,
+            "send_output": output,
+            "inp_with_grad": inp_with_grad,
+            "fwd_inputs": fwd_inputs,
+        }
+
+    def backward(
+        self,
+        ubid: Any,
+        mb_idx: int,
+        outputs_or_loss: list,
+        upstream_grads: Any,
+        pre_detach_outs: Any,
+        detached_outs: Any,
+        inp_with_grad: Any,
+        out_with_grad: Any,
+        compute_stream: torch.cuda.Stream,
+    ) -> dict | None:
+        if pre_detach_outs is None:
+            if upstream_grads is not None:
+                with torch.cuda.stream(compute_stream):
+                    torch.autograd.backward(outputs_or_loss, upstream_grads)
+            else:
+                with torch.cuda.stream(compute_stream):
+                    outputs_or_loss[0].backward()
+        else:
+            bwd_pairs = [
+                (p, d.grad)
+                for p, d in zip(pre_detach_outs, detached_outs)
+                if (isinstance(d, torch.Tensor) and d.requires_grad and d.grad is not None)
+            ]
+            assert bwd_pairs, (
+                f"BWD ubid={ubid} mb={mb_idx}: detached boundary has no tensors "
+                "with a materialized grad"
+            )
+            if bwd_pairs:
+                outputs_bwd = [p for p, _g in bwd_pairs]
+                grads_bwd = [g for _p, g in bwd_pairs]
+                with torch.cuda.stream(compute_stream):
+                    torch.autograd.backward(outputs_bwd, grads_bwd)
+
+        if inp_with_grad:
+            output_grads = [t.grad for t in inp_with_grad if t.grad is not None]
+            if output_grads:
+                return {"send_output": output_grads}
+        return None
+
+    @staticmethod
+    def grad_with_param_layout(weight: Parameter, grad: torch.Tensor) -> torch.Tensor:
+        if (
+            grad.dtype == weight.dtype
+            and grad.device == weight.device
+            and grad.layout == weight.layout
+            and tuple(grad.stride()) == tuple(weight.stride())
+        ):
+            return grad
+        if weight.layout == torch.strided and grad.layout != torch.strided:
+            grad = grad.to_dense()
+        out = torch.empty_strided(
+            tuple(weight.shape),
+            tuple(weight.stride()),
+            dtype=weight.dtype,
+            device=weight.device,
+        )
+        out.copy_(grad)
+        return out
+
+    def fused_backward(self, stage_outputs_or_loss: list, output_grads: Any, weights: list) -> None:
+        wts = [w for w in weights if w.requires_grad]
+        if not wts:
+            return
+        dweights = torch.autograd.grad(
+            stage_outputs_or_loss,
+            inputs=wts,
+            grad_outputs=output_grads,
+            retain_graph=False,
+            allow_unused=True,
+        )
+        for w, dw in zip(wts, dweights):
+            if dw is None:
+                continue
+            dw = self.grad_with_param_layout(w, dw)
+            if w.grad is None:
+                w.grad = dw
+            else:
+                if (
+                    w.grad.dtype != w.dtype
+                    or w.grad.device != w.device
+                    or w.grad.layout != w.layout
+                    or tuple(w.grad.stride()) != tuple(w.stride())
+                ):
+                    w.grad = self.grad_with_param_layout(w, w.grad)
+                w.grad += dw
+
+    def bucket_backward_input(
+        self,
+        stage_outputs_or_loss: list,
+        output_grads: Any,
+        input_values: list,
+        weights: Any,
+    ):
+        weights = list(weights)
+
+        if output_grads is None:
+            output_grads = [torch.ones_like(o) for o in stage_outputs_or_loss]
+
+        input_values = [inp for inp in input_values if inp.requires_grad]
+        if not input_values:
+            self.fused_backward(stage_outputs_or_loss, output_grads, weights)
+            for i, t in enumerate(stage_outputs_or_loss):
+                if isinstance(t, torch.Tensor):
+                    stage_outputs_or_loss[i] = t.detach()
+            return (), []
+
+        stage_output_grad_fns = list(filter(None, map(_get_grad_fn_or_grad_acc, stage_outputs_or_loss)))
+        stage_input_grad_fns = list(filter(None, map(_get_grad_fn_or_grad_acc, input_values)))
+        weight_grad_fns = list(filter(None, map(_get_grad_fn_or_grad_acc, weights)))
+
+        reverse_edges_dict = construct_reverse_graph(stage_output_grad_fns)
+        param_groups = get_param_groups(stage_input_grad_fns, weight_grad_fns, reverse_edges_dict)
+
+        handles = []
+        for param_group in param_groups:
+            for i, intermediate in enumerate(param_group["intermediates"]):
+                def get_hook(pg, idx):
+                    def hook(grad_inputs):
+                        if pg.get("grads") is None:
+                            pg["grads"] = [None] * len(pg["intermediates"])
+                        pg["grads"][idx] = tuple(grad_inputs)
+                    return hook
+                handles.append(intermediate.register_prehook(get_hook(param_group, i)))
+
+        dinputs = torch.autograd.grad(
+            stage_outputs_or_loss,
+            inputs=input_values,
+            grad_outputs=output_grads,
+            retain_graph=True,
+            allow_unused=True,
+        )
+        for inp, dinput in zip(input_values, dinputs):
+            if inp.grad is None:
+                inp.grad = dinput
+            else:
+                inp.grad += dinput
+
+        for i, t in enumerate(stage_outputs_or_loss):
+            if isinstance(t, torch.Tensor):
+                stage_outputs_or_loss[i] = t.detach()
+
+        for handle in handles:
+            handle.remove()
+
+        return dinputs, param_groups
+
+    def bucket_backward_weight(
+        self,
+        weights: Any,
+        param_groups: list,
+        ubid: Any | None = None,
+        mb_idx: int | None = None,
+    ) -> None:
+        if not param_groups:
+            return
+
+        grad_acc_to_weight: dict[Node, Parameter] = {}
+
+        for weight in weights:
+            grad_acc = _get_grad_fn_or_grad_acc(weight)
+            grad_acc_to_weight[grad_acc] = weight
+
+        for param_group in param_groups:
+            valid_edges: list[GradientEdge] = []
+            valid_grad_outputs: list[torch.Tensor] = []
+
+            for grads_tuple, intermediate in zip(
+                param_group.get("grads", []), param_group["intermediates"]
+            ):
+                if grads_tuple is None:
+                    continue
+                for i, grad in enumerate(grads_tuple):
+                    if grad is not None:
+                        valid_edges.append(GradientEdge(intermediate, i))
+                        valid_grad_outputs.append(grad)
+
+            del param_group["intermediates"]
+
+            if not valid_edges:
+                continue
+
+            weight_edges = tuple(GradientEdge(w, 0) for w in param_group["params"])
+            if not weight_edges:
+                continue
+
+            dweights = torch.autograd.grad(
+                valid_edges,
+                weight_edges,
+                grad_outputs=valid_grad_outputs,
+                retain_graph=False,
+            )
+
+            del param_group["grads"]
+
+            for grad_acc, dw in zip(param_group["params"], dweights):
+                if dw is None or grad_acc not in grad_acc_to_weight:
+                    continue
+                weight = grad_acc_to_weight[grad_acc]
+                dw = self.grad_with_param_layout(weight, dw)
+                if weight.grad is None:
+                    weight.grad = dw
+                else:
+                    if (
+                        weight.grad.dtype != weight.dtype
+                        or weight.grad.device != weight.device
+                        or weight.grad.layout != weight.layout
+                        or tuple(weight.grad.stride()) != tuple(weight.stride())
+                    ):
+                        weight.grad = self.grad_with_param_layout(weight, weight.grad)
+                    weight.grad += dw
+
+
+@dataclass
+class DagExecutor:
+    """Execute a loaded TrainingDAG in sorted order."""
+
+    runtime: RuntimeState
+    stages: StageStore
+    buffers: BufferStore
+    events: EventStore
+    params: ParamStorage
+    communication: CommunicationExecutor
+    compute: ComputeExecutor
+    logger: Any
+
+    @staticmethod
+    def _node_meta(node: Any) -> dict:
+        return getattr(node, "node_meta", {}) or {}
+
+    def _node_bucket_key(self, node: Any) -> Any | None:
+        return self._node_meta(node).get("bucket_key")
+
+    def _sync_payload_ubid(self, node: Any) -> Any | None:
+        bk = self._node_bucket_key(node)
+        if bk is not None:
+            return bk
+        if node.data_preds:
+            pred_bk = self._node_bucket_key(node.data_preds[0])
+            if pred_bk is not None:
+                return pred_bk
+        return None
+
+    def _sync_payload_ubids(self, node: Any) -> list[Any]:
+        sync_ubids = self._node_meta(node).get("sync_ubids")
+        if sync_ubids:
+            return list(sync_ubids)
+        ubid = self._sync_payload_ubid(node)
+        return [ubid] if ubid is not None else []
+
+    def _rf_enter(self, label: str):
+        if not self.runtime.pytorch_profiler_enabled:
+            return None
+        mt = torch.autograd.set_multithreading_enabled(False)
+        mt.__enter__()
+        rf = torch.profiler.record_function(label)
+        rf.__enter__()
+        return (rf, mt)
+
+    @staticmethod
+    def _rf_exit(rf) -> None:
+        if rf is not None:
+            rf_ctx, mt = rf
+            rf_ctx.__exit__(None, None, None)
+            mt.__exit__(None, None, None)
+
+    @staticmethod
+    def _node_tag_str(node: Any) -> str:
+        tag = getattr(node, "tag", None)
+        if not isinstance(tag, dict) or not tag:
+            return "{}"
+        items = sorted(tag.items(), key=lambda kv: kv[0])
+        return "{" + ",".join(f"{k}={v}" for k, v in items) + "}"
+
+    def _wait_for_all_gather(self, compute_node: Any) -> None:
+        compute_stream = self.runtime.stream_for_task(compute_node)
+        for pred in compute_node.data_preds:
+            if pred.task_type == TaskType.ALL_GATHER:
+                ag_evt = self.events.all_gather.get(pred.uid)
+                if ag_evt is not None:
+                    compute_stream.wait_event(ag_evt)
+
+    def run(
+        self,
+        dag: Any,
+        sorted_dag_nodes: list[Any],
+        inputs: Any,
+        labels: Any,
+        loss_buffer: list,
+        loss_fn=None,
+    ) -> None:
+        """Run one iteration of the loaded TrainingDAG."""
+        assert dag is not None, "load_training_dag() must be called before run_dag()"
+        assert sorted_dag_nodes is not None, "load_training_dag() must initialize sorted node order"
+
+        self.params.drain_pending_frees()
+        debug_enabled = self.logger.isEnabledFor(logging.DEBUG)
+
+        self.buffers.reset()
+        self.events.reset()
+        self.params.clear_param_grads()
+        default_stream = self.runtime.default_stream()
+        self.params.zero_grad_buffers(default_stream)
+        zero_evt = torch.cuda.Event()
+        zero_evt.record(default_stream)
+        for stream in self.runtime.streams.values():
+            if stream is not default_stream:
+                stream.wait_event(zero_evt)
+        comp_events: dict[Any, torch.cuda.Event] = {}
+
+        self.buffers.init_refcounts(dag)
+        last_comp_event_by_stream: dict[str, torch.cuda.Event] = {}
+
+        for node in sorted_dag_nodes:
+            task_type = node.task_type
+            batch = node.batches[0]
+            mb_idx = batch.mb_idx
+            ubid = self._node_bucket_key(node)
+            node_stream = self.runtime.stream_for_task(node)
+            node_stream_id = self.runtime.stream_id(node)
+            node_tag = self._node_tag_str(node)
+
+            if debug_enabled:
+                self.logger.debug(
+                    f"run_dag dispatch: {task_type.value} "
+                    f"tag={node_tag}"
+                )
+
+            task_label = f"{task_type.value}:{node_tag}:uid{node.uid}"
+            self.runtime.nvtx_push(task_label)
+            rf = self._rf_enter(task_label)
+
+            match task_type:
+                case TaskType.SEND:
+                    compute_node = node.data_preds[0]
+                    node_stream.wait_event(comp_events[compute_node.uid])
+                    send_data = self.buffers.task[compute_node.uid]["send_output"]
+                    self.communication.send(send_data, node.peer_pp_rank, stream=node_stream)
+                    send_buf = self.buffers.task.get(compute_node.uid)
+                    if isinstance(send_buf, dict):
+                        send_buf["send_output"] = None
+                    self.buffers.release(compute_node.uid)
+
+                case TaskType.RECV:
+                    compute_node = node.data_succs[0]
+                    comp_evt = last_comp_event_by_stream.get(self.runtime.stream_id(compute_node))
+                    if comp_evt is not None:
+                        node_stream.wait_event(comp_evt)
+                    if compute_node.task_type == TaskType.FWD:
+                        recv_ubid = self._node_bucket_key(compute_node)
+                        recv_tensors = self.communication.recv_fwd(
+                            recv_ubid, node.peer_pp_rank, stream=node_stream
+                        )
+                    else:
+                        fwd_uid = compute_node.node_meta.get("fwd_uid")
+                        fwd_key = (compute_node.node_meta.get("bucket_key"), fwd_uid)
+                        shape_meta = self.buffers.task[("shape_ref",) + fwd_key]
+                        recv_tensors = self.communication.recv_bwd(
+                            shape_meta, node.peer_pp_rank, stream=node_stream
+                        )
+                    self.buffers.task[node.uid] = recv_tensors
+                    recv_evt = torch.cuda.Event()
+                    recv_evt.record(node_stream)
+                    self.events.recv[node.uid] = recv_evt
+
+                case TaskType.FWD_A2A:
+                    fwd_pred = next(p for p in node.data_preds if p.task_type == TaskType.FWD)
+                    node_stream.wait_event(comp_events[fwd_pred.uid])
+                    tensor_idx = self._node_meta(node)["a2a_tensor_idx"]
+                    fwd_buf = dict(self.buffers.task[fwd_pred.uid])
+                    self.buffers.release(fwd_pred.uid)
+                    detached_outs = list(fwd_buf["detached_outs"])
+                    detached_outs[tensor_idx] = self.communication.all_to_all(
+                        detached_outs[tensor_idx], stream=node_stream
+                    ).requires_grad_(True)
+                    fwd_buf["detached_outs"] = detached_outs
+                    self.buffers.task[node.uid] = fwd_buf
+                    a2a_evt = torch.cuda.Event()
+                    a2a_evt.record(node_stream)
+                    self.events.a2a[node.uid] = a2a_evt
+
+                case TaskType.BWD_A2A:
+                    bwd_pred = next(
+                        p for p in node.data_preds
+                        if p.task_type in (TaskType.BWD, TaskType.BWD_I)
+                    )
+                    node_stream.wait_event(comp_events[bwd_pred.uid])
+                    tensor_idx = self._node_meta(node)["a2a_tensor_idx"]
+                    bwd_buf = dict(self.buffers.task[bwd_pred.uid])
+                    self.buffers.release(bwd_pred.uid)
+                    inp_grads = list(bwd_buf["inp_grads"])
+                    grad_a2a_out = inp_grads[tensor_idx]
+                    assert grad_a2a_out is not None, (
+                        f"BWD_A2A tag={node_tag}: grad at a2a_tensor_idx={tensor_idx} is None"
+                    )
+                    if not grad_a2a_out.is_contiguous():
+                        grad_a2a_out = grad_a2a_out.contiguous()
+                    inp_grads[tensor_idx] = self.communication.all_to_all(
+                        grad_a2a_out, stream=node_stream
+                    )
+                    bwd_buf["inp_grads"] = inp_grads
+                    self.buffers.task[node.uid] = bwd_buf
+                    a2a_evt = torch.cuda.Event()
+                    a2a_evt.record(node_stream)
+                    self.events.a2a[node.uid] = a2a_evt
+
+                case TaskType.ALL_REDUCE:
+                    bwd_node = node.data_preds[0]
+                    ar_ubids = self._sync_payload_ubids(node)
+                    assert ar_ubids, (
+                        f"ALL_REDUCE node uid={node.uid} has no sync_payload_ubids"
+                    )
+                    node_stream.wait_event(comp_events[bwd_node.uid])
+                    for ar_ubid in ar_ubids:
+                        self.communication.all_reduce_grads(ar_ubid, stream=node_stream)
+                    ar_evt = torch.cuda.Event()
+                    ar_evt.record(node_stream)
+                    self.events.all_reduce[node.uid] = ar_evt
+                    self.buffers.release(bwd_node.uid)
+
+                case TaskType.REDUCE_SCATTER:
+                    bwd_node = node.data_preds[0]
+                    rs_ubid = self._node_bucket_key(node)
+                    assert rs_ubid is not None, (
+                        f"REDUCE_SCATTER node uid={node.uid} has no bucket_key"
+                    )
+                    node_stream.wait_event(comp_events[bwd_node.uid])
+                    rs_bytes = self.communication.reduce_scatter(rs_ubid, stream=node_stream)
+                    rs_evt = torch.cuda.Event()
+                    rs_evt.record(node_stream)
+                    self.events.reduce_scatter[node.uid] = rs_evt
+                    if rs_bytes:
+                        self.params.defer_free_full_grads(rs_ubid, rs_evt)
+                    if self._node_meta(node).get("zero_free_full_params_after"):
+                        assert False and "Param free should happen after a compute node"
+                    self.buffers.release(bwd_node.uid)
+
+                case TaskType.ALL_GATHER:
+                    ag_ubid = self._node_bucket_key(node)
+                    assert ag_ubid is not None, (
+                        f"ALL_GATHER node uid={node.uid} has no bucket_key"
+                    )
+                    self.params.all_gather_full_params(ag_ubid, stream=node_stream)
+                    ag_evt = torch.cuda.Event()
+                    ag_evt.record(node_stream)
+                    self.events.all_gather[node.uid] = ag_evt
+
+                case TaskType.FWD:
+                    recv_pred = next(
+                        (p for p in node.data_preds if p.task_type == TaskType.RECV), None
+                    )
+                    if recv_pred is not None and recv_pred.uid in self.events.recv:
+                        node_stream.wait_event(self.events.recv.pop(recv_pred.uid))
+
+                    a2a_pred = next(
+                        (p for p in node.data_preds if p.task_type == TaskType.FWD_A2A), None
+                    )
+                    if a2a_pred is not None and a2a_pred.uid in self.events.a2a:
+                        node_stream.wait_event(self.events.a2a.pop(a2a_pred.uid))
+
+                    fwd_data_pred = next(
+                        (p for p in node.data_preds
+                         if p.task_type in (TaskType.FWD, TaskType.FWD_A2A)), None
+                    )
+                    if fwd_data_pred is not None:
+                        input_tensors = self.buffers.task[fwd_data_pred.uid]["detached_outs"]
+                        self.buffers.release(fwd_data_pred.uid)
+                    elif recv_pred is not None:
+                        input_tensors = self.buffers.task[recv_pred.uid]
+                        self.buffers.release(recv_pred.uid)
+                    else:
+                        input_tensors = inputs
+
+                    self._wait_for_all_gather(node)
+
+                    fwd_out = self.compute.forward(ubid, input_tensors, node_stream)
+                    self.buffers.task[node.uid] = fwd_out
+                    fwd_key = (node.node_meta.get("bucket_key"), node.uid)
+                    self.buffers.task[("shape_ref",) + fwd_key] = [
+                        (t.shape, t.dtype) for t in fwd_out["out_with_grad"]
+                    ]
+                    self.buffers.task[fwd_key] = fwd_out
+                    evt = torch.cuda.Event()
+                    evt.record(node_stream)
+                    comp_events[node.uid] = evt
+                    last_comp_event_by_stream[node_stream_id] = evt
+                    if self._node_meta(node).get("zero_free_full_params_after"):
+                        self.params.defer_free_full_params(ubid, evt)
+
+                case TaskType.BWD:
+                    recv_pred = next(
+                        (p for p in node.data_preds if p.task_type == TaskType.RECV), None
+                    )
+                    if recv_pred is not None and recv_pred.uid in self.events.recv:
+                        node_stream.wait_event(self.events.recv.pop(recv_pred.uid))
+                    self._wait_for_all_gather(node)
+
+                    a2a_pred = next(
+                        (p for p in node.data_preds if p.task_type == TaskType.BWD_A2A), None
+                    )
+                    if a2a_pred is not None and a2a_pred.uid in self.events.a2a:
+                        node_stream.wait_event(self.events.a2a.pop(a2a_pred.uid))
+
+                    fwd_uid = node.node_meta.get("fwd_uid")
+                    fwd_key = (node.node_meta.get("bucket_key"), fwd_uid)
+                    fwd_out = self.buffers.task[fwd_key]
+
+                    if self._node_meta(node).get("compute_loss", False):
+                        assert loss_fn is not None
+                        if self.logger.isEnabledFor(logging.DEBUG):
+                            self.compute.log_compute_loss_inputs(labels, node, fwd_key, fwd_out)
+                        with torch.cuda.stream(node_stream):
+                            outputs_or_loss = [loss_fn(fwd_out["out_with_grad"][0], labels)]
+                        upstream_grads = None
+                    elif recv_pred is not None:
+                        upstream_grads = self.buffers.task[recv_pred.uid]
+                        self.buffers.release(recv_pred.uid)
+                        if not isinstance(upstream_grads, (list, tuple)):
+                            upstream_grads = [upstream_grads]
+                        outputs_or_loss = fwd_out["out_with_grad"]
+                        upstream_grads = list(upstream_grads)
+                    else:
+                        outputs_or_loss = fwd_out["out_with_grad"]
+                        upstream_grads = None
+
+                    if a2a_pred is not None:
+                        a2a_buf = self.buffers.task[a2a_pred.uid]
+                        pre_detach_outs = fwd_out["pre_detach_outs"]
+                        detached_outs = fwd_out["detached_outs"]
+                        for d, g in zip(detached_outs, a2a_buf["inp_grads"]):
+                            if (
+                                d is not None
+                                and isinstance(d, torch.Tensor)
+                                and d.requires_grad
+                                and g is not None
+                            ):
+                                d.grad = g
+                        self.buffers.release(a2a_pred.uid)
+                    else:
+                        bwd_pred = next(
+                            (p for p in node.data_preds
+                             if p.task_type in (TaskType.BWD, TaskType.BWD_I)), None
+                        )
+                        if bwd_pred is not None and recv_pred is None:
+                            prev_buf = self.buffers.task[bwd_pred.uid]
+                            pre_detach_outs = fwd_out.get("pre_detach_outs")
+                            detached_outs = fwd_out.get("detached_outs")
+                            for d, g in zip(detached_outs, prev_buf["inp_grads"]):
+                                if (
+                                    d is not None
+                                    and isinstance(d, torch.Tensor)
+                                    and d.requires_grad
+                                    and g is not None
+                                ):
+                                    d.grad = g
+                            self.buffers.release(bwd_pred.uid)
+                        else:
+                            pre_detach_outs = None
+                            detached_outs = None
+
+                    inp_with_grad = fwd_out.get("inp_with_grad")
+                    if self._node_meta(node).get("zero_alloc_full_grads_before"):
+                        self.params.alloc_full_grads(ubid, node_stream)
+
+                    bwd_out = self.compute.backward(
+                        ubid, mb_idx, outputs_or_loss, upstream_grads,
+                        pre_detach_outs, detached_outs, inp_with_grad,
+                        fwd_out.get("out_with_grad"),
+                        node_stream,
+                    )
+                    buf = bwd_out if bwd_out is not None else {}
+                    fwd_inputs_full = fwd_out.get("fwd_inputs")
+                    buf["inp_grads"] = (
+                        [t.grad if (t is not None and t.requires_grad) else None
+                         for t in fwd_inputs_full]
+                        if fwd_inputs_full is not None
+                        else [t.grad for t in (inp_with_grad or [])]
+                    )
+                    self.params.accumulate_zero_param_grads_to_flat(ubid, node_stream)
+                    self.buffers.task[node.uid] = buf
+                    fwd_out.clear()
+                    del self.buffers.task[fwd_key]
+                    evt = torch.cuda.Event()
+                    evt.record(node_stream)
+                    comp_events[node.uid] = evt
+                    self.events.backward[ubid] = evt
+                    last_comp_event_by_stream[node_stream_id] = evt
+                    if self._node_meta(node).get("zero_free_full_params_after"):
+                        self.params.defer_free_full_params(ubid, evt)
+
+                case TaskType.BWD_I:
+                    recv_pred = next(
+                        (p for p in node.data_preds if p.task_type == TaskType.RECV), None
+                    )
+                    if recv_pred is not None and recv_pred.uid in self.events.recv:
+                        node_stream.wait_event(self.events.recv.pop(recv_pred.uid))
+                    self._wait_for_all_gather(node)
+
+                    fwd_uid = node.node_meta.get("fwd_uid")
+                    fwd_key = (node.node_meta.get("bucket_key"), fwd_uid)
+                    fwd_out = self.buffers.task[fwd_key]
+
+                    if self._node_meta(node).get("compute_loss", False):
+                        assert loss_fn is not None
+                        if self.logger.isEnabledFor(logging.DEBUG):
+                            self.compute.log_compute_loss_inputs(labels, node, fwd_key, fwd_out)
+                        with torch.cuda.stream(node_stream):
+                            stage_outputs_or_loss = [loss_fn(fwd_out["out_with_grad"][0], labels)]
+                        output_grads = None
+                    elif recv_pred is not None:
+                        upstream_raw = self.buffers.task[recv_pred.uid]
+                        self.buffers.release(recv_pred.uid)
+                        if not isinstance(upstream_raw, (list, tuple)):
+                            upstream_raw = [upstream_raw]
+                        stage_outputs_or_loss = fwd_out["out_with_grad"]
+                        output_grads = list(upstream_raw)
+                    else:
+                        stage_outputs_or_loss = fwd_out["out_with_grad"]
+                        output_grads = None
+
+                    bwd_a2a_pred = next(
+                        (p for p in node.data_preds if p.task_type == TaskType.BWD_A2A), None
+                    )
+                    if (
+                        bwd_a2a_pred is not None
+                        and not self._node_meta(node).get("compute_loss", False)
+                        and recv_pred is None
+                    ):
+                        a2a_buf = self.buffers.task[bwd_a2a_pred.uid]
+                        detached_outs = fwd_out["detached_outs"]
+                        output_grads_full = [
+                            a2a_buf["inp_grads"][i]
+                            for i, t in enumerate(detached_outs)
+                            if t is not None and getattr(t, "requires_grad", False)
+                        ]
+                        pairs = [
+                            (o, g) for o, g in zip(stage_outputs_or_loss, output_grads_full)
+                            if g is not None
+                        ]
+                        stage_outputs_or_loss = [p[0] for p in pairs]
+                        output_grads = [p[1] for p in pairs]
+                        self.buffers.release(bwd_a2a_pred.uid)
+
+                    input_values = fwd_out.get("inp_with_grad") or []
+                    weights = self.stages.bucket(ubid).weights()
+
+                    with torch.cuda.stream(node_stream):
+                        dinputs, param_groups = self.compute.bucket_backward_input(
+                            stage_outputs_or_loss, output_grads, input_values, iter(weights)
+                        )
+                    fwd_inputs_full = fwd_out.get("fwd_inputs")
+                    if fwd_inputs_full is not None:
+                        inp_grads_full = [
+                            t.grad if (t is not None and t.requires_grad) else None
+                            for t in fwd_inputs_full
+                        ]
+                    else:
+                        inp_grads_full = list(dinputs)
+                    self.buffers.task[node.uid] = {
+                        "inp_grads": inp_grads_full,
+                        "param_groups": param_groups,
+                    }
+                    if dinputs:
+                        self.buffers.task[node.uid]["send_output"] = list(dinputs)
+                    fwd_out.clear()
+                    del stage_outputs_or_loss, fwd_out
+                    del self.buffers.task[fwd_key]
+                    evt = torch.cuda.Event()
+                    evt.record(node_stream)
+                    comp_events[node.uid] = evt
+                    self.events.backward[ubid] = evt
+                    last_comp_event_by_stream[node_stream_id] = evt
+                    if self._node_meta(node).get("zero_free_full_params_after"):
+                        self.params.defer_free_full_params(ubid, evt)
+
+                case TaskType.BWD_W:
+                    self._wait_for_all_gather(node)
+                    if self._node_meta(node).get("zero_alloc_full_grads_before"):
+                        self.params.alloc_full_grads(ubid, node_stream)
+                    bwdi_node = next(p for p in node.data_preds if p.task_type == TaskType.BWD_I)
+                    param_groups = self.buffers.task[bwdi_node.uid]["param_groups"]
+                    weights = self.stages.bucket(ubid).weights()
+                    with torch.cuda.stream(node_stream):
+                        self.compute.bucket_backward_weight(
+                            iter(weights), param_groups, ubid=ubid, mb_idx=mb_idx
+                        )
+                    self.params.accumulate_zero_param_grads_to_flat(ubid, node_stream)
+                    self.buffers.task[node.uid] = {}
+                    self.buffers.release(bwdi_node.uid)
+                    evt = torch.cuda.Event()
+                    evt.record(node_stream)
+                    comp_events[node.uid] = evt
+                    self.events.backward[ubid] = evt
+                    last_comp_event_by_stream[node_stream_id] = evt
+                    if self._node_meta(node).get("zero_free_full_params_after"):
+                        self.params.defer_free_full_params(ubid, evt)
+
+                case TaskType.UPD:
+                    self._update(node_stream, loss_buffer)
+
+                case TaskType.ORDER_DUMMY:
+                    pass
+
+            self._rf_exit(rf)
+            self.runtime.nvtx_pop()
+
+    def _update(self, stream: torch.cuda.Stream, loss_buffer: list):
+        self.params.drain_pending_frees()
+        if self.params.has_zero_shard_optimizers():
+            self.params.step_zero_shard_optimizers(stream, self.events.reduce_scatter)
+            losses = loss_buffer
+            loss_buffer.clear()
+            torch.cuda.synchronize()
+            return losses
+
+        for ar_evt in self.events.all_reduce.values():
+            stream.wait_event(ar_evt)
+
+        for ubid, bucket in self.stages.buckets.items():
+            if bucket.optimizer is None:
+                continue
+            bwd_evt = self.events.backward.get(ubid)
+            if bwd_evt is not None:
+                stream.wait_event(bwd_evt)
+
+            with torch.cuda.stream(stream):
+                bucket.optimizer.step()
+
+        losses = loss_buffer
+        loss_buffer.clear()
+
+        torch.cuda.synchronize()
+
+        return {
+            "losses": losses,
+        }
diff --git a/src/fx.py b/src/fx.py
new file mode 100644
index 0000000..d568d7b
--- /dev/null
+++ b/src/fx.py
@@ -0,0 +1,694 @@
+import torch
+import torch.fx as fx
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Any
+import inspect
+import importlib
+import json
+import operator
+
+from .state import create_logger, LOG_LEVEL
+
+logger = create_logger("fx", LOG_LEVEL)
+PIPER_ANNOTATIONS_META_KEY = "piper_annotations"
+
+
+def _encode_arg(a):
+    if isinstance(a, fx.Node):
+        return {"__node__": a.name}
+    if isinstance(a, torch.device):
+        return {"__device__": str(a)}
+    if isinstance(a, torch.dtype):
+        return {"__dtype__": str(a).replace("torch.", "")}
+    if isinstance(a, slice):
+        return {
+            "__slice__": True,
+            "start": _encode_arg(a.start),
+            "stop": _encode_arg(a.stop),
+            "step": _encode_arg(a.step),
+        }
+    if a is Ellipsis:
+        return {"__ellipsis__": True}
+    if isinstance(a, tuple):
+        return {"__tuple__": [_encode_arg(x) for x in a]}
+    if isinstance(a, list):
+        return [_encode_arg(x) for x in a]
+    if isinstance(a, dict):
+        return {k: _encode_arg(v) for k, v in a.items()}
+    return a
+
+
+def _decode_arg(a, name_to_node):
+    if isinstance(a, dict):
+        if "__node__" in a:
+            return name_to_node[a["__node__"]]
+        if "__device__" in a:
+            return torch.device(a["__device__"])
+        if "__dtype__" in a:
+            return getattr(torch, a["__dtype__"])
+        if "__slice__" in a:
+            return slice(
+                _decode_arg(a["start"], name_to_node),
+                _decode_arg(a["stop"], name_to_node),
+                _decode_arg(a["step"], name_to_node),
+            )
+        if "__ellipsis__" in a:
+            return Ellipsis
+        if "__tuple__" in a:
+            return tuple(_decode_arg(x, name_to_node) for x in a["__tuple__"])
+        return {k: _decode_arg(v, name_to_node) for k, v in a.items()}
+    if isinstance(a, list):
+        return [_decode_arg(x, name_to_node) for x in a]
+    return a
+
+
+def _is_op_overload(obj):
+    return (
+        obj.__class__.__module__.startswith("torch._ops")
+        or obj.__class__.__name__.startswith("OpOverload")
+    )
+
+
+def _serialize_target(t):
+    if isinstance(t, str):
+        return {"kind": "string", "value": t}
+
+    target_str = str(t)
+    if target_str.startswith("torch.ops."):
+        path = target_str[len("torch.ops."):]
+        return {"kind": "torch_op", "path": path}
+
+    if getattr(t, "__module__", "") == "torch._VariableFunctionsClass":
+        public_name = t.__name__
+        if hasattr(torch, public_name):
+            return {"kind": "py_func", "module": "torch", "qualname": public_name}
+        raise ValueError(f"No public torch alias for {t}")
+
+    if _is_op_overload(t) or getattr(t, "__module__", "").startswith("torch._ops"):
+        return {"kind": "torch_op", "path": str(t)}
+
+    if inspect.isfunction(t) or inspect.isbuiltin(t):
+        mod = inspect.getmodule(t)
+
+        if mod is not None:
+            mod_name = mod.__name__
+            if mod_name.startswith("torch.ops"):
+                target_str = str(t)
+                if target_str.startswith("torch.ops."):
+                    path = target_str[len("torch.ops."):]
+                    return {"kind": "torch_op", "path": path}
+                module_parts = mod_name.split(".")
+                if len(module_parts) >= 3:
+                    namespace = module_parts[2]
+                    path = f"{namespace}.{t.__name__}"
+                    return {"kind": "torch_op", "path": path}
+
+        if t.__name__ == "apply":
+            qualname = getattr(t, "__qualname__", "")
+            mod = inspect.getmodule(t)
+            if "." in qualname:
+                class_name = qualname.split(".")[0]
+                if mod and hasattr(mod, class_name):
+                    func_class = getattr(mod, class_name)
+                    if isinstance(func_class, type) and issubclass(func_class, torch.autograd.Function):
+                        return {
+                            "kind": "py_obj",
+                            "module": func_class.__module__,
+                            "qualname": func_class.__qualname__ + ".apply",
+                        }
+
+                import sys
+
+                for module_obj in sys.modules.values():
+                    if module_obj is not None and hasattr(module_obj, class_name):
+                        try:
+                            func_class = getattr(module_obj, class_name)
+                            if isinstance(func_class, type) and issubclass(func_class, torch.autograd.Function):
+                                return {
+                                    "kind": "py_obj",
+                                    "module": func_class.__module__,
+                                    "qualname": func_class.__qualname__ + ".apply",
+                                }
+                        except (TypeError, AttributeError):
+                            continue
+
+                if class_name.startswith("AllToAll"):
+                    func_class = globals().get(class_name)
+                    if isinstance(func_class, type) and issubclass(func_class, torch.autograd.Function):
+                        return {
+                            "kind": "py_obj",
+                            "module": func_class.__module__,
+                            "qualname": func_class.__qualname__ + ".apply",
+                        }
+
+        mod = inspect.getmodule(t)
+        if mod is None:
+            raise ValueError(f"Cannot serialize function without module: {t}")
+        return {"kind": "py_func", "module": mod.__name__, "qualname": t.__name__}
+
+    if inspect.isclass(t):
+        mod = t.__module__
+        return {"kind": "py_obj", "module": mod, "qualname": t.__qualname__}
+
+    if t in operator.__dict__.values():
+        return {"kind": "py_func", "module": "operator", "qualname": t.__name__}
+
+    mod = getattr(t, "__module__", None)
+    name = getattr(t, "__name__", None)
+    if mod and name:
+        return {"kind": "py_func", "module": mod, "qualname": name}
+
+    raise NotImplementedError(f"Unsupported target type: {t} ({type(t)})")
+
+
+def _resolve_qualname(mod, qualname):
+    obj = mod
+    for part in qualname.split("."):
+        obj = getattr(obj, part)
+    return obj
+
+
+def _deserialize_target(payload):
+    kind = payload["kind"]
+
+    if kind == "string":
+        return payload["value"]
+
+    if kind == "py_func":
+        if payload["module"].startswith("torch.ops"):
+            module_parts = payload["module"].split(".")
+            if len(module_parts) >= 3:
+                namespace = module_parts[2]
+                path = f"{namespace}.{payload['qualname']}"
+                return _deserialize_target({"kind": "torch_op", "path": path})
+        mod = importlib.import_module(payload["module"])
+        return _resolve_qualname(mod, payload["qualname"])
+
+    if kind == "py_obj":
+        mod = importlib.import_module(payload["module"])
+        try:
+            return _resolve_qualname(mod, payload["qualname"])
+        except AttributeError as e:
+            raise AttributeError(
+                f"Failed to resolve qualname '{payload['qualname']}' "
+                f"in module '{payload['module']}': {e}"
+            ) from e
+
+    if kind == "torch_op":
+        obj = torch.ops
+        path = payload["path"]
+        try:
+            for part in path.split("."):
+                obj = getattr(obj, part)
+            return obj
+        except AttributeError:
+            if path.startswith("higher_order."):
+                op_name = path.split(".", 1)[1]
+                op_to_module = {
+                    "triton_kernel_wrapper_mutation": "torch._higher_order_ops.triton_kernel_wrap",
+                    "triton_kernel_wrapper_functional": "torch._higher_order_ops.triton_kernel_wrap",
+                }
+                if op_name in op_to_module:
+                    try:
+                        importlib.import_module(op_to_module[op_name])
+                        obj = torch.ops
+                        for part in path.split("."):
+                            obj = getattr(obj, part)
+                        return obj
+                    except (ImportError, AttributeError):
+                        pass
+            raise AttributeError(
+                f"Failed to resolve torch.ops path '{path}'. "
+                f"Available attributes: {[x for x in dir(obj) if not x.startswith('_')][:20]}"
+            )
+
+    raise NotImplementedError(f"Unknown target kind: {kind}")
+
+
+def _serialize_graphmodule(gm: fx.GraphModule) -> str:
+    nodes = []
+    for n in gm.graph.nodes:
+        nodes.append({
+            "name": n.name,
+            "op": n.op,
+            "target": (
+                _serialize_target(n.target)
+                if n.op in ("call_function", "call_method", "call_module", "get_attr")
+                else None
+            ),
+            "args": _encode_arg(n.args),
+            "kwargs": _encode_arg(n.kwargs),
+        })
+
+    submodules = {}
+    for name, module in gm.named_children():
+        if isinstance(module, fx.GraphModule):
+            submodules[name] = _serialize_graphmodule(module)
+
+    data = {
+        "nodes": nodes,
+        "state_dict": {k: v.detach().cpu().tolist() for k, v in gm.state_dict().items()},
+        "param_devices": {k: str(v.device) for k, v in gm.state_dict().items()},
+        "submodules": submodules,
+    }
+    serialized = json.dumps(data, ensure_ascii=False)
+
+    del data
+    del nodes
+
+    return serialized
+
+
+def _unwrap_output_arg(decoded):
+    if isinstance(decoded, (tuple, list)) and len(decoded) == 1 and isinstance(decoded[0], (tuple, list)):
+        return decoded[0]
+    return decoded
+
+
+def _deserialize_graphmodule(s: str) -> fx.GraphModule:
+    data = json.loads(s)
+    g = fx.Graph()
+    name_to_node = {}
+
+    for n in data["nodes"]:
+        op = n["op"]
+        if op == "placeholder":
+            node = g.placeholder(n["name"])
+        elif op == "output":
+            decoded = _decode_arg(n["args"], name_to_node)
+            node = g.output(_unwrap_output_arg(decoded))
+        elif op == "call_function":
+            target = _deserialize_target(n["target"])
+            args = _decode_arg(n["args"], name_to_node)
+            kwargs = _decode_arg(n["kwargs"], name_to_node)
+            node = g.call_function(target, tuple(args), kwargs)
+        elif op == "call_method":
+            target = _deserialize_target(n["target"])
+            args = _decode_arg(n["args"], name_to_node)
+            kwargs = _decode_arg(n["kwargs"], name_to_node)
+            node = g.call_method(target, tuple(args), kwargs)
+        elif op == "call_module":
+            target = _deserialize_target(n["target"])
+            args = _decode_arg(n["args"], name_to_node)
+            kwargs = _decode_arg(n["kwargs"], name_to_node)
+            node = g.call_module(target, tuple(args), kwargs)
+        elif op == "get_attr":
+            target = _deserialize_target(n["target"])
+            node = g.get_attr(target)
+        else:
+            raise NotImplementedError(f"op {op} not handled")
+        name_to_node[n["name"]] = node
+
+    root_module = torch.nn.Module()
+    submodules = data.get("submodules", {})
+    for module_name, serialized_submodule in submodules.items():
+        submodule_gm = _deserialize_graphmodule(serialized_submodule)
+        parts = module_name.split(".")
+        if len(parts) == 1:
+            root_module.add_module(module_name, submodule_gm)
+        else:
+            current = root_module
+            for part in parts[:-1]:
+                if not hasattr(current, part):
+                    current.add_module(part, torch.nn.Module())
+                current = getattr(current, part)
+            current.add_module(parts[-1], submodule_gm)
+
+    gm = fx.GraphModule(root_module, g)
+    state = {k: torch.tensor(v) for k, v in data["state_dict"].items()}
+    gm.load_state_dict(state, strict=False)
+    return gm
+
+
+@dataclass
+class AnnotationSegment:
+    segment_id: int
+    stage_id: int
+    tag: dict[str, int]
+    gm: fx.GraphModule
+    input_idxs: list[int]
+    param_idxs: list[int]
+    graphargs: list[Any]
+    placeholders: list[fx.Node]
+    a2a_boundary_after: dict[str, Any] | None = None
+
+
+def _inject_piper_annotation(node: fx.Node, name: str, index: int, uid: int) -> None:
+    custom = node.meta.setdefault("custom", {})
+    annotation = {"name": name, "index": int(index), "uid": int(uid)}
+    custom[PIPER_ANNOTATIONS_META_KEY] = (annotation,)
+    custom["name"] = name
+    custom["index"] = int(index)
+
+
+def _meta_tensor_like(example, *, requires_grad: bool, as_parameter: bool):
+    sym_int_type = getattr(torch, "SymInt", ())
+    sym_float_type = getattr(torch, "SymFloat", ())
+    sym_bool_type = getattr(torch, "SymBool", ())
+
+    if isinstance(example, torch.Tensor):
+        t = torch.empty(example.shape, dtype=example.dtype, device="meta")
+        t.requires_grad_(requires_grad)
+    elif isinstance(example, (sym_int_type, int)):
+        # Symbolic/python integers do not have .shape/.dtype; represent them as
+        # singleton meta tensors so downstream graph-arg handling remains uniform.
+        t = torch.empty((1,), dtype=torch.int64, device="meta")
+    elif isinstance(example, (sym_float_type, float)):
+        t = torch.empty((1,), dtype=torch.float32, device="meta")
+    elif isinstance(example, (sym_bool_type, bool)):
+        t = torch.empty((1,), dtype=torch.bool, device="meta")
+    else:
+        # Fallback for unknown scalar-like values.
+        t = torch.empty((1,), dtype=torch.float32, device="meta")
+
+    if as_parameter:
+        return torch.nn.Parameter(t, requires_grad=requires_grad)
+    return t
+
+
+def _iter_arg_nodes(arg: Any) -> list[fx.Node]:
+    nodes: list[fx.Node] = []
+
+    def _walk(v: Any) -> None:
+        if isinstance(v, fx.Node):
+            nodes.append(v)
+        elif isinstance(v, (list, tuple)):
+            for item in v:
+                _walk(item)
+        elif isinstance(v, dict):
+            for item in v.values():
+                _walk(item)
+
+    _walk(arg)
+    return nodes
+
+
+def _example_value(node: fx.Node) -> Any:
+    return node.meta.get("example_value", node.meta.get("val"))
+
+
+def _placeholder_is_runtime_input(node: fx.Node) -> bool:
+    ex = _example_value(node)
+    if not isinstance(ex, torch.Tensor):
+        return False
+    if isinstance(ex, torch.nn.Parameter):
+        return False
+    if bool(getattr(ex, "requires_grad", False)):
+        return False
+    if "self" in node.name or "grapharg" in node.meta:
+        return False
+    return True
+
+
+def _grapharg_for_placeholder(node: fx.Node) -> Any:
+    ex = _example_value(node)
+    requires_grad = bool(getattr(ex, "requires_grad", False))
+    as_parameter = isinstance(ex, torch.nn.Parameter) or requires_grad
+    return _meta_tensor_like(ex, requires_grad=requires_grad, as_parameter=as_parameter)
+
+
+def _grapharg_for_cross_value(node: fx.Node) -> Any:
+    ex = _example_value(node)
+    requires_grad = bool(getattr(ex, "requires_grad", False))
+    return _meta_tensor_like(ex, requires_grad=requires_grad, as_parameter=False)
+
+
+def _validate_and_get_annotation_stack(node: fx.Node) -> tuple[dict[str, int], ...]:
+    custom = node.meta.get("custom")
+    if not isinstance(custom, dict):
+        return ()
+
+    if PIPER_ANNOTATIONS_META_KEY not in custom:
+        return ()
+
+    raw_stack = custom[PIPER_ANNOTATIONS_META_KEY]
+    if not isinstance(raw_stack, (list, tuple)) or not raw_stack:
+        raise ValueError(
+            "Piper annotation metadata must be a non-empty list/tuple of "
+            f"{{'name': str, 'index': int, 'uid': int}} entries. "
+            f"Node {node.name!r} has {raw_stack!r}."
+        )
+
+    stack: list[dict[str, int]] = []
+    for annotation in raw_stack:
+        if not isinstance(annotation, dict):
+            raise ValueError(
+                f"Piper annotation entry on node {node.name!r} must be a dict, "
+                f"got {annotation!r}."
+            )
+        if set(annotation) - {"name", "index", "uid"}:
+            raise ValueError(
+                "Piper annotation entries may only contain 'name', 'index', and 'uid'. "
+                f"Node {node.name!r} has {annotation!r}."
+            )
+        name = annotation.get("name")
+        index = annotation.get("index")
+        uid = annotation.get("uid")
+        if not isinstance(name, str) or not name:
+            raise ValueError(
+                f"Piper annotation 'name' must be a non-empty string on node {node.name!r}: "
+                f"{annotation!r}."
+            )
+        if not isinstance(index, int):
+            raise ValueError(
+                f"Piper annotation 'index' must be an int on node {node.name!r}: "
+                f"{annotation!r}."
+            )
+        if not isinstance(uid, int):
+            raise ValueError(
+                f"Piper annotation 'uid' must be an int on node {node.name!r}: "
+                f"{annotation!r}."
+            )
+        stack.append({"name": name, "index": int(index), "uid": int(uid)})
+    return tuple(stack)
+
+
+def _annotation_stack_key(stack: tuple[dict[str, int], ...]) -> tuple[tuple[str, int, int], ...]:
+    return tuple((a["name"], int(a["index"]), int(a["uid"])) for a in stack)
+
+
+def _tag_from_stack(stack: tuple[dict[str, int], ...]) -> dict[str, int]:
+    tag: dict[str, int] = {}
+    for annotation in stack:
+        tag[annotation["name"]] = int(annotation["index"])
+    return tag
+
+
+def _select_boundary_tensor_idx(nodes: list[fx.Node]) -> int | None:
+    best_idx = None
+    best_score = -1
+    for idx, node in enumerate(nodes):
+        ex = _example_value(node)
+        if not isinstance(ex, torch.Tensor):
+            continue
+        score = 1
+        if ex.is_floating_point():
+            score += 2
+        if bool(getattr(ex, "requires_grad", False)):
+            score += 1
+        if score > best_score:
+            best_score = score
+            best_idx = idx
+    if best_idx is not None:
+        return best_idx
+    return 0 if nodes else None
+
+
+def split_gm_by_annotations(gm: fx.GraphModule) -> tuple[fx.GraphModule, list[AnnotationSegment]]:
+    """Split a GraphModule into contiguous subgraphs by Piper annotation stack.
+
+    Piper annotations are carried in ``node.meta['custom']['piper_annotations']``.
+    The full stack, including nested annotations, is part of the split key. The
+    schedule-facing tag for each segment is the stack projected to
+    ``{annotation_name: auto_index}``.
+    """
+    nodes = list(gm.graph.nodes)
+    compute_nodes = [
+        node for node in nodes
+        if node.op not in ("placeholder", "get_attr", "output")
+    ]
+
+    node_stacks: dict[fx.Node, tuple[dict[str, int], ...]] = {}
+    for node in nodes:
+        stack = _validate_and_get_annotation_stack(node)
+        if node in compute_nodes:
+            node_stacks[node] = stack
+
+    annotated_compute = [node for node in compute_nodes if node_stacks.get(node)]
+    if not annotated_compute:
+        return gm, []
+
+    unannotated = [node.name for node in compute_nodes if not node_stacks.get(node)]
+    if unannotated:
+        raise ValueError(
+            "Found compute nodes without Piper annotations in a graph that uses "
+            f"piper.annotate: {unannotated[:16]}. Wrap all model compute in "
+            "piper.annotate(...)."
+        )
+
+    segment_keys: list[tuple[tuple[str, int, int], ...]] = []
+    segment_stacks: list[tuple[dict[str, int], ...]] = []
+    node_seg: dict[fx.Node, int] = {}
+    current_key: tuple[tuple[str, int, int], ...] | None = None
+    for node in compute_nodes:
+        stack = node_stacks[node]
+        key = _annotation_stack_key(stack)
+        if key != current_key:
+            segment_keys.append(key)
+            segment_stacks.append(stack)
+            current_key = key
+        node_seg[node] = len(segment_keys) - 1
+
+    n_segs = len(segment_stacks)
+    if n_segs == 0:
+        return gm, []
+
+    runtime_placeholders = {
+        node for node in nodes
+        if node.op == "placeholder" and _placeholder_is_runtime_input(node)
+    }
+    grapharg_placeholders = {
+        node for node in nodes
+        if node.op == "placeholder" and node not in runtime_placeholders
+    }
+
+    value_seg: dict[fx.Node, int] = dict(node_seg)
+    for node in runtime_placeholders:
+        value_seg[node] = 0
+
+    def _consumer_seg(user: fx.Node) -> int | None:
+        if user.op == "output":
+            return n_segs - 1
+        return node_seg.get(user)
+
+    node_max_user_seg: dict[fx.Node, int] = {}
+    for node, seg in value_seg.items():
+        user_segs = [
+            user_seg for user in node.users
+            for user_seg in [_consumer_seg(user)]
+            if user_seg is not None
+        ]
+        node_max_user_seg[node] = max(user_segs) if user_segs else seg
+
+    seg_cross_in: list[list[fx.Node]] = [[] for _ in range(n_segs)]
+    for node in nodes:
+        if node not in value_seg:
+            continue
+        seg = value_seg[node]
+        max_seg = node_max_user_seg[node]
+        for target_seg in range(seg + 1, max_seg + 1):
+            seg_cross_in[target_seg].append(node)
+
+    segments: list[AnnotationSegment] = []
+    output_node = next((node for node in nodes if node.op == "output"), None)
+
+    for seg in range(n_segs):
+        sub_g = fx.Graph()
+        remap: dict[fx.Node, fx.Node] = {}
+        new_input_idxs: list[int] = []
+        new_param_idxs: list[int] = []
+        new_graphargs: list[Any] = []
+        pos = 0
+
+        def _add_placeholder(node: fx.Node, name: str, *, is_runtime_input: bool) -> None:
+            nonlocal pos
+            if node in remap:
+                return
+            new_ph = sub_g.placeholder(name)
+            new_ph.type = node.type
+            new_ph.meta.update(node.meta)
+            remap[node] = new_ph
+            if is_runtime_input:
+                new_input_idxs.append(pos)
+                new_graphargs.append(_grapharg_for_cross_value(node))
+            else:
+                new_param_idxs.append(pos)
+                new_graphargs.append(_grapharg_for_placeholder(node))
+            pos += 1
+
+        if seg == 0:
+            for node in nodes:
+                if node in runtime_placeholders:
+                    _add_placeholder(node, node.name, is_runtime_input=True)
+        else:
+            for node in seg_cross_in[seg]:
+                _add_placeholder(node, f"_xseg_{node.name}", is_runtime_input=True)
+
+        seg_compute_nodes = [
+            node for node in nodes
+            if node.op not in ("placeholder", "get_attr", "output")
+            and node_seg[node] == seg
+        ]
+        needed_grapharg_placeholders: set[fx.Node] = set()
+        needed_getattrs: set[fx.Node] = set()
+        for node in seg_compute_nodes:
+            for dep in [*node.all_input_nodes, *_iter_arg_nodes(node.kwargs)]:
+                if dep.op == "placeholder" and dep in grapharg_placeholders:
+                    needed_grapharg_placeholders.add(dep)
+                elif dep.op == "get_attr":
+                    needed_getattrs.add(dep)
+
+        if output_node is not None and seg == n_segs - 1:
+            for dep in _iter_arg_nodes(output_node.args):
+                if dep.op == "placeholder" and dep in grapharg_placeholders:
+                    needed_grapharg_placeholders.add(dep)
+                elif dep.op == "get_attr":
+                    needed_getattrs.add(dep)
+
+        for node in nodes:
+            if node in needed_grapharg_placeholders:
+                _add_placeholder(node, node.name, is_runtime_input=False)
+
+        for node in nodes:
+            if node.op == "get_attr" and node in needed_getattrs and node not in remap:
+                new_ga = sub_g.get_attr(node.target)
+                new_ga.type = node.type
+                new_ga.meta.update(node.meta)
+                remap[node] = new_ga
+
+        for node in seg_compute_nodes:
+            missing = [dep.name for dep in node.all_input_nodes if dep not in remap and node_seg.get(dep) != seg]
+            if missing:
+                raise ValueError(
+                    f"Cannot split annotated graph at segment {seg}; node {node.name!r} "
+                    f"has unmapped external dependencies {missing}."
+                )
+            remap[node] = sub_g.node_copy(node, arg_transform=lambda x, r=remap: r[x])
+
+        if seg == n_segs - 1:
+            if output_node is None:
+                sub_g.output(())
+            else:
+                sub_g.output(fx.map_arg(output_node.args[0], lambda x: remap[x]))
+            boundary_after = None
+        else:
+            out_nodes = [remap[node] for node in seg_cross_in[seg + 1]]
+            sub_g.output(tuple(out_nodes) if len(out_nodes) != 1 else out_nodes[0])
+            boundary_after = {
+                "tensor_idx": _select_boundary_tensor_idx(seg_cross_in[seg + 1]),
+                "reshape_input": None,
+                "reshape_output": None,
+                "from_tag": _tag_from_stack(segment_stacks[seg]),
+                "to_tag": _tag_from_stack(segment_stacks[seg + 1]),
+            }
+
+        sub_g.lint()
+        seg_gm = fx.GraphModule(gm, sub_g)
+        placeholders = [node for node in seg_gm.graph.nodes if node.op == "placeholder"]
+        tag = _tag_from_stack(segment_stacks[seg])
+        segments.append(
+            AnnotationSegment(
+                segment_id=seg,
+                stage_id=int(tag.get("PP", seg)),
+                tag=tag,
+                gm=seg_gm,
+                input_idxs=new_input_idxs,
+                param_idxs=new_param_idxs,
+                graphargs=new_graphargs,
+                placeholders=placeholders,
+                a2a_boundary_after=boundary_after,
+            )
+        )
+
+    return gm, segments
diff --git a/src/ordering.py b/src/ordering.py
new file mode 100644
index 0000000..88bea28
--- /dev/null
+++ b/src/ordering.py
@@ -0,0 +1,178 @@
+from .dag import (
+    TrainingDAG,
+    TrainingDAGEdge,
+    _has_path,
+    _topological_levels,
+    _topological_order,
+)
+
+_DEFAULT_STREAM = "default_stream"
+_OTHER_COMM_KINDS = {
+    "REDUCE_COMM",
+    "ALL_GATHER_COMM",
+    "REDUCE_SCATTER_COMM",
+    "A2A_COMM",
+}
+
+
+def _serial_topological_order(
+    dag: TrainingDAG,
+    topo_levels: dict[str, int] | None = None,
+) -> list[str]:
+    """Serialize topological levels into a deterministic dispatch order.
+
+    Nodes with lower topological levels always come first. Within a level,
+    priority order is: SEND > other comm > compute > RECV.
+    """
+    if topo_levels is None:
+        topo_levels = _topological_levels(dag)
+
+    base_order = _topological_order(dag)
+    topo_idx = {uid: i for i, uid in enumerate(base_order)}
+
+    def node_priority(uid: str) -> int:
+        kind = dag.nodes[uid].node_kind
+        if kind == "SEND_COMM":
+            return 0
+        if kind in _OTHER_COMM_KINDS:
+            return 1
+        if kind == "RECV_COMM":
+            return 3
+        return 2
+
+    return sorted(
+        dag.nodes,
+        key=lambda uid: (topo_levels[uid], node_priority(uid), topo_idx[uid]),
+    )
+
+
+def _resolve_default_stream_order(dag: TrainingDAG) -> None:
+    """Create a total ordering over default-stream COMPUTE nodes.
+
+    Whenever multiple default-stream compute nodes share a topological level,
+    chain them with temporal edges in descending order of downstream
+    dependencies (more downstream nodes -> earlier in the chain). Downstream
+    count is the size of each compute node's transitive successor set, which in
+    a well-formed training DAG terminates at UPD.
+
+    The new edges shift topological levels, so after each chain insertion we
+    recompute levels and rescan from the earliest level. The pass terminates
+    when every default-stream compute node sits at a unique level.
+    """
+    def _is_default_compute(uid: str) -> bool:
+        node = dag.nodes[uid]
+        return node.stream == _DEFAULT_STREAM and node.node_kind == "COMPUTE"
+
+    compute_uids = [uid for uid in dag.nodes if _is_default_compute(uid)]
+    if len(compute_uids) < 2:
+        return
+
+    def _downstream_count(uid: str) -> int:
+        seen: set[str] = set()
+        stack = list(dag.succs.get(uid, set()))
+        while stack:
+            v = stack.pop()
+            if v in seen:
+                continue
+            seen.add(v)
+            stack.extend(dag.succs.get(v, set()))
+        return len(seen)
+
+    while True:
+        topo_levels = _topological_levels(dag)
+        by_level: dict[int, list[str]] = {}
+        for uid in compute_uids:
+            by_level.setdefault(topo_levels[uid], []).append(uid)
+
+        conflict_level: int | None = None
+        for level in sorted(by_level):
+            if len(by_level[level]) > 1:
+                conflict_level = level
+                break
+        if conflict_level is None:
+            return
+
+        # Same-level nodes have no path between them, otherwise their levels
+        # would differ. Order by downstream count desc; break ties by uid.
+        group = by_level[conflict_level]
+        ordered = sorted(group, key=lambda u: (-_downstream_count(u), u))
+        for src, dst in zip(ordered, ordered[1:]):
+            dag.add_edge(
+                TrainingDAGEdge(
+                    src_uid=src,
+                    dst_uid=dst,
+                    dep_kind="temporal",
+                    tensor_name=None,
+                )
+            )
+
+
+def resolve_total_order_per_stream(dag: TrainingDAG) -> None:
+    """Serialize the default stream, then strictly chain non-default streams.
+
+    The default-stream pass runs first so the per-non-default-stream anchors
+    below see the post-serialization topological order of default-stream nodes.
+
+    Non-default stream nodes are ordered by the topological order of the
+    default-stream nodes they directly depend on or are depended on by.
+    """
+    _resolve_default_stream_order(dag)
+
+    topo = _topological_order(dag)
+    topo_idx = {uid: i for i, uid in enumerate(topo)}
+    topo_levels = _topological_levels(dag)
+    default_uids = [
+        uid for uid in topo
+        if dag.nodes[uid].stream == _DEFAULT_STREAM
+    ]
+    streams = sorted({
+        n.stream for n in dag.nodes.values()
+        if n.stream != _DEFAULT_STREAM
+    })
+    if not default_uids or not streams:
+        return
+
+    for stream in streams:
+        associated_by_default: dict[str, list[str]] = {uid: [] for uid in default_uids}
+        default_anchors_by_stream_uid: dict[str, list[str]] = {}
+        for edge in dag.edges:
+            src = dag.nodes[edge.src_uid]
+            dst = dag.nodes[edge.dst_uid]
+            if src.stream == _DEFAULT_STREAM and dst.stream == stream:
+                default_anchors_by_stream_uid.setdefault(edge.dst_uid, []).append(edge.src_uid)
+            elif src.stream == stream and dst.stream == _DEFAULT_STREAM:
+                default_anchors_by_stream_uid.setdefault(edge.src_uid, []).append(edge.dst_uid)
+
+        for stream_uid in {
+            uid for uid, node in dag.nodes.items()
+            if node.stream == stream
+        }:
+            default_anchors = default_anchors_by_stream_uid.get(stream_uid, [])
+            if not default_anchors:
+                continue
+            anchor_uid = min(default_anchors, key=lambda uid: (topo_levels[uid], topo_idx[uid]))
+            associated_by_default.setdefault(anchor_uid, []).append(stream_uid)
+
+        current_uid: str | None = None
+        for default_uid in default_uids:
+            stream_uids = sorted(
+                set(associated_by_default.get(default_uid, [])),
+                key=lambda u: (topo_levels[u], topo_idx[u]),
+            )
+            for next_uid in stream_uids:
+                if current_uid is not None and current_uid != next_uid:
+                    if _has_path(dag, next_uid, current_uid):
+                        raise ValueError(
+                            "resolve_total_order_per_stream would create a cycle while ordering "
+                            f"stream={stream}: {current_uid} -> {next_uid}"
+                        )
+                    if not _has_path(dag, current_uid, next_uid):
+                        dag.add_edge(
+                            TrainingDAGEdge(
+                                src_uid=current_uid,
+                                dst_uid=next_uid,
+                                dep_kind="temporal",
+                                tensor_name=None,
+                            )
+                        )
+                current_uid = next_uid
diff --git a/src/piper.py b/src/piper.py
index 0a353ac..df32a51 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -1,2710 +1,84 @@
-import os
 import time
-from collections import Counter
-from dataclasses import dataclass, field
-from typing import Any
+from contextlib import contextmanager
+from typing import Iterator
 
 import ray
 import torch.fx as fx
 from torch._dynamo.backends.registry import register_backend
 
-from .piper_graph_transform import _profile_and_split_gm, _split_gm_by_stages, split_by_a2a, bucket_stage
-from .piper_exec import TaskType
-from .piper_utils import LOG_LEVEL, create_logger, piper_metadata
+from .fx import PIPER_ANNOTATIONS_META_KEY, split_gm_by_annotations
+from .dag import (
+    TrainingDAG,
+    TrainingDAGEdge,
+    TrainingDAGNode,
+    build_training_dag,
+)
+from .directives import (
+    _apply_order_directive,
+    _apply_split_backward_stencil,
+    _apply_split_directive,
+    _bucket_matched_fwd_nodes,
+    _parse_order_directive,
+    _validate_schedule_tags_exist,
+    _validate_split_backward_order_stencil,
+    apply_schedule_directives,
+)
+from .ordering import (
+    _serial_topological_order,
+    resolve_total_order_per_stream,
+)
+from .state import LOG_LEVEL, create_logger, piper_metadata
+from .visualization import (
+    log_training_dag_dependencies,
+    print_training_dag_order,
+    render_training_dag,
+)
+from .zero import _add_inter_chain_temporal_edges, _prune_zero_lifetime_metadata
 
 logger = create_logger("piper_backend", LOG_LEVEL)
-_ANY_TAG_INDEX = "__ANY_TAG_INDEX__"
-_FORWARD_PASS = "F"
-_FUSED_BWD_PASS = "B"
-_BWD_INPUT_PASS = "BI"
-_BWD_WEIGHT_PASS = "BW"
-_DEFAULT_STREAM = "default_stream"
-_VALID_ORDER_PASSES = frozenset({
-    _FORWARD_PASS,
-    _FUSED_BWD_PASS,
-    _BWD_INPUT_PASS,
-    _BWD_WEIGHT_PASS,
-})
+_ANNOTATION_STACK: list[dict[str, int]] = []
+_ANNOTATION_COUNTS: dict[str, int] = {}
+_ANNOTATION_UID = 0
 
 
-def _is_backward_activation_subkind(subkind: str | None) -> bool:
-    return subkind in ("BWD", "BWD_I")
+def _reset_annotation_state() -> None:
+    global _ANNOTATION_UID
+    _ANNOTATION_STACK.clear()
+    _ANNOTATION_COUNTS.clear()
+    _ANNOTATION_UID = 0
 
 
-def _is_backward_weight_subkind(subkind: str | None) -> bool:
-    return subkind in ("BWD", "BWD_W")
+@contextmanager
+def annotate(name: str) -> Iterator[dict[str, int]]:
+    """Annotate traced model code with a Piper schedule tag.
 
-
-def _is_backward_compute_subkind(subkind: str | None) -> bool:
-    return subkind in ("BWD", "BWD_I", "BWD_W")
-
-def _collect_triton_constant_args(gm: fx.GraphModule) -> dict[int, Any]:
-    try:
-        from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
-    except ImportError:
-        return {}
-    out: dict[int, Any] = {}
-    for node in gm.graph.nodes:
-        if node.op != "call_function":
-            continue
-        target_str = getattr(node.target, "__name__", "") or str(node.target)
-        if "triton_kernel_wrapper" not in target_str:
-            continue
-        idx = node.kwargs.get("constant_args_idx")
-        if idx is not None and idx in kernel_side_table.constant_args:
-            out[int(idx)] = kernel_side_table.constant_args[idx]
-    return out
-
-
-@dataclass(frozen=True)
-class TrainingDAGEdge:
-    src_uid: str
-    dst_uid: str
-    dep_kind: str  # "data" | "temporal"
-    tensor_name: str | None = None
-
-
-@dataclass
-class TrainingDAGNode:
-    uid: str
-    node_kind: str  # "COMPUTE" | "SEND_COMM" | "RECV_COMM" | "REDUCE_COMM" | "ALL_GATHER_COMM" | "REDUCE_SCATTER_COMM" | "A2A_COMM" | "ORDER_DUMMY"
-    compute_subkind: str | None  # "FWD" | "BWD" | "BWD_I" | "BWD_W" when node_kind == "COMPUTE"
-    tag: dict[str, int | None]
-    device: list[int] | None
-    stream: str
-    node_meta: dict[str, Any] = field(default_factory=dict)
-
-
-@dataclass
-class TrainingDAG:
-    nodes: dict[str, TrainingDAGNode] = field(default_factory=dict)
-    edges: list[TrainingDAGEdge] = field(default_factory=list)
-    succs: dict[str, set[str]] = field(default_factory=dict)
-    preds: dict[str, set[str]] = field(default_factory=dict)
-    edge_keys: set[tuple[str, str, str, str | None]] = field(default_factory=set)
-
-    def add_node(self, node: TrainingDAGNode) -> None:
-        if node.uid in self.nodes:
-            raise ValueError(f"Duplicate node uid: {node.uid}")
-        self.nodes[node.uid] = node
-        self.succs[node.uid] = set()
-        self.preds[node.uid] = set()
-
-    def add_edge(self, edge: TrainingDAGEdge) -> None:
-        if edge.src_uid not in self.nodes or edge.dst_uid not in self.nodes:
-            raise ValueError(f"Edge references unknown node: {edge}")
-        edge_key = (edge.src_uid, edge.dst_uid, edge.dep_kind, edge.tensor_name)
-        if edge_key in self.edge_keys:
-            return
-        self.edge_keys.add(edge_key)
-        self.edges.append(edge)
-        self.succs[edge.src_uid].add(edge.dst_uid)
-        self.preds[edge.dst_uid].add(edge.src_uid)
-
-
-
-def _flatten_output_nodes(out_arg: Any) -> list[fx.Node]:
-    out_nodes: list[fx.Node] = []
-
-    def _walk(v: Any) -> None:
-        if isinstance(v, fx.Node):
-            out_nodes.append(v)
-        elif isinstance(v, (tuple, list)):
-            for x in v:
-                _walk(x)
-        elif isinstance(v, dict):
-            for x in v.values():
-                _walk(x)
-
-    _walk(out_arg)
-    return out_nodes
-
-
-
-def _node_io_names(seg_gm: fx.GraphModule) -> tuple[list[str], list[str]]:
-    inputs = [n.name for n in seg_gm.graph.nodes if n.op == "placeholder"]
-    out_node = next((n for n in seg_gm.graph.nodes if n.op == "output"), None)
-    if out_node is None:
-        return inputs, []
-    outputs = [n.name for n in _flatten_output_nodes(out_node.args[0])]
-    return inputs, outputs
-
-
-def _infer_ep_idx(segment_idx: int) -> int | None:
-    """Heuristic: odd A2A segments are expert regions, even segments are non-expert."""
-    if segment_idx % 2 == 0:
-        return None
-    return segment_idx // 2
-
-
-def _format_tag(tag: dict[str, int | None]) -> str:
-    if not tag:
-        return "(no-tags)"
-    keys = sorted(tag.keys(), key=lambda k: (k != "PP", k != "EP", k))
-    return ", ".join(f"{k}={tag[k]}" for k in keys)
-
-
-def _with_pass_tag(tag: dict[str, Any], pass_value: str | None) -> dict[str, Any]:
-    t = dict(tag)
-    t["PASS"] = pass_value
-    return t
-
-
-def _format_device(device: list[int] | None) -> str:
-    return "None" if device is None else str(list(device))
-
-
-def _debug_render_training_dag(training_dag: TrainingDAG, output_path: str = "out/training_dag") -> None:
-    """Render TrainingDAG with node labels as tags and data-dependency edges."""
-    try:
-        import graphviz
-    except ImportError:
-        logger.warning("graphviz Python package not installed; skipping TrainingDAG render")
-        return
-
-    dot = graphviz.Digraph("TrainingDAG", comment="Training DAG (tag-labelled)")
-    dot.attr(rankdir="LR")
-    topo_levels = _topological_levels(training_dag)
-
-    def _format_node_meta_lines(node: TrainingDAGNode) -> str:
-        keys = (
-            "bucket_key",
-            "zero_alloc_full_grads_before",
-            "zero_free_full_params_after",
-        )
-        lines = [
-            f"{key}={node.node_meta[key]!r}"
-            for key in keys
-            if key in node.node_meta
-        ]
-        return "\\n".join(lines)
-
-    for uid, node in training_dag.nodes.items():
-        topo_label = f"topo={topo_levels.get(uid, '?')}"
-        meta_label = _format_node_meta_lines(node)
-        meta_suffix = f"\\n{meta_label}" if meta_label else ""
-        if node.node_kind in (
-            "SEND_COMM",
-            "RECV_COMM",
-            "REDUCE_COMM",
-            "ALL_GATHER_COMM",
-            "REDUCE_SCATTER_COMM",
-            "A2A_COMM",
-        ):
-            label = (
-                f"{topo_label}\\n{node.node_kind}\\n{_format_tag(node.tag)}\\n"
-                f"{_format_device(node.device)}\\nstream={node.stream}"
-                f"{meta_suffix}"
-            )
-            shape = "box"
-        else:
-            subkind = node.compute_subkind or "COMPUTE"
-            label = (
-                f"{topo_label}\\n{subkind}\\n{_format_tag(node.tag)}\\n{_format_device(node.device)}\\nstream={node.stream}"
-                f"{meta_suffix}"
-            )
-            shape = "ellipse"
-        dot.node(uid, label=label, shape=shape)
-
-    nodes_by_level: dict[int, list[str]] = {}
-    for uid, level in topo_levels.items():
-        nodes_by_level.setdefault(level, []).append(uid)
-    for level, uids in sorted(nodes_by_level.items()):
-        with dot.subgraph(name=f"topo_{level}") as sub:
-            sub.attr(rank="same")
-            for uid in sorted(uids):
-                sub.node(uid)
-    level_reps = [
-        sorted(uids)[0]
-        for _level, uids in sorted(nodes_by_level.items())
-        if uids
-    ]
-    for src_uid, dst_uid in zip(level_reps, level_reps[1:]):
-        dot.edge(src_uid, dst_uid, style="invis", weight="100")
-
-    for edge in training_dag.edges:
-        if edge.dep_kind == "data":
-            dot.edge(edge.src_uid, edge.dst_uid)
-        elif edge.dep_kind == "temporal":
-            dot.edge(edge.src_uid, edge.dst_uid, style="dashed", color="blue")
-
-    out = dot.render(output_path, format="png", cleanup=True)
-    logger.info("TrainingDAG debug graph saved to %s", out)
-
-
-def _log_training_dag_dependencies(training_dag: TrainingDAG) -> None:
-    """Log predecessor/successor tags for each TrainingDAG node."""
-    def _sort_key(item: tuple[str, TrainingDAGNode]) -> tuple[int, int]:
-        _uid, node = item
-        if node.node_kind == "COMPUTE":
-            return (int(node.node_meta.get("stage_id", 10**9)), int(node.node_meta.get("segment_id", 10**9)))
-        return (10**9, 10**9)
-
-    for uid, node in sorted(training_dag.nodes.items(), key=_sort_key):
-        pred_tags = [
-            _format_tag(training_dag.nodes[p].tag)
-            for p in sorted(training_dag.preds.get(uid, []), key=lambda x: x)
-        ]
-        succ_tags = [
-            _format_tag(training_dag.nodes[s].tag)
-            for s in sorted(training_dag.succs.get(uid, []), key=lambda x: x)
-        ]
-        logger.debug(
-            "TrainingDAG node=%s kind=%s subkind=%s tag=(%s) device=%s stream=%s preds=[%s] succs=[%s]",
-            uid,
-            node.node_kind,
-            node.compute_subkind,
-            _format_tag(node.tag),
-            _format_device(node.device),
-            node.stream,
-            ", ".join(pred_tags),
-            ", ".join(succ_tags),
-        )
-
-
-def _training_dag_task_type(node: TrainingDAGNode) -> TaskType:
-    if node.node_kind == "COMPUTE":
-        if node.compute_subkind == "FWD":
-            return TaskType.FWD
-        if node.compute_subkind == "BWD":
-            return TaskType.BWD
-        if node.compute_subkind == "BWD_I":
-            return TaskType.BWD_I
-        if node.compute_subkind == "BWD_W":
-            return TaskType.BWD_W
-    if node.node_kind == "UPD":
-        return TaskType.UPD
-    if node.node_kind == "SEND_COMM":
-        return TaskType.SEND
-    if node.node_kind == "RECV_COMM":
-        return TaskType.RECV
-    if node.node_kind == "ALL_GATHER_COMM":
-        return TaskType.ALL_GATHER
-    if node.node_kind == "REDUCE_SCATTER_COMM":
-        return TaskType.REDUCE_SCATTER
-    if node.node_kind == "REDUCE_COMM":
-        return TaskType.ALL_REDUCE
-    if node.node_kind == "A2A_COMM":
-        return TaskType.FWD_A2A if node.tag.get("PASS") == "F" else TaskType.BWD_A2A
-    if node.node_kind == "ORDER_DUMMY":
-        return TaskType.ORDER_DUMMY
-    return TaskType.FWD
-
-
-def print_training_dag_order(
-    dag: TrainingDAG,
-    label: str = "",
-    rank: int = 0,
-    out_dir: str = "out",
-) -> None:
-    """Write and log the actor dispatch order for a TrainingDAG."""
-    topo_levels = _topological_levels(dag)
-    topo = _serial_topological_order(dag, topo_levels)
-    data_preds: dict[str, list[str]] = {uid: [] for uid in dag.nodes}
-    data_succs: dict[str, list[str]] = {uid: [] for uid in dag.nodes}
-    temporal_preds: dict[str, list[str]] = {uid: [] for uid in dag.nodes}
-    temporal_succs: dict[str, list[str]] = {uid: [] for uid in dag.nodes}
-    for e in dag.edges:
-        if e.dep_kind == "data":
-            data_preds[e.dst_uid].append(e.src_uid)
-            data_succs[e.src_uid].append(e.dst_uid)
-        else:
-            temporal_preds[e.dst_uid].append(e.src_uid)
-            temporal_succs[e.src_uid].append(e.dst_uid)
-
-    header = f"--- TrainingDAG execution order{': ' + label if label else ''} ---"
-    lines = [header]
-    for uid in topo:
-        node = dag.nodes[uid]
-        task_type = _training_dag_task_type(node)
-        meta = node.node_meta
-        extra = []
-        for key in ("bucket_key", "fwd_uid", "peer_pp_rank", "from_uid", "to_uid"):
-            if key in meta and meta[key] is not None:
-                extra.append(f"{key}={meta[key]}")
-        extra_str = ("  " + " ".join(extra)) if extra else ""
-        line = (
-            f"  topo={topo_levels[uid]:<3d}  "
-            f"{task_type.value:<14s}  kind={node.node_kind:<19s}  uid={node.uid:<24s}  "
-            f"tag=({_format_tag(node.tag)})  stream={node.stream}{extra_str}  "
-            f"data_preds={sorted(data_preds[node.uid])} data_succs={sorted(data_succs[node.uid])}  "
-            f"temp_preds={sorted(temporal_preds[node.uid])} temp_succs={sorted(temporal_succs[node.uid])}"
-        )
-        lines.append(line)
-
-    lines.append("-" * len(header))
-
-    os.makedirs(out_dir, exist_ok=True)
-    out_path = os.path.join(out_dir, f"training_dag_order_rank{rank}")
-    with open(out_path, "w") as f:
-        f.write("\n".join(lines) + "\n")
-    logger.info("TrainingDAG execution order saved to %s", out_path)
-
-
-def build_training_dag(
-    stage_submodules: list[tuple[int, fx.GraphModule, list[int], list[int], list, list]],
-    *,
-    enable_ep: bool,
-    dp_degree: int,
-    stage_tag_name: str = "PP",
-) -> TrainingDAG:
-    """Build a TrainingDAG where each node is one stage/a2a segment GraphModule."""
-    dag = TrainingDAG()
-
-    global_prev_uid: str | None = None
-
-    for stage_id, stage_gm, input_idxs, param_idxs, graphargs, _placeholders in stage_submodules:
-        if enable_ep and dp_degree > 1:
-            segments, boundary_infos = split_by_a2a(stage_gm, graphargs, input_idxs, param_idxs)
-        else:
-            segments = [(stage_gm, input_idxs, param_idxs, graphargs)]
-            boundary_infos = []
-
-        for seg_id, (seg_gm, seg_in, seg_param, seg_args) in enumerate(segments):
-            uid = f"s{stage_id}.seg{seg_id}"
-            tag: dict[str, int | None] = {stage_tag_name: stage_id}
-            if seg_id % 2 == 1:
-                boundary_name = None
-                if seg_id - 1 < len(boundary_infos):
-                    boundary_name = boundary_infos[seg_id - 1].get("name")
-                if boundary_name:
-                    tag[boundary_name] = _infer_ep_idx(seg_id)
-            tag = _with_pass_tag(tag, "F")
-            in_names, out_names = _node_io_names(seg_gm)
-            boundary_after = boundary_infos[seg_id] if seg_id < len(boundary_infos) else None
-            node = TrainingDAGNode(
-                uid=uid,
-                node_kind="COMPUTE",
-                compute_subkind="FWD",
-                tag=tag,
-                device=None,
-                stream="default_stream",
-                node_meta={
-                    "bucket_key": uid,
-                    "stage_id": stage_id,
-                    "segment_id": seg_id,
-                    "gm": seg_gm,
-                    "input_idxs": list(seg_in),
-                    "param_idxs": list(seg_param),
-                    "graphargs": list(seg_args),
-                    "input_names": in_names,
-                    "output_names": out_names,
-                    "a2a_boundary_after": boundary_after,
-                    "triton_constant_args": _collect_triton_constant_args(seg_gm),
-                },
-            )
-            dag.add_node(node)
-
-            # Explicit activation-flow ordering in stage/segment order.
-            if global_prev_uid is not None:
-                dag.add_edge(TrainingDAGEdge(src_uid=global_prev_uid, dst_uid=uid, dep_kind="data"))
-            global_prev_uid = uid
-
-    # Build backward-pass nodes and dependencies by reversing forward data edges.
-    fwd_compute_uids = [
-        uid for uid, node in dag.nodes.items()
-        if node.node_kind == "COMPUTE" and node.compute_subkind == "FWD"
-    ]
-    fwd_uid_to_bwd_uid: dict[str, str] = {}
-    for fwd_uid in fwd_compute_uids:
-        fwd_node = dag.nodes[fwd_uid]
-        bwd_uid = f"{fwd_uid}.bwd"
-        fwd_uid_to_bwd_uid[fwd_uid] = bwd_uid
-        dag.add_node(
-            TrainingDAGNode(
-                uid=bwd_uid,
-                node_kind="COMPUTE",
-                compute_subkind="BWD",
-                tag=_with_pass_tag(fwd_node.tag, "B"),
-                device=fwd_node.device,
-                stream="default_stream",
-                node_meta={
-                    "fwd_uid": fwd_uid,
-                    "bucket_key": fwd_node.node_meta.get("bucket_key", fwd_uid),
-                    "compute_loss": False,
-                },
-            )
-        )
-
-    fwd_edges = [
-        e for e in dag.edges
-        if e.dep_kind == "data"
-        and dag.nodes[e.src_uid].node_kind == "COMPUTE"
-        and dag.nodes[e.dst_uid].node_kind == "COMPUTE"
-        and dag.nodes[e.src_uid].compute_subkind == "FWD"
-        and dag.nodes[e.dst_uid].compute_subkind == "FWD"
-    ]
-
-    # Reverse every forward dependency for the backward pass:
-    # FWD:  u -> v    ==>    BWD: v' -> u'
-    for e in fwd_edges:
-        bwd_src = fwd_uid_to_bwd_uid[e.dst_uid]
-        bwd_dst = fwd_uid_to_bwd_uid[e.src_uid]
-        dag.add_edge(
-            TrainingDAGEdge(
-                src_uid=bwd_src,
-                dst_uid=bwd_dst,
-                dep_kind="data",
-                tensor_name=e.tensor_name,
-            )
-        )
-
-    # Bridge from forward phase into backward phase:
-    # last_fwd -> first_bwd (where first_bwd is the BWD node paired with last_fwd).
-    if fwd_compute_uids:
-        def _fwd_sort_key(uid: str) -> tuple[int, int]:
-            n = dag.nodes[uid]
-            return (
-                int(n.node_meta.get("stage_id", -1)),
-                int(n.node_meta.get("segment_id", -1)),
-            )
-
-        last_fwd_uid = max(fwd_compute_uids, key=_fwd_sort_key)
-        first_bwd_uid = fwd_uid_to_bwd_uid[last_fwd_uid]
-        dag.add_edge(
-            TrainingDAGEdge(
-                src_uid=last_fwd_uid,
-                dst_uid=first_bwd_uid,
-                dep_kind="data",
-                tensor_name=None,
-            )
-        )
-
-        # Mark loss computation point: first BWD node (earliest in BWD execution).
-        if first_bwd_uid in dag.nodes:
-            dag.nodes[first_bwd_uid].node_meta["compute_loss"] = True
-
-    # Add explicit UPD node with temporal dependency from every BWD compute node.
-    # UPD participates in the compute temporal chain and is not scheduled from
-    # data edges.
-    upd_uid = "upd.0"
-    dag.add_node(
-        TrainingDAGNode(
-            uid=upd_uid,
-            node_kind="UPD",
-            compute_subkind=None,
-            tag={"PASS": None},
-            device=None,
-            stream="default_stream",
-            node_meta={},
-        )
-    )
-    for uid, node in list(dag.nodes.items()):
-        if node.node_kind == "COMPUTE" and node.compute_subkind == "BWD":
-            dag.add_edge(
-                TrainingDAGEdge(
-                    src_uid=uid,
-                    dst_uid=upd_uid,
-                    dep_kind="temporal",
-                    tensor_name=None,
-                )
-            )
-
-    return dag
-
-
-def _match_filter(tag: dict[str, int | None], flt: dict[str, Any]) -> bool:
-    for k, v in flt.items():
-        if k not in tag:
-            return False
-        if v == _ANY_TAG_INDEX:
-            # "*" wildcard matches any concrete index for this tag, but not None.
-            if tag[k] is None:
-                return False
-        elif tag[k] != v:
-            return False
-    return True
-
-
-def _apply_split_backward_stencil(
-    dag: TrainingDAG,
-    split_base_keys: set[tuple[tuple[str, Any], ...]],
-) -> None:
-    if not split_base_keys:
-        return
-    split_filters = [dict(key) for key in split_base_keys]
-    for node in list(dag.nodes.values()):
-        if (
-            node.node_kind != "COMPUTE"
-            or node.compute_subkind != "BWD"
-            or not any(_match_filter(node.tag, flt) for flt in split_filters)
-        ):
-            continue
-        _split_fused_bwd_node(dag, node.uid)
-
-
-def _split_fused_bwd_node(dag: TrainingDAG, bwd_uid: str) -> None:
-    bwd_i = dag.nodes[bwd_uid]
-    if bwd_i.node_kind != "COMPUTE" or bwd_i.compute_subkind != "BWD":
-        raise ValueError(f"expected fused BWD compute node, got {bwd_uid}: {bwd_i}")
-    bwd_w_uid = f"{bwd_uid}.bw"
-    if bwd_w_uid in dag.nodes:
-        raise ValueError(f"duplicate BWD_W uid while splitting {bwd_uid}: {bwd_w_uid}")
-
-    bwd_i.compute_subkind = "BWD_I"
-    bwd_i.tag = _with_pass_tag(bwd_i.tag, _BWD_INPUT_PASS)
-
-    bwd_w_meta = dict(bwd_i.node_meta)
-    bwd_w_meta["compute_loss"] = False
-    dag.add_node(
-        TrainingDAGNode(
-            uid=bwd_w_uid,
-            node_kind="COMPUTE",
-            compute_subkind="BWD_W",
-            tag=_with_pass_tag(bwd_i.tag, _BWD_WEIGHT_PASS),
-            device=list(bwd_i.device) if bwd_i.device is not None else None,
-            stream=bwd_i.stream,
-            node_meta=bwd_w_meta,
-        )
-    )
-
-    for edge in [
-        e for e in list(dag.edges)
-        if e.dep_kind == "temporal"
-        and e.src_uid == bwd_uid
-        and dag.nodes[e.dst_uid].node_kind == "UPD"
-    ]:
-        _remove_edge(dag, edge)
-        dag.add_edge(
-            TrainingDAGEdge(
-                src_uid=bwd_w_uid,
-                dst_uid=edge.dst_uid,
-                dep_kind="temporal",
-                tensor_name=edge.tensor_name,
-            )
-        )
-
-    dag.add_edge(
-        TrainingDAGEdge(
-            src_uid=bwd_uid,
-            dst_uid=bwd_w_uid,
-            dep_kind="data",
-            tensor_name=None,
-        )
-    )
-
-
-def _normalize_filter_devices_directive(
-    directive: Any
-) -> tuple[str, list[dict[str, Any]], list[int], str | None, str | None, str | None, bool, bool, int | None]:
-    if isinstance(directive, dict):
-        op = directive.get("op")
-        if op not in ("place", "replicate", "shard"):
-            raise ValueError(f"Unsupported directive op: {op}")
-        if "filter" not in directive:
-            raise ValueError(
-                f"{op} directive requires 'filter' (singular), not 'filters': {directive}"
-            )
-        if "filters" in directive:
-            raise ValueError(
-                f"{op} directive uses deprecated 'filters'; use 'filter' (singular): {directive}"
-            )
-        filter_spec = directive.get("filter")
-        devices = directive.get("devices", directive.get("device"))
-        stream = directive.get("stream")
-        gather_stream = directive.get("gather_stream")
-        reduce_stream = directive.get("reduce_stream")
-        if op == "replicate":
-            if stream is not None:
-                raise ValueError(
-                    f"replicate directive does not accept 'stream'; use 'gather_stream' and/or 'reduce_stream': {directive}"
-                )
-        else:
-            if gather_stream is not None or reduce_stream is not None:
-                raise ValueError(
-                    f"{op} directive does not accept 'gather_stream'/'reduce_stream'; use 'stream': {directive}"
-                )
-        shard_params = bool(directive.get("shard_params", False))
-        shard_grads = bool(directive.get("shard_grads", False))
-        bucket_size_raw = directive.get("bucket_size", None)
-        bucket_size = None if bucket_size_raw is None else int(bucket_size_raw)
-        if not isinstance(devices, list) or not devices:
-            raise ValueError(f"place directive requires non-empty devices list: {directive}")
-        n = _normalize_filter_spec(filter_spec, directive)
-        return (
-            op,
-            [n],
-            [int(d) for d in devices],
-            (None if stream is None else str(stream)),
-            (None if gather_stream is None else str(gather_stream)),
-            (None if reduce_stream is None else str(reduce_stream)),
-            shard_params,
-            shard_grads,
-            bucket_size,
-        )
-
-    raise ValueError(f"Schedule directives must be JSON objects, got: {type(directive)}")
-
-
-def _normalize_filter_spec(filter_spec: Any, directive: Any) -> dict[str, Any]:
-    """Normalize a single filter spec into {tag_name: value}."""
-    def _norm_value(k: str, v: Any) -> Any:
-        if k == "PASS":
-            if not isinstance(v, str):
-                raise ValueError(
-                    f"'PASS' filter value must be a string in {{'F','B','BI','BW'}}: {directive}"
-                )
-            if v not in ("F", "B", "BI", "BW"):
-                raise ValueError(
-                    f"Invalid 'PASS' filter value '{v}'. Allowed: 'F', 'B', 'BI', 'BW': {directive}"
-                )
-            return v
-        if v == "*":
-            return _ANY_TAG_INDEX
-        if v is None:
-            return None
-        if isinstance(v, int):
-            return int(v)
-        if isinstance(v, str):
-            # Keep pass-like symbolic tags (e.g., "FWD"/"BWD") as strings.
-            try:
-                return int(v)
-            except ValueError:
-                return v
-        return v
-
-    if isinstance(filter_spec, dict):
-        out: dict[str, Any] = {}
-        for k, v in filter_spec.items():
-            if not isinstance(k, str):
-                raise ValueError(f"Filter keys must be strings: {directive}")
-            out[k] = _norm_value(k, v)
-        return out
-    if isinstance(filter_spec, list) and all(
-        isinstance(item, (list, tuple)) and len(item) == 2
-        for item in filter_spec
-    ):
-        out: dict[str, Any] = {}
-        for k, v in filter_spec:
-            if not isinstance(k, str):
-                raise ValueError(f"Filter tuple tag must be a string: {directive}")
-            out[k] = _norm_value(k, v)
-        return out
-    raise ValueError(
-        f"Unsupported filter format. Expected dict or list[tuple[str, value]]: {directive}"
-    )
-
-
-def _iter_filter_specs(spec: Any):
-    if isinstance(spec, dict):
-        yield spec
-        return
-    if isinstance(spec, list):
-        if all(
-            isinstance(item, (list, tuple))
-            and len(item) == 2
-            and isinstance(item[0], str)
-            for item in spec
-        ):
-            yield spec
-            return
-        for item in spec:
-            yield from _iter_filter_specs(item)
-
-
-def _directive_mentions_tag(directive: dict[str, Any], tag_name: str) -> bool:
-    specs = []
-    if "filter" in directive:
-        specs.append(directive["filter"])
-    if isinstance(directive.get("filters"), list):
-        specs.append(directive["filters"])
-    for spec in _iter_filter_specs(specs):
-        if isinstance(spec, dict) and tag_name in spec:
-            return True
-        if isinstance(spec, list):
-            for item in spec:
-                if isinstance(item, tuple) and len(item) == 2 and item[0] == tag_name:
-                    return True
-                if isinstance(item, list) and len(item) == 2 and item[0] == tag_name:
-                    return True
-    return False
-
-
-def _directives_enable_ep(directives: Any) -> bool:
-    if not isinstance(directives, list):
-        return False
-    return any(
-        isinstance(directive, dict)
-        and (directive.get("op") == "shard" or _directive_mentions_tag(directive, "EP"))
-        for directive in directives
-    )
-
-
-def _normalize_split_directive(directive: Any) -> tuple[dict[str, Any], str, int]:
-    if not isinstance(directive, dict):
-        raise ValueError(f"split directive must be a dict: {directive}")
-    if directive.get("op") != "split":
-        raise ValueError(f"Unsupported split directive op: {directive}")
-    if "filter" not in directive:
-        raise ValueError(f"split directive requires 'filter': {directive}")
-    dim_name = directive.get("dim_name")
-    if not isinstance(dim_name, str) or not dim_name:
-        raise ValueError(f"split directive requires non-empty string dim_name: {directive}")
-    num_microbatches = int(directive.get("num_microbatches", 0))
-    if num_microbatches <= 0:
-        raise ValueError(f"split directive requires num_microbatches > 0: {directive}")
-    flt = _normalize_filter_spec(directive["filter"], directive)
-    return flt, dim_name, num_microbatches
-
-
-def _is_filter_spec(spec: Any) -> bool:
-    if isinstance(spec, dict):
-        return True
-    if isinstance(spec, list):
-        return all(
-            isinstance(item, (list, tuple))
-            and len(item) == 2
-            and isinstance(item[0], str)
-            for item in spec
-        )
-    return False
-
-
-def _normalize_order_directive(directive: Any) -> list[list[dict[str, Any]]]:
-    if not isinstance(directive, dict):
-        raise ValueError(f"order directive must be a dict: {directive}")
-    if directive.get("op") != "order":
-        raise ValueError(f"Unsupported order directive op: {directive}")
-    raw_filters = directive.get("filters")
-    if not isinstance(raw_filters, list) or len(raw_filters) < 2:
-        raise ValueError(
-            f"order directive requires filters list with at least 2 groups: {directive}"
-        )
-
-    filter_groups: list[list[dict[str, Any]]] = []
-    for group_idx, raw_group in enumerate(raw_filters):
-        if _is_filter_spec(raw_group):
-            raw_group_items = [raw_group]
-        elif isinstance(raw_group, list) and raw_group:
-            raw_group_items = raw_group
-        else:
-            raise ValueError(
-                f"order directive filter group[{group_idx}] must be a non-empty list "
-                f"of filters: {directive}"
-            )
-
-        group = []
-        for raw_filter in raw_group_items:
-            if not _is_filter_spec(raw_filter):
-                raise ValueError(
-                    f"order directive filter group[{group_idx}] contains invalid "
-                    f"filter spec: {raw_filter}"
-                )
-            flt = _normalize_filter_spec(raw_filter, directive)
-            pass_value = flt.get("PASS")
-            if pass_value is not None and pass_value not in _VALID_ORDER_PASSES:
-                raise ValueError(
-                    f"order directive has unsupported pass={pass_value!r}; "
-                    f"expected one of {sorted(_VALID_ORDER_PASSES)}"
-                )
-            group.append(flt)
-        filter_groups.append(group)
-
-    return filter_groups
-
-
-def _filter_key_without(flt: dict[str, Any], ignored_keys: set[str]) -> tuple[tuple[str, Any], ...]:
-    return tuple(sorted((k, v) for k, v in flt.items() if k not in ignored_keys))
-
-
-def _validate_split_backward_order_stencil(
-    directives: list[Any],
-) -> set[tuple[tuple[str, Any], ...]]:
-    """Validate BI/BW order entries and return base filter keys to split.
-
-    This is intentionally narrow: it validates only the current split-backward
-    stencil until the schedule JSON grows a full schema validation pass.
-    """
-    split_keys: set[tuple[tuple[str, Any], ...]] = set()
-    split_entries: dict[tuple[tuple[str, Any], ...], list[tuple[int, tuple[int, int], str]]] = {}
-
-    for directive_idx, raw in enumerate(directives):
-        if not isinstance(raw, dict) or raw.get("op") != "order":
-            continue
-        filter_groups = _normalize_order_directive(raw)
-        seen_in_row: dict[tuple[tuple[str, Any], ...], dict[str, tuple[int, int]]] = {}
-        for group_idx, group in enumerate(filter_groups):
-            for filter_idx, flt in enumerate(group):
-                filter_pos = (group_idx, filter_idx)
-                pass_value = flt.get("PASS")
-                if pass_value is None:
-                    continue
-                if pass_value == _FUSED_BWD_PASS:
-                    continue
-                if pass_value not in (_BWD_INPUT_PASS, _BWD_WEIGHT_PASS):
-                    continue
-                key = _filter_key_without(flt, {"PASS"})
-                by_pass = seen_in_row.setdefault(key, {})
-                if pass_value in by_pass:
-                    raise ValueError(
-                        f"order directive[{directive_idx}] has duplicate split-backward "
-                        f"{pass_value} entry for {dict(key)} at indices "
-                        f"{by_pass[pass_value]} and {filter_pos}"
-                    )
-                by_pass[pass_value] = filter_pos
-                split_keys.add(key)
-                split_entries.setdefault(key, []).append((directive_idx, filter_pos, pass_value))
-
-    for key, entries in sorted(split_entries.items()):
-        bi = [entry for entry in entries if entry[2] == _BWD_INPUT_PASS]
-        bw = [entry for entry in entries if entry[2] == _BWD_WEIGHT_PASS]
-        if len(bi) != 1 or len(bw) != 1:
-            raise ValueError(
-                f"split backward order entries for {dict(key)} must contain exactly "
-                f"one BI and one BW; got BI={len(bi)} BW={len(bw)}"
-            )
-        if bi[0][0] != bw[0][0]:
-            raise ValueError(
-                f"split backward order entries for {dict(key)} must appear in the "
-                "same order directive row"
-            )
-        if bi[0][1] > bw[0][1]:
-            raise ValueError(
-                f"split backward order entries for {dict(key)} must place BI before BW"
-            )
-
-    return split_keys
-
-
-def _iter_data_edges(dag: TrainingDAG) -> list[TrainingDAGEdge]:
-    return [e for e in dag.edges if e.dep_kind == "data"]
-
-
-def _remove_edge(dag: TrainingDAG, edge: TrainingDAGEdge) -> None:
-    edge_key = (edge.src_uid, edge.dst_uid, edge.dep_kind, edge.tensor_name)
-    if edge_key not in dag.edge_keys:
-        return
-    dag.edge_keys.remove(edge_key)
-    dag.edges = [
-        e for e in dag.edges
-        if not (
-            e.src_uid == edge.src_uid
-            and e.dst_uid == edge.dst_uid
-            and e.dep_kind == edge.dep_kind
-            and e.tensor_name == edge.tensor_name
-        )
-    ]
-    if edge.src_uid in dag.succs:
-        dag.succs[edge.src_uid].discard(edge.dst_uid)
-    if edge.dst_uid in dag.preds:
-        dag.preds[edge.dst_uid].discard(edge.src_uid)
-
-
-def _device_to_physical_pp_rank(dag: TrainingDAG) -> dict[tuple[int, ...], int]:
-    device_keys = sorted({
-        tuple(sorted(node.device))
-        for node in dag.nodes.values()
-        if node.device is not None
-    })
-    return {key: idx for idx, key in enumerate(device_keys)}
-
-
-def _insert_send_recv_comm_nodes(dag: TrainingDAG, comm_stream: str | None = None) -> None:
-    data_edges = _iter_data_edges(dag)
-    device_to_pp_rank = _device_to_physical_pp_rank(dag)
-    send_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "SEND_COMM")
-    recv_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "RECV_COMM")
-    for edge in data_edges:
-        src = dag.nodes[edge.src_uid]
-        dst = dag.nodes[edge.dst_uid]
-        if src.node_kind != "COMPUTE" or dst.node_kind != "COMPUTE":
-            continue
-        if src.device is None or dst.device is None:
-            raise ValueError(f"cannot insert send/recv for unplaced edge {src.uid} -> {dst.uid}")
-        if src.device == dst.device:
-            continue
-        src_device_key = tuple(sorted(src.device))
-        dst_device_key = tuple(sorted(dst.device))
-        send_uid = f"send.{send_idx}"
-        send_idx += 1
-        recv_uid = f"recv.{recv_idx}"
-        recv_idx += 1
-        stream_name = comm_stream if comm_stream is not None else "pp_stream"
-        send_node = TrainingDAGNode(
-            uid=send_uid,
-            node_kind="SEND_COMM",
-            compute_subkind=None,
-            tag=dict(src.tag),
-            device=(None if src.device is None else list(src.device)),
-            stream=stream_name,
-            node_meta={
-                "from_uid": src.uid,
-                "to_uid": dst.uid,
-                "peer_pp_rank": device_to_pp_rank[dst_device_key],
-                "bucket_key": src.node_meta.get("bucket_key"),
-            },
-        )
-        recv_node = TrainingDAGNode(
-            uid=recv_uid,
-            node_kind="RECV_COMM",
-            compute_subkind=None,
-            tag=dict(dst.tag),
-            device=(None if dst.device is None else list(dst.device)),
-            stream=stream_name,
-            node_meta={
-                "from_uid": src.uid,
-                "to_uid": dst.uid,
-                "peer_pp_rank": device_to_pp_rank[src_device_key],
-                "bucket_key": dst.node_meta.get("bucket_key"),
-            },
-        )
-        dag.add_node(send_node)
-        dag.add_node(recv_node)
-        _remove_edge(dag, edge)
-        dag.add_edge(TrainingDAGEdge(src_uid=src.uid, dst_uid=send_uid, dep_kind="data", tensor_name=edge.tensor_name))
-        dag.add_edge(TrainingDAGEdge(src_uid=recv_uid, dst_uid=dst.uid, dep_kind="data", tensor_name=edge.tensor_name))
-
-
-def _replicate_update_nodes_by_device(dag: TrainingDAG) -> None:
-    """Replicate UPD nodes per upstream device set so split components stay homogeneous."""
-    upd_nodes = [n for n in list(dag.nodes.values()) if n.node_kind == "UPD" and ".rep" not in n.uid]
-    for upd in upd_nodes:
-        in_edges = [e for e in list(dag.edges) if e.dep_kind == "temporal" and e.dst_uid == upd.uid]
-        by_dev: dict[tuple[int, ...], list[TrainingDAGEdge]] = {}
-        for e in in_edges:
-            src = dag.nodes[e.src_uid]
-            if src.device is None:
-                continue
-            key = tuple(sorted(src.device))
-            by_dev.setdefault(key, []).append(e)
-        if len(by_dev) <= 1:
-            if len(by_dev) == 1:
-                upd.device = list(next(iter(by_dev.keys())))
-            continue
-        created_uids: list[str] = []
-        for i_upd, (dev_key, dev_edges) in enumerate(sorted(by_dev.items(), key=lambda kv: kv[0])):
-            nu = upd.uid if i_upd == 0 else f"{upd.uid}.rep{i_upd}"
-            if i_upd == 0:
-                upd.device = list(dev_key)
-            else:
-                dag.add_node(
-                    TrainingDAGNode(
-                        uid=nu,
-                        node_kind="UPD",
-                        compute_subkind=None,
-                        tag=dict(upd.tag),
-                        device=list(dev_key),
-                        stream=upd.stream,
-                        node_meta=dict(upd.node_meta),
-                    )
-                )
-            created_uids.append(nu)
-            for e in dev_edges:
-                _remove_edge(dag, e)
-                dag.add_edge(
-                    TrainingDAGEdge(
-                        src_uid=e.src_uid,
-                        dst_uid=nu,
-                        dep_kind=e.dep_kind,
-                        tensor_name=e.tensor_name,
-                    )
-                )
-        # Duplicate outgoing edges of the original UPD to all replicas.
-        out_edges = [e for e in list(dag.edges) if e.dep_kind == "temporal" and e.src_uid == upd.uid]
-        if out_edges:
-            for e in out_edges:
-                for nu in created_uids[1:]:
-                    dag.add_edge(
-                        TrainingDAGEdge(
-                            src_uid=nu,
-                            dst_uid=e.dst_uid,
-                            dep_kind=e.dep_kind,
-                            tensor_name=e.tensor_name,
-                        )
-                    )
-
-
-def _remove_node_and_incident_edges(dag: TrainingDAG, uid: str) -> None:
-    incident = [e for e in dag.edges if e.src_uid == uid or e.dst_uid == uid]
-    for e in incident:
-        _remove_edge(dag, e)
-    dag.nodes.pop(uid, None)
-    dag.succs.pop(uid, None)
-    dag.preds.pop(uid, None)
-    for s in dag.succs.values():
-        s.discard(uid)
-    for p in dag.preds.values():
-        p.discard(uid)
-
-
-def _rewire_bwd_successors_through_sync(
-    dag: TrainingDAG,
-    bwd_uid: str,
-    sync_uid: str,
-) -> None:
-    """Add update ordering through a gradient sync comm node.
-
-    BWD data successors carry activation gradients, including P2P sends to an
-    upstream pipeline stage.  Gradient synchronization is for parameter grads,
-    so it must not be inserted into those data paths.  UPD dependencies are
-    temporal; add sync -> UPD temporal edges so the
-    update waits for gradient synchronization as well as the BWD compute.
-    """
-    upd_temporal_outs = [
-        e for e in list(dag.edges)
-        if e.dep_kind == "temporal"
-        and e.src_uid == bwd_uid
-        and dag.nodes[e.dst_uid].node_kind == "UPD"
-    ]
-    for e in upd_temporal_outs:
-        dag.add_edge(
-            TrainingDAGEdge(
-                src_uid=sync_uid,
-                dst_uid=e.dst_uid,
-                dep_kind="temporal",
-                tensor_name=None,
-            )
-        )
-
-
-def _bucket_matched_fwd_nodes(
-    dag: TrainingDAG,
-    filters: list[dict[str, Any]],
-    bucket_size_mb: int,
-) -> None:
-    if bucket_size_mb <= 0:
-        raise ValueError(f"bucket_size must be > 0 MB, got {bucket_size_mb}")
-    bucket_size_bytes = int(bucket_size_mb) * 1024 * 1024
-
-    fwd_targets = [
-        uid for uid, node in dag.nodes.items()
-        if node.node_kind == "COMPUTE"
-        and node.compute_subkind == "FWD"
-        and any(_match_filter(node.tag, flt) for flt in filters)
-    ]
-
-    # Deterministic rewrite order.
-    fwd_targets.sort(key=lambda uid: (
-        int(dag.nodes[uid].node_meta.get("stage_id", 10**9)),
-        int(dag.nodes[uid].node_meta.get("segment_id", 10**9)),
-    ))
-
-    for fwd_uid in fwd_targets:
-        if fwd_uid not in dag.nodes:
-            continue
-        fwd_node = dag.nodes[fwd_uid]
-        bwd_uid = f"{fwd_uid}.bwd"
-        bwd_node = dag.nodes.get(bwd_uid)
-        if bwd_node is None or bwd_node.node_kind != "COMPUTE" or bwd_node.compute_subkind != "BWD":
-            raise ValueError(f"Expected corresponding BWD node for {fwd_uid}, missing {bwd_uid}")
-
-        gm = fwd_node.node_meta.get("gm")
-        graphargs = fwd_node.node_meta.get("graphargs")
-        input_idxs = fwd_node.node_meta.get("input_idxs")
-        param_idxs = fwd_node.node_meta.get("param_idxs")
-        if gm is None or graphargs is None or input_idxs is None or param_idxs is None:
-            continue
-
-        def _param_bytes(gargs: list, pidxs: list[int]) -> int:
-            total = 0
-            for i in pidxs:
-                if i < 0 or i >= len(gargs):
-                    continue
-                t = gargs[i]
-                if t is not None and hasattr(t, "numel") and hasattr(t, "element_size"):
-                    total += int(t.numel()) * int(t.element_size())
-            return total
-
-        def _placeholder_names(module: fx.GraphModule) -> list[str]:
-            return [n.name for n in module.graph.nodes if n.op == "placeholder"]
-
-        def _param_names(module: fx.GraphModule, pidxs: list[int]) -> list[str]:
-            names = _placeholder_names(module)
-            return [
-                names[i] if 0 <= i < len(names) else f"<invalid-param-idx:{i}>"
-                for i in pidxs
-            ]
-
-        pre_bytes = _param_bytes(graphargs, param_idxs)
-        logger.info(
-            "bucket_stage pre-size node=%s stage=%s seg=%s bucket_size_value=%d "
-            "bucket_size_bytes_passed=%d bucket_size_mib_passed=%.6f param_bytes=%d",
-            fwd_uid,
-            fwd_node.node_meta.get("stage_id"),
-            fwd_node.node_meta.get("segment_id"),
-            int(bucket_size_mb),
-            bucket_size_bytes,
-            bucket_size_bytes / (1024 * 1024),
-            pre_bytes,
-        )
-
-        try:
-            buckets = bucket_stage(
-                gm,
-                graphargs,
-                input_idxs,
-                param_idxs,
-                bucket_size_bytes=bucket_size_bytes,
-                debug_name=fwd_uid,
-            )
-        except Exception as exc:
-            logger.warning(
-                "replicate bucketing skipped for node %s (stage=%s seg=%s): %s",
-                fwd_uid,
-                fwd_node.node_meta.get("stage_id"),
-                fwd_node.node_meta.get("segment_id"),
-                exc,
-            )
-            continue
-        if len(buckets) <= 1:
-            continue
-        original_param_names = set(_param_names(gm, param_idxs))
-        lowered_param_counts: Counter[str] = Counter()
-        for bi, (_bgm, _bin, b_param, b_args) in enumerate(buckets):
-            post_bytes = _param_bytes(b_args, b_param)
-            bucket_param_names = _param_names(_bgm, b_param)
-            lowered_param_counts.update(bucket_param_names)
-            logger.info(
-                "bucket_stage bucket-size node=%s bucket=%d/%d param_bytes=%d params=%d names=%s",
-                fwd_uid,
-                bi,
-                len(buckets),
-                post_bytes,
-                len(bucket_param_names),
-                bucket_param_names,
-            )
-        missing_param_names = sorted(original_param_names - set(lowered_param_counts))
-        unknown_param_names = sorted(set(lowered_param_counts) - original_param_names)
-        duplicate_param_names = sorted(
-            name for name, count in lowered_param_counts.items() if count != 1
-        )
-        logger.info(
-            "bucket_stage lowered-ownership node=%s original_params=%d lowered_param_refs=%d "
-            "unique_lowered_params=%d missing_params=%d duplicate_params=%d unknown_params=%d",
-            fwd_uid,
-            len(original_param_names),
-            sum(lowered_param_counts.values()),
-            len(lowered_param_counts),
-            len(missing_param_names),
-            len(duplicate_param_names),
-            len(unknown_param_names),
-        )
-        if missing_param_names or duplicate_param_names or unknown_param_names:
-            logger.warning(
-                "bucket_stage lowered-ownership-invalid node=%s missing=%s duplicates=%s unknown=%s",
-                fwd_uid,
-                missing_param_names[:16],
-                duplicate_param_names[:16],
-                unknown_param_names[:16],
-            )
-
-        # Snapshot old incident edges before rewrite.
-        in_fwd = [e for e in dag.edges if e.dst_uid == fwd_uid]
-        out_fwd = [e for e in dag.edges if e.src_uid == fwd_uid]
-        in_bwd = [e for e in dag.edges if e.dst_uid == bwd_uid]
-        out_bwd = [e for e in dag.edges if e.src_uid == bwd_uid]
-
-        orig_stage = int(fwd_node.node_meta.get("stage_id", -1))
-        orig_seg = int(fwd_node.node_meta.get("segment_id", -1))
-
-        fwd_bucket_uids: list[str] = []
-        bwd_bucket_uids: list[str] = []
-        for bi, (bgm, b_in, b_param, b_args) in enumerate(buckets):
-            f_uid = f"{fwd_uid}.bucket{bi}"
-            b_uid = f"{f_uid}.bwd"
-            fwd_bucket_uids.append(f_uid)
-            bwd_bucket_uids.append(b_uid)
-            dag.add_node(
-                TrainingDAGNode(
-                    uid=f_uid,
-                    node_kind="COMPUTE",
-                    compute_subkind="FWD",
-                    tag=_with_pass_tag(fwd_node.tag, "F"),
-                    device=list(fwd_node.device) if fwd_node.device is not None else None,
-                    stream=fwd_node.stream,
-                    node_meta={
-                        "stage_id": orig_stage,
-                        "segment_id": orig_seg * 1000 + bi,
-                        "gm": bgm,
-                        "input_idxs": list(b_in),
-                        "param_idxs": list(b_param),
-                        "graphargs": list(b_args),
-                        "input_names": [n.name for n in bgm.graph.nodes if n.op == "placeholder"],
-                        "output_names": [],
-                        "a2a_boundary_after": None,
-                        "bucket_idx": bi,
-                        "num_buckets": len(buckets),
-                        "bucket_key": f_uid,
-                    },
-                )
-            )
-            dag.add_node(
-                TrainingDAGNode(
-                    uid=b_uid,
-                    node_kind="COMPUTE",
-                    compute_subkind="BWD",
-                    tag=_with_pass_tag(fwd_node.tag, "B"),
-                    device=list(bwd_node.device) if bwd_node.device is not None else None,
-                    stream=bwd_node.stream,
-                    node_meta={
-                        "fwd_uid": f_uid,
-                        "bucket_idx": bi,
-                        "num_buckets": len(buckets),
-                        "bucket_key": f_uid,
-                        "compute_loss": bool(bwd_node.node_meta.get("compute_loss", False)) and bi == (len(buckets) - 1),
-                    },
-                )
-            )
-
-        first_fwd = fwd_bucket_uids[0]
-        last_fwd = fwd_bucket_uids[-1]
-        # Mirror backward bucket chain in reverse order.
-        first_bwd = bwd_bucket_uids[-1]
-        last_bwd = bwd_bucket_uids[0]
-
-        def _remap(uid: str) -> str:
-            if uid == fwd_uid:
-                return last_fwd
-            if uid == bwd_uid:
-                return first_bwd
-            return uid
-
-        # Remove originals and their incident edges.
-        _remove_node_and_incident_edges(dag, fwd_uid)
-        _remove_node_and_incident_edges(dag, bwd_uid)
-
-        # Re-attach incoming/outgoing edges at bucket chain boundaries.
-        for e in in_fwd:
-            dag.add_edge(TrainingDAGEdge(src_uid=_remap(e.src_uid), dst_uid=first_fwd, dep_kind=e.dep_kind, tensor_name=e.tensor_name))
-        for e in out_fwd:
-            dst = first_bwd if e.dst_uid == bwd_uid else e.dst_uid
-            dag.add_edge(TrainingDAGEdge(src_uid=last_fwd, dst_uid=dst, dep_kind=e.dep_kind, tensor_name=e.tensor_name))
-        for e in in_bwd:
-            dag.add_edge(TrainingDAGEdge(src_uid=_remap(e.src_uid), dst_uid=first_bwd, dep_kind=e.dep_kind, tensor_name=e.tensor_name))
-        for e in out_bwd:
-            dag.add_edge(TrainingDAGEdge(src_uid=last_bwd, dst_uid=_remap(e.dst_uid), dep_kind=e.dep_kind, tensor_name=e.tensor_name))
-
-        # Internal chain edges.
-        for i in range(len(fwd_bucket_uids) - 1):
-            dag.add_edge(TrainingDAGEdge(src_uid=fwd_bucket_uids[i], dst_uid=fwd_bucket_uids[i + 1], dep_kind="data", tensor_name=None))
-        for i in range(len(bwd_bucket_uids) - 1, 0, -1):
-            dag.add_edge(TrainingDAGEdge(src_uid=bwd_bucket_uids[i], dst_uid=bwd_bucket_uids[i - 1], dep_kind="data", tensor_name=None))
-
-
-def _insert_reduce_comm_nodes(
-    dag: TrainingDAG,
-    filters: list[dict[str, Any]],
-    devices: list[int],
-    comm_stream: str | None = None,
-) -> None:
-    reduce_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "REDUCE_COMM")
-    expected = sorted(int(d) for d in devices)
-    for node in list(dag.nodes.values()):
-        if node.node_kind != "COMPUTE" or not _is_backward_weight_subkind(node.compute_subkind):
-            continue
-        if not any(_match_filter(node.tag, flt) for flt in filters):
-            continue
-        if node.device is None:
-            raise ValueError(
-                f"replicate requires placed backward weight-gradient nodes, but node {node.uid} has device=None; "
-                f"expected devices={devices}"
-            )
-        got = sorted(int(d) for d in node.device)
-        if got != expected:
-            raise ValueError(
-                f"replicate device mismatch for node {node.uid}: "
-                f"expected devices={devices}, node_devices={node.device}"
-            )
-        reduce_uid = f"reduce.{reduce_idx}"
-        reduce_idx += 1
-        reduce_node = TrainingDAGNode(
-            uid=reduce_uid,
-            node_kind="REDUCE_COMM",
-            compute_subkind=None,
-            tag=dict(node.tag),
-            device=list(node.device),
-            stream=comm_stream if comm_stream is not None else "default_stream",
-            node_meta={
-                "bwd_uid": node.uid,
-                "bucket_key": node.node_meta.get("bucket_key"),
-            },
-        )
-        dag.add_node(reduce_node)
-        dag.add_edge(
-            TrainingDAGEdge(
-                src_uid=node.uid,
-                dst_uid=reduce_uid,
-                dep_kind="data",
-                tensor_name=None,
-            )
-        )
-        _rewire_bwd_successors_through_sync(dag, node.uid, reduce_uid)
-
-
-def _node_has_trainable_params(dag: TrainingDAG, node: TrainingDAGNode) -> bool:
-    meta = node.node_meta
-    if _is_backward_compute_subkind(node.compute_subkind):
-        fwd_uid = meta.get("fwd_uid")
-        if isinstance(fwd_uid, str) and fwd_uid in dag.nodes:
-            meta = dag.nodes[fwd_uid].node_meta
-    graphargs = meta.get("graphargs")
-    param_idxs = meta.get("param_idxs")
-    if graphargs is None or param_idxs is None:
-        return True
-    return any(
-        0 <= int(i) < len(graphargs)
-        and graphargs[int(i)] is not None
-        and bool(getattr(graphargs[int(i)], "requires_grad", False))
-        for i in param_idxs
-    )
-
-
-def _insert_all_gather_comm_nodes(
-    dag: TrainingDAG,
-    filters: list[dict[str, Any]],
-    devices: list[int],
-    comm_stream: str | None = None,
-) -> None:
-    ag_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "ALL_GATHER_COMM")
-    expected = sorted(int(d) for d in devices)
-    for node in list(dag.nodes.values()):
-        if (
-            node.node_kind != "COMPUTE"
-            or node.compute_subkind not in ("FWD", "BWD", "BWD_I", "BWD_W")
-        ):
-            continue
-        if not any(_match_filter(node.tag, flt) for flt in filters):
-            continue
-        if not _node_has_trainable_params(dag, node):
-            continue
-        if node.device is None:
-            raise ValueError(
-                f"replicate(shard_params=True) requires placed nodes, but node {node.uid} has device=None; "
-                f"expected devices={devices}"
-            )
-        got = sorted(int(d) for d in node.device)
-        if got != expected:
-            raise ValueError(
-                f"replicate device mismatch for node {node.uid}: "
-                f"expected devices={devices}, node_devices={node.device}"
-            )
-        ag_uid = f"all_gather.{ag_idx}"
-        ag_idx += 1
-        ag_tag = dict(node.tag)
-        ag_node = TrainingDAGNode(
-            uid=ag_uid,
-            node_kind="ALL_GATHER_COMM",
-            compute_subkind=None,
-            tag=ag_tag,
-            device=list(node.device),
-            stream=comm_stream if comm_stream is not None else "default_stream",
-            node_meta={
-                "compute_uid": node.uid,
-                "bucket_key": node.node_meta.get("bucket_key"),
-            },
-        )
-        dag.add_node(ag_node)
-        node.node_meta["zero_free_full_params_after"] = True
-        if node.compute_subkind == "BWD_W":
-            for pred_uid in sorted(dag.preds.get(node.uid, set())):
-                pred = dag.nodes[pred_uid]
-                if pred.node_kind == "COMPUTE" and pred.compute_subkind == "BWD_I":
-                    dag.add_edge(
-                        TrainingDAGEdge(
-                            src_uid=pred.uid,
-                            dst_uid=ag_uid,
-                            dep_kind="data",
-                            tensor_name=None,
-                        )
-                    )
-        # ALL_GATHER is a pre-dependency of compute: AG -> COMPUTE
-        dag.add_edge(
-            TrainingDAGEdge(
-                src_uid=ag_uid,
-                dst_uid=node.uid,
-                dep_kind="data",
-                tensor_name=None,
-            )
-        )
-
-
-def _insert_reduce_scatter_comm_nodes(
-    dag: TrainingDAG,
-    filters: list[dict[str, Any]],
-    devices: list[int],
-    comm_stream: str | None = None,
-) -> None:
-    rs_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "REDUCE_SCATTER_COMM")
-    expected = sorted(int(d) for d in devices)
-    for node in list(dag.nodes.values()):
-        if node.node_kind != "COMPUTE" or not _is_backward_weight_subkind(node.compute_subkind):
-            continue
-        if not any(_match_filter(node.tag, flt) for flt in filters):
-            continue
-        if not _node_has_trainable_params(dag, node):
-            continue
-        if node.device is None:
-            raise ValueError(
-                f"replicate(shard_grads/shard_params) requires placed backward weight-gradient nodes, but node {node.uid} has device=None; "
-                f"expected devices={devices}"
-            )
-        got = sorted(int(d) for d in node.device)
-        if got != expected:
-            raise ValueError(
-                f"replicate device mismatch for node {node.uid}: "
-                f"expected devices={devices}, node_devices={node.device}"
-            )
-        rs_uid = f"reduce_scatter.{rs_idx}"
-        rs_idx += 1
-        rs_node = TrainingDAGNode(
-            uid=rs_uid,
-            node_kind="REDUCE_SCATTER_COMM",
-            compute_subkind=None,
-            tag=dict(node.tag),
-            device=list(node.device),
-            stream=comm_stream if comm_stream is not None else "default_stream",
-            node_meta={
-                "bwd_uid": node.uid,
-                "bucket_key": node.node_meta.get("bucket_key"),
-            },
-        )
-        node.node_meta["zero_alloc_full_grads_before"] = True
-        if node.node_meta.pop("zero_free_full_params_after", None) is not None:
-            rs_node.node_meta["zero_free_full_params_after"] = True
-        dag.add_node(rs_node)
-        dag.add_edge(
-            TrainingDAGEdge(
-                src_uid=node.uid,
-                dst_uid=rs_uid,
-                dep_kind="data",
-                tensor_name=None,
-            )
-        )
-        _rewire_bwd_successors_through_sync(dag, node.uid, rs_uid)
-
-
-def _insert_shard_a2a_comm_nodes(
-    dag: TrainingDAG,
-    filters: list[dict[str, Any]],
-    devices: list[int],
-    comm_stream: str | None = None,
-) -> None:
-    expected = sorted(int(d) for d in devices)
-    a2a_idx = sum(1 for n in dag.nodes.values() if n.node_kind == "A2A_COMM")
-
-    matched_uids = [
-        uid for uid, node in dag.nodes.items()
-        if node.node_kind == "COMPUTE" and any(_match_filter(node.tag, flt) for flt in filters)
-    ]
-
-    def _a2a_boundary_for_edge(src_node: TrainingDAGNode, dst_node: TrainingDAGNode) -> dict[str, Any]:
-        # Backward data edges are reversed from the forward graph:
-        #     FWD: B -> A    becomes    BWD: A.bwd -> B.bwd
-        # The A2A tensor position is defined by the original forward producer,
-        # which is the fwd node paired with the backward edge destination.
-        if (
-            _is_backward_activation_subkind(src_node.compute_subkind)
-            and _is_backward_activation_subkind(dst_node.compute_subkind)
-        ):
-            fwd_producer_uid = dst_node.node_meta.get("fwd_uid")
-            if not isinstance(fwd_producer_uid, str) or fwd_producer_uid not in dag.nodes:
-                raise ValueError(
-                    f"A2A_COMM could not resolve forward producer for BWD edge "
-                    f"{src_node.uid}->{dst_node.uid}: fwd_uid={fwd_producer_uid!r}"
-                )
-            producer = dag.nodes[fwd_producer_uid]
-        else:
-            producer = src_node
-
-        binfo = producer.node_meta.get("a2a_boundary_after")
-        if not isinstance(binfo, dict) or binfo.get("tensor_idx") is None:
-            raise ValueError(
-                f"A2A_COMM missing tensor_idx for edge {src_node.uid}->{dst_node.uid}; "
-                f"producer={producer.uid} boundary_info={binfo!r}"
-            )
-        return binfo
-
-    for uid in matched_uids:
-        if uid not in dag.nodes:
-            continue
-        node = dag.nodes[uid]
-        if node.device is None or sorted(int(d) for d in node.device) != expected:
-            raise ValueError(
-                f"shard requires matched node {uid} to have devices={devices}, got node_devices={node.device}"
-            )
-
-        if node.compute_subkind == "FWD":
-            node.node_meta["apply_zero"] = False
-        elif _is_backward_compute_subkind(node.compute_subkind):
-            fwd_uid = node.node_meta.get("fwd_uid")
-            if isinstance(fwd_uid, str) and fwd_uid in dag.nodes:
-                dag.nodes[fwd_uid].node_meta["apply_zero"] = False
-
-        # Shard replaces replicate-style grad/param sync comms around matched
-        # compute nodes; remove existing gather/reduce comm nodes attached to
-        # this compute node before inserting A2A edges.
-        removable_sync_kinds = {"ALL_GATHER_COMM", "REDUCE_COMM", "REDUCE_SCATTER_COMM"}
-        removable_comm_uids: set[str] = set()
-        for e in list(dag.edges):
-            if e.dep_kind != "data":
-                continue
-            if e.src_uid == uid and e.dst_uid in dag.nodes:
-                other = dag.nodes[e.dst_uid]
-                if other.node_kind in removable_sync_kinds:
-                    removable_comm_uids.add(other.uid)
-            elif e.dst_uid == uid and e.src_uid in dag.nodes:
-                other = dag.nodes[e.src_uid]
-                if other.node_kind in removable_sync_kinds:
-                    removable_comm_uids.add(other.uid)
-        for comm_uid in sorted(removable_comm_uids):
-            if comm_uid not in dag.nodes:
-                continue
-            # Bypass removed sync-comm nodes so dataflow remains connected:
-            # pred -> COMM -> succ  ==>  pred -> succ
-            in_comm = [e for e in list(dag.edges) if e.dep_kind == "data" and e.dst_uid == comm_uid]
-            out_comm = [e for e in list(dag.edges) if e.dep_kind == "data" and e.src_uid == comm_uid]
-            for ie in in_comm:
-                for oe in out_comm:
-                    dag.add_edge(
-                        TrainingDAGEdge(
-                            src_uid=ie.src_uid,
-                            dst_uid=oe.dst_uid,
-                            dep_kind="data",
-                            tensor_name=(oe.tensor_name if oe.tensor_name is not None else ie.tensor_name),
-                        )
-                    )
-            _remove_node_and_incident_edges(dag, comm_uid)
-        if removable_comm_uids:
-            node.node_meta.pop("zero_free_full_params_after", None)
-        if node.compute_subkind == "BWD_W":
-            continue
-
-        def _is_a2a_compute_edge(e: TrainingDAGEdge) -> bool:
-            src = dag.nodes[e.src_uid]
-            dst = dag.nodes[e.dst_uid]
-            if src.node_kind != "COMPUTE" or dst.node_kind != "COMPUTE":
-                return False
-            if node.compute_subkind == "FWD":
-                return src.compute_subkind == "FWD" and dst.compute_subkind == "FWD"
-            if _is_backward_activation_subkind(node.compute_subkind):
-                return (
-                    _is_backward_activation_subkind(src.compute_subkind)
-                    and _is_backward_activation_subkind(dst.compute_subkind)
-                )
-            return False
-
-        incoming = [
-            e for e in list(dag.edges)
-            if e.dep_kind == "data"
-            and e.dst_uid == uid
-            and _is_a2a_compute_edge(e)
-        ]
-        outgoing = [
-            e for e in list(dag.edges)
-            if e.dep_kind == "data"
-            and e.src_uid == uid
-            and _is_a2a_compute_edge(e)
-        ]
-
-        for e in incoming:
-            src = dag.nodes[e.src_uid]
-            if src.device is None or sorted(int(d) for d in src.device) != expected:
-                raise ValueError(
-                    f"shard precondition failed for incoming edge {e.src_uid}->{uid}: "
-                    f"upstream compute must be replicated on devices={devices}, got {src.device}"
-                )
-            if src.tag.get("PASS") != node.tag.get("PASS"):
-                raise ValueError(
-                    f"A2A_COMM requires matching compute pass tags; "
-                    f"incoming edge {e.src_uid}->{uid} has src.PASS={src.tag.get('PASS')} "
-                    f"dst.PASS={node.tag.get('PASS')}"
-                )
-        for e in outgoing:
-            dst = dag.nodes[e.dst_uid]
-            if dst.device is None or sorted(int(d) for d in dst.device) != expected:
-                raise ValueError(
-                    f"shard precondition failed for outgoing edge {uid}->{e.dst_uid}: "
-                    f"downstream compute must be replicated on devices={devices}, got {dst.device}"
-                )
-            if dst.tag.get("PASS") != node.tag.get("PASS"):
-                raise ValueError(
-                    f"A2A_COMM requires matching compute pass tags; "
-                    f"outgoing edge {uid}->{e.dst_uid} has src.PASS={node.tag.get('PASS')} "
-                    f"dst.PASS={dst.tag.get('PASS')}"
-                )
-
-        for e in incoming:
-            src_node = dag.nodes[e.src_uid]
-            binfo = _a2a_boundary_for_edge(src_node, node)
-            a2a_tensor_idx = binfo["tensor_idx"]
-            comm_uid = f"a2a.{a2a_idx}"
-            a2a_idx += 1
-            comm_node = TrainingDAGNode(
-                uid=comm_uid,
-                node_kind="A2A_COMM",
-                compute_subkind=None,
-                tag=dict(node.tag),
-                device=list(node.device) if node.device is not None else None,
-                stream=comm_stream if comm_stream is not None else "default_stream",
-                node_meta={
-                    "direction": "incoming",
-                    "target_uid": uid,
-                    "a2a_tensor_idx": a2a_tensor_idx,
-                    "bucket_key": node.node_meta.get("bucket_key", src_node.node_meta.get("bucket_key")),
-                },
-            )
-            dag.add_node(comm_node)
-            _remove_edge(dag, e)
-            dag.add_edge(TrainingDAGEdge(src_uid=e.src_uid, dst_uid=comm_uid, dep_kind="data", tensor_name=e.tensor_name))
-            dag.add_edge(TrainingDAGEdge(src_uid=comm_uid, dst_uid=uid, dep_kind="data", tensor_name=e.tensor_name))
-
-        for e in outgoing:
-            dst_node = dag.nodes[e.dst_uid]
-            binfo = _a2a_boundary_for_edge(node, dst_node)
-            a2a_tensor_idx = binfo["tensor_idx"]
-            comm_uid = f"a2a.{a2a_idx}"
-            a2a_idx += 1
-            comm_node = TrainingDAGNode(
-                uid=comm_uid,
-                node_kind="A2A_COMM",
-                compute_subkind=None,
-                tag=dict(node.tag),
-                device=list(node.device) if node.device is not None else None,
-                stream=comm_stream if comm_stream is not None else "default_stream",
-                node_meta={
-                    "direction": "outgoing",
-                    "source_uid": uid,
-                    "a2a_tensor_idx": a2a_tensor_idx,
-                    "bucket_key": node.node_meta.get("bucket_key", dst_node.node_meta.get("bucket_key")),
-                },
-            )
-            dag.add_node(comm_node)
-            _remove_edge(dag, e)
-            dag.add_edge(TrainingDAGEdge(src_uid=uid, dst_uid=comm_uid, dep_kind="data", tensor_name=e.tensor_name))
-            dag.add_edge(TrainingDAGEdge(src_uid=comm_uid, dst_uid=e.dst_uid, dep_kind="data", tensor_name=e.tensor_name))
-
-
-def _topological_order(dag: TrainingDAG) -> list[str]:
-    in_deg: dict[str, int] = {uid: 0 for uid in dag.nodes}
-    out_edges_by_src: dict[str, list[TrainingDAGEdge]] = {uid: [] for uid in dag.nodes}
-    for e in dag.edges:
-        if e.dst_uid in in_deg:
-            in_deg[e.dst_uid] += 1
-        if e.src_uid in out_edges_by_src:
-            out_edges_by_src[e.src_uid].append(e)
-    q = sorted(uid for uid, d in in_deg.items() if d == 0)
-    out: list[str] = []
-    while q:
-        cur_level = q
-        q = []
-        for u in cur_level:
-            out.append(u)
-            # Decrement per outgoing edge (not per unique successor), because we
-            # can have parallel edges between the same pair (e.g., data + temporal).
-            for e in out_edges_by_src.get(u, []):
-                v = e.dst_uid
-                if v not in in_deg:
-                    continue
-                in_deg[v] -= 1
-                if in_deg[v] == 0:
-                    q.append(v)
-        q.sort()
-    if len(out) != len(dag.nodes):
-        remaining = [uid for uid, d in in_deg.items() if d > 0]
-        raise ValueError(
-            "TrainingDAG topological sort failed: unresolved incoming edges remain; "
-            f"possible real cycle or malformed parallel-edge bookkeeping. Remaining nodes: {remaining[:8]}"
-        )
-    return out
-
-
-def _topological_levels(dag: TrainingDAG) -> dict[str, int]:
-    """Return each node's topological level.
-
-    Source nodes are level 0. Every other node is one level after its latest
-    predecessor, so independent successors can share the same level.
-    """
-    topo = _topological_order(dag)
-    levels: dict[str, int] = {}
-    for uid in topo:
-        pred_levels = [
-            levels[pred_uid]
-            for pred_uid in dag.preds.get(uid, set())
-            if pred_uid in levels
-        ]
-        levels[uid] = (max(pred_levels) + 1) if pred_levels else 0
-    return levels
-
-
-def _serial_topological_order(
-    dag: TrainingDAG,
-    topo_levels: dict[str, int] | None = None,
-) -> list[str]:
-    """Serialize topological levels into a deterministic dispatch order.
-
-    Nodes with lower topological levels always come first. Within a level,
-    priority order is: SEND > other comm > compute > RECV.
-    """
-    if topo_levels is None:
-        topo_levels = _topological_levels(dag)
-
-    base_order = _topological_order(dag)
-    topo_idx = {uid: i for i, uid in enumerate(base_order)}
-
-    _OTHER_COMM_KINDS = {
-        "REDUCE_COMM",
-        "ALL_GATHER_COMM",
-        "REDUCE_SCATTER_COMM",
-        "A2A_COMM",
-    }
-
-    def node_priority(uid: str) -> int:
-        kind = dag.nodes[uid].node_kind
-        if kind == "SEND_COMM":
-            return 0
-        if kind in _OTHER_COMM_KINDS:
-            return 1
-        if kind == "RECV_COMM":
-            return 3
-        return 2
-
-    return sorted(
-        dag.nodes,
-        key=lambda uid: (topo_levels[uid], node_priority(uid), topo_idx[uid]),
-    )
-
-
-def _has_path(dag: TrainingDAG, src_uid: str, dst_uid: str) -> bool:
-    seen: set[str] = set()
-    stack = [src_uid]
-    while stack:
-        uid = stack.pop()
-        if uid == dst_uid:
-            return True
-        if uid in seen:
-            continue
-        seen.add(uid)
-        stack.extend(dag.succs.get(uid, set()))
-    return False
-
-
-def _resolve_default_stream_order(dag: TrainingDAG) -> None:
-    """Create a total ordering over default-stream COMPUTE nodes.
-
-    Whenever multiple default-stream compute nodes share a topological level,
-    chain them with temporal edges in descending order of downstream
-    dependencies (more downstream nodes -> earlier in the chain). Downstream
-    count is the size of each compute node's transitive successor set, which in
-    a well-formed training DAG terminates at UPD.
-
-    The new edges shift topological levels, so after each chain insertion we
-    recompute levels and rescan from the earliest level. The pass terminates
-    when every default-stream compute node sits at a unique level.
-    """
-    def _is_default_compute(uid: str) -> bool:
-        node = dag.nodes[uid]
-        return node.stream == _DEFAULT_STREAM and node.node_kind == "COMPUTE"
-
-    compute_uids = [uid for uid in dag.nodes if _is_default_compute(uid)]
-    if len(compute_uids) < 2:
-        return
-
-    def _downstream_count(uid: str) -> int:
-        seen: set[str] = set()
-        stack = list(dag.succs.get(uid, set()))
-        while stack:
-            v = stack.pop()
-            if v in seen:
-                continue
-            seen.add(v)
-            stack.extend(dag.succs.get(v, set()))
-        return len(seen)
-
-    while True:
-        topo_levels = _topological_levels(dag)
-        by_level: dict[int, list[str]] = {}
-        for uid in compute_uids:
-            by_level.setdefault(topo_levels[uid], []).append(uid)
-
-        conflict_level: int | None = None
-        for level in sorted(by_level):
-            if len(by_level[level]) > 1:
-                conflict_level = level
-                break
-        if conflict_level is None:
-            return
-
-        # Same-level nodes have no path between them (otherwise their levels
-        # would differ), so chaining them in any direction is acyclic. Order by
-        # downstream count desc; break ties by uid for determinism.
-        group = by_level[conflict_level]
-        ordered = sorted(group, key=lambda u: (-_downstream_count(u), u))
-        for src, dst in zip(ordered, ordered[1:]):
-            dag.add_edge(
-                TrainingDAGEdge(
-                    src_uid=src,
-                    dst_uid=dst,
-                    dep_kind="temporal",
-                    tensor_name=None,
-                )
-            )
-
-
-def resolve_total_order_per_stream(dag: TrainingDAG) -> None:
-    """Serialize the default stream, then add temporal edges so each non-default
-    logical stream is a strict chain.
-
-    The default-stream pass runs first so the per-non-default-stream anchors
-    below see the post-serialization topological order of default-stream nodes.
-
-    Non-default stream nodes are ordered by the topological order of the
-    default-stream nodes they directly depend on or are depended on by.
+    Piper assigns the integer index for each tag name automatically in the
+    order annotation scopes are entered during tracing.
     """
-    _resolve_default_stream_order(dag)
-
-    topo = _topological_order(dag)
-    topo_idx = {uid: i for i, uid in enumerate(topo)}
-    topo_levels = _topological_levels(dag)
-    default_uids = [
-        uid for uid in topo
-        if dag.nodes[uid].stream == _DEFAULT_STREAM
-    ]
-    streams = sorted({
-        n.stream for n in dag.nodes.values()
-        if n.stream != _DEFAULT_STREAM
-    })
-    if not default_uids or not streams:
-        return
-
-    for stream in streams:
-        associated_by_default: dict[str, list[str]] = {uid: [] for uid in default_uids}
-        default_anchors_by_stream_uid: dict[str, list[str]] = {}
-        for edge in dag.edges:
-            src = dag.nodes[edge.src_uid]
-            dst = dag.nodes[edge.dst_uid]
-            if src.stream == _DEFAULT_STREAM and dst.stream == stream:
-                default_anchors_by_stream_uid.setdefault(edge.dst_uid, []).append(edge.src_uid)
-            elif src.stream == stream and dst.stream == _DEFAULT_STREAM:
-                default_anchors_by_stream_uid.setdefault(edge.src_uid, []).append(edge.dst_uid)
-
-        for stream_uid in {
-            uid for uid, node in dag.nodes.items()
-            if node.stream == stream
-        }:
-            default_anchors = default_anchors_by_stream_uid.get(stream_uid, [])
-            if not default_anchors:
-                continue
-            anchor_uid = min(default_anchors, key=lambda uid: (topo_levels[uid], topo_idx[uid]))
-            associated_by_default.setdefault(anchor_uid, []).append(stream_uid)
-
-        current_uid: str | None = None
-        for default_uid in default_uids:
-            stream_uids = sorted(
-                set(associated_by_default.get(default_uid, [])),
-                key=lambda u: (topo_levels[u], topo_idx[u]),
-            )
-            for next_uid in stream_uids:
-                if current_uid is not None and current_uid != next_uid:
-                    if _has_path(dag, next_uid, current_uid):
-                        raise ValueError(
-                            "resolve_total_order_per_stream would create a cycle while ordering "
-                            f"stream={stream}: {current_uid} -> {next_uid}"
-                        )
-                    if not _has_path(dag, current_uid, next_uid):
-                        dag.add_edge(
-                            TrainingDAGEdge(
-                                src_uid=current_uid,
-                                dst_uid=next_uid,
-                                dep_kind="temporal",
-                                tensor_name=None,
-                            )
-                        )
-                current_uid = next_uid
-
-
-def _prune_zero_lifetime_metadata(dag: TrainingDAG) -> list[list[str]]:
-    """Prune redundant ZeRO param/grad lifetimes across same-bucket compute chains.
-
-    Returns the union of gradient and parameter chains used for pruning, one
-    list per chain ordered by topo. The caller passes these to
-    ``_add_inter_chain_temporal_edges`` *after*
-    ``resolve_total_order_per_stream`` runs, so the inter-chain check sees the
-    post-stream-serialization topo levels for the gather_stream and
-    reduce_stream nodes.
-    """
-    topo_levels = _topological_levels(dag)
-    topo = _serial_topological_order(dag, topo_levels)
-
-    def _is_compute(uid: str) -> bool:
-        node = dag.nodes[uid]
-        return (
-            node.node_kind == "COMPUTE"
-            and node.compute_subkind in ("FWD", "BWD", "BWD_I", "BWD_W")
-        )
-
-    def _chain_bucket(uid: str) -> Any:
-        return dag.nodes[uid].node_meta.get("bucket_key")
-
-    def _compute_uids_with(topo_order: list[str], predicate) -> list[str]:
-        return [
-            uid for uid in topo_order
-            if uid in dag.nodes and _is_compute(uid) and predicate(uid)
-        ]
-
-    def _has_direct_compute_dependency(src_uid: str, dst_uid: str) -> bool:
-        return any(
-            e.src_uid == src_uid
-            and e.dst_uid == dst_uid
-            and e.dep_kind in ("data", "temporal")
-            and e.src_uid in dag.nodes
-            and e.dst_uid in dag.nodes
-            and _is_compute(e.src_uid)
-            and _is_compute(e.dst_uid)
-            for e in dag.edges
-        )
-
-    def _build_chains(candidates: list[str], topo_order: list[str]) -> list[list[str]]:
-        chains: list[list[str]] = []
-        by_bucket: dict[Any, list[str]] = {}
-        for uid in candidates:
-            by_bucket.setdefault(_chain_bucket(uid), []).append(uid)
-        topo_idx = {uid: i for i, uid in enumerate(topo_order)}
-        for bucket, uids in sorted(by_bucket.items(), key=lambda item: str(item[0])):
-            uid_set = set(uids)
-            links: dict[str, set[str]] = {uid: set() for uid in uids}
-            for e in dag.edges:
-                if (
-                    e.dep_kind in ("data", "temporal")
-                    and e.src_uid in uid_set
-                    and e.dst_uid in uid_set
-                    and _is_compute(e.src_uid)
-                    and _is_compute(e.dst_uid)
-                ):
-                    links[e.src_uid].add(e.dst_uid)
-                    links[e.dst_uid].add(e.src_uid)
-
-            seen: set[str] = set()
-            for uid in uids:
-                if uid in seen:
-                    continue
-                component: list[str] = []
-                stack = [uid]
-                seen.add(uid)
-                while stack:
-                    cur = stack.pop()
-                    component.append(cur)
-                    for nxt in sorted(links[cur], key=lambda u: topo_idx[u]):
-                        if nxt not in seen:
-                            seen.add(nxt)
-                            stack.append(nxt)
-                chains.append(sorted(component, key=lambda u: topo_idx[u]))
-        return chains
-
-    def _data_preds(uid: str) -> list[TrainingDAGEdge]:
-        return [
-            e for e in list(dag.edges)
-            if e.dep_kind == "data" and e.dst_uid == uid
-        ]
-
-    def _data_succs(uid: str) -> list[TrainingDAGEdge]:
-        return [
-            e for e in list(dag.edges)
-            if e.dep_kind == "data" and e.src_uid == uid
-        ]
-
-    def _all_gather_preds(uid: str) -> list[str]:
-        return sorted(
-            e.src_uid
-            for e in dag.edges
-            if e.dep_kind == "data"
-            and e.dst_uid == uid
-            and e.src_uid in dag.nodes
-            and dag.nodes[e.src_uid].node_kind == "ALL_GATHER_COMM"
-        )
-
-    def _reduce_scatter_succs(uid: str) -> list[str]:
-        return sorted(
-            e.dst_uid
-            for e in dag.edges
-            if e.dep_kind == "data"
-            and e.src_uid == uid
-            and e.dst_uid in dag.nodes
-            and dag.nodes[e.dst_uid].node_kind == "REDUCE_SCATTER_COMM"
-        )
-
-    def _remove_all_gather(ag_uid: str, compute_uid: str, bucket: Any) -> None:
-        if ag_uid not in dag.nodes:
-            return
-        in_ag = _data_preds(ag_uid)
-        out_ag = _data_succs(ag_uid)
-        for ie in in_ag:
-            for oe in out_ag:
-                dag.add_edge(
-                    TrainingDAGEdge(
-                        src_uid=ie.src_uid,
-                        dst_uid=oe.dst_uid,
-                        dep_kind="data",
-                        tensor_name=(oe.tensor_name if oe.tensor_name is not None else ie.tensor_name),
-                    )
-                )
-        _remove_node_and_incident_edges(dag, ag_uid)
-
-    def _remove_reduce_scatter(rs_uid: str, compute_uid: str, bucket: Any) -> None:
-        if rs_uid not in dag.nodes:
-            return
-        _remove_node_and_incident_edges(dag, rs_uid)
-
-    has_grad_sync = any(
-        n.node_kind in ("REDUCE_COMM", "REDUCE_SCATTER_COMM")
-        for n in dag.nodes.values()
-    )
-    has_all_gathers = any(n.node_kind == "ALL_GATHER_COMM" for n in dag.nodes.values())
-
-    grad_chains: list[list[str]] = []
-    param_chains: list[list[str]] = []
-
-    # Gradient lifetimes: keep one full-grad allocation and one reduce-scatter
-    # per directly-connected same-bucket gradient chain.
-    if has_grad_sync:
-        grad_candidates = _compute_uids_with(
-            topo,
-            lambda uid: bool(dag.nodes[uid].node_meta.get("zero_alloc_full_grads_before"))
-        )
-        if grad_candidates:
-            grad_chains = _build_chains(grad_candidates, topo)
-            covered = {uid for chain in grad_chains for uid in chain}
-            assert covered == set(grad_candidates), (
-                "zero_lifetime_prune: gradient mode did not assign every grad-allocation "
-                f"compute node to a chain; missing={sorted(set(grad_candidates) - covered)}"
-            )
-
-            for chain in grad_chains:
-                bucket = _chain_bucket(chain[0])
-                assert all(_chain_bucket(uid) == bucket for uid in chain), (
-                    f"zero_lifetime_prune: gradient chain has mixed buckets: "
-                    f"{[(uid, _chain_bucket(uid)) for uid in chain]}"
-                )
-                logger.info(
-                    "zero_lifetime_prune_grad_chain bucket=%s nodes=%s keep_alloc_for=%s keep_reduce_scatter_for=%s",
-                    bucket,
-                    chain,
-                    chain[0],
-                    chain[-1],
-                )
-
-                for uid in chain:
-                    rs_uids = _reduce_scatter_succs(uid)
-                    assert rs_uids, (
-                        f"zero_lifetime_prune: gradient chain node {uid} has "
-                        "zero_alloc_full_grads_before but no outgoing reduce-scatter"
-                    )
-
-                for uid in chain[1:]:
-                    dag.nodes[uid].node_meta.pop("zero_alloc_full_grads_before", None)
-                for uid in chain[:-1]:
-                    for rs_uid in _reduce_scatter_succs(uid):
-                        _remove_reduce_scatter(rs_uid, uid, bucket)
-
-                assert dag.nodes[chain[0]].node_meta.get("zero_alloc_full_grads_before"), (
-                    f"zero_lifetime_prune: gradient chain root {chain[0]} lost grad allocation"
-                )
-                for prev_uid, uid in zip(chain, chain[1:]):
-                    assert _has_direct_compute_dependency(prev_uid, uid), (
-                        f"zero_lifetime_prune: gradient chain is not directly linked: "
-                        f"{prev_uid} does not have a direct compute dependency to {uid}; chain={chain}"
-                    )
-                    assert not dag.nodes[uid].node_meta.get("zero_alloc_full_grads_before"), (
-                        f"zero_lifetime_prune: non-root gradient chain node {uid} still allocates grads"
-                    )
-                live_rs = [
-                    (uid, rs_uid)
-                    for uid in chain
-                    for rs_uid in _reduce_scatter_succs(uid)
-                ]
-                assert live_rs, (
-                    f"zero_lifetime_prune: gradient chain has no live reduce-scatter; chain={chain}"
-                )
-                assert all(uid == chain[-1] for uid, _rs_uid in live_rs), (
-                    f"zero_lifetime_prune: only chain tail {chain[-1]} should keep reduce-scatter, "
-                    f"but live reduce-scatters are {live_rs}"
-                )
-
-    # Parameter lifetimes: keep one all-gather and one full-param free per
-    # directly-connected same-bucket parameter chain.  This pass intentionally
-    # does not change grad allocation metadata or remove reduce-scatters.
-    if has_all_gathers:
-        topo = _serial_topological_order(dag, _topological_levels(dag))
-        ag_by_compute = {uid: _all_gather_preds(uid) for uid in topo if uid in dag.nodes and _is_compute(uid)}
-        ag_by_compute = {uid: ags for uid, ags in ag_by_compute.items() if ags}
-
-        candidates = _compute_uids_with(topo, lambda uid: uid in ag_by_compute)
-        param_chains = _build_chains(candidates, topo)
-
-        covered = {uid for chain in param_chains for uid in chain}
-        assert covered == set(candidates), (
-            "zero_lifetime_prune: parameter mode did not assign every all-gathered "
-            f"compute node to a chain; missing={sorted(set(candidates) - covered)}"
-        )
-
-        for chain in param_chains:
-            bucket = _chain_bucket(chain[0])
-            assert all(_chain_bucket(uid) == bucket for uid in chain), (
-                f"zero_lifetime_prune: parameter chain has mixed buckets: "
-                f"{[(uid, _chain_bucket(uid)) for uid in chain]}"
-            )
-
-            for uid in chain[1:]:
-                for ag_uid in ag_by_compute[uid]:
-                    _remove_all_gather(ag_uid, uid, bucket)
-
-            tail = chain[-1]
-            for uid in chain:
-                dag.nodes[uid].node_meta.pop("zero_free_full_params_after", None)
-                for rs_uid in _reduce_scatter_succs(uid):
-                    dag.nodes[rs_uid].node_meta.pop("zero_free_full_params_after", None)
-            dag.nodes[tail].node_meta["zero_free_full_params_after"] = True
-
-            remaining_ag = {uid: _all_gather_preds(uid) for uid in chain}
-            assert remaining_ag[chain[0]], (
-                f"zero_lifetime_prune: parameter chain root {chain[0]} lost all-gather"
-            )
-            for prev_uid, uid in zip(chain, chain[1:]):
-                assert _has_direct_compute_dependency(prev_uid, uid), (
-                    f"zero_lifetime_prune: parameter chain is not directly linked: "
-                    f"{prev_uid} does not have a direct compute dependency to {uid}; chain={chain}"
-                )
-                assert not remaining_ag[uid], (
-                    f"zero_lifetime_prune: non-root parameter chain node {uid} still "
-                    f"has all-gather predecessors {remaining_ag[uid]}; chain={chain}"
-                )
-            assert dag.nodes[tail].node_meta.get("zero_free_full_params_after"), (
-                f"zero_lifetime_prune: parameter chain tail {tail} should free params"
-            )
-            for uid in chain[:-1]:
-                assert not dag.nodes[uid].node_meta.get("zero_free_full_params_after"), (
-                    f"zero_lifetime_prune: non-tail parameter chain node {uid} still frees params"
-                )
-            stale_rs_param_frees = [
-                rs_uid
-                for uid in chain
-                for rs_uid in _reduce_scatter_succs(uid)
-                if dag.nodes[rs_uid].node_meta.get("zero_free_full_params_after")
-            ]
-            assert not stale_rs_param_frees, (
-                f"zero_lifetime_prune: reduce-scatters still own param free metadata: "
-                f"{stale_rs_param_frees}; chain={chain}"
-            )
-
-    return grad_chains + param_chains
-
-
-def _add_inter_chain_temporal_edges(
-    dag: TrainingDAG,
-    chains: list[list[str]],
-) -> None:
-    """For each consecutive same-bucket chain pair (parameter or gradient),
-    force the next chain's bucket materialization to wait for the prior chain's
-    bucket free.
-
-    Parameter chains: prev_tail compute frees full params, next root's
-    all-gather predecessor materializes them. Edge: prev_tail -> ag_uid.
-
-    Gradient chains: prev_tail's reduce-scatter successor frees full grads,
-    next root compute allocates them. Edge: rs_uid -> next_root.
-
-    Without these edges ``_serial_topological_order``'s other-comm-before-
-    compute priority can dispatch the next bucket op before the prior free
-    runs. Must be called after ``resolve_total_order_per_stream`` so the
-    gather_stream and reduce_stream chains have populated topo levels.
-    """
-    if not chains:
-        return
-
-    topo_levels = _topological_levels(dag)
-
-    def _chain_bucket(uid: str) -> Any:
-        return dag.nodes[uid].node_meta.get("bucket_key")
-
-    def _all_gather_preds(uid: str) -> list[str]:
-        return sorted(
-            e.src_uid
-            for e in dag.edges
-            if e.dep_kind == "data"
-            and e.dst_uid == uid
-            and e.src_uid in dag.nodes
-            and dag.nodes[e.src_uid].node_kind == "ALL_GATHER_COMM"
-        )
-
-    def _reduce_scatter_succs(uid: str) -> list[str]:
-        return sorted(
-            e.dst_uid
-            for e in dag.edges
-            if e.dep_kind == "data"
-            and e.src_uid == uid
-            and e.dst_uid in dag.nodes
-            and dag.nodes[e.dst_uid].node_kind == "REDUCE_SCATTER_COMM"
-        )
-
-    def _maybe_add_edge(src_uid: str, dst_uid: str) -> None:
-        if topo_levels.get(src_uid, 0) < topo_levels.get(dst_uid, 0):
-            return
-        if _has_path(dag, src_uid, dst_uid):
-            return
-        if _has_path(dag, dst_uid, src_uid):
-            return
-        dag.add_edge(TrainingDAGEdge(
-            src_uid=src_uid,
-            dst_uid=dst_uid,
-            dep_kind="temporal",
-            tensor_name=None,
-        ))
-
-    # Group chains by type (param vs grad) and bucket, then within each group
-    # pair consecutive chains by root topo and add the type-specific edge.
-    param_by_bucket: dict[Any, list[list[str]]] = {}
-    grad_by_bucket: dict[Any, list[list[str]]] = {}
-    for chain in chains:
-        bucket = _chain_bucket(chain[0])
-        if _all_gather_preds(chain[0]):
-            param_by_bucket.setdefault(bucket, []).append(chain)
-        if dag.nodes[chain[0]].node_meta.get("zero_alloc_full_grads_before"):
-            grad_by_bucket.setdefault(bucket, []).append(chain)
-
-    for bucket_chains in param_by_bucket.values():
-        bucket_chains.sort(key=lambda c: topo_levels.get(c[0], 0))
-        for prev_chain, next_chain in zip(bucket_chains, bucket_chains[1:]):
-            for ag_uid in _all_gather_preds(next_chain[0]):
-                _maybe_add_edge(prev_chain[-1], ag_uid)
-
-    for bucket_chains in grad_by_bucket.values():
-        bucket_chains.sort(key=lambda c: topo_levels.get(c[0], 0))
-        for prev_chain, next_chain in zip(bucket_chains, bucket_chains[1:]):
-            for rs_uid in _reduce_scatter_succs(prev_chain[-1]):
-                _maybe_add_edge(rs_uid, next_chain[0])
-
-
-def _apply_split_directive(
-    dag: TrainingDAG,
-    flt: dict[str, Any],
-    dim_name: str,
-    num_microbatches: int,
-) -> None:
-    matched = {uid for uid, n in dag.nodes.items() if _match_filter(n.tag, flt)}
-    if not matched:
-        logger.warning("split(filter=%s): no nodes matched", flt)
-        return
-
-    # UPD nodes are global reduction/update sinks and must not be duplicated
-    # by microbatch split; duplicated predecessors should continue to target
-    # the same UPD node(s).
-    matched = {uid for uid in matched if dag.nodes[uid].node_kind != "UPD"}
-    if not matched:
-        logger.warning("split(filter=%s): only UPD nodes matched; nothing to split", flt)
-        return
-
-    topo = _topological_order(dag)
-    idx = {u: i for i, u in enumerate(topo)}
-    mpos = sorted(idx[u] for u in matched)
-    lo, hi = mpos[0], mpos[-1]
-    interleaved = [
-        u for u in topo[lo:hi + 1]
-        if u not in matched and dag.nodes[u].node_kind != "UPD"
-    ]
-    if interleaved:
-        raise ValueError(
-            f"split requires a contiguous sub-DAG; found interleaved non-matching nodes: {interleaved[:8]}"
-        )
-
-    incoming_boundary = [e for e in list(dag.edges) if e.dst_uid in matched and e.src_uid not in matched]
-    outgoing_boundary = [e for e in list(dag.edges) if e.src_uid in matched and e.dst_uid not in matched]
-
-    sources = {
-        u for u in matched
-        if not any(pred in matched for pred in dag.preds.get(u, set()))
-    }
-    sinks = {
-        u for u in matched
-        if not any(succ in matched for succ in dag.succs.get(u, set()))
-    }
-
-    # Ensure outside edges only touch source/sink boundaries.
-    for e in incoming_boundary:
-        if dag.nodes[e.src_uid].node_kind == "UPD" or dag.nodes[e.dst_uid].node_kind == "UPD":
-            continue
-        if e.dst_uid not in sources:
-            raise ValueError(
-                f"split boundary violation: incoming outside edge targets non-source node {e.dst_uid}"
-            )
-    for e in outgoing_boundary:
-        if dag.nodes[e.src_uid].node_kind == "UPD" or dag.nodes[e.dst_uid].node_kind == "UPD":
-            continue
-        if e.src_uid not in sinks:
-            raise ValueError(
-                f"split boundary violation: outgoing outside edge starts at non-sink node {e.src_uid}"
-            )
-
-    inside_edges = [e for e in list(dag.edges) if e.src_uid in matched and e.dst_uid in matched]
-
-    # Tag originals as microbatch 0.
-    for uid in matched:
-        dag.nodes[uid].tag[dim_name] = 0
-
-    # Create copies for microbatches 1..N-1.
-    copy_uid: dict[tuple[str, int], str] = {}
-    for mb in range(1, num_microbatches):
-        for uid in topo:
-            if uid not in matched:
-                continue
-            nu = f"{uid}.split{dim_name}{mb}"
-            copy_uid[(uid, mb)] = nu
-
-    uid_meta_fields = (
-        "fwd_uid",
-        "compute_uid",
-        "bwd_uid",
-        "source_uid",
-        "target_uid",
-        "src_uid",
-        "dst_uid",
-        "from_uid",
-        "to_uid",
-    )
-    for mb in range(1, num_microbatches):
-        for uid in topo:
-            if uid not in matched:
-                continue
-            n = dag.nodes[uid]
-            nu = copy_uid[(uid, mb)]
-            copied_meta = dict(n.node_meta)
-            for k in uid_meta_fields:
-                v = copied_meta.get(k)
-                if isinstance(v, str) and v in matched:
-                    copied_meta[k] = copy_uid[(v, mb)]
-            dag.add_node(
-                TrainingDAGNode(
-                    uid=nu,
-                    node_kind=n.node_kind,
-                    compute_subkind=n.compute_subkind,
-                    tag={**n.tag, dim_name: mb},
-                    device=list(n.device) if n.device is not None else None,
-                    stream=n.stream,
-                    node_meta=copied_meta,
-                )
-            )
-
-    # Duplicate internal sub-DAG edges for each copied microbatch.
-    for mb in range(1, num_microbatches):
-        for e in inside_edges:
-            dag.add_edge(
-                TrainingDAGEdge(
-                    src_uid=copy_uid[(e.src_uid, mb)],
-                    dst_uid=copy_uid[(e.dst_uid, mb)],
-                    dep_kind=e.dep_kind,
-                    tensor_name=e.tensor_name,
-                )
-            )
-
-    # Duplicate incoming edges onto copied sources.
-    for mb in range(1, num_microbatches):
-        for e in incoming_boundary:
-            dag.add_edge(
-                TrainingDAGEdge(
-                    src_uid=e.src_uid,
-                    dst_uid=copy_uid[(e.dst_uid, mb)],
-                    dep_kind=e.dep_kind,
-                    tensor_name=e.tensor_name,
-                )
-            )
-
-    # Duplicate outgoing edges from copied sinks.
-    for mb in range(1, num_microbatches):
-        for e in outgoing_boundary:
-            dag.add_edge(
-                TrainingDAGEdge(
-                    src_uid=copy_uid[(e.src_uid, mb)],
-                    dst_uid=e.dst_uid,
-                    dep_kind=e.dep_kind,
-                    tensor_name=e.tensor_name,
-                )
-            )
-
-
-def _match_set_contiguous_subdag(
-    dag: TrainingDAG,
-    matched: set[str],
-    *,
-    context: str,
-) -> tuple[set[str], set[str], set[str], set[str]]:
-    if not matched:
-        raise ValueError(f"{context}: filter matched zero nodes")
-    # Contiguity should be dependency-based, not tied to one arbitrary
-    # topological ordering (which can interleave independent branches).
-    # A non-matching node is considered interleaving iff it is on some path
-    # between matched nodes: matched -> ... -> node -> ... -> matched.
-    interleaved: list[str] = []
-    for u in dag.nodes.keys():
-        if u in matched:
-            continue
-        # Has matched ancestor?
-        has_matched_ancestor = False
-        seen = {u}
-        stack = list(dag.preds.get(u, set()))
-        while stack:
-            cur = stack.pop()
-            if cur in seen:
-                continue
-            seen.add(cur)
-            if cur in matched:
-                has_matched_ancestor = True
-                break
-            stack.extend(dag.preds.get(cur, set()))
-        if not has_matched_ancestor:
-            continue
-
-        # Has matched descendant?
-        has_matched_descendant = False
-        seen = {u}
-        stack = list(dag.succs.get(u, set()))
-        while stack:
-            cur = stack.pop()
-            if cur in seen:
-                continue
-            seen.add(cur)
-            if cur in matched:
-                has_matched_descendant = True
-                break
-            stack.extend(dag.succs.get(cur, set()))
-        if has_matched_descendant:
-            interleaved.append(u)
-
-    if interleaved:
-        raise ValueError(
-            f"{context}: matched nodes are not contiguous; interleaved non-matching nodes: {interleaved[:8]}"
-        )
-
-    sources = {
-        u for u in matched
-        if not any(pred in matched for pred in dag.preds.get(u, set()))
-    }
-    sinks = {
-        u for u in matched
-        if not any(succ in matched for succ in dag.succs.get(u, set()))
-    }
-    if not sources or not sinks:
-        raise ValueError(f"{context}: failed to identify non-empty source/sink sets")
-    compute_nodes = {u for u in matched if dag.nodes[u].node_kind == "COMPUTE"}
-
-    def _has_upstream_compute(u: str) -> bool:
-        seen = {u}
-        stack = list(dag.preds.get(u, set()))
-        while stack:
-            cur = stack.pop()
-            if cur in seen or cur not in matched:
-                continue
-            seen.add(cur)
-            if dag.nodes[cur].node_kind == "COMPUTE":
-                return True
-            stack.extend(dag.preds.get(cur, set()))
-        return False
-
-    def _has_downstream_compute(u: str) -> bool:
-        seen = {u}
-        stack = list(dag.succs.get(u, set()))
-        while stack:
-            cur = stack.pop()
-            if cur in seen or cur not in matched:
-                continue
-            seen.add(cur)
-            if dag.nodes[cur].node_kind == "COMPUTE":
-                return True
-            stack.extend(dag.succs.get(cur, set()))
-        return False
-
-    compute_sources = {u for u in compute_nodes if not _has_upstream_compute(u)}
-    compute_sinks = {u for u in compute_nodes if not _has_downstream_compute(u)}
-    if not compute_sources or not compute_sinks:
-        raise ValueError(f"{context}: failed to identify non-empty compute source/sink sets")
-
-    return sources, sinks, compute_sources, compute_sinks
-
-
-@dataclass(frozen=True)
-class _OrderSegment:
-    source_uids: set[str]
-    sink_uids: set[str]
-
-
-def _single_device_for_nodes(dag: TrainingDAG, uids: set[str]) -> list[int] | None:
-    device_keys = {
-        tuple(n.device)
-        for uid in uids
-        for n in [dag.nodes[uid]]
-        if n.device is not None
-    }
-    if len(device_keys) == 1:
-        return list(next(iter(device_keys)))
-    return None
-
-
-def _add_order_dummy_node(
-    dag: TrainingDAG,
-    *,
-    uid: str,
-    directive_idx: int,
-    group_idx: int,
-    role: str,
-    grouped_uids: set[str],
-) -> str:
-    dag.add_node(
-        TrainingDAGNode(
-            uid=uid,
-            node_kind="ORDER_DUMMY",
-            compute_subkind=None,
-            tag={"order": directive_idx, "group": group_idx},
-            device=_single_device_for_nodes(dag, grouped_uids),
-            stream=_DEFAULT_STREAM,
-            node_meta={"role": role},
-        )
-    )
-    return uid
-
-
-def _add_temporal_order_edge(dag: TrainingDAG, src_uid: str, dst_uid: str) -> None:
-    logger.debug("Adding temporal dependency edge for order directive: %s -> %s", src_uid, dst_uid)
-    dag.add_edge(
-        TrainingDAGEdge(
-            src_uid=src_uid,
-            dst_uid=dst_uid,
-            dep_kind="temporal",
-            tensor_name=None,
-        )
-    )
-
-
-def _apply_order_directive(
-    dag: TrainingDAG,
-    filter_groups: list[list[dict[str, Any]]],
-    *,
-    directive_idx: int,
-) -> None:
-    segments: list[_OrderSegment] = []
-
-    for group_idx, group in enumerate(filter_groups):
-        group_sources: list[set[str]] = []
-        group_sinks: list[set[str]] = []
-        grouped_uids: set[str] = set()
-        for filter_idx, flt in enumerate(group):
-            matched = {
-                uid for uid, n in dag.nodes.items()
-                if n.node_kind != "ORDER_DUMMY" and _match_filter(n.tag, flt)
-            }
-            _srcs, _snks, csrcs, csnks = _match_set_contiguous_subdag(
-                dag,
-                matched,
-                context=f"order filter[{group_idx}][{filter_idx}]={flt}",
-            )
-            group_sources.append(csrcs)
-            group_sinks.append(csnks)
-            grouped_uids.update(matched)
-
-        if len(group) == 1:
-            segments.append(_OrderSegment(source_uids=group_sources[0], sink_uids=group_sinks[0]))
-            continue
-
-        src_uid = _add_order_dummy_node(
-            dag,
-            uid=f"order.{directive_idx}.group{group_idx}.source",
-            directive_idx=directive_idx,
-            group_idx=group_idx,
-            role="source",
-            grouped_uids=grouped_uids,
-        )
-        sink_uid = _add_order_dummy_node(
-            dag,
-            uid=f"order.{directive_idx}.group{group_idx}.sink",
-            directive_idx=directive_idx,
-            group_idx=group_idx,
-            role="sink",
-            grouped_uids=grouped_uids,
-        )
-        for csrcs in group_sources:
-            for v in csrcs:
-                _add_temporal_order_edge(dag, src_uid, v)
-        for csnks in group_sinks:
-            for u in csnks:
-                _add_temporal_order_edge(dag, u, sink_uid)
-        segments.append(_OrderSegment(source_uids={src_uid}, sink_uids={sink_uid}))
-
-    for i in range(len(segments) - 1):
-        for u in segments[i].sink_uids:
-            for v in segments[i + 1].source_uids:
-                _add_temporal_order_edge(dag, u, v)
-
-
-def apply_schedule_directives(training_dag: TrainingDAG, directives: list[Any] | None) -> None:
-    if not directives:
-        return
-    split_backward_keys = _validate_split_backward_order_stencil(directives)
-
-    # Microbatch expansion must run before split-backward lowering so schedules
-    # can mix fused B and split BI/BW for the same bucket on different MBs.
-    for i, raw in enumerate(directives):
-        if not isinstance(raw, dict) or raw.get("op") != "split":
-            continue
-        flt, dim_name, num_microbatches = _normalize_split_directive(raw)
-        logger.info(
-            "Applying directive[%d]: split(filter=%s, dim_name=%s, num_microbatches=%d)",
-            i, flt, dim_name, num_microbatches
-        )
-        _apply_split_directive(training_dag, flt, dim_name, num_microbatches)
-
-    _apply_split_backward_stencil(training_dag, split_backward_keys)
-    place_streams: set[str] = set()
-    for i, raw in enumerate(directives):
-        if isinstance(raw, dict) and raw.get("op") != "place":
-            continue
-        op, filters, devices, stream, _gather_stream, _reduce_stream, shard_params, shard_grads, bucket_size = _normalize_filter_devices_directive(raw)
-        logger.info(
-            "Applying directive[%d]: %s(filters=%s, devices=%s, stream=%s, shard_params=%s, shard_grads=%s, bucket_size=%s)",
-            i, op, filters, devices, stream, shard_params, shard_grads, bucket_size
-        )
-        for node in training_dag.nodes.values():
-            if node.node_kind not in ("COMPUTE", "UPD"):
-                continue
-            if any(_match_filter(node.tag, flt) for flt in filters):
-                node.device = list(devices)
-        if stream is not None:
-            place_streams.add(stream)
-
-    if place_streams:
-        if len(place_streams) != 1:
-            raise ValueError(f"place directives must agree on stream, got {sorted(place_streams)}")
-        place_stream = next(iter(place_streams))
-    else:
-        place_stream = None
-    _replicate_update_nodes_by_device(training_dag)
-    _insert_send_recv_comm_nodes(training_dag, comm_stream=place_stream)
-
-    for i, raw in enumerate(directives):
-        if isinstance(raw, dict) and raw.get("op") in ("place", "split", "order"):
-            continue
-        op, filters, devices, stream, gather_stream, reduce_stream, shard_params, shard_grads, bucket_size = _normalize_filter_devices_directive(raw)
-        logger.info(
-            "Applying directive[%d]: %s(filters=%s, devices=%s, stream=%s, gather_stream=%s, reduce_stream=%s, shard_params=%s, shard_grads=%s, bucket_size=%s)",
-            i, op, filters, devices, stream, gather_stream, reduce_stream, shard_params, shard_grads, bucket_size
-        )
-        if op == "replicate":
-            if bucket_size is not None:
-                _bucket_matched_fwd_nodes(training_dag, filters, int(bucket_size))
-            if shard_params:
-                _insert_all_gather_comm_nodes(training_dag, filters, devices, comm_stream=gather_stream)
-                _insert_reduce_scatter_comm_nodes(training_dag, filters, devices, comm_stream=reduce_stream)
-            elif shard_grads:
-                _insert_reduce_scatter_comm_nodes(training_dag, filters, devices, comm_stream=reduce_stream)
-            else:
-                _insert_reduce_comm_nodes(training_dag, filters, devices, comm_stream=reduce_stream)
-        elif op == "shard":
-            _insert_shard_a2a_comm_nodes(training_dag, filters, devices, comm_stream=stream)
-        else:
-            raise ValueError(f"Unsupported directive op after normalization: {op}")
+    if not isinstance(name, str) or not name:
+        raise ValueError(f"piper.annotate requires a non-empty string tag name, got {name!r}")
+
+    global _ANNOTATION_UID
+    index = _ANNOTATION_COUNTS.get(name, 0)
+    _ANNOTATION_COUNTS[name] = index + 1
+    uid = _ANNOTATION_UID
+    _ANNOTATION_UID += 1
+
+    annotation = {"name": name, "index": int(index), "uid": int(uid)}
+    _ANNOTATION_STACK.append(annotation)
+    fx_metadata_stack = tuple(dict(item) for item in _ANNOTATION_STACK)
+    try:
+        with fx.traceback.annotate({
+            PIPER_ANNOTATIONS_META_KEY: fx_metadata_stack,
+            "name": name,
+            "index": int(index),
+        }):
+            yield annotation
+    finally:
+        popped = _ANNOTATION_STACK.pop()
+        if popped is not annotation:
+            raise RuntimeError("piper.annotate stack corrupted during tracing")
 
-    # Final pass for order directives (add temporal dependencies across sub-DAGs).
-    for i, raw in enumerate(directives):
-        if not isinstance(raw, dict) or raw.get("op") != "order":
-            continue
-        filter_groups = _normalize_order_directive(raw)
-        logger.info("Applying directive[%d]: order(filters=%s)", i, filter_groups)
-        _apply_order_directive(training_dag, filter_groups, directive_idx=i)
 
 
 def _split_global_training_dag_by_pp_rank(training_dag: TrainingDAG) -> list[TrainingDAG]:
@@ -2793,42 +167,22 @@ def _dag_device_key(d: TrainingDAG) -> tuple[int, ...]:
 
 @register_backend
 def piper(gm, example_inputs, **kwargs):
-    """TrainingDAG backend: split by stage/a2a and lower schedule directives."""
+    """TrainingDAG backend: split by Piper annotations and lower schedule directives."""
     del example_inputs, kwargs
 
     schedule_info = getattr(piper_metadata, "schedule_info", {}) or {}
     schedule_directives = getattr(piper_metadata, "schedule_directives", None)
-    num_stages = int(schedule_info.get("num_stages", schedule_info["pp_degree"]))
-    has_annotations = any(
-        isinstance(node.meta.get("custom"), dict) and node.meta["custom"].get("stage") is not None
-        for node in gm.graph.nodes
-    )
+    _top_level_gm, annotation_segments = split_gm_by_annotations(gm)
 
-    stage_tag_name = "PP"
-    if has_annotations:
-        for node in gm.graph.nodes:
-            custom = node.meta.get("custom")
-            if isinstance(custom, dict) and custom.get("stage") is not None:
-                if isinstance(custom.get("name"), str) and custom.get("name"):
-                    stage_tag_name = custom["name"]
-                break
-    if has_annotations:
-        _top_level_gm, stage_submodules = _split_gm_by_stages(gm)
-    else:
-        logger.info(
-            "No stage annotations found, profiling graph to split into %d stages",
-            num_stages,
+    if not annotation_segments:
+        raise ValueError(
+            "No Piper annotations found in the traced graph. Wrap model compute "
+            "with src.piper.annotate(...) before compiling with Piper."
         )
-        _top_level_gm, stage_submodules = _profile_and_split_gm(gm, num_stages)
 
     # Build and store the new directed DAG representation for later scheduling transforms.
-    dp_degree = int(os.environ.get("PIPER_DP_DEGREE", "1"))
-    training_dag = build_training_dag(
-        stage_submodules,
-        enable_ep=_directives_enable_ep(schedule_directives),
-        dp_degree=dp_degree,
-        stage_tag_name=stage_tag_name,
-    )
+    training_dag = build_training_dag(annotation_segments)
+    _validate_schedule_tags_exist(training_dag, schedule_directives)
     apply_schedule_directives(
         training_dag,
         schedule_directives,
@@ -2840,9 +194,10 @@ def piper(gm, example_inputs, **kwargs):
         zero_chains = _prune_zero_lifetime_metadata(subdag)
         resolve_total_order_per_stream(subdag)
         _add_inter_chain_temporal_edges(subdag, zero_chains)
-        _log_training_dag_dependencies(subdag)
-        print_training_dag_order(subdag, label=f"pp{i}", rank=i, out_dir=output_dir)
-        _debug_render_training_dag(subdag, output_path=os.path.join(output_dir, f"training_dag_pp{i}"))
+        if getattr(piper_metadata, "visualize_dag", False):
+            log_training_dag_dependencies(subdag)
+            print_training_dag_order(subdag, label=f"pp{i}", rank=i, out_dir=output_dir)
+            render_training_dag(subdag, output_path=f"{output_dir}/training_dag_pp{i}")
     piper_metadata.per_pp_training_dags = per_pp_training_dags
 
     logger.info(
diff --git a/src/piper_actor.py b/src/piper_actor.py
deleted file mode 100644
index d047a37..0000000
--- a/src/piper_actor.py
+++ /dev/null
@@ -1,2151 +0,0 @@
-import ray
-import torch
-import logging
-import os
-import re
-from typing import Any, Dict, List
-import gc
-from concurrent.futures import Future, ThreadPoolExecutor
-from torch.nn import Parameter
-from torch.autograd.graph import GradientEdge, Node
-from torch.autograd.graph import set_warn_on_accumulate_grad_stream_mismatch
-import torch.distributed as dist
-from collections import defaultdict
-
-from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
-
-from .piper_utils import (
-    _serialize_graphmodule,
-    _deserialize_graphmodule,
-    create_logger,
-    LOG_LEVEL,
-)
-from .backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
-from .piper_exec import TaskType
-from .piper import _serial_topological_order, _training_dag_task_type
-
-CLEANUP_MEMORY = False
-
-logger = create_logger("piper_actor", LOG_LEVEL)
-
-def _get_rank(pp_rank, dp_rank, pp_degree):
-    return pp_rank + dp_rank * pp_degree
-
-
-def _create_actors(
-    num_actors,
-    optim_class,
-    profile=False,
-    no_nvtx: bool = False,
-    pg=None,
-    temp_dir: str = None,
-    use_inductor: bool = False,
-    pp_outer: bool = False,
-):
-    dp_rank = int(os.environ["PIPER_DP_RANK"])
-    world_size = int(os.environ["PIPER_WORLD_SIZE"])
-    dp_degree = int(os.environ["PIPER_DP_DEGREE"])
-    pp_degree = int(os.environ["PIPER_PP_DEGREE"])
-
-    from .piper_utils import piper_metadata
-
-    for pp_rank in range(num_actors):
-        global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
-        nsight_env = {"nsight": {
-            "t": "cuda,cudnn,cublas,nvtx",
-            "sample": "process-tree",
-            "backtrace": "dwarf",
-            "cudabacktrace": "sync:0,memory:0",
-            "python-backtrace": "cuda",
-            "stop-on-exit": "true",
-        }} if profile else {}
-        nccl_env = {
-            "env_vars": {
-                # "NCCL_SOCKET_IFNAME": "ens32",
-                # "GLOO_SOCKET_IFNAME": "ens32",
-                # "NCCL_P2P_DISABLE": "1",
-                # "NCCL_DEBUG": "INFO",
-                **({"TMPDIR": temp_dir} if (profile and temp_dir) else {}),
-            }
-        }
-        # When pp_outer=True, one bundle corresponds to one pipeline stage and
-        # holds all DP replicas for that stage (placement group shape is
-        # [{"GPU": dp}] * pp). Otherwise one bundle is one DP replica holding
-        # all PP ranks (shape is [{"GPU": pp}] * dp).
-        bundle_index = pp_rank if pp_outer else dp_rank
-        actor = PiperActor.options(
-            num_gpus=0.6,
-            runtime_env={**nsight_env, **nccl_env},
-            scheduling_strategy=PlacementGroupSchedulingStrategy(
-                placement_group=pg,
-                placement_group_bundle_index=bundle_index,
-            ),
-        ).remote(
-            pp_rank,
-            optim_class,
-            world_size,
-            dp_rank=dp_rank,
-            dp_degree=dp_degree,
-            pp_degree=pp_degree,
-            no_nvtx=no_nvtx,
-            use_inductor=use_inductor,
-        )
-        piper_metadata.actors[pp_rank] = actor
-
-
-@ray.remote
-class PiperActor:
-    def __init__(
-        self,
-        pp_rank,
-        optim_class,
-        world_size,
-        dp_rank=0,
-        dp_degree=1,
-        pp_degree=1,
-        no_nvtx: bool = False,
-        use_inductor: bool = False,
-    ):
-        self.logger = create_logger("piper_actor", LOG_LEVEL)
-
-        # BWD_I uses torch.autograd.grad (not .backward), so AccumulateGrad nodes are
-        # traversed for stream-sync bookkeeping but never accumulate to p.grad.
-        # Suppress the spurious stream-mismatch warning.
-        set_warn_on_accumulate_grad_stream_mismatch(False)
-
-        self.pp_rank = pp_rank
-        self.optim_class = optim_class
-        self.no_nvtx = no_nvtx
-        self.use_inductor = bool(use_inductor)
-
-        self.dp_rank = dp_rank
-        self.dp_degree = dp_degree
-        self.pp_degree = pp_degree
-        self.world_size = world_size
-
-        self.dp_group = None
-        self.ep_group = None  # separate NCCL communicator for expert-parallel (all2all) ops
-        # Per-direction communicators: (src_global_rank, dst_global_rank) -> ProcessGroup
-        self.pp_groups = {}
-        self.pp_lo_hi = None
-        self.pp_hi_lo = None
-        self.device = "cuda"
-
-        self.global_rank = _get_rank(pp_rank, dp_rank, pp_degree)
-
-        self.logger.debug(
-            f"Initializing Ray actor {self.global_rank} GPU {os.environ['CUDA_VISIBLE_DEVICES']}"
-        )
-
-        self.input = None
-        self.labels = None
-
-        self.streams: dict[str, torch.cuda.Stream] = {}
-
-        # map stage id -> original GraphModule (for hook registration)
-        self.graph_modules = dict()
-        # map stage id -> model parameters used by the fx.Graph with holes (None values) for input tensors
-        self.forward_args = dict()
-        # map stage id -> input idx -> input tensor metadata
-        self.forward_input_meta = defaultdict(dict)
-        # accumuate loss for each microbatch
-        self.loss = []
-
-        # DAG execution state
-        self.dag = None
-        self.sorted_dag_nodes = None
-
-        # PyTorch profiler state (enabled on demand via start_pytorch_profiler).
-        self._pytorch_profiler_enabled = False
-        self._torch_profiler = None
-
-        # Unified inter-task buffer.  Keyed by task.uid (int), storing whatever
-        # data the task produced for downstream consumers.
-        # Cleared at the start of each run_dag() call.
-        self.task_buffer: dict = {}
-        self.task_buffer_refcounts: dict[int, int] = {}
-
-        # CUDA events for synchronisation between streams
-        self.recv_events: dict = {}   # task_uid -> cuda.Event (for RECV tasks)
-        self.a2a_events: dict = {}    # task_uid -> cuda.Event (for FWD_A2A/BWD_A2A tasks)
-        self.ar_events: dict = {}     # task_uid -> cuda.Event (for ALL_REDUCE tasks)
-        self.rs_events: dict = {}     # task_uid -> cuda.Event (for REDUCE_SCATTER tasks)
-        self.ag_events: dict = {}     # ALL_GATHER task_uid -> cuda.Event
-        self.bwd_events: dict = {}    # bucket_key -> cuda.Event (for BWD/BWD_I/BWD_W tasks)
-
-        # Per-bucket data keyed by bucket_key (populated by _load_stage).
-        # "Bucket" here is a globally unique unit of computation; each stage may
-        # contribute one or more buckets.  The actor is agnostic to which stage
-        # owns a bucket.
-        self.bucket_fwd_fns: dict = {}          # ubid -> bucket forward runner
-        self.bucket_fwd_args: dict = {}         # ubid -> args list (with None holes for inputs)
-        self.bucket_input_idxs: dict = {}       # ubid -> list of input slot indices
-        self.bucket_param_idxs: dict = {}       # ubid -> list of param slot indices
-        self.bucket_param_names: dict = {}      # ubid -> [FX placeholder name per param]
-        self.bucket_optims: dict = {}           # ubid -> optimizer (or None)
-        self.bucket_trainable_param_idxs: dict = {}  # ubid -> list of trainable param slot indices
-        self.bucket_ac_subgraph_counts: dict = {}    # ubid -> number of sequential AC subgraphs
-        self.bucket_flat_params: dict = {}       # ubid -> full flat param tensor for sharded params
-        self.bucket_flat_grads: dict = {}        # ubid -> flat grad tensor for sharded grads
-        self.bucket_shard_params: dict = {}      # ubid -> local optimizer shard param tensor
-        self.bucket_shard_optims: dict = {}      # ubid -> local shard optimizer
-        self.bucket_rs_grads: dict = {}          # ubid -> reduced local shard grad tensor
-        self.grad_buffer_dtype = torch.float32   # sharded gradient buffers stay fp32
-        self.param_shard_info: dict = {}         # ubid -> (shard_start, shard_size, orig_numel)
-        self.bucket_param_view_specs: dict = {}  # ubid -> [(param, offset, numel, shape)]
-        self.full_params_fresh: dict = {}        # ubid -> bool
-        self.param_sharded_ubids: set = set()
-        self.grad_sharded_ubids: set = set()
-        self.zero_managed_ubids: set = set()
-        self.cleanup_executor = ThreadPoolExecutor(max_workers=1)
-        self.pending_param_frees: dict[Any, Future] = {}
-        self.pending_grad_frees: dict[Any, Future] = {}
-
-        # Non-trainable constant tensor attributes (e.g. freqs_cis, mask) pushed
-        # from the coordinator before compilation so _load_stage can fill them in
-        # instead of zero-initializing.  Keyed by bare attribute name (e.g. "freqs_cis").
-        self.model_const_attrs: dict = {}
-
-    def get_and_reset_peak_memory_stats(self) -> tuple:
-        """Return (global_rank, max_memory_allocated_bytes) and reset peak stats."""
-        max_alloc = torch.cuda.max_memory_allocated()
-        torch.cuda.reset_peak_memory_stats()
-        return self.global_rank, max_alloc
-
-    def reset_peak_memory(self):
-        torch.cuda.reset_peak_memory_stats()
-
-    def _nvtx_push(self, label: str) -> None:
-        if not self.no_nvtx:
-            torch.cuda.nvtx.range_push(label)
-
-    def _nvtx_pop(self) -> None:
-        if not self.no_nvtx:
-            torch.cuda.nvtx.range_pop()
-
-    def start_pytorch_profiler(self) -> None:
-        """Begin a torch.profiler session spanning the next run_dag iterations.
-
-        Each node's execution in _run_dag_body is wrapped in a record_function
-        labelled the same as its NVTX range, so the resulting trace identifies
-        per-node work.
-        """
-        self._torch_profiler = torch.profiler.profile(
-            activities=[
-                torch.profiler.ProfilerActivity.CPU,
-                torch.profiler.ProfilerActivity.CUDA,
-            ],
-        )
-        self._torch_profiler.__enter__()
-        self._pytorch_profiler_enabled = True
-        self.logger.info(f"Actor {self.global_rank}: PyTorch profiler started")
-
-    def stop_pytorch_profiler(self, profile_dir: str) -> str:
-        """End the profiler session and export a chrome trace.
-
-        The file is named ``dp{dp_rank}_pp{pp_rank}.json`` inside *profile_dir*
-        so the harness can group same-dp-rank actors. Returns the written path.
-        """
-        self._pytorch_profiler_enabled = False
-        self._torch_profiler.__exit__(None, None, None)
-        os.makedirs(profile_dir, exist_ok=True)
-        filepath = os.path.join(
-            profile_dir, f"dp{self.dp_rank}_pp{self.pp_rank}.json"
-        )
-        self._torch_profiler.export_chrome_trace(filepath)
-        self._torch_profiler = None
-        self.logger.info(
-            f"Actor {self.global_rank}: PyTorch profiler trace written to {filepath}"
-        )
-        return filepath
-
-    def _rf_enter(self, label: str):
-        """Enter a profiler record_function for *label* (no-op unless profiling).
-
-        Returns the entered context (or None) to pass to _rf_exit, mirroring the
-        _nvtx_push/_nvtx_pop pattern so the large match block need not be
-        re-indented under a ``with``.
-
-        While profiling, we also disable multithreaded autograd for the duration
-        of the task. record_function pushes its range onto the *calling* thread's
-        thread-local stack, but the autograd engine normally runs backward on a
-        separate per-device worker thread, so kernels it launches fall outside the
-        label window and appear un-labelled on the GPU streams. Running backward on
-        the calling thread makes the label span every backward kernel. This perturbs
-        timing slightly, which is acceptable for a diagnostic profiling run.
-        """
-        if not self._pytorch_profiler_enabled:
-            return None
-        mt = torch.autograd.set_multithreading_enabled(False)
-        mt.__enter__()
-        rf = torch.profiler.record_function(label)
-        rf.__enter__()
-        return (rf, mt)
-
-    def _rf_exit(self, rf) -> None:
-        if rf is not None:
-            rf_ctx, mt = rf
-            rf_ctx.__exit__(None, None, None)
-            mt.__exit__(None, None, None)
-
-    def load_input(self, inputs):
-        self.inputs = [inp.to(self.device) for inp in inputs]
-        self.logger.debug(f"Actor {self.global_rank} loaded inputs {len(self.inputs)}")
-
-    def load_labels(self, labels):
-        self.labels = labels.to(self.device)
-        self.logger.debug(f"Actor {self.global_rank} loaded labels {self.labels.shape}")
-
-    def load_const_attrs(self, const_attrs: dict) -> None:
-        """Store non-trainable constant tensor attributes (e.g. freqs_cis, mask).
-
-        *const_attrs* maps bare attribute name → CPU tensor.  Values are moved to
-        the actor's device so ``_load_stage`` can copy them directly.
-        """
-        self.model_const_attrs = {k: v.to(self.device) for k, v in const_attrs.items()}
-
-    def get_node_ip_and_free_port(self):
-        import socket
-
-        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-            s.bind(("0.0.0.0", 0))
-            port = s.getsockname()[1]
-        return ray.util.get_node_ip_address(), port
-
-    def _join_process_groups(self, master_addr, master_port):
-
-        self.logger.info(f"Actor {self.global_rank} using GPU {os.environ['CUDA_VISIBLE_DEVICES']}, master addr {master_addr}:{master_port}")
-
-        init_method = f"tcp://{master_addr}:{master_port}"
-
-        self.device = f"cuda:{self.global_rank % torch.cuda.device_count()}"
-        torch.cuda.set_device(self.device)
-
-        if self.pp_degree > 1 or self.dp_degree > 1:
-            dist.init_process_group(
-                "nccl",
-                init_method=init_method,
-                rank=self.global_rank,
-                world_size=self.world_size,
-            )
-
-            if self.dp_degree > 1:
-                self._join_dp_process_group()
-            if self.pp_degree > 1:
-                self._join_pp_process_group()
-
-            self.logger.info(f"Actor {self.global_rank} joined process groups")
-
-    def _join_dp_process_group(self):
-        num_dp_groups = self.world_size // self.dp_degree
-        for dp_group_id in range(num_dp_groups):
-            group_ranks = [
-                (dp_group_id + num_dp_groups * i) for i in range(self.dp_degree)
-            ]
-            # Two separate NCCL communicators over the same ranks: one for allreduce,
-            # one for all2all.  Sharing a communicator causes both op types to run on
-            # the same internal NCCL proxy stream, which prevents true overlap.
-            process_group = dist.new_group(ranks=group_ranks, backend="nccl")
-            ep_process_group = dist.new_group(ranks=group_ranks, backend="nccl")
-            if self.global_rank % num_dp_groups == dp_group_id:
-                self.dp_group = process_group
-                self.ep_group = ep_process_group
-
-    def _join_pp_process_group(self):
-        num_pp_groups = self.world_size // self.pp_degree
-
-        for pp_group_id in range(num_pp_groups):
-            group_ranks = [
-                (pp_group_id * self.pp_degree + i) for i in range(self.pp_degree)
-            ]
-            lo_hi_group = dist.new_group(ranks=group_ranks, backend="nccl")
-            hi_lo_group = dist.new_group(ranks=group_ranks, backend="nccl")
-
-            if self.global_rank in group_ranks:
-                self.pp_lo_hi = lo_hi_group
-                self.pp_hi_lo = hi_lo_group
-
-    def _derive_dag_bucket_modes(self, training_dag: Any) -> None:
-        self.param_sharded_ubids = set()
-        self.grad_sharded_ubids = set()
-        for node in training_dag.nodes.values():
-            meta = getattr(node, "node_meta", {}) or {}
-            ubid = meta.get("bucket_key")
-            if ubid is None:
-                continue
-            if node.node_kind == "ALL_GATHER_COMM":
-                self.param_sharded_ubids.add(ubid)
-            elif node.node_kind == "REDUCE_SCATTER_COMM":
-                self.grad_sharded_ubids.add(ubid)
-        self.zero_managed_ubids = self.param_sharded_ubids | self.grad_sharded_ubids
-
-    @staticmethod
-    def _node_meta(node: Any) -> dict:
-        meta = getattr(node, "node_meta", None)
-        return meta if isinstance(meta, dict) else {}
-
-    def _node_bucket_key(self, node: Any) -> Any | None:
-        return self._node_meta(node).get("bucket_key")
-
-    def _relocate_meta_devices(self, gm) -> None:
-        """Rewrite baked ``torch.device('meta')`` literals to the actor device.
-
-        Meta-device compilation captures whatever device was current at trace
-        time (inside ``type_as`` / ``.to(device=x.device)`` / device-aware
-        factory ops like ``torch.zeros(..., device=...)``). That literal
-        round-trips through serialization as ``meta`` and would otherwise make
-        those ops emit meta tensors at runtime, mismatching the CUDA inputs.
-        Lifted params/buffers/inputs are unaffected (placed on device directly),
-        so this only touches device literals embedded in node args/kwargs.
-        """
-        import torch.fx as fx
-
-        device = self.device
-        replaced = 0
-
-        def _fix(value):
-            nonlocal replaced
-            if isinstance(value, torch.device):
-                if value.type == "meta":
-                    replaced += 1
-                    return torch.device(device)
-                return value
-            if isinstance(value, tuple):
-                return tuple(_fix(v) for v in value)
-            if isinstance(value, list):
-                return [_fix(v) for v in value]
-            if isinstance(value, dict):
-                return {k: _fix(v) for k, v in value.items()}
-            return value
-
-        for module in gm.modules():
-            if not isinstance(module, fx.GraphModule):
-                continue
-            before = replaced
-            for node in module.graph.nodes:
-                node.args = _fix(node.args)
-                node.kwargs = _fix(node.kwargs)
-            if replaced != before:
-                module.recompile()
-
-    def _load_stage(
-        self,
-        stage_id: int,
-        modules_data: list,
-        a2a_boundaries: dict = None,
-        use_activation_checkpointing: bool = False,
-    ) -> None:
-        """Load a (possibly bucketed) stage.
-
-        *modules_data* is a list of dicts, one per module/bucket, each with keys:
-        ``gm_data``, ``graphargs``, ``input_idxs``, ``param_idxs``, ``bucket_key``.
-
-        A non-bucketed stage is represented as a single-element list.
-        All per-bucket data structures are keyed by ``bucket_key`` so run_dag
-        can look up bucket data without knowing which stage owns a bucket.
-        """
-        self.logger.debug(
-            f"Loading stage {stage_id} ({len(modules_data)} module(s)) on actor {self.global_rank}"
-        )
-
-        g = torch.Generator(device=self.device)
-        g.manual_seed(1000 * self.global_rank + stage_id)
-
-        first_gm = None
-
-        for b_idx, bd in enumerate(modules_data):
-            ubid: Any = bd["bucket_key"]
-            ac_num_subgraphs = int(bd.get("ac_num_subgraphs", 1))
-            ac_requested_subgraphs = int(bd.get("ac_requested_subgraphs", ac_num_subgraphs))
-            gms = [_deserialize_graphmodule(gm_data) for gm_data in bd["gm_data_list"]] if "gm_data_list" in bd else [_deserialize_graphmodule(bd["gm_data"])]
-            for gm in gms:
-                self._relocate_meta_devices(gm)
-            if self.use_inductor:
-                compiled_gms = []
-                for subgraph_idx, gm in enumerate(gms):
-                    compiled_gm = torch.compile(gm)
-                    compiled_gms.append(compiled_gm)
-                    self.logger.debug(
-                        f"[load_stage_compile] rank={self.global_rank} stage={stage_id} "
-                        f"ubid={ubid} subgraph={subgraph_idx} compiled=True"
-                    )
-                gms = compiled_gms
-
-            if b_idx == 0:
-                first_gm = gms[0]
-
-            forward_args = list(bd["graphargs"])
-            b_input_idxs = list(bd["input_idxs"])
-            b_param_idxs = list(bd["param_idxs"])
-            apply_zero = bool(bd.get("apply_zero", True))
-            shared_placeholder_names = list(
-                bd.get("shared_placeholder_names", bd.get("placeholder_names", []))
-            )
-            # Extract FX placeholder names for each param index.
-            self.bucket_param_names[ubid] = [
-                shared_placeholder_names[i] if i < len(shared_placeholder_names) else f"ubid{ubid}_p{i}"
-                for i in b_param_idxs
-            ]
-
-            # Save input tensor metadata for pre-allocating FWD recv buffers.
-            # Stored as a list of (shape, dtype, requires_grad) in input-slot order.
-            recv_meta = []
-            for i in b_input_idxs:
-                meta = forward_args[i]
-                if meta is not None:
-                    recv_meta.append((tuple(meta.shape), meta.dtype,
-                                      bool(getattr(meta, "requires_grad", False))))
-                forward_args[i] = None  # clear slot; run_dag will fill it at execution time
-            self.forward_input_meta[ubid] = recv_meta
-
-            self.bucket_input_idxs[ubid] = b_input_idxs
-
-            # Realize parameter tensors.
-            realized = [None] * len(forward_args)
-            for i, arg in enumerate(forward_args):
-                if arg is None:
-                    continue
-                t = torch.empty(arg.shape, dtype=arg.dtype, device=self.device)
-                if arg.requires_grad:
-                    t.requires_grad_(True)
-                    torch.nn.init.normal_(t, mean=0.0, std=0.02, generator=g)
-                else:
-                    # Non-trainable slot: try to fill from const attrs (freqs_cis, mask, …)
-                    # before falling back to zeros.  Dynamo names direct model attrs as
-                    # "l_self_<attr_name>", so strip that prefix to get the bare name.
-                    ph_name = shared_placeholder_names[i] if i < len(shared_placeholder_names) else ""
-                    attr_name = re.sub(r'^l_self_', '', ph_name)
-                    const_val = self.model_const_attrs.get(attr_name)
-                    if (
-                        const_val is not None
-                        and tuple(const_val.shape) == tuple(arg.shape)
-                        and const_val.dtype == arg.dtype
-                    ):
-                        t.copy_(const_val)
-                    else:
-                        t.zero_()
-                realized[i] = t
-
-            shared_name_to_idx = {
-                name: i for i, name in enumerate(shared_placeholder_names)
-            }
-            subgraph_specs = []
-            for gm in gms:
-                sub_placeholder_names = [
-                    n.name for n in gm.graph.nodes if n.op == "placeholder"
-                ]
-                dynamic_names = [
-                    name for name in sub_placeholder_names
-                    if name not in shared_name_to_idx
-                ]
-                subgraph_specs.append((gm.forward, sub_placeholder_names, dynamic_names))
-
-            def _bucket_forward_runner(
-                shared_args,
-                _specs=subgraph_specs,
-                _name_to_idx=shared_name_to_idx,
-                _use_ac=use_activation_checkpointing,
-                _stage_id=stage_id,
-                _ubid=ubid,
-                _shared_placeholder_names=tuple(shared_placeholder_names),
-            ):
-                out = None
-                for subgraph_idx, (forward_impl, placeholder_names, dynamic_names) in enumerate(_specs):
-                    if len(dynamic_names) == 0:
-                        dynamic_values = []
-                    elif len(dynamic_names) == 1:
-                        dyn_arg = out
-                        if isinstance(dyn_arg, (tuple, list)) and len(dyn_arg) == 1:
-                            dyn_arg = dyn_arg[0]
-                        dynamic_values = [dyn_arg]
-                    else:
-                        if not isinstance(out, (tuple, list)):
-                            raise RuntimeError(
-                                f"Bucket forward expected {len(dynamic_names)} dynamic inputs "
-                                f"but previous subgraph produced {type(out).__name__}"
-                            )
-                        dynamic_values = list(out)
-                        if len(dynamic_values) != len(dynamic_names):
-                            raise RuntimeError(
-                                f"Bucket forward expected {len(dynamic_names)} dynamic inputs "
-                                f"but previous subgraph produced {len(dynamic_values)} values"
-                            )
-                    dynamic_name_to_value = {
-                        name: value for name, value in zip(dynamic_names, dynamic_values)
-                    }
-                    call_args = []
-                    for name in placeholder_names:
-                        if name in dynamic_name_to_value:
-                            call_args.append(dynamic_name_to_value[name])
-                        else:
-                            call_args.append(shared_args[_name_to_idx[name]])
-                    if _use_ac:
-                        out = torch.utils.checkpoint.checkpoint(
-                            forward_impl,
-                            *call_args,
-                            use_reentrant=False,
-                        )
-                    else:
-                        out = forward_impl(*call_args)
-                return out
-
-            self.bucket_fwd_fns[ubid] = _bucket_forward_runner
-            self.bucket_fwd_args[ubid] = realized
-            self.bucket_param_idxs[ubid] = b_param_idxs
-            self.bucket_ac_subgraph_counts[ubid] = ac_num_subgraphs
-            trainable_idxs = [
-                i for i in b_param_idxs
-                if realized[i] is not None and realized[i].requires_grad
-            ]
-            self.bucket_trainable_param_idxs[ubid] = trainable_idxs
-
-            zero_managed = (
-                self.dp_degree > 1
-                and apply_zero
-                and bool(trainable_idxs)
-                and ubid in self.zero_managed_ubids
-            )
-            params_sharded = ubid in self.param_sharded_ubids
-            grads_sharded = ubid in self.grad_sharded_ubids
-
-            if zero_managed:
-                trainable = [realized[i] for i in trainable_idxs]
-                flat_params = torch.cat([p.detach().view(-1) for p in trainable]).contiguous()
-                flat_params.requires_grad_(False)
-                orig_numel = flat_params.numel()
-                dp = self.dp_degree
-                shard_size = (orig_numel + dp - 1) // dp
-                padded_numel = shard_size * dp
-                if padded_numel > orig_numel:
-                    padded = flat_params.new_zeros(padded_numel)
-                    padded[:orig_numel].copy_(flat_params)
-                    flat_params = padded
-
-                offset = 0
-                view_specs = []
-                for idx, p in zip(trainable_idxs, trainable):
-                    numel = p.numel()
-                    realized[idx] = realized[idx].detach()
-                    realized[idx].data = flat_params[offset:offset + numel].view(p.shape)
-                    realized[idx].requires_grad_(True)
-                    realized[idx].grad_dtype = self.grad_buffer_dtype
-                    view_specs.append((realized[idx], offset, numel, tuple(p.shape)))
-                    offset += numel
-
-                shard_start = self.dp_rank * shard_size
-                if params_sharded:
-                    shard_param = flat_params[shard_start:shard_start + shard_size].detach().clone()
-                else:
-                    shard_param = flat_params[shard_start:shard_start + shard_size]
-                shard_param.requires_grad_(True)
-                # Keep ZeRO comm/storage buffers in fp32, but let the optimizer consume
-                # grads in the shard-param dtype to match Adam's bf16 foreach path.
-                shard_param.grad_dtype = None
-
-                self.bucket_flat_params[ubid] = flat_params
-                self.bucket_flat_grads[ubid] = (
-                    None
-                    if grads_sharded
-                    else torch.zeros(padded_numel, dtype=self.grad_buffer_dtype, device=self.device)
-                )
-                self.bucket_shard_params[ubid] = shard_param
-                self.bucket_shard_optims[ubid] = self.optim_class([shard_param])
-                self.bucket_rs_grads[ubid] = (
-                    torch.zeros(shard_size, dtype=self.grad_buffer_dtype, device=self.device)
-                    if grads_sharded
-                    else None
-                )
-                self.param_shard_info[ubid] = (shard_start, shard_size, orig_numel)
-                self.bucket_param_view_specs[ubid] = view_specs
-                self.full_params_fresh[ubid] = False
-
-                if params_sharded:
-                    storage = flat_params.untyped_storage()
-                    if storage.size() != 0:
-                        storage.resize_(0)
-
-                optim = None
-            else:
-                # Keep trainable params as separate tensors for the non-ZeRO path.
-                self.bucket_param_view_specs[ubid] = []
-                self.bucket_flat_params[ubid] = None
-                self.bucket_flat_grads[ubid] = None
-                trainable_for_optim = [realized[i] for i in trainable_idxs]
-                optim = self.optim_class(trainable_for_optim, fused=True) if trainable_for_optim else None
-            self.bucket_optims[ubid] = optim
-
-        # Keep first GraphModule for compatibility with external inspection tools.
-        self.graph_modules[stage_id] = first_gm
-
-    # -----------------------------------------------------------------------
-    # DAG-based execution
-    # -----------------------------------------------------------------------
-
-    def _stream_id(self, node_or_stream: Any) -> str:
-        if isinstance(node_or_stream, str):
-            return node_or_stream
-        return str(getattr(node_or_stream, "stream", "default_stream"))
-
-    def _initialize_streams_for_training_dag(self, training_dag: Any) -> None:
-        stream_ids = {
-            self._stream_id(n)
-            for n in training_dag.nodes.values()
-            if getattr(n, "stream", None) is not None
-        }
-        stream_ids.add("default_stream")
-        self.streams = {
-            stream_id: torch.cuda.Stream(device=self.device)
-            for stream_id in sorted(stream_ids)
-        }
-
-        # Force cuBLAS context initialization on every logical stream used by
-        # this DAG so the first backward pass does not hit lazy CUDA warnings.
-        for stream in self.streams.values():
-            with torch.cuda.stream(stream):
-                _w = torch.zeros(4, 4, device=self.device)
-                torch.mm(_w, _w)
-
-    def _stream_for_id(self, stream_id: str) -> "torch.cuda.Stream":
-        assert stream_id in self.streams, (
-            f"TrainingDAG referenced stream={stream_id!r}, but load_training_dag "
-            f"initialized only {sorted(self.streams)}"
-        )
-        return self.streams[stream_id]
-
-    def _stream_for_task(self, task: Any) -> "torch.cuda.Stream":
-        return self._stream_for_id(self._stream_id(task))
-
-    def _default_stream(self) -> "torch.cuda.Stream":
-        return self._stream_for_id("default_stream")
-
-    def load_training_dag(self, training_dag: Any) -> None:
-        """Load per-PP-rank TrainingDAG compute nodes using existing _load_stage logic.
-
-        This is an adapter for the new TrainingDAG representation where each
-        COMPUTE/FWD node with ``node_meta['gm']`` is equivalent to one bucket/module.
-        The runtime executes TrainingDAG nodes via ``run_dag``; this method
-        only populates actor-side bucket/module state.
-        """
-        if training_dag is None:
-            raise ValueError("load_training_dag requires non-None training_dag")
-        if not hasattr(training_dag, "nodes"):
-            raise TypeError("load_training_dag expected object with 'nodes' field")
-
-        self._derive_dag_bucket_modes(training_dag)
-        self._initialize_streams_for_training_dag(training_dag)
-
-        # Clear any prior module state before loading a new training DAG.
-        self.graph_modules.clear()
-        self.forward_args.clear()
-        self.forward_input_meta.clear()
-
-        stage_to_modules: dict[int, list[dict]] = defaultdict(list)
-
-        # Deterministic order by stage/segment then uid.
-        compute_nodes = [
-            n for n in training_dag.nodes.values()
-            if getattr(n, "node_kind", None) == "COMPUTE"
-            and getattr(n, "compute_subkind", None) == "FWD"
-        ]
-        compute_nodes.sort(
-            key=lambda n: (
-                int(getattr(n, "node_meta", {}).get("stage_id", 10**9)),
-                int(getattr(n, "node_meta", {}).get("segment_id", 10**9)),
-                str(getattr(n, "uid", "")),
-            )
-        )
-
-        seen_bucket_keys: set[Any] = set()
-        skipped_dupe_fwd_nodes = 0
-        for node in compute_nodes:
-            meta = getattr(node, "node_meta", {}) or {}
-            bucket_key = meta.get("bucket_key", getattr(node, "uid", None))
-            if bucket_key in seen_bucket_keys:
-                skipped_dupe_fwd_nodes += 1
-                continue
-            seen_bucket_keys.add(bucket_key)
-            gm = meta.get("gm")
-            gm_data = meta.get("gm_data")
-            stage_id = meta.get("stage_id")
-            input_idxs = meta.get("input_idxs")
-            param_idxs = meta.get("param_idxs")
-            graphargs = meta.get("graphargs")
-            input_names = meta.get("input_names", [])
-            output_names = meta.get("output_names", [])
-
-            if gm_data is None and gm is None:
-                raise ValueError(
-                    f"load_training_dag: compute node {getattr(node, 'uid', '<unknown>')} "
-                    f"is missing required metadata field 'gm_data' (or fallback 'gm')"
-                )
-            if stage_id is None or input_idxs is None or param_idxs is None or graphargs is None:
-                raise ValueError(
-                    f"load_training_dag: compute node {getattr(node, 'uid', '<unknown>')} "
-                    f"is missing required metadata fields"
-                )
-
-            module_data = {
-                "gm_data": gm_data if gm_data is not None else _serialize_graphmodule(gm),
-                "graphargs": list(graphargs),
-                "input_idxs": list(input_idxs),
-                "param_idxs": list(param_idxs),
-                "placeholder_names": list(input_names),
-                "output_names": list(output_names),
-                "shared_placeholder_names": list(input_names),
-                "bucket_key": bucket_key,
-                "ac_num_subgraphs": 1,
-                "ac_requested_subgraphs": 1,
-                "apply_zero": bool(meta.get("apply_zero", True)),
-                "training_dag_uid": getattr(node, "uid", None),
-                "triton_constant_args": dict(meta.get("triton_constant_args", {})),
-            }
-            stage_to_modules[int(stage_id)].append(module_data)
-
-        assert stage_to_modules, (
-            "load_training_dag: no FWD compute nodes with gm metadata found"
-        )
-
-        for stage_id in sorted(stage_to_modules.keys()):
-            self._load_stage(
-                stage_id=stage_id,
-                modules_data=stage_to_modules[stage_id],
-                a2a_boundaries={},
-                use_activation_checkpointing=False,
-            )
-
-        # Build runtime adjacency on TrainingDAG nodes so run_dag can execute directly.
-        for n in training_dag.nodes.values():
-            n.data_preds = []
-            n.data_succs = []
-            n.temporal_preds = []
-            n.temporal_succs = []
-            if n.node_kind == "SEND_COMM":
-                n.peer_pp_rank = n.node_meta.get("peer_pp_rank")
-            elif n.node_kind == "RECV_COMM":
-                n.peer_pp_rank = n.node_meta.get("peer_pp_rank")
-            else:
-                n.peer_pp_rank = None
-        for e in training_dag.edges:
-            if e.src_uid not in training_dag.nodes or e.dst_uid not in training_dag.nodes:
-                continue
-            src = training_dag.nodes[e.src_uid]
-            dst = training_dag.nodes[e.dst_uid]
-            if e.dep_kind == "temporal":
-                src.temporal_succs.append(dst)
-                dst.temporal_preds.append(src)
-            else:
-                src.data_succs.append(dst)
-                dst.data_preds.append(src)
-
-        for n in training_dag.nodes.values():
-            n.task_type = _training_dag_task_type(n)
-            mb = n.tag.get("MB", 0)
-            st = n.tag.get("PP", 0)
-            n.batches = [type("RuntimeBatch", (), {"stage_id": st, "mb_idx": mb})()]
-
-        self.dag = training_dag
-        self.sorted_dag_nodes = [
-            training_dag.nodes[uid] for uid in _serial_topological_order(training_dag)
-        ]
-
-    def _clear_param_grads(self) -> None:
-        """Restore standard PyTorch semantics: inactive params keep grad=None."""
-        for ubid, trainable_idxs in self.bucket_trainable_param_idxs.items():
-            args = self.bucket_fwd_args[ubid]
-            for idx in trainable_idxs:
-                t = args[idx]
-                if t is not None:
-                    t.grad = None
-
-    def _accumulate_zero_param_grads_to_flat(
-        self, ubid: Any | None, stream: "torch.cuda.Stream"
-    ) -> None:
-        """Accumulate ZeRO-managed param grads into the fp32 flat grad buffer."""
-        if ubid is None or ubid not in self.zero_managed_ubids or self.dp_degree <= 1:
-            return
-        if ubid in self.grad_sharded_ubids:
-            self._wait_pending_free(self.pending_grad_frees, ubid)
-        specs = self.bucket_param_view_specs.get(ubid, [])
-        if not specs:
-            return
-        with torch.cuda.stream(stream):
-            flat_grads = self.bucket_flat_grads.get(ubid)
-            if flat_grads is None:
-                shard_info = self.param_shard_info.get(ubid)
-                if shard_info is None:
-                    return
-                _shard_start, shard_size, _orig_numel = shard_info
-                flat_grads = torch.zeros(
-                    shard_size * self.dp_degree,
-                    dtype=self.grad_buffer_dtype,
-                    device=self.device,
-                )
-                self.bucket_flat_grads[ubid] = flat_grads
-            for param, offset, numel, _shape in specs:
-                grad = param.grad
-                if grad is None:
-                    continue
-                flat_grads[offset:offset + numel].add_(grad.detach().reshape(-1).to(flat_grads.dtype))
-                param.grad = None
-
-    def _sync_payload_ubid(self, node: Any) -> Any | None:
-        bk = self._node_bucket_key(node)
-        if bk is not None:
-            return bk
-        if node.data_preds:
-            pred_bk = self._node_bucket_key(node.data_preds[0])
-            if pred_bk is not None:
-                return pred_bk
-        return None
-
-    def _sync_payload_ubids(self, node: Any) -> list[Any]:
-        sync_ubids = self._node_meta(node).get("sync_ubids")
-        if sync_ubids:
-            return list(sync_ubids)
-        ubid = self._sync_payload_ubid(node)
-        return [ubid] if ubid is not None else []
-
-    def _wait_pending_free(self, pending: dict[Any, Future], ubid: Any | None) -> None:
-        if ubid is None:
-            return
-        fut = pending.pop(ubid, None)
-        if fut is not None:
-            fut.result()
-
-    def _drain_pending_frees(self) -> None:
-        for pending in (self.pending_param_frees, self.pending_grad_frees):
-            futures = list(pending.values())
-            pending.clear()
-            for fut in futures:
-                fut.result()
-
-    def _defer_free_full_params(self, ubid: Any | None, evt: torch.cuda.Event) -> None:
-        if ubid is None or ubid not in self.param_sharded_ubids:
-            return
-        self._wait_pending_free(self.pending_param_frees, ubid)
-        self.pending_param_frees[ubid] = self.cleanup_executor.submit(
-            self._wait_then_free_full_params,
-            ubid,
-            evt,
-        )
-
-    def _defer_free_full_grads(self, ubid: Any | None, evt: torch.cuda.Event) -> None:
-        if ubid is None or ubid not in self.grad_sharded_ubids:
-            return
-        self._wait_pending_free(self.pending_grad_frees, ubid)
-        self.pending_grad_frees[ubid] = self.cleanup_executor.submit(
-            self._wait_then_free_full_grads,
-            ubid,
-            evt,
-        )
-
-    def _wait_then_free_full_params(self, ubid: Any, evt: torch.cuda.Event) -> None:
-        evt.synchronize()
-        self._free_full_params(ubid)
-
-    def _wait_then_free_full_grads(self, ubid: Any, evt: torch.cuda.Event) -> None:
-        evt.synchronize()
-        self._free_full_grads(ubid)
-
-    def _init_task_buffer_refcounts(self, dag: Any) -> None:
-        nodes_iter = dag.nodes.values() if isinstance(dag.nodes, dict) else dag.nodes
-        self.task_buffer_refcounts = {
-            node.uid: len(node.data_succs)
-            for node in nodes_iter
-            if node.data_succs
-        }
-
-    def _release_task_buffer_uid(self, uid: int) -> None:
-        remaining = self.task_buffer_refcounts.get(uid)
-        if remaining is None:
-            self.task_buffer.pop(uid, None)
-            return
-        remaining -= 1
-        if remaining <= 0:
-            self.task_buffer_refcounts.pop(uid, None)
-            self.task_buffer.pop(uid, None)
-        else:
-            self.task_buffer_refcounts[uid] = remaining
-
-    def run_dag(self, loss_fn=None):
-        # Mark the entire iteration boundary for the NVTX timeline.
-        iter_idx = getattr(self, "_iter_counter", 0)
-        self._iter_counter = iter_idx + 1
-        self._nvtx_push(f"iter_{iter_idx}_rank_{self.global_rank}")
-        self._run_dag_body(loss_fn=loss_fn)
-        self._nvtx_pop()
-
-    def _run_dag_body(self, loss_fn=None):
-        """Execute the loaded TrainingDAG in topological order.
-
-        All inter-task data is routed through ``task_buffer``, keyed by the
-        producing task's ``uid``.  Each task method receives its inputs as
-        arguments and returns its outputs; ``run_dag`` is the only place that
-        reads from and writes to ``task_buffer``.
-
-        Synchronisation contract:
-        - Before a SEND node: the node's stream waits on the compute event so the
-          activation / gradient tensor is fully computed before the send begins.
-        - Before a compute node that has a RECV predecessor: the compute stream
-          waits on the recv_event so the recv buffer is populated before compute reads it.
-        - RECV records a recv_event after the dist.recv completes.
-        """
-        assert self.dag is not None, "load_training_dag() must be called before run_dag()"
-        assert self.sorted_dag_nodes is not None, "load_training_dag() must initialize sorted node order"
-        self._drain_pending_frees()
-        debug_enabled = self.logger.isEnabledFor(logging.DEBUG)
-        # Reset per-iteration state
-        self.task_buffer = {}
-        self.task_buffer_refcounts = {}
-        self.recv_events = {}
-        self.a2a_events = {}
-        self.ar_events = {}
-        self.rs_events = {}
-        self.ag_events = {}
-        self.bwd_events = {}
-        self._clear_param_grads()
-        default_stream = self._default_stream()
-        with torch.cuda.stream(default_stream):
-            for buf in self.bucket_flat_grads.values():
-                if buf is not None:
-                    buf.zero_()
-            for buf in self.bucket_rs_grads.values():
-                if buf is not None:
-                    buf.zero_()
-        zero_evt = torch.cuda.Event()
-        zero_evt.record(default_stream)
-        for stream in self.streams.values():
-            if stream is not default_stream:
-                stream.wait_event(zero_evt)
-        comp_events: dict = {}  # task_uid -> cuda.Event for compute tasks
-
-        self._init_task_buffer_refcounts(self.dag)
-        last_comp_event_by_stream: dict[str, torch.cuda.Event] = {}
-        def _node_tag_str(node: Any) -> str:
-            tag = getattr(node, "tag", None)
-            if not isinstance(tag, dict) or not tag:
-                return "{}"
-            items = sorted(tag.items(), key=lambda kv: kv[0])
-            return "{" + ",".join(f"{k}={v}" for k, v in items) + "}"
-
-        def _wait_for_all_gather(compute_node: Any) -> None:
-            compute_stream = self._stream_for_task(compute_node)
-            for pred in compute_node.data_preds:
-                if pred.task_type == TaskType.ALL_GATHER:
-                    ag_evt = self.ag_events.get(pred.uid)
-                    if ag_evt is not None:
-                        compute_stream.wait_event(ag_evt)
-
-        for node in self.sorted_dag_nodes:
-
-            task_type = node.task_type
-            batch = node.batches[0]
-            mb_idx = batch.mb_idx
-            ubid = self._node_bucket_key(node)  # None for non-compute nodes
-            node_stream = self._stream_for_task(node)
-            node_stream_id = self._stream_id(node)
-            node_tag = _node_tag_str(node)
-
-            if debug_enabled:
-                self.logger.debug(
-                    f"run_dag dispatch: {task_type.value} "
-                    f"tag={node_tag}"
-                )
-
-            # Timeline marker visible in NVTX traces / PyTorch profiler.
-            _task_label = f"{task_type.value}:{node_tag}:uid{node.uid}"
-            self._nvtx_push(_task_label)
-            _rf = self._rf_enter(_task_label)
-
-            match task_type:
-
-                case TaskType.SEND:
-                    # data_preds[0] is the compute task whose output we send.
-                    compute_node = node.data_preds[0]
-                    node_stream.wait_event(comp_events[compute_node.uid])
-                    send_data = self.task_buffer[compute_node.uid]["send_output"]
-                    self._exec_send(send_data, node.peer_pp_rank, stream=node_stream)
-                    # Free the send buffer.  For FWD/FWD_A2A the whole entry is stale
-                    # after the send (task_buffer[(ubid,mb)] is the BWD consumer).
-                    # For BWD/BWD_I/BWD_A2A keep the dict but drop the send tensor —
-                    # BWD_W still needs param_groups (BWD_I), co-located BWD still
-                    # needs inp_grads (BWD/BWD_A2A).
-                    _send_buf = self.task_buffer.get(compute_node.uid)
-                    if isinstance(_send_buf, dict):
-                        _send_buf["send_output"] = None
-                    self._release_task_buffer_uid(compute_node.uid)
-
-                case TaskType.RECV:
-                    # data_succs[0] is the downstream compute task.
-                    compute_node = node.data_succs[0]
-                    comp_evt = last_comp_event_by_stream.get(self._stream_id(compute_node))
-                    if comp_evt is not None:
-                        node_stream.wait_event(comp_evt)
-                    if compute_node.task_type == TaskType.FWD:
-                        # FWD recv: pre-allocate based on target bucket's input metadata.
-                        recv_ubid = self._node_bucket_key(compute_node)
-                        recv_tensors = self._exec_recv_fwd(
-                            recv_ubid, node.peer_pp_rank, stream=node_stream
-                        )
-                    else:
-                        # BWD recv: pre-allocate based on FWD output shapes.
-                        fwd_uid = compute_node.node_meta.get("fwd_uid")
-                        fwd_key = (compute_node.node_meta.get("bucket_key"), fwd_uid)
-                        shape_meta = self.task_buffer[("shape_ref",) + fwd_key]
-                        recv_tensors = self._exec_recv_bwd(
-                            shape_meta, node.peer_pp_rank, stream=node_stream
-                        )
-                    self.task_buffer[node.uid] = recv_tensors
-                    recv_evt = torch.cuda.Event()
-                    recv_evt.record(node_stream)
-                    self.recv_events[node.uid] = recv_evt
-
-                case TaskType.FWD_A2A:
-                    fwd_pred = next(p for p in node.data_preds if p.task_type == TaskType.FWD)
-                    node_stream.wait_event(comp_events[fwd_pred.uid])
-                    tensor_idx = self._node_meta(node)["a2a_tensor_idx"]
-                    # Pass-through: copy upstream FWD task_buffer, apply A2A only at tensor_idx.
-                    fwd_buf = dict(self.task_buffer[fwd_pred.uid])
-                    # FWD_A2A now owns the copy; release predecessor after consuming it.
-                    self._release_task_buffer_uid(fwd_pred.uid)
-                    detached_outs = list(fwd_buf["detached_outs"])
-                    detached_outs[tensor_idx] = self._exec_a2a(
-                        detached_outs[tensor_idx], stream=node_stream
-                    ).requires_grad_(True)
-                    fwd_buf["detached_outs"] = detached_outs
-                    self.task_buffer[node.uid] = fwd_buf
-                    a2a_evt = torch.cuda.Event()
-                    a2a_evt.record(node_stream)
-                    self.a2a_events[node.uid] = a2a_evt
-                    # node_stream.wait_event(a2a_evt)
-
-                case TaskType.BWD_A2A:
-                    bwd_pred = next(
-                        p for p in node.data_preds
-                        if p.task_type in (TaskType.BWD, TaskType.BWD_I)
-                    )
-                    node_stream.wait_event(comp_events[bwd_pred.uid])
-                    tensor_idx = self._node_meta(node)["a2a_tensor_idx"]
-                    # Pass-through: copy upstream BWD/BWD_I task_buffer, apply reversed A2A only at tensor_idx.
-                    bwd_buf = dict(self.task_buffer[bwd_pred.uid])
-                    self._release_task_buffer_uid(bwd_pred.uid)
-                    inp_grads = list(bwd_buf["inp_grads"])
-                    grad_a2a_out = inp_grads[tensor_idx]
-                    assert grad_a2a_out is not None, (
-                        f"BWD_A2A tag={node_tag}: grad at a2a_tensor_idx={tensor_idx} is None"
-                    )
-                    if not grad_a2a_out.is_contiguous():
-                        grad_a2a_out = grad_a2a_out.contiguous()
-                    inp_grads[tensor_idx] = self._exec_a2a(grad_a2a_out, stream=node_stream)
-                    bwd_buf["inp_grads"] = inp_grads
-                    self.task_buffer[node.uid] = bwd_buf
-                    a2a_evt = torch.cuda.Event()
-                    a2a_evt.record(node_stream)
-                    self.a2a_events[node.uid] = a2a_evt
-                    # node_stream.wait_event(a2a_evt)
-
-                case TaskType.ALL_REDUCE:
-                    bwd_node = node.data_preds[0]
-                    ar_ubids = self._sync_payload_ubids(node)
-                    assert ar_ubids, (
-                        f"ALL_REDUCE node uid={node.uid} has no sync_payload_ubids"
-                    )
-                    node_stream.wait_event(comp_events[bwd_node.uid])
-                    for ubid in ar_ubids:
-                        self._exec_all_reduce_grads(ubid, stream=node_stream)
-                    ar_evt = torch.cuda.Event()
-                    ar_evt.record(node_stream)
-                    self.ar_events[node.uid] = ar_evt
-                    self._release_task_buffer_uid(bwd_node.uid)
-
-                case TaskType.REDUCE_SCATTER:
-                    bwd_node = node.data_preds[0]
-                    rs_ubid = self._node_bucket_key(node)
-                    assert rs_ubid is not None, (
-                        f"REDUCE_SCATTER node uid={node.uid} has no bucket_key"
-                    )
-                    node_stream.wait_event(comp_events[bwd_node.uid])
-                    rs_bytes = self._exec_reduce_scatter(rs_ubid, stream=node_stream)
-                    rs_evt = torch.cuda.Event()
-                    rs_evt.record(node_stream)
-                    self.rs_events[node.uid] = rs_evt
-                    if rs_bytes:
-                        self._defer_free_full_grads(rs_ubid, rs_evt)
-                    if self._node_meta(node).get("zero_free_full_params_after"):
-                        assert False and "Param free should happen after a compute node"
-                    self._release_task_buffer_uid(bwd_node.uid)
-
-                case TaskType.ALL_GATHER:
-                    ag_ubid = self._node_bucket_key(node)
-                    assert ag_ubid is not None, (
-                        f"ALL_GATHER node uid={node.uid} has no bucket_key"
-                    )
-                    self._exec_all_gather(ag_ubid, stream=node_stream)
-                    ag_evt = torch.cuda.Event()
-                    ag_evt.record(node_stream)
-                    self.ag_events[node.uid] = ag_evt
-
-                case TaskType.FWD:
-                    # Wait on any RECV predecessor.
-                    recv_pred = next(
-                        (p for p in node.data_preds if p.task_type == TaskType.RECV), None
-                    )
-                    if recv_pred is not None and recv_pred.uid in self.recv_events:
-                        node_stream.wait_event(self.recv_events.pop(recv_pred.uid))
-
-                    # Wait on any FWD_A2A predecessor.
-                    a2a_pred = next(
-                        (p for p in node.data_preds if p.task_type == TaskType.FWD_A2A), None
-                    )
-                    if a2a_pred is not None and a2a_pred.uid in self.a2a_events:
-                        node_stream.wait_event(self.a2a_events.pop(a2a_pred.uid))
-
-                    # Gather input tensors from task_buffer.
-                    # FWD(bkt=0 of first stage): inputs come from self.inputs.
-                    # FWD(bkt=0 of non-first stage): inputs come from RECV or prev FWD (co-located).
-                    # FWD(bkt>0): inputs come from prev FWD or FWD_A2A output.
-                    fwd_data_pred = next(
-                        (p for p in node.data_preds
-                         if p.task_type in (TaskType.FWD, TaskType.FWD_A2A)), None
-                    )
-                    if fwd_data_pred is not None:
-                        # Both FWD and FWD_A2A task_buffer entries carry "detached_outs".
-                        input_tensors = self.task_buffer[fwd_data_pred.uid]["detached_outs"]
-                        self._release_task_buffer_uid(fwd_data_pred.uid)
-                    elif recv_pred is not None:
-                        input_tensors = self.task_buffer[recv_pred.uid]
-                        self._release_task_buffer_uid(recv_pred.uid)
-                    else:
-                        input_tensors = self.inputs  # first stage, first bucket
-
-                    _wait_for_all_gather(node)
-
-                    fwd_out = self._forward_dag(ubid, mb_idx, input_tensors, node_stream)
-                    self.task_buffer[node.uid] = fwd_out
-                    # Shape ref for BWD RECV: store lightweight metadata (shape, dtype) so
-                    # _exec_recv_bwd can pre-allocate receive buffers without holding live tensors.
-                    bkey = node.node_meta.get("bucket_key")
-                    fwd_key = (bkey, node.uid)
-                    self.task_buffer[("shape_ref",) + fwd_key] = [(t.shape, t.dtype) for t in fwd_out["out_with_grad"]]
-                    self.task_buffer[fwd_key] = fwd_out  # per-node FWD data for BWD
-                    evt = torch.cuda.Event()
-                    evt.record(node_stream)
-                    comp_events[node.uid] = evt
-                    last_comp_event_by_stream[node_stream_id] = evt
-                    if self._node_meta(node).get("zero_free_full_params_after"):
-                        self._defer_free_full_params(ubid, evt)
-
-                case TaskType.BWD:
-                    # Wait on upstream grad RECV if present.
-                    recv_pred = next(
-                        (p for p in node.data_preds if p.task_type == TaskType.RECV), None
-                    )
-                    if recv_pred is not None and recv_pred.uid in self.recv_events:
-                        node_stream.wait_event(self.recv_events.pop(recv_pred.uid))
-                    _wait_for_all_gather(node)
-
-                    # Wait on any BWD_A2A predecessor.
-                    a2a_pred = next(
-                        (p for p in node.data_preds if p.task_type == TaskType.BWD_A2A), None
-                    )
-                    if a2a_pred is not None and a2a_pred.uid in self.a2a_events:
-                        node_stream.wait_event(self.a2a_events.pop(a2a_pred.uid))
-
-                    # Resolve outputs and upstream grads via per-mb ubid lookup.
-                    fwd_uid = node.node_meta.get("fwd_uid")
-                    fwd_key = (node.node_meta.get("bucket_key"), fwd_uid)
-                    fwd_out = self.task_buffer[fwd_key]
-
-                    if self._node_meta(node).get("compute_loss", False):
-                        assert loss_fn is not None
-                        if self.logger.isEnabledFor(logging.DEBUG):
-                            self._log_compute_loss_inputs(node, fwd_key, fwd_out)
-                        with torch.cuda.stream(node_stream):
-                            outputs_or_loss = [loss_fn(fwd_out["out_with_grad"][0], self.labels)]
-                        upstream_grads = None
-                    elif recv_pred is not None:
-                        upstream_grads = self.task_buffer[recv_pred.uid]
-                        self._release_task_buffer_uid(recv_pred.uid)
-                        if not isinstance(upstream_grads, (list, tuple)):
-                            upstream_grads = [upstream_grads]
-                        outputs_or_loss = fwd_out["out_with_grad"]
-                        upstream_grads = list(upstream_grads)
-                    else:
-                        outputs_or_loss = fwd_out["out_with_grad"]
-                        upstream_grads = None  # last stage, no recv
-
-                    if a2a_pred is not None:
-                        a2a_buf = self.task_buffer[a2a_pred.uid]
-                        pre_detach_outs = fwd_out["pre_detach_outs"]
-                        detached_outs = fwd_out["detached_outs"]
-                        # Set .grad for ALL requires_grad detached boundary tensors from
-                        # BWD_A2A's inp_grads.  inp_grads[tensor_idx] is the reversed A2A
-                        # gradient; other positions carry pass-through grads (residuals, etc.)
-                        # computed by the preceding BWD bucket.  The assertion in _backward_dag
-                        # requires every requires_grad entry to have a non-None .grad.
-                        for i, (d, g) in enumerate(zip(detached_outs, a2a_buf["inp_grads"])):
-                            if d is not None and isinstance(d, torch.Tensor) and d.requires_grad and g is not None:
-                                d.grad = g
-                        self._release_task_buffer_uid(a2a_pred.uid)
-                    else:
-                        bwd_pred = next(
-                            (p for p in node.data_preds
-                             if p.task_type in (TaskType.BWD, TaskType.BWD_I)), None
-                        )
-                        if bwd_pred is not None and recv_pred is None:
-                            # Co-located boundary-bridge: the downstream BWD stored
-                            # inp_grads for D' (its re-detached inputs). D' is parallel
-                            # to this stage's detached_outs (D), so copy grad explicitly
-                            # — same pattern as BWD_A2A.
-                            # recv_pred is None ensures this is a true co-located pair,
-                            # not a scheduling mb-chain edge from a cross-rank predecessor.
-                            prev_buf = self.task_buffer[bwd_pred.uid]
-                            pre_detach_outs = fwd_out.get("pre_detach_outs")
-                            detached_outs = fwd_out.get("detached_outs")
-                            for d, g in zip(detached_outs, prev_buf["inp_grads"]):
-                                if (d is not None and isinstance(d, torch.Tensor)
-                                        and d.requires_grad and g is not None):
-                                    d.grad = g
-                            self._release_task_buffer_uid(bwd_pred.uid)
-                        else:
-                            # Last (or only) stage in backward order, or has a RECV for
-                            # upstream grads (cross-rank predecessor): standard backward.
-                            pre_detach_outs = None
-                            detached_outs = None
-
-                    inp_with_grad = fwd_out.get("inp_with_grad")
-                    if self._node_meta(node).get("zero_alloc_full_grads_before"):
-                        self._alloc_full_grads(ubid, node_stream)
-
-                    bwd_out = self._backward_dag(
-                        ubid, mb_idx, outputs_or_loss, upstream_grads,
-                        pre_detach_outs, detached_outs, inp_with_grad,
-                        fwd_out.get("out_with_grad"),
-                        node_stream,
-                    )
-                    buf = bwd_out if bwd_out is not None else {}
-                    # Full-length inp_grads parallel to fwd_inputs / detached_outs so
-                    # BWD_A2A can index with a2a_tensor_idx without filtering offset.
-                    fwd_inputs_full = fwd_out.get("fwd_inputs")
-                    buf["inp_grads"] = (
-                        [t.grad if (t is not None and t.requires_grad) else None
-                         for t in fwd_inputs_full]
-                        if fwd_inputs_full is not None
-                        else [t.grad for t in (inp_with_grad or [])]
-                    )
-                    self._accumulate_zero_param_grads_to_flat(ubid, node_stream)
-                    self.task_buffer[node.uid] = buf
-                    # FWD stored the same dict under both node.uid and (ubid, mb_idx).
-                    # Clear the dict in-place so any remaining alias stops keeping
-                    # activations and autograd edges alive through the optimizer step.
-                    fwd_out.clear()
-                    del self.task_buffer[fwd_key]
-                    evt = torch.cuda.Event()
-                    evt.record(node_stream)
-                    comp_events[node.uid] = evt
-                    self.bwd_events[ubid] = evt
-                    last_comp_event_by_stream[node_stream_id] = evt
-                    if self._node_meta(node).get("zero_free_full_params_after"):
-                        self._defer_free_full_params(ubid, evt)
-
-                case TaskType.BWD_I:
-                    # Wait on upstream grad RECV if present.
-                    recv_pred = next(
-                        (p for p in node.data_preds if p.task_type == TaskType.RECV), None
-                    )
-                    if recv_pred is not None and recv_pred.uid in self.recv_events:
-                        node_stream.wait_event(self.recv_events.pop(recv_pred.uid))
-                    _wait_for_all_gather(node)
-
-                    # Resolve stage_outputs_or_loss and output_grads.
-                    fwd_uid = node.node_meta.get("fwd_uid")
-                    fwd_key = (node.node_meta.get("bucket_key"), fwd_uid)
-                    fwd_out = self.task_buffer[fwd_key]
-
-                    if self._node_meta(node).get("compute_loss", False):
-                        assert loss_fn is not None
-                        if self.logger.isEnabledFor(logging.DEBUG):
-                            self._log_compute_loss_inputs(node, fwd_key, fwd_out)
-                        with torch.cuda.stream(node_stream):
-                            stage_outputs_or_loss = [loss_fn(fwd_out["out_with_grad"][0], self.labels)]
-                        output_grads = None
-                    elif recv_pred is not None:
-                        upstream_raw = self.task_buffer[recv_pred.uid]
-                        self._release_task_buffer_uid(recv_pred.uid)
-                        if not isinstance(upstream_raw, (list, tuple)):
-                            upstream_raw = [upstream_raw]
-                        stage_outputs_or_loss = fwd_out["out_with_grad"]
-                        output_grads = list(upstream_raw)
-                    else:
-                        stage_outputs_or_loss = fwd_out["out_with_grad"]
-                        output_grads = None
-
-                    # If a BWD_A2A precedes this bucket, use the reversed grad as
-                    # output_grads so backward flows correctly through the A2A boundary.
-                    bwd_a2a_pred = next(
-                        (p for p in node.data_preds if p.task_type == TaskType.BWD_A2A), None
-                    )
-                    if (
-                        bwd_a2a_pred is not None
-                        and not self._node_meta(node).get("compute_loss", False)
-                        and recv_pred is None
-                    ):
-                        a2a_buf = self.task_buffer[bwd_a2a_pred.uid]
-                        # BWD_A2A passes through BWD_I(bkt+1)'s task_buffer with
-                        # inp_grads[tensor_idx] replaced by the reversed A2A gradient.
-                        # Build output_grads aligned with out_with_grad (requires_grad
-                        # positions of detached_outs) then filter to non-None entries.
-                        detached_outs = fwd_out["detached_outs"]
-                        output_grads_full = [
-                            a2a_buf["inp_grads"][i]
-                            for i, t in enumerate(detached_outs)
-                            if t is not None and getattr(t, "requires_grad", False)
-                        ]
-                        pairs = [
-                            (o, g) for o, g in zip(stage_outputs_or_loss, output_grads_full)
-                            if g is not None
-                        ]
-                        stage_outputs_or_loss = [p[0] for p in pairs]
-                        output_grads = [p[1] for p in pairs]
-                        self._release_task_buffer_uid(bwd_a2a_pred.uid)
-
-                    input_values = fwd_out.get("inp_with_grad") or []
-
-                    # Weights for this bucket.
-                    b_fwd_args = self.bucket_fwd_args[ubid]
-                    weights = [b_fwd_args[i] for i in self.bucket_param_idxs[ubid]
-                               if b_fwd_args[i] is not None]
-
-                    with torch.cuda.stream(node_stream):
-                        dinputs, param_groups = self._bucket_backward_input(
-                            stage_outputs_or_loss, output_grads, input_values, iter(weights)
-                        )
-                    # Full-length inp_grads parallel to fwd_inputs / detached_outs.
-                    fwd_inputs_full = fwd_out.get("fwd_inputs")
-                    if fwd_inputs_full is not None:
-                        inp_grads_full = [
-                            t.grad if (t is not None and t.requires_grad) else None
-                            for t in fwd_inputs_full
-                        ]
-                    else:
-                        inp_grads_full = list(dinputs)
-                    # Store param_groups for BWD_W (accessed via data_succs edge).
-                    self.task_buffer[node.uid] = {
-                        "inp_grads": inp_grads_full,
-                        "param_groups": param_groups,
-                    }
-                    # Propagate input grad to SEND (if this is the first bucket and has predecessors).
-                    if dinputs:
-                        self.task_buffer[node.uid]["send_output"] = list(dinputs)
-                    # FWD stored the same dict under both node.uid and (ubid, mb_idx).
-                    # Clear the dict in-place so any remaining alias stops keeping
-                    # activations and autograd edges alive through the optimizer step.
-                    fwd_out.clear()
-                    del stage_outputs_or_loss, fwd_out
-                    del self.task_buffer[fwd_key]
-                    evt = torch.cuda.Event()
-                    evt.record(node_stream)
-                    comp_events[node.uid] = evt
-                    self.bwd_events[ubid] = evt
-                    last_comp_event_by_stream[node_stream_id] = evt
-                    if self._node_meta(node).get("zero_free_full_params_after"):
-                        self._defer_free_full_params(ubid, evt)
-
-                case TaskType.BWD_W:
-                    _wait_for_all_gather(node)
-                    if self._node_meta(node).get("zero_alloc_full_grads_before"):
-                        self._alloc_full_grads(ubid, node_stream)
-                    # data_preds[0] is the BWD_I task for bkt=K-1 (head of chain).
-                    # For bkt<K-1 the chain-link edge (BWD_W predecessor) is prepended
-                    # before Pass 2 appends the BWD_I edge, so data_preds[0] would be
-                    # the wrong task.  Find BWD_I explicitly by type to be safe.
-                    bwdi_node = next(p for p in node.data_preds if p.task_type == TaskType.BWD_I)
-                    param_groups = self.task_buffer[bwdi_node.uid]["param_groups"]
-                    b_fwd_args = self.bucket_fwd_args[ubid]
-                    weights = [b_fwd_args[i] for i in self.bucket_param_idxs[ubid]
-                               if b_fwd_args[i] is not None]
-                    with torch.cuda.stream(node_stream):
-                        self._bucket_backward_weight(iter(weights), param_groups, ubid=ubid, mb_idx=mb_idx)
-                    self._accumulate_zero_param_grads_to_flat(ubid, node_stream)
-                    self.task_buffer[node.uid] = {}
-                    # BWD_I buffer is no longer needed: _bucket_backward_weight already
-                    # deleted param_groups["intermediates"] and param_groups["grads"] and
-                    # the autograd graph was freed by retain_graph=False.  Drop the shell.
-                    self._release_task_buffer_uid(bwdi_node.uid)
-                    evt = torch.cuda.Event()
-                    evt.record(node_stream)
-                    comp_events[node.uid] = evt
-                    self.bwd_events[ubid] = evt
-                    last_comp_event_by_stream[node_stream_id] = evt
-                    if self._node_meta(node).get("zero_free_full_params_after"):
-                        self._defer_free_full_params(ubid, evt)
-
-                case TaskType.UPD:
-                    self._update(node_stream)
-
-                case TaskType.ORDER_DUMMY:
-                    pass
-
-            self._rf_exit(_rf)
-            self._nvtx_pop()
-
-    def _log_compute_loss_inputs(self, node: Any, fwd_key: Any, fwd_out: dict) -> None:
-        def _summarize_value(value: Any) -> str:
-            if isinstance(value, torch.Tensor):
-                return (
-                    f"Tensor(shape={tuple(value.shape)}, dtype={value.dtype}, "
-                    f"requires_grad={value.requires_grad}, device={value.device})"
-                )
-            if isinstance(value, (list, tuple)):
-                return "[" + ", ".join(_summarize_value(v) for v in value) + "]"
-            if value is None:
-                return "None"
-            return type(value).__name__
-
-        self.logger.debug(
-            "compute_loss inputs: rank=%s node_uid=%s node_type=%s tag=%s "
-            "fwd_key=%s labels=%s out_with_grad=%s pre_detach_outs=%s "
-            "detached_outs=%s send_output=%s",
-            self.global_rank,
-            getattr(node, "uid", None),
-            getattr(getattr(node, "task_type", None), "value", None),
-            getattr(node, "tag", None),
-            fwd_key,
-            _summarize_value(self.labels),
-            _summarize_value(fwd_out.get("out_with_grad")),
-            _summarize_value(fwd_out.get("pre_detach_outs")),
-            _summarize_value(fwd_out.get("detached_outs")),
-            _summarize_value(fwd_out.get("send_output")),
-        )
-
-    def _exec_send(self, send_data, peer_pp_rank: int, stream) -> None:
-        """Send tensors to peer_pp_rank on the stream assigned to the SEND node.
-
-        The caller (run_dag) must have already made the node stream wait on the
-        compute event before calling this method.
-        """
-        global_dst_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
-
-        with torch.cuda.stream(stream):
-            tensors = send_data if isinstance(send_data, (list, tuple)) else [send_data]
-            use_lo_hi = global_dst_rank > self.global_rank
-            pp_group = self.pp_lo_hi if use_lo_hi else self.pp_hi_lo
-            for tensor in tensors:
-                dist.send(tensor, dst=global_dst_rank, group=pp_group)
-
-    def _exec_recv_fwd(self, recv_ubid: Any, peer_pp_rank: int, stream) -> list:
-        """Receive FWD activations from peer_pp_rank into pre-allocated buffers.
-
-        Buffer shapes are derived from the target bucket's stored input metadata.
-        Returns the received tensor list (stored in task_buffer by run_dag).
-        """
-        global_src_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
-
-        buf = [
-            torch.empty(shape, dtype=dtype, requires_grad=requires_grad, device=self.device)
-            for shape, dtype, requires_grad in self.forward_input_meta[recv_ubid]
-        ]
-        with torch.cuda.stream(stream):
-            use_hi_lo = global_src_rank > self.global_rank
-            pp_group = self.pp_hi_lo if use_hi_lo else self.pp_lo_hi
-            for tensor in buf:
-                dist.recv(tensor, src=global_src_rank, group=pp_group)
-        return buf
-
-    def _exec_recv_bwd(self, shape_meta: list, peer_pp_rank: int, stream) -> list:
-        """Receive BWD upstream gradients from peer_pp_rank.
-
-        Buffer shapes are derived from shape_meta, a list of (shape, dtype) tuples
-        stored in task_buffer[("shape_ref", ubid)] after the corresponding FWD.
-        Returns the received gradient list (stored in task_buffer by run_dag).
-        """
-        global_src_rank = _get_rank(peer_pp_rank, self.dp_rank, self.pp_degree)
-
-        buf = [torch.empty(shape, dtype=dtype, device=self.device) for shape, dtype in shape_meta]
-        with torch.cuda.stream(stream):
-            use_hi_lo = global_src_rank > self.global_rank
-            pp_group = self.pp_hi_lo if use_hi_lo else self.pp_lo_hi
-            for tensor in buf:
-                dist.recv(tensor, src=global_src_rank, group=pp_group)
-        return buf
-
-    def _exec_a2a(self, input_tensor: torch.Tensor, stream) -> torch.Tensor:
-        """Apply dist.all_to_all_single on the node stream and return the output tensor.
-
-        Used for both FWD_A2A and BWD_A2A — the operation is symmetric.
-        Stream waits and event recording are handled by run_dag.
-        """
-        output_buf = torch.empty_like(input_tensor, device=self.device)
-        with torch.cuda.stream(stream):
-            dist.all_to_all_single(output_buf, input_tensor, group=self.ep_group)
-        return output_buf
-
-    def _forward_dag(self, ubid: Any, mb_idx: int, input_tensors, compute_stream) -> dict:
-        """Run the forward function for bucket *ubid* and return a result dict.
-
-        The result dict always contains:
-        - ``"send_output"``: raw output tuple/tensor for SEND (last bucket only)
-        - ``"out_with_grad"``: list of output tensors that require grad (last bucket only)
-        - ``"pre_detach_outs"`` / ``"detached_outs"``: boundary tensors (non-last bucket only)
-        - ``"inp_with_grad"``: input tensors that require grad (always; empty list if none)
-
-        run_dag stores the result in task_buffer[node.uid] and passes fields to
-        downstream tasks as appropriate.
-        """
-        fwd_fn = self.bucket_fwd_fns[ubid]
-        fwd_args = self.bucket_fwd_args[ubid]
-        input_idxs = self.bucket_input_idxs[ubid]
-
-        # Place input tensors into the args array.
-        # For co-located stages, input_tensors may be the raw output tuple from
-        # the previous stage's FWD (a tuple or a single tensor).
-        if not isinstance(input_tensors, (list, tuple)):
-            input_tensors = [input_tensors]
-
-        for i, tensor in zip(input_idxs, input_tensors):
-            if isinstance(tensor, (list, tuple)):
-                tensor = tensor[0]
-            # Detach activations arriving from a co-located stage so backward
-            # treats them as leaves (same device, no cross-stage grad flow).
-            if tensor.requires_grad:
-                tensor = tensor.detach().requires_grad_(True)
-            fwd_args[i] = tensor
-
-        # Full input list (after double-detach), parallel to detached_outs of the
-        # predecessor FWD/FWD_A2A.  Stored so that BWD can build a full-length
-        # inp_grads list (None for non-grad entries) that BWD_A2A can index with
-        # a2a_tensor_idx without any requires_grad filtering offset.
-        fwd_inputs = [fwd_args[i] for i in input_idxs]
-
-        inp_with_grad = [t for t in fwd_inputs if t is not None and t.requires_grad]
-
-        # Run the forward function.
-        with torch.cuda.stream(compute_stream):
-            output = fwd_fn(fwd_args)
-
-        # Clear input slots so we don't hold stale tensor references.
-        for i in input_idxs:
-            fwd_args[i] = None
-
-        out_list = list(output) if isinstance(output, tuple) else [output]
-
-        # Is this the last bucket of its stage?  Determined from DAG structure in
-        # run_dag, but here we use a simpler proxy: if the bucket has an A2A
-        # boundary entry it is NOT the last bucket.  More precisely, run_dag
-        # calls us with correct inputs; we always produce both boundary and
-        # last-bucket fields and let run_dag consume what's relevant.
-        possibly_detached = [
-            t.detach().requires_grad_(True) if isinstance(t, torch.Tensor) and t.requires_grad else t
-            for t in out_list
-        ]
-        out_with_grad = [t for t in out_list if isinstance(t, torch.Tensor) and t.requires_grad]
-
-        return {
-            "pre_detach_outs": out_list,
-            "detached_outs": possibly_detached,
-            "out_with_grad": out_with_grad,
-            "send_output": output,    # raw output for SEND node
-            "inp_with_grad": inp_with_grad,
-            "fwd_inputs": fwd_inputs,
-        }
-
-    def _backward_dag(
-        self,
-        ubid: Any,
-        mb_idx: int,
-        outputs_or_loss: list,
-        upstream_grads,
-        pre_detach_outs,
-        detached_outs,
-        inp_with_grad,
-        out_with_grad,
-        compute_stream,
-    ) -> dict | None:
-        """Fused backward pass (BWD) for a single bucket.
-
-        Handles both:
-        - Last-bucket-of-stage case: backward from out_with_grad / loss.
-        - Non-last-bucket case: backward through the (pre_detach, detached) boundary.
-
-        Returns a dict with ``"send_output"`` (input grad list) when this is the
-        first-bucket-of-stage and the stage has a predecessor, or None otherwise.
-        """
-        if pre_detach_outs is None:
-            # Last bucket (or only bucket): backward from stage outputs or loss.
-            if upstream_grads is not None:
-                with torch.cuda.stream(compute_stream):
-                    torch.autograd.backward(outputs_or_loss, upstream_grads)
-            else:
-                with torch.cuda.stream(compute_stream):
-                    outputs_or_loss[0].backward()
-        else:
-            # Non-last bucket: propagate through the bucket boundary.
-            bwd_pairs = [
-                (p, d.grad)
-                for p, d in zip(pre_detach_outs, detached_outs)
-                if (isinstance(d, torch.Tensor) and d.requires_grad and d.grad is not None)
-            ]
-            assert bwd_pairs, (
-                f"BWD ubid={ubid} mb={mb_idx}: detached boundary has no tensors "
-                "with a materialized grad"
-            )
-            if bwd_pairs:
-                outputs_bwd = [p for p, _g in bwd_pairs]
-                grads_bwd = [g for _p, g in bwd_pairs]
-                with torch.cuda.stream(compute_stream):
-                    torch.autograd.backward(outputs_bwd, grads_bwd)
-
-        # Collect stage-input grads for SEND — applies to both last-bucket and
-        # non-last-bucket paths (e.g. when downstream of BWD_A2A).
-        if inp_with_grad:
-            output_grads = [t.grad for t in inp_with_grad if t.grad is not None]
-            if output_grads:
-                return {"send_output": output_grads}
-        return None
-
-    @staticmethod
-    def _grad_with_param_layout(weight: Parameter, grad: torch.Tensor) -> torch.Tensor:
-        if (
-            grad.dtype == weight.dtype
-            and grad.device == weight.device
-            and grad.layout == weight.layout
-            and tuple(grad.stride()) == tuple(weight.stride())
-        ):
-            return grad
-        if weight.layout == torch.strided and grad.layout != torch.strided:
-            grad = grad.to_dense()
-        out = torch.empty_strided(
-            tuple(weight.shape),
-            tuple(weight.stride()),
-            dtype=weight.dtype,
-            device=weight.device,
-        )
-        out.copy_(grad)
-        return out
-
-    def _fused_backward(self, stage_outputs_or_loss: list, output_grads, weights: list) -> None:
-        """Fused backward writing weight grads directly into ``weight.grad``.
-
-        Used for the first pipeline stage, whose inputs (token ids) do not
-        require grad. The split B/W path relies on input-grad prehooks firing to
-        capture intermediate grads, but with no input grads to compute those
-        prehooks never run and stage 0 would never accumulate weight gradients.
-        Computing dW in one pass here (and no-opping the paired BWD_W) keeps the
-        first stage training without redundant work.
-        """
-        wts = [w for w in weights if w.requires_grad]
-        if not wts:
-            return
-        dweights = torch.autograd.grad(
-            stage_outputs_or_loss,
-            inputs=wts,
-            grad_outputs=output_grads,
-            retain_graph=False,
-            allow_unused=True,
-        )
-        for w, dw in zip(wts, dweights):
-            if dw is None:
-                continue
-            dw = self._grad_with_param_layout(w, dw)
-            if w.grad is None:
-                w.grad = dw
-            else:
-                if (
-                    w.grad.dtype != w.dtype
-                    or w.grad.device != w.device
-                    or w.grad.layout != w.layout
-                    or tuple(w.grad.stride()) != tuple(w.stride())
-                ):
-                    w.grad = self._grad_with_param_layout(w, w.grad)
-                w.grad += dw
-
-    def _bucket_backward_input(
-        self,
-        stage_outputs_or_loss: list,
-        output_grads,
-        input_values: list,
-        weights,
-    ):
-        """Compute input gradients for a single bucket (BWD_I).
-
-        Matches ``torch.distributed.pipelining.stage_backward_input`` exactly,
-        with "stage" replaced by "bucket".  Returns ``(dinputs, param_groups)``.
-        """
-        from .backward_utils import get_param_groups, construct_reverse_graph, _get_grad_fn_or_grad_acc
-
-        weights = list(weights)
-
-        if output_grads is None:
-            output_grads = [torch.ones_like(o) for o in stage_outputs_or_loss]
-
-        input_values = [inp for inp in input_values if inp.requires_grad]
-        if not input_values:
-            # First pipeline stage: no input gradient to compute or send. The
-            # split B/W mechanism would never fire its prehooks here, so compute
-            # weight grads in a single fused backward and return empty
-            # param_groups so the paired BWD_W is a no-op.
-            self._fused_backward(stage_outputs_or_loss, output_grads, weights)
-            for i, t in enumerate(stage_outputs_or_loss):
-                if not isinstance(t, torch.Tensor):
-                    continue
-                stage_outputs_or_loss[i] = t.detach()
-            return (), []
-
-        stage_output_grad_fns = list(filter(None, map(_get_grad_fn_or_grad_acc, stage_outputs_or_loss)))
-        stage_input_grad_fns = list(filter(None, map(_get_grad_fn_or_grad_acc, input_values)))
-        weight_grad_fns = list(filter(None, map(_get_grad_fn_or_grad_acc, weights)))
-
-        reverse_edges_dict = construct_reverse_graph(stage_output_grad_fns)
-        param_groups = get_param_groups(stage_input_grad_fns, weight_grad_fns, reverse_edges_dict)
-
-        handles = []
-        for param_group in param_groups:
-            for i, intermediate in enumerate(param_group["intermediates"]):
-                def get_hook(pg, idx):
-                    def hook(grad_inputs):
-                        if pg.get("grads") is None:
-                            pg["grads"] = [None] * len(pg["intermediates"])
-                        pg["grads"][idx] = tuple(grad_inputs)
-                    return hook
-                handles.append(intermediate.register_prehook(get_hook(param_group, i)))
-
-        dinputs = torch.autograd.grad(
-            stage_outputs_or_loss,
-            inputs=input_values,
-            grad_outputs=output_grads,
-            retain_graph=True,
-            allow_unused=True,
-        )
-        for inp, dinput in zip(input_values, dinputs):
-            if inp.grad is None:
-                inp.grad = dinput
-            else:
-                inp.grad += dinput
-
-        # Match torch.distributed.pipelining.stage_backward_input(): once dinputs
-        # are computed, these outputs are no longer needed by the later weight
-        # pass, so detach them to let autograd release the dead part of the graph.
-        for i, t in enumerate(stage_outputs_or_loss):
-            if not isinstance(t, torch.Tensor):
-                continue
-            stage_outputs_or_loss[i] = t.detach()
-
-        for handle in handles:
-            handle.remove()
-
-        return dinputs, param_groups
-
-    def _bucket_backward_weight(self, weights, param_groups: list, ubid: Any | None = None, mb_idx: int | None = None) -> None:
-        """Compute weight gradients for a single bucket (BWD_W).
-
-        Matches ``torch.distributed.pipelining.stage_backward_weight`` exactly,
-        with "stage" replaced by "bucket".  Accumulates gradients into param.grad.
-        """
-        if not param_groups:
-            # First-stage BWD_W is a no-op: BWD_I already ran the fused backward and
-            # wrote weight.grad directly, so there are no param_groups to consume.
-            # Skip the grad-accumulator preamble (one throwaway view per weight).
-            return
-
-        grad_acc_to_weight: Dict[Node, Parameter] = {}
-        _grad_with_param_layout = self._grad_with_param_layout
-
-        for weight in weights:
-            grad_acc = _get_grad_fn_or_grad_acc(weight)
-            grad_acc_to_weight[grad_acc] = weight
-
-        for param_group in param_groups:
-            valid_edges: List[GradientEdge] = []
-            valid_grad_outputs: List[torch.Tensor] = []
-
-            for grads_tuple, intermediate in zip(
-                param_group.get("grads", []), param_group["intermediates"]
-            ):
-                if grads_tuple is None:
-                    continue
-                for i, grad in enumerate(grads_tuple):
-                    if grad is not None:
-                        valid_edges.append(GradientEdge(intermediate, i))
-                        valid_grad_outputs.append(grad)
-
-            del param_group["intermediates"]
-
-            if not valid_edges:
-                continue
-
-            weight_edges = tuple(GradientEdge(w, 0) for w in param_group["params"])
-            if not weight_edges:
-                continue
-
-            dweights = torch.autograd.grad(
-                valid_edges,
-                weight_edges,
-                grad_outputs=valid_grad_outputs,
-                retain_graph=False,
-            )
-
-            del param_group["grads"]
-
-            for grad_acc, dw in zip(param_group["params"], dweights):
-                if dw is None or grad_acc not in grad_acc_to_weight:
-                    continue
-                weight = grad_acc_to_weight[grad_acc]
-                dw = _grad_with_param_layout(weight, dw)
-                if weight.grad is None:
-                    weight.grad = dw
-                else:
-                    if (
-                        weight.grad.dtype != weight.dtype
-                        or weight.grad.device != weight.device
-                        or weight.grad.layout != weight.layout
-                        or tuple(weight.grad.stride()) != tuple(weight.stride())
-                    ):
-                        weight.grad = _grad_with_param_layout(weight, weight.grad)
-                    weight.grad += dw
-
-    def _exec_all_reduce_grads(self, ubid: Any, stream) -> int:
-        """All-reduce each materialized parameter grad for *ubid* on *stream*.
-
-        Returns the total payload bytes actually dispatched at runtime.
-        """
-        assert ubid is not None, "_exec_all_reduce_grads requires a non-None ubid"
-        if not self._has_trainable_params_for_collective(ubid, "_exec_all_reduce_grads"):
-            return 0
-        args = self.bucket_fwd_args.get(ubid, [])
-        trainable_idxs = self.bucket_trainable_param_idxs.get(ubid, [])
-        grad_tensors = []
-        for idx in trainable_idxs:
-            param = args[idx]
-            assert param is not None, (
-                f"_exec_all_reduce_grads: ubid={ubid} idx={idx} param is None"
-            )
-            assert param.grad is not None, (
-                f"_exec_all_reduce_grads: ubid={ubid} idx={idx} param.grad is None"
-            )
-            grad_tensors.append((idx, param.grad))
-
-        total_bytes = sum(grad.numel() * grad.element_size() for _, grad in grad_tensors)
-        with torch.cuda.stream(stream):
-            for _, grad in grad_tensors:
-                dist.all_reduce(grad, group=self.dp_group)
-        return total_bytes
-
-    def _has_trainable_params_for_collective(self, ubid: Any, op_name: str) -> bool:
-        trainable_idxs = self.bucket_trainable_param_idxs.get(ubid, [])
-        if trainable_idxs:
-            return True
-        self.logger.warning(
-            "%s: skipping collective for ubid=%s because it has no trainable param indices",
-            op_name,
-            ubid,
-        )
-        return False
-
-    def _alloc_full_params(self, ubid: Any) -> None:
-        assert ubid is not None, "_alloc_full_params requires a non-None ubid"
-        assert ubid in self.param_sharded_ubids, (
-            f"_alloc_full_params: ubid={ubid} is not in param_sharded_ubids={self.param_sharded_ubids}"
-        )
-        self._wait_pending_free(self.pending_param_frees, ubid)
-        assert self.param_shard_info.get(ubid) is not None, (
-            f"_alloc_full_params: missing param_shard_info for ubid={ubid}"
-        )
-        full = self.bucket_flat_params.get(ubid)
-        assert full is not None, (
-            f"_alloc_full_params: missing bucket_flat_params buffer for ubid={ubid}"
-        )
-        specs = self.bucket_param_view_specs.get(ubid, [])
-        assert specs, f"_alloc_full_params: missing bucket_param_view_specs for ubid={ubid}"
-        storage = full.untyped_storage()
-        required_bytes = full.numel() * full.element_size()
-        storage.resize_(required_bytes)
-        self.logger.debug(
-            f"[alloc_full_params] rank={self.global_rank} ubid={ubid}: "
-            f"numel={full.numel()} required_bytes={required_bytes} "
-            f"storage_size={storage.size()} fresh={self.full_params_fresh.get(ubid, False)}"
-        )
-        for param, offset, numel, shape in specs:
-            param.data = full[offset:offset + numel].view(shape)
-            param.requires_grad_(True)
-        param_names = self.bucket_param_names.get(ubid, [])
-        zero_storage = []
-        for i, (param, offset, numel, shape) in enumerate(specs):
-            p_storage = param.untyped_storage()
-            if p_storage.size() == 0:
-                name = param_names[i] if i < len(param_names) else f"param{i}"
-                zero_storage.append(
-                    f"{name}: offset={offset} numel={numel} shape={shape} stride={tuple(param.stride())}"
-                )
-        assert not zero_storage, (
-            f"[alloc_full_params_zero_storage] rank={self.global_rank} ubid={ubid}: "
-            + " | ".join(zero_storage)
-        )
-
-    def _free_full_params(self, ubid: Any) -> None:
-        assert ubid is not None, "_free_full_params requires a non-None ubid"
-        assert ubid in self.param_sharded_ubids, (
-            f"_free_full_params: ubid={ubid} is not in param_sharded_ubids={self.param_sharded_ubids}"
-        )
-        full = self.bucket_flat_params.get(ubid)
-        assert full is not None, (
-            f"_free_full_params: missing bucket_flat_params buffer for ubid={ubid}"
-        )
-        storage = full.untyped_storage()
-        self.logger.debug(
-            f"[free_full_params] rank={self.global_rank} ubid={ubid}: "
-            f"storage_size_before={storage.size()} fresh_before={self.full_params_fresh.get(ubid, False)}"
-        )
-        storage.resize_(0)
-        self.full_params_fresh[ubid] = False
-
-    def _alloc_full_grads(self, ubid: Any, stream: "torch.cuda.Stream") -> None:
-        assert ubid is not None, "_alloc_full_grads requires a non-None ubid"
-        assert ubid in self.grad_sharded_ubids, (
-            f"_alloc_full_grads: ubid={ubid} is not in grad_sharded_ubids={self.grad_sharded_ubids}"
-        )
-        self._wait_pending_free(self.pending_grad_frees, ubid)
-        specs = self.bucket_param_view_specs.get(ubid, [])
-        assert specs, f"_alloc_full_grads: missing bucket_param_view_specs for ubid={ubid}"
-        shard_info = self.param_shard_info.get(ubid)
-        assert shard_info is not None, (
-            f"_alloc_full_grads: missing param_shard_info for ubid={ubid}"
-        )
-        shard_size = shard_info[1]
-        with torch.cuda.stream(stream):
-            if self.bucket_flat_grads.get(ubid) is None:
-                self.bucket_flat_grads[ubid] = torch.zeros(
-                    shard_size * self.dp_degree,
-                    dtype=self.grad_buffer_dtype,
-                    device=self.device,
-                )
-            if self.bucket_rs_grads.get(ubid) is None:
-                self.bucket_rs_grads[ubid] = torch.zeros(
-                    shard_size, dtype=self.grad_buffer_dtype, device=self.device
-                )
-
-    def _free_full_grads(self, ubid: Any) -> None:
-        assert ubid is not None, "_free_full_grads requires a non-None ubid"
-        assert ubid in self.grad_sharded_ubids, (
-            f"_free_full_grads: ubid={ubid} is not in grad_sharded_ubids={self.grad_sharded_ubids}"
-        )
-        specs = self.bucket_param_view_specs.get(ubid, [])
-        assert specs, f"_free_full_grads: missing bucket_param_view_specs for ubid={ubid}"
-        for param, *_ in specs:
-            param.grad = None
-        self.bucket_flat_grads[ubid] = None
-
-    def _exec_reduce_scatter(self, ubid: Any, stream) -> int:
-        assert ubid is not None, "_exec_reduce_scatter requires a non-None ubid"
-        if not self._has_trainable_params_for_collective(ubid, "_exec_reduce_scatter"):
-            return 0
-        assert ubid in self.grad_sharded_ubids, (
-            f"_exec_reduce_scatter: ubid={ubid} is not in grad_sharded_ubids={self.grad_sharded_ubids}"
-        )
-        assert self.param_shard_info.get(ubid) is not None, (
-            f"_exec_reduce_scatter: missing param_shard_info for ubid={ubid}"
-        )
-        flat_grads = self.bucket_flat_grads.get(ubid)
-        rs_out = self.bucket_rs_grads.get(ubid)
-        assert flat_grads is not None, (
-            f"_exec_reduce_scatter: missing bucket_flat_grads buffer for ubid={ubid}"
-        )
-        assert rs_out is not None, (
-            f"_exec_reduce_scatter: missing bucket_rs_grads buffer for ubid={ubid}"
-        )
-        with torch.cuda.stream(stream):
-            total_bytes = flat_grads.numel() * flat_grads.element_size()
-            tmp = torch.empty_like(rs_out)
-            dist.reduce_scatter_tensor(tmp, flat_grads, group=self.dp_group)
-            rs_out.add_(tmp)
-            return total_bytes
-
-    def _exec_all_gather(
-        self,
-        ubid: Any,
-        stream,
-    ) -> int:
-        assert ubid is not None, "_exec_all_gather requires a non-None ubid"
-        if not self._has_trainable_params_for_collective(ubid, "_exec_all_gather"):
-            return 0
-        assert ubid in self.param_sharded_ubids, (
-            f"_exec_all_gather: ubid={ubid} is not in param_sharded_ubids={self.param_sharded_ubids}"
-        )
-        self._alloc_full_params(ubid)
-        flat_params = self.bucket_flat_params.get(ubid)
-        shard_in = self.bucket_shard_params.get(ubid)
-        assert flat_params is not None, (
-            f"_exec_all_gather: missing bucket_flat_params buffer for ubid={ubid}"
-        )
-        assert shard_in is not None, (
-            f"_exec_all_gather: missing bucket_shard_params buffer for ubid={ubid}"
-        )
-        assert not self.full_params_fresh.get(ubid, False), (
-            f"_exec_all_gather: ubid={ubid} dispatched but full params are already fresh; "
-            "DAG is constructing a redundant ALL_GATHER"
-        )
-        with torch.cuda.stream(stream):
-            self.logger.debug(
-                f"[all_gather_begin] rank={self.global_rank} ubid={ubid}: "
-                f"flat_numel={flat_params.numel()} flat_storage={flat_params.untyped_storage().size()} "
-                f"shard_numel={shard_in.numel()} shard_storage={shard_in.untyped_storage().size()}"
-            )
-            dist.all_gather_into_tensor(flat_params, shard_in, group=self.dp_group)
-            self.full_params_fresh[ubid] = True
-            self.logger.debug(
-                f"[all_gather_end] rank={self.global_rank} ubid={ubid}: "
-                f"flat_storage={flat_params.untyped_storage().size()}"
-            )
-            return flat_params.numel() * flat_params.element_size()
-
-    def _update(self, stream, *deps):
-        self._drain_pending_frees()
-        if self.bucket_shard_optims:
-            for evt in self.rs_events.values():
-                stream.wait_event(evt)
-
-            for ubid, shard_optim in self.bucket_shard_optims.items():
-                shard_param = self.bucket_shard_params[ubid]
-                rs_grad = self.bucket_rs_grads.get(ubid)
-                if rs_grad is None:
-                    continue
-                with torch.cuda.stream(stream):
-                    shard_param.grad = rs_grad.to(shard_param.dtype)
-                    shard_optim.step()
-                shard_param.grad = None
-
-            for ubid in self.param_sharded_ubids:
-                for param, *_ in self.bucket_param_view_specs.get(ubid, []):
-                    param.grad = None
-                full = self.bucket_flat_params.get(ubid)
-                if full is not None:
-                    storage = full.untyped_storage()
-                    if storage.size() != 0:
-                        storage.resize_(0)
-                self.full_params_fresh[ubid] = False
-
-            losses = self.loss
-            self.loss.clear()
-            torch.cuda.synchronize()
-            return losses
-
-        for ar_evt in self.ar_events.values():
-            stream.wait_event(ar_evt)
-
-        for ubid, optim in self.bucket_optims.items():
-            if optim is None:
-                continue
-            bwd_evt = self.bwd_events.get(ubid)
-            if bwd_evt is not None:
-                stream.wait_event(bwd_evt)
-
-            with torch.cuda.stream(stream):
-                optim.step()
-
-        losses = self.loss
-        self.loss.clear()
-
-        torch.cuda.synchronize()
-
-        return {
-            "losses": losses,
-        }
diff --git a/src/piper_exec.py b/src/piper_exec.py
deleted file mode 100644
index e35b5a6..0000000
--- a/src/piper_exec.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from enum import Enum
-
-
-class TaskType(Enum):
-    FWD = "forward"
-    BWD = "backward"
-    UPD = "update"
-    BWD_I = "backward_input"
-    BWD_W = "backward_weight"
-    FWD_BWD = "forward_backward"
-    SEND = "send"
-    RECV = "recv"
-    ALL_REDUCE = "all_reduce"
-    REDUCE_SCATTER = "reduce_scatter"
-    ALL_GATHER = "all_gather"
-    FWD_A2A = "forward_a2a"
-    BWD_A2A = "backward_a2a"
-    ORDER_DUMMY = "order_dummy"
diff --git a/src/piper_graph_transform.py b/src/piper_graph_transform.py
deleted file mode 100644
index 122e699..0000000
--- a/src/piper_graph_transform.py
+++ /dev/null
@@ -1,1683 +0,0 @@
-import hashlib
-import json
-import torch
-import torch.fx as fx
-from collections import defaultdict
-from pathlib import Path
-import operator
-
-from .piper_utils import create_logger, LOG_LEVEL
-
-logger = create_logger("piper_graph_transform", LOG_LEVEL)
-
-
-def _profile_and_split_gm(gm, num_stages) -> tuple[fx.GraphModule, list[tuple[int, fx.GraphModule, list[int], list]]]:
-    """
-    Profile each node in the graph module, then split into num_stages stages
-    of roughly equal execution time. This replaces annotation-based splitting
-    when users don't provide stage annotations.
-
-    Returns the same format as _split_gm_by_stages:
-        (top_level_gm, [(stage_id, stage_gm, input_idxs, param_idxs, graphargs, placeholders), ...])
-    """
-    # Step 1: Collect compute nodes (everything that isn't placeholder, get_attr, or output)
-    compute_nodes = []
-    for node in gm.graph.nodes:
-        if node.op in ("placeholder", "get_attr", "output"):
-            continue
-        compute_nodes.append(node)
-
-    if not compute_nodes:
-        return gm, []
-
-    # Check for cached split from a previous profiling run
-    cache_dir = Path.home() / ".cache" / "piper" / "splits"
-
-    def _stable_target(target):
-        """Return a stable string for a node target, avoiding memory addresses."""
-        if isinstance(target, str):
-            return target
-        if callable(target) and hasattr(target, '__module__') and hasattr(target, '__qualname__'):
-            return f"{target.__module__}.{target.__qualname__}"
-        return str(target)
-
-    graph_signature = hashlib.sha256(
-        json.dumps([(n.name, n.op, _stable_target(n.target)) for n in compute_nodes] + [num_stages]).encode()
-    ).hexdigest()[:16]
-    cache_file = cache_dir / f"{graph_signature}.json"
-
-    if cache_file.exists():
-        try:
-            cached = json.loads(cache_file.read_text())
-            split_node_names = cached["split_node_names"]
-            # Rebuild split_indices from cached node names
-            node_name_to_idx = {n.name: i for i, n in enumerate(compute_nodes)}
-            split_indices = [node_name_to_idx[name] for name in split_node_names]
-            logger.info(f"Loaded cached split from {cache_file} (splits: {split_node_names})")
-
-            # Assign stages and inject annotations
-            stage_assignments = {}
-            current_stage = 0
-            split_iter = iter(split_indices)
-            next_split = next(split_iter, len(compute_nodes))
-            for idx, node in enumerate(compute_nodes):
-                if idx == next_split:
-                    current_stage += 1
-                    next_split = next(split_iter, len(compute_nodes))
-                stage_assignments[node] = current_stage
-
-            for node, stage_id in stage_assignments.items():
-                if 'custom' not in node.meta:
-                    node.meta['custom'] = {}
-                node.meta['custom']['stage'] = stage_id
-
-            return _split_gm_by_stages(gm)
-        except Exception as e:
-            logger.warning(f"Failed to load cached split from {cache_file}: {e}, re-profiling")
-
-    # Step 2: Build environment mapping for executing individual nodes
-    # We need real tensors on device to profile. To handle models too large for a single
-    # GPU, we eagerly free intermediate tensors once all their consumers have been profiled.
-    env = {}
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-
-    # Build reference counts: for each node, count how many compute nodes (+ the output node)
-    # consume it. When refcount hits 0 after profiling a consumer, we free the tensor.
-    compute_node_set = set(compute_nodes)
-    ref_counts = {}  # node.name -> int
-    for node in compute_nodes:
-        for inp in node.all_input_nodes:
-            ref_counts[inp.name] = ref_counts.get(inp.name, 0) + 1
-
-    def _materialize_tensor(ex, device):
-        """Create a real tensor on device from an example (possibly meta) tensor."""
-        if isinstance(ex, torch.Tensor):
-            shape = tuple(ex.shape)
-            if ex.device.type != "meta" and ex.device.type == device:
-                return ex
-            if ex.is_floating_point():
-                t = torch.randn(shape, dtype=ex.dtype, device=device)
-            else:
-                t = torch.ones(shape, dtype=ex.dtype, device=device)
-            if isinstance(ex, torch.nn.Parameter):
-                t = torch.nn.Parameter(t, requires_grad=ex.requires_grad)
-            return t
-        return ex
-
-    def _ensure_in_env(node):
-        """Lazily materialize a placeholder or get_attr node into env on first access."""
-        if node.name in env:
-            return
-        if node.op == "placeholder":
-            ex = node.meta.get("example_value")
-            if ex is not None:
-                env[node.name] = _materialize_tensor(ex, device)
-            else:
-                env[node.name] = None
-        elif node.op == "get_attr":
-            target_atoms = node.target.split(".")
-            attr = gm
-            for atom in target_atoms:
-                attr = getattr(attr, atom)
-            if isinstance(attr, torch.Tensor):
-                env[node.name] = _materialize_tensor(attr, device)
-            else:
-                env[node.name] = attr
-
-    def _ensure_real(val):
-        """Recursively replace any meta tensors with real tensors on device."""
-        if isinstance(val, torch.Tensor) and val.device.type == "meta":
-            return _materialize_tensor(val, device)
-        elif isinstance(val, (list, tuple)):
-            return type(val)(_ensure_real(v) for v in val)
-        elif isinstance(val, dict):
-            return {k: _ensure_real(v) for k, v in val.items()}
-        return val
-
-    def _fetch_arg(arg):
-        if isinstance(arg, fx.Node):
-            _ensure_in_env(arg)
-            val = env.get(arg.name)
-            real_val = _ensure_real(val)
-            if real_val is not val:
-                env[arg.name] = real_val  # cache so future accesses also get real tensor
-            return real_val
-        elif isinstance(arg, (list, tuple)):
-            vals = [_fetch_arg(a) for a in arg]
-            return type(arg)(vals)
-        elif isinstance(arg, dict):
-            return {k: _fetch_arg(v) for k, v in arg.items()}
-        elif isinstance(arg, torch.Tensor) and arg.device.type == "meta":
-            # Constants baked into node.args at trace time (e.g. RoPE frequency
-            # matrices) are meta tensors when the model was traced on meta device.
-            # Materialize them on the profiling device so call_function nodes don't fail.
-            return _materialize_tensor(arg, device)
-        return arg
-
-    def _dec_ref(node):
-        """Decrement refcount for a node and free its env entry when it reaches 0."""
-        name = node.name
-        if name not in ref_counts:
-            return
-        ref_counts[name] -= 1
-        if ref_counts[name] <= 0 and name in env and isinstance(env[name], torch.Tensor):
-            del env[name]
-
-    def _exec_node(node, args, kwargs, fn):
-        """Execute a single graph node and return the result."""
-        if node.op == "call_method":
-            return getattr(args[0], node.target)(*args[1:], **kwargs)
-        elif node.op == "call_module":
-            return fn(*args, **kwargs)
-        else:
-            return fn(*args, **kwargs)
-
-    # Step 3: Profile each compute node with eager memory freeing
-    # Use no_grad to prevent autograd graph accumulation — we only need forward timing.
-    node_times = {}  # node -> time in ms
-
-    num_warmup = 3
-    num_profile = 5
-    for node in compute_nodes:
-        args = _fetch_arg(node.args)
-        kwargs = _fetch_arg(node.kwargs)
-
-        # Check for None inputs from failed predecessor nodes
-        has_none_input = False
-        for inp in node.all_input_nodes:
-            if inp.op not in ("placeholder", "get_attr") and env.get(inp.name) is None:
-                has_none_input = True
-                break
-
-        if has_none_input:
-            node_times[node.name] = 0.0
-            env[node.name] = None
-            for inp in node.all_input_nodes:
-                _dec_ref(inp)
-            continue
-
-        # Resolve the callable
-        if node.op == "call_function":
-            fn = node.target
-        elif node.op == "call_method":
-            fn = getattr(type(args[0]), node.target) if args else None
-        elif node.op == "call_module":
-            fn = gm.get_submodule(node.target)
-            fn.to(device)
-        else:
-            node_times[node.name] = 0.0
-            env[node.name] = None
-            for inp in node.all_input_nodes:
-                _dec_ref(inp)
-            continue
-
-        # Execute and profile under no_grad to prevent autograd graph accumulation
-        try:
-            with torch.no_grad():
-                for _ in range(num_warmup):
-                    result = _exec_node(node, args, kwargs, fn)
-
-                if device == "cuda":
-                    torch.cuda.synchronize()
-
-                start_event = torch.cuda.Event(enable_timing=True)
-                end_event = torch.cuda.Event(enable_timing=True)
-                start_event.record()
-                for _ in range(num_profile):
-                    result = _exec_node(node, args, kwargs, fn)
-                end_event.record()
-                torch.cuda.synchronize()
-                elapsed = start_event.elapsed_time(end_event) / num_profile
-            node_times[node.name] = elapsed
-            env[node.name] = result
-        except Exception as e:
-            logger.warning(f"Failed to profile node {node.name} ({node.op}): {e}")
-            node_times[node.name] = 0.0
-            try:
-                with torch.no_grad():
-                    env[node.name] = _exec_node(node, args, kwargs, fn)
-            except Exception:
-                env[node.name] = None
-
-        # Move call_module submodules back to meta to free GPU memory
-        if node.op == "call_module":
-            fn.to("meta")
-
-        # Decrement refcounts for all inputs of this node
-        for inp in node.all_input_nodes:
-            _dec_ref(inp)
-
-    # Step 4: Partition compute nodes into num_stages groups of roughly equal time
-    total_time = sum(node_times.get(n.name, 0.0) for n in compute_nodes)
-    target_time = total_time / num_stages
-
-    # Precompute prefix sums for fast time range queries
-    prefix_times = [0.0]
-    for n in compute_nodes:
-        prefix_times.append(prefix_times[-1] + node_times.get(n.name, 0.0))
-
-    def _count_cross_boundary_deps(split_idx):
-        """Count how many compute nodes before split_idx are used by nodes at or after split_idx."""
-        before_set = set(compute_nodes[:split_idx])
-        cross = set()
-        for node in compute_nodes[split_idx:]:
-            for arg in node.all_input_nodes:
-                if arg in before_set and arg.op not in ("placeholder", "get_attr"):
-                    cross.add(arg)
-        return len(cross)
-
-    def _find_best_split(ideal_idx, stage_start_idx):
-        """Search all valid split points and return the one closest to ideal_idx in time."""
-        ideal_time = prefix_times[ideal_idx]
-        best_split = None
-        best_time_dist = float('inf')
-        for candidate in range(stage_start_idx + 1, len(compute_nodes)):
-            n_deps = _count_cross_boundary_deps(candidate)
-            if n_deps <= 1:
-                time_dist = abs(prefix_times[candidate] - ideal_time)
-                if time_dist < best_time_dist:
-                    best_time_dist = time_dist
-                    best_split = candidate
-        return best_split
-
-    # Find split points: we need (num_stages - 1) splits
-    split_indices = []
-    stage_start_idx = 0
-    for s in range(num_stages - 1):
-        # Target cumulative time for end of this stage
-        remaining_time = prefix_times[-1] - prefix_times[stage_start_idx]
-        remaining_stages = num_stages - s
-        stage_target = remaining_time / remaining_stages
-
-        target_cum_time = prefix_times[stage_start_idx] + stage_target
-        # Find the ideal split index (first node where cumulative time exceeds target)
-        ideal_idx = stage_start_idx + 1
-        for i in range(stage_start_idx + 1, len(compute_nodes)):
-            if prefix_times[i] >= target_cum_time:
-                ideal_idx = i
-                break
-        else:
-            ideal_idx = len(compute_nodes) - 1
-
-        best_split = _find_best_split(ideal_idx, stage_start_idx)
-        if best_split is not None:
-            split_indices.append(best_split)
-            time_offset = prefix_times[best_split] - prefix_times[ideal_idx]
-            logger.debug(
-                f"Stage {s}/{s+1} boundary: split before node {compute_nodes[best_split].name} "
-                f"(index {best_split}, time offset from ideal: {time_offset:+.3f} ms)"
-            )
-            stage_start_idx = best_split
-        else:
-            logger.warning(f"Could not find valid split point near node {ideal_idx}, skipping")
-
-    # Save split to cache
-    try:
-        cache_dir.mkdir(parents=True, exist_ok=True)
-        split_node_names = [compute_nodes[i].name for i in split_indices]
-        cache_file.write_text(json.dumps({"split_node_names": split_node_names}, indent=2))
-        logger.debug(f"Saved split cache to {cache_file}")
-    except Exception as e:
-        logger.warning(f"Failed to save split cache: {e}")
-
-    # Assign stages based on split indices
-    stage_assignments = {}
-    current_stage = 0
-    split_iter = iter(split_indices)
-    next_split = next(split_iter, len(compute_nodes))
-    for idx, node in enumerate(compute_nodes):
-        if idx == next_split:
-            current_stage += 1
-            next_split = next(split_iter, len(compute_nodes))
-        stage_assignments[node] = current_stage
-
-    # Ensure example_value metadata matches the profiled result's requires_grad.
-    # This prevents assertions in _forward_impl from failing when non-grad tensors
-    # (e.g., masks, positional encodings) are passed between stages as outputs.
-    for node in compute_nodes:
-        result = env.get(node.name)
-        if result is not None and isinstance(result, torch.Tensor) and not result.requires_grad:
-            ex = node.meta.get("example_value")
-            if ex is not None and isinstance(ex, torch.Tensor) and ex.requires_grad:
-                node.meta["example_value"] = ex.detach()
-
-    # Inject stage annotations into node metadata so _split_gm_by_stages can handle it
-    for node, stage_id in stage_assignments.items():
-        if 'custom' not in node.meta:
-            node.meta['custom'] = {}
-        node.meta['custom']['stage'] = stage_id
-
-    # Log stage times
-    stage_times = defaultdict(float)
-    stage_node_counts = defaultdict(int)
-    for node, stage_id in stage_assignments.items():
-        stage_times[stage_id] += node_times.get(node.name, 0.0)
-        stage_node_counts[stage_id] += 1
-    for stage_id in sorted(stage_times.keys()):
-        logger.debug(
-            f"Stage {stage_id}: {stage_times[stage_id]:.3f} ms "
-            f"({stage_node_counts[stage_id]} nodes)"
-        )
-
-    # Free all profiling tensors before training starts
-    import gc
-    env.clear()
-    del env
-    gc.collect()
-    torch.cuda.empty_cache()
-
-    # Delegate to _split_gm_by_stages which handles all the submodule creation
-    return _split_gm_by_stages(gm)
-
-
-def _meta_tensor_like(example, *, requires_grad: bool, as_parameter: bool):
-    sym_int_type = getattr(torch, "SymInt", ())
-    sym_float_type = getattr(torch, "SymFloat", ())
-    sym_bool_type = getattr(torch, "SymBool", ())
-
-    if isinstance(example, torch.Tensor):
-        t = torch.empty(example.shape, dtype=example.dtype, device="meta")
-        t.requires_grad_(requires_grad)
-    elif isinstance(example, (sym_int_type, int)):
-        # Symbolic/python integers do not have .shape/.dtype; represent them as
-        # singleton meta tensors so downstream graph-arg handling remains uniform.
-        t = torch.empty((1,), dtype=torch.int64, device="meta")
-    elif isinstance(example, (sym_float_type, float)):
-        t = torch.empty((1,), dtype=torch.float32, device="meta")
-    elif isinstance(example, (sym_bool_type, bool)):
-        t = torch.empty((1,), dtype=torch.bool, device="meta")
-    else:
-        # Fallback for unknown scalar-like values.
-        t = torch.empty((1,), dtype=torch.float32, device="meta")
-
-    if as_parameter:
-        return torch.nn.Parameter(t, requires_grad=requires_grad)
-    return t
-
-def _split_gm_by_stages(gm) -> tuple[fx.GraphModule, list[tuple[int, fx.GraphModule, list[int], list]]]:
-    """
-    Transform a graph module with stage annotations by extracting
-    stage computations into submodules and distributing the stage
-    submodules to Ray actors.
-    
-    Returns:
-        A tuple of (new_graph_module, list_of_stage_info) where:
-        - new_graph_module: The transformed top-level graph module
-        - list_of_stage_info: List of (stage_id, submodule, input_idxs, all_inputs) tuples for each extracted stage
-    """
-    # Collect all nodes with custom metadata
-    nodes_with_metadata = []
-    for node in gm.graph.nodes:
-        if 'custom' in node.meta:
-            custom_meta = node.meta['custom']
-            # Extract stage from metadata
-            if isinstance(custom_meta, dict):
-                stage_annotation_id = custom_meta.get('stage')
-                if stage_annotation_id is not None:
-                    nodes_with_metadata.append((node, stage_annotation_id))
-    
-    if not nodes_with_metadata:
-        return gm, []
-
-    # Group nodes by stage ID for creating stage modules
-    stage_code = defaultdict(list)  # stage_id -> list of all nodes for this stage
-    
-    for node, stage_annotation_id in nodes_with_metadata:
-        stage_code[stage_annotation_id].append(node)
-    
-    stage_modules = {}
-    
-    # Process stages in sorted order to track outputs from previous stages
-    for stage_annotation_id, stage_nodes in sorted(stage_code.items(), key=lambda x: x[0]):
-        stage_graph = fx.Graph()
-        stage_node_mapping = {}
-        
-        stage_node_set = set(stage_nodes)
-        
-        # Collect all nodes from other stages to identify stage boundaries
-        other_stage_nodes = set()
-        for other_stage_id, other_stage_nodes_list in stage_code.items():
-            if other_stage_id != stage_annotation_id:
-                other_stage_nodes.update(other_stage_nodes_list)
-        
-        # Check if any nodes are outputs from previous stages
-        prev_stage_outputs = set()
-        for prev_stage_id in range(stage_annotation_id):
-            if prev_stage_id in stage_modules:
-                _, _, _, prev_outputs, _, _, _, _, _, _ = stage_modules[prev_stage_id]
-                prev_stage_outputs.update(prev_outputs)
-        
-        # Find all inputs needed by this stage (nodes that are not in the stage set)
-        # Check both args and kwargs to find all dependencies
-        stage_inputs = set()
-        get_attr_nodes = set()  # Track get_attr nodes separately
-        
-        def extract_nodes_from_arg(arg):
-            """Recursively extract all fx.Node objects from an argument"""
-            nodes = []
-            if isinstance(arg, fx.Node):
-                nodes.append(arg)
-            elif isinstance(arg, (list, tuple)):
-                for item in arg:
-                    nodes.extend(extract_nodes_from_arg(item))
-            elif isinstance(arg, dict):
-                for value in arg.values():
-                    nodes.extend(extract_nodes_from_arg(value))
-            return nodes
-        
-        for node in stage_nodes:
-            # Check args - use both map_arg and explicit extraction for robustness
-            for arg in fx.graph.map_arg(node.args, lambda n: n):
-                if isinstance(arg, fx.Node) and arg not in stage_node_set:
-                    if arg.op == "get_attr":
-                        get_attr_nodes.add(arg)
-                    elif arg.op == "placeholder":
-                        # Placeholders are always inputs
-                        stage_inputs.add(arg)
-                    elif arg in other_stage_nodes or arg in prev_stage_outputs:
-                        # Nodes from other stages or outputs from previous stages are inputs
-                        stage_inputs.add(arg)
-                    else:
-                        # Could be computed in this stage or an input - will determine later
-                        stage_inputs.add(arg)
-            
-            # Check kwargs - explicitly extract nodes from dict values
-            # fx.graph.map_arg should work, but let's also manually check kwargs dict
-            if node.kwargs:
-                # Use map_arg first (should handle most cases)
-                for arg in fx.graph.map_arg(node.kwargs, lambda n: n):
-                    if isinstance(arg, fx.Node) and arg not in stage_node_set:
-                        if arg.op == "get_attr":
-                            get_attr_nodes.add(arg)
-                        elif arg.op == "placeholder":
-                            # Placeholders are always inputs
-                            stage_inputs.add(arg)
-                        elif arg in other_stage_nodes or arg in prev_stage_outputs:
-                            # Nodes from other stages or outputs from previous stages are inputs
-                            stage_inputs.add(arg)
-                        else:
-                            # Could be computed in this stage or an input - will determine later
-                            stage_inputs.add(arg)
-                
-                # Also explicitly extract nodes from kwargs dict values as a fallback
-                for kwarg_value in extract_nodes_from_arg(node.kwargs):
-                    if kwarg_value not in stage_node_set:
-                        if kwarg_value.op == "get_attr":
-                            get_attr_nodes.add(kwarg_value)
-                        elif kwarg_value.op == "placeholder":
-                            stage_inputs.add(kwarg_value)
-                        elif kwarg_value in other_stage_nodes or kwarg_value in prev_stage_outputs:
-                            stage_inputs.add(kwarg_value)
-                        else:
-                            stage_inputs.add(kwarg_value)
-        
-        # Copy get_attr nodes (parameters) to the stage graph first
-        # These should be available directly in the stage, not passed as inputs
-        for node in gm.graph.nodes:
-            if node in get_attr_nodes:
-                new_get_attr = stage_graph.node_copy(node, lambda n: n)
-                stage_node_mapping[node] = new_get_attr
-        
-        # Create placeholders for inputs with proper ordering
-        # For stages > 0, inputs from previous stage outputs should be ordered
-        # according to their order in the previous stage's outputs
-        input_placeholders = {}
-        input_order = []  # Track order of inputs
-        
-        # Get outputs from previous stage (if it exists) to determine ordering
-        prev_stage_outputs_ordered = []
-        if stage_annotation_id > 0:
-            prev_stage_id = stage_annotation_id - 1
-            if prev_stage_id in stage_modules:
-                _, _, _, prev_stage_outputs_list, _, _, _, _, _, _ = stage_modules[prev_stage_id]
-                prev_stage_outputs_ordered = prev_stage_outputs_list
-        
-        # For stages > 0, first add inputs that come from previous stage outputs
-        # in the order they appear in the previous stage's outputs
-        if stage_annotation_id > 0 and prev_stage_outputs_ordered:
-            for prev_output_node in prev_stage_outputs_ordered:
-                if prev_output_node in stage_inputs and prev_output_node not in input_placeholders:
-                    # If the input is already a placeholder, use node_copy to preserve all metadata
-                    if prev_output_node.op == "placeholder":
-                        placeholder = stage_graph.node_copy(prev_output_node, lambda n: n)
-                    else:
-                        # For computed nodes, create a placeholder but preserve metadata
-                        placeholder = stage_graph.placeholder(prev_output_node.name)
-                        # Copy all metadata from the original node
-                        placeholder.meta.update(prev_output_node.meta)
-                    stage_node_mapping[prev_output_node] = placeholder
-                    input_placeholders[prev_output_node] = placeholder
-                    input_order.append(prev_output_node)
-        
-        # Then add any remaining inputs in original graph order
-        # (these are typically placeholders or other inputs not from previous stage)
-        for node in gm.graph.nodes:
-            if node in stage_inputs and node not in input_placeholders:
-                # If the input is already a placeholder, use node_copy to preserve all metadata
-                if node.op == "placeholder":
-                    placeholder = stage_graph.node_copy(node, lambda n: n)
-                else:
-                    # For computed nodes, create a placeholder but preserve metadata
-                    placeholder = stage_graph.placeholder(node.name)
-                    # Copy all metadata from the original node
-                    placeholder.meta.update(node.meta)
-                stage_node_mapping[node] = placeholder
-                input_placeholders[node] = placeholder
-                input_order.append(node)
-        
-        # Find outputs of this stage (nodes used outside the stage set)
-        # First, collect all nodes from other stages to identify stage boundaries
-        other_stage_nodes = set()
-        for other_stage_id, other_stage_nodes_list in stage_code.items():
-            if other_stage_id != stage_annotation_id:
-                other_stage_nodes.update(other_stage_nodes_list)
-        
-        # Check if any nodes are outputs from previous stages
-        prev_stage_outputs = set()
-        for prev_stage_id in range(stage_annotation_id):
-            if prev_stage_id in stage_modules:
-                _, _, _, prev_outputs, _, _, _, _, _, _ = stage_modules[prev_stage_id]
-                prev_stage_outputs.update(prev_outputs)
-        
-        # Find all nodes computed in this stage (dependencies of stage_nodes)
-        # These are nodes that are arguments to stage_nodes but not inputs from other stages
-        # We need to recursively collect all dependencies
-        stage_computed_nodes = set(stage_nodes)
-        visited = set()
-        
-        def collect_dependencies(node):
-            """Recursively collect all node dependencies that are computed in this stage"""
-            if node in visited:
-                return
-            visited.add(node)
-            
-            # Skip if already in computed nodes
-            if node in stage_computed_nodes:
-                return
-            
-            # Skip placeholders, get_attr, nodes from other stages, and outputs from previous stages
-            if (node.op == "placeholder" or node.op == "get_attr" or 
-                node in other_stage_nodes or node in prev_stage_outputs):
-                return
-            
-            # This node is computed in this stage (it's a dependency of a stage node)
-            if node in stage_inputs:
-                # Remove from inputs if it's computed in this stage
-                stage_inputs.discard(node)
-            stage_computed_nodes.add(node)
-            
-            # Recursively collect dependencies of this node from args
-            for arg in fx.graph.map_arg(node.args, lambda n: n):
-                if isinstance(arg, fx.Node):
-                    collect_dependencies(arg)
-            
-            # Recursively collect dependencies from kwargs - use both map_arg and explicit extraction
-            if node.kwargs:
-                # Use map_arg first
-                for arg in fx.graph.map_arg(node.kwargs, lambda n: n):
-                    if isinstance(arg, fx.Node):
-                        collect_dependencies(arg)
-                
-                # Also explicitly extract nodes from kwargs dict values as a fallback
-                for kwarg_node in extract_nodes_from_arg(node.kwargs):
-                    collect_dependencies(kwarg_node)
-        
-        # Collect all dependencies of stage nodes
-        for stage_node in stage_nodes:
-            # Collect from args
-            for arg in fx.graph.map_arg(stage_node.args, lambda n: n):
-                if isinstance(arg, fx.Node):
-                    collect_dependencies(arg)
-            
-            # Collect from kwargs - use both map_arg and explicit extraction
-            if stage_node.kwargs:
-                for arg in fx.graph.map_arg(stage_node.kwargs, lambda n: n):
-                    if isinstance(arg, fx.Node):
-                        collect_dependencies(arg)
-                
-                # Also explicitly extract nodes from kwargs dict values
-                for kwarg_node in extract_nodes_from_arg(stage_node.kwargs):
-                    collect_dependencies(kwarg_node)
-        
-        # Find outputs: nodes computed in this stage that are used by other stages
-        # Check if users have stage annotations from other stages, or if they're in already-processed stages
-        # Only consider non-placeholder nodes as outputs — placeholders (e.g. model attributes
-        # like freqs_cis, mask) are copied into each stage via get_attr or re-created as
-        # placeholders, so they don't need to be passed between stages.
-        stage_outputs = []
-        for node in stage_computed_nodes:
-            if node.op == "placeholder":
-                continue
-            for user in node.users:
-                # Check if user has a stage annotation from a different stage
-                user_in_other_stage = False
-                if 'custom' in user.meta:
-                    custom_meta = user.meta['custom']
-                    if isinstance(custom_meta, dict):
-                        user_stage = custom_meta.get('stage')
-                        if user_stage is not None and user_stage != stage_annotation_id:
-                            user_in_other_stage = True
-                
-                # Also check if user is in an already-processed stage's computed nodes
-                if not user_in_other_stage:
-                    for other_stage_id in range(stage_annotation_id):
-                        if other_stage_id in stage_modules:
-                            _, _, _, _, _, other_ordered_nodes, _, _, _, _ = stage_modules[other_stage_id]
-                            if user in other_ordered_nodes:
-                                user_in_other_stage = True
-                                break
-                
-                # Check if user is in another stage (annotated nodes)
-                if not user_in_other_stage and user in other_stage_nodes:
-                    user_in_other_stage = True
-                
-                if user_in_other_stage:
-                    if node not in stage_outputs:
-                        stage_outputs.append(node)
-                # Also check if user is the output node (which uses stage outputs)
-                elif user.op == "output":
-                    if node not in stage_outputs:
-                        stage_outputs.append(node)
-        
-        # If no external users, use the last node(s) as output
-        if not stage_outputs:
-            # Use the last computed node in topological order
-            ordered_computed = [n for n in gm.graph.nodes if n in stage_computed_nodes]
-            stage_outputs = [ordered_computed[-1]] if ordered_computed else []
-        
-        # Preserve original graph order by iterating through nodes in original order
-        # This ensures correctness of computation order
-        # Include ALL computed nodes, not just annotated ones
-        ordered_stage_nodes = []
-        for node in gm.graph.nodes:
-            if node in stage_computed_nodes:
-                ordered_stage_nodes.append(node)
-        
-        # Copy stage nodes to the stage graph in original graph order
-        for node in ordered_stage_nodes:
-            new_stage_node = stage_graph.node_copy(
-                node,
-                lambda n: stage_node_mapping.get(n, input_placeholders.get(n))
-            )
-            stage_node_mapping[node] = new_stage_node
-
-        # Create output node
-        if stage_outputs:
-            output_values = [stage_node_mapping[node] for node in stage_outputs]
-            if len(output_values) == 1:
-                stage_graph.output(output_values[0])
-            else:
-                stage_graph.output(tuple(output_values))
-        else:
-            # No outputs, create a dummy output
-            stage_graph.output(stage_graph.placeholder('dummy'))
-        
-        stage_gm = fx.GraphModule(torch.nn.Module(), stage_graph)
-        
-        # Store the stage module and related info
-        for node in ordered_stage_nodes:
-            if node.op == "placeholder":
-                input_placeholders[node] = node
-
-        # Load the module on the corresponding actor
-        # Track which inputs come from previous stage outputs
-        input_idxs = []
-        param_idxs = []
-        graphargs = []
-        placeholders = stage_gm.graph.find_nodes(op="placeholder")
-        
-        # Create reverse mapping from placeholder to original node
-        placeholder_to_original = {placeholder: orig_node for orig_node, placeholder in input_placeholders.items()}
-        
-        # Get outputs from previous stage (if it exists)
-        prev_stage_outputs = set()
-        prev_stage_id = stage_annotation_id - 1
-        if prev_stage_id in stage_modules:
-            _, _, _, prev_stage_outputs_list, _, _, _, _, _, _ = stage_modules[prev_stage_id]
-            _, _, _, prev_stage_outputs_list, _, _, _, _, _, _ = stage_modules[prev_stage_id]
-            prev_stage_outputs = set(prev_stage_outputs_list)
-        
-        for i, placeholder in enumerate(placeholders):
-            ex = placeholder.meta["example_value"]
-
-            # Parameter-like placeholders
-            is_param_like = ("grapharg" in placeholder.meta) or ("self" in placeholder.name)
-
-            # we may not actually need to call _metathis if we're already placing example inputs on meta, 
-            # but it's guaranteed to be safe with this function call
-            graphargs.append(
-                _meta_tensor_like(
-                    ex,
-                    requires_grad=bool(getattr(ex, "requires_grad", False)),
-                    as_parameter=is_param_like,
-                )
-            )
-
-            if isinstance(ex, torch.nn.Parameter):
-                param_idxs.append(i)
-            # For the first stage, the input indices are everything that's not an attribute
-            if stage_annotation_id == 0:
-                # Only runtime tensor activations should be stage inputs.
-                # Symbolic placeholders (e.g. SymInt shape env entries) are not
-                # fed by run_dag and must stay out of input_idxs.
-                if isinstance(ex, torch.Tensor) and ('self' not in placeholder.name):
-                    input_idxs.append(i)
-            # For subsequent stages, check if this placeholder corresponds to an output from the previous stage
-            else:
-                orig_node = placeholder_to_original.get(placeholder)
-                is_from_prev_stage = orig_node is not None and orig_node in prev_stage_outputs
-                if is_from_prev_stage:
-                    input_idxs.append(i)
-
-        assert set(input_idxs) & set(param_idxs) == set(), "Input and parameter indices should be disjoint"
-
-        stage_modules[stage_annotation_id] = (
-            stage_gm,
-            input_placeholders,
-            input_order,
-            stage_outputs,
-            stage_nodes,
-            ordered_stage_nodes,
-            stage_annotation_id,
-            input_idxs,
-            param_idxs,
-            graphargs,
-        )
-
-    # Create a new top-level graph and replace stage nodes with call_module
-    new_graph = fx.Graph()
-    node_mapping = {}
-    
-    # Copy placeholders
-    for node in gm.graph.nodes:
-        if node.op == "placeholder":
-            new_node = new_graph.node_copy(node, lambda n: node_mapping.get(n, n))
-            node_mapping[node] = new_node
-    
-    # Track which stage calls have been replaced
-    stage_call_replaced = {}
-    
-    # Copy all get_attr nodes (parameters) so they're available for stage calls
-    for node in gm.graph.nodes:
-        if node.op == "get_attr":
-            if node not in node_mapping:
-                new_node = new_graph.node_copy(node, lambda n: node_mapping.get(n, n))
-                node_mapping[node] = new_node
-    
-    # Process nodes in the original graph order to preserve structure
-    for node in gm.graph.nodes:
-        if node.op == "placeholder":
-            # Already handled
-            continue
-        
-        if node.op == "output":
-            # Handle output separately
-            continue
-        
-        if node.op == "get_attr":
-            # Already handled above
-            continue
-        
-        if node in node_mapping:
-            # Already processed
-            continue
-        
-        # Check if this node belongs to a stage and find which stage call it's part of
-        in_stage = False
-        for stage_annotation_id, stage_nodes in stage_code.items():
-            if node in stage_nodes:
-                in_stage = True
-                
-                # Get the stage module info
-                stage_gm, input_placeholders, input_order, stage_outputs, stage_nodes, ordered_stage_nodes, _, _, _, _= stage_modules[stage_annotation_id]
-                
-                # Check if we've already created the call_module for this stage call
-                if stage_annotation_id not in stage_call_replaced:
-                    # Find first node in this stage's nodes (in original graph order)
-                    first_node = None
-                    for n in gm.graph.nodes:
-                        if n in stage_nodes:
-                            first_node = n
-                            break
-                    
-                    if first_node is None:
-                        first_node = stage_nodes[0]
-                    
-                    # Map inputs in the same order as placeholders (original graph order)
-                    mapped_args = []
-                    for input_node in input_order:
-                        if input_node in node_mapping:
-                            mapped_args.append(node_mapping[input_node])
-                        else:
-                            # Input node not yet mapped, need to copy it first
-                            new_input_node = new_graph.node_copy(input_node, lambda n: node_mapping.get(n, n))
-                            node_mapping[input_node] = new_input_node
-                            mapped_args.append(new_input_node)
-                    
-                    # Create call_module node for the stage
-                    module_name = f"stage_{stage_annotation_id}"
-                    call_node = new_graph.call_module(module_name, tuple(mapped_args), {})
-                    stage_call_replaced[stage_annotation_id] = (call_node, stage_outputs)
-                
-                # Map stage nodes to the call_module output
-                call_node, stage_outputs = stage_call_replaced[stage_annotation_id]
-                
-                # Only create output mapping for output nodes (nodes used outside stage)
-                if node in stage_outputs:
-                    if len(stage_outputs) == 1:
-                        # Single output, use call_node directly
-                        node_mapping[node] = call_node
-                    else:
-                        # Multiple outputs, need to getitem
-                        output_idx = stage_outputs.index(node)
-                        getitem_node = new_graph.call_function(
-                            operator.getitem,
-                            (call_node, output_idx)
-                        )
-                        node_mapping[node] = getitem_node
-                else:
-                    node_mapping[node] = call_node
-                break
-        
-        if not in_stage:
-            # Regular node, copy it
-            new_node = new_graph.node_copy(node, lambda n: node_mapping.get(n, n))
-            node_mapping[node] = new_node
-    
-    
-    # Handle output node
-    output_node = None
-    for node in gm.graph.nodes:
-        if node.op == "output":
-            output_node = node
-            break
-    
-    if output_node:
-        output_args = fx.graph.map_arg(output_node.args, lambda n: node_mapping.get(n, n))
-        if isinstance(output_args, tuple) and len(output_args) == 1:
-            new_graph.output(output_args[0])
-        else:
-            new_graph.output(output_args)
-    
-    # Create root module and add stage modules as submodules
-    root_module = torch.nn.Module()
-    submodule_list = []
-    for stage_annotation_id, (stage_gm, placeholders, _, _, _, _, _, input_idxs, param_idxs, params) in stage_modules.items():
-        module_name = f"stage_{stage_annotation_id}"
-        root_module.add_module(module_name, stage_gm)
-        submodule_list.append((stage_annotation_id, stage_gm, input_idxs, param_idxs, params, placeholders))
-    
-    new_gm = fx.GraphModule(root_module, new_graph)
-    
-    # Copy parameters, buffers, and modules from original module
-    for name, param in gm.named_parameters(recurse=False):
-        if name not in new_gm._parameters:
-            new_gm.register_parameter(name, param)
-    
-    for name, buffer in gm.named_buffers(recurse=False):
-        if name not in new_gm._buffers:
-            new_gm.register_buffer(name, buffer)
-    
-    for name, module in gm.named_children():
-        if name not in [f"stage_{stage_annotation_id}" for stage_annotation_id in stage_modules.keys()]:
-            new_gm.add_module(name, module)
-    
-    new_gm.recompile()
-    
-    return new_gm, submodule_list
-
-
-
-# ---------------------------------------------------------------------------
-# bucket_stage - stage-level parameter bucketing (placeholder params)
-# ---------------------------------------------------------------------------
-
-
-def bucket_stage(
-    stage_gm: fx.GraphModule,
-    graphargs: list,
-    input_idxs: list[int],
-    param_idxs: list[int],
-    bucket_size_bytes: int = 25 * 1024 * 1024,
-    debug_name: str | None = None,
-) -> list[tuple[fx.GraphModule, list[int], list[int], list]]:
-    """Split a stage GraphModule into per-parameter-bucket sub-modules.
-
-    This function handles stages produced by :func:`_split_gm_by_stages`
-    where trainable parameters are passed as **placeholder inputs** identified
-    by *param_idxs*.
-
-    Args:
-        stage_gm: The stage ``fx.GraphModule`` to split.
-        graphargs: Flat arg list for ``stage_gm.forward``; entries at
-            *param_idxs* are meta tensors, entries at *input_idxs* are None.
-        input_idxs: Positions of activation-input placeholders.
-        param_idxs: Positions of parameter placeholders.
-        bucket_size_bytes: Target bucket size (default 25 MB).
-        debug_name: Optional label included in bucket planning logs.
-
-    Returns:
-        A list of ``(bucket_gm, bucket_input_idxs, bucket_param_idxs,
-        bucket_graphargs)`` tuples in execution order.  Returns a
-        single-element list containing the original stage when no split is
-        needed.
-    """
-    nodes = list(stage_gm.graph.nodes)
-    node_idx: dict[fx.Node, int] = {nd: i for i, nd in enumerate(nodes)}
-    ph_nodes = [nd for nd in nodes if nd.op == "placeholder"]
-
-    param_ph_set = {ph_nodes[i] for i in param_idxs if i < len(ph_nodes)}
-    param_ph_list = [ph_nodes[i] for i in param_idxs if i < len(ph_nodes)]
-    input_ph_set  = {ph_nodes[i] for i in input_idxs  if i < len(ph_nodes)}
-
-    if not param_ph_list:
-        return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))]
-
-    compute_set: frozenset[int] = frozenset(
-        i for i, nd in enumerate(nodes)
-        if nd.op not in ("placeholder", "get_attr", "output")
-    )
-
-    def _use_range(pnd: fx.Node) -> tuple[int, int]:
-        idxs = [node_idx[u] for u in pnd.users if node_idx[u] in compute_set]
-        return (min(idxs), max(idxs)) if idxs else (node_idx[pnd], node_idx[pnd])
-
-    def _size(pnd: fx.Node) -> int:
-        i = ph_nodes.index(pnd)
-        if i < len(graphargs) and graphargs[i] is not None and hasattr(graphargs[i], "numel"):
-            return int(graphargs[i].numel() * graphargs[i].element_size())
-        ev = pnd.meta.get("example_value")
-        if ev is not None and hasattr(ev, "numel"):
-            return int(ev.numel() * ev.element_size())
-        return 0
-
-    debug_name = debug_name or getattr(stage_gm, "__class__", type(stage_gm)).__name__
-    param_ranges = {pn: _use_range(pn) for pn in param_ph_list}
-    param_sizes = {pn: _size(pn) for pn in param_ph_list}
-
-    # Greedy bucket assignment
-    bucket_id: dict[fx.Node, int] = {}
-    cur_b, cur_sz = 0, 0
-    for pn in param_ph_list:
-        sz = param_sizes[pn]
-        if cur_sz + sz > bucket_size_bytes and cur_sz > 0:
-            cur_b += 1
-            cur_sz = 0
-        bucket_id[pn] = cur_b
-        cur_sz += sz
-    n_init = cur_b + 1
-
-    # Singleton promotion
-    def _init_range(b: int) -> tuple[int, int]:
-        ms = [pn for pn in param_ph_list if bucket_id[pn] == b]
-        if not ms:
-            return (0, 0)
-        return (min(param_ranges[pn][0] for pn in ms), max(param_ranges[pn][1] for pn in ms))
-
-    init_ranges = [_init_range(b) for b in range(n_init)]
-
-    def _bucket_at(idx: int) -> int:
-        for b, (lo, hi) in enumerate(init_ranges):
-            if lo <= idx <= hi:
-                return b
-        return 0
-
-    sn = n_init
-    for pn in param_ph_list:
-        f, l = param_ranges[pn]
-        if _bucket_at(f) != _bucket_at(l):
-            bucket_id[pn] = sn
-            sn += 1
-
-    # Build bucket list, sort, merge overlapping ranges
-    bm: dict[int, list[fx.Node]] = defaultdict(list)
-    for pn in param_ph_list:
-        bm[bucket_id[pn]].append(pn)
-
-    blist: list[tuple[int, int, list[fx.Node]]] = []
-    for members in bm.values():
-        f = min(param_ranges[pn][0] for pn in members)
-        l = max(param_ranges[pn][1] for pn in members)
-        blist.append((f, l, members))
-    blist.sort(key=lambda t: t[0])
-
-    merged: list[tuple[int, int, list[fx.Node]]] = []
-    for f, l, ms in blist:
-        if merged and f <= merged[-1][1]:
-            pf, pl, pm = merged[-1]
-            merged[-1] = (pf, max(pl, l), pm + ms)
-        else:
-            merged.append((f, l, ms))
-
-    def _bucket_bytes(members: list[fx.Node]) -> int:
-        return sum(param_sizes[pn] for pn in members)
-
-    def _format_members(members: list[fx.Node], *, limit: int = 8) -> str:
-        def _one(pn: fx.Node) -> str:
-            return f"{pn.name}:{param_sizes[pn]}"
-
-        if len(members) <= limit:
-            return "[" + ", ".join(_one(pn) for pn in members) + "]"
-        head = ", ".join(_one(pn) for pn in members[:4])
-        tail = ", ".join(_one(pn) for pn in members[-2:])
-        return f"[{head}, ... ({len(members) - 6} more), {tail}]"
-
-    def _log_bucket_plan(phase: str, seg_ranges: list[tuple[int, int, list[fx.Node]]]) -> None:
-        ownership: dict[fx.Node, list[int]] = defaultdict(list)
-        for seg_idx, (_f, _l, members) in enumerate(seg_ranges):
-            for pn in members:
-                ownership[pn].append(seg_idx)
-        missing = [pn.name for pn in param_ph_list if pn not in ownership]
-        duplicates = [
-            (pn.name, buckets)
-            for pn, buckets in ownership.items()
-            if len(buckets) != 1
-        ]
-        total_bytes = sum(param_sizes[pn] for pn in param_ph_list)
-        max_bucket_bytes = max((_bucket_bytes(ms) for _f, _l, ms in seg_ranges), default=0)
-        logger.info(
-            "bucket_stage plan phase=%s node=%s bucket_size_bytes=%d bucket_size_mib=%.6f "
-            "params=%d total_param_bytes=%d initial_buckets=%d final_buckets=%d "
-            "owned_params=%d unique_owned_params=%d missing_params=%d duplicate_params=%d max_bucket_bytes=%d",
-            phase,
-            debug_name,
-            bucket_size_bytes,
-            bucket_size_bytes / (1024 * 1024),
-            len(param_ph_list),
-            total_bytes,
-            n_init,
-            len(seg_ranges),
-            sum(len(ms) for _f, _l, ms in seg_ranges),
-            len(ownership),
-            len(missing),
-            len(duplicates),
-            max_bucket_bytes,
-        )
-        if missing or duplicates:
-            logger.warning(
-                "bucket_stage ownership-invalid phase=%s node=%s missing=%s duplicates=%s",
-                phase,
-                debug_name,
-                missing[:16],
-                duplicates[:16],
-            )
-
-        merge_candidates: list[tuple[int, int, int]] = []
-        for left_idx in range(len(seg_ranges) - 1):
-            left_bytes = _bucket_bytes(seg_ranges[left_idx][2])
-            right_bytes = _bucket_bytes(seg_ranges[left_idx + 1][2])
-            combined = left_bytes + right_bytes
-            if combined <= bucket_size_bytes:
-                merge_candidates.append((left_idx, left_idx + 1, combined))
-
-        for seg_idx, (first, last, members) in enumerate(seg_ranges):
-            bytes_ = _bucket_bytes(members)
-            if bytes_ <= bucket_size_bytes:
-                status = "within_limit"
-            elif len(members) == 1:
-                status = "single_param_over_limit"
-            else:
-                status = "forced_multi_param_over_limit"
-            logger.info(
-                "bucket_stage bucket-plan phase=%s node=%s bucket=%d/%d param_bytes=%d "
-                "params=%d range=%d:%d status=%s members=%s",
-                phase,
-                debug_name,
-                seg_idx,
-                len(seg_ranges),
-                bytes_,
-                len(members),
-                first,
-                last,
-                status,
-                _format_members(members),
-            )
-        if merge_candidates:
-            logger.warning(
-                "bucket_stage fullness-warning phase=%s node=%s mergeable_adjacent_pairs=%s "
-                "bucket_size_bytes=%d",
-                phase,
-                debug_name,
-                merge_candidates[:16],
-                bucket_size_bytes,
-            )
-        else:
-            logger.info(
-                "bucket_stage fullness-ok phase=%s node=%s adjacent_pairs=%d bucket_size_bytes=%d",
-                phase,
-                debug_name,
-                max(0, len(seg_ranges) - 1),
-                bucket_size_bytes,
-            )
-
-    _log_bucket_plan("range_merge", merged)
-
-    alias_methods = {
-        "view", "_unsafe_view", "reshape", "transpose", "permute", "t",
-        "movedim", "moveaxis", "swapdims", "swapaxes",
-        "select", "narrow", "slice", "split", "chunk", "unbind",
-        "unsqueeze", "squeeze", "flatten", "expand", "diagonal",
-        "detach", "alias", "as_strided",
-    }
-    multi_output_alias_methods = {"split", "chunk", "unbind"}
-
-    alias_function_names = {
-        "view", "_unsafe_view", "reshape", "transpose", "permute", "t",
-        "movedim", "moveaxis", "swapdims", "swapaxes",
-        "select", "narrow", "slice", "split", "chunk", "unbind",
-        "unsqueeze", "squeeze", "flatten", "expand", "diagonal",
-        "detach", "alias", "as_strided",
-    }
-    multi_output_alias_function_names = {"split", "chunk", "unbind"}
-    hard_forbid_cross_bucket_prefixes = ("bfloat16_",)
-
-    def _target_name(target: object) -> str | None:
-        if isinstance(target, str):
-            return target
-        return getattr(target, "__name__", None)
-
-    def _alias_passthrough_sources(nd: fx.Node) -> set[fx.Node]:
-        if nd.op == "call_method":
-            if nd.target in alias_methods:
-                base = next(iter(nd.all_input_nodes), None)
-                return set(alias_sources.get(base, set())) if base is not None else set()
-            return set()
-
-        if nd.op != "call_function":
-            return set()
-
-        if nd.target == operator.getitem:
-            base = nd.args[0] if nd.args else None
-            if isinstance(base, fx.Node):
-                return set(alias_sources.get(base, set()))
-            return set()
-
-        name = _target_name(nd.target)
-        if name in alias_function_names:
-            base = next(iter(nd.all_input_nodes), None)
-            return set(alias_sources.get(base, set())) if base is not None else set()
-        return set()
-
-    alias_sources: dict[fx.Node, set[fx.Node]] = {}
-    for nd in nodes:
-        if nd.op == "placeholder":
-            alias_sources[nd] = {nd} if nd in param_ph_set else set()
-            continue
-        if nd.op in ("get_attr", "output"):
-            alias_sources[nd] = set()
-            continue
-        alias_sources[nd] = _alias_passthrough_sources(nd)
-
-    def _compute_seg_metadata(
-        seg_ranges: list[tuple[int, int, list[fx.Node]]]
-    ) -> tuple[dict[fx.Node, int], dict[fx.Node, int], list[list[fx.Node]]]:
-        """Return (node_seg, node_max_user_seg, seg_cross_in) for segment ranges."""
-        cut_after = [seg_ranges[i][1] for i in range(len(seg_ranges) - 1)]
-
-        def _seg_of(idx: int) -> int:
-            return sum(1 for c in cut_after if c < idx)
-
-        node_seg: dict[fx.Node, int] = {}
-        for nd in nodes:
-            if nd.op == "output":
-                node_seg[nd] = len(seg_ranges) - 1
-            elif nd.op == "placeholder":
-                if nd in param_ph_set:
-                    user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
-                    node_seg[nd] = _seg_of(min(user_idxs)) if user_idxs else 0
-                else:
-                    node_seg[nd] = 0
-            elif nd.op == "get_attr":
-                user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
-                node_seg[nd] = _seg_of(min(user_idxs)) if user_idxs else 0
-            else:
-                node_seg[nd] = _seg_of(node_idx[nd])
-
-        node_max_user_seg: dict[fx.Node, int] = {}
-        for nd in nodes:
-            if nd.op == "output":
-                continue
-            node_max_user_seg[nd] = (
-                max(node_seg[u] for u in nd.users) if nd.users else node_seg[nd]
-            )
-
-        seg_cross_in: list[list[fx.Node]] = [[] for _ in range(len(seg_ranges))]
-        for nd in nodes:
-            if nd.op == "output":
-                continue
-            s = node_seg[nd]
-            mu = node_max_user_seg[nd]
-            # Only values that cross a segment boundary must be forwarded.
-            if mu <= s:
-                continue
-            for seg in range(s + 1, mu + 1):
-                seg_cross_in[seg].append(nd)
-
-        return node_seg, node_max_user_seg, seg_cross_in
-
-    n_segs = len(merged)
-    if n_segs == 1:
-        return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))]
-
-    while True:
-        node_seg, node_max_user_seg, seg_cross_in = _compute_seg_metadata(merged)
-        merged_boundary = False
-        for seg in range(1, len(merged)):
-            alias_crossers = [
-                nd for nd in seg_cross_in[seg]
-                if alias_sources.get(nd) or any(
-                    nd.name.startswith(prefix) for prefix in hard_forbid_cross_bucket_prefixes
-                )
-            ]
-            if not alias_crossers:
-                continue
-            pf, _pl, pms = merged[seg - 1]
-            _cf, cl, cms = merged[seg]
-            merged[seg - 1] = (pf, cl, pms + cms)
-            del merged[seg]
-            merged_boundary = True
-            break
-        if not merged_boundary:
-            break
-
-    _log_bucket_plan("final", merged)
-
-    n_segs = len(merged)
-    if n_segs == 1:
-        return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))]
-
-    # Build sub-graphs
-    results: list[tuple[fx.GraphModule, list[int], list[int], list]] = []
-
-    for seg in range(n_segs):
-        sub_g = fx.Graph()
-        remap: dict[fx.Node, fx.Node] = {}
-        new_input_idxs: list[int] = []
-        new_param_idxs: list[int] = []
-        new_graphargs: list = []
-        pos = 0
-
-        def _meta_tensor_for(nd: fx.Node):
-            """Return a meta tensor matching nd's shape/dtype, or None if unavailable."""
-            ev = nd.meta.get("example_value")
-            if ev is None:
-                ev = nd.meta.get("val")
-            if ev is not None and hasattr(ev, "shape"):
-                return torch.empty(ev.shape, dtype=ev.dtype, device="meta", requires_grad=ev.requires_grad)
-            return None
-
-        def _add_ph(nd: fx.Node, name: str, is_input: bool) -> None:
-            nonlocal pos
-            new_ph = sub_g.placeholder(name)
-            new_ph.type = nd.type
-            remap[nd] = new_ph
-            if is_input:
-                new_input_idxs.append(pos)
-                meta = _meta_tensor_for(nd)
-                if meta is None:
-                    # Fallback: use the corresponding entry from the incoming graphargs.
-                    # This handles sub-graphs produced by split_by_a2a whose placeholder
-                    # nodes are freshly created and lack example_value metadata.
-                    i_orig = ph_nodes.index(nd) if nd in ph_nodes else -1
-                    if 0 <= i_orig < len(graphargs):
-                        meta = graphargs[i_orig]
-                new_graphargs.append(meta)
-            else:
-                new_param_idxs.append(pos)
-                i_orig = ph_nodes.index(nd) if nd in ph_nodes else -1
-                new_graphargs.append(graphargs[i_orig] if 0 <= i_orig < len(graphargs) else None)
-            pos += 1
-
-        if seg == 0:
-            # Non-param placeholders (activation inputs + other non-param inputs).
-            for nd in nodes:
-                if nd.op == "placeholder" and nd not in param_ph_set:
-                    _add_ph(nd, nd.name, is_input=(nd in input_ph_set))
-        else:
-            # Cross-segment activation inputs from the previous segment.
-            for orig in seg_cross_in[seg]:
-                _add_ph(orig, f"_xseg_{orig.name}", is_input=True)
-
-        # Parameter placeholders belonging to this segment.
-        for nd in nodes:
-            if nd.op == "placeholder" and nd in param_ph_set and node_seg[nd] == seg:
-                _add_ph(nd, nd.name, is_input=False)
-
-        # get_attr nodes for this segment.
-        for nd in nodes:
-            if nd.op == "get_attr" and node_seg[nd] == seg:
-                new_ga = sub_g.get_attr(nd.target)
-                new_ga.type = nd.type
-                remap[nd] = new_ga
-
-        # Compute nodes for this segment (graph order preserved).
-        for nd in nodes:
-            if nd.op in ("placeholder", "get_attr", "output"):
-                continue
-            if node_seg[nd] != seg:
-                continue
-            remap[nd] = sub_g.node_copy(nd, arg_transform=lambda x, r=remap: r[x])
-
-        # Output node.
-        if seg == n_segs - 1:
-            orig_out = next(nd for nd in nodes if nd.op == "output")
-            sub_g.output(fx.map_arg(orig_out.args[0], lambda x: remap[x]))
-        else:
-            out_nodes = [remap[orig] for orig in seg_cross_in[seg + 1]]
-            sub_g.output(tuple(out_nodes) if len(out_nodes) != 1 else out_nodes[0])
-
-        sub_g.lint()
-        results.append((fx.GraphModule(stage_gm, sub_g), new_input_idxs, new_param_idxs, new_graphargs))
-
-    return results
-
-
-# ---------------------------------------------------------------------------
-# split_by_a2a: split an FX GraphModule at all-to-all annotation boundaries
-# ---------------------------------------------------------------------------
-
-def split_by_a2a(
-    stage_gm: fx.GraphModule,
-    graphargs: list,
-    input_idxs: list[int],
-    param_idxs: list[int],
-) -> tuple[list[tuple], list[dict]]:
-    """Split a stage GraphModule at all-to-all annotation boundaries.
-
-    Each all-to-all annotation (``node.meta['custom']['collective'] ==
-    'all_to_all_single'``) marks a tensor that will be communicated via an
-    expert-parallel all-to-all operation.  The annotations must come in pairs;
-    the graph is cut at each boundary so that the A2A operation itself is
-    handled at the DAG level (FWD_A2A / BWD_A2A task types) rather than being
-    embedded in the FX graph.
-
-    Args:
-        stage_gm: The stage ``fx.GraphModule`` to split.
-        graphargs: Flat arg list for ``stage_gm.forward``.
-        input_idxs: Positions of activation-input placeholders.
-        param_idxs: Positions of parameter placeholders.
-
-    Returns:
-        ``(segments, boundary_infos)`` where:
-
-        * *segments* is a list of ``(gm, input_idxs, param_idxs, graphargs)``
-          tuples in execution order.  If no A2A annotations are found the
-          original stage is returned as a single-element list with an empty
-          *boundary_infos*.
-
-        * *boundary_infos* is a list of dicts, one per A2A boundary (length ==
-          number of annotation blocks).  Each dict has keys:
-
-          - ``"tensor_idx"`` (int): index in the output tuple of the preceding
-            segment of the tensor to communicate.
-          - ``"reshape_input"`` (tuple | None): reshape the tensor to this
-            shape before the A2A send.
-          - ``"reshape_output"`` (tuple | None): reshape the A2A result to
-            this shape before feeding the next segment.
-
-    Raises:
-        ValueError: If the number of A2A annotation blocks is not even
-            (annotations must come in pairs).
-    """
-    nodes = list(stage_gm.graph.nodes)
-    node_idx: dict[fx.Node, int] = {nd: i for i, nd in enumerate(nodes)}
-    ph_nodes = [nd for nd in nodes if nd.op == "placeholder"]
-    param_ph_set = {ph_nodes[i] for i in param_idxs if i < len(ph_nodes)}
-    input_ph_set = {ph_nodes[i] for i in input_idxs if i < len(ph_nodes)}
-
-    # ------------------------------------------------------------------
-    # 1. Find all A2A annotation blocks (same logic as _insert_a2a_ops)
-    # ------------------------------------------------------------------
-    annotated_nodes = []
-    for idx, node in enumerate(nodes):
-        # Skip non-compute nodes: get_attr (parameters) and placeholders may
-        # inherit annotations from the enclosing `with annotate(...)` block
-        # but are not A2A compute boundaries.
-        if node.op in ("placeholder", "get_attr", "output"):
-            continue
-        custom = node.meta.get("custom")
-        if isinstance(custom, dict) and custom.get("collective") == "all_to_all_single":
-            reshape = custom.get("reshape")
-            annotation_key = tuple(sorted(custom.items()))
-            annotated_nodes.append((idx, node, annotation_key, reshape))
-
-    if not annotated_nodes:
-        return [(stage_gm, list(input_idxs), list(param_idxs), list(graphargs))], []
-
-    # Group CONSECUTIVE annotated nodes with the same annotation_key into blocks,
-    # but only if they are graph-adjacent (no other compute nodes between them).
-    # Adjacency is required to handle models where all A2A annotations share the
-    # same key (e.g. dispatch and gather both tagged "all_to_all_single"): without
-    # the adjacency check they would all merge into one block.
-    # The grouping still handles the case where a single A2A boundary spans
-    # multiple truly-adjacent nodes (e.g. reshape + contiguous inside one
-    # `with annotate(...)` block).
-    annotated_idx_set = {idx for idx, _, _, _ in annotated_nodes}
-    blocks: list = []
-    for idx, node, annotation_key, reshape in annotated_nodes:
-        if blocks and blocks[-1]["annotation_key"] == annotation_key:
-            prev_last_idx = blocks[-1]["last_idx"]
-            # Only merge if no non-annotated compute nodes exist in the gap.
-            gap_has_compute = any(
-                nodes[j].op not in ("placeholder", "get_attr", "output")
-                and j not in annotated_idx_set
-                for j in range(prev_last_idx + 1, idx)
-            )
-            if not gap_has_compute:
-                blocks[-1]["nodes"].append((idx, node))
-                blocks[-1]["last_idx"] = idx
-                continue
-        blocks.append({
-            "nodes": [(idx, node)],
-            "last_idx": idx,
-            "reshape": reshape,
-            "annotation_key": annotation_key,
-        })
-    n_boundaries = len(blocks)
-
-    if n_boundaries % 2 != 0:
-        raise ValueError(
-            f"split_by_a2a: expected an even number of A2A annotation blocks "
-            f"(got {n_boundaries}).  Annotations must come in pairs.\n"
-            f"Blocks: " + ", ".join(
-                f"[{' '.join(nd.name for _, nd in b['nodes'])}]" for b in blocks
-            )
-        )
-
-    # ------------------------------------------------------------------
-    # 2. For each block, find the "boundary node" — the block node that
-    #    has users outside the block.  This becomes the A2A output tensor.
-    # ------------------------------------------------------------------
-    boundary_nodes: list[tuple[fx.Node, tuple | None, tuple | None]] = []
-    for block in blocks:
-        block_node_set = {nd for _, nd in block["nodes"]}
-        reshape_hint = block["reshape"]
-        reshape_input = None
-        reshape_output = None
-        if isinstance(reshape_hint, (list, tuple)) and len(reshape_hint) == 2:
-            kind, shape = reshape_hint
-            if kind == "input":
-                reshape_input = tuple(shape)
-            elif kind == "output":
-                reshape_output = tuple(shape)
-
-        # Find output node of the block
-        output_node = None
-        for _, nd in block["nodes"]:
-            for user in nd.users:
-                if user not in block_node_set:
-                    output_node = nd
-                    break
-            if output_node is not None:
-                break
-        if output_node is None:
-            # Fallback: last node in the block by graph position
-            output_node = max((nd for _, nd in block["nodes"]),
-                              key=lambda n: node_idx[n])
-        boundary_nodes.append((output_node, reshape_input, reshape_output))
-
-    # ------------------------------------------------------------------
-    # 3. Assign each node to a segment.
-    #    Segments are separated by boundary_nodes.  Node `n` goes into
-    #    segment `s` where `s` is the number of boundary nodes that appear
-    #    strictly before `n` in graph order (i.e. boundary node itself is in
-    #    the segment BEFORE the cut).
-    # ------------------------------------------------------------------
-    boundary_positions = [node_idx[bn] for bn, _, _ in boundary_nodes]
-    # boundary_positions[i] is the graph index of the i-th boundary node.
-    # Segment i contains nodes with graph index in [prev_cut+1, boundary_pos_i].
-    # Segment n_boundaries contains all nodes after the last boundary.
-    n_segs = n_boundaries + 1
-
-    compute_set: frozenset[int] = frozenset(
-        i for i, nd in enumerate(nodes)
-        if nd.op not in ("placeholder", "get_attr", "output")
-    )
-
-    def _seg_of_idx(idx: int) -> int:
-        """Return the segment index for a graph-node at position idx."""
-        s = 0
-        for bp in boundary_positions:
-            if idx > bp:
-                s += 1
-            else:
-                break
-        return s
-
-    node_seg: dict[fx.Node, int] = {}
-    for nd in nodes:
-        if nd.op == "output":
-            node_seg[nd] = n_segs - 1
-        elif nd.op == "placeholder":
-            if nd in param_ph_set:
-                user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
-                node_seg[nd] = _seg_of_idx(min(user_idxs)) if user_idxs else 0
-            else:
-                node_seg[nd] = 0
-        elif nd.op == "get_attr":
-            user_idxs = [node_idx[u] for u in nd.users if node_idx[u] in compute_set]
-            node_seg[nd] = _seg_of_idx(min(user_idxs)) if user_idxs else 0
-        else:
-            node_seg[nd] = _seg_of_idx(node_idx[nd])
-
-    # ------------------------------------------------------------------
-    # 4. Compute cross-segment inputs (same pattern as bucket_stage)
-    # ------------------------------------------------------------------
-    node_max_user_seg: dict[fx.Node, int] = {}
-    for nd in nodes:
-        if nd.op == "output":
-            continue
-        node_max_user_seg[nd] = (
-            max(node_seg[u] for u in nd.users) if nd.users else node_seg[nd]
-        )
-
-    seg_cross_in: list[list[fx.Node]] = [[] for _ in range(n_segs)]
-    for nd in nodes:
-        if nd.op in ("get_attr", "output"):
-            continue
-        if nd.op == "placeholder" and nd in param_ph_set:
-            continue
-        s = node_seg[nd]
-        mu = node_max_user_seg[nd]
-        for seg in range(s + 1, mu + 1):
-            seg_cross_in[seg].append(nd)
-
-    # ------------------------------------------------------------------
-    # 5. Build per-segment sub-graphs (same pattern as bucket_stage)
-    # ------------------------------------------------------------------
-    results: list[tuple[fx.GraphModule, list[int], list[int], list]] = []
-    boundary_infos: list[dict] = []
-
-    for seg in range(n_segs):
-        sub_g = fx.Graph()
-        remap: dict[fx.Node, fx.Node] = {}
-        new_input_idxs: list[int] = []
-        new_param_idxs: list[int] = []
-        new_graphargs: list = []
-        pos = 0
-
-        def _meta_tensor_for(nd: fx.Node):
-            ev = nd.meta.get("example_value")
-            if ev is None:
-                ev = nd.meta.get("val")
-            if ev is not None and hasattr(ev, "shape"):
-                return torch.empty(ev.shape, dtype=ev.dtype, device="meta",
-                                   requires_grad=getattr(ev, "requires_grad", False))
-            return None
-
-        def _add_ph(nd: fx.Node, name: str, is_input: bool) -> None:
-            nonlocal pos
-            new_ph = sub_g.placeholder(name)
-            new_ph.type = nd.type
-            remap[nd] = new_ph
-            if is_input:
-                new_input_idxs.append(pos)
-                new_graphargs.append(_meta_tensor_for(nd))
-            else:
-                new_param_idxs.append(pos)
-                i_orig = ph_nodes.index(nd) if nd in ph_nodes else -1
-                new_graphargs.append(graphargs[i_orig] if 0 <= i_orig < len(graphargs) else None)
-            pos += 1
-
-        if seg == 0:
-            for nd in nodes:
-                if nd.op == "placeholder" and nd not in param_ph_set:
-                    _add_ph(nd, nd.name, is_input=(nd in input_ph_set))
-        else:
-            for orig in seg_cross_in[seg]:
-                _add_ph(orig, f"_xseg_{orig.name}", is_input=True)
-
-        for nd in nodes:
-            if nd.op == "placeholder" and nd in param_ph_set and node_seg[nd] == seg:
-                _add_ph(nd, nd.name, is_input=False)
-
-        for nd in nodes:
-            if nd.op == "get_attr" and node_seg[nd] == seg:
-                new_ga = sub_g.get_attr(nd.target)
-                new_ga.type = nd.type
-                remap[nd] = new_ga
-
-        for nd in nodes:
-            if nd.op in ("placeholder", "get_attr", "output"):
-                continue
-            if node_seg[nd] != seg:
-                continue
-            remap[nd] = sub_g.node_copy(nd, arg_transform=lambda x, r=remap: r[x])
-
-        if seg == n_segs - 1:
-            orig_out = next(nd for nd in nodes if nd.op == "output")
-            sub_g.output(fx.map_arg(orig_out.args[0], lambda x: remap[x]))
-        else:
-            # Output is all cross-segment values for the next segment.
-            out_nodes = [remap[orig] for orig in seg_cross_in[seg + 1]]
-            sub_g.output(tuple(out_nodes) if len(out_nodes) != 1 else out_nodes[0])
-
-            # Record which output position holds the A2A boundary tensor.
-            # boundary_nodes[seg] is the A2A output node for the boundary AFTER seg.
-            boundary_nd, reshape_input, reshape_output = boundary_nodes[seg]
-            cross_out_list = seg_cross_in[seg + 1]
-            if boundary_nd in cross_out_list:
-                tensor_idx = cross_out_list.index(boundary_nd)
-            else:
-                raise ValueError(
-                    f"split_by_a2a: A2A boundary node '{boundary_nd.name}' "
-                    f"(segment {seg}) is not in seg_cross_in[{seg + 1}]; "
-                    f"seg_cross_in = {[n.name for n in cross_out_list]}"
-                )
-            boundary_custom = boundary_nd.meta.get("custom") if hasattr(boundary_nd, "meta") else None
-            boundary_name = (
-                boundary_custom.get("name")
-                if isinstance(boundary_custom, dict)
-                else None
-            )
-            boundary_infos.append({
-                "tensor_idx": tensor_idx,
-                "reshape_input": reshape_input,
-                "reshape_output": reshape_output,
-                "name": boundary_name,
-            })
-
-        sub_g.lint()
-        results.append((fx.GraphModule(stage_gm, sub_g), new_input_idxs, new_param_idxs, new_graphargs))
-
-    return results, boundary_infos
diff --git a/src/piper_utils.py b/src/piper_utils.py
deleted file mode 100644
index d410a0b..0000000
--- a/src/piper_utils.py
+++ /dev/null
@@ -1,399 +0,0 @@
-import sys
-import torch
-import inspect
-import logging
-import json, importlib, operator
-import torch.fx as fx
-from typing import Optional
-
-LOG_LEVEL = "INFO"
-
-"""
-Logger utility
-"""
-
-def create_logger(name: str, log_level: str):
-    match log_level:
-        case "DEBUG":
-            log_level = logging.DEBUG
-        case "INFO":
-            log_level = logging.INFO
-        case "WARNING":
-            log_level = logging.WARNING
-        case "ERROR":
-            log_level = logging.ERROR
-
-    logger = logging.getLogger(name)
-    logger.setLevel(log_level)
-
-    if not logger.handlers:
-        handler = logging.StreamHandler(sys.stdout)
-        fmt = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-        handler.setFormatter(logging.Formatter(fmt))
-        logger.addHandler(handler)
-        logger.propagate = False
-
-    return logger
-
-
-
-
-"""
-Piper thread local storage for tracking Piper actors, stages, and microbatches
-"""
-
-class PiperMetadata:
-    actors = dict()
-    visualize_dag: bool = True  # Whether to render per-rank DAG PNGs after compilation
-    output_dir: str = "out"  # Base directory for debug artifacts emitted during runs
-    training_dag = None  # DAG of annotated model segments and transform-inserted nodes
-    per_pp_training_dags = None  # Per-PP-rank DAGs built by the TrainingDAG backend
-    compiled_data_store = None  # Ray actor used to share compiled DAGs across DP ranks
-    schedule_directives: list = []  # Program of DAG transform directives (e.g., place(...))
-    schedule_directives_file: Optional[str] = None  # JSON source for schedule_directives
-    schedule_info: dict = {}  # Derived schedule facts such as pp/dp/mbs
-
-piper_metadata = PiperMetadata()
-
-
-"""
-Serialize/deserialize an fx.GraphModule
-"""
-
-def _encode_arg(a):
-    if isinstance(a, fx.Node):
-        return {"__node__": a.name}
-    if isinstance(a, torch.device):
-        return {"__device__": str(a)}
-    if isinstance(a, torch.dtype):
-        return {"__dtype__": str(a).replace("torch.", "")}
-    if isinstance(a, slice):
-        return {"__slice__": True,
-                "start": _encode_arg(a.start),
-                "stop": _encode_arg(a.stop),
-                "step": _encode_arg(a.step)}
-    if a is Ellipsis:
-        return {"__ellipsis__": True}
-    if isinstance(a, tuple):  # <-- preserve tuples
-        return {"__tuple__": [_encode_arg(x) for x in a]}
-    if isinstance(a, list):
-        return [_encode_arg(x) for x in a]
-    if isinstance(a, dict):
-        return {k: _encode_arg(v) for k, v in a.items()}
-    return a
-
-def _decode_arg(a, name_to_node):
-    if isinstance(a, dict):
-        if "__node__" in a:
-            return name_to_node[a["__node__"]]
-        if "__device__" in a:
-            return torch.device(a["__device__"])
-        if "__dtype__" in a:
-            return getattr(torch, a["__dtype__"])
-        if "__slice__" in a:
-            return slice(
-                _decode_arg(a["start"], name_to_node),
-                _decode_arg(a["stop"], name_to_node),
-                _decode_arg(a["step"], name_to_node),
-            )
-        if "__ellipsis__" in a:
-            return Ellipsis
-        if "__tuple__" in a:  # <-- reconstruct tuples
-            return tuple(_decode_arg(x, name_to_node) for x in a["__tuple__"])
-        # generic dict
-        return {k: _decode_arg(v, name_to_node) for k, v in a.items()}
-    if isinstance(a, list):
-        return [_decode_arg(x, name_to_node) for x in a]
-    return a
-
-def _is_op_overload(obj):
-    # Works across PyTorch versions without importing private types directly
-    return obj.__class__.__module__.startswith("torch._ops") or obj.__class__.__name__.startswith("OpOverload")
-
-def _serialize_target(t):
-    # print("SERIALIZING", t, type(t))
-    # call_method uses a string method name, pass through
-    if isinstance(t, str):
-        return {"kind": "string", "value": t}
-
-    target_str = str(t)
-    if target_str.startswith("torch.ops."):
-            # Remove "torch.ops." prefix to get the path
-            path = target_str[len("torch.ops."):]
-            return {"kind": "torch_op", "path": path}
-
-    # Handle _VariableFunctionsClass
-    if getattr(t, "__module__", "") == "torch._VariableFunctionsClass":
-        public_name = t.__name__
-        if hasattr(torch, public_name):
-            return {"kind": "py_func", "module": "torch", "qualname": public_name}
-        else:
-            raise ValueError(f"No public torch alias for {t}")
-
-    # torch.ops.* (aten, prim, etc.)
-    if _is_op_overload(t) or (getattr(t, "__module__", "").startswith("torch._ops")):
-        return {"kind": "torch_op", "path": str(t)}  # e.g. "aten.add.Tensor" or "aten.add"
-
-    # regular python function or built-in
-    if inspect.isfunction(t) or inspect.isbuiltin(t):
-        mod = inspect.getmodule(t)
-
-        # TODO: This may not be working as expected, since we use another 
-        # workaround during deserialization. 
-        # Check if inspect.getmodule returned torch.ops.* namespace
-        # These should be serialized as torch_op, not py_func
-        if mod is not None:
-            mod_name = mod.__name__
-            if mod_name.startswith("torch.ops"):
-                # This is a torch.ops function, serialize as torch_op
-                target_str = str(t)
-                if target_str.startswith("torch.ops."):
-                    path = target_str[len("torch.ops."):]
-                    return {"kind": "torch_op", "path": path}
-                # If str(t) doesn't work, try to construct path from module and name
-                # e.g., module="torch.ops.higher_order", name="triton_kernel_wrapper_mutation"
-                # -> path="higher_order.triton_kernel_wrapper_mutation"
-                module_parts = mod_name.split(".")
-                if len(module_parts) >= 3:
-                    namespace = module_parts[2]  # e.g., "higher_order"
-                    path = f"{namespace}.{t.__name__}"
-                    return {"kind": "torch_op", "path": path}
-        # Special handling for torch.autograd.Function.apply methods
-        # These are static methods that may have the wrong module in inspect.getmodule
-        if t.__name__ == "apply":
-            qualname = getattr(t, "__qualname__", "")
-            mod = inspect.getmodule(t)
-            
-            # If qualname contains a class name (e.g., "AllToAllSingleFunction.apply")
-            if "." in qualname:
-                class_name = qualname.split(".")[0]
-                # Try to find the class by searching common modules
-                # First try the module where the function was found
-                if mod and hasattr(mod, class_name):
-                    func_class = getattr(mod, class_name)
-                    if isinstance(func_class, type) and issubclass(func_class, torch.autograd.Function):
-                        # Serialize as the Function class with .apply
-                        return {"kind": "py_obj", "module": func_class.__module__, "qualname": func_class.__qualname__ + ".apply"}
-                
-                # If not found, search sys.modules for the class
-                # This handles the case where inspect.getmodule returns torch.autograd.function
-                # instead of the actual module where the Function class is defined
-                import sys
-                for module_name, module_obj in sys.modules.items():
-                    if module_obj is not None and hasattr(module_obj, class_name):
-                        try:
-                            func_class = getattr(module_obj, class_name)
-                            if isinstance(func_class, type) and issubclass(func_class, torch.autograd.Function):
-                                # Serialize as the Function class with .apply
-                                return {"kind": "py_obj", "module": func_class.__module__, "qualname": func_class.__qualname__ + ".apply"}
-                        except (TypeError, AttributeError):
-                            continue
-                
-                # If still not found and we have a qualname, try to import the module directly
-                # based on common patterns (e.g., if class is AllToAllSingleFunction, try src.piper_graph_transform)
-                if class_name.startswith("AllToAll"):
-                    try:
-                        from src import piper_graph_transform
-                        if hasattr(piper_graph_transform, class_name):
-                            func_class = getattr(piper_graph_transform, class_name)
-                            if isinstance(func_class, type) and issubclass(func_class, torch.autograd.Function):
-                                return {"kind": "py_obj", "module": func_class.__module__, "qualname": func_class.__qualname__ + ".apply"}
-                    except (ImportError, AttributeError):
-                        pass
-        
-        mod = inspect.getmodule(t)
-        if mod is None:
-            raise ValueError(f"Cannot serialize function without module: {t}")
-        return {"kind": "py_func", "module": mod.__name__, "qualname": t.__name__}
-
-    # classes or callables rarely appear as call_function targets, but support anyway
-    if inspect.isclass(t):
-        mod = t.__module__
-        return {"kind": "py_obj", "module": mod, "qualname": t.__qualname__}
-
-    # operator functions (already covered by py_func, but ensure resolvable)
-    if t in operator.__dict__.values():
-        return {"kind": "py_func", "module": "operator", "qualname": t.__name__}
-
-    # last resort: try module+name
-    mod = getattr(t, "__module__", None)
-    name = getattr(t, "__name__", None)
-    if mod and name:
-        return {"kind": "py_func", "module": mod, "qualname": name}
-
-    raise NotImplementedError(f"Unsupported target type: {t} ({type(t)})")
-
-def _resolve_qualname(mod, qualname):
-    obj = mod
-    for part in qualname.split("."):
-        obj = getattr(obj, part)
-    return obj
-
-def _deserialize_target(payload):
-    # print("DESERIALIZING", payload, flush=True)
-    kind = payload["kind"]
-
-    if kind == "string":
-        return payload["value"]
-
-    if kind == "py_func":
-        #TODO: this is a workaround to deserialize torch.ops.* functions
-        # if module is torch.ops.*, redirect to torch_op deserialization
-        # this needs to be dealt with during the serialization. 
-        if payload["module"].startswith("torch.ops"):
-            # Reconstruct the path from module and qualname
-            # e.g., module="torch.ops.higher_order", qualname="triton_kernel_wrapper_mutation"
-            # -> path="higher_order.triton_kernel_wrapper_mutation"
-            module_parts = payload["module"].split(".")
-            if len(module_parts) >= 3:
-                namespace = module_parts[2]  # e.g., "higher_order"
-                path = f"{namespace}.{payload['qualname']}"
-                # Recursively call with torch_op payload
-                torch_op_payload = {"kind": "torch_op", "path": path}
-                return _deserialize_target(torch_op_payload)
-        mod = importlib.import_module(payload["module"])
-        return _resolve_qualname(mod, payload["qualname"])
-
-    if kind == "py_obj":
-        mod = importlib.import_module(payload["module"])
-        try:
-            return _resolve_qualname(mod, payload["qualname"])
-        except AttributeError as e:
-            # Provide better error message for debugging
-            raise AttributeError(
-                f"Failed to resolve qualname '{payload['qualname']}' in module '{payload['module']}': {e}"
-            ) from e
-
-    if kind == "torch_op":
-        obj = torch.ops
-        path = payload["path"]
-        try:
-            for part in payload["path"].split("."):
-                obj = getattr(obj, part)
-            return obj
-        except AttributeError:
-                # if operator doesn't exist, try importing implementation module to trigger registration
-                if path.startswith("higher_order."):
-                    op_name = path.split(".", 1)[1]
-                    op_to_module = {
-                        "triton_kernel_wrapper_mutation": "torch._higher_order_ops.triton_kernel_wrap",
-                        "triton_kernel_wrapper_functional": "torch._higher_order_ops.triton_kernel_wrap",
-                    }
-                    if op_name in op_to_module:
-                        try:
-                            # Import to trigger registration
-                            importlib.import_module(op_to_module[op_name])
-                            # Try again
-                            obj = torch.ops
-                            for part in path.split("."):
-                                obj = getattr(obj, part)
-                            return obj
-                        except (ImportError, AttributeError):
-                            pass
-                raise AttributeError(
-                    f"Failed to resolve torch.ops path '{path}'. "
-                    f"Available attributes: {[x for x in dir(obj) if not x.startswith('_')][:20]}"
-                )
-
-    raise NotImplementedError(f"Unknown target kind: {kind}")
-
-def _serialize_graphmodule(gm: fx.GraphModule) -> str:
-    nodes = []
-    for n in gm.graph.nodes:
-        nodes.append({
-            "name": n.name,
-            "op": n.op,
-            "target": _serialize_target(n.target) if n.op in ("call_function", "call_method", "call_module", "get_attr") else None,
-            "args": _encode_arg(n.args),
-            "kwargs": _encode_arg(n.kwargs),
-        })
-
-    # Serialize all direct child modules that are GraphModules
-    submodules = {}
-    for name, module in gm.named_children():
-        if isinstance(module, fx.GraphModule):
-            # Recursively serialize GraphModule submodules
-            submodules[name] = _serialize_graphmodule(module)
-
-    data = {
-        "nodes": nodes,
-        "state_dict": {k: v.detach().cpu().tolist() for k, v in gm.state_dict().items()},
-        # save which device parameters were on, optional:
-        "param_devices": {k: str(v.device) for k, v in gm.state_dict().items()},
-        "submodules": submodules,  # Add serialized submodules
-    }
-    serialized = json.dumps(data, ensure_ascii=False)
-
-    del data
-    del nodes
-
-    return serialized
-
-def _unwrap_output_arg(decoded):
-    # FX stores output as (value,), where value may itself be a tuple.
-    # Inductor expects the inner tuple directly.
-    if isinstance(decoded, (tuple, list)) and len(decoded) == 1 and isinstance(decoded[0], (tuple, list)):
-        return decoded[0]
-    return decoded
-
-def _deserialize_graphmodule(s: str) -> fx.GraphModule:
-    data = json.loads(s)
-    g = fx.Graph()
-    name_to_node = {}
-
-    for n in data["nodes"]:
-        op = n["op"]
-        if op == "placeholder":
-            node = g.placeholder(n["name"])
-        elif op == "output":
-            decoded = _decode_arg(n["args"], name_to_node)
-            node = g.output(_unwrap_output_arg(decoded))
-        elif op == "call_function":
-            target = _deserialize_target(n["target"])
-            args = _decode_arg(n["args"], name_to_node)
-            kwargs = _decode_arg(n["kwargs"], name_to_node)
-            node = g.call_function(target, tuple(args), kwargs)
-        elif op == "call_method":
-            target = _deserialize_target(n["target"])
-            args = _decode_arg(n["args"], name_to_node)
-            kwargs = _decode_arg(n["kwargs"], name_to_node)
-            node = g.call_method(target, tuple(args), kwargs)
-        elif op == "call_module":
-            target = _deserialize_target(n["target"])
-            args = _decode_arg(n["args"], name_to_node)
-            kwargs = _decode_arg(n["kwargs"], name_to_node)
-            node = g.call_module(target, tuple(args), kwargs)
-        elif op == "get_attr":
-            target = _deserialize_target(n["target"])
-            node = g.get_attr(target)
-        else:
-            raise NotImplementedError(f"op {op} not handled")
-        name_to_node[n["name"]] = node
-
-    # Create root module and add submodules before creating GraphModule
-    root_module = torch.nn.Module()
-    
-    # Deserialize and add submodules
-    submodules = data.get("submodules", {})
-    for module_name, serialized_submodule in submodules.items():
-        # Recursively deserialize submodule
-        submodule_gm = _deserialize_graphmodule(serialized_submodule)
-        # Add as direct child module (handles both simple names and nested paths)
-        # For nested paths like "layer.expert_0", we need to create intermediate modules
-        parts = module_name.split(".")
-        if len(parts) == 1:
-            # Simple name, add directly
-            root_module.add_module(module_name, submodule_gm)
-        else:
-            # Nested path, create intermediate modules
-            current = root_module
-            for part in parts[:-1]:
-                if not hasattr(current, part):
-                    current.add_module(part, torch.nn.Module())
-                current = getattr(current, part)
-            current.add_module(parts[-1], submodule_gm)
-    
-    gm = fx.GraphModule(root_module, g)
-    state = {k: torch.tensor(v) for k, v in data["state_dict"].items()}
-    gm.load_state_dict(state, strict=False)
-    return gm
diff --git a/src/runtime.py b/src/runtime.py
new file mode 100644
index 0000000..3c5cc37
--- /dev/null
+++ b/src/runtime.py
@@ -0,0 +1,495 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+import torch
+import torch.distributed as dist
+from concurrent.futures import Future, ThreadPoolExecutor
+
+
+@dataclass
+class BufferStore:
+    """Per-iteration task outputs and their consumer refcounts."""
+
+    task: dict[Any, Any] = field(default_factory=dict)
+    refcounts: dict[Any, int] = field(default_factory=dict)
+
+    def reset(self) -> None:
+        self.task.clear()
+        self.refcounts.clear()
+
+    def init_refcounts(self, dag: Any) -> None:
+        nodes_iter = dag.nodes.values() if isinstance(dag.nodes, dict) else dag.nodes
+        self.refcounts = {
+            node.uid: len(node.data_succs)
+            for node in nodes_iter
+            if node.data_succs
+        }
+
+    def release(self, uid: Any) -> None:
+        remaining = self.refcounts.get(uid)
+        if remaining is None:
+            self.task.pop(uid, None)
+            return
+
+        remaining -= 1
+        if remaining <= 0:
+            self.refcounts.pop(uid, None)
+            self.task.pop(uid, None)
+        else:
+            self.refcounts[uid] = remaining
+
+
+@dataclass
+class EventStore:
+    """CUDA events produced by non-compute DAG tasks during one iteration."""
+
+    recv: dict[Any, Any] = field(default_factory=dict)
+    a2a: dict[Any, Any] = field(default_factory=dict)
+    all_reduce: dict[Any, Any] = field(default_factory=dict)
+    reduce_scatter: dict[Any, Any] = field(default_factory=dict)
+    all_gather: dict[Any, Any] = field(default_factory=dict)
+    backward: dict[Any, Any] = field(default_factory=dict)
+
+    def reset(self) -> None:
+        self.recv.clear()
+        self.a2a.clear()
+        self.all_reduce.clear()
+        self.reduce_scatter.clear()
+        self.all_gather.clear()
+        self.backward.clear()
+
+
+@dataclass
+class BucketState:
+    """Loaded runtime state for one globally unique compute bucket."""
+
+    forward_fn: Any = None
+    forward_args: list[Any] = field(default_factory=list)
+    forward_input_meta: list[Any] = field(default_factory=list)
+    input_idxs: list[int] = field(default_factory=list)
+    param_idxs: list[int] = field(default_factory=list)
+    param_names: list[str] = field(default_factory=list)
+    optimizer: Any = None
+    trainable_param_idxs: list[int] = field(default_factory=list)
+    activation_checkpoint_subgraph_count: int = 1
+
+    flat_params: Any = None
+    flat_grads: Any = None
+    shard_param: Any = None
+    shard_optimizer: Any = None
+    reduce_scatter_grads: Any = None
+    param_shard_info: tuple[int, int, int] | None = None
+    param_view_specs: list[Any] = field(default_factory=list)
+    full_params_fresh: bool = False
+
+    def weights(self) -> list[Any]:
+        return [
+            self.forward_args[idx]
+            for idx in self.param_idxs
+            if self.forward_args[idx] is not None
+        ]
+
+    def trainable_params(self) -> list[Any]:
+        return [
+            self.forward_args[idx]
+            for idx in self.trainable_param_idxs
+            if self.forward_args[idx] is not None
+        ]
+
+
+@dataclass
+class StageStore:
+    """Loaded stage and bucket state owned by one actor."""
+
+    graph_modules: dict[Any, Any] = field(default_factory=dict)
+    buckets: dict[Any, BucketState] = field(default_factory=dict)
+
+    param_sharded_ubids: set[Any] = field(default_factory=set)
+    grad_sharded_ubids: set[Any] = field(default_factory=set)
+    zero_managed_ubids: set[Any] = field(default_factory=set)
+
+    def clear_loaded_modules(self) -> None:
+        self.graph_modules.clear()
+        self.buckets.clear()
+
+    def ensure_bucket(self, ubid: Any) -> BucketState:
+        return self.buckets.setdefault(ubid, BucketState())
+
+    def bucket(self, ubid: Any) -> BucketState:
+        try:
+            return self.buckets[ubid]
+        except KeyError as exc:
+            raise KeyError(f"Unknown bucket_key {ubid!r}") from exc
+
+    def get_bucket(self, ubid: Any) -> BucketState | None:
+        return self.buckets.get(ubid)
+
+
+@dataclass
+class RuntimeState:
+    """Actor-local distributed runtime environment."""
+
+    pp_rank: int
+    dp_rank: int
+    dp_degree: int
+    pp_degree: int
+    world_size: int
+    no_nvtx: bool = False
+    device: str = "cuda"
+    dp_group: Any = None
+    ep_group: Any = None
+    pp_lo_hi: Any = None
+    pp_hi_lo: Any = None
+    streams: dict[str, torch.cuda.Stream] = field(default_factory=dict)
+    pytorch_profiler_enabled: bool = False
+    torch_profiler: Any = None
+
+    @property
+    def global_rank(self) -> int:
+        return self.pp_rank + self.dp_rank * self.pp_degree
+
+    def pipeline_peer_global_rank(self, pp_rank: int) -> int:
+        return pp_rank + self.dp_rank * self.pp_degree
+
+    def stream_id(self, node_or_stream: Any) -> str:
+        if isinstance(node_or_stream, str):
+            return node_or_stream
+        return str(getattr(node_or_stream, "stream", "default_stream"))
+
+    def initialize_streams_for_training_dag(self, training_dag: Any) -> None:
+        stream_ids = {
+            self.stream_id(n)
+            for n in training_dag.nodes.values()
+            if getattr(n, "stream", None) is not None
+        }
+        stream_ids.add("default_stream")
+        self.streams = {
+            stream_id: torch.cuda.Stream(device=self.device)
+            for stream_id in sorted(stream_ids)
+        }
+
+        # Force cuBLAS context initialization on every logical stream used by
+        # this DAG so the first backward pass does not hit lazy CUDA warnings.
+        for stream in self.streams.values():
+            with torch.cuda.stream(stream):
+                w = torch.zeros(4, 4, device=self.device)
+                torch.mm(w, w)
+
+    def stream_for_id(self, stream_id: str) -> torch.cuda.Stream:
+        assert stream_id in self.streams, (
+            f"TrainingDAG referenced stream={stream_id!r}, but load_training_dag "
+            f"initialized only {sorted(self.streams)}"
+        )
+        return self.streams[stream_id]
+
+    def stream_for_task(self, task: Any) -> torch.cuda.Stream:
+        return self.stream_for_id(self.stream_id(task))
+
+    def default_stream(self) -> torch.cuda.Stream:
+        return self.stream_for_id("default_stream")
+
+    def nvtx_push(self, label: str) -> None:
+        if not self.no_nvtx:
+            torch.cuda.nvtx.range_push(label)
+
+    def nvtx_pop(self) -> None:
+        if not self.no_nvtx:
+            torch.cuda.nvtx.range_pop()
+
+
+@dataclass
+class ParamStorage:
+    """ZeRO parameter and gradient storage owned by one actor."""
+
+    runtime: RuntimeState
+    stages: StageStore
+    logger: Any
+    grad_buffer_dtype: torch.dtype = torch.float32
+    cleanup_executor: ThreadPoolExecutor = field(
+        default_factory=lambda: ThreadPoolExecutor(max_workers=1)
+    )
+    pending_param_frees: dict[Any, Future] = field(default_factory=dict)
+    pending_grad_frees: dict[Any, Future] = field(default_factory=dict)
+
+    def clear_param_grads(self) -> None:
+        for bucket in self.stages.buckets.values():
+            for idx in bucket.trainable_param_idxs:
+                param = bucket.forward_args[idx]
+                if param is not None:
+                    param.grad = None
+
+    def zero_grad_buffers(self, stream: torch.cuda.Stream) -> None:
+        with torch.cuda.stream(stream):
+            for bucket in self.stages.buckets.values():
+                if bucket.flat_grads is not None:
+                    bucket.flat_grads.zero_()
+                if bucket.reduce_scatter_grads is not None:
+                    bucket.reduce_scatter_grads.zero_()
+
+    def wait_pending_free(
+        self,
+        pending: dict[Any, Future],
+        ubid: Any | None,
+    ) -> None:
+        if ubid is None:
+            return
+        fut = pending.pop(ubid, None)
+        if fut is not None:
+            fut.result()
+
+    def drain_pending_frees(self) -> None:
+        for pending in (self.pending_param_frees, self.pending_grad_frees):
+            futures = list(pending.values())
+            pending.clear()
+            for fut in futures:
+                fut.result()
+
+    def accumulate_zero_param_grads_to_flat(
+        self,
+        ubid: Any | None,
+        stream: torch.cuda.Stream,
+    ) -> None:
+        if ubid is None or ubid not in self.stages.zero_managed_ubids or self.runtime.dp_degree <= 1:
+            return
+        if ubid in self.stages.grad_sharded_ubids:
+            self.wait_pending_free(self.pending_grad_frees, ubid)
+        bucket = self.stages.bucket(ubid)
+        specs = bucket.param_view_specs
+        if not specs:
+            return
+        with torch.cuda.stream(stream):
+            flat_grads = bucket.flat_grads
+            if flat_grads is None:
+                shard_info = bucket.param_shard_info
+                if shard_info is None:
+                    return
+                _shard_start, shard_size, _orig_numel = shard_info
+                flat_grads = torch.zeros(
+                    shard_size * self.runtime.dp_degree,
+                    dtype=self.grad_buffer_dtype,
+                    device=self.runtime.device,
+                )
+                bucket.flat_grads = flat_grads
+            for param, offset, numel, _shape in specs:
+                grad = param.grad
+                if grad is None:
+                    continue
+                flat_grads[offset:offset + numel].add_(
+                    grad.detach().reshape(-1).to(flat_grads.dtype)
+                )
+                param.grad = None
+
+    def defer_free_full_params(self, ubid: Any | None, evt: torch.cuda.Event) -> None:
+        if ubid is None or ubid not in self.stages.param_sharded_ubids:
+            return
+        self.wait_pending_free(self.pending_param_frees, ubid)
+        self.pending_param_frees[ubid] = self.cleanup_executor.submit(
+            self._wait_then_free_full_params,
+            ubid,
+            evt,
+        )
+
+    def defer_free_full_grads(self, ubid: Any | None, evt: torch.cuda.Event) -> None:
+        if ubid is None or ubid not in self.stages.grad_sharded_ubids:
+            return
+        self.wait_pending_free(self.pending_grad_frees, ubid)
+        self.pending_grad_frees[ubid] = self.cleanup_executor.submit(
+            self._wait_then_free_full_grads,
+            ubid,
+            evt,
+        )
+
+    def _wait_then_free_full_params(self, ubid: Any, evt: torch.cuda.Event) -> None:
+        evt.synchronize()
+        self.free_full_params(ubid)
+
+    def _wait_then_free_full_grads(self, ubid: Any, evt: torch.cuda.Event) -> None:
+        evt.synchronize()
+        self.free_full_grads(ubid)
+
+    def alloc_full_params(self, ubid: Any) -> None:
+        assert ubid is not None, "alloc_full_params requires a non-None ubid"
+        assert ubid in self.stages.param_sharded_ubids, (
+            f"alloc_full_params: ubid={ubid} is not in param_sharded_ubids="
+            f"{self.stages.param_sharded_ubids}"
+        )
+        bucket = self.stages.bucket(ubid)
+        self.wait_pending_free(self.pending_param_frees, ubid)
+        assert bucket.param_shard_info is not None, (
+            f"alloc_full_params: missing param_shard_info for ubid={ubid}"
+        )
+        full = bucket.flat_params
+        assert full is not None, (
+            f"alloc_full_params: missing flat_params buffer for ubid={ubid}"
+        )
+        specs = bucket.param_view_specs
+        assert specs, f"alloc_full_params: missing param_view_specs for ubid={ubid}"
+        storage = full.untyped_storage()
+        required_bytes = full.numel() * full.element_size()
+        storage.resize_(required_bytes)
+        self.logger.debug(
+            f"[alloc_full_params] rank={self.runtime.global_rank} ubid={ubid}: "
+            f"numel={full.numel()} required_bytes={required_bytes} "
+            f"storage_size={storage.size()} fresh={bucket.full_params_fresh}"
+        )
+        for param, offset, numel, shape in specs:
+            param.data = full[offset:offset + numel].view(shape)
+            param.requires_grad_(True)
+        zero_storage = []
+        for i, (param, offset, numel, shape) in enumerate(specs):
+            p_storage = param.untyped_storage()
+            if p_storage.size() == 0:
+                name = bucket.param_names[i] if i < len(bucket.param_names) else f"param{i}"
+                zero_storage.append(
+                    f"{name}: offset={offset} numel={numel} "
+                    f"shape={shape} stride={tuple(param.stride())}"
+                )
+        assert not zero_storage, (
+            f"[alloc_full_params_zero_storage] rank={self.runtime.global_rank} "
+            f"ubid={ubid}: " + " | ".join(zero_storage)
+        )
+
+    def free_full_params(self, ubid: Any) -> None:
+        assert ubid is not None, "free_full_params requires a non-None ubid"
+        assert ubid in self.stages.param_sharded_ubids, (
+            f"free_full_params: ubid={ubid} is not in param_sharded_ubids="
+            f"{self.stages.param_sharded_ubids}"
+        )
+        bucket = self.stages.bucket(ubid)
+        full = bucket.flat_params
+        assert full is not None, (
+            f"free_full_params: missing flat_params buffer for ubid={ubid}"
+        )
+        storage = full.untyped_storage()
+        self.logger.debug(
+            f"[free_full_params] rank={self.runtime.global_rank} ubid={ubid}: "
+            f"storage_size_before={storage.size()} fresh_before={bucket.full_params_fresh}"
+        )
+        storage.resize_(0)
+        bucket.full_params_fresh = False
+
+    def alloc_full_grads(self, ubid: Any, stream: torch.cuda.Stream) -> None:
+        assert ubid is not None, "alloc_full_grads requires a non-None ubid"
+        assert ubid in self.stages.grad_sharded_ubids, (
+            f"alloc_full_grads: ubid={ubid} is not in grad_sharded_ubids="
+            f"{self.stages.grad_sharded_ubids}"
+        )
+        bucket = self.stages.bucket(ubid)
+        self.wait_pending_free(self.pending_grad_frees, ubid)
+        specs = bucket.param_view_specs
+        assert specs, f"alloc_full_grads: missing param_view_specs for ubid={ubid}"
+        shard_info = bucket.param_shard_info
+        assert shard_info is not None, (
+            f"alloc_full_grads: missing param_shard_info for ubid={ubid}"
+        )
+        shard_size = shard_info[1]
+        with torch.cuda.stream(stream):
+            if bucket.flat_grads is None:
+                bucket.flat_grads = torch.zeros(
+                    shard_size * self.runtime.dp_degree,
+                    dtype=self.grad_buffer_dtype,
+                    device=self.runtime.device,
+                )
+            if bucket.reduce_scatter_grads is None:
+                bucket.reduce_scatter_grads = torch.zeros(
+                    shard_size,
+                    dtype=self.grad_buffer_dtype,
+                    device=self.runtime.device,
+                )
+
+    def free_full_grads(self, ubid: Any) -> None:
+        assert ubid is not None, "free_full_grads requires a non-None ubid"
+        assert ubid in self.stages.grad_sharded_ubids, (
+            f"free_full_grads: ubid={ubid} is not in grad_sharded_ubids="
+            f"{self.stages.grad_sharded_ubids}"
+        )
+        bucket = self.stages.bucket(ubid)
+        specs = bucket.param_view_specs
+        assert specs, f"free_full_grads: missing param_view_specs for ubid={ubid}"
+        for param, *_ in specs:
+            param.grad = None
+        bucket.flat_grads = None
+
+    def all_gather_full_params(self, ubid: Any, stream: torch.cuda.Stream) -> int:
+        assert ubid is not None, "all_gather_full_params requires a non-None ubid"
+        if not self._has_trainable_params_for_collective(ubid, "all_gather_full_params"):
+            return 0
+        assert ubid in self.stages.param_sharded_ubids, (
+            f"all_gather_full_params: ubid={ubid} is not in param_sharded_ubids="
+            f"{self.stages.param_sharded_ubids}"
+        )
+        bucket = self.stages.bucket(ubid)
+        self.alloc_full_params(ubid)
+        flat_params = bucket.flat_params
+        shard_in = bucket.shard_param
+        assert flat_params is not None, (
+            f"all_gather_full_params: missing flat_params buffer for ubid={ubid}"
+        )
+        assert shard_in is not None, (
+            f"all_gather_full_params: missing shard_param buffer for ubid={ubid}"
+        )
+        assert not bucket.full_params_fresh, (
+            f"all_gather_full_params: ubid={ubid} dispatched but full params are already fresh; "
+            "DAG is constructing a redundant ALL_GATHER"
+        )
+        with torch.cuda.stream(stream):
+            self.logger.debug(
+                f"[all_gather_begin] rank={self.runtime.global_rank} ubid={ubid}: "
+                f"flat_numel={flat_params.numel()} flat_storage={flat_params.untyped_storage().size()} "
+                f"shard_numel={shard_in.numel()} shard_storage={shard_in.untyped_storage().size()}"
+            )
+            dist.all_gather_into_tensor(flat_params, shard_in, group=self.runtime.dp_group)
+            bucket.full_params_fresh = True
+            self.logger.debug(
+                f"[all_gather_end] rank={self.runtime.global_rank} ubid={ubid}: "
+                f"flat_storage={flat_params.untyped_storage().size()}"
+            )
+            return flat_params.numel() * flat_params.element_size()
+
+    def has_zero_shard_optimizers(self) -> bool:
+        return any(bucket.shard_optimizer is not None for bucket in self.stages.buckets.values())
+
+    def step_zero_shard_optimizers(
+        self,
+        stream: torch.cuda.Stream,
+        reduce_scatter_events: dict[Any, torch.cuda.Event],
+    ) -> None:
+        for evt in reduce_scatter_events.values():
+            stream.wait_event(evt)
+
+        for bucket in self.stages.buckets.values():
+            shard_optim = bucket.shard_optimizer
+            if shard_optim is None:
+                continue
+            shard_param = bucket.shard_param
+            rs_grad = bucket.reduce_scatter_grads
+            if rs_grad is None:
+                continue
+            with torch.cuda.stream(stream):
+                shard_param.grad = rs_grad.to(shard_param.dtype)
+                shard_optim.step()
+            shard_param.grad = None
+
+        for ubid in self.stages.param_sharded_ubids:
+            bucket = self.stages.bucket(ubid)
+            for param, *_ in bucket.param_view_specs:
+                param.grad = None
+            full = bucket.flat_params
+            if full is not None:
+                storage = full.untyped_storage()
+                if storage.size() != 0:
+                    storage.resize_(0)
+            bucket.full_params_fresh = False
+
+    def _has_trainable_params_for_collective(self, ubid: Any, op_name: str) -> bool:
+        bucket = self.stages.get_bucket(ubid)
+        if bucket is not None and bucket.trainable_param_idxs:
+            return True
+        self.logger.warning(
+            "%s: skipping collective for ubid=%s because it has no trainable param indices",
+            op_name,
+            ubid,
+        )
+        return False
diff --git a/src/piper_schedule.py b/src/schedule.py
similarity index 66%
rename from src/piper_schedule.py
rename to src/schedule.py
index cad00e6..dbfd6d8 100644
--- a/src/piper_schedule.py
+++ b/src/schedule.py
@@ -9,13 +9,10 @@ def load_schedule_directives(path: str) -> list[dict]:
     if not isinstance(directives, list):
         raise ValueError(f"schedule directives file must contain a list, got: {type(directives)}")
 
-    for directive in directives:
+    for i, directive in enumerate(directives):
         if not isinstance(directive, dict):
-            continue
-        if "filter" in directive:
-            directive["filter"] = _normalize_filter_list(directive["filter"])
-        if "filters" in directive and isinstance(directive["filters"], list):
-            directive["filters"] = _normalize_order_filters(directive["filters"])
+            raise ValueError(f"schedule directive[{i}] must be an object, got: {type(directive)}")
+        _validate_directive_shape(directive, i)
         if directive.get("op") == "split" and directive.get("num_microbatches") == "__MBS__":
             raise ValueError(
                 "split.num_microbatches must be encoded in the schedule JSON; "
@@ -76,52 +73,33 @@ def derive_schedule_info(directives: list[dict], schedule_path: str) -> dict[str
     }
 
 
-def _normalize_filter_list(spec):
-    # JSON encodes tuples as lists; the schedule normalizer expects list[tuple[tag, value]].
-    if isinstance(spec, list):
-        out = []
-        for item in spec:
-            if (
-                isinstance(item, list)
-                and len(item) == 2
-                and isinstance(item[0], str)
-            ):
-                out.append((item[0], item[1]))
-            else:
-                out.append(item)
-        return out
-    return spec
-
-
-def _is_filter_list(spec):
-    return isinstance(spec, list) and all(
-        isinstance(item, list)
-        and len(item) == 2
-        and isinstance(item[0], str)
-        for item in spec
-    )
-
-
-def _normalize_order_filters(filters):
-    normalized = []
-    for item in filters:
-        if _is_filter_list(item):
-            normalized.append(_normalize_filter_list(item))
-        elif isinstance(item, list):
-            normalized.append([
-                _normalize_filter_list(f) if _is_filter_list(f) else f
-                for f in item
-            ])
-        else:
-            normalized.append(item)
-    return normalized
+def _validate_directive_shape(directive: dict, idx: int) -> None:
+    op = directive.get("op")
+    if op in {"place", "replicate", "shard", "split"}:
+        if not isinstance(directive.get("filter"), dict):
+            raise ValueError(f"{op} directive[{idx}] requires object field 'filter': {directive}")
+        if "filters" in directive:
+            raise ValueError(f"{op} directive[{idx}] does not accept field 'filters': {directive}")
+    elif op == "order":
+        filters = directive.get("filters")
+        if not isinstance(filters, list) or not filters:
+            raise ValueError(f"order directive[{idx}] requires non-empty list field 'filters': {directive}")
+        for group_idx, group in enumerate(filters):
+            if not isinstance(group, list) or not group:
+                raise ValueError(
+                    f"order directive[{idx}] group[{group_idx}] must be a non-empty list: {directive}"
+                )
+            for filter_idx, flt in enumerate(group):
+                if not isinstance(flt, dict):
+                    raise ValueError(
+                        f"order directive[{idx}] group[{group_idx}][{filter_idx}] "
+                        f"must be a filter object: {directive}"
+                    )
+    else:
+        raise ValueError(f"schedule directive[{idx}] has unsupported op: {directive}")
 
 
 def _filter_value(filter_spec, key: str):
     if isinstance(filter_spec, dict):
         return filter_spec.get(key)
-    if isinstance(filter_spec, list):
-        for item in filter_spec:
-            if isinstance(item, tuple) and len(item) == 2 and item[0] == key:
-                return item[1]
     return None
diff --git a/src/state.py b/src/state.py
new file mode 100644
index 0000000..9a923ce
--- /dev/null
+++ b/src/state.py
@@ -0,0 +1,52 @@
+import sys
+import logging
+from typing import Optional
+
+LOG_LEVEL = "INFO"
+
+"""
+Logger utility
+"""
+
+def create_logger(name: str, log_level: str):
+    match log_level:
+        case "DEBUG":
+            log_level = logging.DEBUG
+        case "INFO":
+            log_level = logging.INFO
+        case "WARNING":
+            log_level = logging.WARNING
+        case "ERROR":
+            log_level = logging.ERROR
+
+    logger = logging.getLogger(name)
+    logger.setLevel(log_level)
+
+    if not logger.handlers:
+        handler = logging.StreamHandler(sys.stdout)
+        fmt = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+        handler.setFormatter(logging.Formatter(fmt))
+        logger.addHandler(handler)
+        logger.propagate = False
+
+    return logger
+
+
+
+
+"""
+Piper thread local storage for tracking Piper actors, stages, and microbatches
+"""
+
+class PiperMetadata:
+    actors = dict()
+    visualize_dag: bool = False  # Whether to render per-rank DAG PNGs after compilation
+    output_dir: str = "out"  # Base directory for debug artifacts emitted during runs
+    training_dag = None  # DAG of annotated model segments and transform-inserted nodes
+    per_pp_training_dags = None  # Per-PP-rank DAGs built by the TrainingDAG backend
+    compiled_data_store = None  # Ray actor used to share compiled DAGs across DP ranks
+    schedule_directives: list = []  # Program of DAG transform directives (e.g., place(...))
+    schedule_directives_file: Optional[str] = None  # JSON source for schedule_directives
+    schedule_info: dict = {}  # Derived schedule facts such as pp/dp/mbs
+
+piper_metadata = PiperMetadata()
diff --git a/src/tasks.py b/src/tasks.py
new file mode 100644
index 0000000..d84e582
--- /dev/null
+++ b/src/tasks.py
@@ -0,0 +1,51 @@
+from enum import Enum
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from .dag import TrainingDAGNode
+
+
+class TaskType(Enum):
+    FWD = "forward"
+    BWD = "backward"
+    UPD = "update"
+    BWD_I = "backward_input"
+    BWD_W = "backward_weight"
+    FWD_BWD = "forward_backward"
+    SEND = "send"
+    RECV = "recv"
+    ALL_REDUCE = "all_reduce"
+    REDUCE_SCATTER = "reduce_scatter"
+    ALL_GATHER = "all_gather"
+    FWD_A2A = "forward_a2a"
+    BWD_A2A = "backward_a2a"
+    ORDER_DUMMY = "order_dummy"
+
+
+def training_dag_task_type(node: "TrainingDAGNode") -> TaskType:
+    if node.node_kind == "COMPUTE":
+        if node.compute_subkind == "FWD":
+            return TaskType.FWD
+        if node.compute_subkind == "BWD":
+            return TaskType.BWD
+        if node.compute_subkind == "BWD_I":
+            return TaskType.BWD_I
+        if node.compute_subkind == "BWD_W":
+            return TaskType.BWD_W
+    if node.node_kind == "UPD":
+        return TaskType.UPD
+    if node.node_kind == "SEND_COMM":
+        return TaskType.SEND
+    if node.node_kind == "RECV_COMM":
+        return TaskType.RECV
+    if node.node_kind == "ALL_GATHER_COMM":
+        return TaskType.ALL_GATHER
+    if node.node_kind == "REDUCE_SCATTER_COMM":
+        return TaskType.REDUCE_SCATTER
+    if node.node_kind == "REDUCE_COMM":
+        return TaskType.ALL_REDUCE
+    if node.node_kind == "A2A_COMM":
+        return TaskType.FWD_A2A if node.tag.get("PASS") == "F" else TaskType.BWD_A2A
+    if node.node_kind == "ORDER_DUMMY":
+        return TaskType.ORDER_DUMMY
+    return TaskType.FWD
diff --git a/src/visualization.py b/src/visualization.py
new file mode 100644
index 0000000..1131c6a
--- /dev/null
+++ b/src/visualization.py
@@ -0,0 +1,412 @@
+from __future__ import annotations
+
+import os
+from collections.abc import Sequence
+from contextlib import suppress
+from dataclasses import dataclass
+from html import escape
+from pathlib import Path
+
+from .dag import TrainingDAG, TrainingDAGNode, _topological_levels
+from .ordering import _serial_topological_order
+from .state import LOG_LEVEL, create_logger
+from .tasks import training_dag_task_type
+
+logger = create_logger("visualization", LOG_LEVEL)
+
+_VALID_PASSES = frozenset({"F", "B", "BI", "BW"})
+_PASS_COLOR: dict[str, str] = {
+    "F": "#FFE08A",
+    "B": "#A8E6A1",
+    "BI": "#A9D6FF",
+    "BW": "#C6B6FF",
+}
+
+
+@dataclass(frozen=True)
+class _ScheduleOp:
+    pp: int
+    mb: int
+    pass_: str
+
+
+_ScheduleSlot = list[_ScheduleOp]
+
+
+def _format_tag(tag: dict[str, int | None]) -> str:
+    if not tag:
+        return "(no-tags)"
+    keys = sorted(tag.keys(), key=lambda k: (k != "PP", k != "EP", k))
+    return ", ".join(f"{k}={tag[k]}" for k in keys)
+
+
+def _format_device(device: list[int] | None) -> str:
+    return "None" if device is None else str(list(device))
+
+
+def render_training_dag(training_dag: TrainingDAG, output_path: str = "out/training_dag") -> None:
+    """Render TrainingDAG with node labels as tags and data-dependency edges."""
+    import graphviz
+
+    dot = graphviz.Digraph("TrainingDAG", comment="Training DAG (tag-labelled)")
+    dot.attr(rankdir="LR")
+    topo_levels = _topological_levels(training_dag)
+
+    def _format_node_meta_lines(node: TrainingDAGNode) -> str:
+        keys = (
+            "bucket_key",
+            "zero_alloc_full_grads_before",
+            "zero_free_full_params_after",
+        )
+        lines = [
+            f"{key}={node.node_meta[key]!r}"
+            for key in keys
+            if key in node.node_meta
+        ]
+        return "\\n".join(lines)
+
+    for uid, node in training_dag.nodes.items():
+        topo_label = f"topo={topo_levels.get(uid, '?')}"
+        meta_label = _format_node_meta_lines(node)
+        meta_suffix = f"\\n{meta_label}" if meta_label else ""
+        if node.node_kind in (
+            "SEND_COMM",
+            "RECV_COMM",
+            "REDUCE_COMM",
+            "ALL_GATHER_COMM",
+            "REDUCE_SCATTER_COMM",
+            "A2A_COMM",
+        ):
+            label = (
+                f"{topo_label}\\n{node.node_kind}\\n{_format_tag(node.tag)}\\n"
+                f"{_format_device(node.device)}\\nstream={node.stream}"
+                f"{meta_suffix}"
+            )
+            shape = "box"
+        else:
+            subkind = node.compute_subkind or "COMPUTE"
+            label = (
+                f"{topo_label}\\n{subkind}\\n{_format_tag(node.tag)}\\n{_format_device(node.device)}\\nstream={node.stream}"
+                f"{meta_suffix}"
+            )
+            shape = "ellipse"
+        dot.node(uid, label=label, shape=shape)
+
+    nodes_by_level: dict[int, list[str]] = {}
+    for uid, level in topo_levels.items():
+        nodes_by_level.setdefault(level, []).append(uid)
+    for level, uids in sorted(nodes_by_level.items()):
+        with dot.subgraph(name=f"topo_{level}") as sub:
+            sub.attr(rank="same")
+            for uid in sorted(uids):
+                sub.node(uid)
+    level_reps = [
+        sorted(uids)[0]
+        for _level, uids in sorted(nodes_by_level.items())
+        if uids
+    ]
+    for src_uid, dst_uid in zip(level_reps, level_reps[1:]):
+        dot.edge(src_uid, dst_uid, style="invis", weight="100")
+
+    for edge in training_dag.edges:
+        if edge.dep_kind == "data":
+            dot.edge(edge.src_uid, edge.dst_uid)
+        elif edge.dep_kind == "temporal":
+            dot.edge(edge.src_uid, edge.dst_uid, style="dashed", color="blue")
+
+    out = dot.render(output_path, format="png", cleanup=True)
+    logger.info("TrainingDAG debug graph saved to %s", out)
+
+
+def log_training_dag_dependencies(training_dag: TrainingDAG) -> None:
+    """Log predecessor/successor tags for each TrainingDAG node."""
+    def _sort_key(item: tuple[str, TrainingDAGNode]) -> tuple[int, int]:
+        _uid, node = item
+        if node.node_kind == "COMPUTE":
+            return (int(node.node_meta.get("stage_id", 10**9)), int(node.node_meta.get("segment_id", 10**9)))
+        return (10**9, 10**9)
+
+    for uid, node in sorted(training_dag.nodes.items(), key=_sort_key):
+        pred_tags = [
+            _format_tag(training_dag.nodes[p].tag)
+            for p in sorted(training_dag.preds.get(uid, []), key=lambda x: x)
+        ]
+        succ_tags = [
+            _format_tag(training_dag.nodes[s].tag)
+            for s in sorted(training_dag.succs.get(uid, []), key=lambda x: x)
+        ]
+        logger.debug(
+            "TrainingDAG node=%s kind=%s subkind=%s tag=(%s) device=%s stream=%s preds=[%s] succs=[%s]",
+            uid,
+            node.node_kind,
+            node.compute_subkind,
+            _format_tag(node.tag),
+            _format_device(node.device),
+            node.stream,
+            ", ".join(pred_tags),
+            ", ".join(succ_tags),
+        )
+
+
+def print_training_dag_order(
+    dag: TrainingDAG,
+    label: str = "",
+    rank: int = 0,
+    out_dir: str = "out",
+) -> None:
+    """Write and log the actor dispatch order for a TrainingDAG."""
+    topo_levels = _topological_levels(dag)
+    topo = _serial_topological_order(dag, topo_levels)
+    data_preds: dict[str, list[str]] = {uid: [] for uid in dag.nodes}
+    data_succs: dict[str, list[str]] = {uid: [] for uid in dag.nodes}
+    temporal_preds: dict[str, list[str]] = {uid: [] for uid in dag.nodes}
+    temporal_succs: dict[str, list[str]] = {uid: [] for uid in dag.nodes}
+    for e in dag.edges:
+        if e.dep_kind == "data":
+            data_preds[e.dst_uid].append(e.src_uid)
+            data_succs[e.src_uid].append(e.dst_uid)
+        else:
+            temporal_preds[e.dst_uid].append(e.src_uid)
+            temporal_succs[e.src_uid].append(e.dst_uid)
+
+    header = f"--- TrainingDAG execution order{': ' + label if label else ''} ---"
+    lines = [header]
+    for uid in topo:
+        node = dag.nodes[uid]
+        task_type = training_dag_task_type(node)
+        meta = node.node_meta
+        extra = []
+        for key in ("bucket_key", "fwd_uid", "peer_pp_rank", "from_uid", "to_uid"):
+            if key in meta and meta[key] is not None:
+                extra.append(f"{key}={meta[key]}")
+        extra_str = ("  " + " ".join(extra)) if extra else ""
+        line = (
+            f"  topo={topo_levels[uid]:<3d}  "
+            f"{task_type.value:<14s}  kind={node.node_kind:<19s}  uid={node.uid:<24s}  "
+            f"tag=({_format_tag(node.tag)})  stream={node.stream}{extra_str}  "
+            f"data_preds={sorted(data_preds[node.uid])} data_succs={sorted(data_succs[node.uid])}  "
+            f"temp_preds={sorted(temporal_preds[node.uid])} temp_succs={sorted(temporal_succs[node.uid])}"
+        )
+        lines.append(line)
+
+    lines.append("-" * len(header))
+
+    os.makedirs(out_dir, exist_ok=True)
+    out_path = os.path.join(out_dir, f"training_dag_order_rank{rank}")
+    with open(out_path, "w", encoding="utf-8") as f:
+        f.write("\n".join(lines) + "\n")
+    logger.info("TrainingDAG execution order saved to %s", out_path)
+
+
+def visualize_order_directives(
+    order_directives: list[dict],
+    output_path: str | Path = "out/schedule",
+    fmt: str = "png",
+) -> str:
+    """Render a 2-D schedule grid, one row per physical rank."""
+    rows = _rows_from_order_directives(order_directives)
+    placements = _compute_columns(rows)
+    max_col = max(
+        (col + _slot_width(rows[rank][slot_idx]) - 1 for (rank, slot_idx), col in placements.items()),
+        default=-1,
+    )
+
+    table: list[list[_ScheduleSlot | None]] = [
+        [None for _ in range(max_col + 1)]
+        for _ in rows
+    ]
+    for key, col in placements.items():
+        rank, slot_idx = key
+        table[rank][col] = rows[rank][slot_idx]
+
+    output = Path(output_path)
+    if output.suffix:
+        fmt = output.suffix.lstrip(".")
+        output = output.with_suffix("")
+
+    output.parent.mkdir(parents=True, exist_ok=True)
+    rendered_suffix = f".{fmt}"
+    _cleanup_visualizer_sidecars(output, keep_suffix=rendered_suffix)
+    import graphviz
+
+    dot = graphviz.Digraph("PipelineSchedule")
+    dot.attr(rankdir="LR")
+    dot.node("schedule", label=_schedule_html_table(table), shape="plain")
+    try:
+        return dot.render(str(output), format=fmt, cleanup=True)
+    finally:
+        _cleanup_visualizer_sidecars(output, keep_suffix=rendered_suffix)
+
+
+def _op_width(op: _ScheduleOp) -> int:
+    if op.pass_ == "B":
+        return 2
+    return 1
+
+
+def _slot_width(slot: _ScheduleSlot) -> int:
+    return sum(_op_width(op) for op in slot)
+
+
+def _cleanup_visualizer_sidecars(
+    output_base: Path,
+    keep_suffix: str | None = None,
+) -> None:
+    for suffix in (".dot", ".txt"):
+        if suffix == keep_suffix:
+            continue
+        with suppress(FileNotFoundError):
+            output_base.with_suffix(suffix).unlink()
+
+
+def _rows_from_order_directives(order_directives: list[dict]) -> list[list[_ScheduleSlot]]:
+    rows = []
+    for directive in order_directives:
+        if directive.get("op") != "order":
+            raise ValueError(f"expected order directive, got {directive}")
+        filters = directive.get("filters")
+        if not isinstance(filters, list):
+            raise ValueError(f"order directive requires filters list: {directive}")
+        row: list[_ScheduleSlot] = []
+        for filter_slot in _parse_filter_slots(filters):
+            slot: _ScheduleSlot = []
+            for flt in filter_slot:
+                spec = _filter_to_dict(flt)
+                pass_value = spec["PASS"]
+                if pass_value not in _VALID_PASSES:
+                    raise ValueError(
+                        f"unsupported PASS={pass_value!r}; expected one of {sorted(_VALID_PASSES)}"
+                    )
+                slot.append(_ScheduleOp(pp=int(spec["PP"]), mb=int(spec["MB"]), pass_=pass_value))
+            row.append(slot)
+        rows.append(row)
+    return rows
+
+
+def _parse_filter_slots(filters: Sequence[object]) -> list[list[dict]]:
+    """Group the directive's filter entries into visual schedule slots."""
+    slots: list[list[dict]] = []
+    for item in filters:
+        if not isinstance(item, list) or not item:
+            raise ValueError(f"invalid order filter group: {item}")
+        if not all(isinstance(flt, dict) for flt in item):
+            raise ValueError(f"invalid order filter group: {item}")
+        slots.append(list(item))
+    return slots
+
+
+def _filter_to_dict(flt: object) -> dict:
+    if isinstance(flt, dict):
+        return dict(flt)
+    raise ValueError(f"invalid order filter: {flt}")
+
+
+def _compute_columns(rows: list[list[_ScheduleSlot]]) -> dict[tuple[int, int], int]:
+    key_by_op: dict[tuple[int, int, str], tuple[int, int]] = {}
+    for rank, row in enumerate(rows):
+        for slot_idx, slot in enumerate(row):
+            for op in slot:
+                op_key = (op.pp, op.mb, op.pass_)
+                if op_key in key_by_op:
+                    raise ValueError(f"duplicate scheduled op: {op_key}")
+                key_by_op[op_key] = (rank, slot_idx)
+
+    preds: dict[tuple[int, int], set[tuple[int, int]]] = {
+        (rank, slot_idx): set()
+        for rank, row in enumerate(rows)
+        for slot_idx, _slot in enumerate(row)
+    }
+    succs: dict[tuple[int, int], set[tuple[int, int]]] = {
+        key: set() for key in preds
+    }
+
+    def add_edge(src: tuple[int, int] | None, dst: tuple[int, int] | None) -> None:
+        if src is None or dst is None or src == dst:
+            return
+        preds[dst].add(src)
+        succs[src].add(dst)
+
+    for rank, row in enumerate(rows):
+        for slot_idx in range(1, len(row)):
+            add_edge((rank, slot_idx - 1), (rank, slot_idx))
+
+    for rank, row in enumerate(rows):
+        for slot_idx, slot in enumerate(row):
+            dst = (rank, slot_idx)
+            for op in slot:
+                if op.pass_ == "F":
+                    add_edge(key_by_op.get((op.pp - 1, op.mb, "F")), dst)
+                elif op.pass_ == "B":
+                    add_edge(key_by_op.get((op.pp, op.mb, "F")), dst)
+                    add_edge(key_by_op.get((op.pp + 1, op.mb, "B")), dst)
+                elif op.pass_ == "BI":
+                    add_edge(key_by_op.get((op.pp, op.mb, "F")), dst)
+                    add_edge(key_by_op.get((op.pp + 1, op.mb, "BI")), dst)
+                elif op.pass_ == "BW":
+                    add_edge(key_by_op.get((op.pp, op.mb, "BI")), dst)
+                else:
+                    raise ValueError(f"unsupported pass={op.pass_!r}")
+
+    columns = {key: 0 for key in preds}
+    pending = {key: set(value) for key, value in preds.items()}
+    ready = [key for key, value in pending.items() if not value]
+    visited = 0
+
+    while ready:
+        key = ready.pop(0)
+        visited += 1
+        for succ in sorted(succs[key]):
+            rank, slot_idx = key
+            columns[succ] = max(columns[succ], columns[key] + _slot_width(rows[rank][slot_idx]))
+            pending[succ].discard(key)
+            if not pending[succ]:
+                ready.append(succ)
+
+    if visited != len(preds):
+        blocked = [key for key, value in pending.items() if value]
+        raise ValueError(f"schedule dependencies contain a cycle; blocked={blocked[:8]}")
+    return columns
+
+
+def _schedule_html_table(table: list[list[_ScheduleSlot | None]]) -> str:
+    max_cols = max((len(row) for row in table), default=0)
+    lines = ['<<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="6">']
+    lines.append('<TR><TD BGCOLOR="#eeeeee">rank</TD>')
+    for col in range(max_cols):
+        lines.append(f'<TD BGCOLOR="#eeeeee">t{col}</TD>')
+    lines.append("</TR>")
+    for rank, row in enumerate(table):
+        lines.append(f'<TR><TD BGCOLOR="#eeeeee">r{rank}</TD>')
+        col = 0
+        while col < len(row):
+            slot = row[col]
+            if slot is None:
+                lines.append('<TD WIDTH="72" HEIGHT="36"></TD>')
+                col += 1
+            else:
+                colspan = _slot_width(slot)
+                colspan_attr = f' COLSPAN="{colspan}"' if colspan > 1 else ""
+                width = 72 * colspan
+                if len(slot) == 1:
+                    op = slot[0]
+                    color = _PASS_COLOR[op.pass_]
+                    label = f"{escape(op.pass_)}<BR/>PP{op.pp} MB{op.mb}"
+                    lines.append(
+                        f'<TD BGCOLOR="{color}" WIDTH="{width}" HEIGHT="36"{colspan_attr}>{label}</TD>'
+                    )
+                else:
+                    inner = ['<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="6">']
+                    for op in slot:
+                        color = _PASS_COLOR[op.pass_]
+                        label = f"{escape(op.pass_)}<BR/>PP{op.pp} MB{op.mb}"
+                        inner.append(
+                            f'<TR><TD BGCOLOR="{color}" WIDTH="{width}" HEIGHT="36">{label}</TD></TR>'
+                        )
+                    inner.append("</TABLE>")
+                    lines.append(
+                        f'<TD WIDTH="{width}"{colspan_attr} CELLPADDING="0">{"".join(inner)}</TD>'
+                    )
+                col += colspan
+        lines.append("</TR>")
+    lines.append("</TABLE>>")
+    return "".join(lines)
diff --git a/src/zero.py b/src/zero.py
new file mode 100644
index 0000000..5025128
--- /dev/null
+++ b/src/zero.py
@@ -0,0 +1,369 @@
+from typing import Any
+
+from .dag import (
+    TrainingDAG,
+    TrainingDAGEdge,
+    _has_path,
+    _remove_node_and_incident_edges,
+    _topological_levels,
+)
+from .ordering import _serial_topological_order
+from .state import LOG_LEVEL, create_logger
+
+logger = create_logger("zero", LOG_LEVEL)
+
+
+def _prune_zero_lifetime_metadata(dag: TrainingDAG) -> list[list[str]]:
+    """Prune redundant ZeRO param/grad lifetimes across same-bucket compute chains.
+
+    Returns the union of gradient and parameter chains used for pruning, one
+    list per chain ordered by topo. The caller passes these to
+    ``_add_inter_chain_temporal_edges`` *after*
+    ``resolve_total_order_per_stream`` runs, so the inter-chain check sees the
+    post-stream-serialization topo levels for the gather_stream and
+    reduce_stream nodes.
+    """
+    topo_levels = _topological_levels(dag)
+    topo = _serial_topological_order(dag, topo_levels)
+
+    def _is_compute(uid: str) -> bool:
+        node = dag.nodes[uid]
+        return (
+            node.node_kind == "COMPUTE"
+            and node.compute_subkind in ("FWD", "BWD", "BWD_I", "BWD_W")
+        )
+
+    def _chain_bucket(uid: str) -> Any:
+        return dag.nodes[uid].node_meta.get("bucket_key")
+
+    def _compute_uids_with(topo_order: list[str], predicate) -> list[str]:
+        return [
+            uid for uid in topo_order
+            if uid in dag.nodes and _is_compute(uid) and predicate(uid)
+        ]
+
+    def _has_direct_compute_dependency(src_uid: str, dst_uid: str) -> bool:
+        return any(
+            e.src_uid == src_uid
+            and e.dst_uid == dst_uid
+            and e.dep_kind in ("data", "temporal")
+            and e.src_uid in dag.nodes
+            and e.dst_uid in dag.nodes
+            and _is_compute(e.src_uid)
+            and _is_compute(e.dst_uid)
+            for e in dag.edges
+        )
+
+    def _build_chains(candidates: list[str], topo_order: list[str]) -> list[list[str]]:
+        chains: list[list[str]] = []
+        by_bucket: dict[Any, list[str]] = {}
+        for uid in candidates:
+            by_bucket.setdefault(_chain_bucket(uid), []).append(uid)
+        topo_idx = {uid: i for i, uid in enumerate(topo_order)}
+        for bucket, uids in sorted(by_bucket.items(), key=lambda item: str(item[0])):
+            uid_set = set(uids)
+            links: dict[str, set[str]] = {uid: set() for uid in uids}
+            for e in dag.edges:
+                if (
+                    e.dep_kind in ("data", "temporal")
+                    and e.src_uid in uid_set
+                    and e.dst_uid in uid_set
+                    and _is_compute(e.src_uid)
+                    and _is_compute(e.dst_uid)
+                ):
+                    links[e.src_uid].add(e.dst_uid)
+                    links[e.dst_uid].add(e.src_uid)
+
+            seen: set[str] = set()
+            for uid in uids:
+                if uid in seen:
+                    continue
+                component: list[str] = []
+                stack = [uid]
+                seen.add(uid)
+                while stack:
+                    cur = stack.pop()
+                    component.append(cur)
+                    for nxt in sorted(links[cur], key=lambda u: topo_idx[u]):
+                        if nxt not in seen:
+                            seen.add(nxt)
+                            stack.append(nxt)
+                chains.append(sorted(component, key=lambda u: topo_idx[u]))
+        return chains
+
+    def _data_preds(uid: str) -> list[TrainingDAGEdge]:
+        return [
+            e for e in list(dag.edges)
+            if e.dep_kind == "data" and e.dst_uid == uid
+        ]
+
+    def _data_succs(uid: str) -> list[TrainingDAGEdge]:
+        return [
+            e for e in list(dag.edges)
+            if e.dep_kind == "data" and e.src_uid == uid
+        ]
+
+    def _all_gather_preds(uid: str) -> list[str]:
+        return sorted(
+            e.src_uid
+            for e in dag.edges
+            if e.dep_kind == "data"
+            and e.dst_uid == uid
+            and e.src_uid in dag.nodes
+            and dag.nodes[e.src_uid].node_kind == "ALL_GATHER_COMM"
+        )
+
+    def _reduce_scatter_succs(uid: str) -> list[str]:
+        return sorted(
+            e.dst_uid
+            for e in dag.edges
+            if e.dep_kind == "data"
+            and e.src_uid == uid
+            and e.dst_uid in dag.nodes
+            and dag.nodes[e.dst_uid].node_kind == "REDUCE_SCATTER_COMM"
+        )
+
+    def _remove_all_gather(ag_uid: str, compute_uid: str, bucket: Any) -> None:
+        del compute_uid, bucket
+        if ag_uid not in dag.nodes:
+            return
+        in_ag = _data_preds(ag_uid)
+        out_ag = _data_succs(ag_uid)
+        for ie in in_ag:
+            for oe in out_ag:
+                dag.add_edge(
+                    TrainingDAGEdge(
+                        src_uid=ie.src_uid,
+                        dst_uid=oe.dst_uid,
+                        dep_kind="data",
+                        tensor_name=(oe.tensor_name if oe.tensor_name is not None else ie.tensor_name),
+                    )
+                )
+        _remove_node_and_incident_edges(dag, ag_uid)
+
+    def _remove_reduce_scatter(rs_uid: str, compute_uid: str, bucket: Any) -> None:
+        del compute_uid, bucket
+        if rs_uid not in dag.nodes:
+            return
+        _remove_node_and_incident_edges(dag, rs_uid)
+
+    has_grad_sync = any(
+        n.node_kind in ("REDUCE_COMM", "REDUCE_SCATTER_COMM")
+        for n in dag.nodes.values()
+    )
+    has_all_gathers = any(n.node_kind == "ALL_GATHER_COMM" for n in dag.nodes.values())
+
+    grad_chains: list[list[str]] = []
+    param_chains: list[list[str]] = []
+
+    # Gradient lifetimes: keep one full-grad allocation and one reduce-scatter
+    # per directly-connected same-bucket gradient chain.
+    if has_grad_sync:
+        grad_candidates = _compute_uids_with(
+            topo,
+            lambda uid: bool(dag.nodes[uid].node_meta.get("zero_alloc_full_grads_before"))
+        )
+        if grad_candidates:
+            grad_chains = _build_chains(grad_candidates, topo)
+            covered = {uid for chain in grad_chains for uid in chain}
+            assert covered == set(grad_candidates), (
+                "zero_lifetime_prune: gradient mode did not assign every grad-allocation "
+                f"compute node to a chain; missing={sorted(set(grad_candidates) - covered)}"
+            )
+
+            for chain in grad_chains:
+                bucket = _chain_bucket(chain[0])
+                assert all(_chain_bucket(uid) == bucket for uid in chain), (
+                    f"zero_lifetime_prune: gradient chain has mixed buckets: "
+                    f"{[(uid, _chain_bucket(uid)) for uid in chain]}"
+                )
+                logger.info(
+                    "zero_lifetime_prune_grad_chain bucket=%s nodes=%s keep_alloc_for=%s keep_reduce_scatter_for=%s",
+                    bucket,
+                    chain,
+                    chain[0],
+                    chain[-1],
+                )
+
+                for uid in chain:
+                    rs_uids = _reduce_scatter_succs(uid)
+                    assert rs_uids, (
+                        f"zero_lifetime_prune: gradient chain node {uid} has "
+                        "zero_alloc_full_grads_before but no outgoing reduce-scatter"
+                    )
+
+                for uid in chain[1:]:
+                    dag.nodes[uid].node_meta.pop("zero_alloc_full_grads_before", None)
+                for uid in chain[:-1]:
+                    for rs_uid in _reduce_scatter_succs(uid):
+                        _remove_reduce_scatter(rs_uid, uid, bucket)
+
+                assert dag.nodes[chain[0]].node_meta.get("zero_alloc_full_grads_before"), (
+                    f"zero_lifetime_prune: gradient chain root {chain[0]} lost grad allocation"
+                )
+                for prev_uid, uid in zip(chain, chain[1:]):
+                    assert _has_direct_compute_dependency(prev_uid, uid), (
+                        f"zero_lifetime_prune: gradient chain is not directly linked: "
+                        f"{prev_uid} does not have a direct compute dependency to {uid}; chain={chain}"
+                    )
+                    assert not dag.nodes[uid].node_meta.get("zero_alloc_full_grads_before"), (
+                        f"zero_lifetime_prune: non-root gradient chain node {uid} still allocates grads"
+                    )
+                live_rs = [
+                    (uid, rs_uid)
+                    for uid in chain
+                    for rs_uid in _reduce_scatter_succs(uid)
+                ]
+                assert live_rs, (
+                    f"zero_lifetime_prune: gradient chain has no live reduce-scatter; chain={chain}"
+                )
+                assert all(uid == chain[-1] for uid, _rs_uid in live_rs), (
+                    f"zero_lifetime_prune: only chain tail {chain[-1]} should keep reduce-scatter, "
+                    f"but live reduce-scatters are {live_rs}"
+                )
+
+    # Parameter lifetimes: keep one all-gather and one full-param free per
+    # directly-connected same-bucket parameter chain. This pass intentionally
+    # does not change grad allocation metadata or remove reduce-scatters.
+    if has_all_gathers:
+        topo = _serial_topological_order(dag, _topological_levels(dag))
+        ag_by_compute = {uid: _all_gather_preds(uid) for uid in topo if uid in dag.nodes and _is_compute(uid)}
+        ag_by_compute = {uid: ags for uid, ags in ag_by_compute.items() if ags}
+
+        candidates = _compute_uids_with(topo, lambda uid: uid in ag_by_compute)
+        param_chains = _build_chains(candidates, topo)
+
+        covered = {uid for chain in param_chains for uid in chain}
+        assert covered == set(candidates), (
+            "zero_lifetime_prune: parameter mode did not assign every all-gathered "
+            f"compute node to a chain; missing={sorted(set(candidates) - covered)}"
+        )
+
+        for chain in param_chains:
+            bucket = _chain_bucket(chain[0])
+            assert all(_chain_bucket(uid) == bucket for uid in chain), (
+                f"zero_lifetime_prune: parameter chain has mixed buckets: "
+                f"{[(uid, _chain_bucket(uid)) for uid in chain]}"
+            )
+
+            for uid in chain[1:]:
+                for ag_uid in ag_by_compute[uid]:
+                    _remove_all_gather(ag_uid, uid, bucket)
+
+            tail = chain[-1]
+            for uid in chain:
+                dag.nodes[uid].node_meta.pop("zero_free_full_params_after", None)
+                for rs_uid in _reduce_scatter_succs(uid):
+                    dag.nodes[rs_uid].node_meta.pop("zero_free_full_params_after", None)
+            dag.nodes[tail].node_meta["zero_free_full_params_after"] = True
+
+            remaining_ag = {uid: _all_gather_preds(uid) for uid in chain}
+            assert remaining_ag[chain[0]], (
+                f"zero_lifetime_prune: parameter chain root {chain[0]} lost all-gather"
+            )
+            for prev_uid, uid in zip(chain, chain[1:]):
+                assert _has_direct_compute_dependency(prev_uid, uid), (
+                    f"zero_lifetime_prune: parameter chain is not directly linked: "
+                    f"{prev_uid} does not have a direct compute dependency to {uid}; chain={chain}"
+                )
+                assert not remaining_ag[uid], (
+                    f"zero_lifetime_prune: non-root parameter chain node {uid} still "
+                    f"has all-gather predecessors {remaining_ag[uid]}; chain={chain}"
+                )
+            assert dag.nodes[tail].node_meta.get("zero_free_full_params_after"), (
+                f"zero_lifetime_prune: parameter chain tail {tail} should free params"
+            )
+            for uid in chain[:-1]:
+                assert not dag.nodes[uid].node_meta.get("zero_free_full_params_after"), (
+                    f"zero_lifetime_prune: non-tail parameter chain node {uid} still frees params"
+                )
+            stale_rs_param_frees = [
+                rs_uid
+                for uid in chain
+                for rs_uid in _reduce_scatter_succs(uid)
+                if dag.nodes[rs_uid].node_meta.get("zero_free_full_params_after")
+            ]
+            assert not stale_rs_param_frees, (
+                f"zero_lifetime_prune: reduce-scatters still own param free metadata: "
+                f"{stale_rs_param_frees}; chain={chain}"
+            )
+
+    return grad_chains + param_chains
+
+
+def _add_inter_chain_temporal_edges(
+    dag: TrainingDAG,
+    chains: list[list[str]],
+) -> None:
+    """Force later same-bucket ZeRO chains to wait for earlier bucket frees.
+
+    Parameter chains: prev_tail compute frees full params, next root's
+    all-gather predecessor materializes them. Edge: prev_tail -> ag_uid.
+
+    Gradient chains: prev_tail's reduce-scatter successor frees full grads,
+    next root compute allocates them. Edge: rs_uid -> next_root.
+    """
+    if not chains:
+        return
+
+    topo_levels = _topological_levels(dag)
+
+    def _chain_bucket(uid: str) -> Any:
+        return dag.nodes[uid].node_meta.get("bucket_key")
+
+    def _all_gather_preds(uid: str) -> list[str]:
+        return sorted(
+            e.src_uid
+            for e in dag.edges
+            if e.dep_kind == "data"
+            and e.dst_uid == uid
+            and e.src_uid in dag.nodes
+            and dag.nodes[e.src_uid].node_kind == "ALL_GATHER_COMM"
+        )
+
+    def _reduce_scatter_succs(uid: str) -> list[str]:
+        return sorted(
+            e.dst_uid
+            for e in dag.edges
+            if e.dep_kind == "data"
+            and e.src_uid == uid
+            and e.dst_uid in dag.nodes
+            and dag.nodes[e.dst_uid].node_kind == "REDUCE_SCATTER_COMM"
+        )
+
+    def _maybe_add_edge(src_uid: str, dst_uid: str) -> None:
+        if topo_levels.get(src_uid, 0) < topo_levels.get(dst_uid, 0):
+            return
+        if _has_path(dag, src_uid, dst_uid):
+            return
+        if _has_path(dag, dst_uid, src_uid):
+            return
+        dag.add_edge(TrainingDAGEdge(
+            src_uid=src_uid,
+            dst_uid=dst_uid,
+            dep_kind="temporal",
+            tensor_name=None,
+        ))
+
+    # Group chains by type and bucket, then add type-specific temporal edges
+    # between consecutive same-bucket chains.
+    param_by_bucket: dict[Any, list[list[str]]] = {}
+    grad_by_bucket: dict[Any, list[list[str]]] = {}
+    for chain in chains:
+        bucket = _chain_bucket(chain[0])
+        if _all_gather_preds(chain[0]):
+            param_by_bucket.setdefault(bucket, []).append(chain)
+        if dag.nodes[chain[0]].node_meta.get("zero_alloc_full_grads_before"):
+            grad_by_bucket.setdefault(bucket, []).append(chain)
+
+    for bucket_chains in param_by_bucket.values():
+        bucket_chains.sort(key=lambda c: topo_levels.get(c[0], 0))
+        for prev_chain, next_chain in zip(bucket_chains, bucket_chains[1:]):
+            for ag_uid in _all_gather_preds(next_chain[0]):
+                _maybe_add_edge(prev_chain[-1], ag_uid)
+
+    for bucket_chains in grad_by_bucket.values():
+        bucket_chains.sort(key=lambda c: topo_levels.get(c[0], 0))
+        for prev_chain, next_chain in zip(bucket_chains, bucket_chains[1:]):
+            for rs_uid in _reduce_scatter_succs(prev_chain[-1]):
+                _maybe_add_edge(rs_uid, next_chain[0])
diff --git a/test/models/mixtral-dtensor-a2a.py b/test/models/mixtral-dtensor-a2a.py
deleted file mode 100644
index cfff4c5..0000000
--- a/test/models/mixtral-dtensor-a2a.py
+++ /dev/null
@@ -1,354 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-from dataclasses import dataclass
-from typing import Optional
-import math
-import torch.cuda.nvtx as nvtx
-
-import torch
-import torch.nn as nn
-from torch import Tensor
-from torch.nn import functional as F
-from torch.distributed.tensor import DTensor, Shard, Replicate, DeviceMesh, distribute_tensor
-from torch.distributed import get_rank, get_world_size
-from torch.distributed.tensor import Placement
-
-def distribute_module_parameters(mod: nn.Module, device_mesh: DeviceMesh, placement: list[Placement]) -> None:
-    """Distribute all parameters of a module with the given placement."""
-    for name, param in mod.named_parameters(recurse=False):
-        # Distribute the parameter
-        dist_param = nn.Parameter(
-            distribute_tensor(param, device_mesh, placement)
-        )
-        # Register the distributed parameter back to the module
-        mod.register_parameter(name, dist_param)
-
-def find_multiple(n: int, k: int) -> int:
-    if n % k == 0:
-        return n
-    return n + k - (n % k)
-
-
-@dataclass
-class ModelArgs:
-    block_size: int = 2048
-    vocab_size: int = 32000
-    n_layer: int = 32
-    n_head: int = 32
-    dim: int = 4096
-    intermediate_size: int = None
-    n_local_heads: int = -1
-    head_dim: int = 64
-    rope_base: float = 10000
-    norm_eps: float = 1e-5
-    num_experts: int = 8
-    num_activated_experts: int = 2
-
-    def __post_init__(self):
-        if self.n_local_heads == -1:
-            self.n_local_heads = self.n_head
-        if self.intermediate_size is None:
-            hidden_dim = 4 * self.dim
-            n_hidden = int(2 * hidden_dim / 3)
-            self.intermediate_size = find_multiple(n_hidden, 256)
-        self.head_dim = self.dim // self.n_head
-
-    @classmethod
-    def from_name(cls, name: str):
-        if name in transformer_configs:
-            return cls(**transformer_configs[name])
-        # fuzzy search
-        config = [config for config in transformer_configs if config in str(name).upper() or config in str(name)]
-        assert len(config) == 1, name
-        return cls(**transformer_configs[config[0]])
-
-
-transformer_configs = {
-    "small": dict(block_size=512, n_layer=8, n_head=8, n_local_heads=2, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "medium": dict(block_size=1024, n_layer=32, n_head=32, n_local_heads=8, dim=2048, intermediate_size=3584, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "Mixtral-8x7B-v0.1": dict(block_size=32768, n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-}
-
-class KVCache(nn.Module):
-    def __init__(self, max_batch_size, max_seq_length, n_heads, head_dim, dtype=torch.bfloat16):
-        super().__init__()
-        cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
-        self.register_buffer('k_cache', torch.zeros(cache_shape, dtype=dtype))
-        self.register_buffer('v_cache', torch.zeros(cache_shape, dtype=dtype))
-
-    def update(self, input_pos, k_val, v_val):
-        # input_pos: [S], k_val: [B, H, S, D]
-        assert input_pos.shape[0] == k_val.shape[2]
-
-        k_out = self.k_cache
-        v_out = self.v_cache
-        k_out[:, :, input_pos] = k_val
-        v_out[:, :, input_pos] = v_val
-
-        return k_out, v_out
-
-class Transformer(nn.Module):
-    def __init__(self, config: ModelArgs, device_mesh: DeviceMesh) -> None:
-        super().__init__()
-        self.config = config
-        self.device_mesh = device_mesh
-
-        # Initialize layers normally
-        self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
-        self.layers = nn.ModuleList(TransformerBlock(config, device_mesh) for _ in range(config.n_layer))
-        self.norm = RMSNorm(config.dim, device_mesh, eps=config.norm_eps)
-        self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
-        
-        # Distribute parameters with Replicate placement
-        distribute_module_parameters(self.tok_embeddings, device_mesh, [Replicate()])
-        distribute_module_parameters(self.output, device_mesh, [Replicate()])
-        distribute_module_parameters(self.norm, device_mesh, [Replicate()])
-        for layer in self.layers:
-            distribute_module_parameters(layer, device_mesh, [Replicate()])
-
-        self.freqs_cis: Optional[DTensor] = distribute_tensor(
-            precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base),
-            device_mesh,
-            [Replicate()])
-        self.causal_mask: Optional[DTensor] = distribute_tensor(
-            torch.tril(torch.ones(self.config.block_size, self.config.block_size, dtype=torch.bool)),
-            device_mesh,
-            [Replicate()])
-        
-        self.mask_cache: Optional[Tensor] = None
-        self.max_batch_size = -1
-        self.max_seq_length = -1
-
-    def setup_caches(self, max_batch_size, max_seq_length):
-        assert False, "Should not be called"
-        if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
-            return
-        head_dim = self.config.dim // self.config.n_head
-        max_seq_length = find_multiple(max_seq_length, 8)
-        self.max_seq_length = max_seq_length
-        self.max_batch_size = max_batch_size
-        for b in self.layers:
-            b.attention.kv_cache = KVCache(max_batch_size, max_seq_length, self.config.n_local_heads, head_dim)
-
-        self.freqs_cis: Optional[Tensor] = distribute_tensor(
-            precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base),
-            device_mesh,
-            [Replicate()])        
-        self.causal_mask = distribute_tensor(
-            torch.tril(torch.ones(self.config.block_size, self.config.block_size, dtype=torch.bool)),
-            device_mesh,
-            [Replicate()])
-
-    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
-        assert self.freqs_cis is not None, "Caches must be initialized first"
-        mask = self.causal_mask[None, None, input_pos]
-        freqs_cis = self.freqs_cis[input_pos]
-        x = self.tok_embeddings(idx)
-
-        for i, layer in enumerate(self.layers):
-            x = layer(x, input_pos, freqs_cis, mask)
-        x = self.norm(x)
-        logits = self.output(x)
-        return logits
-
-    @classmethod
-    def from_name(cls, name: str, device_mesh: DeviceMesh):
-        return cls(ModelArgs.from_name(name), device_mesh)
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, config: ModelArgs, device_mesh: DeviceMesh) -> None:
-        super().__init__()
-        self.attention = Attention(config, device_mesh)
-        self.block_sparse_moe = MOEFeedForward(config, device_mesh)
-        self.ffn_norm = RMSNorm(config.dim, device_mesh, config.norm_eps)
-        self.attention_norm = RMSNorm(config.dim, device_mesh, config.norm_eps)
-
-    def forward(self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor) -> Tensor:
-        h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos)
-        out = h + self.block_sparse_moe(self.ffn_norm(h))
-        return out
-
-
-class Attention(nn.Module):
-    def __init__(self, config: ModelArgs, device_mesh: DeviceMesh):
-        super().__init__()
-        assert config.dim % config.n_head == 0
-
-        total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
-        # key, query, value projections for all heads, but in a batch
-        # Initialize layers normally
-        self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
-        self.wo = nn.Linear(config.dim, config.dim, bias=False)
-        
-        # Distribute parameters with Replicate placement
-        distribute_module_parameters(self.wqkv, device_mesh, [Replicate()])
-        distribute_module_parameters(self.wo, device_mesh, [Replicate()])
-        self.kv_cache = None
-
-        self.n_head = config.n_head
-        self.head_dim = config.head_dim
-        self.n_local_heads = config.n_local_heads
-        self.dim = config.dim
-        self._register_load_state_dict_pre_hook(self.load_hook)
-
-    def load_hook(self, state_dict, prefix, *args):
-        if prefix + "wq.weight" in state_dict:
-            wq = state_dict.pop(prefix + "wq.weight")
-            wk = state_dict.pop(prefix + "wk.weight")
-            wv = state_dict.pop(prefix + "wv.weight")
-            state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
-
-    def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
-        bsz, seqlen, _ = x.shape
-
-        kv_size = self.n_local_heads * self.head_dim
-        q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
-
-        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
-        k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
-        v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
-
-        q = apply_rotary_emb(q, freqs_cis)
-        k = apply_rotary_emb(k, freqs_cis)
-
-        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
-
-        if self.kv_cache is not None:
-            k, v = self.kv_cache.update(input_pos, k, v)
-
-        k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
-        v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
-        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
-
-        y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
-
-        y = self.wo(y)
-        return y
-
-
-class ConditionalFeedForward(nn.Module):
-    def __init__(self, config, device_mesh: DeviceMesh):
-        super().__init__()
-        self.w1 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
-        self.w2 = nn.Parameter(torch.empty(config.num_experts, config.dim, config.intermediate_size))
-        self.w3 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
-        
-        self.num_experts = config.num_experts
-        self.device_mesh = device_mesh
-        self.ep_size = device_mesh.size(0)
-        self.num_local_experts = config.num_experts // self.ep_size
-        self.config = config
-        
-        # Distribute parameters with Shard(0) placement
-        distribute_module_parameters(self, device_mesh, [Shard(0)])
-        
-        self.rank = get_rank()
-
-    def forward(self, x: Tensor, dispatch_mask: Tensor, combine_weights: Tensor, capacity: int) -> Tensor:
-        # all to all
-        print(f"x: {x.shape}")
-        x_local = x.to_local()
-        print(f"x_local: {x_local.shape}")
-        expert_inputs = torch.einsum("tec,tm->ecm", dispatch_mask.type_as(x_local[0]).to_local(), x_local) # [E, C, D]
-        buf = torch.empty_like(expert_inputs)
-        torch.distributed.all_to_all_single(buf, expert_inputs, group=self.device_mesh.get_group())
-        expert_inputs = buf.reshape(self.ep_size, self.num_local_experts, -1, self.config.dim)
-
-        x1 = F.silu(torch.einsum('glcd, lmd -> glcm', expert_inputs, self.w1.to_local()))
-        x3 = torch.einsum('glcd, lmd -> glcm', expert_inputs, self.w3.to_local())
-        expert_output =  torch.einsum('glcm, ldm -> glcd', (x1 * x3), self.w2.to_local())
-
-        # all to all
-        expert_output = expert_output.reshape(self.num_experts, -1, self.config.dim)
-        buf = torch.empty_like(expert_output)
-        torch.distributed.all_to_all_single(buf, expert_output, group=self.device_mesh.get_group())
-        
-        combined_output = torch.einsum("tec,ecd->td", combine_weights.to_local(), buf)
-        combined_output = DTensor.from_local(combined_output, self.device_mesh, [Shard(0)])
-        return combined_output
-
-class MOEFeedForward(nn.Module):
-    def __init__(self, config, device_mesh: DeviceMesh) -> None:
-        super().__init__()
-        # Initialize layers normally
-        self.gate = nn.Linear(config.dim, config.num_experts, bias=False)
-        self.cond_ffn = ConditionalFeedForward(config, device_mesh)
-        
-        # Distribute gate parameters with Replicate placement
-        distribute_module_parameters(self.gate, device_mesh, [Replicate()])
-        self.dim = config.dim
-        self.num_activated_experts = config.num_activated_experts
-        self.rank = get_rank()
-        self.device_mesh = device_mesh
-        
-    def forward(self, x: Tensor) -> Tensor:
-        # Save original shape to reshape output back
-        shape = x.shape
-        x = x.view(-1, self.dim)
-        # T = num_tokens, E = num_experts, D = hidden dim, A = activated experts
-        # x: [T, D]
-        scores = self.gate(x) # [T, E]
-        gates = F.softmax(scores, dim=-1)
-        expert_weights, expert_indices = torch.topk(gates, self.num_activated_experts, dim=-1) # [T, A], [T, A]
-        expert_weights /= expert_weights.sum(dim=-1, keepdim=True) # [T, A]
-
-        num_experts = scores.shape[-1]
-        mask = F.one_hot(expert_indices, num_classes=num_experts).sum(dim=1).bool()  # [T, E]
-        capacity = x.shape[0] # // num_experts * 2
-        # print(f"capacity: {capacity}, expert_counts: {mask.sum(dim=0)}")
-        locations = torch.cumsum(mask.to_local(), dim=0) - 1 # [T, E]
-        locations = DTensor.from_local(locations, self.device_mesh, [Shard(0)])
-        locations_sc = F.one_hot(locations * mask, capacity).float() # [T, E, C]
-        combine_weights = torch.einsum("se,sec->sec", gates * mask, locations_sc)
-        dispatch_mask = combine_weights.bool()
-
-        output = self.cond_ffn(x, dispatch_mask, combine_weights, capacity)
-        return output.view(shape)
-
-
-class RMSNorm(nn.Module):
-    def __init__(self, dim: int, device_mesh: DeviceMesh, eps: float = 1e-5):
-        super().__init__()
-        self.eps = eps
-        # Initialize weight normally (ones for RMSNorm)
-        self.weight = nn.Parameter(torch.ones(dim))
-        
-        # Distribute parameters with Replicate placement
-        distribute_module_parameters(self, device_mesh, [Replicate()])
-
-    def _norm(self, x):
-        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
-
-    def forward(self, x: Tensor) -> Tensor:
-        output = self._norm(x.float()).type_as(x)
-        return output * self.weight
-
-
-def precompute_freqs_cis(
-    seq_len: int, n_elem: int, base: int = 10000
-) -> Tensor:
-    freqs = 1.0 / (base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem))
-    t = torch.arange(seq_len, device=freqs.device)
-    freqs = torch.outer(t, freqs)
-    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
-    cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
-    return cache.to(dtype=torch.bfloat16)
-
-
-def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
-    xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
-    freqs_cis = freqs_cis.reshape(1, xshaped.size(1), 1, xshaped.size(3), 2)
-    x_out2 = torch.stack(
-        [
-            xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
-            xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
-        ],
-        -1,
-    )
-
-    x_out2 = x_out2.flatten(3)
-    return x_out2.type_as(x)
\ No newline at end of file
diff --git a/test/models/mixtral-dtensor.py b/test/models/mixtral-dtensor.py
deleted file mode 100644
index fbc1a8b..0000000
--- a/test/models/mixtral-dtensor.py
+++ /dev/null
@@ -1,343 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-from dataclasses import dataclass
-from typing import Optional
-import math
-import torch.cuda.nvtx as nvtx
-
-import torch
-import torch.nn as nn
-from torch import Tensor
-from torch.nn import functional as F
-from torch.distributed.tensor import DTensor, Shard, Replicate, DeviceMesh, distribute_tensor
-from torch.distributed import get_rank, get_world_size
-from torch.distributed.tensor import Placement
-
-def distribute_module_parameters(mod: nn.Module, device_mesh: DeviceMesh, placement: list[Placement]) -> None:
-    """Distribute all parameters of a module with the given placement."""
-    for name, param in mod.named_parameters(recurse=False):
-        # Distribute the parameter
-        dist_param = nn.Parameter(
-            distribute_tensor(param, device_mesh, placement)
-        )
-        # Register the distributed parameter back to the module
-        mod.register_parameter(name, dist_param)
-
-def find_multiple(n: int, k: int) -> int:
-    if n % k == 0:
-        return n
-    return n + k - (n % k)
-
-
-@dataclass
-class ModelArgs:
-    block_size: int = 2048
-    vocab_size: int = 32000
-    n_layer: int = 32
-    n_head: int = 32
-    dim: int = 4096
-    intermediate_size: int = None
-    n_local_heads: int = -1
-    head_dim: int = 64
-    rope_base: float = 10000
-    norm_eps: float = 1e-5
-    num_experts: int = 8
-    num_activated_experts: int = 2
-
-    def __post_init__(self):
-        if self.n_local_heads == -1:
-            self.n_local_heads = self.n_head
-        if self.intermediate_size is None:
-            hidden_dim = 4 * self.dim
-            n_hidden = int(2 * hidden_dim / 3)
-            self.intermediate_size = find_multiple(n_hidden, 256)
-        self.head_dim = self.dim // self.n_head
-
-    @classmethod
-    def from_name(cls, name: str):
-        if name in transformer_configs:
-            return cls(**transformer_configs[name])
-        # fuzzy search
-        config = [config for config in transformer_configs if config in str(name).upper() or config in str(name)]
-        assert len(config) == 1, name
-        return cls(**transformer_configs[config[0]])
-
-
-transformer_configs = {
-    "small": dict(block_size=512, n_layer=8, n_head=8, n_local_heads=2, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "medium": dict(block_size=1024, n_layer=32, n_head=32, n_local_heads=8, dim=2048, intermediate_size=3584, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "Mixtral-8x7B-v0.1": dict(block_size=32768, n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-}
-
-class KVCache(nn.Module):
-    def __init__(self, max_batch_size, max_seq_length, n_heads, head_dim, dtype=torch.bfloat16):
-        super().__init__()
-        cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
-        self.register_buffer('k_cache', torch.zeros(cache_shape, dtype=dtype))
-        self.register_buffer('v_cache', torch.zeros(cache_shape, dtype=dtype))
-
-    def update(self, input_pos, k_val, v_val):
-        # input_pos: [S], k_val: [B, H, S, D]
-        assert input_pos.shape[0] == k_val.shape[2]
-
-        k_out = self.k_cache
-        v_out = self.v_cache
-        k_out[:, :, input_pos] = k_val
-        v_out[:, :, input_pos] = v_val
-
-        return k_out, v_out
-
-class Transformer(nn.Module):
-    def __init__(self, config: ModelArgs, device_mesh: DeviceMesh) -> None:
-        super().__init__()
-        self.config = config
-        self.device_mesh = device_mesh
-
-        # Initialize layers normally
-        self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
-        self.layers = nn.ModuleList(TransformerBlock(config, device_mesh) for _ in range(config.n_layer))
-        self.norm = RMSNorm(config.dim, device_mesh, eps=config.norm_eps)
-        self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
-        
-        # Distribute parameters with Replicate placement
-        distribute_module_parameters(self.tok_embeddings, device_mesh, [Replicate()])
-        distribute_module_parameters(self.output, device_mesh, [Replicate()])
-        distribute_module_parameters(self.norm, device_mesh, [Replicate()])
-        for layer in self.layers:
-            distribute_module_parameters(layer, device_mesh, [Replicate()])
-
-        self.freqs_cis: Optional[DTensor] = distribute_tensor(
-            precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base),
-            device_mesh,
-            [Replicate()])
-        self.causal_mask: Optional[DTensor] = distribute_tensor(
-            torch.tril(torch.ones(self.config.block_size, self.config.block_size, dtype=torch.bool)),
-            device_mesh,
-            [Replicate()])
-        
-        self.mask_cache: Optional[Tensor] = None
-        self.max_batch_size = -1
-        self.max_seq_length = -1
-
-    def setup_caches(self, max_batch_size, max_seq_length):
-        assert False, "Should not be called"
-        if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
-            return
-        head_dim = self.config.dim // self.config.n_head
-        max_seq_length = find_multiple(max_seq_length, 8)
-        self.max_seq_length = max_seq_length
-        self.max_batch_size = max_batch_size
-        for b in self.layers:
-            b.attention.kv_cache = KVCache(max_batch_size, max_seq_length, self.config.n_local_heads, head_dim)
-
-        self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base)
-        self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
-
-    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
-        assert self.freqs_cis is not None, "Caches must be initialized first"
-        mask = self.causal_mask[None, None, input_pos]
-        freqs_cis = self.freqs_cis[input_pos]
-        x = self.tok_embeddings(idx)
-
-        for i, layer in enumerate(self.layers):
-            x = layer(x, input_pos, freqs_cis, mask)
-        x = self.norm(x)
-        logits = self.output(x)
-        return logits
-
-    @classmethod
-    def from_name(cls, name: str, device_mesh: DeviceMesh):
-        return cls(ModelArgs.from_name(name), device_mesh)
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, config: ModelArgs, device_mesh: DeviceMesh) -> None:
-        super().__init__()
-        self.attention = Attention(config, device_mesh)
-        self.block_sparse_moe = MOEFeedForward(config, device_mesh)
-        self.ffn_norm = RMSNorm(config.dim, device_mesh, config.norm_eps)
-        self.attention_norm = RMSNorm(config.dim, device_mesh, config.norm_eps)
-
-    def forward(self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor) -> Tensor:
-        h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos)
-        out = h + self.block_sparse_moe(self.ffn_norm(h))
-        return out
-
-
-class Attention(nn.Module):
-    def __init__(self, config: ModelArgs, device_mesh: DeviceMesh):
-        super().__init__()
-        assert config.dim % config.n_head == 0
-
-        total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
-        # key, query, value projections for all heads, but in a batch
-        # Initialize layers normally
-        self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
-        self.wo = nn.Linear(config.dim, config.dim, bias=False)
-        
-        # Distribute parameters with Replicate placement
-        distribute_module_parameters(self.wqkv, device_mesh, [Replicate()])
-        distribute_module_parameters(self.wo, device_mesh, [Replicate()])
-        self.kv_cache = None
-
-        self.n_head = config.n_head
-        self.head_dim = config.head_dim
-        self.n_local_heads = config.n_local_heads
-        self.dim = config.dim
-        self._register_load_state_dict_pre_hook(self.load_hook)
-
-    def load_hook(self, state_dict, prefix, *args):
-        if prefix + "wq.weight" in state_dict:
-            wq = state_dict.pop(prefix + "wq.weight")
-            wk = state_dict.pop(prefix + "wk.weight")
-            wv = state_dict.pop(prefix + "wv.weight")
-            state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
-
-    def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
-        bsz, seqlen, _ = x.shape
-
-        kv_size = self.n_local_heads * self.head_dim
-        q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
-
-        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
-        k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
-        v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
-
-        q = apply_rotary_emb(q, freqs_cis)
-        k = apply_rotary_emb(k, freqs_cis)
-
-        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
-
-        if self.kv_cache is not None:
-            k, v = self.kv_cache.update(input_pos, k, v)
-
-        k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
-        v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
-        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
-
-        y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
-
-        y = self.wo(y)
-        return y
-
-
-class ConditionalFeedForward(nn.Module):
-    def __init__(self, config, device_mesh: DeviceMesh):
-        super().__init__()
-        self.w1 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
-        self.w2 = nn.Parameter(torch.empty(config.num_experts, config.dim, config.intermediate_size))
-        self.w3 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
-        
-        self.num_experts = config.num_experts
-        self.device_mesh = device_mesh
-        self.ep_size = device_mesh.size(0)
-        self.num_local_experts = config.num_experts // self.ep_size
-        self.config = config
-        
-        # Distribute parameters with Shard(0) placement
-        distribute_module_parameters(self, device_mesh, [Shard(0)])
-        
-        self.rank = get_rank()
-
-    def forward(self, x: Tensor, dispatch_mask: Tensor, combine_weights: Tensor, capacity: int) -> Tensor:
-        """
-        x: [T, D]
-        dispatch_mask: [T, E, C]
-        combine_weights: [T, E, C]
-        capacity: int
-        """
-        # all gather
-        expert_inputs = torch.einsum("tec,td->ecd", dispatch_mask.type_as(x[0]), x) # [E, C, D]
-        # reduce scatter
-        x1 = F.silu(torch.einsum('ecd, emd -> ecm', expert_inputs, self.w1))
-        # reduce scatter
-        x3 = torch.einsum('ecd, emd -> ecm', expert_inputs, self.w3)
-        expert_output =  torch.einsum('ecm, edm -> ecd', (x1 * x3), self.w2)
-        # all gather
-        expert_output = expert_output.redistribute(self.device_mesh, [Replicate()])
-        combined_output = torch.einsum("tec,ecd->td", combine_weights, expert_output)
-        return combined_output
-
-class MOEFeedForward(nn.Module):
-    def __init__(self, config, device_mesh: DeviceMesh) -> None:
-        super().__init__()
-        # Initialize layers normally
-        self.gate = nn.Linear(config.dim, config.num_experts, bias=False)
-        self.cond_ffn = ConditionalFeedForward(config, device_mesh)
-        
-        # Distribute gate parameters with Replicate placement
-        distribute_module_parameters(self.gate, device_mesh, [Replicate()])
-        self.dim = config.dim
-        self.num_activated_experts = config.num_activated_experts
-        self.rank = get_rank()
-        self.device_mesh = device_mesh
-        
-    def forward(self, x: Tensor) -> Tensor:
-        # Save original shape to reshape output back
-        shape = x.shape
-        x = x.view(-1, self.dim)
-        # T = num_tokens, E = num_experts, D = hidden dim, A = activated experts
-        # x: [T, D]
-        scores = self.gate(x) # [T, E]
-        gates = F.softmax(scores, dim=-1)
-        expert_weights, expert_indices = torch.topk(gates, self.num_activated_experts, dim=-1) # [T, A], [T, A]
-        expert_weights /= expert_weights.sum(dim=-1, keepdim=True) # [T, A]
-
-        num_experts = scores.shape[-1]
-        mask = F.one_hot(expert_indices, num_classes=num_experts).sum(dim=1).bool()  # [T, E]
-        capacity = x.shape[0] #// num_experts * 2
-        locations = torch.cumsum(mask.to_local(), dim=0) - 1 # [T, E]
-        locations = DTensor.from_local(locations, self.device_mesh, [Shard(0)])
-        locations_sc = F.one_hot(locations * mask, capacity).float() # [T, E, C]
-        combine_weights = torch.einsum("se,sec->sec", gates * mask, locations_sc)
-        dispatch_mask = combine_weights.bool()
-
-        output = self.cond_ffn(x, dispatch_mask, combine_weights, capacity)
-        return output.view(shape)
-
-
-class RMSNorm(nn.Module):
-    def __init__(self, dim: int, device_mesh: DeviceMesh, eps: float = 1e-5):
-        super().__init__()
-        self.eps = eps
-        # Initialize weight normally (ones for RMSNorm)
-        self.weight = nn.Parameter(torch.ones(dim))
-        
-        # Distribute parameters with Replicate placement
-        distribute_module_parameters(self, device_mesh, [Replicate()])
-
-    def _norm(self, x):
-        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
-
-    def forward(self, x: Tensor) -> Tensor:
-        output = self._norm(x.float()).type_as(x)
-        return output * self.weight
-
-
-def precompute_freqs_cis(
-    seq_len: int, n_elem: int, base: int = 10000
-) -> Tensor:
-    freqs = 1.0 / (base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem))
-    t = torch.arange(seq_len, device=freqs.device)
-    freqs = torch.outer(t, freqs)
-    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
-    cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
-    return cache.to(dtype=torch.bfloat16)
-
-
-def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
-    xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
-    freqs_cis = freqs_cis.reshape(1, xshaped.size(1), 1, xshaped.size(3), 2)
-    x_out2 = torch.stack(
-        [
-            xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
-            xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
-        ],
-        -1,
-    )
-
-    x_out2 = x_out2.flatten(3)
-    return x_out2.type_as(x)
\ No newline at end of file
diff --git a/test/models/mixtral-orig.py b/test/models/mixtral-orig.py
deleted file mode 100644
index b04324b..0000000
--- a/test/models/mixtral-orig.py
+++ /dev/null
@@ -1,288 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-from dataclasses import dataclass
-from typing import Optional
-import math
-import torch.cuda.nvtx as nvtx
-
-import torch
-import torch.nn as nn
-from torch import Tensor
-from torch.nn import functional as F
-from torch.distributed import get_rank, get_world_size
-
-def find_multiple(n: int, k: int) -> int:
-    if n % k == 0:
-        return n
-    return n + k - (n % k)
-
-
-@dataclass
-class ModelArgs:
-    block_size: int = 2048
-    vocab_size: int = 32000
-    n_layer: int = 32
-    n_head: int = 32
-    dim: int = 4096
-    intermediate_size: int = None
-    n_local_heads: int = -1
-    head_dim: int = 64
-    rope_base: float = 10000
-    norm_eps: float = 1e-5
-    num_experts: int = 8
-    num_activated_experts: int = 2
-
-    def __post_init__(self):
-        if self.n_local_heads == -1:
-            self.n_local_heads = self.n_head
-        if self.intermediate_size is None:
-            hidden_dim = 4 * self.dim
-            n_hidden = int(2 * hidden_dim / 3)
-            self.intermediate_size = find_multiple(n_hidden, 256)
-        self.head_dim = self.dim // self.n_head
-
-    @classmethod
-    def from_name(cls, name: str):
-        if name in transformer_configs:
-            return cls(**transformer_configs[name])
-        # fuzzy search
-        config = [config for config in transformer_configs if config in str(name).upper() or config in str(name)]
-        assert len(config) == 1, name
-        return cls(**transformer_configs[config[0]])
-
-
-transformer_configs = {
-    "small": dict(block_size=512, n_layer=8, n_head=8, n_local_heads=2, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "medium": dict(block_size=1024, n_layer=32, n_head=32, n_local_heads=8, dim=2048, intermediate_size=3584, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "Mixtral-8x7B-v0.1": dict(block_size=32768, n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-}
-
-class KVCache(nn.Module):
-    def __init__(self, max_batch_size, max_seq_length, n_heads, head_dim, dtype=torch.bfloat16):
-        super().__init__()
-        cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
-        self.register_buffer('k_cache', torch.zeros(cache_shape, dtype=dtype))
-        self.register_buffer('v_cache', torch.zeros(cache_shape, dtype=dtype))
-
-    def update(self, input_pos, k_val, v_val):
-        # input_pos: [S], k_val: [B, H, S, D]
-        assert input_pos.shape[0] == k_val.shape[2]
-
-        k_out = self.k_cache
-        v_out = self.v_cache
-        k_out[:, :, input_pos] = k_val
-        v_out[:, :, input_pos] = v_val
-
-        return k_out, v_out
-
-class Transformer(nn.Module):
-    def __init__(self, config: ModelArgs) -> None:
-        super().__init__()
-        self.config = config
-
-        # Initialize layers normally
-        self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
-        self.layers = nn.ModuleList(TransformerBlock(config) for _ in range(config.n_layer))
-        self.norm = RMSNorm(config.dim, eps=config.norm_eps)
-        self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
-
-        self.freqs_cis: Optional[Tensor] = precompute_freqs_cis(
-            self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base
-        ).to(f"cuda:{get_rank()}")
-        self.causal_mask: Optional[Tensor] = torch.tril(
-            torch.ones(self.config.block_size, self.config.block_size, dtype=torch.bool)
-        ).to(f"cuda:{get_rank()}")
-        
-        self.mask_cache: Optional[Tensor] = None
-        self.max_batch_size = -1
-        self.max_seq_length = -1
-
-    def setup_caches(self, max_batch_size, max_seq_length):
-        assert False, "Should not be called"
-        if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
-            return
-        head_dim = self.config.dim // self.config.n_head
-        max_seq_length = find_multiple(max_seq_length, 8)
-        self.max_seq_length = max_seq_length
-        self.max_batch_size = max_batch_size
-        for b in self.layers:
-            b.attention.kv_cache = KVCache(max_batch_size, max_seq_length, self.config.n_local_heads, head_dim)
-
-        self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base)
-        self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
-
-    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
-        assert self.freqs_cis is not None, "Caches must be initialized first"
-        mask = self.causal_mask[None, None, input_pos]
-        freqs_cis = self.freqs_cis[input_pos]
-        x = self.tok_embeddings(idx)
-
-        for i, layer in enumerate(self.layers):
-            x = layer(x, input_pos, freqs_cis, mask)
-        x = self.norm(x)
-        logits = self.output(x)
-        return logits
-
-    @classmethod
-    def from_name(cls, name: str):
-        return cls(ModelArgs.from_name(name))
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, config: ModelArgs) -> None:
-        super().__init__()
-        self.attention = Attention(config)
-        self.block_sparse_moe = MOEFeedForward(config)
-        self.ffn_norm = RMSNorm(config.dim, config.norm_eps)
-        self.attention_norm = RMSNorm(config.dim, config.norm_eps)
-
-    def forward(self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor) -> Tensor:
-        h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos)
-        out = h + self.block_sparse_moe(self.ffn_norm(h))
-        return out
-
-
-class Attention(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-        assert config.dim % config.n_head == 0
-
-        total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
-        # key, query, value projections for all heads, but in a batch
-        # Initialize layers normally
-        self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
-        self.wo = nn.Linear(config.dim, config.dim, bias=False)
-        
-        self.kv_cache = None
-
-        self.n_head = config.n_head
-        self.head_dim = config.head_dim
-        self.n_local_heads = config.n_local_heads
-        self.dim = config.dim
-        self._register_load_state_dict_pre_hook(self.load_hook)
-
-    def load_hook(self, state_dict, prefix, *args):
-        if prefix + "wq.weight" in state_dict:
-            wq = state_dict.pop(prefix + "wq.weight")
-            wk = state_dict.pop(prefix + "wk.weight")
-            wv = state_dict.pop(prefix + "wv.weight")
-            state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
-
-    def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
-        bsz, seqlen, _ = x.shape
-
-        kv_size = self.n_local_heads * self.head_dim
-        q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
-
-        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
-        k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
-        v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
-
-        q = apply_rotary_emb(q, freqs_cis)
-        k = apply_rotary_emb(k, freqs_cis)
-
-        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
-
-        if self.kv_cache is not None:
-            k, v = self.kv_cache.update(input_pos, k, v)
-
-        k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
-        v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
-        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
-
-        y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
-
-        y = self.wo(y)
-        return y
-
-
-class ConditionalFeedForward(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.w1 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
-        self.w2 = nn.Parameter(torch.empty(config.num_experts, config.dim, config.intermediate_size))
-        self.w3 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
-        
-        self.num_experts = config.num_experts
-        self.config = config
-        
-        self.rank = get_rank()
-
-    def forward(self, x: Tensor, expert_indices: Tensor, expert_weights: Tensor) -> Tensor:
-        w1_weights = self.w1[expert_indices] # [T, A, D, D]
-        w3_weights = self.w3[expert_indices] # [T, A, D, D]
-        w2_weights = self.w2[expert_indices]  # [T, A, D, D]
-        x1 = F.silu(torch.einsum('ti,taoi -> tao', x, w1_weights))
-        x3 = torch.einsum('ti, taoi -> tao', x, w3_weights)
-        expert_outs =  torch.einsum('tao, taio -> tai', (x1 * x3), w2_weights)
-        combined_output = torch.einsum('tai,ta -> ti', expert_outs, expert_weights)
-        return combined_output
-
-class MOEFeedForward(nn.Module):
-    def __init__(self, config) -> None:
-        super().__init__()
-        # Initialize layers normally
-        self.gate = nn.Linear(config.dim, config.num_experts, bias=False)
-        self.cond_ffn = ConditionalFeedForward(config)
-        
-        self.dim = config.dim
-        self.num_activated_experts = config.num_activated_experts
-        self.rank = get_rank()
-        
-    def forward(self, x: Tensor) -> Tensor:
-        # Save original shape to reshape output back
-        original_shape = x.shape  # [batch_size, seq_len, dim] or [seq_len, dim]
-        x = x.view(-1, self.dim)
-        # T = num_tokens, E = num_experts, D = hidden dim, A = activated experts
-        # x: [T, D]
-        scores = self.gate(x) # [T, E]
-        gates = F.softmax(scores, dim=-1)
-        expert_weights, expert_indices = torch.topk(gates, self.num_activated_experts, dim=-1) # [T, A], [T, A]
-        expert_weights /= expert_weights.sum(dim=-1, keepdim=True) # [T, A]
-
-        output = self.cond_ffn(x, expert_indices, expert_weights)
-        return output.view(original_shape)
-
-
-class RMSNorm(nn.Module):
-    def __init__(self, dim: int, eps: float = 1e-5):
-        super().__init__()
-        self.eps = eps
-        # Initialize weight normally (ones for RMSNorm)
-        self.weight = nn.Parameter(torch.ones(dim))
-
-    def _norm(self, x):
-        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
-
-    def forward(self, x: Tensor) -> Tensor:
-        output = self._norm(x.float()).type_as(x)
-        return output * self.weight
-
-
-def precompute_freqs_cis(
-    seq_len: int, n_elem: int, base: int = 10000
-) -> Tensor:
-    freqs = 1.0 / (base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem))
-    t = torch.arange(seq_len, device=freqs.device)
-    freqs = torch.outer(t, freqs)
-    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
-    cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
-    return cache.to(dtype=torch.bfloat16)
-
-
-def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
-    xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
-    freqs_cis = freqs_cis.reshape(1, xshaped.size(1), 1, xshaped.size(3), 2)
-    x_out2 = torch.stack(
-        [
-            xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
-            xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
-        ],
-        -1,
-    )
-
-    x_out2 = x_out2.flatten(3)
-    return x_out2.type_as(x)
\ No newline at end of file
diff --git a/test/models/mixtral.py b/test/models/mixtral.py
deleted file mode 100644
index c63e913..0000000
--- a/test/models/mixtral.py
+++ /dev/null
@@ -1,356 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-from dataclasses import dataclass
-from typing import Optional
-import math
-import torch.cuda.nvtx as nvtx
-
-import torch
-import torch.nn as nn
-from torch import Tensor
-from torch.nn import functional as F
-from torch.distributed import get_world_size
-
-def find_multiple(n: int, k: int) -> int:
-    if n % k == 0:
-        return n
-    return n + k - (n % k)
-
-
-@dataclass
-class ModelArgs:
-    block_size: int = 2048
-    vocab_size: int = 32000
-    n_layer: int = 32
-    n_head: int = 32
-    dim: int = 4096
-    intermediate_size: int = None
-    n_local_heads: int = -1
-    head_dim: int = 64
-    rope_base: float = 10000
-    norm_eps: float = 1e-5
-    num_experts: int = 8
-    num_activated_experts: int = 2
-
-    def __post_init__(self):
-        if self.n_local_heads == -1:
-            self.n_local_heads = self.n_head
-        if self.intermediate_size is None:
-            hidden_dim = 4 * self.dim
-            n_hidden = int(2 * hidden_dim / 3)
-            self.intermediate_size = find_multiple(n_hidden, 256)
-        self.head_dim = self.dim // self.n_head
-
-    @classmethod
-    def from_name(cls, name: str):
-        if name in transformer_configs:
-            return cls(**transformer_configs[name])
-        # fuzzy search
-        config = [config for config in transformer_configs if config in str(name).upper() or config in str(name)]
-        assert len(config) == 1, name
-        return cls(**transformer_configs[config[0]])
-
-
-transformer_configs = {
-    "30M": dict(block_size=128, n_layer=4, n_head=2, n_local_heads=1, dim=256, intermediate_size=512, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "280M": dict(block_size=512, n_layer=4, n_head=4, n_local_heads=2, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "6B": dict(block_size=512, n_layer=4, n_head=8, n_local_heads=4, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "Mixtral-8x7B-v0.1": dict(block_size=1024, n_layer=2, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    # "Mixtral-8x22B-v0.1": dict(block_size=2048, n_layer=4, n_head=48, n_local_heads=8, dim=6144, intermediate_size=16384, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "Mixtral-8x22B-v0.1": dict(block_size=1024, n_layer=4, n_head=32, n_local_heads=8, dim=2048, intermediate_size=16384, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-}
-
-class KVCache(nn.Module):
-    def __init__(self, max_batch_size, max_seq_length, n_heads, head_dim):
-        super().__init__()
-        cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
-        self.register_buffer('k_cache', torch.zeros(cache_shape, dtype=dtype))
-        self.register_buffer('v_cache', torch.zeros(cache_shape, dtype=dtype))
-
-    def update(self, input_pos, k_val, v_val):
-        # input_pos: [S], k_val: [B, H, S, D]
-        assert input_pos.shape[0] == k_val.shape[2]
-
-        k_out = self.k_cache
-        v_out = self.v_cache
-        k_out[:, :, input_pos] = k_val
-        v_out[:, :, input_pos] = v_val
-
-        return k_out, v_out
-
-class Transformer(nn.Module):
-    def __init__(self, config: ModelArgs) -> None:
-        super().__init__()
-        self.config = config
-
-        # Initialize layers normally
-        self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
-        self.layers = nn.ModuleList(TransformerBlock(config) for _ in range(config.n_layer))
-        self.norm = RMSNorm(config.dim, eps=config.norm_eps)
-        self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
-
-        self.freqs_cis: Optional[Tensor] = precompute_freqs_cis(
-            self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base
-        )
-        self.causal_mask: Optional[Tensor] = torch.tril(
-            torch.ones(self.config.block_size, self.config.block_size, dtype=torch.bool)
-        )
-        
-        self.mask_cache: Optional[Tensor] = None
-        self.max_batch_size = -1
-        self.max_seq_length = -1
-
-    def setup_caches(self, max_batch_size, max_seq_length):
-        assert False, "Should not be called"
-        if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
-            return
-        head_dim = self.config.dim // self.config.n_head
-        max_seq_length = find_multiple(max_seq_length, 8)
-        self.max_seq_length = max_seq_length
-        self.max_batch_size = max_batch_size
-        for b in self.layers:
-            b.attention.kv_cache = KVCache(max_batch_size, max_seq_length, self.config.n_local_heads, head_dim)
-
-        self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base).to(torch.bfloat16)
-        self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
-
-    # def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
-    #     """
-    #     2 Stages
-    #     """
-    #     self.first_arg = idx.shape
-    #     mask = self.causal_mask[None, None, input_pos]
-    #     freqs_cis = self.freqs_cis[input_pos]
-    #     x = self.tok_embeddings(idx)
-    #     for layer in self.layers:
-    #         x = layer(x, input_pos, freqs_cis, mask)
-    #     x = self.norm(x)
-    #     logits = self.output(x)
-    #     return logits
-    
-    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
-        """
-        2 Stages
-        """
-        self.first_arg = idx.shape
-        with torch.fx.traceback.annotate({"stage": 0}):
-            mask = self.causal_mask[None, None, input_pos]
-            freqs_cis = self.freqs_cis[input_pos]
-            x = self.tok_embeddings(idx)
-            for layer in self.layers[:len(self.layers)//2]:
-                x = layer(x, input_pos, freqs_cis, mask)
-        
-        with torch.fx.traceback.annotate({"stage": 1}):
-            for layer in self.layers[len(self.layers)//2:]:
-                x = layer(x, input_pos, freqs_cis, mask)
-            x = self.norm(x)
-            logits = self.output(x)
-        return logits
-
-    # def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
-    #     """
-    #     4 Stages
-    #     """
-    #     self.first_arg = idx.shape
-    #     with torch.fx.traceback.annotate({"stage": 0}):
-    #         mask = self.causal_mask[None, None, input_pos]
-    #         freqs_cis = self.freqs_cis[input_pos]
-    #         x = self.tok_embeddings(idx)
-    #         for layer in self.layers[:len(self.layers)//4]:
-    #             x = layer(x, input_pos, freqs_cis, mask)
-        
-    #     with torch.fx.traceback.annotate({"stage": 1}):
-    #         for layer in self.layers[len(self.layers)//4:len(self.layers)//2]:
-    #             x = layer(x, input_pos, freqs_cis, mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 2}):
-    #         for layer in self.layers[len(self.layers)//2:3*len(self.layers)//4]:
-    #             x = layer(x, input_pos, freqs_cis, mask)
-
-    #     with torch.fx.traceback.annotate({"stage": 3}):
-    #         for layer in self.layers[3*len(self.layers)//4:]:
-    #             x = layer(x, input_pos, freqs_cis, mask)
-    #         x = self.norm(x)
-    #         logits = self.output(x)
-    #     return logits
-
-    @classmethod
-    def from_name(cls, name: str):
-        return cls(ModelArgs.from_name(name))
-
-class TransformerBlock(nn.Module):
-    def __init__(self, config: ModelArgs) -> None:
-        super().__init__()
-        self.attention = Attention(config)
-        self.block_sparse_moe = MOEFeedForward(config)
-        self.ffn_norm = RMSNorm(config.dim, config.norm_eps)
-        self.attention_norm = RMSNorm(config.dim, config.norm_eps)
-
-    def forward(self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor) -> Tensor:
-        h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos)
-        out = h + self.block_sparse_moe(self.ffn_norm(h))
-        return out
-
-
-class Attention(nn.Module):
-    def __init__(self, config: ModelArgs):
-        super().__init__()
-        assert config.dim % config.n_head == 0
-
-        total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
-        # key, query, value projections for all heads, but in a batch
-        # Initialize layers normally
-        self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
-        self.wo = nn.Linear(config.dim, config.dim, bias=False)
-        
-        self.kv_cache = None
-
-        self.n_head = config.n_head
-        self.head_dim = config.head_dim
-        self.n_local_heads = config.n_local_heads
-        self.dim = config.dim
-        self._register_load_state_dict_pre_hook(self.load_hook)
-
-    def load_hook(self, state_dict, prefix, *args):
-        if prefix + "wq.weight" in state_dict:
-            wq = state_dict.pop(prefix + "wq.weight")
-            wk = state_dict.pop(prefix + "wk.weight")
-            wv = state_dict.pop(prefix + "wv.weight")
-            state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
-
-    def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
-        bsz, seqlen, _ = x.shape
-
-        kv_size = self.n_local_heads * self.head_dim
-        q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
-
-        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
-        k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
-        v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
-
-        q = apply_rotary_emb(q, freqs_cis)
-        k = apply_rotary_emb(k, freqs_cis)
-
-        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
-
-        if self.kv_cache is not None:
-            k, v = self.kv_cache.update(input_pos, k, v)
-
-        k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
-        v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
-        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
-
-        y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
-
-        y = self.wo(y)
-        return y
-
-
-class ConditionalFeedForward(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-
-        self.w1 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
-        self.w2 = nn.Parameter(torch.empty(config.num_experts, config.dim, config.intermediate_size))
-        self.w3 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
-
-    def forward(self, x: Tensor, dispatch_mask: Tensor, combine_weights: Tensor, capacity: int) -> Tensor:
-        """
-        x: [T, D]
-        dispatch_mask: [T, E, C]
-        combine_weights: [T, E, C]
-        capacity: int
-        """
-        with torch.fx.traceback.annotate({
-            "collective": "all_to_all_single",
-            "group": "ep",
-            "reshape": ("output", (2, self.config.num_experts, -1, self.config.dim))
-        }):
-            expert_inputs = torch.einsum("tec,tm->ecm", dispatch_mask.type_as(x[0]), x) # [E, C, D]
-        x1 = F.silu(torch.einsum('...ecd, emd -> ...ecm', expert_inputs, self.w1))
-        x3 = torch.einsum('...ecd, emd -> ...ecm', expert_inputs, self.w3)
-        with torch.fx.traceback.annotate({
-            "collective": "all_to_all_single",
-            "group": "ep",
-            "reshape": ("input", (self.config.num_experts, -1, self.config.dim))
-        }):
-            expert_output =  torch.einsum('...ecm, edm -> ...ecd', (x1 * x3), self.w2) # [..., E, C, D]
-        combined_output = torch.einsum("tec,ecd->td", combine_weights, expert_output)
-        return combined_output
-
-class MOEFeedForward(nn.Module):
-    def __init__(self, config) -> None:
-        super().__init__()
-        # Initialize layers normally
-        self.gate = nn.Linear(config.dim, config.num_experts, bias=False)
-        self.cond_ffn = ConditionalFeedForward(config)
-        
-        self.dim = config.dim
-        self.num_activated_experts = config.num_activated_experts
-        
-    def forward(self, x: Tensor) -> Tensor:
-        # Save original shape to reshape output back
-        original_shape = x.shape  # [batch_size, seq_len, dim] or [seq_len, dim]
-        x = x.view(-1, self.dim)
-        # T = num_tokens, E = num_experts, D = hidden dim, A = activated experts
-        # x: [T, D]
-        scores = self.gate(x) # [T, E]
-        gates = F.softmax(scores, dim=-1)
-        expert_weights, expert_indices = torch.topk(gates, self.num_activated_experts, dim=-1) # [T, A], [T, A]
-        expert_weights /= expert_weights.sum(dim=-1, keepdim=True) # [T, A]
-
-        num_experts = scores.shape[-1]
-        mask = F.one_hot(expert_indices, num_classes=num_experts).sum(dim=1).bool()  # [T, E]
-        capacity = x.shape[0] # // num_experts * 2
-        locations = torch.cumsum(mask, dim=0) - 1 # [T, E]
-        locations_sc = F.one_hot(locations * mask, capacity).to(dtype=x.dtype) # [T, E, C]
-        combine_weights = torch.einsum("se,sec->sec", gates * mask, locations_sc)
-        dispatch_mask = combine_weights.bool()
-
-        output = self.cond_ffn(x, dispatch_mask, combine_weights, capacity)
-        return output.view(original_shape)
-
-
-class RMSNorm(nn.Module):
-    def __init__(self, dim: int, eps: float = 1e-5):
-        super().__init__()
-        self.eps = eps
-        # Initialize weight normally (ones for RMSNorm)
-        self.weight = nn.Parameter(torch.ones(dim))
-
-    def _norm(self, x):
-        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
-
-    def forward(self, x: Tensor) -> Tensor:
-        output = self._norm(x.float()).type_as(x)
-        return output * self.weight
-
-
-def precompute_freqs_cis(
-    seq_len: int, n_elem: int, base: int = 10000
-) -> Tensor:
-    freqs = 1.0 / (base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem))
-    t = torch.arange(seq_len, device=freqs.device)
-    freqs = torch.outer(t, freqs)
-    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
-    cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
-    return cache
-
-
-def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
-    xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
-    freqs_cis = freqs_cis.reshape(1, xshaped.size(1), 1, xshaped.size(3), 2)
-    x_out2 = torch.stack(
-        [
-            xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
-            xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
-        ],
-        -1,
-    )
-
-    x_out2 = x_out2.flatten(3)
-    return x_out2.type_as(x)
\ No newline at end of file
diff --git a/test/models/mixtral_a2a.py b/test/models/mixtral_a2a.py
deleted file mode 100644
index affbffd..0000000
--- a/test/models/mixtral_a2a.py
+++ /dev/null
@@ -1,314 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-from dataclasses import dataclass
-from typing import Optional
-import math
-import torch.cuda.nvtx as nvtx
-
-import torch
-import torch.nn as nn
-from torch import Tensor
-from torch.nn import functional as F
-from torch.distributed import get_rank
-from torch.distributed.tensor import DeviceMesh
-
-
-def find_multiple(n: int, k: int) -> int:
-    if n % k == 0:
-        return n
-    return n + k - (n % k)
-
-
-@dataclass
-class ModelArgs:
-    block_size: int = 2048
-    vocab_size: int = 32000
-    n_layer: int = 32
-    n_head: int = 32
-    dim: int = 4096
-    intermediate_size: int = None
-    n_local_heads: int = -1
-    head_dim: int = 64
-    rope_base: float = 10000
-    norm_eps: float = 1e-5
-    num_experts: int = 8
-    num_activated_experts: int = 2
-
-    def __post_init__(self):
-        if self.n_local_heads == -1:
-            self.n_local_heads = self.n_head
-        if self.intermediate_size is None:
-            hidden_dim = 4 * self.dim
-            n_hidden = int(2 * hidden_dim / 3)
-            self.intermediate_size = find_multiple(n_hidden, 256)
-        self.head_dim = self.dim // self.n_head
-
-    @classmethod
-    def from_name(cls, name: str):
-        if name in transformer_configs:
-            return cls(**transformer_configs[name])
-        # fuzzy search
-        config = [config for config in transformer_configs if config in str(name).upper() or config in str(name)]
-        assert len(config) == 1, name
-        return cls(**transformer_configs[config[0]])
-
-
-transformer_configs = {
-    "tiny": dict(block_size=128, n_layer=2, n_head=2, n_local_heads=1, dim=256, intermediate_size=512, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "small": dict(block_size=512, n_layer=8, n_head=8, n_local_heads=2, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "medium": dict(block_size=512, n_layer=24, n_head=32, n_local_heads=8, dim=1024, intermediate_size=2048, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-    "Mixtral-8x7B-v0.1": dict(block_size=32768, n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
-}
-
-class KVCache(nn.Module):
-    def __init__(self, max_batch_size, max_seq_length, n_heads, head_dim, dtype=torch.bfloat16):
-        super().__init__()
-        cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
-        self.register_buffer('k_cache', torch.zeros(cache_shape, dtype=dtype))
-        self.register_buffer('v_cache', torch.zeros(cache_shape, dtype=dtype))
-
-    def update(self, input_pos, k_val, v_val):
-        # input_pos: [S], k_val: [B, H, S, D]
-        assert input_pos.shape[0] == k_val.shape[2]
-
-        k_out = self.k_cache
-        v_out = self.v_cache
-        k_out[:, :, input_pos] = k_val
-        v_out[:, :, input_pos] = v_val
-
-        return k_out, v_out
-
-class Transformer(nn.Module):
-    def __init__(self, config: ModelArgs, group: Optional[DeviceMesh] = None) -> None:
-        super().__init__()
-        self.config = config
-        self.group = group
-
-        # Initialize layers normally
-        self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
-        self.layers = nn.ModuleList(TransformerBlock(config, group) for _ in range(config.n_layer))
-        self.norm = RMSNorm(config.dim, group, eps=config.norm_eps)
-        self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
-
-        self.freqs_cis: Optional[Tensor] = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base).to(f"cuda:{get_rank()}")
-        self.causal_mask: Optional[Tensor] = torch.tril(torch.ones(self.config.block_size, self.config.block_size, dtype=torch.bool)).to(f"cuda:{get_rank()}")
-        
-        self.mask_cache: Optional[Tensor] = None
-        self.max_batch_size = -1
-        self.max_seq_length = -1
-
-    def setup_caches(self, max_batch_size, max_seq_length):
-        assert False, "Should not be called"
-        if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
-            return
-        head_dim = self.config.dim // self.config.n_head
-        max_seq_length = find_multiple(max_seq_length, 8)
-        self.max_seq_length = max_seq_length
-        self.max_batch_size = max_batch_size
-        for b in self.layers:
-            b.attention.kv_cache = KVCache(max_batch_size, max_seq_length, self.config.n_local_heads, head_dim)
-
-        self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base)
-        self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
-
-    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
-        assert self.freqs_cis is not None, "Caches must be initialized first"
-        mask = self.causal_mask[None, None, input_pos]
-        freqs_cis = self.freqs_cis[input_pos]
-        x = self.tok_embeddings(idx) if self.tok_embeddings else idx
-
-        for i, layer in enumerate(self.layers):
-            x = layer(x, input_pos, freqs_cis, mask)
-        x = self.norm(x) if self.norm else x
-        logits = self.output(x) if self.output else x
-        return logits
-
-    @classmethod
-    def from_name(cls, name: str, group: DeviceMesh):
-        return cls(ModelArgs.from_name(name), group)
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, config: ModelArgs, group: Optional[DeviceMesh] = None) -> None:
-        super().__init__()
-        self.attention = Attention(config, group)
-        self.block_sparse_moe = MOEFeedForward(config, group)
-        self.ffn_norm = RMSNorm(config.dim, group, config.norm_eps)
-        self.attention_norm = RMSNorm(config.dim, group, config.norm_eps)
-
-    def forward(self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor) -> Tensor:
-        h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos)
-        out = h + self.block_sparse_moe(self.ffn_norm(h))
-        return out
-
-
-class Attention(nn.Module):
-    def __init__(self, config: ModelArgs, group: Optional[DeviceMesh] = None):
-        super().__init__()
-        assert config.dim % config.n_head == 0
-
-        total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
-        # key, query, value projections for all heads, but in a batch
-        # Initialize layers normally
-        self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
-        self.wo = nn.Linear(config.dim, config.dim, bias=False)
-        
-        self.kv_cache = None
-        self.n_head = config.n_head
-        self.head_dim = config.head_dim
-        self.n_local_heads = config.n_local_heads
-        self.dim = config.dim
-        self._register_load_state_dict_pre_hook(self.load_hook)
-
-    def load_hook(self, state_dict, prefix, *args):
-        if prefix + "wq.weight" in state_dict:
-            wq = state_dict.pop(prefix + "wq.weight")
-            wk = state_dict.pop(prefix + "wk.weight")
-            wv = state_dict.pop(prefix + "wv.weight")
-            state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
-
-    def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
-        bsz, seqlen, _ = x.shape
-
-        kv_size = self.n_local_heads * self.head_dim
-        q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
-
-        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
-        k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
-        v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
-
-        q = apply_rotary_emb(q, freqs_cis)
-        k = apply_rotary_emb(k, freqs_cis)
-
-        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
-
-        if self.kv_cache is not None:
-            k, v = self.kv_cache.update(input_pos, k, v)
-
-        k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
-        v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
-        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
-
-        y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
-
-        y = self.wo(y)
-        return y
-
-
-class ConditionalFeedForward(nn.Module):
-    def __init__(self, config, group: Optional[DeviceMesh] = None):
-        super().__init__()
-        self.num_experts = config.num_experts
-        self.group = group
-        self.ep_size = 2
-        self.num_local_experts = config.num_experts // self.ep_size
-        self.config = config
-
-        self.w1 = nn.Parameter(torch.empty(self.num_local_experts, config.intermediate_size, config.dim))
-        self.w2 = nn.Parameter(torch.empty(self.num_local_experts, config.dim, config.intermediate_size))
-        self.w3 = nn.Parameter(torch.empty(self.num_local_experts, config.intermediate_size, config.dim))
-        
-        self.rank = get_rank()
-
-    def forward(self, x: Tensor, dispatch_mask: Tensor, combine_weights: Tensor, capacity: int) -> Tensor:
-        """
-        x: [T, D]
-        dispatch_mask: [T, E, C]
-        combine_weights: [T, E, C]
-        capacity: int
-        """
-        expert_inputs = torch.einsum("tec,tm->ecm", dispatch_mask.type_as(x[0]), x) # [E, C, D]
-        buf = torch.empty_like(expert_inputs)
-        torch.distributed.all_to_all_single(buf, expert_inputs, group=self.group)
-        expert_inputs = buf
-
-        expert_inputs = expert_inputs.reshape(self.ep_size, self.num_local_experts, -1, self.config.dim) # [G, L, C, D]
-        x1 = F.silu(torch.einsum('glcd, lmd -> glcm', expert_inputs, self.w1))
-        x3 = torch.einsum('glcd, lmd -> glcm', expert_inputs, self.w3)
-        expert_output =  torch.einsum('glcm, ldm -> glcd', (x1 * x3), self.w2) # [G, L, C, D]
-
-        expert_output = expert_output.reshape(self.num_experts, -1, self.config.dim) # [E, C, D]
-        buf = torch.empty_like(expert_output)
-        torch.distributed.all_to_all_single(buf, expert_output, group=self.group)
-        expert_output = buf
-        
-        combined_output = torch.einsum("tec,ecd->td", combine_weights, expert_output)
-        return combined_output
-
-class MOEFeedForward(nn.Module):
-    def __init__(self, config, group: Optional[DeviceMesh] = None) -> None:
-        super().__init__()
-        # Initialize layers normally
-        self.gate = nn.Linear(config.dim, config.num_experts, bias=False)
-        self.cond_ffn = ConditionalFeedForward(config, group)
-        
-        self.dim = config.dim
-        self.num_activated_experts = config.num_activated_experts
-        self.rank = get_rank()
-        self.group = group
-        
-    def forward(self, x: Tensor) -> Tensor:
-        # Save original shape to reshape output back
-        shape = x.shape
-        x = x.view(-1, self.dim)
-        # T = num_tokens, E = num_experts, D = hidden dim, A = activated experts
-        # x: [T, D]
-        scores = self.gate(x) # [T, E]
-        gates = F.softmax(scores, dim=-1)
-        expert_weights, expert_indices = torch.topk(gates, self.num_activated_experts, dim=-1) # [T, A], [T, A]
-        expert_weights /= expert_weights.sum(dim=-1, keepdim=True) # [T, A]
-
-        num_experts = scores.shape[-1]
-        mask = F.one_hot(expert_indices, num_classes=num_experts).sum(dim=1).bool()  # [T, E]
-        capacity = x.shape[0] # // num_experts * 2
-        locations = torch.cumsum(mask, dim=0) - 1 # [T, E]
-        locations_sc = F.one_hot(locations * mask, capacity).float() # [T, E, C]
-        combine_weights = torch.einsum("se,sec->sec", gates * mask, locations_sc)
-        dispatch_mask = combine_weights.bool()
-
-        output = self.cond_ffn(x, dispatch_mask, combine_weights, capacity)
-        return output.view(shape)
-
-
-class RMSNorm(nn.Module):
-    def __init__(self, dim: int, group: Optional[DeviceMesh] = None, eps: float = 1e-5):
-        super().__init__()
-        self.eps = eps
-        # Initialize weight normally (ones for RMSNorm)
-        self.weight = nn.Parameter(torch.ones(dim))
-        
-    def _norm(self, x):
-        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
-
-    def forward(self, x: Tensor) -> Tensor:
-        output = self._norm(x.float()).type_as(x)
-        return output * self.weight
-
-
-def precompute_freqs_cis(
-    seq_len: int, n_elem: int, base: int = 10000
-) -> Tensor:
-    freqs = 1.0 / (base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem))
-    t = torch.arange(seq_len, device=freqs.device)
-    freqs = torch.outer(t, freqs)
-    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
-    cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
-    return cache.to(dtype=torch.bfloat16)
-
-
-def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
-    xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
-    freqs_cis = freqs_cis.reshape(1, xshaped.size(1), 1, xshaped.size(3), 2)
-    x_out2 = torch.stack(
-        [
-            xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
-            xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
-        ],
-        -1,
-    )
-
-    x_out2 = x_out2.flatten(3)
-    return x_out2.type_as(x)
\ No newline at end of file
diff --git a/test/pippy_profiling/1f1b_event_profile.py b/test/pippy_profiling/1f1b_event_profile.py
deleted file mode 100644
index 5904935..0000000
--- a/test/pippy_profiling/1f1b_event_profile.py
+++ /dev/null
@@ -1,300 +0,0 @@
-"""
-CUDA-event timing for pipeline parallel Schedule1F1B.
-Measures per-rank forward and backward timings (backward not split) without using torch.profiler.
-"""
-
-import argparse
-from collections import defaultdict
-
-import torch
-import torch.distributed as dist
-import torch.multiprocessing as mp
-import torch.nn as nn
-from torch.cuda import Event
-from torch.distributed.pipelining import (
-    SplitPoint,
-    Schedule1F1B,
-    pipeline,
-)
-
-from model import Transformer, LLAMA_3B
-
-
-def op_key(op) -> str:
-    """Normalize schedule op to key 'F' | 'B'."""
-    try:
-        s = str(op)
-    except Exception:
-        s = f"{op}"
-    s = s.upper()
-    if "FORWARD" in s or s == "F":
-        return "F"
-    if "BACKWARD" in s or s == "B":
-        return "B"
-    return s  # fallback
-
-
-class TimingTracker:
-    """Collect CUDA event pairs for forward/backward timings per microbatch."""
-
-    def __init__(self, device: torch.device):
-        self.device = device
-        self.reset_iteration()
-
-    def reset_iteration(self):
-        self.fwd_pairs = []  # list of (mb, start, end)
-        self.bwd_pairs = []
-        self._fwd_ctr = 0
-        self._bwd_ctr = 0
-
-    def next_fwd_mb(self):
-        mb = self._fwd_ctr
-        self._fwd_ctr += 1
-        return mb
-
-    def next_bwd_input_mb(self):
-        mb = self._bwd_ctr
-        self._bwd_ctr += 1
-        return mb
-
-    # alias for clarity
-    next_bwd_weight_mb = next_bwd_input_mb
-
-    def add_fwd(self, mb: int):
-        torch.cuda.set_device(self.device)
-        start, end = Event(enable_timing=True), Event(enable_timing=True)
-        self.fwd_pairs.append((mb, start, end))
-        return start, end
-
-    def add_bwd(self, mb: int):
-        torch.cuda.set_device(self.device)
-        start, end = Event(enable_timing=True), Event(enable_timing=True)
-        self.bwd_pairs.append((mb, start, end))
-        return start, end
-
-    def summarize_iteration(self):
-        """Return per-op ordered lists of seconds: {'F': [...], 'B': [...]}."""
-        if not torch.cuda.is_available():
-            return {"F": [], "B": []}
-        torch.cuda.set_device(self.device)
-        torch.cuda.synchronize(self.device)
-
-        def to_list(pairs):
-            return [s.elapsed_time(e) / 1000.0 for _, s, e in pairs]
-
-        return {
-            "F": to_list(self.fwd_pairs),
-            "B": to_list(self.bwd_pairs),
-        }
-
-
-def wrap_stage_forward(stage, tracker: TimingTracker):
-    """Wrap forward to record CUDA event timing."""
-    orig_forward = stage.submod.forward
-
-    def wrapped_forward(self, *args, **kwargs):
-        mb = int(tracker.next_fwd_mb())
-        f_start, f_end = tracker.add_fwd(mb)
-        f_start.record()
-        out = orig_forward(self, *args, **kwargs)
-        f_end.record()
-        return out
-
-    stage.submod.forward = wrapped_forward
-
-
-def wrap_stage_backward(stage, tracker: TimingTracker):
-    """Wrap backward (activation/weight together in 1f1b) to time gradients."""
-    orig_bwd_one = stage.__class__.backward_one_chunk
-
-    def _normalize_mb(mb_val):
-        try:
-            return int(mb_val)
-        except Exception:
-            try:
-                return int(mb_val.item())
-            except Exception:
-                return int(tracker.next_bwd_input_mb())
-
-    def wrapped_bwd_one(self, *args, **kwargs):
-        raw_mb = kwargs.get("bwd_chunk_id", args[0] if args else tracker.next_bwd_input_mb())
-        mb = _normalize_mb(raw_mb)
-        b_start, b_end = tracker.add_bwd(mb)
-        b_start.record()
-        result = orig_bwd_one(self, *args, **kwargs)
-        b_end.record()
-        return result
-
-    stage.backward_one_chunk = wrapped_bwd_one.__get__(stage, stage.__class__)
-
-
-def setup(rank, world_size):
-    import os
-    os.environ["MASTER_ADDR"] = "localhost"
-    os.environ["MASTER_PORT"] = "29500"
-    dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
-
-
-def cleanup():
-    dist.destroy_process_group()
-
-
-def run_pipeline(rank, world_size, mb_size, num_mbs, seq_len, model_cfg):
-    device = torch.device(f"cuda:{rank}")
-    torch.cuda.set_device(device)
-
-    setup(rank, world_size)
-    dist.barrier()
-    print(f"[Rank {rank}] using torch device {device}")
-
-    model = Transformer(model_cfg).to(device)
-
-    def tokenwise_loss_fn(outputs, targets):
-        loss_fn = nn.CrossEntropyLoss()
-        outputs = outputs.reshape(-1, model_cfg.vocab_size)
-        targets = targets.reshape(-1)
-        return loss_fn(outputs, targets)
-
-    layers_per_rank = model_cfg.n_layers // world_size
-    split_spec = {
-        f"layers.{i * layers_per_rank}": SplitPoint.BEGINNING for i in range(1, world_size)
-    }
-
-    example_input = torch.randint(
-        low=0,
-        high=model_cfg.vocab_size,
-        size=(mb_size, seq_len),
-        dtype=torch.long,
-        device=device,
-    )
-
-    pipe = pipeline(module=model, mb_args=(example_input,), split_spec=split_spec)
-    stage = pipe.build_stage(rank, device=device)
-
-    tracker = TimingTracker(device)
-    wrap_stage_forward(stage, tracker)
-    wrap_stage_backward(stage, tracker)
-
-    optimizer = torch.optim.Adam(stage.submod.parameters(), lr=1e-3)
-    pipe_schedule = Schedule1F1B(stage, n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn)
-
-    x = torch.randint(
-        low=0,
-        high=model_cfg.vocab_size,
-        size=(mb_size * num_mbs, seq_len),
-        dtype=torch.long,
-        device=device,
-    )
-    y = torch.randint(
-        low=0,
-        high=model_cfg.vocab_size,
-        size=(mb_size * num_mbs, seq_len),
-        dtype=torch.long,
-        device=device,
-    )
-
-    warmup_steps = 2
-    measure_steps = 1
-    total_tokens = mb_size * num_mbs * seq_len
-
-    # Warmup
-    for _ in range(warmup_steps):
-        optimizer.zero_grad(set_to_none=True)
-        if rank != world_size - 1:
-            pipe_schedule.step(x)
-        else:
-            _ = pipe_schedule.step(target=y, losses=[])
-        optimizer.step()
-        torch.cuda.synchronize()
-        dist.barrier()
-
-    iter_times = []
-    avg_accum = {"F": defaultdict(list), "B": defaultdict(list)}
-
-    for _ in range(measure_steps):
-        dist.barrier()
-        torch.cuda.synchronize()
-
-        tracker.reset_iteration()
-
-        iter_start = Event(enable_timing=True)
-        iter_end = Event(enable_timing=True)
-        iter_start.record()
-
-        optimizer.zero_grad(set_to_none=True)
-
-        if rank != world_size - 1:
-            pipe_schedule.step(x)
-        else:
-            losses = []
-            _ = pipe_schedule.step(target=y, losses=losses)
-
-        optimizer.step()
-        torch.cuda.synchronize()
-        iter_end.record()
-        torch.cuda.synchronize()
-        dist.barrier()
-
-        per_op_lists = tracker.summarize_iteration()
-        for key_in, times in per_op_lists.items():
-            norm_key = op_key(key_in)
-            for idx, t in enumerate(times):
-                avg_accum[norm_key][idx].append(t)
-
-        if rank == world_size - 1:
-            iter_times.append(iter_start.elapsed_time(iter_end) / 1000.0)
-
-    # Compute per-op per-occurrence averages across measured steps
-    op_idx_avg = {
-        op: {idx: sum(times) / len(times) for idx, times in idx_dict.items()}
-        for op, idx_dict in avg_accum.items()
-    }
-
-    if rank == world_size - 1 and iter_times:
-        avg_iter = sum(iter_times) / len(iter_times)
-        tput = total_tokens / avg_iter
-
-        print(f"[Rank {rank}] Total iter time: {sum(iter_times):.6f}s")
-        print(f"[Rank {rank}] Avg iter time:   {avg_iter:.6f}s")
-        print(f"[Rank {rank}] Throughput:      {tput:.2f} tokens/s")
-        if 'losses' in locals():
-            print(f"[Rank {rank}] final losses: {losses}")
-
-    # Print pipeline order with average op timings for this rank
-    print(f"\n=== Pipeline order for rank {rank} (avg over {measure_steps} steps) ===")
-    op_counters = defaultdict(int)
-    for step, action in enumerate(pipe_schedule.pipeline_order[rank]):
-        if action is None:
-            continue
-        stage_idx, op, mb = action
-        key = op_key(op)
-        idx = op_counters[key]
-        op_counters[key] += 1
-        t_val = op_idx_avg.get(key, {}).get(idx)
-        t_str = f"{t_val:.6f}s" if t_val is not None else "n/a"
-        print(f"t={step:03d} | stage={stage_idx} | op={op} | microbatch={mb} | time={t_str}")
-
-    dist.barrier()
-    cleanup()
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--microbatch_size", type=int, default=16)
-    parser.add_argument("--num_microbatches", type=int, default=4)
-    parser.add_argument("--seq_len", type=int, default=256)
-    args = parser.parse_args()
-
-    world_size = 2
-    mp.set_start_method("spawn", force=True)
-
-    mp.spawn(
-        run_pipeline,
-        args=(world_size, args.microbatch_size, args.num_microbatches, args.seq_len, LLAMA_3B),
-        nprocs=world_size,
-        join=True,
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/test/pippy_profiling/1f1b_expected_schedule.dot b/test/pippy_profiling/1f1b_expected_schedule.dot
deleted file mode 100644
index cab5522..0000000
--- a/test/pippy_profiling/1f1b_expected_schedule.dot
+++ /dev/null
@@ -1,118 +0,0 @@
-digraph PipelineDAG_1F1B {
-  rankdir=LR;
-  node [shape=box, fontname="Helvetica", fontsize=10];
-
-  // -----------------------
-  // Nodes (durations used in calculations)
-  // -----------------------
-  subgraph cluster_s0 {
-    label="Stage 0 (rank 0)";
-    style=rounded;
-
-    S0_F0 [label="S0 F mb0\nt=000 274.75ms"];
-    S0_F1 [label="S0 F mb1\nt=003 274.75ms"];
-    S0_B0 [label="S0 B mb0\nt=004 551ms"];
-    S0_F2 [label="S0 F mb2\nt=005 274.75ms"];
-    S0_B1 [label="S0 B mb1\nt=006 551ms"];
-    S0_F3 [label="S0 F mb3\nt=007 274.75ms"];
-    S0_B2 [label="S0 B mb2\nt=008 551ms"];
-    S0_B3 [label="S0 B mb3\nt=010 551ms"];
-
-    Update0 [shape=box, style="rounded", label="Update (rank 0)\n40ms"];
-  }
-
-  subgraph cluster_s1 {
-    label="Stage 1 (rank 1)";
-    style=rounded;
-
-    S1_F1 [label="S1 F mb1\nt=001 274.75ms"];
-    S1_B0 [label="S1 B mb0\nt=002 551ms"];
-    S1_F2 [label="S1 F mb2\nt=003 274.75ms"];
-    S1_B1 [label="S1 B mb1\nt=004 551ms"];
-    S1_F3 [label="S1 F mb3\nt=005 274.75ms"];
-    S1_B2 [label="S1 B mb2\nt=006 551ms"];
-    S1_F4 [label="S1 F mb4\nt=007 274.75ms"];
-    S1_B3 [label="S1 B mb3\nt=008 551ms"];
-
-    Update1 [shape=box, style="rounded", label="Update (rank 1)\n40ms"];
-  }
-
-  Done [shape=oval, label="Done\n0ms"];
-
-  // -----------------------
-  // Temporal edges (serialization on each stage)
-  // -----------------------
-  edge [color=black, penwidth=1.2];
-
-  // Stage 0 order
-  S0_F0 -> S0_F1 -> S0_B0 -> S0_F2 -> S0_B1 -> S0_F3 -> S0_B2 -> S0_B3 -> Update0;
-
-  // Stage 1 order
-  S1_F1 -> S1_B0 -> S1_F2 -> S1_B1 -> S1_F3 -> S1_B2 -> S1_F4 -> S1_B3 -> Update1;
-
-  // -----------------------
-  // Intra-microbatch logical deps (within a stage)
-  // -----------------------
-  edge [color=gray50, style=dashed, penwidth=1.0];
-
-  // Stage 0: Fm -> Bm
-  S0_F0 -> S0_B0;
-  S0_F1 -> S0_B1;
-  S0_F2 -> S0_B2;
-  S0_F3 -> S0_B3;
-
-  // Stage 1: F(m+1) -> Bm (offseted microbatch labeling in your trace)
-  S1_F1 -> S1_B0;
-  S1_F2 -> S1_B1;
-  S1_F3 -> S1_B2;
-  S1_F4 -> S1_B3;
-
-  // -----------------------
-  // Cross-stage data deps
-  // -----------------------
-  // Forward activations (offset mapping): S0_Fm -> S1_F(m+1)
-  edge [color=blue, penwidth=2.0];
-  S0_F0 -> S1_F1;
-  S0_F1 -> S1_F2;
-  S0_F2 -> S1_F3;
-  S0_F3 -> S1_F4;
-
-  // Backward grads: S1_Bm -> S0_Bm
-  edge [color=red, penwidth=2.0];
-  S1_B0 -> S0_B0;
-  S1_B1 -> S0_B1;
-  S1_B2 -> S0_B2;
-  S1_B3 -> S0_B3;
-
-  // -----------------------
-  // Done joins both updates
-  // -----------------------
-  edge [color=purple, style=dashed, penwidth=1.5];
-  Update0 -> Done;
-  Update1 -> Done;
-
-  // ============================================================
-  // Highlight critical path edges (longest path to Done)
-  // Chain sums to 4168.75ms with F=274.75, B=551, U=40
-  // ============================================================
-  edge [color=green, penwidth=4.0];
-
-  // activation + stage1 compute + grad back
-  S0_F0 -> S1_F1;
-  S1_F1 -> S1_B0;
-  S1_B0 -> S0_B0;
-
-  // stage0 tail (forced by temporal order + future grad dependency)
-  S0_B0 -> S0_F2;
-  S0_F2 -> S0_B1;
-  S0_B1 -> S0_F3;
-
-  // activation to stage1 last forward, then last backward, then final stage0 backward
-  S0_F3 -> S1_F4;
-  S1_F4 -> S1_B3;
-  S1_B3 -> S0_B3;
-
-  // update + done
-  S0_B3 -> Update0;
-  Update0 -> Done;
-}
diff --git a/test/pippy_profiling/1f1b_expected_schedule.png b/test/pippy_profiling/1f1b_expected_schedule.png
deleted file mode 100644
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diff --git a/test/pippy_profiling/zb1p_expected_schedule.dot b/test/pippy_profiling/zb1p_expected_schedule.dot
deleted file mode 100644
index a119c49..0000000
--- a/test/pippy_profiling/zb1p_expected_schedule.dot
+++ /dev/null
@@ -1,122 +0,0 @@
-digraph PipelineDAG {
-  rankdir=LR;
-  node [shape=box, fontname="Helvetica", fontsize=10];
-
-  // -----------------------
-  // Nodes (durations used in calculations)
-  // -----------------------
-  subgraph cluster_s0 {
-    label="Stage 0 (rank 0)";
-    style=rounded;
-
-    S0_F0 [label="S0 F mb0\nt=000 274.75ms"];
-    S0_F1 [label="S0 F mb1\nt=002 274.75ms"];
-    S0_I0 [label="S0 I mb0\nt=003 0ms"];
-    S0_W0 [label="S0 W mb0\nt=004 551ms"];
-    S0_F2 [label="S0 F mb2\nt=005 274.75ms"];
-    S0_I1 [label="S0 I mb1\nt=006 0ms"];
-    S0_W1 [label="S0 W mb1\nt=007 551ms"];
-    S0_F3 [label="S0 F mb3\nt=008 274.75ms"];
-    S0_I2 [label="S0 I mb2\nt=009 0ms"];
-    S0_W2 [label="S0 W mb2\nt=010 551ms"];
-    S0_I3 [label="S0 I mb3\nt=011 0ms"];
-    S0_W3 [label="S0 W mb3\nt=012 551ms"];
-  }
-
-  subgraph cluster_s1 {
-    label="Stage 1 (rank 1)";
-    style=rounded;
-
-    S1_F0 [label="S1 F mb0\nt=001 274.75ms"];
-    S1_I0 [label="S1 I mb0\nt=002 275.5ms"];
-    S1_F1 [label="S1 F mb1\nt=003 274.75ms"];
-    S1_I1 [label="S1 I mb1\nt=004 275.5ms"];
-    S1_W0 [label="S1 W mb0\nt=005 275.5ms"];
-    S1_F2 [label="S1 F mb2\nt=006 274.75ms"];
-    S1_I2 [label="S1 I mb2\nt=007 275.5ms"];
-    S1_W1 [label="S1 W mb1\nt=008 275.5ms"];
-    S1_F3 [label="S1 F mb3\nt=009 274.75ms"];
-    S1_I3 [label="S1 I mb3\nt=010 275.5ms"];
-    S1_W2 [label="S1 W mb2\nt=011 275.5ms"];
-    S1_W3 [label="S1 W mb3\nt=012 275.5ms"];
-  }
-
-  // Iteration end when both ranks complete their last op
-  Done [shape=oval, label="Done\n0ms"];
-
-  // -----------------------
-  // Temporal edges (serialization on each stage)
-  // -----------------------
-  edge [color=black, penwidth=1.2];
-
-  // Stage 0 order
-  S0_F0 -> S0_F1 -> S0_I0 -> S0_W0 -> S0_F2 -> S0_I1 -> S0_W1 -> S0_F3 -> S0_I2 -> S0_W2 -> S0_I3 -> S0_W3;
-
-  // Stage 1 order
-  S1_F0 -> S1_I0 -> S1_F1 -> S1_I1 -> S1_W0 -> S1_F2 -> S1_I2 -> S1_W1 -> S1_F3 -> S1_I3 -> S1_W2 -> S1_W3;
-
-  // -----------------------
-  // Intra-microbatch logical deps (within a stage)
-  // -----------------------
-  edge [color=gray50, style=dashed, penwidth=1.0];
-
-  // Stage 0: F -> I -> W
-  S0_F0 -> S0_I0 -> S0_W0;
-  S0_F1 -> S0_I1 -> S0_W1;
-  S0_F2 -> S0_I2 -> S0_W2;
-  S0_F3 -> S0_I3 -> S0_W3;
-
-  // Stage 1: F -> I -> W
-  S1_F0 -> S1_I0 -> S1_W0;
-  S1_F1 -> S1_I1 -> S1_W1;
-  S1_F2 -> S1_I2 -> S1_W2;
-  S1_F3 -> S1_I3 -> S1_W3;
-
-  // -----------------------
-  // Cross-stage data deps
-  // -----------------------
-  edge [color=blue, penwidth=2.0];
-  S0_F0 -> S1_F0;
-  S0_F1 -> S1_F1;
-  S0_F2 -> S1_F2;
-  S0_F3 -> S1_F3;
-
-  edge [color=red, penwidth=2.0];
-  S1_I0 -> S0_I0;
-  S1_I1 -> S0_I1;
-  S1_I2 -> S0_I2;
-  S1_I3 -> S0_I3;
-
-  // -----------------------
-  // Join (Done)
-  // -----------------------
-  edge [color=purple, style=dashed, penwidth=1.5];
-  S0_W3 -> Done;
-  S1_W3 -> Done;
-
-  // ============================================================
-  // Highlight critical path edges (longest path to Done)
-  // ============================================================
-  edge [color=green, penwidth=4.0];
-
-  // activation dep mb0
-  S0_F0 -> S1_F0;
-
-  // stage1 temporal + grad dep back to stage0
-  S1_F0 -> S1_I0;
-  S1_I0 -> S0_I0;
-
-  // stage0 temporal tail to end (critical)
-  S0_I0 -> S0_W0;
-  S0_W0 -> S0_F2;
-  S0_F2 -> S0_I1;
-  S0_I1 -> S0_W1;
-  S0_W1 -> S0_F3;
-  S0_F3 -> S0_I2;
-  S0_I2 -> S0_W2;
-  S0_W2 -> S0_I3;
-  S0_I3 -> S0_W3;
-
-  // done
-  S0_W3 -> Done;
-}
diff --git a/test/pippy_profiling/zb1p_expected_schedule.png b/test/pippy_profiling/zb1p_expected_schedule.png
deleted file mode 100644
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diff --git a/test/pippy_profiling/zb_1f1b_memory_profiling.py b/test/pippy_profiling/zb_1f1b_memory_profiling.py
deleted file mode 100644
index 6da4fe5..0000000
--- a/test/pippy_profiling/zb_1f1b_memory_profiling.py
+++ /dev/null
@@ -1,206 +0,0 @@
-import os
-import torch
-import torch.multiprocessing as mp
-import argparse
-import torch.distributed as dist
-import torch.nn as nn
-import subprocess
-from torch.distributed.pipelining import pipeline, SplitPoint, ScheduleInterleavedZeroBubble, Schedule1F1B
-from torch.profiler import profile, ProfilerActivity, record_function, schedule
-from model import Transformer, LLAMA_DEBUG, LLAMA_3B, LLAMA_1B
-
-def setup(rank, world_size):
-    os.environ["MASTER_ADDR"] = "localhost"
-    os.environ["MASTER_PORT"] = "29500"
-
-    backend = "nccl" if torch.cuda.is_available() else "gloo"
-    dist.init_process_group(backend=backend, rank=rank, world_size=world_size)
-    if backend == "nccl":
-        torch.cuda.set_device(rank)
-
-def cleanup():
-    dist.destroy_process_group()
-
-def wrap_stage_forward(stage, name):
-    orig_forward = stage.submod.forward
-    mb_counter = {"i": 0}
-
-    def wrapped_forward(self, *args, **kwargs):
-        i = mb_counter["i"]
-        mb_counter["i"] += 1
-
-        with record_function(f"{name}_F_mb{i}"):
-            return orig_forward(self, *args, **kwargs)
-
-    stage.submod.forward = wrapped_forward
-
-def wrap_stage_backward_ZB(stage, name):
-    orig_bwd_one = stage.__class__.backward_one_chunk
-    def wrapped_bwd_one(self, *args, **kwargs):
-        mb = kwargs.get("bwd_chunk_id", args[0] if args else "unk")
-        with record_function(f"{name}_B_mb{mb}"):
-            return orig_bwd_one(self, *args, **kwargs)
-    stage.backward_one_chunk = wrapped_bwd_one.__get__(stage, stage.__class__)
-
-    orig_bwd_w = stage.__class__.backward_weight_one_chunk
-    def wrapped_bwd_w(self, *args, **kwargs):
-       mb = kwargs.get("bwd_chunk_id", args[0] if args else "unk")
-       with record_function(f"{name}_W_mb{mb}"):
-            return orig_bwd_w(self, *args, **kwargs)
-    stage.backward_weight_one_chunk = wrapped_bwd_w.__get__(stage, stage.__class__)
-
-def wrap_stage_backward_1f1b(stage, name):
-    orig_bwd_one = stage.__class__.backward_one_chunk
-    def wrapped_bwd_one(self, *args, **kwargs):
-        mb = kwargs.get("bwd_chunk_id", args[0] if args else "unk")
-        with record_function(f"{name}_B_mb{mb}"):
-            return orig_bwd_one(self, *args, **kwargs)
-    stage.backward_one_chunk = wrapped_bwd_one.__get__(stage, stage.__class__)
-
-
-def run_pipeline(rank, world_size, schedule_name, mb_size, num_mbs, result_queue, model_cfg=LLAMA_DEBUG):
-    setup(rank, world_size)
-
-    device = torch.device(f"cuda:{rank}") if torch.cuda.is_available() else torch.device("cpu")
-
-    model = Transformer(model_cfg)
-    model.to(device)
-
-    # Helper loss function that automatically reshapes output and targets tensors
-    def tokenwise_loss_fn(outputs, targets):
-        loss_fn = nn.CrossEntropyLoss()
-        outputs = outputs.reshape(-1, model_cfg.vocab_size)
-        targets = targets.reshape(-1)
-        return loss_fn(outputs, targets)
-
-    # Partition the model into world_size stages and create the stage for this rank
-    layers_per_rank = model_cfg.n_layers // world_size
-
-    split_spec = {
-        f"layers.{i * layers_per_rank}": SplitPoint.BEGINNING
-        for i in range(1, world_size)
-    }
-
-    if rank == 0:
-        print(model_cfg.n_layers)
-        print(world_size)
-        print(layers_per_rank)
-        print(split_spec)
-
-    example_input = torch.randint(
-        low=0,
-        high=model_cfg.vocab_size,
-        size=(mb_size, model_cfg.max_seq_len),
-        dtype=torch.long,
-        device=device
-    )
-    
-    pipe = pipeline(
-        module=model,
-        mb_args=(example_input,),
-        split_spec=split_spec,
-    )
-    
-    stage = pipe.build_stage(rank, device=device) # Currently assumes one stage per rank  
-    schedule = None      
-    wrap_stage_forward(stage, f"stage{rank}")
-    if schedule_name == "1f1b":
-        wrap_stage_backward_1f1b(stage, f"stage{rank}")
-        schedule = Schedule1F1B(stage, n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn)
-    else:
-        wrap_stage_backward_ZB(stage, f"stage{rank}")
-        schedule = ScheduleInterleavedZeroBubble([stage], n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn)
-
-    # Print the pipeline schedule
-    print(f"\n=== Pipeline order for rank {rank} ===")
-    for step, action in enumerate(schedule.pipeline_order[rank]):
-        if action is None:
-            continue
-        stage_idx, op, mb = action
-        print(f"t={step:03d} | stage={stage_idx} | op={op} | microbatch={mb}")
-
-    prof_schedule = torch.profiler.schedule(wait=0, warmup=0, active=1, repeat=1)
-
-    # Generate random training tensors
-    x = torch.randint(
-        low=0,
-        high=model_cfg.vocab_size,
-        size=(mb_size * num_mbs, model_cfg.max_seq_len),
-        dtype=torch.long,
-        device=device                 
-    )
-
-    y = torch.randint(
-        low=0, 
-        high=model_cfg.vocab_size, 
-        size=(mb_size * num_mbs, model_cfg.max_seq_len), 
-        dtype=torch.long,
-        device=device
-    )
-
-    # Initialize profiler activity based on device availability
-    activities = [ProfilerActivity.CPU]
-    if torch.cuda.is_available():
-        activities += [ProfilerActivity.CUDA]
-
-    with profile(
-        activities=activities, 
-        schedule=prof_schedule,
-        profile_memory=True,
-        with_stack=True,
-        record_shapes=True,
-        experimental_config=torch._C._profiler._ExperimentalConfig(
-            verbose=True,
-            profile_all_threads=True,
-            capture_overload_names=True,
-        )) as prof:
-        if rank == 0:
-            schedule.step(x)
-        elif rank == 1:
-            losses = []
-            output = schedule.step(target=y, losses=losses)
-            print(f"final losses: {losses}")
-        prof.step()
-
-    trace_dir = os.path.join(os.path.dirname(__file__), "traces")
-    out_path = os.path.join(trace_dir, f"trace_{rank}.json")
-    prof.export_chrome_trace(out_path)
-    
-    dist.barrier()
-    if rank == 1:
-        # Create traces folder if it doesn't exist
-        os.makedirs(trace_dir, exist_ok=True)
-
-        merged_out = os.path.join(trace_dir, "merged_trace.json")
-        cmd = ["python", "filter_trace.py", os.path.join(trace_dir,"trace_0.json"), os.path.join(trace_dir,"user_only_trace_0.json")]
-        subprocess.run(cmd, check=True)
-
-        cmd = ["python", "filter_trace.py", os.path.join(trace_dir, "trace_1.json"),  os.path.join(trace_dir,"user_only_trace_1.json")]
-        subprocess.run(cmd, check=True)
-
-        cmd = ["python", "merge_traces.py", os.path.join(trace_dir,"user_only_trace_0.json"), os.path.join(trace_dir,"user_only_trace_1.json"), merged_out]
-        subprocess.run(cmd, check=True)
-        print(f"Merged trace available at {merged_out}")
-    
-    cleanup()
-   
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--microbatch_size', type=int, default=64)
-    parser.add_argument('--num_microbatches', type=int, default=256)
-    parser.add_argument("--schedule", type=str, default="1f1b", choices=["1f1b", "zb"])
-    args = parser.parse_args()
-    
-    world_size = 4
-
-    mp.set_start_method("spawn", force=True)
-    manager = mp.Manager()
-    result_queue = manager.Queue()
-
-    mp.spawn(
-        run_pipeline,
-        args=(world_size, args.schedule, args.microbatch_size, args.num_microbatches, result_queue),
-        nprocs=world_size,
-        join=True,
-    )
diff --git a/test/pippy_profiling/zerobubble_event_profile.py b/test/pippy_profiling/zerobubble_event_profile.py
deleted file mode 100644
index 804eecc..0000000
--- a/test/pippy_profiling/zerobubble_event_profile.py
+++ /dev/null
@@ -1,318 +0,0 @@
-import os
-import argparse
-from collections import defaultdict
-
-import torch
-import torch.multiprocessing as mp
-import torch.distributed as dist
-import torch.nn as nn
-from torch.cuda import Event
-from torch.distributed.pipelining import pipeline, SplitPoint, ScheduleInterleavedZeroBubble
-
-from model import Transformer, LLAMA_3B
-
-
-def op_key(op) -> str:
-    """Normalize schedule op to string key 'F' | 'I' | 'W'."""
-    try:
-        s = str(op)
-    except Exception:
-        s = f"{op}"
-    s = s.upper()
-    if "FORWARD" in s:
-        return "F"
-    if "BACKWARD_INPUT" in s or s == "I":
-        return "I"
-    if "BACKWARD_WEIGHT" in s or s == "W":
-        return "W"
-    return s  # fall back (will not match counters)
-
-
-class TimingTracker:
-    """Collect CUDA event pairs for forward/backward timings per microbatch."""
-
-    def __init__(self, device: torch.device):
-        self.device = device
-        self.reset_iteration()
-
-    def reset_iteration(self):
-        self.fwd_pairs = []  # list of (mb, start, end)
-        self.bwd_input_pairs = []
-        self.bwd_weight_pairs = []
-        self._fwd_ctr = 0
-        self._bwd_input_ctr = 0
-        self._bwd_weight_ctr = 0
-
-    def next_fwd_mb(self):
-        mb = self._fwd_ctr
-        self._fwd_ctr += 1
-        return mb
-
-    def next_bwd_input_mb(self):
-        mb = self._bwd_input_ctr
-        self._bwd_input_ctr += 1
-        return mb
-
-    def next_bwd_weight_mb(self):
-        mb = self._bwd_weight_ctr
-        self._bwd_weight_ctr += 1
-        return mb
-
-    def add_fwd(self, mb: int):
-        torch.cuda.set_device(self.device)
-        start, end = Event(enable_timing=True), Event(enable_timing=True)
-        self.fwd_pairs.append((mb, start, end))
-        return start, end
-
-    def add_bwd_input(self, mb: int):
-        torch.cuda.set_device(self.device)
-        start, end = Event(enable_timing=True), Event(enable_timing=True)
-        self.bwd_input_pairs.append((mb, start, end))
-        return start, end
-
-    def add_bwd_weight(self, mb: int):
-        torch.cuda.set_device(self.device)
-        start, end = Event(enable_timing=True), Event(enable_timing=True)
-        self.bwd_weight_pairs.append((mb, start, end))
-        return start, end
-
-    def summarize_iteration(self):
-        """Return per-op ordered lists of seconds: {'F': [...], 'I': [...], 'W': [...]}."""
-        if not torch.cuda.is_available():
-            return {"F": [], "I": [], "W": []}
-        torch.cuda.set_device(self.device)
-        torch.cuda.synchronize(self.device)
-
-        def to_list(pairs):
-            return [s.elapsed_time(e) / 1000.0 for _, s, e in pairs]
-
-        return {
-            "F": to_list(self.fwd_pairs),
-            "I": to_list(self.bwd_input_pairs),
-            "W": to_list(self.bwd_weight_pairs),
-        }
-
-
-def wrap_stage_forward(stage, tracker: TimingTracker):
-    """Wrap forward to record CUDA event timing."""
-    orig_forward = stage.submod.forward
-
-    def wrapped_forward(self, *args, **kwargs):
-        mb = int(tracker.next_fwd_mb())
-        f_start, f_end = tracker.add_fwd(mb)
-        f_start.record()
-        out = orig_forward(self, *args, **kwargs)
-        f_end.record()
-        return out
-
-    stage.submod.forward = wrapped_forward
-
-def wrap_stage_backward(stage, tracker: TimingTracker):
-    """Wrap backward passes to time activation and weight gradients separately."""
-    orig_bwd_one = stage.__class__.backward_one_chunk
-    def _normalize_mb(mb_val):
-        try:
-            return int(mb_val)
-        except Exception:
-            try:
-                return int(mb_val.item())
-            except Exception:
-                return int(tracker.next_bwd_input_mb())
-
-    def wrapped_bwd_one(self, *args, **kwargs):
-        raw_mb = kwargs.get("bwd_chunk_id", args[0] if args else tracker.next_bwd_input_mb())
-        mb = _normalize_mb(raw_mb)
-        b_start, b_end = tracker.add_bwd_input(mb)
-        b_start.record()
-        result = orig_bwd_one(self, *args, **kwargs)
-        b_end.record()
-        return result
-    
-    stage.backward_one_chunk = wrapped_bwd_one.__get__(stage, stage.__class__)
-
-    orig_bwd_w = stage.__class__.backward_weight_one_chunk
-
-    def wrapped_bwd_w(self, *args, **kwargs):
-        raw_mb = kwargs.get("bwd_chunk_id", args[0] if args else tracker.next_bwd_weight_mb())
-        mb = _normalize_mb(raw_mb)
-        w_start, w_end = tracker.add_bwd_weight(mb)
-        w_start.record()
-        result = orig_bwd_w(self, *args, **kwargs)
-        w_end.record()
-        return result
-
-    stage.backward_weight_one_chunk = wrapped_bwd_w.__get__(stage, stage.__class__)
-
-
-def setup(rank, world_size):
-    os.environ["MASTER_ADDR"] = "localhost"
-    os.environ["MASTER_PORT"] = "29500"
-    dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
-
-
-def cleanup():
-    dist.destroy_process_group()
-
-
-def run_pipeline(rank, world_size, mb_size, num_mbs, seq_len, model_cfg):
-    setup(rank, world_size)
-    dist.barrier()
-    device = torch.device(f"cuda:{rank}")
-    torch.cuda.set_device(device)
-    print(f"[Rank {rank}] using torch device {device}")
-
-    model = Transformer(model_cfg).to(device)
-
-    def tokenwise_loss_fn(outputs, targets):
-        loss_fn = nn.CrossEntropyLoss()
-        outputs = outputs.reshape(-1, model_cfg.vocab_size)
-        targets = targets.reshape(-1)
-        return loss_fn(outputs, targets)
-
-    layers_per_rank = model_cfg.n_layers // world_size
-    split_spec = {
-        f"layers.{i * layers_per_rank}": SplitPoint.BEGINNING for i in range(1, world_size)
-    }
-
-    example_input = torch.randint(
-        low=0,
-        high=model_cfg.vocab_size,
-        size=(mb_size, seq_len),
-        dtype=torch.long,
-        device=device,
-    )
-
-    pipe = pipeline(module=model, mb_args=(example_input,), split_spec=split_spec)
-    stage = pipe.build_stage(rank, device=device)
-
-    tracker = TimingTracker(device)
-    wrap_stage_forward(stage, tracker)
-    wrap_stage_backward(stage, tracker)
-
-    optimizer = torch.optim.Adam(stage.submod.parameters(), lr=1e-3)
-    pipe_schedule = ScheduleInterleavedZeroBubble(
-        [stage], n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn
-    )
-
-    x = torch.randint(
-        low=0,
-        high=model_cfg.vocab_size,
-        size=(mb_size * num_mbs, seq_len),
-        dtype=torch.long,
-        device=device,
-    )
-    y = torch.randint(
-        low=0,
-        high=model_cfg.vocab_size,
-        size=(mb_size * num_mbs, seq_len),
-        dtype=torch.long,
-        device=device,
-    )
-
-    warmup_steps = 2
-    measure_steps = 1
-    total_tokens = mb_size * num_mbs * seq_len
-
-    # Warmup
-    for _ in range(warmup_steps):
-        optimizer.zero_grad(set_to_none=True)
-        if rank != world_size - 1:
-            pipe_schedule.step(x)
-        else:
-            _ = pipe_schedule.step(target=y, losses=[])
-        optimizer.step()
-        torch.cuda.synchronize()
-        dist.barrier()
-
-    iter_times = []
-    avg_accum = {"F": defaultdict(list), "I": defaultdict(list), "W": defaultdict(list)}
-
-    for _ in range(measure_steps):
-        dist.barrier()
-        torch.cuda.synchronize()
-
-        tracker.reset_iteration()
-
-        iter_start = Event(enable_timing=True)
-        iter_end = Event(enable_timing=True)
-        iter_start.record()
-
-        optimizer.zero_grad(set_to_none=True)
-
-        if rank != world_size - 1:
-            pipe_schedule.step(x)
-        else:
-            losses = []
-            _ = pipe_schedule.step(target=y, losses=losses)
-
-        optimizer.step()
-
-        torch.cuda.synchronize()
-        iter_end.record()
-        torch.cuda.synchronize()
-        dist.barrier()
-
-        per_op_lists = tracker.summarize_iteration()
-        # accumulate by occurrence index (per op) to align with pipeline order
-        for key_in, times in per_op_lists.items():
-            norm_key = op_key(key_in)
-            for idx, t in enumerate(times):
-                avg_accum[norm_key][idx].append(t)
-
-        if rank == world_size - 1:
-            iter_times.append(iter_start.elapsed_time(iter_end) / 1000.0)
-
-    # Compute per-op per-occurrence averages across measured steps
-    op_idx_avg = {
-        op: {idx: sum(times) / len(times) for idx, times in idx_dict.items()}
-        for op, idx_dict in avg_accum.items()
-    }
-
-    if rank == world_size - 1 and iter_times:
-        avg_iter = sum(iter_times) / len(iter_times)
-        tput = total_tokens / avg_iter
-
-        print(f"[Rank {rank}] Total iter time: {sum(iter_times):.6f}s")
-        print(f"[Rank {rank}] Avg iter time:   {avg_iter:.6f}s")
-        print(f"[Rank {rank}] Throughput:      {tput:.2f} tokens/s")
-        if rank == world_size - 1 and 'losses' in locals():
-            print(f"[Rank {rank}] final losses: {losses}")
-
-    # Print pipeline order with average op timings for this rank
-    print(f"\n=== Pipeline order for rank {rank} (avg over {measure_steps} steps) ===")
-    op_counters = defaultdict(int)
-    for step, action in enumerate(pipe_schedule.pipeline_order[rank]):
-        if action is None:
-            continue
-        stage_idx, op, mb = action
-        key = op_key(op)
-        idx = op_counters[key]
-        op_counters[key] += 1
-        t_val = op_idx_avg.get(key, {}).get(idx)
-        t_str = f"{t_val:.6f}s" if t_val is not None else "n/a"
-        print(f"t={step:03d} | stage={stage_idx} | op={op} | microbatch={mb} | time={t_str}")
-
-    dist.barrier()
-    cleanup()
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--microbatch_size", type=int, default=16)
-    parser.add_argument("--num_microbatches", type=int, default=4)
-    parser.add_argument("--seq_len", type=int, default=256)
-    args = parser.parse_args()
-
-    world_size = 2
-    mp.set_start_method("spawn", force=True)
-
-    mp.spawn(
-        run_pipeline,
-        args=(world_size, args.microbatch_size, args.num_microbatches, args.seq_len, LLAMA_3B),
-        nprocs=world_size,
-        join=True,
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/test/pippy_profiling/zerobubble_example.py b/test/pippy_profiling/zerobubble_example.py
deleted file mode 100644
index 0d3a189..0000000
--- a/test/pippy_profiling/zerobubble_example.py
+++ /dev/null
@@ -1,270 +0,0 @@
-import os
-import torch
-import torch.multiprocessing as mp
-import argparse
-import torch.distributed as dist
-import torch.nn as nn
-import time
-import subprocess
-from torch.cuda import Event
-from torch.distributed.pipelining import pipeline, SplitPoint, ScheduleInterleavedZeroBubble, Schedule1F1B, ScheduleInterleaved1F1B
-from torch.profiler import profile, ProfilerActivity, record_function, schedule as profiler_schedule
-from model import Transformer, LLAMA_DEBUG, LLAMA_3B, LLAMA_1B
-
-
-def wrap_stage_forward(stage, name):
-    """Tag each microbatch forward pass for profiling."""
-    orig_forward = stage.submod.forward
-    mb_counter = {"i": 0}
-
-    def wrapped_forward(self, *args, **kwargs):
-        i = mb_counter["i"]
-        mb_counter["i"] += 1
-
-        with record_function(f"{name}_F_mb{i}"):
-            return orig_forward(self, *args, **kwargs)
-
-    stage.submod.forward = wrapped_forward
-
-
-def wrap_stage_backward(stage, name):
-    """Tag activation and weight backward passes for profiling."""
-    orig_bwd_one = stage.__class__.backward_one_chunk
-
-    def wrapped_bwd_one(self, *args, **kwargs):
-        mb = kwargs.get("bwd_chunk_id", args[0] if args else "unk")
-        with record_function(f"{name}_B_mb{mb}"):
-            return orig_bwd_one(self, *args, **kwargs)
-
-    stage.backward_one_chunk = wrapped_bwd_one.__get__(stage, stage.__class__)
-
-    orig_bwd_w = stage.__class__.backward_weight_one_chunk
-
-    def wrapped_bwd_w(self, *args, **kwargs):
-        mb = kwargs.get("bwd_chunk_id", args[0] if args else "unk")
-        with record_function(f"{name}_W_mb{mb}"):
-            return orig_bwd_w(self, *args, **kwargs)
-
-    stage.backward_weight_one_chunk = wrapped_bwd_w.__get__(stage, stage.__class__)
-
-def setup(rank, world_size):
-    os.environ["MASTER_ADDR"] = "localhost"
-    os.environ["MASTER_PORT"] = "29500"
-
-    backend = "nccl"
-    dist.init_process_group(backend=backend, rank=rank, world_size=world_size)
-
-
-def cleanup():
-    dist.destroy_process_group()
-
-
-def run_pipeline(rank, world_size, mb_size, num_mbs, seq_len, model_cfg):
-    setup(rank, world_size)
-    dist.barrier()
-    device = torch.device(f"cuda:{rank}")
-    torch.cuda.set_device(device)
-    print(f"[Rank {rank}] using torch device {device}")
-
-    model = Transformer(model_cfg)
-    model.to(device)
-
-    # Helper loss function that automatically reshapes output and targets tensors
-    def tokenwise_loss_fn(outputs, targets):
-        loss_fn = nn.CrossEntropyLoss()
-        outputs = outputs.reshape(-1, model_cfg.vocab_size)
-        targets = targets.reshape(-1)
-        return loss_fn(outputs, targets)
-
-    # Partition the model into world_size stages and create the stage for this rank
-    layers_per_rank = model_cfg.n_layers // world_size
-
-    split_spec = {
-        f"layers.{i * layers_per_rank}": SplitPoint.BEGINNING
-        for i in range(1, world_size)
-    }
-
-    example_input = torch.randint(
-        low=0,
-        high=model_cfg.vocab_size,
-        size=(mb_size, seq_len),
-        dtype=torch.long,
-        device=device
-    )
-    
-    pipe = pipeline(
-        module=model,
-        mb_args=(example_input,),
-        split_spec=split_spec,
-    )
-
-    stage = pipe.build_stage(rank, device=device)  # Currently assumes one stage per rank
-
-    # Add profiling markers to forward / backward passes (names align with zerobubble.py)
-    wrap_stage_forward(stage, f"stage{rank}")
-    wrap_stage_backward(stage, f"stage{rank}")
-
-    # Optimizer for this stage's parameters; profiled per-iteration
-    optimizer = torch.optim.Adam(stage.submod.parameters(), lr=1e-3)
-
-    # pipe_schedule = ScheduleInterleavedZeroBubble([stage], n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn)
-    pipe_schedule = Schedule1F1B(stage, n_microbatches=num_mbs, loss_fn=tokenwise_loss_fn)
-
-    # Print the pipeline schedule
-    print(f"\n=== Pipeline order for rank {rank} ===")
-    for step, action in enumerate(pipe_schedule.pipeline_order[rank]):
-        if action is None:
-            continue
-        stage_idx, op, mb = action
-        print(f"t={step:03d} | stage={stage_idx} | op={op} | microbatch={mb}")
-
-    # Generate random training tensors
-    x = torch.randint(
-        low=0,
-        high=model_cfg.vocab_size,
-        size=(mb_size * num_mbs, seq_len),
-        dtype=torch.long,
-        device=device                 
-    )
-
-    y = torch.randint(
-        low=0, 
-        high=model_cfg.vocab_size, 
-        size=(mb_size * num_mbs, seq_len), 
-        dtype=torch.long,
-        device=device
-    )
-
-    total_tokens = mb_size * num_mbs * seq_len
-
-    warmup_steps = 2
-    measure_steps = 4
-
-    for _ in range(warmup_steps):
-        optimizer.zero_grad(set_to_none=True)
-        if rank != world_size - 1:
-            pipe_schedule.step(x)
-        else:
-            _losses = []
-            _ = pipe_schedule.step(target=y, losses=_losses)
-        optimizer.step()
-        torch.cuda.synchronize()
-        dist.barrier()
-
-    elapsed_times = []
-
-    # ---------------------------
-    # Profiling + timing section
-    # ---------------------------
-
-    activities = [ProfilerActivity.CPU]
-    if torch.cuda.is_available():
-        activities.append(ProfilerActivity.CUDA)
-
-    # Profile the full measure_steps window (after warmup) and export chrome trace.
-    prof_schedule = profiler_schedule(wait=0, warmup=0, active=measure_steps, repeat=1)
-
-    trace_dir = os.path.join(os.path.dirname(__file__), "traces")
-    os.makedirs(trace_dir, exist_ok=True)
-    trace_path = os.path.join(trace_dir, f"trace_{rank}.json")
-
-    def _remove_if_exists(path: str):
-        try:
-            os.remove(path)
-        except FileNotFoundError:
-            pass
-
-    with profile(
-        activities=activities,
-        schedule=prof_schedule,
-        with_stack=False,
-        record_shapes=False,
-    ) as prof:
-        for _ in range(measure_steps):
-            dist.barrier()
-            torch.cuda.synchronize()
-
-            start = torch.cuda.Event(enable_timing=True)
-            end = torch.cuda.Event(enable_timing=True)
-
-            start.record()
-
-            optimizer.zero_grad(set_to_none=True)
-
-            if rank != world_size - 1:
-                pipe_schedule.step(x)
-            else:
-                losses = []
-                _ = pipe_schedule.step(target=y, losses=losses)
-
-            with record_function(f"optimizer_step_rank{rank}"):
-                optimizer.step()
-            
-            torch.cuda.synchronize()
-            end.record()
-            torch.cuda.synchronize()
-            dist.barrier()
-
-            if rank == world_size - 1:
-                elapsed_ms = start.elapsed_time(end)
-                elapsed_times.append(elapsed_ms / 1000.0)
-
-            prof.step()
-
-    _remove_if_exists(trace_path)
-    prof.export_chrome_trace(trace_path)
-    dist.barrier()  # ensure both traces are written before merging
-
-    if rank == world_size - 1:
-        print(f"[Rank {rank}] chrome trace saved to {trace_path}")
-
-        # Create user-only traces and merge (mirrors zerobubble.py workflow)
-        trace0 = os.path.join(trace_dir, "trace_0.json")
-        trace1 = os.path.join(trace_dir, "trace_1.json")
-        user0 = os.path.join(trace_dir, "user_only_trace_0.json")
-        user1 = os.path.join(trace_dir, "user_only_trace_1.json")
-        merged = os.path.join(trace_dir, "merged_trace.json")
-        merged_raw = os.path.join(trace_dir, "merged_trace_raw.json")
-
-        for p in (user0, user1, merged, merged_raw):
-            _remove_if_exists(p)
-
-        subprocess.run(["python", "filter_trace.py", trace0, user0], check=True)
-        subprocess.run(["python", "filter_trace.py", trace1, user1], check=True)
-        subprocess.run(["python", "merge_traces.py", user0, user1, merged], check=True)
-        subprocess.run(["python", "merge_traces.py", trace0, trace1, merged_raw], check=True)
-        print(f"[Rank {rank}] merged trace available at {merged}")
-        print(f"[Rank {rank}] raw merged trace available at {merged_raw}")
-
-    if rank == world_size - 1:
-        avg_elapsed = sum(elapsed_times) / len(elapsed_times)
-        tokens_per_sec = total_tokens / avg_elapsed
-        print(f"[Rank {rank}] Total elapsed: {sum(elapsed_times):.6f}s (CUDA events)")
-        print(f"[Rank {rank}] Avg elapsed: {avg_elapsed:.6f}s (CUDA events)")
-        print(f"[Rank {rank}] Throughput: {tokens_per_sec:.2f} tokens/s")
-        print(f"[Rank {rank}] final losses: {losses}")
-
-    dist.barrier()
-    cleanup()
-
-   
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--microbatch_size', type=int, default=16)
-    parser.add_argument('--num_microbatches', type=int, default=4)
-    parser.add_argument('--seq_len', type=int, default=256)
-    # parser.add_argument("--schedule", type=str, default="1f1b", choices=["1f1b", "zb"])
-    args = parser.parse_args()
-    
-    world_size = 2
-
-    mp.set_start_method("spawn", force=True)
-    manager = mp.Manager()
-
-    mp.spawn(
-        run_pipeline,
-        args=(world_size, args.microbatch_size, args.num_microbatches, args.seq_len, LLAMA_3B),
-        nprocs=world_size,
-        join=True,
-    )
diff --git a/test/schedules/build_order_directives.py b/test/schedules/build_order_directives.py
deleted file mode 100644
index 52e74de..0000000
--- a/test/schedules/build_order_directives.py
+++ /dev/null
@@ -1,638 +0,0 @@
-"""Build JSON order directives for common pipeline schedules."""
-
-from __future__ import annotations
-
-from collections import defaultdict
-from contextlib import suppress
-from dataclasses import dataclass
-from html import escape
-from pathlib import Path
-import subprocess
-from typing import Literal
-
-Pass = Literal["F", "B", "BI", "BW"]
-_VALID_PASSES = frozenset({"F", "B", "BI", "BW"})
-
-
-@dataclass(frozen=True)
-class _Op:
-    pp: int
-    mb: int
-    pass_: Pass
-
-
-# A "slot" is one timestep in a rank's row. A slot holds one op normally; when
-# multiple ops are nested in the same order-directive filter group they share a
-# slot and visualize as a vertically-stacked cell, indicating that they run
-# interleaved within the same scheduling block.
-_Slot = list[_Op]
-
-
-_PASS_COLOR: dict[str, str] = {
-    "F": "#FFE08A",
-    "B": "#A8E6A1",
-    "BI": "#A9D6FF",
-    "BW": "#C6B6FF",
-}
-
-
-def _filter(pp: int, mb: int, pass_: Pass) -> list[list[int | str]]:
-    return [["PP", pp], ["MB", mb], ["PASS", pass_]]
-
-
-def _order_directive(ops: list[_Op]) -> dict:
-    return _order_directive_from_slots([[op] for op in ops])
-
-
-def _order_directive_from_slots(slots: list[_Slot]) -> dict:
-    return {
-        "op": "order",
-        "filters": [
-            [_filter(op.pp, op.mb, op.pass_) for op in slot]
-            for slot in slots
-        ],
-    }
-
-
-def _op_width(op: _Op) -> int:
-    if op.pass_ == "B":
-        return 2
-    return 1
-
-
-def _slot_width(slot: _Slot) -> int:
-    return sum(_op_width(op) for op in slot)
-
-
-def _cleanup_visualizer_sidecars(
-    output_base: Path,
-    keep_suffix: str | None = None,
-) -> None:
-    for suffix in (".dot", ".txt"):
-        if suffix == keep_suffix:
-            continue
-        with suppress(FileNotFoundError):
-            output_base.with_suffix(suffix).unlink()
-
-
-def visualize_order_directives(
-    order_directives: list[dict],
-    output_path: str | Path = "out/schedule",
-    fmt: str = "png",
-) -> str:
-    """Render a 2-D schedule grid, one row per physical rank.
-
-    The order directives do not carry explicit ``None`` slots. Columns are
-    reconstructed as earliest feasible logical times from per-rank temporal
-    order and FWD/BWD pipeline data dependencies.
-    """
-    rows = _rows_from_order_directives(order_directives)
-    placements = _compute_columns(rows)
-    max_col = max(
-        (col + _slot_width(rows[rank][slot_idx]) - 1 for (rank, slot_idx), col in placements.items()),
-        default=-1,
-    )
-
-    table: list[list[_Slot | None]] = [
-        [None for _ in range(max_col + 1)]
-        for _ in rows
-    ]
-    for key, col in placements.items():
-        rank, slot_idx = key
-        table[rank][col] = rows[rank][slot_idx]
-
-    output = Path(output_path)
-    if output.suffix:
-        fmt = output.suffix.lstrip(".")
-        output = output.with_suffix("")
-
-    output.parent.mkdir(parents=True, exist_ok=True)
-    rendered_suffix = f".{fmt}"
-    _cleanup_visualizer_sidecars(output, keep_suffix=rendered_suffix)
-    try:
-        import graphviz
-
-        dot = graphviz.Digraph("PipelineSchedule")
-        dot.attr(rankdir="LR")
-        dot.node("schedule", label=_html_table(table), shape="plain")
-        try:
-            return dot.render(str(output), format=fmt, cleanup=True)
-        finally:
-            _cleanup_visualizer_sidecars(output, keep_suffix=rendered_suffix)
-    except ImportError:
-        dot_path = output.with_suffix(".dot")
-        rendered_path = output.with_suffix(f".{fmt}")
-        dot_path.write_text(_dot_source(table), encoding="utf-8")
-        rendered_ok = False
-        try:
-            subprocess.run(
-                ["dot", f"-T{fmt}", str(dot_path), "-o", str(rendered_path)],
-                check=True,
-            )
-            rendered_ok = True
-            return str(rendered_path)
-        except (FileNotFoundError, subprocess.CalledProcessError):
-            fallback = output.with_suffix(".txt")
-            fallback.write_text(_format_text_table(table), encoding="utf-8")
-            return str(fallback)
-        finally:
-            if rendered_ok:
-                _cleanup_visualizer_sidecars(output, keep_suffix=rendered_suffix)
-
-
-def build_1f1b_schedule(n_ranks: int, n_mbs: int) -> list[dict]:
-    """Return one 1F1B order directive per rank.
-
-    This preserves the old PipelineSchedule builder's non-None per-rank op order:
-    rank r warms up with ``n_ranks - 1 - r`` forwards, then alternates forward
-    and fused backward until all microbatches have completed.
-    """
-    _validate_positive("n_ranks", n_ranks)
-    _validate_positive("n_mbs", n_mbs)
-
-    rows: list[list[_Op]] = [[] for _ in range(n_ranks)]
-    fwd_mb = [0] * n_ranks
-    bwd_mb = [0] * n_ranks
-
-    for rank in range(n_ranks):
-        warmup = n_ranks - 1 - rank
-        for _ in range(min(warmup, n_mbs)):
-            rows[rank].append(_Op(pp=rank, mb=fwd_mb[rank], pass_="F"))
-            fwd_mb[rank] += 1
-
-    for rank in range(n_ranks):
-        while bwd_mb[rank] < n_mbs:
-            if fwd_mb[rank] < n_mbs:
-                rows[rank].append(_Op(pp=rank, mb=fwd_mb[rank], pass_="F"))
-                fwd_mb[rank] += 1
-            rows[rank].append(_Op(pp=rank, mb=bwd_mb[rank], pass_="B"))
-            bwd_mb[rank] += 1
-
-    return [_order_directive(row) for row in rows]
-
-
-def build_zerobubble_schedule(n_ranks: int, n_mbs: int) -> list[dict]:
-    """Return one ZeroBubble order directive per rank.
-
-    This is the old ZB-1 schedule builder expressed as order directives. Each
-    rank owns one pipeline stage, fused backward is split into BI/BW, and BW is
-    deferred by ``rank`` backward-input steps.
-    """
-    _validate_positive("n_ranks", n_ranks)
-    _validate_positive("n_mbs", n_mbs)
-
-    rows: list[list[_Op]] = []
-    for rank in range(n_ranks):
-        warmup = min(n_ranks - 1 - rank, n_mbs)
-        fwd_bwd_ops = n_mbs - warmup
-        cooldown_ops = n_mbs - fwd_bwd_ops
-        deferred_bw_count = rank
-
-        fwd_mb = 0
-        bwd_mb = 0
-        bw_mb = 0
-        bwdi_count = 0
-        emitted_bw_count = 0
-        ops: list[_Op] = []
-
-        for op_idx in range(warmup + fwd_bwd_ops + cooldown_ops):
-            if op_idx < warmup:
-                ops.append(_Op(pp=rank, mb=fwd_mb, pass_="F"))
-                fwd_mb += 1
-                continue
-
-            if op_idx < warmup + fwd_bwd_ops:
-                ops.append(_Op(pp=rank, mb=fwd_mb, pass_="F"))
-                fwd_mb += 1
-
-            ops.append(_Op(pp=rank, mb=bwd_mb, pass_="BI"))
-            bwd_mb += 1
-            bwdi_count += 1
-
-            if bwdi_count > deferred_bw_count:
-                ops.append(_Op(pp=rank, mb=bw_mb, pass_="BW"))
-                bw_mb += 1
-                emitted_bw_count += 1
-
-        while emitted_bw_count < bwdi_count:
-            ops.append(_Op(pp=rank, mb=bw_mb, pass_="BW"))
-            bw_mb += 1
-            emitted_bw_count += 1
-
-        rows.append(ops)
-
-    return [_order_directive(row) for row in rows]
-
-
-def build_interleaved_1f1b_schedule(
-    n_ranks: int,
-    n_mbs: int,
-    n_virtual_stages: int,
-) -> list[dict]:
-    """Return one interleaved 1F1B order directive per physical rank.
-
-    Rank ``r`` owns virtual pipeline stages ``r + k * n_ranks``. The op order is
-    the old interleaved PipelineSchedule order with the placeholder ``None`` slots
-    omitted.
-    """
-    _validate_positive("n_ranks", n_ranks)
-    _validate_positive("n_mbs", n_mbs)
-    _validate_positive("n_virtual_stages", n_virtual_stages)
-
-    microbatches_per_round = n_ranks
-
-    def warmup_ops(rank: int) -> int:
-        return min(
-            (n_virtual_stages - 1) * n_ranks + 2 * (n_ranks - 1 - rank),
-            n_mbs * n_virtual_stages,
-        )
-
-    def forward_stage_index(step: int, rank: int) -> int:
-        local_index = (step // microbatches_per_round) % n_virtual_stages
-        return local_index * n_ranks + rank
-
-    def backward_stage_index(step: int, rank: int, warmup: int) -> int:
-        local_index = (
-            n_virtual_stages
-            - 1
-            - ((step - warmup) // microbatches_per_round) % n_virtual_stages
-        )
-        return local_index * n_ranks + rank
-
-    rows: list[list[_Op]] = []
-    for rank in range(n_ranks):
-        warmup = warmup_ops(rank)
-        microbatch_ops = n_virtual_stages * n_mbs
-        fwd_bwd_ops = microbatch_ops - warmup
-        cooldown_ops = microbatch_ops - fwd_bwd_ops
-
-        fwd_mb: dict[int, int] = defaultdict(int)
-        bwd_mb: dict[int, int] = defaultdict(int)
-        ops: list[_Op] = []
-
-        for op_idx in range(warmup + fwd_bwd_ops + cooldown_ops):
-            if op_idx < warmup:
-                stage = forward_stage_index(op_idx, rank)
-                mb = fwd_mb[stage]
-                fwd_mb[stage] += 1
-                ops.append(_Op(pp=stage, mb=mb, pass_="F"))
-            elif op_idx < warmup + fwd_bwd_ops:
-                stage = forward_stage_index(op_idx, rank)
-                mb = fwd_mb[stage]
-                fwd_mb[stage] += 1
-                ops.append(_Op(pp=stage, mb=mb, pass_="F"))
-
-                stage = backward_stage_index(op_idx, rank, warmup)
-                mb = bwd_mb[stage]
-                bwd_mb[stage] += 1
-                ops.append(_Op(pp=stage, mb=mb, pass_="B"))
-            else:
-                stage = backward_stage_index(op_idx, rank, warmup)
-                mb = bwd_mb[stage]
-                bwd_mb[stage] += 1
-                ops.append(_Op(pp=stage, mb=mb, pass_="B"))
-
-        rows.append(ops)
-
-    return [_order_directive(row) for row in rows]
-
-
-def build_dualpipev_schedule(n_ranks: int, n_mbs: int) -> list[dict]:
-    """Return DualPipeV order directives with overlapped FWD/BWD slots.
-
-    Rank ``r`` owns V-layout stages ``r`` and ``2 * n_ranks - 1 - r``. The
-    old ``FWD_BWD`` chunk is represented as one nested order filter group
-    containing the corresponding FWD filter and fused BWD filter.
-    """
-    _validate_positive("n_ranks", n_ranks)
-    _validate_positive("n_mbs", n_mbs)
-    if n_mbs < 2 * n_ranks:
-        raise ValueError(
-            f"dualpipev requires n_mbs >= 2 * n_ranks, got n_mbs={n_mbs}, "
-            f"n_ranks={n_ranks}"
-        )
-
-    rows: list[list[_Slot]] = [[] for _ in range(n_ranks)]
-
-    for rank in range(n_ranks):
-        s0 = rank
-        s1 = 2 * n_ranks - 1 - rank
-        slots = rows[rank]
-        counts: dict[tuple[int, str], int] = {}
-        weight_queue: list[tuple[int, int]] = []
-
-        def count(stage: int, key: str) -> int:
-            return counts.get((stage, key), 0)
-
-        def inc(stage: int, key: str) -> None:
-            counts[(stage, key)] = count(stage, key) + 1
-
-        def inc_bwd(stage: int) -> None:
-            inc(stage, "i")
-            inc(stage, "w")
-
-        def append_op(stage: int, mb: int, pass_: Pass) -> None:
-            slots.append([_Op(pp=stage, mb=mb, pass_=pass_)])
-
-        def fwd(stage: int) -> None:
-            mb = count(stage, "f")
-            append_op(stage, mb, "F")
-            inc(stage, "f")
-
-        def bwdi(stage: int) -> None:
-            mb = count(stage, "i")
-            append_op(stage, mb, "BI")
-            weight_queue.append((stage, mb))
-            inc(stage, "i")
-
-        def drain_w() -> None:
-            if not weight_queue:
-                return
-            stage, mb = weight_queue.pop(0)
-            append_op(stage, mb, "BW")
-            inc(stage, "w")
-
-        def full_bwd(stage: int) -> None:
-            mb = count(stage, "i")
-            append_op(stage, mb, "B")
-            inc_bwd(stage)
-
-        def overlap_fb(fwd_stage: int, bwd_stage: int) -> None:
-            fwd_mb = count(fwd_stage, "f")
-            bwd_mb = count(bwd_stage, "i")
-            slots.append([
-                _Op(pp=fwd_stage, mb=fwd_mb, pass_="F"),
-                _Op(pp=bwd_stage, mb=bwd_mb, pass_="B"),
-            ])
-            inc(fwd_stage, "f")
-            inc_bwd(bwd_stage)
-
-        # Phase 1: F0 warmup.
-        for _ in range((n_ranks - rank - 1) * 2):
-            fwd(s0)
-
-        # Phase 2: F0F1.
-        for _ in range(rank + 1):
-            fwd(s0)
-            fwd(s1)
-
-        # Phase 3: I1 W1 F1.
-        for _ in range(n_ranks - rank - 1):
-            bwdi(s1)
-            drain_w()
-            fwd(s1)
-
-        # Phase 4: Main overlapped F0B1 + F1B0.
-        for i in range(n_mbs - n_ranks * 2 + rank + 1):
-            if i == 0 and rank == n_ranks - 1:
-                fwd(s0)
-                full_bwd(s1)
-            else:
-                overlap_fb(s0, s1)
-            overlap_fb(s1, s0)
-
-        # Phase 5: B1 + F1B0.
-        for _ in range(n_ranks - rank - 1):
-            full_bwd(s1)
-            overlap_fb(s1, s0)
-
-        # Phase 6: B1B0, switching the second half to split backward.
-        enable_zb = False
-        for i in range(rank + 1):
-            if i == (rank + 1) // 2 and rank % 2 == 1:
-                enable_zb = True
-            (bwdi if enable_zb else full_bwd)(s1)
-            if i == (rank + 1) // 2 and rank % 2 == 0:
-                enable_zb = True
-            (bwdi if enable_zb else full_bwd)(s0)
-
-        # Phase 7: W0 B0.
-        for _ in range(n_ranks - rank - 1):
-            drain_w()
-            (bwdi if enable_zb else full_bwd)(s0)
-
-        # Phase 8: W0.
-        for _ in range(rank + 1):
-            drain_w()
-
-    return [_order_directive_from_slots(row) for row in rows]
-
-
-def _validate_positive(name: str, value: int) -> None:
-    if not isinstance(value, int) or value <= 0:
-        raise ValueError(f"{name} must be a positive integer, got {value!r}")
-
-
-def _rows_from_order_directives(order_directives: list[dict]) -> list[list[_Slot]]:
-    rows = []
-    for directive in order_directives:
-        if directive.get("op") != "order":
-            raise ValueError(f"expected order directive, got {directive}")
-        filters = directive.get("filters")
-        if not isinstance(filters, list):
-            raise ValueError(f"order directive requires filters list: {directive}")
-        row: list[_Slot] = []
-        for filter_slot in _parse_filter_slots(filters):
-            slot: _Slot = []
-            for flt in filter_slot:
-                spec = _filter_to_dict(flt)
-                pass_value = spec["PASS"]
-                if pass_value not in _VALID_PASSES:
-                    raise ValueError(
-                        f"unsupported PASS={pass_value!r}; expected one of {sorted(_VALID_PASSES)}"
-                    )
-                slot.append(_Op(pp=int(spec["PP"]), mb=int(spec["MB"]), pass_=pass_value))
-            row.append(slot)
-        rows.append(row)
-    return rows
-
-
-def _is_filter_spec(flt: object) -> bool:
-    if isinstance(flt, dict):
-        return True
-    if isinstance(flt, list):
-        return all(
-            isinstance(item, (list, tuple))
-            and len(item) == 2
-            and isinstance(item[0], str)
-            for item in flt
-        )
-    return False
-
-
-def _parse_filter_slots(filters: list) -> list[list]:
-    """Group the directive's filter entries into slots.
-
-    Each top-level entry in ``filters`` corresponds to one slot (one timestep
-    on the owning rank). A bare filter spec becomes a single-op slot. A list of
-    filter specs becomes a multi-op slot whose ops run interleaved at the same
-    scheduling step and visualize stacked vertically in one block.
-    """
-    slots: list[list] = []
-    for item in filters:
-        if _is_filter_spec(item):
-            slots.append([item])
-        elif isinstance(item, list):
-            slots.append(list(item))
-        else:
-            raise ValueError(f"invalid order filter group: {item}")
-    return slots
-
-
-def _filter_to_dict(flt: object) -> dict:
-    if isinstance(flt, dict):
-        return dict(flt)
-    if isinstance(flt, list):
-        out = {}
-        for item in flt:
-            if not isinstance(item, (list, tuple)) or len(item) != 2:
-                raise ValueError(f"invalid order filter item: {item}")
-            out[item[0]] = item[1]
-        return out
-    raise ValueError(f"invalid order filter: {flt}")
-
-
-def _compute_columns(rows: list[list[_Slot]]) -> dict[tuple[int, int], int]:
-    # Each (rank, slot_idx) is one timeslot. Multiple ops within a slot share
-    # that slot's column; the slot's width is max(op width) across its ops.
-    key_by_op: dict[tuple[int, int, str], tuple[int, int]] = {}
-    for rank, row in enumerate(rows):
-        for slot_idx, slot in enumerate(row):
-            for op in slot:
-                op_key = (op.pp, op.mb, op.pass_)
-                if op_key in key_by_op:
-                    raise ValueError(f"duplicate scheduled op: {op_key}")
-                key_by_op[op_key] = (rank, slot_idx)
-
-    preds: dict[tuple[int, int], set[tuple[int, int]]] = {
-        (rank, slot_idx): set()
-        for rank, row in enumerate(rows)
-        for slot_idx, _slot in enumerate(row)
-    }
-    succs: dict[tuple[int, int], set[tuple[int, int]]] = {
-        key: set() for key in preds
-    }
-
-    def add_edge(src: tuple[int, int] | None, dst: tuple[int, int] | None) -> None:
-        if src is None or dst is None or src == dst:
-            return
-        preds[dst].add(src)
-        succs[src].add(dst)
-
-    for rank, row in enumerate(rows):
-        for slot_idx in range(1, len(row)):
-            add_edge((rank, slot_idx - 1), (rank, slot_idx))
-
-    for rank, row in enumerate(rows):
-        for slot_idx, slot in enumerate(row):
-            dst = (rank, slot_idx)
-            for op in slot:
-                if op.pass_ == "F":
-                    add_edge(key_by_op.get((op.pp - 1, op.mb, "F")), dst)
-                elif op.pass_ == "B":
-                    add_edge(key_by_op.get((op.pp, op.mb, "F")), dst)
-                    add_edge(key_by_op.get((op.pp + 1, op.mb, "B")), dst)
-                elif op.pass_ == "BI":
-                    add_edge(key_by_op.get((op.pp, op.mb, "F")), dst)
-                    add_edge(key_by_op.get((op.pp + 1, op.mb, "BI")), dst)
-                elif op.pass_ == "BW":
-                    add_edge(key_by_op.get((op.pp, op.mb, "BI")), dst)
-                else:
-                    raise ValueError(f"unsupported pass={op.pass_!r}")
-
-    columns = {key: 0 for key in preds}
-    pending = {key: set(value) for key, value in preds.items()}
-    ready = [key for key, value in pending.items() if not value]
-    visited = 0
-
-    while ready:
-        key = ready.pop(0)
-        visited += 1
-        for succ in sorted(succs[key]):
-            rank, slot_idx = key
-            columns[succ] = max(columns[succ], columns[key] + _slot_width(rows[rank][slot_idx]))
-            pending[succ].discard(key)
-            if not pending[succ]:
-                ready.append(succ)
-
-    if visited != len(preds):
-        blocked = [key for key, value in pending.items() if value]
-        raise ValueError(f"schedule dependencies contain a cycle; blocked={blocked[:8]}")
-    return columns
-
-
-def _html_table(table: list[list[_Slot | None]]) -> str:
-    max_cols = max((len(row) for row in table), default=0)
-    lines = ['<<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="6">']
-    lines.append('<TR><TD BGCOLOR="#eeeeee">rank</TD>')
-    for col in range(max_cols):
-        lines.append(f'<TD BGCOLOR="#eeeeee">t{col}</TD>')
-    lines.append("</TR>")
-    for rank, row in enumerate(table):
-        lines.append(f'<TR><TD BGCOLOR="#eeeeee">r{rank}</TD>')
-        col = 0
-        while col < len(row):
-            slot = row[col]
-            if slot is None:
-                lines.append('<TD WIDTH="72" HEIGHT="36"></TD>')
-                col += 1
-            else:
-                colspan = _slot_width(slot)
-                colspan_attr = f' COLSPAN="{colspan}"' if colspan > 1 else ""
-                width = 72 * colspan
-                if len(slot) == 1:
-                    op = slot[0]
-                    color = _PASS_COLOR[op.pass_]
-                    label = f"{escape(op.pass_)}<BR/>PP{op.pp} MB{op.mb}"
-                    lines.append(
-                        f'<TD BGCOLOR="{color}" WIDTH="{width}" HEIGHT="36"{colspan_attr}>{label}</TD>'
-                    )
-                else:
-                    # Multi-op slot: ops run interleaved and stack vertically in
-                    # one block. Use a nested table so each op gets its own
-                    # colored row spanning the full slot width.
-                    inner = ['<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="6">']
-                    for op in slot:
-                        color = _PASS_COLOR[op.pass_]
-                        label = f"{escape(op.pass_)}<BR/>PP{op.pp} MB{op.mb}"
-                        inner.append(
-                            f'<TR><TD BGCOLOR="{color}" WIDTH="{width}" HEIGHT="36">{label}</TD></TR>'
-                        )
-                    inner.append("</TABLE>")
-                    lines.append(
-                        f'<TD WIDTH="{width}"{colspan_attr} CELLPADDING="0">{"".join(inner)}</TD>'
-                    )
-                col += colspan
-        lines.append("</TR>")
-    lines.append("</TABLE>>")
-    return "".join(lines)
-
-
-def _dot_source(table: list[list[_Op | None]]) -> str:
-    return "\n".join([
-        "digraph PipelineSchedule {",
-        "  rankdir=LR;",
-        f"  schedule [shape=plain label={_html_table(table)}];",
-        "}",
-        "",
-    ])
-
-
-def _format_text_table(table: list[list[_Slot | None]]) -> str:
-    lines = []
-    for rank, row in enumerate(table):
-        cells = []
-        col = 0
-        while col < len(row):
-            slot = row[col]
-            if slot is None:
-                cells.append("--------")
-                col += 1
-            else:
-                width = _slot_width(slot)
-                # Stacked ops in one slot join with "/" to flag interleaving.
-                label = "/".join(f"{op.pass_}:s{op.pp}:m{op.mb}" for op in slot)
-                cells.append(label.ljust(8 * width, "="))
-                col += width
-        lines.append(f"rank {rank}: " + " | ".join(cells))
-    return "\n".join(lines) + "\n"
diff --git a/test/schedules/build_schedule.py b/test/schedules/build_schedule.py
deleted file mode 100644
index ebfb7c1..0000000
--- a/test/schedules/build_schedule.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""Compatibility entry point for the schedule-building test harness."""
-
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-
-_REPO_ROOT = Path(__file__).resolve().parents[2]
-if str(_REPO_ROOT) not in sys.path:
-    sys.path.insert(0, str(_REPO_ROOT))
-
-from test.test_harness import main
-
-
-if __name__ == "__main__":
-    main()
diff --git a/test/schedules/llama_pp.json b/test/schedules/llama_pp.json
deleted file mode 100644
index a93ee90..0000000
--- a/test/schedules/llama_pp.json
+++ /dev/null
@@ -1,14 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": [["PP", 0]],
-    "devices": [0],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 1]],
-    "devices": [1],
-    "stream": "pp_stream"
-  }
-]
diff --git a/test/schedules/qwen_pp2.json b/test/schedules/qwen_pp2.json
deleted file mode 100644
index a93ee90..0000000
--- a/test/schedules/qwen_pp2.json
+++ /dev/null
@@ -1,14 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": [["PP", 0]],
-    "devices": [0],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 1]],
-    "devices": [1],
-    "stream": "pp_stream"
-  }
-]
diff --git a/test/schedules/qwen_pp2_dp2_ep2.json b/test/schedules/qwen_pp2_dp2_ep2.json
deleted file mode 100644
index c183ee1..0000000
--- a/test/schedules/qwen_pp2_dp2_ep2.json
+++ /dev/null
@@ -1,38 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
-    "reduce_stream": "dp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
-    "reduce_stream": "dp_stream"
-  },
-  {
-    "op": "shard",
-    "filter": [["PP", 0], ["EP", "*"]],
-    "devices": [0, 2],
-    "stream": "ep_stream"
-  },
-  {
-    "op": "shard",
-    "filter": [["PP", 1], ["EP", "*"]],
-    "devices": [1, 3],
-    "stream": "ep_stream"
-  }
-]
diff --git a/test/schedules/qwen_pp2_dp2_ep2_dualpipev.json b/test/schedules/qwen_pp2_dp2_ep2_dualpipev.json
deleted file mode 100644
index a97f416..0000000
--- a/test/schedules/qwen_pp2_dp2_ep2_dualpipev.json
+++ /dev/null
@@ -1,74 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 2]],
-    "devices": [1, 3],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 3]],
-    "devices": [0, 2],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
-    "reduce_stream": "dp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
-    "reduce_stream": "dp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 2]],
-    "devices": [1, 3],
-    "reduce_stream": "dp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 3]],
-    "devices": [0, 2],
-    "reduce_stream": "dp_stream"
-  },
-  {
-    "op": "shard",
-    "filter": [["PP", 0], ["EP", "*"]],
-    "devices": [0, 2],
-    "stream": "ep_stream"
-  },
-  {
-    "op": "shard",
-    "filter": [["PP", 1], ["EP", "*"]],
-    "devices": [1, 3],
-    "stream": "ep_stream"
-  },
-  {
-    "op": "shard",
-    "filter": [["PP", 2], ["EP", "*"]],
-    "devices": [1, 3],
-    "stream": "ep_stream"
-  },
-  {
-    "op": "shard",
-    "filter": [["PP", 3], ["EP", "*"]],
-    "devices": [0, 2],
-    "stream": "ep_stream"
-  }
-]
diff --git a/test/schedules/qwen_pp2_dp2_ep2_interleaved.json b/test/schedules/qwen_pp2_dp2_ep2_interleaved.json
deleted file mode 100644
index 0929510..0000000
--- a/test/schedules/qwen_pp2_dp2_ep2_interleaved.json
+++ /dev/null
@@ -1,50 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 2]],
-    "devices": [0, 2],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 3]],
-    "devices": [1, 3],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
-    "reduce_stream": "dp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
-    "reduce_stream": "dp_stream"
-  },
-  {
-    "op": "shard",
-    "filter": [["PP", 0], ["EP", "*"]],
-    "devices": [0, 2],
-    "stream": "ep_stream"
-  },
-  {
-    "op": "shard",
-    "filter": [["PP", 1], ["EP", "*"]],
-    "devices": [1, 3],
-    "stream": "ep_stream"
-  }
-]
diff --git a/test/schedules/qwen_pp2_interleaved.json b/test/schedules/qwen_pp2_interleaved.json
deleted file mode 100644
index 3702849..0000000
--- a/test/schedules/qwen_pp2_interleaved.json
+++ /dev/null
@@ -1,26 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": [["PP", 0]],
-    "devices": [0],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 1]],
-    "devices": [1],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 2]],
-    "devices": [0],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 3]],
-    "devices": [1],
-    "stream": "pp_stream"
-  }
-]
diff --git a/test/schedules/qwen_pp4.json b/test/schedules/qwen_pp4.json
deleted file mode 100644
index 3702849..0000000
--- a/test/schedules/qwen_pp4.json
+++ /dev/null
@@ -1,26 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": [["PP", 0]],
-    "devices": [0],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 1]],
-    "devices": [1],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 2]],
-    "devices": [0],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 3]],
-    "devices": [1],
-    "stream": "pp_stream"
-  }
-]
diff --git a/test/schedules/qwen_pp_dp.json b/test/schedules/qwen_pp_dp.json
deleted file mode 100644
index 20286c1..0000000
--- a/test/schedules/qwen_pp_dp.json
+++ /dev/null
@@ -1,26 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
-    "stream": "dp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
-    "stream": "dp_stream"
-  }
-]
diff --git a/test/schedules/qwen_pp_dp_ep_overlap.json b/test/schedules/qwen_pp_dp_ep_overlap.json
deleted file mode 100644
index d47c2b5..0000000
--- a/test/schedules/qwen_pp_dp_ep_overlap.json
+++ /dev/null
@@ -1,52 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
-    "reduce_stream": "dp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
-    "reduce_stream": "dp_stream"
-  },
-  {
-    "op": "shard",
-    "filter": [["PP", 0], ["EP", "*"]],
-    "devices": [0, 2],
-    "stream": "ep_stream"
-  },
-  {
-    "op": "shard",
-    "filter": [["PP", 1], ["EP", "*"]],
-    "devices": [1, 3],
-    "stream": "ep_stream"
-  },
-  {
-    "op": "split",
-    "filter": [],
-    "dim_name": "MB",
-    "num_microbatches": 2
-  },
-  {
-    "op": "order",
-    "filters": [[["PP", 0], ["MB", 0], ["PASS", "F"]], [["PP", 0], ["MB", 1], ["PASS", "F"]], [["PP", 0], ["MB", 0], ["PASS", "B"]], [["PP", 0], ["MB", 1], ["PASS", "B"]]]
-  },
-  {
-    "op": "order",
-    "filters": [[["PP", 1], ["MB", 0], ["PASS", "F"]], [[["PP", 1], ["MB", 0], ["PASS", "B"]], [["PP", 1], ["MB", 1], ["PASS", "F"]]], [["PP", 1], ["MB", 1], ["PASS", "B"]]]
-  }
-]
diff --git a/test/schedules/qwen_pp_dp_ep_zero2.json b/test/schedules/qwen_pp_dp_ep_zero2.json
deleted file mode 100644
index 2a79c82..0000000
--- a/test/schedules/qwen_pp_dp_ep_zero2.json
+++ /dev/null
@@ -1,40 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
-    "stream": "dp_stream",
-    "shard_grads": true
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
-    "stream": "dp_stream",
-    "shard_grads": true
-  },
-  {
-    "op": "shard",
-    "filter": [["PP", 0], ["EP", "*"]],
-    "devices": [0, 2],
-    "stream": "dp_stream"
-  },
-  {
-    "op": "shard",
-    "filter": [["PP", 1], ["EP", "*"]],
-    "devices": [1, 3],
-    "stream": "dp_stream"
-  }
-]
diff --git a/test/schedules/qwen_pp_dp_ep_zero3.json b/test/schedules/qwen_pp_dp_ep_zero3.json
deleted file mode 100644
index 7d24f20..0000000
--- a/test/schedules/qwen_pp_dp_ep_zero3.json
+++ /dev/null
@@ -1,42 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
-    "stream": "dp_stream",
-    "shard_grads": true,
-    "shard_params": true
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
-    "stream": "dp_stream",
-    "shard_grads": true,
-    "shard_params": true
-  },
-  {
-    "op": "shard",
-    "filter": [["PP", 0], ["EP", "*"]],
-    "devices": [0, 2],
-    "stream": "dp_stream"
-  },
-  {
-    "op": "shard",
-    "filter": [["PP", 1], ["EP", "*"]],
-    "devices": [1, 3],
-    "stream": "dp_stream"
-  }
-]
diff --git a/test/schedules/qwen_pp_dp_zero2.json b/test/schedules/qwen_pp_dp_zero2.json
deleted file mode 100644
index c1c11cb..0000000
--- a/test/schedules/qwen_pp_dp_zero2.json
+++ /dev/null
@@ -1,28 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 0]],
-    "devices": [0, 2],
-    "stream": "dp_stream",
-    "shard_grads": true
-  },
-  {
-    "op": "replicate",
-    "filter": [["PP", 1]],
-    "devices": [1, 3],
-    "stream": "dp_stream",
-    "shard_grads": true
-  }
-]
diff --git a/test/test_graph_transform.py b/test/test_graph_transform.py
index 8752fc3..496f3b1 100644
--- a/test/test_graph_transform.py
+++ b/test/test_graph_transform.py
@@ -1,62 +1,29 @@
-"""
-Correctness test for _split_gm_by_stages and _profile_and_split_gm.
-
-Verifies that when the stage GraphModules produced by each split function are
-composed (run sequentially, feeding each stage's outputs into the next), the
-final output matches the original model's output on the same input.
-
-Both split functions are designed to operate on FX graphs captured by
-torch.compile (AOT autograd), where model parameters are lifted to placeholder
-inputs rather than represented as get_attr nodes.  This test captures graphs
-in that same way via a custom torch.compile backend.
-
-Usage:
-    python -m test.test_graph_transform
-"""
-import argparse
-import copy
-import sys
+import pytest
 import torch
-import torch.nn as nn
 import torch.fx as fx
+import torch.nn as nn
 
-from src.piper_graph_transform import _split_gm_by_stages, _profile_and_split_gm
-
-
-# ---------------------------------------------------------------------------
-# Simple test model
-# ---------------------------------------------------------------------------
+from src.piper import _reset_annotation_state, annotate
+from src.fx import split_gm_by_annotations
 
-class _Net(nn.Module):
-    """Three-layer MLP that is easily splittable into two stages."""
 
-    def __init__(self, dim: int = 32):
+class _AnnotatedNet(nn.Module):
+    def __init__(self, dim: int = 8):
         super().__init__()
         self.fc1 = nn.Linear(dim, dim, bias=False)
         self.fc2 = nn.Linear(dim, dim, bias=False)
         self.fc3 = nn.Linear(dim, dim, bias=False)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = torch.relu(self.fc1(x))
-        x = torch.relu(self.fc2(x))
-        x = self.fc3(x)
-        return x
-
+        with annotate("PP"):
+            x = torch.relu(self.fc1(x))
+            with annotate("EP"):
+                x = torch.relu(self.fc2(x))
+        with annotate("PP"):
+            return self.fc3(x)
 
-# ---------------------------------------------------------------------------
-# FX graph capture via torch.compile
-# ---------------------------------------------------------------------------
 
 def _capture_gm(model: nn.Module, x: torch.Tensor):
-    """
-    Capture the FX GraphModule that torch.compile produces for *model*(*x*).
-
-    Returns (gm, example_inputs, ref_output) where:
-      - gm            : the FX GraphModule with proper example_value metadata
-      - example_inputs: real tensors corresponding to gm's placeholder nodes
-                        (lifted parameters first, then the data input x)
-      - ref_output    : model(x) evaluated once with real weights
-    """
     captured: dict = {}
 
     def _backend(gm, example_inputs):
@@ -64,194 +31,95 @@ def _backend(gm, example_inputs):
         captured["inputs"] = list(example_inputs)
         return gm.forward
 
-    with torch.no_grad():
-        ref_output = model(x).detach().clone()
-
+    _reset_annotation_state()
     compiled = torch.compile(model, backend=_backend, fullgraph=True)
     with torch.no_grad():
-        compiled(x)
-
-    return captured["gm"], captured["inputs"], ref_output
-
-
-# ---------------------------------------------------------------------------
-# Stage annotation
-# ---------------------------------------------------------------------------
-
-def _annotate_stages(gm: fx.GraphModule, num_stages: int) -> None:
-    """
-    Divide compute nodes (everything except placeholder / get_attr / output)
-    into *num_stages* equal-ish chunks and stamp ``node.meta['custom']['stage']``.
-    """
-    compute = [n for n in gm.graph.nodes if n.op not in ("placeholder", "get_attr", "output")]
-    chunk = max(1, (len(compute) + num_stages - 1) // num_stages)
-    for i, node in enumerate(compute):
-        stage = min(i // chunk, num_stages - 1)
-        node.meta.setdefault("custom", {})["stage"] = stage
+        ref = compiled(x).detach().clone()
 
+    return captured["gm"], captured["inputs"], ref
 
-# ---------------------------------------------------------------------------
-# Running the split stages
-# ---------------------------------------------------------------------------
 
-def _build_ph_map(gm: fx.GraphModule, example_inputs: list) -> dict:
-    """
-    Return a dict mapping each placeholder node's *name* to its corresponding
-    real tensor from *example_inputs*.  Used to fill parameter slots in stage
-    GraphModules by name matching.
-    """
-    ph_nodes = [n for n in gm.graph.nodes if n.op == "placeholder"]
-    return {node.name: val for node, val in zip(ph_nodes, example_inputs)}
+def _placeholder_values(gm: fx.GraphModule, example_inputs: list) -> dict[str, object]:
+    placeholders = [node for node in gm.graph.nodes if node.op == "placeholder"]
+    return {node.name: value for node, value in zip(placeholders, example_inputs)}
 
 
-def _run_stage_chain(
-    submodules,
-    orig_ph_map: dict,
-) -> torch.Tensor:
-    """
-    Execute stage GraphModules sequentially, feeding each stage's outputs into
-    the next stage's inter-stage placeholder inputs.
-
-    For each stage_gm:
-      - Placeholders whose names appear in *orig_ph_map* (parameters / original
-        data inputs) are filled from that map.
-      - Remaining placeholders (inter-stage activations from the previous stage)
-        are filled from the previous stage's output in order.
-
-    Returns the final stage's output tensor.
-    """
+def _run_segments(segments, placeholder_values: dict[str, object]) -> torch.Tensor:
     prev_output = None
 
-    for stage_id, stage_gm, _input_idxs, _param_idxs, _graphargs, _placeholders in sorted(
-        submodules, key=lambda t: t[0]
-    ):
-        ph_nodes = list(stage_gm.graph.find_nodes(op="placeholder"))
-        args: list = [None] * len(ph_nodes)
-
-        # 1. Fill placeholders by name from the original graph's inputs.
-        for i, node in enumerate(ph_nodes):
-            if node.name in orig_ph_map:
-                args[i] = orig_ph_map[node.name]
-
-        # 2. Fill remaining (inter-stage) placeholders from the previous stage's
-        #    output, in left-to-right placeholder order.
-        if prev_output is not None:
-            inter = (
-                (prev_output,)
-                if isinstance(prev_output, torch.Tensor)
-                else tuple(prev_output)
-            )
-            unfilled = [i for i, v in enumerate(args) if v is None]
-            for pos, idx in enumerate(unfilled):
-                if pos < len(inter):
-                    args[idx] = inter[pos]
+    for segment in segments:
+        ph_nodes = [node for node in segment.gm.graph.nodes if node.op == "placeholder"]
+        args = []
+        activation_inputs = (
+            ()
+            if prev_output is None
+            else prev_output
+            if isinstance(prev_output, tuple)
+            else (prev_output,)
+        )
+        activation_iter = iter(activation_inputs)
+
+        for node in ph_nodes:
+            if node.name in placeholder_values:
+                args.append(placeholder_values[node.name])
+            else:
+                args.append(next(activation_iter))
 
         with torch.no_grad():
-            prev_output = stage_gm(*args)
+            prev_output = segment.gm(*args)
 
-    # Unwrap single-element tuples that some output nodes produce.
-    if isinstance(prev_output, (list, tuple)) and len(prev_output) == 1:
+    if isinstance(prev_output, (tuple, list)) and len(prev_output) == 1:
         prev_output = prev_output[0]
     return prev_output
 
 
-# ---------------------------------------------------------------------------
-# Tests
-# ---------------------------------------------------------------------------
-
-def test_split_gm_by_stages(verbose: bool = False) -> None:
-    """
-    _split_gm_by_stages: stage GMs composed sequentially reproduce the
-    original model's output.
-    """
+def test_split_gm_by_annotations_handles_nested_annotation_segments() -> None:
     torch.manual_seed(0)
-    dim = 32
-    model = _Net(dim)
-    x = torch.randn(4, dim)
+    model = _AnnotatedNet()
+    x = torch.randn(2, 8)
 
     gm, example_inputs, ref = _capture_gm(model, x)
-    orig_ph_map = _build_ph_map(gm, example_inputs)
+    _, segments = split_gm_by_annotations(gm)
 
-    # Annotate the captured graph so _split_gm_by_stages knows where to split.
-    _annotate_stages(gm, num_stages=2)
+    assert [segment.tag for segment in segments] == [
+        {"PP": 0},
+        {"PP": 0, "EP": 0},
+        {"PP": 1},
+    ]
+    assert [segment.stage_id for segment in segments] == [0, 0, 1]
+    assert [segment.a2a_boundary_after["from_tag"] for segment in segments[:-1]] == [
+        {"PP": 0},
+        {"PP": 0, "EP": 0},
+    ]
+    assert [segment.a2a_boundary_after["to_tag"] for segment in segments[:-1]] == [
+        {"PP": 0, "EP": 0},
+        {"PP": 1},
+    ]
 
-    _, submodules = _split_gm_by_stages(gm)
+    result = _run_segments(segments, _placeholder_values(gm, example_inputs))
 
-    assert len(submodules) == 2, (
-        f"_split_gm_by_stages: expected 2 stages, got {len(submodules)}"
-    )
+    assert torch.allclose(ref, result, atol=1e-6)
 
-    result = _run_stage_chain(submodules, orig_ph_map)
 
-    max_diff = (ref - result.detach()).abs().max().item()
-    assert torch.allclose(ref, result.detach(), atol=1e-5), (
-        f"_split_gm_by_stages output mismatch: max abs diff = {max_diff:.2e}"
-    )
+def test_split_gm_by_annotations_ignores_non_piper_custom_metadata() -> None:
+    gm = fx.symbolic_trace(nn.Sequential(nn.ReLU()))
+    relu = next(node for node in gm.graph.nodes if node.op == "call_module")
+    relu.meta["custom"] = {"name": "PP", "index": 0}
 
-    if verbose:
-        print(f"  max_diff={max_diff:.2e}")
+    _, segments = split_gm_by_annotations(gm)
 
+    assert segments == []
 
-def test_profile_and_split_gm(verbose: bool = False) -> None:
-    """
-    _profile_and_split_gm: auto-profiled stages composed sequentially
-    reproduce the original model's output.
 
-    _profile_and_split_gm may move call_module submodules to meta device
-    during profiling.  Because torch.compile's AOT-traced graphs lift
-    parameters to placeholder inputs (no call_module / get_attr nodes for
-    weights), this is not an issue for simple models.  A deepcopy of the
-    model is used for tracing to avoid any side-effects on the original.
-    """
+def test_split_gm_by_annotations_rejects_mixed_annotated_and_unannotated_compute() -> None:
     torch.manual_seed(0)
-    dim = 32
-    model = _Net(dim)
-    x = torch.randn(4, dim)
-
-    gm, example_inputs, ref = _capture_gm(model, x)
-    orig_ph_map = _build_ph_map(gm, example_inputs)
-
-    # Use a fresh capture on a deep copy so profiling doesn't mutate `gm`.
-    gm_for_profile, _, _ = _capture_gm(copy.deepcopy(model), x)
-
-    # No stage annotations; _profile_and_split_gm determines the split.
-    _, submodules = _profile_and_split_gm(gm_for_profile, num_stages=2)
-
-    assert len(submodules) == 2, (
-        f"_profile_and_split_gm: expected 2 stages, got {len(submodules)}"
+    gm, _, _ = _capture_gm(_AnnotatedNet(), torch.randn(2, 8))
+    first_compute = next(
+        node
+        for node in gm.graph.nodes
+        if node.op not in ("placeholder", "get_attr", "output")
     )
+    first_compute.meta.pop("custom", None)
 
-    # Run stage chain with the *original* example_inputs (real weights).
-    result = _run_stage_chain(submodules, orig_ph_map)
-
-    max_diff = (ref - result.detach()).abs().max().item()
-    assert torch.allclose(ref, result.detach(), atol=1e-5), (
-        f"_profile_and_split_gm output mismatch: max abs diff = {max_diff:.2e}"
-    )
-
-    if verbose:
-        print(f"  max_diff={max_diff:.2e}")
-
-
-# ---------------------------------------------------------------------------
-# Entry point
-# ---------------------------------------------------------------------------
-
-def main() -> None:
-    p = argparse.ArgumentParser(description="Graph-transform correctness tests")
-    p.add_argument("--verbose", action="store_true")
-    args = p.parse_args()
-
-    print("Running test_split_gm_by_stages ...", flush=True)
-    test_split_gm_by_stages(verbose=args.verbose)
-    print("  PASSED")
-
-    print("Running test_profile_and_split_gm ...", flush=True)
-    test_profile_and_split_gm(verbose=args.verbose)
-    print("  PASSED")
-
-    print("\nAll tests passed.")
-
-
-if __name__ == "__main__":
-    main()
+    with pytest.raises(ValueError, match="without Piper annotations"):
+        split_gm_by_annotations(gm)
diff --git a/test/test_mixtral.py b/test/test_mixtral.py
deleted file mode 100644
index b55d37d..0000000
--- a/test/test_mixtral.py
+++ /dev/null
@@ -1,191 +0,0 @@
-"""
-test_dag.py – end-to-end test for the DAG-based execution path (piper_exec_dag).
-
-Run exactly like test_mixtral.py:
-
-    python -m test.test_dag [--model tiny] [--pp 2] [--dp 1] ...
-
-The main difference is that each training step is dispatched through
-piper_exec_dag() rather than the legacy piper_exec() scheduler.
-"""
-import logging
-import ray
-import torch
-import argparse
-import json
-import time
-import os
-import numpy as np
-
-logger = logging.getLogger(__name__)
-
-from ray.util.placement_group import (
-    placement_group,
-    placement_group_table,
-    remove_placement_group,
-)
-
-from src.piper_coordinator import PiperProgramCoordinator
-from src.piper_compile import piper_setup
-from src.piper import piper_exec_dag
-from src.piper_utils import piper_metadata
-
-from .models.mixtral import Transformer, ModelArgs
-from .schedule_helpers import (
-    build_1f1b_schedule,
-    build_gpipe_schedule,
-    print_schedule,
-    NO_PP_SCHEDULE,
-    INTERLEAVED_1F1B_PP2_MB4_SCHEDULE,
-    DUALPIPEV_MB6_SCHEDULE,
-    ZEROBUBBLE_MB4_SCHEDULE,
-)
-
-
-def main(args, pg):
-    world_size = args.dp * args.pp
-    mbs = args.mbs
-    batch_size = args.batch_size
-
-    match args.model:
-        case "30M":
-            config = ModelArgs.from_name("30M")
-        case "280M":
-            config = ModelArgs.from_name("280M")
-        case "6B":
-            config = ModelArgs.from_name("6B")
-        case "7b":
-            config = ModelArgs.from_name("Mixtral-8x7B-v0.1")
-        case "22b":
-            config = ModelArgs.from_name("Mixtral-8x22B-v0.1")
-
-    match args.schedule:
-        case "no-pp":
-            schedule = NO_PP_SCHEDULE
-        case "1f1b":
-            schedule = build_1f1b_schedule(args.mbs, args.pp)
-        case "gpipe":
-            schedule = build_gpipe_schedule(args.mbs, args.pp)
-        case "interleaved-1f1b":
-            schedule = INTERLEAVED_1F1B_PP2_MB4_SCHEDULE
-        case "dualpipev":
-            schedule = DUALPIPEV_MB6_SCHEDULE
-        case "zerobubble":
-            schedule = ZEROBUBBLE_MB4_SCHEDULE
-
-    print("Schedule:")
-    print_schedule(schedule)
-
-    x = torch.randint(0, config.vocab_size, (batch_size, config.block_size))
-    input_pos = torch.arange(config.block_size)
-    y = torch.randn(batch_size, config.block_size, config.vocab_size)
-
-    loss_fn = torch.nn.CrossEntropyLoss()
-
-    piper_setup(
-        Transformer,
-        model_args=(config,),
-        optim_fn=torch.optim.Adam,
-        example_inputs=[x, input_pos],
-        example_outputs=y,
-        schedule=schedule,
-        naive_gradient_sync=args.naive_grad_sync,
-        activation_checkpointing=False,
-        bucketing=args.bucketing,
-        model_dtype=torch.bfloat16,
-        pg=pg,
-        nsight=args.nsight,
-    )
-
-    print(f"Running {args.warmup} warmup iterations (dag path)...")
-    for _ in range(args.warmup):
-        piper_exec_dag(loss_fn)
-        time.sleep(1)
-
-    actors = piper_metadata.actors
-
-    print(f"Running {args.iters} timed iterations (dag path)...")
-    iter_times = []
-    for _ in range(args.iters):
-        start = time.perf_counter()
-        losses = piper_exec_dag(loss_fn)
-        end = time.perf_counter()
-        iter_times.append(end - start)
-        time.sleep(1)
-
-    dp_rank = int(os.environ["PIPER_DP_RANK"])
-    print(
-        f"rank {dp_rank} iter time= {np.mean(iter_times):.5f} ± {np.std(iter_times):.5f} s "
-        f"({len(iter_times)} samples)\n"
-        f"rank {dp_rank} throughput= "
-        f"{(args.batch_size * args.mbs * config.block_size) / np.mean(iter_times):.3f} tokens/s"
-    )
-
-    if args.tracing:
-        ray.get([actor.set_tracing.remote(True) for actor in actors.values()])
-        print(f"Running {args.trace_iters} tracing iterations...")
-        for _ in range(args.trace_iters):
-            piper_exec_dag(loss_fn)
-            ray.get([actor.flush_timing_events.remote() for actor in actors.values()])
-            time.sleep(1)
-        trace_data_ret = ray.get([actor.get_trace_data.remote() for actor in actors.values()])
-        for rank, trace_data in trace_data_ret:
-            for key in trace_data:
-                all_times = trace_data[key]
-                print(
-                    f"rank {rank} {key} time= {np.mean(all_times):.3f} ± "
-                    f"{np.std(all_times):.3f} ms ({len(all_times)} samples)"
-                )
-
-    os.makedirs("out", exist_ok=True)
-    timeline_filename = f"out/mixtral-dag-pp{args.pp}-dp{args.dp}-{args.schedule}"
-    ray.timeline(timeline_filename)
-    print(f"Ray timeline saved to: {timeline_filename}")
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description="Test DAG-based piper execution with the Mixtral model"
-    )
-    parser.add_argument(
-        "--model",
-        choices=["30M", "280M", "6B", "7b", "22b"],
-        default="30M",
-    )
-    parser.add_argument(
-        "--schedule",
-        choices=["gpipe", "1f1b", "no-pp", "interleaved-1f1b", "dualpipev", "zerobubble"],
-        default="1f1b",
-    )
-    parser.add_argument("--dp", type=int, default=1)
-    parser.add_argument("--pp", type=int, default=2)
-    parser.add_argument("--batch-size", type=int, default=4)
-    parser.add_argument("--mbs", type=int, default=4)
-    parser.add_argument("--warmup", type=int, default=3)
-    parser.add_argument("--iters", type=int, default=5)
-    parser.add_argument("--trace-iters", type=int, default=3)
-    parser.add_argument("--tracing", action="store_true", default=False)
-    parser.add_argument("--naive-grad-sync", action="store_true", default=False)
-    parser.add_argument("--bucketing", action="store_true", default=False,
-                        help="Split stages into per-param-bucket sub-modules for overlapped all-reduce")
-    parser.add_argument("--nsight", action="store_true", default=False,
-                        help="Whether to use Nsight Systems for tracing")
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    print(args)
-    ray.init(
-        namespace="mixtral",
-        log_to_driver=True,
-        include_dashboard=False,
-        _temp_dir="/m-coriander/coriander/mfris/piper/ray_tmp",
-    )
-    pg = placement_group([{"CPU": args.pp, "GPU": args.pp}] * args.dp, strategy="PACK")
-    ray.get(pg.ready(), timeout=600)
-    print(placement_group_table(pg))
-    piper_coordinator = PiperProgramCoordinator.remote(pp_degree=args.pp, dp_degree=args.dp)
-    handles = piper_coordinator.run_program.remote(main, args, pg)
-    ray.get(handles)
-    ray.shutdown()
diff --git a/test/test_order_directive.py b/test/test_order_directive.py
index f140e16..54223b9 100644
--- a/test/test_order_directive.py
+++ b/test/test_order_directive.py
@@ -1,14 +1,21 @@
+import pytest
+import torch.fx as fx
+
 from src.piper import (
     TrainingDAG,
     TrainingDAGEdge,
     TrainingDAGNode,
+    apply_schedule_directives,
     _apply_order_directive,
     _apply_split_backward_stencil,
     _apply_split_directive,
-    _normalize_order_directive,
+    _bucket_matched_fwd_nodes,
+    _parse_order_directive,
     _serial_topological_order,
+    _validate_schedule_tags_exist,
     _validate_split_backward_order_stencil,
 )
+import src.directives as directives
 
 
 def _compute(uid: str, tag: dict) -> TrainingDAGNode:
@@ -35,21 +42,28 @@ def _bwd(uid: str, fwd_uid: str, tag: dict) -> TrainingDAGNode:
     )
 
 
-def test_normalize_order_directive_accepts_nested_filter_groups() -> None:
+def _dummy_gm() -> fx.GraphModule:
+    graph = fx.Graph()
+    x = graph.placeholder("x")
+    graph.output(x)
+    return fx.GraphModule({}, graph)
+
+
+def test_parse_order_directive_accepts_nested_filter_groups() -> None:
     directive = {
         "op": "order",
         "filters": [
             [
-                [["PP", 0], ["MB", 0], ["PASS", "F"]],
-                [["PP", 0], ["MB", 1], ["PASS", "F"]],
+                {"PP": 0, "MB": 0, "PASS": "F"},
+                {"PP": 0, "MB": 1, "PASS": "F"},
             ],
             [
-                [["PP", 0], ["MB", 0], ["PASS", "B"]],
+                {"PP": 0, "MB": 0, "PASS": "B"},
             ],
         ],
     }
 
-    groups = _normalize_order_directive(directive)
+    groups = _parse_order_directive(directive)
 
     assert groups == [
         [
@@ -62,21 +76,17 @@ def test_normalize_order_directive_accepts_nested_filter_groups() -> None:
     ]
 
 
-def test_normalize_order_directive_wraps_legacy_flat_filters() -> None:
+def test_parse_order_directive_rejects_flat_filter_groups() -> None:
     directive = {
         "op": "order",
         "filters": [
-            [["PP", 0], ["MB", 0], ["PASS", "F"]],
-            [["PP", 0], ["MB", 0], ["PASS", "B"]],
+            {"PP": 0, "MB": 0, "PASS": "F"},
+            {"PP": 0, "MB": 0, "PASS": "B"},
         ],
     }
 
-    groups = _normalize_order_directive(directive)
-
-    assert groups == [
-        [{"PP": 0, "MB": 0, "PASS": "F"}],
-        [{"PP": 0, "MB": 0, "PASS": "B"}],
-    ]
+    with pytest.raises(ValueError, match="must be a non-empty list"):
+        _parse_order_directive(directive)
 
 
 def test_split_backward_stencil_handles_nested_filter_groups() -> None:
@@ -84,11 +94,11 @@ def test_split_backward_stencil_handles_nested_filter_groups() -> None:
         "op": "order",
         "filters": [
             [
-                [["PP", 0], ["MB", 0], ["PASS", "BI"]],
-                [["PP", 0], ["MB", 0], ["PASS", "BW"]],
+                {"PP": 0, "MB": 0, "PASS": "BI"},
+                {"PP": 0, "MB": 0, "PASS": "BW"},
             ],
             [
-                [["PP", 0], ["MB", 1], ["PASS", "F"]],
+                {"PP": 0, "MB": 1, "PASS": "F"},
             ],
         ],
     }
@@ -102,9 +112,9 @@ def test_split_backward_stencil_allows_mixed_fused_and_split_microbatches() -> N
     directive = {
         "op": "order",
         "filters": [
-            [[["PP", 0], ["MB", 0], ["PASS", "B"]]],
-            [[["PP", 0], ["MB", 1], ["PASS", "BI"]]],
-            [[["PP", 0], ["MB", 1], ["PASS", "BW"]]],
+            [{"PP": 0, "MB": 0, "PASS": "B"}],
+            [{"PP": 0, "MB": 1, "PASS": "BI"}],
+            [{"PP": 0, "MB": 1, "PASS": "BW"}],
         ],
     }
     dag = TrainingDAG()
@@ -124,6 +134,39 @@ def test_split_backward_stencil_allows_mixed_fused_and_split_microbatches() -> N
     assert dag.nodes[f"{split_bwd_uid}.bw"].compute_subkind == "BWD_W"
 
 
+def test_bucket_rewrite_resolves_split_microbatch_bwd_by_metadata(monkeypatch) -> None:
+    dag = TrainingDAG()
+    fwd = _compute("fwd", {"PP": 0, "PASS": "F"})
+    fwd.node_meta.update({
+        "stage_id": 0,
+        "segment_id": 0,
+        "gm": _dummy_gm(),
+        "graphargs": [],
+        "input_idxs": [],
+        "param_idxs": [],
+    })
+    dag.add_node(fwd)
+    dag.add_node(_bwd("bwd", "fwd", {"PP": 0, "PASS": "B"}))
+    dag.add_edge(TrainingDAGEdge("fwd", "bwd", "data"))
+
+    _apply_split_directive(dag, {}, "MB", 2)
+
+    def fake_bucket_stage(*_args, **_kwargs):
+        return [
+            (_dummy_gm(), [], [], []),
+            (_dummy_gm(), [], [], []),
+        ]
+
+    monkeypatch.setattr(directives, "bucket_stage", fake_bucket_stage)
+
+    _bucket_matched_fwd_nodes(dag, [{"PP": 0}], 25)
+
+    assert "fwd.bucket0" in dag.nodes
+    assert "fwd.bucket0.bwd" in dag.nodes
+    assert "fwd.splitMB1.bucket0" in dag.nodes
+    assert "fwd.splitMB1.bucket0.bwd" in dag.nodes
+
+
 def test_apply_order_directive_groups_nested_subdags_with_dummy_source_sink() -> None:
     dag = TrainingDAG()
     for node in [
@@ -172,3 +215,40 @@ def test_apply_order_directive_groups_nested_subdags_with_dummy_source_sink() ->
     assert topo.index("lane0.last") < topo.index(sink_uid)
     assert topo.index("lane1") < topo.index(sink_uid)
     assert topo.index(sink_uid) < topo.index("next")
+
+
+def test_validate_schedule_tags_exist_rejects_tags_missing_from_model() -> None:
+    dag = TrainingDAG()
+    dag.add_node(_compute("fwd", {"PP": 0, "PASS": "F"}))
+
+    directives = [
+        {"op": "place", "filter": {"TP": 0}, "devices": [0]},
+        {"op": "order", "filters": [[{"PP": 0, "MB": 0, "PASS": "F"}]]},
+    ]
+
+    with pytest.raises(ValueError, match="not found in model annotations"):
+        _validate_schedule_tags_exist(dag, directives)
+
+
+def test_validate_schedule_tags_exist_ignores_runtime_tags() -> None:
+    dag = TrainingDAG()
+    dag.add_node(_compute("fwd", {"PP": 0, "PASS": "F"}))
+
+    _validate_schedule_tags_exist(
+        dag,
+        [
+            {"op": "split", "filter": {}, "dim_name": "MB", "num_microbatches": 2},
+            {"op": "order", "filters": [[{"PP": 0, "MB": 0, "PASS": "F"}]]},
+        ],
+    )
+
+
+def test_apply_schedule_directives_rejects_unmatched_place_directive() -> None:
+    dag = TrainingDAG()
+    dag.add_node(_compute("fwd", {"PP": 0, "PASS": "F"}))
+
+    with pytest.raises(ValueError, match="matched zero nodes"):
+        apply_schedule_directives(
+            dag,
+            [{"op": "place", "filter": {"PP": 1}, "devices": [0]}],
+        )
diff --git a/test/test_piper_schedule.py b/test/test_piper_schedule.py
new file mode 100644
index 0000000..46b2eed
--- /dev/null
+++ b/test/test_piper_schedule.py
@@ -0,0 +1,72 @@
+import json
+
+import pytest
+
+from src.schedule import load_schedule_directives, load_schedule_info
+
+
+def test_load_schedule_directives_accepts_current_filter_shape(tmp_path) -> None:
+    schedule_path = tmp_path / "schedule.json"
+    schedule_path.write_text(
+        json.dumps([
+            {"op": "place", "filter": {"PP": 0}, "devices": [0, 1]},
+            {
+                "op": "order",
+                "filters": [
+                    [{"PP": 0, "MB": 0, "PASS": "F"}],
+                    [{"PP": 0, "MB": 0, "PASS": "B"}],
+                ],
+            },
+            {"op": "split", "filter": {}, "dim_name": "MB", "num_microbatches": 2},
+        ]),
+        encoding="utf-8",
+    )
+
+    directives = load_schedule_directives(str(schedule_path))
+
+    assert directives[0]["filter"] == {"PP": 0}
+    assert directives[1]["filters"] == [
+        [{"PP": 0, "MB": 0, "PASS": "F"}],
+        [{"PP": 0, "MB": 0, "PASS": "B"}],
+    ]
+
+
+def test_load_schedule_info_derives_parallel_degrees(tmp_path) -> None:
+    schedule_path = tmp_path / "qwen_example.json"
+    schedule_path.write_text(
+        json.dumps([
+            {"op": "place", "filter": {"PP": 0}, "devices": [0, 1]},
+            {"op": "place", "filter": {"PP": 1}, "devices": [2, 3]},
+            {"op": "split", "filter": {}, "dim_name": "MB", "num_microbatches": 4},
+        ]),
+        encoding="utf-8",
+    )
+
+    info = load_schedule_info(str(schedule_path))
+
+    assert info == {
+        "name": "qwen_example",
+        "path": str(schedule_path),
+        "num_stages": 2,
+        "pp_degree": 2,
+        "dp_degree": 2,
+        "num_microbatches": 4,
+    }
+
+
+def test_load_schedule_directives_rejects_placeholder_microbatch_count(tmp_path) -> None:
+    schedule_path = tmp_path / "schedule.json"
+    schedule_path.write_text(
+        json.dumps([
+            {
+                "op": "split",
+                "filter": {},
+                "dim_name": "MB",
+                "num_microbatches": "__MBS__",
+            }
+        ]),
+        encoding="utf-8",
+    )
+
+    with pytest.raises(ValueError, match="__MBS__"):
+        load_schedule_directives(str(schedule_path))

From bbd4ae840a9894fae1e83d883f6a893abe7b4860 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Wed, 3 Jun 2026 21:18:08 +0000
Subject: [PATCH 099/100] artifact dir with commands to run eval

---
 .gitignore                                   |    1 +
 README.md                                    |   13 +
 REFACTOR_PLAN.md                             |  470 -----
 artifact/Dockerfile                          |  104 ++
 artifact/README.md                           |  103 ++
 artifact/e2e_eval.py                         | 1122 ++++++++++++
 artifact/patches/torchtitan-qwen-e2e.patch   |  718 ++++++++
 artifact/run_deepspeed.py                    |  621 +++++++
 artifact/run_megatron.py                     |  349 ++++
 artifact/scripts/make_piper_base_schedule.py |   87 +
 artifact/scripts/run_deepspeed.sh            |   37 +
 artifact/scripts/run_megatron.sh             |   57 +
 artifact/scripts/run_piper.sh                |  127 ++
 artifact/scripts/run_torchtitan.sh           |   62 +
 artifact/sitecustomize.py                    |   64 +
 examples/models/qwen3.py                     |  101 +-
 examples/test_qwen.py                        |   10 +-
 run_piper_remote.sh                          |   83 -
 scripts/copy-logs.sh                         |   52 -
 scripts/export_qwen_sweep_results.py         |  310 ----
 scripts/nccl_benchmark.py                    |  223 ---
 scripts/plot_qwen_e2e_torchtitan_vs_piper.py |  494 ------
 scripts/print_qwen3_9b_output_layer_size.py  |   80 -
 scripts/resync.sh                            |   24 -
 scripts/run-qwen-ec2.sh                      |  326 ----
 scripts/run_ag_rs_bucket_benchmark.sh        |   77 -
 scripts/run_local.sh                         |   17 -
 scripts/run_nccl_benchmark.sh                |  168 --
 scripts/run_qwen_e2e_eval.py                 | 1634 ------------------
 scripts/run_qwen_ec2_sweep.py                |  377 ----
 scripts/test_env.py                          |  161 --
 src/fx.py                                    |    8 +
 32 files changed, 3564 insertions(+), 4516 deletions(-)
 delete mode 100644 REFACTOR_PLAN.md
 create mode 100644 artifact/Dockerfile
 create mode 100644 artifact/README.md
 create mode 100755 artifact/e2e_eval.py
 create mode 100644 artifact/patches/torchtitan-qwen-e2e.patch
 create mode 100644 artifact/run_deepspeed.py
 create mode 100644 artifact/run_megatron.py
 create mode 100755 artifact/scripts/make_piper_base_schedule.py
 create mode 100755 artifact/scripts/run_deepspeed.sh
 create mode 100755 artifact/scripts/run_megatron.sh
 create mode 100755 artifact/scripts/run_piper.sh
 create mode 100755 artifact/scripts/run_torchtitan.sh
 create mode 100644 artifact/sitecustomize.py
 delete mode 100644 run_piper_remote.sh
 delete mode 100755 scripts/copy-logs.sh
 delete mode 100644 scripts/export_qwen_sweep_results.py
 delete mode 100644 scripts/nccl_benchmark.py
 delete mode 100644 scripts/plot_qwen_e2e_torchtitan_vs_piper.py
 delete mode 100644 scripts/print_qwen3_9b_output_layer_size.py
 delete mode 100755 scripts/resync.sh
 delete mode 100755 scripts/run-qwen-ec2.sh
 delete mode 100644 scripts/run_ag_rs_bucket_benchmark.sh
 delete mode 100755 scripts/run_local.sh
 delete mode 100755 scripts/run_nccl_benchmark.sh
 delete mode 100644 scripts/run_qwen_e2e_eval.py
 delete mode 100644 scripts/run_qwen_ec2_sweep.py
 delete mode 100644 scripts/test_env.py

diff --git a/.gitignore b/.gitignore
index 6b55f73..171b266 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,5 +10,6 @@ figs/
 logs/
 .vscode/
 e2e-eval/
+artifact/ec2-scripts/
 .codex
 ec2-trust-policy.json
diff --git a/README.md b/README.md
index 28369c7..80fca53 100644
--- a/README.md
+++ b/README.md
@@ -8,6 +8,8 @@ Piper is a user-controllable distributed training system that decouples distribu
 
 The following installation has been tested with Python 3.10 on Linux with CUDA 12.8 drivers. 
 
+### venv
+
 ```bash
 python3.10 -m venv .venv
 source .venv/bin/activate
@@ -15,6 +17,17 @@ python -m pip install --upgrade pip
 python -m pip install -e .
 ```
 
+### conda
+
+If you do not already have a local Python 3.10 interpreter, create one with conda:
+
+```bash
+conda create -n piper python=3.10 -y
+conda activate piper
+python -m pip install --upgrade pip
+python -m pip install -e .
+```
+
 ## Quickstart
 
 Run the test harness with the `pp2` base schedule and a generated 1F1B order schedule:
diff --git a/REFACTOR_PLAN.md b/REFACTOR_PLAN.md
deleted file mode 100644
index 1331e3b..0000000
--- a/REFACTOR_PLAN.md
+++ /dev/null
@@ -1,470 +0,0 @@
-# Piper Refactor Plan
-
-This document records the agreed refactor direction for the Piper implementation
-under `src/`. It is a planning document, not an implementation checklist that
-must be completed in one change.
-
-## Goals
-
-- Improve module boundaries and make each file responsible for one coherent
-  part of the system.
-- Remove `piper_` from implementation filenames. Only the main backend module
-  should keep the name `piper.py`.
-- Keep `src.piper` as the public user-facing API surface for `annotate`, backend
-  registration, and execution entry points.
-- Preserve the DAG as the single source of scheduling policy, including
-  communication, ZeRO allocation/free behavior, and task order.
-- Reduce `PiperActor` complexity by grouping state and extracting execution
-  helpers while keeping the top-level DAG execution loop easy to read.
-- Make illegal states fail early through schedule/API validation rather than
-  through indirect runtime failures.
-
-## Refactoring Principles
-
-- Separate data structures, parsing, graph transforms, runtime orchestration,
-  and distributed execution.
-- Prefer mechanical moves before behavioral rewrites so diffs remain reviewable.
-- Avoid compatibility shims unless explicitly required.
-- Avoid parallel dictionaries where one typed state object can express ownership.
-- Keep the schedule execution semantics visible: the top-level executor should
-  still iterate over the sorted DAG and dispatch by task type.
-- Keep ZeRO policy out of parameter storage. The DAG decides when to allocate,
-  free, gather, scatter, and sync.
-
-## Target Module Layout
-
-### `piper.py`
-
-Public API and backend registration only:
-
-- `annotate`
-- annotation state reset
-- `piper` backend callback
-- `piper_exec_dag`
-- minimal public glue
-
-`piper.py` should delegate DAG construction, schedule directive application,
-ordering, visualization, and runtime setup to focused modules.
-
-### `actor.py`
-
-Ray actor API shell and lifecycle:
-
-- actor construction
-- process group setup
-- loading inputs, labels, constants, stages, and DAGs
-- profiler/NVTX public methods
-- delegating task execution to `DagExecutor`
-
-The actor should own composed state objects, not a large collection of unrelated
-parallel dictionaries.
-
-### `coordinator.py`
-
-Distributed program coordination:
-
-- `PiperProgramCoordinator`
-- `run_dp_rank`
-- placement group construction
-
-### `compile.py`
-
-Compile-time orchestration:
-
-- `piper_setup`
-- compile-time Ray stores
-- model construction/tracing setup
-- pushing compiled DAG data to actors
-
-### `schedule.py`
-
-Schedule JSON loading and schema-level validation:
-
-- load schedule directives
-- validate current JSON API shape
-- derive schedule info such as PP/DP degrees and microbatch count
-
-### `tasks.py`
-
-Task type enum and task-level constants.
-
-### `state.py`
-
-Shared process-local state and logging:
-
-- `PiperMetadata`
-- global metadata instance
-- `create_logger`
-
-### `fx.py`
-
-FX graph infrastructure:
-
-- GraphModule serialization/deserialization
-- annotation stack validation
-- annotation-based FX splitting into segments
-
-This combines the current FX serialization helpers and FX splitting logic,
-because both are infrastructure around `torch.fx.GraphModule` representation.
-
-### `bucket.py`
-
-Parameter bucket planning and bucket GraphModule construction.
-
-This remains separate from `fx.py` because bucketing has separate invariants and
-is tied to optimizer/parameter ownership rather than generic FX transport.
-
-### `dag.py`
-
-Training DAG data structures, construction, and core graph utilities:
-
-- `TrainingDAG`
-- `TrainingDAGNode`
-- `TrainingDAGEdge`
-- construction of the initial TrainingDAG from annotated FX segments
-- graph mutation helpers
-- basic topological utilities that are not specifically ordering directives
-
-### `directives.py`
-
-Schedule directive parsing, matching, validation, and application.
-
-This file should include communication insertion because communication nodes are
-created as the effect of applying schedule directives:
-
-- `place`
-- `replicate`
-- `shard`
-- `split`
-- `order` parsing handoff
-- send/recv insertion
-- reduce/all-reduce insertion
-- all-gather insertion
-- reduce-scatter insertion
-- A2A insertion
-
-There should not be a separate `comm_transforms.py`; communication transforms
-are part of directive application.
-
-### `ordering.py`
-
-Ordering-specific graph logic:
-
-- serial and per-stream topological ordering
-- order directive sub-DAG matching
-- temporal edge insertion for order directives
-- stream total order resolution
-
-### `zero.py`
-
-ZeRO-specific DAG metadata and graph rewrites:
-
-- ZeRO lifetime metadata pruning
-- inter-chain temporal edge insertion around ZeRO lifetimes
-- helper logic for full-param/full-grad lifetime correctness
-
-This module should operate on the DAG and metadata. It should not own runtime
-parameter storage policy.
-
-### `visualization.py`
-
-Debug/render output:
-
-- TrainingDAG order dumps
-- Graphviz rendering
-- schedule visualization currently produced by `examples/build_schedule.py`
-- dependency logging if it remains debug-output oriented
-
-The example harness should expose a single `--viz` flag. That flag should guard
-both schedule visualization and TrainingDAG visualization; there should not be
-separate schedule-only or DAG-only example flags unless a later use case needs
-that split.
-
-### `backward.py`
-
-Current `backward_utils.py` helpers for split backward/autograd graph traversal.
-
-## Actor Design
-
-`PiperActor` should become a thin Ray shell around typed runtime components.
-
-### `DagExecutor`
-
-Owns the top-level sorted-DAG execution loop.
-
-This should remain the readable control plane:
-
-```python
-for node in sorted_nodes:
-    match task_type:
-        case TaskType.FWD:
-            compute.forward(node)
-        case TaskType.BWD:
-            compute.backward(node)
-        case TaskType.SEND:
-            communication.send(node)
-        case TaskType.RECV:
-            communication.recv(node)
-        case TaskType.ALL_GATHER:
-            communication.all_gather(node)
-        case TaskType.REDUCE_SCATTER:
-            communication.reduce_scatter(node)
-        case TaskType.A2A:
-            communication.a2a(node)
-```
-
-`DagExecutor` also handles metadata-driven lifecycle hooks, because the DAG is
-the source of policy:
-
-```python
-if node.meta.get("zero_alloc_full_grads_before"):
-    stage_store.params.alloc_full_grads(...)
-
-execute_task(node)
-
-if node.meta.get("zero_free_full_params_after"):
-    stage_store.params.free_full_params(...)
-```
-
-### `ComputeExecutor`
-
-Executes compute tasks:
-
-- forward
-- fused backward
-- backward input
-- backward weight
-- loss application
-- optimizer/update execution where appropriate
-
-It should use `StageStore`, `BufferStore`, and `ParamStorage` rather than owning
-parameter or buffer dictionaries itself.
-
-### `CommunicationExecutor`
-
-Executes communication tasks:
-
-- send
-- recv
-- all-reduce
-- reduce-scatter
-- all-gather
-- A2A
-
-Send and recv are generic communication tasks. There are no direction-specific
-send/recv task types in the current implementation and the refactor should not
-introduce them.
-
-### `StageStore`
-
-Owns loaded executable stage/bucket state:
-
-- bucket modules
-- graph args
-- input and parameter indices
-- optimizers
-- bucket metadata
-- parameter storage
-
-### `BucketState`
-
-One executable bucket:
-
-- bucket id
-- GraphModule/submodules
-- placeholder metadata
-- optimizer
-- parameter metadata
-- task/bucket mode metadata
-
-### `ParamStorage`
-
-Storage and tensor lookup only. It should not encode ZeRO scheduling policy.
-
-Responsibilities:
-
-- own parameter tensors or local shards
-- expose args for forward/compute
-- map placeholder names/indices to tensors
-- store and clear full parameter materializations
-- store and clear full gradient buffers
-- expose local shards and grad shards
-- provide trainable parameter views/layout metadata
-
-The DAG and `DagExecutor` decide when to allocate/free/gather/scatter. This
-avoids separate `Zero2ParamState` or `Zero3ParamState` policy classes, because
-ZeRO behavior is already encoded by DAG nodes and metadata.
-
-### `BufferStore`
-
-Owns runtime buffers:
-
-- forward outputs
-- backward outputs
-- recv payloads
-- send payloads
-- task buffers and refcounts
-
-### `RuntimeState`
-
-Owns runtime environment:
-
-- ranks and degrees
-- device
-- process groups
-- streams
-- stream lookup
-- profiler/NVTX helpers
-
-## Staged Implementation Plan
-
-1. Rename files mechanically:
-   - `piper_actor.py` -> `actor.py`
-   - `piper_compile.py` -> `compile.py`
-   - `piper_coordinator.py` -> `coordinator.py`
-   - `piper_exec.py` -> `tasks.py`
-   - `piper_graph_transform.py` -> temporary `fx.py` or split later
-   - `piper_schedule.py` -> `schedule.py`
-   - `piper_utils.py` -> temporary `state.py` or split later
-   - `backward_utils.py` -> `backward.py`
-
-2. Update all imports, tests, and examples. Run unit tests and the Qwen
-   verification matrix before doing semantic moves.
-
-3. Split state and FX helpers:
-   - move metadata/logging into `state.py`
-   - move GraphModule serialization into `fx.py`
-   - move annotation splitting into `fx.py`
-
-4. Move bucket planning into `bucket.py`.
-
-5. Move TrainingDAG dataclasses, initial DAG construction from annotated FX
-   segments, and core graph utilities into `dag.py`.
-
-6. Move schedule directive parsing/application and communication insertion into
-   `directives.py`.
-
-7. Move ordering-specific logic into `ordering.py`.
-
-8. Move ZeRO DAG lifetime/metadata transforms into `zero.py`.
-
-9. Move debug rendering, schedule visualization, and DAG order output into
-   `visualization.py`. Rename the example visualization flag from `--save-viz`
-   to `--viz` and use it to guard both schedule and DAG visualization.
-
-10. Refactor `PiperActor` internally:
-    - introduce `RuntimeState`
-    - introduce `StageStore`
-    - introduce `BucketState`
-    - introduce `ParamStorage`
-    - introduce `BufferStore`
-    - introduce `ComputeExecutor`
-    - introduce `CommunicationExecutor`
-    - introduce `DagExecutor`
-
-11. Keep the top-level DAG loop visible in `DagExecutor`; avoid replacing it
-    with a registry of opaque callbacks unless there is a clear benefit.
-
-12. After each stage:
-    - run `python3 -m py_compile` on touched modules
-    - run `.venv/bin/python -m pytest test`
-    - run direct-file PP-only Qwen smoke tests with the current requested GPU
-      mask (`CUDA_VISIBLE_DEVICES=0,2`) on:
-      - PP2 topology with 1F1B
-      - V-placement PP topology with DualPipeV
-
-DualPipeV verification should use the corresponding V-placement base schedule,
-because the generated DualPipeV order references virtual pipeline stages.
-
-## Current Progress
-
-- Stages 1-9 are implemented:
-  - implementation modules have been renamed away from `piper_*`
-  - state/logging, FX serialization/splitting, bucket planning, DAG
-    construction, directive application, ordering, ZeRO DAG transforms, and
-    visualization have been moved into focused modules
-  - `examples/test_harness.py` uses `--viz` as the single visualization flag,
-    and `piper_setup(..., visualize_dag=False)` is the default
-- Stage 10 is implemented:
-  - `RuntimeState` owns ranks, device selection, process groups, CUDA streams,
-    and profiler/NVTX helpers
-  - `BufferStore` owns per-iteration task outputs and refcounts
-  - `EventStore` owns per-iteration non-compute CUDA events
-  - `StageStore` owns loaded stage/bucket dictionaries and ZeRO bucket-mode sets
-  - `BucketState` owns per-bucket forward args/functions, optimizer state, and
-    ZeRO storage buffers, replacing the previous parallel `bucket_*` maps
-  - `CommunicationExecutor` owns P2P send/recv, A2A, all-reduce, and
-    reduce-scatter execution
-  - `ComputeExecutor` owns bucket forward, fused backward, split backward-input,
-    split backward-weight, and compute-loss debug logging
-  - `ParamStorage` owns ZeRO full-param/full-grad storage, pending async frees,
-    flat gradient accumulation, all-gather for sharded params, and ZeRO shard
-    optimizer cleanup
-  - `DagExecutor` owns the top-level sorted-DAG `match task_type` loop, as
-    intended
-- Stage 10 actor cleanup is implemented.
-
-Latest verification after introducing `RuntimeState`:
-
-- `.venv/bin/python -m py_compile src/*.py examples/*.py examples/models/*.py test/*.py scripts/test_env.py`
-- `.venv/bin/python -m pytest test` (`15 passed`)
-- `CUDA_VISIBLE_DEVICES=2,3` PP2 + 1F1B Qwen run:
-  `out/20260601_203107/results.csv`
-- `CUDA_VISIBLE_DEVICES=2,3` V-placement PP + DualPipeV Qwen run:
-  `out/20260601_203147/results.csv`
-
-Latest verification after introducing `BucketState`:
-
-- `.venv/bin/python -m py_compile src/*.py examples/*.py examples/models/*.py test/*.py scripts/test_env.py`
-- `.venv/bin/python -m pytest test` (`15 passed`)
-- `CUDA_VISIBLE_DEVICES=2,3` PP2 + 1F1B Qwen run:
-  `out/20260601_203620/results.csv`
-- `CUDA_VISIBLE_DEVICES=2,3` V-placement PP + DualPipeV Qwen run:
-  `out/20260601_203700/results.csv`
-
-Latest verification after introducing `CommunicationExecutor`:
-
-- `.venv/bin/python -m py_compile src/*.py examples/*.py examples/models/*.py test/*.py scripts/test_env.py`
-- `.venv/bin/python -m pytest test` (`15 passed`)
-- `CUDA_VISIBLE_DEVICES=0,2` PP2 + 1F1B Qwen run:
-  `out/20260601_204331/results.csv`
-- `CUDA_VISIBLE_DEVICES=0,2` V-placement PP + DualPipeV Qwen run:
-  `out/20260601_204412/results.csv`
-
-Latest verification after introducing `ComputeExecutor`:
-
-- `.venv/bin/python -m py_compile src/*.py examples/*.py examples/models/*.py test/*.py scripts/test_env.py`
-- `.venv/bin/python -m pytest test` (`15 passed`)
-- `CUDA_VISIBLE_DEVICES=0,2` PP2 + 1F1B Qwen run:
-  `out/20260601_204816/results.csv`
-- `CUDA_VISIBLE_DEVICES=0,2` V-placement PP + DualPipeV Qwen run:
-  `out/20260601_204855/results.csv`
-
-Latest verification after introducing `ParamStorage`:
-
-- `.venv/bin/python -m py_compile src/*.py examples/*.py examples/models/*.py test/*.py scripts/test_env.py`
-- `.venv/bin/python -m pytest test` (`15 passed`)
-- `CUDA_VISIBLE_DEVICES=0,2` PP2 + 1F1B Qwen run:
-  `out/20260601_205514/results.csv`
-- `CUDA_VISIBLE_DEVICES=0,2` V-placement PP + DualPipeV Qwen run:
-  `out/20260601_205554/results.csv`
-
-Latest verification after introducing `DagExecutor`:
-
-- `.venv/bin/python -m py_compile src/*.py examples/*.py examples/models/*.py test/*.py scripts/test_env.py`
-- `.venv/bin/python -m pytest test` (`15 passed`)
-- Initial `CUDA_VISIBLE_DEVICES=0,2` PP2 + 1F1B Qwen run exposed an eager
-  actor input-state bug after the loop move; fixed by initializing
-  `PiperActor.inputs`
-- `CUDA_VISIBLE_DEVICES=0,2` PP2 + 1F1B Qwen run after the fix:
-  `out/20260601_210222/results.csv`
-- `CUDA_VISIBLE_DEVICES=0,2` V-placement PP + DualPipeV Qwen run:
-  `out/20260601_210306/results.csv`
-
-## Resolved Decisions
-
-- `visualization.py` should own schedule visualization as well as DAG
-  visualization. The example `--viz` flag should guard both outputs.
-- Send/recv task types remain generic; the refactor should not add directional
-  send/recv variants.
-- `piper_exec_dag` stays in `piper.py`.
diff --git a/artifact/Dockerfile b/artifact/Dockerfile
new file mode 100644
index 0000000..9fd13ba
--- /dev/null
+++ b/artifact/Dockerfile
@@ -0,0 +1,104 @@
+FROM rayproject/ray:2.44.1-py310-cu128
+
+SHELL ["/bin/bash", "-lc"]
+USER root
+WORKDIR /workspace
+
+ARG TORCHTITAN_COMMIT=b01adfb544b4331ecab090ebdb50b2296cd8eb6a
+ARG MEGATRON_REF=26.04-alpha.rc2
+ARG TORCHTITAN_TORCH_INDEX_URL=https://download.pytorch.org/whl/nightly/cu128
+ARG APEX_REF=master
+
+ENV DEBIAN_FRONTEND=noninteractive
+ENV TORCHTITAN_CONDA_ENV=torchtitan
+ENV MEGATRON_CONDA_ENV=megatron
+ENV DEEPSPEED_CONDA_ENV=deepspeed
+ENV PIPER_CONDA_ENV=piper
+ENV MEGATRON_WORKSPACE=/workspace/Megatron-LM
+ENV TORCHTITAN_WORKSPACE=/workspace/torchtitan
+ENV PIPER_WORKSPACE=/workspace/piper
+ENV ARTIFACT_WORKSPACE=/workspace/artifact
+
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends ca-certificates git wget build-essential ninja-build && \
+    . /etc/os-release && \
+    arch="$(dpkg --print-architecture)" && \
+    case "$arch" in \
+        amd64) cuda_arch="x86_64" ;; \
+        arm64) cuda_arch="sbsa" ;; \
+        *) echo "Unsupported architecture: $arch" >&2; exit 1 ;; \
+    esac && \
+    cuda_repo="https://developer.download.nvidia.com/compute/cuda/repos/${ID}${VERSION_ID//./}/${cuda_arch}" && \
+    wget -q "${cuda_repo}/cuda-keyring_1.1-1_all.deb" -O /tmp/cuda-keyring.deb && \
+    dpkg -i /tmp/cuda-keyring.deb && \
+    apt-get update && \
+    apt-get install -y --no-install-recommends \
+        cuda-nsight-systems-12-8 \
+        cuda-nvcc-12-8 \
+        cuda-cudart-dev-12-8 && \
+    rm -rf /var/lib/apt/lists/* /tmp/cuda-keyring.deb
+
+RUN mkdir -p /workspace/artifact/patches
+COPY artifact/patches /workspace/artifact/patches
+
+RUN git clone https://github.com/pytorch/torchtitan.git /workspace/torchtitan && \
+    cd /workspace/torchtitan && \
+    git checkout "${TORCHTITAN_COMMIT}" && \
+    git apply /workspace/artifact/patches/torchtitan-qwen-e2e.patch
+
+RUN git clone https://github.com/NVIDIA/Megatron-LM.git /workspace/Megatron-LM && \
+    cd /workspace/Megatron-LM && \
+    git checkout "${MEGATRON_REF}"
+
+RUN git clone https://github.com/NVIDIA/apex.git /workspace/apex && \
+    cd /workspace/apex && \
+    git checkout "${APEX_REF}"
+
+RUN conda create -y -n torchtitan python=3.10 && \
+    conda run -n torchtitan pip install --no-cache-dir --pre torch --index-url "${TORCHTITAN_TORCH_INDEX_URL}" && \
+    conda run -n torchtitan pip install --no-cache-dir smart_open requests && \
+    conda run -n torchtitan pip install --no-cache-dir --pre --no-deps torchdata --index-url https://download.pytorch.org/whl/nightly/cpu && \
+    conda run -n torchtitan pip install --no-cache-dir -e /workspace/torchtitan && \
+    conda run -n torchtitan pip install --no-cache-dir --upgrade fsspec && \
+    conda clean -afy
+
+RUN conda create -y -n megatron python=3.12 && \
+    conda run -n megatron pip install --no-cache-dir torch --index-url https://download.pytorch.org/whl/cu128 && \
+    conda run -n megatron pip install --no-cache-dir \
+        numpy packaging pandas pydantic pyyaml einops importlib-metadata \
+        nvdlfw-inspect onnx onnxscript tensorboard && \
+    conda run -n megatron pip install --no-cache-dir --no-deps \
+        transformer-engine==2.13.0 \
+        transformer-engine-torch==2.13.0 \
+        transformer-engine-cu12==2.13.0 && \
+    cd /workspace/apex && conda run -n megatron pip install --no-cache-dir --no-build-isolation \
+        --config-settings "--build-option=--cpp_ext" \
+        --config-settings "--build-option=--cuda_ext" \
+        . && \
+    conda run -n megatron pip install --no-cache-dir -e /workspace/Megatron-LM --no-deps && \
+    conda clean -afy
+
+RUN conda create -y -n deepspeed python=3.10 && \
+    conda run -n deepspeed pip install --no-cache-dir torch --index-url https://download.pytorch.org/whl/cu128 && \
+    conda run -n deepspeed pip install --no-cache-dir deepspeed && \
+    conda clean -afy
+
+COPY pyproject.toml requirements.txt README.md /workspace/piper/
+COPY src /workspace/piper/src
+COPY examples /workspace/piper/examples
+COPY test /workspace/piper/test
+
+RUN conda create -y -n piper python=3.10 && \
+    conda run -n piper pip install --no-cache-dir torch==2.10.0 --index-url https://download.pytorch.org/whl/cu128 && \
+    cd /workspace/piper && conda run -n piper pip install --no-cache-dir -e . \
+        --extra-index-url https://download.pytorch.org/whl/cu128 && \
+    conda clean -afy
+
+COPY artifact/e2e_eval.py artifact/run_deepspeed.py artifact/run_megatron.py artifact/sitecustomize.py /workspace/artifact/
+COPY artifact/scripts /workspace/artifact/scripts
+COPY artifact/README.md /workspace/artifact/README.md
+
+RUN chmod +x /workspace/artifact/scripts/*.sh && \
+    mkdir -p /workspace/eval-out /tmp/piper/ray_tmp
+
+WORKDIR /workspace/piper
diff --git a/artifact/README.md b/artifact/README.md
new file mode 100644
index 0000000..4fe4d4d
--- /dev/null
+++ b/artifact/README.md
@@ -0,0 +1,103 @@
+# Piper Qwen E2E Artifact
+
+This directory is the reproducible entrypoint for the paper e2e evals across
+Piper, TorchTitan, Megatron, and DeepSpeed.
+
+## Build
+
+From the piper repository root:
+
+```bash
+docker build -f artifact/Dockerfile -t piper-e2e-artifact:latest .
+```
+
+The image pins TorchTitan to upstream commit
+`b01adfb544b4331ecab090ebdb50b2296cd8eb6a` and applies
+`artifact/patches/torchtitan-qwen-e2e.patch`. Megatron defaults to the stable
+release tag `26.04-alpha.rc2`; pass `--build-arg MEGATRON_REF=<commit-or-tag>`
+to override it.
+
+## Local Single-Node Runs
+
+Run the smallest smoke eval on local GPUs:
+
+```bash
+python artifact/e2e_eval.py \
+  --backend local \
+  --systems torchtitan piper \
+  --sweeps local \
+  --image piper-e2e-artifact:latest
+```
+
+Run the full default matrix locally:
+
+```bash
+python artifact/e2e_eval.py --backend local --image piper-e2e-artifact:latest
+```
+
+Local mode uses one Docker container per experiment and maps
+`artifact/out/e2e-eval/<run_id>` into `/workspace/eval-out` inside the
+container. For local mode, `nnode=1` and `ngpu=pp*dp`. If
+`/m-coriander/coriander/mfris/torchtitan/assets/hf` exists, it is mounted
+read-only into `/workspace/torchtitan/assets/hf` so TorchTitan can find the
+downloaded Qwen tokenizer assets. Override this with
+`--local-torchtitan-assets-path`.
+
+## AWS Existing-Node Runs
+
+AWS mode targets an existing EC2 cluster over SSH. Required environment:
+
+```bash
+export SSH_KEY=/path/to/key.pem
+export HEAD_PUBLIC_IP=<public head ip>
+export HEAD_PRIVATE_IP=<private head ip>
+export WORKER1_PRIVATE_IP=<private worker ip>
+export WORKER2_PRIVATE_IP=<private worker ip>
+export WORKER3_PRIVATE_IP=<private worker ip>
+```
+
+Start containers automatically if the image already exists on every node:
+
+```bash
+python artifact/e2e_eval.py \
+  --backend aws \
+  --aws-start-containers \
+  --image piper-e2e-artifact:latest \
+  --container piper_artifact
+```
+
+If containers are already running, omit `--aws-start-containers`. AWS mode uses
+`nnode=dp` and `ngpu=pp`, matching the original EC2 experiment layout.
+
+## Useful Options
+
+```bash
+python artifact/e2e_eval.py --dry-run
+python artifact/e2e_eval.py --backend local --sweeps local --local-cuda-visible-devices 0
+python artifact/e2e_eval.py --backend local --sweeps local --local-torchtitan-assets-path /path/to/assets/hf
+python artifact/e2e_eval.py --backend local --sweeps local --no-torchtitan-use-bmm-experts
+python artifact/e2e_eval.py --backend local --sweeps local --piper-use-inductor --torchtitan-compile
+python artifact/e2e_eval.py --systems piper torchtitan --sweeps schedule
+python artifact/e2e_eval.py --sweeps zero --bucket-size-mb 25
+python artifact/e2e_eval.py --backend aws --nsight --systems piper
+```
+
+Outputs are written under `artifact/out/e2e-eval/<run_id>/`:
+
+- `results.csv`
+- `<sweep>.png` for combined plots with successful rows
+- `<system>/*.log`
+
+## Notes
+
+- Local Docker runs bind-mount this `artifact/` directory plus Piper `src/`,
+  `examples/`, and `test/` into the container, so runner/script/source edits do
+  not require rebuilding the image. Rebuild only when dependencies or the
+  Dockerfile image contents change.
+- The TorchTitan patch is intentionally limited to the Qwen e2e runtime needs:
+  Qwen3 MoE eval configs, mesh ordering, timing/memory logs, dataset retry
+  behavior, and small runtime compatibility fixes.
+- The piper runner invokes the current tracked entrypoint:
+  `examples/test_harness.py --test-file examples/test_qwen.py`.
+- Piper base schedules are generated per experiment, so the artifact is not
+  limited to the checked-in `examples/base-schedules/*.json` files.
diff --git a/artifact/e2e_eval.py b/artifact/e2e_eval.py
new file mode 100755
index 0000000..25dda2f
--- /dev/null
+++ b/artifact/e2e_eval.py
@@ -0,0 +1,1122 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import math
+import os
+import re
+import shlex
+import shutil
+import signal
+import subprocess
+import sys
+import tempfile
+import time
+from dataclasses import asdict, dataclass
+from pathlib import Path
+from typing import Iterable, Sequence
+
+ANSI_RE = re.compile(r"\x1b\[[0-9;]*m")
+TT_ITER_RE = re.compile(r"Final \d+ iter times.*?avg:\s*([\d.]+)\s*s,\s*std:\s*([\d.]+)\s*s")
+TT_FINAL_BY_RANK_RE = re.compile(
+    r"\[rank(\d+)\].*?Final \d+ iter times.*?avg:\s*([\d.]+)\s*s,\s*std:\s*([\d.]+)\s*s"
+)
+TT_MEM_RE = re.compile(r"\[rank(\d+)\].*?memory:\s*([\d.]+)GiB")
+DS_STEP_RE = re.compile(r"\[Step\s+\d+/\d+\].*?step_time=([\d.]+)s")
+DS_MEM_RE = re.compile(r"\[rank(\d+)\]\s+peak_memory_allocated_gb=([\d.]+)\s+peak_memory_reserved_gb=([\d.]+)")
+MG_ITER_RE = re.compile(r"elapsed time per iteration \(ms\):\s*([\d.]+)")
+MG_MEM_RE = re.compile(r"\[Rank\s+(\d+)\].*?max allocated:\s*([\d.]+)\s*\|")
+OOM_RE = re.compile(r"(?:cuda\s+out\s+of\s+memory|out\s+of\s+memory|std::bad_alloc|\boom\b)", re.IGNORECASE)
+
+DEFAULT_SYSTEMS = ("torchtitan", "megatron", "deepspeed", "piper")
+DEFAULT_SCHEDULE_SWEEP = ("1f1b", "interleaved1f1b", "zerobubble", "dualpipe")
+DEFAULT_IMAGE = "piper-e2e-artifact:latest"
+DEFAULT_CONTAINER = "piper_artifact"
+DEFAULT_LOCAL_TORCHTITAN_ASSETS = Path("/m-coriander/coriander/mfris/torchtitan/assets/hf")
+CONTAINER_OUT = Path("/workspace/eval-out")
+CONTAINER_ARTIFACT = Path("/workspace/artifact")
+CONTAINER_TORCHTITAN_HF_ASSETS = Path("/workspace/torchtitan/assets/hf")
+
+RUNTIME_SCHEDULES = {
+    "1f1b": "1F1B",
+    "interleaved1f1b": "Interleaved1F1B",
+    "zerobubble": "InterleavedZeroBubble",
+    "dualpipe": "DualPipeV",
+}
+PIPER_SCHEDULES = {
+    "1f1b": "1f1b",
+    "interleaved1f1b": "interleaved_1f1b",
+    "zerobubble": "zerobubble",
+    "dualpipe": "dualpipev",
+}
+PIPER_ZERO_STAGES = {"zero0": 0, "zero1": 1, "zero2": 2, "zero3": 3}
+SYSTEM_COLORS = {
+    "torchtitan": "#4C72B0",
+    "megatron": "#55A868",
+    "deepspeed": "#C44E52",
+    "piper": "#8172B3",
+}
+
+
+def remapped_cuda_visible_devices(host_devices: str) -> str:
+    devices = [device.strip() for device in host_devices.split(",") if device.strip()]
+    return ",".join(str(index) for index, _device in enumerate(devices)) or "0"
+
+
+def docker_gpus_request(cuda_visible_devices: str | None) -> str:
+    if cuda_visible_devices is None:
+        return "all"
+    return f'"device={cuda_visible_devices}"'
+
+
+@dataclass(frozen=True)
+class Experiment:
+    system: str
+    sweep: str
+    config: str
+    pp: int
+    dp: int
+    ep: int
+    zero_level: str
+    schedule: str
+    mb_size: int
+    seq_len: int
+    gradient_accumulation: bool = True
+    bucket_size_mb: float | None = None
+    ar_a2a_same_stream: bool = False
+    overlap_chunks: bool = False
+
+
+@dataclass
+class Result:
+    system: str
+    sweep: str
+    config: str
+    pp: int
+    dp: int
+    ep: int
+    zero_level: str
+    schedule: str
+    mb_size: int
+    seq_len: int
+    local_batch_size: int
+    global_batch_size: int
+    nnode: int
+    ngpu: int
+    log_path: str
+    metrics_path: str
+    returncode: int
+    status: str
+    iter_time_mean: float | None
+    iter_time_stddev: float | None
+    peak_memory_gb_by_rank: str
+    failure_reason: str
+
+
+def normalize_schedule(name: str) -> str:
+    mapping = {
+        "1f1b": "1f1b",
+        "interleaved1f1b": "interleaved1f1b",
+        "interleaved-1f1b": "interleaved1f1b",
+        "interleaved_1f1b": "interleaved1f1b",
+        "zerobubble": "zerobubble",
+        "zero-bubble": "zerobubble",
+        "interleavedzerobubble": "zerobubble",
+        "interleaved-zero-bubble": "zerobubble",
+        "dualpipe": "dualpipe",
+        "dualpipev": "dualpipe",
+    }
+    key = name.strip().lower()
+    if key not in mapping:
+        raise ValueError(f"Unsupported schedule: {name}")
+    return mapping[key]
+
+
+def build_experiments(
+    *,
+    systems: Iterable[str],
+    schedule_sweep: Iterable[str],
+    seq_len: int,
+    enabled_sweeps: set[str],
+    gradient_accumulation: bool,
+    ar_a2a_same_stream: bool,
+    overlap_chunks: bool,
+    bucket_size_mb: float | None,
+) -> list[Experiment]:
+    experiments: list[Experiment] = []
+    scalability_defaults = {
+        "config": "qwen3_9b",
+        "ep": 1,
+        "zero_level": "zero0",
+        "schedule": "1f1b",
+        "mb_size": 4,
+    }
+    zero_defaults = {
+        "config": "qwen3_1b",
+        "pp": 8,
+        "dp": 2,
+        "ep": 1,
+        "schedule": "1f1b",
+    }
+    zero_levels_by_system = {
+        "torchtitan": ("zero1", "zero2", "zero3"),
+        "megatron": ("zero1",),
+        "deepspeed": ("zero1",),
+        "piper": ("zero1", "zero2", "zero3"),
+    }
+    zero_sweep_values = (16, 24, 32, 34, 36, 38, 40)
+    schedule_defaults = {
+        "config": "qwen3_9b",
+        "pp": 2,
+        "dp": 2,
+        "ep": 2,
+        "zero_level": "zero1",
+        "mb_size": 4,
+    }
+    local_defaults = {
+        "config": "qwen3_1b",
+        "pp": 1,
+        "dp": 1,
+        "ep": 1,
+        "zero_level": "zero1",
+        "schedule": "1f1b",
+        "mb_size": 8,
+    }
+    supported_schedules_by_system = {
+        "torchtitan": {"1f1b", "interleaved1f1b", "dualpipe"},
+        "megatron": {"1f1b", "interleaved1f1b"},
+        "deepspeed": {"1f1b"},
+        "piper": {"1f1b", "interleaved1f1b", "dualpipe"},
+    }
+
+    for system in systems:
+        if "scalability" in enabled_sweeps:
+            for pp in (4, 8):
+                for dp in (1, 2, 4):
+                    experiments.append(
+                        Experiment(
+                            system=system,
+                            sweep="scalability",
+                            config=str(scalability_defaults["config"]),
+                            pp=pp,
+                            dp=dp,
+                            ep=int(scalability_defaults["ep"]),
+                            zero_level=str(scalability_defaults["zero_level"]),
+                            schedule=str(scalability_defaults["schedule"]),
+                            mb_size=int(scalability_defaults["mb_size"]),
+                            seq_len=seq_len,
+                            gradient_accumulation=gradient_accumulation,
+                            bucket_size_mb=bucket_size_mb,
+                            ar_a2a_same_stream=ar_a2a_same_stream,
+                            overlap_chunks=overlap_chunks,
+                        )
+                    )
+
+        if "zero" in enabled_sweeps:
+            for zero_level in zero_levels_by_system[system]:
+                for sweep_value in zero_sweep_values:
+                    experiments.append(
+                        Experiment(
+                            system=system,
+                            sweep="zero",
+                            config=str(zero_defaults["config"]),
+                            pp=int(zero_defaults["pp"]),
+                            dp=int(zero_defaults["dp"]),
+                            ep=int(zero_defaults["ep"]),
+                            zero_level=zero_level,
+                            schedule=str(zero_defaults["schedule"]),
+                            mb_size=sweep_value,
+                            seq_len=seq_len,
+                            gradient_accumulation=gradient_accumulation,
+                            bucket_size_mb=bucket_size_mb,
+                            ar_a2a_same_stream=ar_a2a_same_stream,
+                            overlap_chunks=overlap_chunks,
+                        )
+                    )
+
+        if "schedule" in enabled_sweeps:
+            for schedule in schedule_sweep:
+                if schedule not in supported_schedules_by_system[system]:
+                    continue
+                experiments.append(
+                    Experiment(
+                        system=system,
+                        sweep="schedule",
+                        config=str(schedule_defaults["config"]),
+                        pp=int(schedule_defaults["pp"]),
+                        dp=int(schedule_defaults["dp"]),
+                        ep=int(schedule_defaults["ep"]),
+                        zero_level=str(schedule_defaults["zero_level"]),
+                        schedule=schedule,
+                        mb_size=int(schedule_defaults["mb_size"]),
+                        seq_len=seq_len,
+                        gradient_accumulation=gradient_accumulation,
+                        bucket_size_mb=bucket_size_mb,
+                        ar_a2a_same_stream=ar_a2a_same_stream,
+                        overlap_chunks=overlap_chunks,
+                    )
+                )
+
+        if "local" in enabled_sweeps:
+            experiments.append(
+                Experiment(
+                    system=system,
+                    sweep="local",
+                    config=str(local_defaults["config"]),
+                    pp=int(local_defaults["pp"]),
+                    dp=int(local_defaults["dp"]),
+                    ep=int(local_defaults["ep"]),
+                    zero_level=str(local_defaults["zero_level"]),
+                    schedule=str(local_defaults["schedule"]),
+                    mb_size=int(local_defaults["mb_size"]),
+                    seq_len=seq_len,
+                    gradient_accumulation=gradient_accumulation,
+                    bucket_size_mb=bucket_size_mb,
+                    ar_a2a_same_stream=ar_a2a_same_stream,
+                    overlap_chunks=overlap_chunks,
+                )
+            )
+    return experiments
+
+
+def local_batch_size(exp: Experiment) -> int:
+    if exp.sweep == "local":
+        return exp.mb_size
+    return exp.pp * 2 * exp.mb_size
+
+
+def global_batch_size(exp: Experiment) -> int:
+    return local_batch_size(exp) * exp.dp
+
+
+def experiment_label(exp: Experiment) -> str:
+    if exp.sweep == "scalability":
+        return f"pp={exp.pp}, dp={exp.dp}"
+    if exp.sweep == "zero":
+        return f"zero={exp.zero_level}, mb={exp.mb_size}"
+    if exp.sweep == "local":
+        return "local"
+    return exp.schedule
+
+
+def piper_model_name(config: str) -> str:
+    return {"qwen3_1b": "1B", "qwen3_9b": "9B"}[config]
+
+
+def piper_num_mbs(exp: Experiment) -> int:
+    if exp.sweep == "local":
+        return 1
+    return exp.pp * 2
+
+
+def slug(exp: Experiment, *, nsight: bool = False) -> str:
+    bucket_part = f"-bucket{exp.bucket_size_mb:g}" if exp.bucket_size_mb is not None else ""
+    nsight_part = "-nsight1" if nsight else ""
+    return (
+        f"{exp.system}-{exp.sweep}-qwen{exp.config.removeprefix('qwen3_')}-"
+        f"sched_{exp.schedule}-pp{exp.pp}-dp{exp.dp}-ep{exp.ep}-"
+        f"{exp.zero_level}{bucket_part}{nsight_part}-bs{exp.mb_size}-"
+        f"sl{exp.seq_len}-mbs{piper_num_mbs(exp)}"
+    )
+
+
+def torchtitan_hf_assets_path(config: str) -> str | None:
+    return {
+        "qwen3_1b": "/workspace/torchtitan/assets/hf/Qwen3-0.6B",
+        "qwen3_9b": "/workspace/torchtitan/assets/hf/Qwen3-8B",
+    }.get(config)
+
+
+def backend_layout(exp: Experiment, backend_name: str) -> tuple[int, int]:
+    if backend_name == "local":
+        return 1, exp.pp * exp.dp
+    return exp.dp, exp.pp
+
+
+def in_container_command(exp: Experiment, args: argparse.Namespace, metrics_container_path: str | None = None) -> list[str]:
+    nnode = "{nnode}"
+    ngpu = "{ngpu}"
+    node_rank = "{node_rank}"
+    master_addr = "{master_addr}"
+
+    if exp.system == "torchtitan":
+        nnode_value, ngpu_value = backend_layout(exp, args.backend)
+        if exp.ep > 1:
+            dp_replicate_degree = 1
+            dp_shard_degree = exp.ep
+        else:
+            dp_replicate_degree = exp.dp if exp.zero_level == "zero1" else 1
+            dp_shard_degree = 1 if exp.zero_level == "zero1" else exp.dp
+        tt_args = [
+            "--parallelism.pipeline_parallel_degree", str(exp.pp),
+            "--parallelism.expert_parallel_degree", str(exp.ep),
+            "--parallelism.pipeline_parallel_schedule", RUNTIME_SCHEDULES[exp.schedule],
+            "--parallelism.pipeline_parallel_microbatch_size", str(exp.mb_size),
+            "--parallelism.data_parallel_replicate_degree", str(dp_replicate_degree),
+            "--parallelism.data_parallel_shard_degree", str(dp_shard_degree),
+            "--training.seq_len", str(exp.seq_len),
+            "--training.local_batch_size", str(local_batch_size(exp)),
+            "--training.global_batch_size", str(global_batch_size(exp)),
+        ]
+        hf_assets_path = torchtitan_hf_assets_path(exp.config)
+        if hf_assets_path is not None:
+            tt_args.extend(["--hf_assets_path", hf_assets_path])
+        if exp.zero_level == "zero2":
+            tt_args.extend(["--parallelism.fsdp_reshard_after_forward", "never"])
+        elif exp.zero_level == "zero3":
+            tt_args.extend(["--parallelism.fsdp_reshard_after_forward", "always"])
+        if exp.schedule == "dualpipe" or not args.torchtitan_compile:
+            tt_args.append("--compile.no-enable")
+        command = [
+            "/workspace/artifact/scripts/run_torchtitan.sh",
+            "--nnode", nnode,
+            "--ngpu", ngpu,
+            "--node-rank", node_rank,
+            "--master-addr", master_addr,
+            "--master-port", "29500",
+            "--module", "qwen3",
+            "--config", exp.config,
+            "--log-rank", ",".join(str(i) for i in range(min(nnode_value * ngpu_value, int(args.max_log_ranks)))),
+        ]
+        if args.nsight:
+            command.append("--nsight")
+        if args.torchtitan_use_bmm_experts:
+            command.append("--use-bmm-experts")
+        command.extend(["--", *tt_args])
+        return command
+
+    if exp.system == "megatron":
+        dp_megatron = exp.dp // exp.ep if exp.ep > 1 else exp.dp
+        command = [
+            "/workspace/artifact/scripts/run_megatron.sh",
+            "--nnode", nnode,
+            "--ngpu", ngpu,
+            "--node-rank", node_rank,
+            "--master-addr", master_addr,
+            "--master-port", "29500",
+            "--model", exp.config,
+            "--pp", str(exp.pp),
+            "--dp", str(dp_megatron),
+            "--ep", str(exp.ep),
+        ]
+        if args.nsight:
+            command.append("--nsight")
+        command.extend([
+            "--",
+            "--micro-bs", str(exp.mb_size),
+            "--global-bs", str(global_batch_size(exp)),
+            "--seq-length", str(exp.seq_len),
+            "--schedule", exp.schedule,
+            "--zero-level", exp.zero_level,
+            "--train-iters", str(args.baseline_steps),
+        ])
+        return command
+
+    if exp.system == "deepspeed":
+        return [
+            "/workspace/artifact/scripts/run_deepspeed.sh",
+            "--nnode", nnode,
+            "--ngpu", ngpu,
+            "--node-rank", node_rank,
+            "--master-addr", master_addr,
+            "--master-port", "29501",
+            "--model", exp.config,
+            "--",
+            "--pp", str(exp.pp),
+            "--dp", str(exp.dp),
+            "--ep", str(exp.ep),
+            "--micro-bs", str(exp.mb_size),
+            "--global-bs", str(global_batch_size(exp)),
+            "--seq-len", str(exp.seq_len),
+            "--schedule", exp.schedule,
+            "--zero-stage", {"zero1": "1", "zero2": "2", "zero3": "3"}[exp.zero_level],
+            "--steps", str(args.baseline_steps),
+        ]
+
+    command = [
+        "/workspace/artifact/scripts/run_piper.sh",
+        "--model", piper_model_name(exp.config),
+        "--schedule", PIPER_SCHEDULES[exp.schedule],
+        "--pp", str(exp.pp),
+        "--dp", str(exp.dp),
+        "--zero-stage", str(PIPER_ZERO_STAGES[exp.zero_level]),
+        "--batch-size", str(exp.mb_size),
+        "--seq-len", str(exp.seq_len),
+        "--mbs", str(piper_num_mbs(exp)),
+        "--warmup", str(args.piper_warmup),
+        "--iters", str(args.piper_iters),
+        "--iteration-sleep", f"{args.piper_iteration_sleep:g}",
+        "--port", str(args.piper_ray_port),
+        "--temp-dir", "/tmp/piper/ray_tmp",
+        "--use-inductor" if args.piper_use_inductor and exp.sweep != "schedule" else "--no-use-inductor",
+    ]
+    if exp.ep > 1:
+        command.append("--ep")
+    if exp.bucket_size_mb is not None:
+        command.extend(["--bucket-size", f"{exp.bucket_size_mb:g}"])
+    if args.nsight:
+        command.append("--nsight")
+    if metrics_container_path:
+        command.extend(["--metrics-out", metrics_container_path])
+    if args.backend == "aws":
+        command.extend(["--address", "{master_addr}"])
+    return command
+
+
+def render_command(command: Sequence[str], *, nnode: int, ngpu: int, node_rank: int, master_addr: str) -> list[str]:
+    values = {
+        "nnode": str(nnode),
+        "ngpu": str(ngpu),
+        "node_rank": str(node_rank),
+        "master_addr": master_addr,
+    }
+    return [part.format(**values) for part in command]
+
+
+class Backend:
+    name: str
+
+    def layout(self, exp: Experiment) -> tuple[int, int]:
+        return backend_layout(exp, self.name)
+
+    def run(self, exp: Experiment, command: Sequence[str], log_path: Path) -> int:
+        raise NotImplementedError
+
+    def prepare_piper(self, args: argparse.Namespace) -> None:
+        return None
+
+    def cleanup_after_experiment(self, exp: Experiment) -> None:
+        return None
+
+    def fetch_file(self, container_path: str, destination: Path) -> bool:
+        return Path(container_path).is_file()
+
+    def dry_run_lines(self, exp: Experiment, command: Sequence[str]) -> list[str]:
+        raise NotImplementedError
+
+
+class LocalBackend(Backend):
+    name = "local"
+
+    def __init__(
+        self,
+        image: str,
+        out_dir: Path,
+        extra_docker_args: Sequence[str],
+        cuda_visible_devices: str | None,
+        torchtitan_assets_path: Path | None,
+    ):
+        self.image = image
+        self.out_dir = out_dir
+        self.extra_docker_args = list(extra_docker_args)
+        self.cuda_visible_devices = cuda_visible_devices
+        self.container_cuda_visible_devices = (
+            remapped_cuda_visible_devices(cuda_visible_devices)
+            if cuda_visible_devices is not None
+            else None
+        )
+        self.artifact_src_dir = Path(__file__).resolve().parent
+        self.workspace_dir = self.artifact_src_dir.parent
+        self.torchtitan_assets_path = torchtitan_assets_path if torchtitan_assets_path and torchtitan_assets_path.is_dir() else None
+
+    def run(self, exp: Experiment, command: Sequence[str], log_path: Path) -> int:
+        nnode, ngpu = self.layout(exp)
+        rendered = render_command(command, nnode=nnode, ngpu=ngpu, node_rank=0, master_addr="127.0.0.1")
+        gpus = docker_gpus_request(self.cuda_visible_devices)
+        docker_cmd = [
+            "docker", "run", "--rm",
+            "--gpus", gpus,
+            "--network", "host",
+            "--ipc", "host",
+            "--shm-size", "32g",
+            "-v", f"{self.out_dir}:{CONTAINER_OUT}",
+            "-v", f"{self.artifact_src_dir}:{CONTAINER_ARTIFACT}:ro",
+            "-v", f"{self.workspace_dir / 'src'}:/workspace/piper/src:ro",
+            "-v", f"{self.workspace_dir / 'examples'}:/workspace/piper/examples:ro",
+            "-v", f"{self.workspace_dir / 'test'}:/workspace/piper/test:ro",
+        ]
+        if self.torchtitan_assets_path is not None:
+            docker_cmd.extend(["-v", f"{self.torchtitan_assets_path}:{CONTAINER_TORCHTITAN_HF_ASSETS}:ro"])
+        if self.cuda_visible_devices is not None:
+            docker_cmd.extend([
+                "-e", f"NVIDIA_VISIBLE_DEVICES={self.cuda_visible_devices}",
+                "-e", f"CUDA_VISIBLE_DEVICES={self.container_cuda_visible_devices}",
+            ])
+        docker_cmd.extend([
+            *self.extra_docker_args,
+            self.image,
+            "bash", "-lc", shlex.join(rendered),
+        ])
+        log_path.parent.mkdir(parents=True, exist_ok=True)
+        with log_path.open("w", encoding="utf-8") as log_file:
+            proc = subprocess.run(docker_cmd, stdout=log_file, stderr=subprocess.STDOUT, text=True)
+        return proc.returncode
+
+    def dry_run_lines(self, exp: Experiment, command: Sequence[str]) -> list[str]:
+        nnode, ngpu = self.layout(exp)
+        rendered = render_command(command, nnode=nnode, ngpu=ngpu, node_rank=0, master_addr="127.0.0.1")
+        cuda_env = (
+            f"-e CUDA_VISIBLE_DEVICES={shlex.quote(str(self.container_cuda_visible_devices))} "
+            if self.cuda_visible_devices is not None
+            else ""
+        )
+        extra_args = " ".join(shlex.quote(arg) for arg in self.extra_docker_args)
+        extra_args = f"{extra_args} " if extra_args else ""
+        gpus = docker_gpus_request(self.cuda_visible_devices)
+        nvidia_env = (
+            f"-e NVIDIA_VISIBLE_DEVICES={shlex.quote(self.cuda_visible_devices)} "
+            if self.cuda_visible_devices is not None
+            else ""
+        )
+        return [
+            f"docker run --rm --gpus {shlex.quote(gpus)} --network host --ipc host "
+            f"{nvidia_env}"
+            f"{cuda_env}"
+            f"-v {shlex.quote(str(self.out_dir))}:{CONTAINER_OUT} "
+            f"-v {shlex.quote(str(self.artifact_src_dir))}:{CONTAINER_ARTIFACT}:ro "
+            f"-v {shlex.quote(str(self.workspace_dir / 'src'))}:/workspace/piper/src:ro "
+            f"-v {shlex.quote(str(self.workspace_dir / 'examples'))}:/workspace/piper/examples:ro "
+            f"-v {shlex.quote(str(self.workspace_dir / 'test'))}:/workspace/piper/test:ro "
+            f"{self._torchtitan_assets_mount_for_dry_run()}"
+            f"{extra_args}{shlex.quote(self.image)} bash -lc {shlex.quote(shlex.join(rendered))}"
+        ]
+
+    def _torchtitan_assets_mount_for_dry_run(self) -> str:
+        if self.torchtitan_assets_path is None:
+            return ""
+        return f"-v {shlex.quote(str(self.torchtitan_assets_path))}:{CONTAINER_TORCHTITAN_HF_ASSETS}:ro "
+
+
+class AwsSshBackend(Backend):
+    name = "aws"
+
+    def __init__(
+        self,
+        *,
+        image: str,
+        container: str,
+        start_containers: bool,
+        ray_port: int,
+    ):
+        self.image = image
+        self.container = container
+        self.start_containers = start_containers
+        self.ray_port = ray_port
+        self.ssh_key = require_env("SSH_KEY")
+        self.head_public_ip = require_env("HEAD_PUBLIC_IP")
+        self.head_private_ip = require_env("HEAD_PRIVATE_IP")
+        self.workers = workers_from_env()
+        if self.start_containers:
+            self._start_containers()
+
+    def _ssh(self, target: str, remote_command: str, *, worker: bool = False) -> list[str]:
+        cmd = [
+            "ssh",
+            "-i", self.ssh_key,
+            "-o", "StrictHostKeyChecking=no",
+        ]
+        if worker:
+            proxy = (
+                "ProxyCommand="
+                f"ssh -i {self.ssh_key} -o StrictHostKeyChecking=no "
+                f"-W %h:%p ubuntu@{self.head_public_ip}"
+            )
+            cmd.extend(["-o", proxy])
+        cmd.extend([f"ubuntu@{target}", remote_command])
+        return cmd
+
+    def _node_specs(self, nnode: int) -> list[tuple[str, str, bool]]:
+        if nnode > 1 and len(self.workers) < nnode - 1:
+            raise RuntimeError(f"Need {nnode - 1} WORKER*_PRIVATE_IP values, found {len(self.workers)}")
+        nodes = [("head", self.head_public_ip, False)]
+        nodes.extend((f"worker{i}", ip, True) for i, ip in enumerate(self.workers[: nnode - 1], start=1))
+        return nodes
+
+    def _docker_exec_command(self, rendered: Sequence[str], *, node_rank: int) -> str:
+        inner = shlex.join(rendered)
+        docker = [
+            "docker", "exec",
+            "-e", f"NODE_RANK={node_rank}",
+            "-e", "NCCL_SOCKET_IFNAME=ens32",
+            "-e", "GLOO_SOCKET_IFNAME=ens32",
+            self.container,
+            "bash", "-lc", inner,
+        ]
+        return shlex.join(docker)
+
+    def _start_containers(self) -> None:
+        remote = (
+            f"docker rm -f {shlex.quote(self.container)} >/dev/null 2>&1 || true; "
+            f"docker run -d --name {shlex.quote(self.container)} --gpus all "
+            "--network host --ipc host --shm-size 32g "
+            f"{shlex.quote(self.image)} sleep infinity"
+        )
+        for label, target, worker in self._node_specs(len(self.workers) + 1):
+            print(f"[aws] starting container on {label}", file=sys.stderr)
+            subprocess.run(self._ssh(target, remote, worker=worker), check=True)
+
+    def prepare_piper(self, args: argparse.Namespace) -> None:
+        nnode = max(1, len(self.workers) + 1)
+        stop = f"docker exec {shlex.quote(self.container)} bash -lc 'conda run -n piper ray stop -f >/dev/null 2>&1 || true'"
+        for _label, target, worker in self._node_specs(nnode):
+            subprocess.run(self._ssh(target, stop, worker=worker), check=False, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+
+        head_cmd = (
+            f"docker exec {shlex.quote(self.container)} bash -lc "
+            + shlex.quote(
+                "conda run -n piper ray start --head "
+                f"--node-ip-address={self.head_private_ip} "
+                f"--port={self.ray_port} "
+                "--disable-usage-stats "
+                "--temp-dir=/tmp/piper/ray_tmp"
+            )
+        )
+        subprocess.run(self._ssh(self.head_public_ip, head_cmd), check=True)
+        for _label, target, worker in self._node_specs(nnode)[1:]:
+            worker_cmd = (
+                f"docker exec {shlex.quote(self.container)} bash -lc "
+                + shlex.quote(
+                    "conda run -n piper ray start "
+                    f"--address={self.head_private_ip}:{self.ray_port} "
+                    "--disable-usage-stats "
+                    "--temp-dir=/tmp/piper/ray_tmp"
+                )
+            )
+            subprocess.run(self._ssh(target, worker_cmd, worker=worker), check=True)
+
+    def run(self, exp: Experiment, command: Sequence[str], log_path: Path) -> int:
+        nnode, ngpu = self.layout(exp)
+        nodes = self._node_specs(nnode)
+        log_path.parent.mkdir(parents=True, exist_ok=True)
+
+        if exp.system == "piper":
+            rendered = render_command(
+                command,
+                nnode=nnode,
+                ngpu=ngpu,
+                node_rank=0,
+                master_addr=self.head_private_ip,
+            )
+            remote = self._docker_exec_command(rendered, node_rank=0)
+            with log_path.open("w", encoding="utf-8") as log_file:
+                proc = subprocess.run(self._ssh(self.head_public_ip, remote), stdout=log_file, stderr=subprocess.STDOUT, text=True)
+            return proc.returncode
+
+        with tempfile.TemporaryDirectory(prefix="piper-e2e-node-logs.") as tmp_dir:
+            tmp_path = Path(tmp_dir)
+            procs: list[tuple[str, subprocess.Popen[str], Path]] = []
+            for node_rank, (label, target, worker) in enumerate(nodes):
+                rendered = render_command(
+                    command,
+                    nnode=nnode,
+                    ngpu=ngpu,
+                    node_rank=node_rank,
+                    master_addr=self.head_private_ip,
+                )
+                remote = self._docker_exec_command(rendered, node_rank=node_rank)
+                node_log = tmp_path / f"{log_path.stem}.{label}.log"
+                handle = node_log.open("w", encoding="utf-8")
+                proc = subprocess.Popen(self._ssh(target, remote, worker=worker), stdout=handle, stderr=subprocess.STDOUT, text=True)
+                handle.close()
+                procs.append((label, proc, node_log))
+
+            returncodes = []
+            for _label, proc, _node_log in procs:
+                returncodes.append(proc.wait())
+            with log_path.open("w", encoding="utf-8") as combined:
+                for label, _proc, node_log in procs:
+                    combined.write(f"===== {label} =====\n")
+                    if node_log.exists():
+                        combined.write(node_log.read_text(encoding="utf-8", errors="replace"))
+                        combined.write("\n")
+            return 0 if all(code == 0 for code in returncodes) else next(code for code in returncodes if code != 0)
+
+    def fetch_file(self, container_path: str, destination: Path) -> bool:
+        remote = f"docker exec {shlex.quote(self.container)} bash -lc {shlex.quote('test -f ' + shlex.quote(container_path) + ' && cat ' + shlex.quote(container_path))}"
+        result = subprocess.run(self._ssh(self.head_public_ip, remote), check=False, capture_output=True, text=True)
+        if result.returncode != 0 or not result.stdout:
+            return False
+        destination.parent.mkdir(parents=True, exist_ok=True)
+        destination.write_text(result.stdout, encoding="utf-8")
+        return True
+
+    def cleanup_after_experiment(self, exp: Experiment) -> None:
+        if exp.system != "piper":
+            return
+        stop = f"docker exec {shlex.quote(self.container)} bash -lc 'conda run -n piper ray stop -f >/dev/null 2>&1 || true'"
+        for _label, target, worker in self._node_specs(max(1, len(self.workers) + 1)):
+            subprocess.run(self._ssh(target, stop, worker=worker), check=False, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+
+    def dry_run_lines(self, exp: Experiment, command: Sequence[str]) -> list[str]:
+        nnode, ngpu = self.layout(exp)
+        nodes = self._node_specs(nnode)
+        lines = []
+        if exp.system == "piper":
+            rendered = render_command(command, nnode=nnode, ngpu=ngpu, node_rank=0, master_addr=self.head_private_ip)
+            lines.append(self._docker_exec_command(rendered, node_rank=0))
+            return lines
+        for node_rank, (label, _target, _worker) in enumerate(nodes):
+            rendered = render_command(command, nnode=nnode, ngpu=ngpu, node_rank=node_rank, master_addr=self.head_private_ip)
+            lines.append(f"{label}: {self._docker_exec_command(rendered, node_rank=node_rank)}")
+        return lines
+
+
+def require_env(name: str) -> str:
+    value = os.environ.get(name)
+    if not value:
+        raise SystemExit(f"Missing required environment variable: {name}")
+    return value
+
+
+def workers_from_env() -> list[str]:
+    workers: list[str] = []
+    index = 1
+    while True:
+        value = os.environ.get(f"WORKER{index}_PRIVATE_IP")
+        if not value:
+            break
+        workers.append(value)
+        index += 1
+    return workers
+
+
+def read_text_lossy(path: Path) -> str:
+    if not path.is_file():
+        return ""
+    return path.read_text(encoding="utf-8", errors="replace").replace("\0", "")
+
+
+def strip_ansi(text: str) -> str:
+    return ANSI_RE.sub("", text)
+
+
+def parse_log(system: str, log_path: Path, metrics_path: Path | None = None) -> tuple[float | None, float | None, str, bool, str]:
+    text = strip_ansi(read_text_lossy(log_path))
+    is_oom = OOM_RE.search(text) is not None
+    reason = "oom" if is_oom else ""
+
+    if system == "torchtitan":
+        peak: dict[int, float] = {}
+        iter_by_rank: dict[int, tuple[float, float]] = {}
+        last_iter: tuple[float, float] | None = None
+        for line in text.splitlines():
+            m = TT_FINAL_BY_RANK_RE.search(line)
+            if m:
+                iter_by_rank[int(m.group(1))] = (float(m.group(2)), float(m.group(3)))
+                last_iter = (float(m.group(2)), float(m.group(3)))
+            m = TT_ITER_RE.search(line)
+            if m:
+                last_iter = (float(m.group(1)), float(m.group(2)))
+            m = TT_MEM_RE.search(line)
+            if m:
+                peak[int(m.group(1))] = max(peak.get(int(m.group(1)), 0.0), float(m.group(2)))
+        iter_mean, iter_std = iter_by_rank.get(0, last_iter or (None, None))
+        peak_str = "/".join(f"{peak[rank]:.2f}" for rank in sorted(peak)) if peak else ""
+        return iter_mean, iter_std, peak_str, is_oom, reason
+
+    if system == "deepspeed":
+        step_times = [float(m.group(1)) for m in DS_STEP_RE.finditer(text)][-5:]
+        peak = {int(m.group(1)): float(m.group(2)) for m in DS_MEM_RE.finditer(text)}
+        peak_str = "/".join(f"{peak[rank]:.2f}" for rank in sorted(peak)) if peak else ""
+        if not step_times:
+            return None, None, peak_str, is_oom, reason
+        mean = sum(step_times) / len(step_times)
+        return mean, pstdev(step_times, mean), peak_str, is_oom, reason
+
+    if system == "megatron":
+        iter_times_s = [float(m.group(1)) / 1000.0 for m in MG_ITER_RE.finditer(text)][-5:]
+        peak = {int(m.group(1)): float(m.group(2)) / 1024.0 for m in MG_MEM_RE.finditer(text)}
+        peak_str = "/".join(f"{peak[rank]:.2f}" for rank in sorted(peak)) if peak else ""
+        if not iter_times_s:
+            return None, None, peak_str, is_oom, reason
+        mean = sum(iter_times_s) / len(iter_times_s)
+        return mean, pstdev(iter_times_s, mean), peak_str, is_oom, reason
+
+    if system == "piper" and metrics_path is not None and metrics_path.is_file():
+        rows = list(csv.DictReader(metrics_path.open("r", encoding="utf-8")))
+        times: list[tuple[float, float, int]] = []
+        peak_values: list[str] = []
+        for row in rows:
+            try:
+                mean = float(row.get("iter_time_mean_s") or "")
+            except ValueError:
+                continue
+            try:
+                std = float(row.get("iter_time_std_s") or 0.0)
+            except ValueError:
+                std = 0.0
+            try:
+                samples = int(float(row.get("samples") or 1))
+            except ValueError:
+                samples = 1
+            times.append((mean, std, samples))
+            for key, value in row.items():
+                if key.startswith("peak_memory_pp") and value:
+                    try:
+                        peak_values.append(f"{key.removeprefix('peak_memory_pp').removesuffix('_gb')}:{float(value):.3f}")
+                    except ValueError:
+                        pass
+        total_samples = sum(samples for _mean, _std, samples in times)
+        if total_samples:
+            mean = sum(mean * samples for mean, _std, samples in times) / total_samples
+            std = sum(std * samples for _mean, std, samples in times) / total_samples
+            return mean, std, ";".join(peak_values), is_oom, reason
+    return None, None, "", is_oom, reason
+
+
+def pstdev(values: Sequence[float], mean: float) -> float:
+    if not values:
+        return 0.0
+    return math.sqrt(sum((value - mean) ** 2 for value in values) / len(values))
+
+
+def write_csv(path: Path, rows: list[Result]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    fieldnames = list(Result.__annotations__.keys())
+    with path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.DictWriter(handle, fieldnames=fieldnames)
+        writer.writeheader()
+        for row in rows:
+            writer.writerow(asdict(row))
+
+
+def row_tps(row: Result) -> float | None:
+    if row.iter_time_mean is None or row.iter_time_mean <= 0:
+        return None
+    return row.global_batch_size * row.seq_len / row.iter_time_mean
+
+
+def result_label(row: Result) -> str:
+    if row.sweep == "scalability":
+        return f"pp={row.pp},dp={row.dp}"
+    if row.sweep == "zero":
+        return f"{row.zero_level},mb={row.mb_size}"
+    if row.sweep == "local":
+        return "local"
+    return row.schedule
+
+
+def write_outputs(base_out: Path, rows: list[Result]) -> None:
+    write_csv(base_out / "results.csv", rows)
+    for sweep in ("scalability", "zero", "schedule", "local"):
+        sweep_rows = [row for row in rows if row.sweep == sweep]
+        if sweep_rows:
+            save_plot(sweep_rows, base_out / f"{sweep}.png", title=f"Qwen3 {sweep} throughput")
+
+
+def save_plot(rows: list[Result], output_path: Path, *, title: str) -> None:
+    ok_rows = [row for row in rows if row.status == "ok" and row_tps(row) is not None]
+    if not ok_rows:
+        return
+    try:
+        import matplotlib
+
+        matplotlib.use("Agg")
+        import matplotlib.pyplot as plt
+    except Exception as exc:  # pragma: no cover - plotting is optional for dry environments
+        print(f"Skipping plot {output_path}: {exc}", file=sys.stderr)
+        return
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    labels = [f"{row.system}\n{result_label(row)}" for row in ok_rows]
+    values = [float(row_tps(row) or 0.0) for row in ok_rows]
+    colors = [SYSTEM_COLORS.get(row.system, "#4C72B0") for row in ok_rows]
+    fig, ax = plt.subplots(figsize=(max(10, len(ok_rows) * 0.7), 5))
+    ax.bar(range(len(ok_rows)), values, color=colors)
+    ax.set_title(title)
+    ax.set_ylabel("Tokens / second")
+    ax.set_xticks(range(len(ok_rows)))
+    ax.set_xticklabels(labels, rotation=45, ha="right")
+    ax.grid(axis="y", linestyle="--", alpha=0.35)
+    fig.tight_layout()
+    fig.savefig(output_path, dpi=200)
+    plt.close(fig)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Run the Piper paper Qwen e2e artifact.")
+    parser.add_argument("--backend", choices=("local", "aws"), default="local")
+    parser.add_argument("--image", default=DEFAULT_IMAGE)
+    parser.add_argument("--container", default=DEFAULT_CONTAINER)
+    parser.add_argument("--aws-start-containers", action="store_true")
+    parser.add_argument("--systems", nargs="+", choices=list(DEFAULT_SYSTEMS), default=list(DEFAULT_SYSTEMS))
+    parser.add_argument("--sweeps", nargs="+", choices=("scalability", "zero", "schedule", "local"), default=["scalability", "zero", "schedule"])
+    parser.add_argument("--schedule-sweep", nargs="+", default=list(DEFAULT_SCHEDULE_SWEEP))
+    parser.add_argument("--seq-len", type=int, default=512)
+    parser.add_argument("--out-dir", default=None)
+    parser.add_argument("--dry-run", action="store_true")
+    parser.add_argument("--nsight", action="store_true")
+    parser.add_argument("--bucket-size-mb", type=float, default=None)
+    parser.add_argument("--gradient-accumulation", dest="gradient_accumulation", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--ar-a2a-same-stream", dest="ar_a2a_same_stream", action=argparse.BooleanOptionalAction, default=False)
+    parser.add_argument("--overlap-chunks", dest="overlap_chunks", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--piper-warmup", type=int, default=3)
+    parser.add_argument("--piper-iters", type=int, default=10)
+    parser.add_argument("--piper-iteration-sleep", type=float, default=0.0)
+    parser.add_argument("--piper-use-inductor", action=argparse.BooleanOptionalAction, default=False)
+    parser.add_argument("--torchtitan-compile", action=argparse.BooleanOptionalAction, default=False)
+    parser.add_argument("--torchtitan-use-bmm-experts", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--piper-ray-port", type=int, default=6379)
+    parser.add_argument("--baseline-steps", type=int, default=8)
+    parser.add_argument("--max-log-ranks", type=int, default=8)
+    parser.add_argument(
+        "--local-cuda-visible-devices",
+        "--cuda-visible-devices",
+        dest="local_cuda_visible_devices",
+        default=None,
+        help=(
+            "Restrict local Docker runs to these host GPU IDs. Inside the filtered "
+            "container, CUDA_VISIBLE_DEVICES is remapped to logical IDs."
+        ),
+    )
+    default_torchtitan_assets = os.environ.get("TORCHTITAN_HF_ASSETS")
+    if default_torchtitan_assets is None and DEFAULT_LOCAL_TORCHTITAN_ASSETS.is_dir():
+        default_torchtitan_assets = str(DEFAULT_LOCAL_TORCHTITAN_ASSETS)
+    parser.add_argument(
+        "--local-torchtitan-assets-path",
+        default=default_torchtitan_assets,
+        help="Host path mounted read-only to /workspace/torchtitan/assets/hf for local TorchTitan runs.",
+    )
+    parser.add_argument("--docker-arg", action="append", default=[], help="Extra argument passed to docker run in local mode.")
+    return parser.parse_args()
+
+
+def make_backend(args: argparse.Namespace, base_out: Path) -> Backend:
+    if args.backend == "local":
+        torchtitan_assets_path = Path(args.local_torchtitan_assets_path).resolve() if args.local_torchtitan_assets_path else None
+        return LocalBackend(
+            args.image,
+            base_out,
+            args.docker_arg,
+            args.local_cuda_visible_devices,
+            torchtitan_assets_path,
+        )
+    return AwsSshBackend(
+        image=args.image,
+        container=args.container,
+        start_containers=args.aws_start_containers,
+        ray_port=args.piper_ray_port,
+    )
+
+
+def main() -> int:
+    args = parse_args()
+    schedule_sweep = [normalize_schedule(item) for item in args.schedule_sweep]
+    base_out = Path(args.out_dir).resolve() if args.out_dir else (Path("artifact") / "out" / "e2e-eval" / time.strftime("%Y%m%d_%H%M%S")).resolve()
+    base_out.mkdir(parents=True, exist_ok=True)
+    backend = make_backend(args, base_out)
+
+    experiments = build_experiments(
+        systems=args.systems,
+        schedule_sweep=schedule_sweep,
+        seq_len=args.seq_len,
+        enabled_sweeps=set(args.sweeps),
+        gradient_accumulation=args.gradient_accumulation,
+        ar_a2a_same_stream=args.ar_a2a_same_stream,
+        overlap_chunks=args.overlap_chunks,
+        bucket_size_mb=args.bucket_size_mb,
+    )
+    grouped: dict[str, list[Experiment]] = {system: [] for system in args.systems}
+    for exp in experiments:
+        grouped[exp.system].append(exp)
+
+    print(f"output directory: {base_out}")
+    print(f"planned experiments: {len(experiments)}")
+    overall_failed = False
+
+    def _terminate(signum: int, _frame) -> None:
+        raise SystemExit(128 + signum)
+
+    signal.signal(signal.SIGINT, _terminate)
+    signal.signal(signal.SIGTERM, _terminate)
+
+    results: list[Result] = []
+    for system in args.systems:
+        system_out = base_out / system
+        system_out.mkdir(parents=True, exist_ok=True)
+        system_experiments = grouped[system]
+
+        for index, exp in enumerate(system_experiments, start=1):
+            exp_slug = slug(exp, nsight=args.nsight)
+            log_path = system_out / f"{index:02d}_{exp_slug}.log"
+            metrics_name = f".{exp_slug}.metrics.csv"
+            host_metrics_path = base_out / metrics_name
+            container_metrics_path = str(CONTAINER_OUT / metrics_name)
+            command = in_container_command(exp, args, container_metrics_path if system == "piper" else None)
+            nnode, ngpu = backend.layout(exp)
+
+            print(f"[{system} {index}/{len(system_experiments)}] {exp.sweep}: {experiment_label(exp)} config={exp.config}")
+            for line in backend.dry_run_lines(exp, command):
+                print("  " + line)
+            if args.dry_run:
+                continue
+
+            if system == "piper":
+                backend.prepare_piper(args)
+            returncode = 1
+            try:
+                returncode = backend.run(exp, command, log_path)
+                if system == "piper" and not host_metrics_path.is_file():
+                    backend.fetch_file(container_metrics_path, host_metrics_path)
+                if system == "piper":
+                    for leaked_json in base_out.glob("*.json"):
+                        leaked_json.unlink()
+                iter_mean, iter_std, peak_str, is_oom, reason = parse_log(
+                    system,
+                    log_path,
+                    metrics_path=host_metrics_path if system == "piper" else None,
+                )
+                if returncode == 0 and iter_mean is not None:
+                    status = "ok"
+                elif is_oom:
+                    status = "oom"
+                    reason = reason or "oom"
+                else:
+                    status = "failed"
+                    reason = reason or f"exit_code={returncode}"
+                if status != "ok":
+                    overall_failed = True
+                results.append(
+                    Result(
+                        system=system,
+                        sweep=exp.sweep,
+                        config=exp.config,
+                        pp=exp.pp,
+                        dp=exp.dp,
+                        ep=exp.ep,
+                        zero_level=exp.zero_level,
+                        schedule=exp.schedule,
+                        mb_size=exp.mb_size,
+                        seq_len=exp.seq_len,
+                        local_batch_size=local_batch_size(exp),
+                        global_batch_size=global_batch_size(exp),
+                        nnode=nnode,
+                        ngpu=ngpu,
+                        log_path=str(log_path),
+                        metrics_path="",
+                        returncode=returncode,
+                        status=status,
+                        iter_time_mean=iter_mean,
+                        iter_time_stddev=iter_std,
+                        peak_memory_gb_by_rank=peak_str,
+                        failure_reason=reason,
+                    )
+                )
+                if host_metrics_path.exists():
+                    host_metrics_path.unlink()
+                write_csv(base_out / "results.csv", results)
+            finally:
+                if host_metrics_path.exists():
+                    host_metrics_path.unlink()
+                backend.cleanup_after_experiment(exp)
+
+    if not args.dry_run:
+        write_outputs(base_out, results)
+        print(f"wrote results under {base_out}")
+    return 1 if overall_failed else 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/artifact/patches/torchtitan-qwen-e2e.patch b/artifact/patches/torchtitan-qwen-e2e.patch
new file mode 100644
index 0000000..042a6a7
--- /dev/null
+++ b/artifact/patches/torchtitan-qwen-e2e.patch
@@ -0,0 +1,718 @@
+diff --git a/torchtitan/config/manager.py b/torchtitan/config/manager.py
+index 9c9ab6cf..335b388b 100644
+--- a/torchtitan/config/manager.py
++++ b/torchtitan/config/manager.py
+@@ -102,18 +102,21 @@ class ConfigManager:
+         # Import config_registry from module based on module specification
+         if module_name in all_supported:
+             # short module from supported module list  (search models first, then experiments)
++            first_exc = None
+             for prefix in ("torchtitan.models", "torchtitan.experiments"):
+                 module_path = f"{prefix}.{module_name}.config_registry"
+                 try:
+                     module = importlib.import_module(module_path)
+                     break
+-                except ImportError:
++                except ImportError as e:
++                    if first_exc is None:
++                        first_exc = e
+                     continue
+             if module is None:
+                 raise ImportError(
+                     f"Cannot import config_registry for module '{module_name}' "
+                     f"from torchtitan.models or torchtitan.experiments"
+-                )
++                ) from first_exc
+         else:
+             # Fully qualified module path: try appending .config_registry first,
+             # then fall back to importing directly (e.g., torchtitan.models.llama3
+diff --git a/torchtitan/components/checkpoint.py b/torchtitan/components/checkpoint.py
+index d31dcda1..1c6becc1 100644
+--- a/torchtitan/components/checkpoint.py
++++ b/torchtitan/components/checkpoint.py
+@@ -22,9 +22,13 @@ import torch.distributed as dist
+ import torch.distributed.checkpoint as dcp
+ import torch.nn as nn
+-from torch.distributed.checkpoint import HuggingFaceStorageWriter
+-from torch.distributed.checkpoint._consolidate_hf_safetensors import (
+-    consolidate_safetensors_files_on_every_rank,
+-)
++try:
++    from torch.distributed.checkpoint import HuggingFaceStorageWriter
++    from torch.distributed.checkpoint._consolidate_hf_safetensors import (
++        consolidate_safetensors_files_on_every_rank,
++    )
++except ImportError:
++    HuggingFaceStorageWriter = None
++    consolidate_safetensors_files_on_every_rank = None
+ from torch.distributed.checkpoint.staging import DefaultStager, StagingOptions
+ from torch.distributed.checkpoint.state_dict import (
+     get_model_state_dict,
+@@ -531,6 +534,10 @@ class CheckpointManager(Configurable):
+         checkpoint_save_id: str | None = None
+         fqn_to_index_mapping: dict[Any, int] | None = None
+         if to_hf:
++            if HuggingFaceStorageWriter is None:
++                raise RuntimeError(
++                    "HuggingFaceStorageWriter is unavailable in this torch build."
++                )
+             assert (
+                 self.sd_adapter is not None
+             ), "trying to save checkpoint in HF safetensors format, but sd_adapter is not provided."
+@@ -956,6 +963,10 @@ class CheckpointManager(Configurable):
+             states = self._flattened_model_states_sd()
+ 
+         if self.last_save_in_hf:
++            if consolidate_safetensors_files_on_every_rank is None:
++                raise RuntimeError(
++                    "HF safetensors consolidation is unavailable in this torch build."
++                )
+             assert (
+                 self.last_save_model_only
+             ), "Only model can be saved when saving in HF safetensors format."
+diff --git a/torchtitan/distributed/parallel_dims.py b/torchtitan/distributed/parallel_dims.py
+index 5fcf646a..a267cd3f 100644
+--- a/torchtitan/distributed/parallel_dims.py
++++ b/torchtitan/distributed/parallel_dims.py
+@@ -93,9 +93,9 @@ class ParallelDims:
+         which is created by flattening the batch and cp dimensions.
+         This API performs the following unflatten operations from the world mesh:
+ 
+-            ["pp", "batch", "cp", "tp"]  # dataloading_mesh
+-            ["pp", "dp_replicate", "fsdp", "tp"]  # dense_mesh
+-            ["pp", "dp_replicate", "efsdp", "ep", "etp"]  # sparse_mesh
++            ["batch", "pp", "cp", "tp"]  # dataloading_mesh
++            ["dp_replicate", "fsdp", "pp", "tp"]  # dense_mesh
++            ["dp_replicate", "efsdp", "ep", "pp", "etp"]  # sparse_mesh
+ 
+         Note: DeviceMesh currently recreates the process group for each dimension.
+         It should share the process group for the same dim group to avoid unnecessary
+@@ -143,19 +143,19 @@ class ParallelDims:
+         )
+         dataloading_mesh = unflatten_mesh(
+             self._world_mesh,
+-            ("pp", "batch", "cp", "tp"),
+-            (self.pp, batch, self.cp, self.tp),
++            ("batch", "pp", "cp", "tp"),
++            (batch, self.pp, self.cp, self.tp),
+         )
+         loss_mesh = dataloading_mesh["batch", "cp"]._flatten("loss_mesh")
+         dense_mesh = unflatten_mesh(
+             self._world_mesh,
+-            ("pp", "dp_replicate", "fsdp", "tp"),
+-            (self.pp, self.dp_replicate, fsdp, self.tp),
++            ("dp_replicate", "fsdp", "pp", "tp"),
++            (self.dp_replicate, fsdp, self.pp, self.tp),
+         )
+         sparse_mesh = unflatten_mesh(
+             self._world_mesh,
+-            ("pp", "dp_replicate", "efsdp", "ep", "etp"),
+-            (self.pp, self.dp_replicate, efsdp, self.ep, self.etp),
++            ("dp_replicate", "efsdp", "ep", "pp", "etp"),
++            (self.dp_replicate, efsdp, self.ep, self.pp, self.etp),
+         )
+ 
+         self._global_meshes = {
+diff --git a/torchtitan/models/llama4/parallelize.py b/torchtitan/models/llama4/parallelize.py
+index 03a0cc21..de795c44 100644
+--- a/torchtitan/models/llama4/parallelize.py
++++ b/torchtitan/models/llama4/parallelize.py
+@@ -1,9 +1,10 @@
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
+ # All rights reserved.
+ #
+ # This source code is licensed under the BSD-style license found in the
+ # LICENSE file in the root directory of this source tree.
+ 
++from typing import NamedTuple
+ from typing import Any
+ 
+ import torch
+@@ -11,9 +12,16 @@ import torch.nn as nn
+ from torch.distributed.device_mesh import DeviceMesh
+ from torch.distributed.fsdp import CPUOffloadPolicy, fully_shard, MixedPrecisionPolicy
+-from torch.distributed.fsdp._fully_shard._fsdp_common import (
+-    FSDPMeshInfo,
+-    ShardPlacementResult,
+-)
++try:
++    from torch.distributed.fsdp._fully_shard._fsdp_common import (
++        FSDPMeshInfo,
++        ShardPlacementResult,
++    )
++except ImportError:
++    from torch.distributed.fsdp._fully_shard._fsdp_common import FSDPMeshInfo
++
++    class ShardPlacementResult(NamedTuple):
++        placement: object
++        mesh_info: FSDPMeshInfo
+ from torch.distributed.tensor import Partial, Replicate, Shard
+ from torch.distributed.tensor.parallel import (
+     ColwiseParallel,
+diff --git a/torchtitan/distributed/pipeline_parallel.py b/torchtitan/distributed/pipeline_parallel.py
+index 7cc6e890..f04bc298 100644
+--- a/torchtitan/distributed/pipeline_parallel.py
++++ b/torchtitan/distributed/pipeline_parallel.py
+@@ -240,6 +240,13 @@ def build_pipeline_schedule(
+         f"with {n_microbatches} microbatches and {num_total_stages} stages."
+     )
+ 
++    if hasattr(schedule, "pipeline_order"):
++        pp_rank = torch.distributed.get_rank(stages[0].group)
++        rank_ops = schedule.pipeline_order.get(pp_rank, [])
++        logger.info(
++            f"PP rank {pp_rank} pipeline operations ({len(rank_ops)} steps): {rank_ops}"
++        )
++
+     if parallelism.pipeline_parallel_expert_parallel_overlap and isinstance(
+         schedule, ScheduleDualPipeV
+     ):
+diff --git a/torchtitan/hf_datasets/text_datasets.py b/torchtitan/hf_datasets/text_datasets.py
+index 51a5126a..f41038a4 100644
+--- a/torchtitan/hf_datasets/text_datasets.py
++++ b/torchtitan/hf_datasets/text_datasets.py
+@@ -25,7 +25,23 @@ from torchtitan.tools.logging import logger
+ 
+ def _load_c4_dataset(dataset_path: str, split: str):
+     """Load C4 dataset with default configuration."""
+-    return load_dataset(dataset_path, name="en", split=split, streaming=True)
++    import time
++
++    from huggingface_hub.errors import HfHubHTTPError
++
++    max_retries = 5
++    for attempt in range(max_retries):
++        try:
++            return load_dataset(dataset_path, name="en", split=split, streaming=True)
++        except HfHubHTTPError as e:
++            if attempt == max_retries - 1:
++                raise
++            wait = 30 * (2**attempt)
++            logger.warning(
++                f"HF API error loading C4 (attempt {attempt + 1}/{max_retries}): {e}. "
++                f"Retrying in {wait}s..."
++            )
++            time.sleep(wait)
+ 
+ 
+ def _process_c4_text(sample: dict[str, Any]) -> str:
+diff --git a/torchtitan/models/common/attention.py b/torchtitan/models/common/attention.py
+index fe40d387..70c1a64a 100644
+--- a/torchtitan/models/common/attention.py
++++ b/torchtitan/models/common/attention.py
+@@ -23,12 +23,10 @@ from torch.nn.attention import sdpa_kernel, SDPBackend
+ from torch.nn.attention.flex_attention import (
+     _mask_mod_signature,
+     _score_mod_signature,
+-    AuxRequest,
+     BlockMask,
+     create_block_mask,
+     flex_attention,
+ )
+-from torch.nn.attention.varlen import varlen_attn
+ from torch.types import Number
+ 
+ from torchtitan.models.common.linear import Linear
+@@ -187,6 +185,8 @@ class VarlenAttentionWrapper(LocalMapAttention):
+         xk_packed = xk_packed.to(torch.bfloat16)
+         xv_packed = xv_packed.to(torch.bfloat16)
+ 
++        from torch.nn.attention.varlen import varlen_attn
++
+         return varlen_attn(
+             xq_packed,
+             xk_packed,
+@@ -260,6 +260,8 @@ class FlexAttentionWrapper(LocalMapAttention):
+         # 2. `self._compiled_flex_attn` is not correct, `self` will be passed in
+         #    as the first argument, which will cause an error.
+         #    `FlexAttentionWrapper._compiled_flex_attn` is correct.
++        from torch.nn.attention.flex_attention import AuxRequest
++
+         out, aux = FlexAttentionWrapper._compiled_flex_attn(
+             q,
+             k,
+@@ -317,7 +319,7 @@ class ScaledDotProductAttentionWrapper(LocalMapAttention):
+         super().__init__()
+         if not self.sdpa_backends:
+             self.sdpa_backends = [
+-                SDPBackend.CUDNN_ATTENTION,
++                # SDPBackend.CUDNN_ATTENTION,
+                 SDPBackend.FLASH_ATTENTION,
+                 SDPBackend.MATH,
+             ]
+diff --git a/torchtitan/models/common/moe/moe.py b/torchtitan/models/common/moe/moe.py
+index acd2de5f..0de207ab 100644
+--- a/torchtitan/models/common/moe/moe.py
++++ b/torchtitan/models/common/moe/moe.py
+@@ -28,6 +28,12 @@ def _run_experts_for_loop(
+     x: torch.Tensor,
+     num_tokens_per_expert: torch.Tensor,
+ ) -> torch.Tensor:
++
++    # Avoid synchronization with the host (may race as implemented right now)
++    # num_tokens_per_expert_host = torch.empty(len(num_tokens_per_expert), dtype=torch.int64, device="cpu", pin_memory=True)
++    # num_tokens_per_expert_host.copy_(num_tokens_per_expert.to(torch.int64), non_blocking=True)
++    # num_tokens_per_expert_list = num_tokens_per_expert_host.tolist()
++
+     # NOTE: this would incur a synchronization between device and host
+     num_tokens_per_expert_list = num_tokens_per_expert.tolist()
+ 
+@@ -78,6 +84,24 @@ def _run_experts_grouped_mm(
+ 
+     return out
+ 
++
++def _run_experts_bmm(
++    w1: torch.Tensor,
++    w2: torch.Tensor,
++    w3: torch.Tensor,
++    x: torch.Tensor,
++    num_tokens_per_expert: torch.Tensor,
++) -> torch.Tensor:
++    dim = x.shape[-1]
++    num_experts = w1.shape[0]
++    counts = num_tokens_per_expert.to(device=x.device, dtype=torch.long)
++    capacity = int(counts.max().item()) if counts.numel() else 0
++    starts = torch.cumsum(counts, dim=0) - counts
++    positions = torch.arange(capacity, device=x.device, dtype=torch.long)
++    zero = x.new_zeros((capacity, dim))
++
++    packed_rows = []
++    for expert_idx in range(num_experts):
++        valid = positions < counts[expert_idx]
++        source_idx = starts[expert_idx] + positions
++        safe_idx = torch.where(valid, source_idx, positions.new_zeros(()))
++        packed_rows.append(torch.where(valid[:, None], x[safe_idx], zero))
++    packed = torch.stack(packed_rows, dim=0)
++
++    h = F.silu(torch.bmm(packed, w1.transpose(-2, -1)))
++    h = h * torch.bmm(packed, w3.transpose(-2, -1))
++    expert_out = torch.bmm(h, w2.transpose(-2, -1))
++
++    out = x.new_zeros(x.shape)
++    for expert_idx in range(num_experts):
++        valid = positions < counts[expert_idx]
++        dest_idx = starts[expert_idx] + positions
++        safe_idx = torch.where(valid, dest_idx, positions.new_zeros(()))
++        src = torch.where(valid[:, None], expert_out[expert_idx], zero)
++        out = out.scatter_add(0, safe_idx[:, None].expand(-1, dim), src)
++    return out
++
++
+ class GroupedExperts(Module):
+     @dataclass(kw_only=True, slots=True)
+     class Config(Module.Config):
+@@ -85,6 +109,7 @@ class GroupedExperts(Module):
+         hidden_dim: int = field(init=False)
+         num_experts: int = field(init=False)
+         use_grouped_mm: bool = True
++        use_bmm: bool = False
+ 
+     def __init__(self, config: Config):
+         super().__init__()
+@@ -103,6 +128,7 @@ class GroupedExperts(Module):
+             torch.empty(config.num_experts, config.hidden_dim, config.dim)
+         )
+         self.use_grouped_mm = config.use_grouped_mm
++        self.use_bmm = config.use_bmm
+ 
+     def forward(
+         self,
+@@ -122,6 +148,9 @@ class GroupedExperts(Module):
+             w2 = self.w2
+             w3 = self.w3
+ 
++        if self.use_bmm:
++            return _run_experts_bmm(w1, w2, w3, x, num_tokens_per_expert)
++
+         if self.use_grouped_mm:
+             # NOTE: If EP is not used, we need to pad the indices
+             #       to prepare for grouped_mm;
+diff --git a/torchtitan/models/qwen3/__init__.py b/torchtitan/models/qwen3/__init__.py
+index d4bace1c..846d9e59 100644
+--- a/torchtitan/models/qwen3/__init__.py
++++ b/torchtitan/models/qwen3/__init__.py
+@@ -272,6 +272,88 @@ qwen3_configs = {
+         ),
+     ),
+     # Qwen3-MoE models
++    "1B-A0.7B": Qwen3Model.Config(
++        vocab_size=151936,
++        dim=1024,
++        n_layers=16,
++        tok_embeddings=Embedding.Config(),
++        output=Linear.Config(),
++        norm=RMSNorm.Config(eps=1e-6),
++        enable_weight_tying=False,
++        layer=Qwen3TransformerBlock.Config(
++            attention_norm=RMSNorm.Config(eps=1e-6),
++            ffn_norm=RMSNorm.Config(eps=1e-6),
++            moe_enabled=True,
++            moe=MoE.Config(
++                hidden_dim=3584,
++                num_experts=4,
++                num_shared_experts=0,
++                score_before_experts=False,
++                load_balance_coeff=None,
++                router=TokenChoiceTopKRouter.Config(
++                    top_k=2,
++                    score_func="softmax",
++                ),
++            ),
++            feed_forward=FeedForward.Config(hidden_dim=3584),
++            attention=GQAttention.Config(
++                n_heads=16,
++                n_kv_heads=8,
++                head_dim=64,
++                q_norm=RMSNorm.Config(eps=1e-6),
++                k_norm=RMSNorm.Config(eps=1e-6),
++                attn_backend="sdpa",
++                rope_backend="cos_sin",
++            ),
++        ),
++        rope=RoPE.Config(
++            dim=64,
++            max_seq_len=2048,
++            theta=1000000.0,
++            backend="cos_sin",
++        ),
++    ),
++    "9B-A3B": Qwen3Model.Config(
++        vocab_size=151936,
++        dim=2048,
++        n_layers=24,
++        tok_embeddings=Embedding.Config(),
++        output=Linear.Config(),
++        norm=RMSNorm.Config(eps=1e-6),
++        enable_weight_tying=False,
++        layer=Qwen3TransformerBlock.Config(
++            attention_norm=RMSNorm.Config(eps=1e-6),
++            ffn_norm=RMSNorm.Config(eps=1e-6),
++            moe_enabled=True,
++            moe=MoE.Config(
++                hidden_dim=7168,
++                num_experts=8,
++                num_shared_experts=0,
++                score_before_experts=False,
++                load_balance_coeff=None,
++                router=TokenChoiceTopKRouter.Config(
++                    top_k=2,
++                    score_func="softmax",
++                ),
++            ),
++            feed_forward=FeedForward.Config(hidden_dim=7168),
++            attention=GQAttention.Config(
++                n_heads=32,
++                n_kv_heads=8,
++                head_dim=64,
++                q_norm=RMSNorm.Config(eps=1e-6),
++                k_norm=RMSNorm.Config(eps=1e-6),
++                attn_backend="sdpa",
++                rope_backend="cos_sin",
++            ),
++        ),
++        rope=RoPE.Config(
++            dim=64,
++            max_seq_len=2048,
++            theta=1000000.0,
++            backend="cos_sin",
++        ),
++    ),
+     "debugmodel_moe": Qwen3Model.Config(
+         vocab_size=2048,
+         dim=256,
+diff --git a/torchtitan/models/qwen3/config_registry.py b/torchtitan/models/qwen3/config_registry.py
+index 80aed1f6..8e9094a8 100644
+--- a/torchtitan/models/qwen3/config_registry.py
++++ b/torchtitan/models/qwen3/config_registry.py
+@@ -11,6 +11,7 @@ from torchtitan.components.optimizer import OptimizersContainer
+ from torchtitan.config import (
+     ActivationCheckpointConfig,
+     ParallelismConfig,
++    CompileConfig,
+     TrainingConfig,
+ )
+ from torchtitan.hf_datasets.text_datasets import (
+@@ -202,13 +203,18 @@ def qwen3_moe_debug() -> Trainer.Config:
+         optimizer=OptimizersContainer.Config(lr=3e-4),
+         lr_scheduler=LRSchedulersContainer.Config(warmup_steps=2),
+         training=TrainingConfig(
+-            local_batch_size=4,
+-            seq_len=4096,
+-            steps=10,
++            global_batch_size=32,  # Should be divisible by pipeline_parallel_microbatch_size
++            local_batch_size=16,  # Should be divisible by pipeline_parallel_microbatch_size
++            seq_len=512,
++            steps=8,  # Few iterations for profiling
++            dtype="bfloat16",
+         ),
+         parallelism=ParallelismConfig(
+-            expert_parallel_degree=1,
++            expert_parallel_degree=2,
+             expert_tensor_parallel_degree=1,
++            pipeline_parallel_degree=2,
++            pipeline_parallel_microbatch_size=4,  # Configurable micro batch size
++            data_parallel_shard_degree=-1,
+         ),
+         checkpoint=CheckpointManager.Config(
+             interval=10,
+@@ -221,6 +227,202 @@ def qwen3_moe_debug() -> Trainer.Config:
+     )
+ 
+ 
++def qwen3_1b_single() -> Trainer.Config:
++    return Trainer.Config(
++        hf_assets_path="./assets/hf/Qwen3-0.6B",
++        model_spec=model_registry("1B-A0.7B"),
++        dataloader=HuggingFaceTextDataLoader.Config(
++            dataset="c4",
++        ),
++        optimizer=OptimizersContainer.Config(lr=8e-4),
++        lr_scheduler=LRSchedulersContainer.Config(warmup_steps=2),
++        training=TrainingConfig(
++            local_batch_size=8,  # Should be divisible by pipeline_parallel_microbatch_size
++            seq_len=32,
++            steps=8,  # Few iterations for profiling
++            dtype="bfloat16",
++        ),
++        parallelism=ParallelismConfig(
++            expert_parallel_degree=1,
++            expert_tensor_parallel_degree=1,
++            pipeline_parallel_degree=1,
++            pipeline_parallel_microbatch_size=1,  # Configurable micro batch size
++            data_parallel_shard_degree=1,
++        ),
++        checkpoint=CheckpointManager.Config(
++            enable=False,
++        ),
++        activation_checkpoint=ActivationCheckpointConfig(
++            mode="none",
++        ),
++        # compile=CompileConfig(enable=True),
++    )
++
++
++def qwen3_1b_single_no_grouped_mm() -> Trainer.Config:
++    cfg = qwen3_1b_single()
++    assert cfg.model_spec is not None
++    assert cfg.model_spec.model.layer.moe is not None
++    cfg.model_spec.model.layer.moe.experts.use_grouped_mm = False
++    return cfg
++
++
++def qwen3_1b() -> Trainer.Config:
++    return Trainer.Config(
++        hf_assets_path="./assets/hf/Qwen3-0.6B",
++        model_spec=model_registry("1B-A0.7B"),
++        dataloader=HuggingFaceTextDataLoader.Config(
++            dataset="c4",
++        ),
++        optimizer=OptimizersContainer.Config(lr=8e-4),
++        lr_scheduler=LRSchedulersContainer.Config(warmup_steps=2),
++        training=TrainingConfig(
++            # global_batch_size=128,  # Should be divisible by pipeline_parallel_microbatch_size
++            local_batch_size=32,  # Should be divisible by pipeline_parallel_microbatch_size
++            seq_len=32,
++            steps=8,  # Few iterations for profiling
++            dtype="bfloat16",
++        ),
++        parallelism=ParallelismConfig(
++            expert_parallel_degree=2,
++            expert_tensor_parallel_degree=1,
++            pipeline_parallel_degree=4,
++            pipeline_parallel_microbatch_size=8,  # Configurable micro batch size
++            data_parallel_shard_degree=-1,
++        ),
++        checkpoint=CheckpointManager.Config(
++            enable=False,
++        ),
++        activation_checkpoint=ActivationCheckpointConfig(
++            mode="none",
++        ),
++        compile=CompileConfig(enable=True),
++    )
++
++
++def qwen3_1b_no_grouped_mm() -> Trainer.Config:
++    cfg = qwen3_1b()
++    assert cfg.model_spec is not None
++    assert cfg.model_spec.model.layer.moe is not None
++    cfg.model_spec.model.layer.moe.experts.use_grouped_mm = False
++    return cfg
++
++
++def qwen3_9b_single() -> Trainer.Config:
++    return Trainer.Config(
++        hf_assets_path="./assets/hf/Qwen3-8B",
++        model_spec=model_registry("9B-A3B"),
++        dataloader=HuggingFaceTextDataLoader.Config(
++            dataset="c4",
++        ),
++        optimizer=OptimizersContainer.Config(lr=8e-4),
++        lr_scheduler=LRSchedulersContainer.Config(warmup_steps=2),
++        training=TrainingConfig(
++            local_batch_size=128,  # Should be divisible by pipeline_parallel_microbatch_size
++            seq_len=512,
++            steps=8,  # Few iterations for profiling
++            dtype="bfloat16",
++        ),
++        parallelism=ParallelismConfig(
++            pipeline_parallel_degree=8,
++            pipeline_parallel_microbatch_size=8,  # Configurable micro batch size
++        ),
++        checkpoint=CheckpointManager.Config(
++            enable=False,
++        ),
++        activation_checkpoint=ActivationCheckpointConfig(
++            mode="none",
++        ),
++        compile=CompileConfig(enable=True),
++    )
++
++def qwen3_9b() -> Trainer.Config:
++    """Generic Qwen3 9B config for runtime PP/DP/EP/schedule/batch/ZeRO overrides."""
++    return Trainer.Config(
++        hf_assets_path="./assets/hf/Qwen3-8B",
++        model_spec=model_registry("9B-A3B"),
++        dataloader=HuggingFaceTextDataLoader.Config(dataset="c4"),
++        optimizer=OptimizersContainer.Config(lr=8e-4),
++        lr_scheduler=LRSchedulersContainer.Config(warmup_steps=2),
++        training=TrainingConfig(
++            global_batch_size=64,
++            local_batch_size=64,
++            seq_len=512,
++            steps=8,
++            dtype="bfloat16",
++        ),
++        parallelism=ParallelismConfig(
++            expert_parallel_degree=1,
++            pipeline_parallel_degree=8,
++            pipeline_parallel_schedule="1F1B",
++            pipeline_parallel_microbatch_size=4,
++            data_parallel_replicate_degree=1,
++            data_parallel_shard_degree=1,
++            fsdp_reshard_after_forward="default",
++        ),
++        checkpoint=CheckpointManager.Config(enable=False),
++        activation_checkpoint=ActivationCheckpointConfig(mode="none"),
++        compile=CompileConfig(enable=True),
++    )
++
++
++def qwen3_30b() -> Trainer.Config:
++    return Trainer.Config(
++        hf_assets_path="./assets/hf/Qwen3-1.7B",
++        model_spec=model_registry("30B-A3B"),
++        dataloader=HuggingFaceTextDataLoader.Config(
++            dataset="c4",
++        ),
++        optimizer=OptimizersContainer.Config(lr=8e-4),
++        lr_scheduler=LRSchedulersContainer.Config(warmup_steps=2),
++        training=TrainingConfig(
++            global_batch_size=256,  # Should be divisible by pipeline_parallel_microbatch_size
++            local_batch_size=128,  # Should be divisible by pipeline_parallel_microbatch_size
++            seq_len=1024,
++            steps=8,  # Few iterations for profiling
++            dtype="bfloat16",
++        ),
++        parallelism=ParallelismConfig(
++            expert_parallel_degree=2,
++            expert_tensor_parallel_degree=1,
++            pipeline_parallel_degree=8,
++            pipeline_parallel_microbatch_size=8,  # Configurable micro batch size
++            data_parallel_shard_degree=-1,
++        ),
++        checkpoint=CheckpointManager.Config(
++            enable=False,
++        ),
++        activation_checkpoint=ActivationCheckpointConfig(
++            mode="none",
++        ),
++        compile=CompileConfig(enable=True),
++    )
++
++
++def qwen3_30b_single() -> Trainer.Config:
++    return Trainer.Config(
++        hf_assets_path="./assets/hf/Qwen3-1.7B",
++        model_spec=model_registry("30B-A3B"),
++        dataloader=HuggingFaceTextDataLoader.Config(
++            dataset="c4",
++        ),
++        optimizer=OptimizersContainer.Config(lr=8e-4),
++        lr_scheduler=LRSchedulersContainer.Config(warmup_steps=2),
++        training=TrainingConfig(
++            local_batch_size=16,  # Should be divisible by pipeline_parallel_microbatch_size
++            seq_len=256,
++            steps=8,  # Few iterations for profiling
++            dtype="bfloat16",
++        ),
++        checkpoint=CheckpointManager.Config(
++            enable=False,
++        ),
++        activation_checkpoint=ActivationCheckpointConfig(
++            mode="none",
++        ),
++        # compile=CompileConfig(enable=True),
++    )
++
+ def sft_qwen3_8b_math() -> Trainer.Config:
+     """Qwen3-8B SFT on GSM8K math dataset."""
+ 
+diff --git a/torchtitan/trainer.py b/torchtitan/trainer.py
+index 6137578f..b65973d4 100644
+--- a/torchtitan/trainer.py
++++ b/torchtitan/trainer.py
+@@ -234,6 +234,12 @@ class Trainer(torch.distributed.checkpoint.stateful.Stateful, Configurable):
+         self.tokenizer = config.tokenizer.build(tokenizer_path=config.hf_assets_path)
+ 
+         # build dataloader
++        # Non-zero local ranks wait for local rank 0 to prime the HuggingFace
++        # dataset cache before making their own requests, avoiding HTTP timeouts
++        # when multiple processes hit the HF servers simultaneously.
++        local_rank = int(os.environ.get("LOCAL_RANK", 0))
++        if local_rank != 0:
++            torch.distributed.barrier()
+         self.dataloader = config.dataloader.build(
+             dp_world_size=batch_degree,
+             dp_rank=batch_rank,
+@@ -241,6 +247,8 @@ class Trainer(torch.distributed.checkpoint.stateful.Stateful, Configurable):
+             seq_len=config.training.seq_len,
+             local_batch_size=config.training.local_batch_size,
+         )
++        if local_rank == 0:
++            torch.distributed.barrier()
+ 
+         # build model (using meta init)
+         model_config = model_spec.model
+@@ -819,8 +827,10 @@ class Trainer(torch.distributed.checkpoint.stateful.Stateful, Configurable):
+                 base_folder=config.dump_folder,
+             ) as memory_profiler,
+         ):
++            iter_times = []
+             data_iterator = self.batch_generator(self.dataloader)
+             while self.should_continue_training():
++                start = time.perf_counter()
+                 self.step += 1
+                 self.gc_handler.run(self.step)
+                 try:
+@@ -852,6 +862,19 @@ class Trainer(torch.distributed.checkpoint.stateful.Stateful, Configurable):
+                         timeout=timedelta(seconds=config.comm.train_timeout_seconds),
+                         parallel_dims=self.parallel_dims,
+                     )
++                end = time.perf_counter()
++                iter_time = end - start
++                iter_times.append(iter_time)
++                logger.info(f"Training iter {self.step} took {iter_time:.5f} s")
++
++        if len(iter_times) > 0:
++            last_n = iter_times[-5:]
++            mean = sum(last_n) / len(last_n)
++            variance = sum((t - mean) ** 2 for t in last_n) / len(last_n)
++            std = variance**0.5
++            logger.info(
++                f"Final {len(last_n)} iter times — avg: {mean:.5f} s, std: {std:.5f} s"
++            )
+ 
+         if torch.distributed.get_rank() == 0:
+             logger.info("Sleeping 2 seconds for other ranks to complete")
diff --git a/artifact/run_deepspeed.py b/artifact/run_deepspeed.py
new file mode 100644
index 0000000..68116fd
--- /dev/null
+++ b/artifact/run_deepspeed.py
@@ -0,0 +1,621 @@
+
+#!/usr/bin/env python3
+"""
+DeepSpeed training script for Qwen3 MoE models.
+
+Parallelism layout (8 GPUs, 2 nodes x 4 GPUs/node):
+  - PP = 4  (intra-node pipeline parallel)
+  - EP = 2  (inter-node expert parallel, splits DP dim)
+  - DP = 2  (data parallel, consumed by EP)
+
+Config:
+  - qwen3_1b: global_bs=128, micro_bs=8, seq_len=2048, grad_accum=8
+  - qwen3_9b: global_bs=256, micro_bs=8, seq_len=2048, grad_accum=16
+
+Topology design (see setup_custom_topology for details):
+  We use ProcessTopology(axes=['data', 'pipe'], dims=[2, 4]) so that the
+  'pipe' axis varies fastest → PP stages map to adjacent ranks (intra-node).
+  Then we pre-register EP process groups with the correct cross-node rank
+  pairs BEFORE deepspeed.initialize(), because DeepSpeed's default EP group
+  creation assumes pipe-slowest layout and would produce wrong groups.
+"""
+
+import argparse
+import os
+import time
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import Dataset
+
+import deepspeed
+from deepspeed.pipe import PipelineModule, LayerSpec
+from deepspeed.runtime.pipe.topology import ProcessTopology
+from deepspeed.utils import groups as ds_groups
+
+# ---------------------------------------------------------------------------
+# Qwen3 architecture constants
+# ---------------------------------------------------------------------------
+ROPE_THETA = 1_000_000.0
+MODEL_CONFIGS = {
+    "qwen3_1b": {
+        "vocab_size": 151936,
+        "dim": 1024,
+        "n_layers": 16,
+        "n_heads": 16,
+        "n_kv_heads": 8,
+        "head_dim": 64,
+        "moe_inter_dim": 3584,
+        "num_experts": 4,
+        "top_k": 2,
+        "seq_len": 2048,
+        "global_batch_size": 128,
+        "micro_batch_size": 8,
+    },
+    "qwen3_9b": {
+        "vocab_size": 151936,
+        "dim": 2048,
+        "n_layers": 24,
+        "n_heads": 32,
+        "n_kv_heads": 8,
+        "head_dim": 64,
+        "moe_inter_dim": 7168,
+        "num_experts": 8,
+        "top_k": 2,
+        "seq_len": 2048,
+        "global_batch_size": 256,
+        "micro_batch_size": 8,
+    },
+}
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(dim))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        norm = x.float().pow(2).mean(-1, keepdim=True).add(self.eps).rsqrt()
+        return (x.float() * norm).type_as(x) * self.weight
+
+
+def precompute_rope(dim: int, seq_len: int, theta: float = 1e6):
+    """Return (cos, sin) each of shape [seq_len, dim//2]."""
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+    t = torch.arange(seq_len, dtype=torch.float32)
+    freqs = torch.outer(t, freqs)  # [seq_len, dim//2]
+    return torch.cos(freqs), torch.sin(freqs)
+
+
+def apply_rope(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor):
+    """x: [B, n_heads, S, head_dim]. cos/sin: [S, head_dim//2]."""
+    half = x.shape[-1] // 2
+    x1, x2 = x[..., :half], x[..., half:]
+    cos = cos[:x.shape[2]].unsqueeze(0).unsqueeze(0)  # [1, 1, S, half]
+    sin = sin[:x.shape[2]].unsqueeze(0).unsqueeze(0)
+    out1 = x1 * cos - x2 * sin
+    out2 = x2 * cos + x1 * sin
+    return torch.cat([out1, out2], dim=-1)
+
+
+def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """Repeat KV heads to match query head count."""
+    if n_rep == 1:
+        return x
+    B, n_kv, S, D = x.shape
+    return x[:, :, None, :, :].expand(B, n_kv, n_rep, S, D).reshape(B, n_kv * n_rep, S, D)
+
+
+# ---------------------------------------------------------------------------
+# Model layers (each takes and returns a single tensor for PipelineModule)
+#
+# Convention: the pipeline tensor is [B, S, D] for hidden states.
+# The first layer converts input_ids → hidden, the last converts hidden → logits.
+# ---------------------------------------------------------------------------
+
+class EmbeddingLayer(nn.Module):
+    """Token embedding: input_ids [B, S] → hidden [B, S, D]."""
+    def __init__(self, vocab_size: int, dim: int):
+        super().__init__()
+        self.tok_emb = nn.Embedding(vocab_size, dim)
+
+    def forward(self, input_ids):
+        return self.tok_emb(input_ids)
+
+
+class TransformerBlock(nn.Module):
+    """
+    Single Qwen3 MoE transformer block.
+    GQA attention + MoE FFN, with pre-norm (RMSNorm) and residual connections.
+
+    Takes hidden [B, S, D], returns hidden [B, S, D].
+    """
+    def __init__(self, model_config: dict[str, int], layer_id: int = 0):
+        super().__init__()
+        self.layer_id = layer_id
+        self.dim = int(model_config["dim"])
+        self.n_heads = int(model_config["n_heads"])
+        self.n_kv_heads = int(model_config["n_kv_heads"])
+        self.head_dim = int(model_config["head_dim"])
+
+        # --- Attention ---
+        self.attn_norm = RMSNorm(self.dim)
+        self.q_proj = nn.Linear(self.dim, self.n_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.n_heads * self.head_dim, self.dim, bias=False)
+        # qk_norm
+        self.q_norm = RMSNorm(self.head_dim)
+        self.k_norm = RMSNorm(self.head_dim)
+        # RoPE cache (registered as buffer so it moves with .to(device))
+        cos, sin = precompute_rope(self.head_dim, int(model_config["seq_len"]), ROPE_THETA)
+        self.register_buffer("rope_cos", cos, persistent=False)
+        self.register_buffer("rope_sin", sin, persistent=False)
+
+        # --- MoE FFN ---
+        self.ffn_norm = RMSNorm(self.dim)
+        # Build a single expert template, then wrap with DeepSpeed MoE
+        expert = SwiGLUExpert(self.dim, int(model_config["moe_inter_dim"]))
+        from deepspeed.moe.layer import MoE
+        self.moe = MoE(
+            hidden_size=self.dim,
+            expert=expert,
+            num_experts=int(model_config["num_experts"]),
+            ep_size=int(model_config["ep_size"]),
+            k=int(model_config["top_k"]),
+            capacity_factor=1.0,
+            eval_capacity_factor=1.0,
+            min_capacity=4,
+            noisy_gate_policy=None,
+            drop_tokens=False,     # no token dropping for training stability
+            use_residual=False,
+            use_tutel=False,
+        )
+
+    def forward(self, hidden: torch.Tensor) -> torch.Tensor:
+        B, S, D = hidden.shape
+
+        # ---------- Self-Attention ----------
+        residual = hidden
+        h = self.attn_norm(hidden)
+
+        q = self.q_proj(h).view(B, S, self.n_heads, self.head_dim)
+        k = self.k_proj(h).view(B, S, self.n_kv_heads, self.head_dim)
+        v = self.v_proj(h).view(B, S, self.n_kv_heads, self.head_dim)
+
+        # qk_norm
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        # [B, heads, S, D]
+        q = q.transpose(1, 2)
+        k = k.transpose(1, 2)
+        v = v.transpose(1, 2)
+
+        # RoPE
+        q = apply_rope(q, self.rope_cos, self.rope_sin)
+        k = apply_rope(k, self.rope_cos, self.rope_sin)
+
+        # GQA: repeat KV heads
+        n_rep = self.n_heads // self.n_kv_heads
+        k = repeat_kv(k, n_rep)
+        v = repeat_kv(v, n_rep)
+
+        # Scaled dot-product attention (causal)
+        attn_out = F.scaled_dot_product_attention(
+            q, k, v, is_causal=True, dropout_p=0.0
+        )  # [B, heads, S, head_dim]
+        attn_out = attn_out.transpose(1, 2).contiguous().view(B, S, -1)
+        hidden = residual + self.o_proj(attn_out)
+
+        # ---------- MoE FFN ----------
+        residual = hidden
+        h = self.ffn_norm(hidden)
+        # DeepSpeed MoE expects [B*S, D] or [B, S, D]; it handles both
+        moe_out, aux_loss, _ = self.moe(h)
+        hidden = residual + moe_out
+
+        return hidden
+
+
+class SwiGLUExpert(nn.Module):
+    """Single SwiGLU expert: gate_proj + up_proj → SiLU → down_proj."""
+    def __init__(self, dim: int, inter_dim: int):
+        super().__init__()
+        self.gate_proj = nn.Linear(dim, inter_dim, bias=False)
+        self.up_proj = nn.Linear(dim, inter_dim, bias=False)
+        self.down_proj = nn.Linear(inter_dim, dim, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
+
+
+class FinalNorm(nn.Module):
+    """Final RMSNorm before LM head."""
+    def __init__(self, dim: int):
+        super().__init__()
+        self.norm = RMSNorm(dim)
+
+    def forward(self, hidden: torch.Tensor) -> torch.Tensor:
+        return self.norm(hidden)
+
+
+class LMHead(nn.Module):
+    """Linear projection to vocab logits: [B, S, D] → [B*S, V]."""
+    def __init__(self, dim: int, vocab_size: int):
+        super().__init__()
+        self.vocab_size = vocab_size
+        self.head = nn.Linear(dim, vocab_size, bias=False)
+
+    def forward(self, hidden: torch.Tensor) -> torch.Tensor:
+        # Flatten for cross-entropy: [B*S, V]
+        return self.head(hidden).view(-1, self.vocab_size)
+
+
+# ---------------------------------------------------------------------------
+# Loss function for PipelineModule
+# ---------------------------------------------------------------------------
+
+def loss_fn(logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor:
+    """Cross-entropy loss. logits: [B*S, V], labels: [B, S] or [B*S]."""
+    labels = labels.view(-1)
+    return F.cross_entropy(logits, labels)
+
+
+# ---------------------------------------------------------------------------
+# Mock dataset
+# ---------------------------------------------------------------------------
+
+class MockTokenDataset(Dataset):
+    """Random token dataset for benchmarking. No real data needed."""
+
+    def __init__(self, vocab_size: int, seq_len: int, num_samples: int, seed: int = 42):
+        self.vocab_size = vocab_size
+        self.seq_len = seq_len
+        self.num_samples = num_samples
+        self.seed = seed
+
+    def __len__(self):
+        return self.num_samples
+
+    def __getitem__(self, idx):
+        rng = torch.Generator()
+        rng.manual_seed(self.seed + idx)
+        tokens = torch.randint(0, self.vocab_size, (self.seq_len + 1,), generator=rng)
+        return tokens[:-1], tokens[1:]  # (input_ids, labels)
+
+
+# ---------------------------------------------------------------------------
+# Pre-register EP groups for custom topology
+# ---------------------------------------------------------------------------
+
+def pre_register_ep_groups(ep_size: int, pp_size: int, dp_size: int):
+    """
+    Pre-register expert-parallel (EP) and expert-data-parallel process groups
+    in DeepSpeed's global groups module BEFORE deepspeed.initialize() is called.
+
+    Why this is needed:
+    ------------------
+    We use ProcessTopology(axes=['data', 'pipe'], dims=[dp, pp]) so that the
+    'pipe' axis is fastest-varying → PP stages are adjacent ranks → intra-node.
+
+    Rank layout with 2 nodes × 4 GPUs:
+        rank 0: data=0, pipe=0  (node 0)
+        rank 1: data=0, pipe=1  (node 0)
+        rank 2: data=0, pipe=2  (node 0)
+        rank 3: data=0, pipe=3  (node 0)
+        rank 4: data=1, pipe=0  (node 1)
+        rank 5: data=1, pipe=1  (node 1)
+        rank 6: data=1, pipe=2  (node 1)
+        rank 7: data=1, pipe=3  (node 1)
+
+    PP groups (varying pipe, same data): [0,1,2,3] and [4,5,6,7]  → intra-node ✓
+    DP groups (varying data, same pipe): [0,4], [1,5], [2,6], [3,7] → cross-node ✓
+
+    EP should align with DP (same PP stage, different data ranks):
+        EP groups: [0,4], [1,5], [2,6], [3,7]  → cross-node ✓
+
+    However, DeepSpeed's _create_expert_and_data_parallel() assumes pipe is the
+    SLOWEST axis and creates EP groups from consecutive ranks: [0,1], [2,3], ...
+    which would be WRONG for our layout (those pair different PP stages on the
+    same node).
+
+    Solution: we pre-populate the _EXPERT_PARALLEL_GROUP and
+    _EXPERT_DATA_PARALLEL_GROUP dicts. When DeepSpeed's MoE._create_process_groups
+    runs, it checks `if group_name not in groups._get_expert_parallel_group_dict()`
+    and skips creation if the groups already exist.
+    """
+    import torch.distributed as dist
+
+    rank = dist.get_rank()
+    world_size = dist.get_world_size()
+    assert world_size == pp_size * dp_size
+
+    group_name = f"ep_size_{ep_size}"
+
+    # EP groups: ranks sharing the same pipe stage but different data index
+    # With axes=['data', 'pipe'], rank = data_idx * pp_size + pipe_idx
+    # So for a given pipe stage p, EP peers are: [p, p + pp_size, p + 2*pp_size, ...]
+    # With dp_size=2, pp_size=4: EP groups = [0,4], [1,5], [2,6], [3,7]
+    for pipe_stage in range(pp_size):
+        ep_ranks = list(range(pipe_stage, world_size, pp_size))
+        # ep_ranks has dp_size elements; chunk into groups of ep_size
+        for start in range(0, len(ep_ranks), ep_size):
+            ranks = ep_ranks[start : start + ep_size]
+            group = dist.new_group(ranks)
+            if rank in ranks:
+                ds_groups._EXPERT_PARALLEL_GROUP[group_name] = group
+                ds_groups._EXPERT_PARALLEL_GROUP_RANKS[group_name] = ranks
+            if rank == 0:
+                print(f"  EP group ({group_name}): {ranks}")
+
+    # Expert-data-parallel groups: ranks with the same EP role but different
+    # data-parallel shards.  With ep_size == dp_size (our case), each EP group
+    # spans the entire DP dimension, so expert-data-parallel is trivially each
+    # rank alone (no all-reduce needed for MoE params).
+    # More generally: for each pipe stage, chunk DP ranks into EP groups,
+    # then expert-data-parallel = ranks at the same position across EP groups.
+    for pipe_stage in range(pp_size):
+        dp_ranks = list(range(pipe_stage, world_size, pp_size))
+        # With ep_size == dp_size, there's only 1 EP group per pipe stage,
+        # so expert-data-parallel groups are just individual ranks.
+        # With ep_size < dp_size, we'd interleave.
+        num_ep_groups = dp_size // ep_size
+        for pos_in_ep in range(ep_size):
+            edp_ranks = [dp_ranks[g * ep_size + pos_in_ep] for g in range(num_ep_groups)]
+            group = dist.new_group(edp_ranks)
+            if rank in edp_ranks:
+                ds_groups._EXPERT_DATA_PARALLEL_GROUP[group_name] = group
+                ds_groups._EXPERT_DATA_PARALLEL_GROUP_RANKS[group_name] = edp_ranks
+            if rank == 0:
+                print(f"  Expert-DP group ({group_name}): {edp_ranks}")
+
+
+# ---------------------------------------------------------------------------
+# Build PipelineModule
+# ---------------------------------------------------------------------------
+
+def build_pipeline_model(model_config: dict[str, int], args):
+    """Construct PipelineModule for the selected Qwen3 model."""
+    layers = []
+
+    # Embedding
+    layers.append(LayerSpec(EmbeddingLayer, int(model_config["vocab_size"]), int(model_config["dim"])))
+
+    for i in range(int(model_config["n_layers"])):
+        layers.append(LayerSpec(TransformerBlock, model_config, layer_id=i))
+
+    # Final norm + LM head
+    layers.append(LayerSpec(FinalNorm, int(model_config["dim"])))
+    layers.append(LayerSpec(LMHead, int(model_config["dim"]), int(model_config["vocab_size"])))
+
+    # -----------------------------------------------------------------------
+    # Topology: PP=4 intra-node, DP=2 cross-node (consumed by EP=2)
+    # 8 GPUs total: 4 PP stages × 2 DP ranks
+    #
+    # We use ProcessTopology with axes=['data', 'pipe'] so that 'pipe' is the
+    # fastest-varying axis.  This ensures PP stages are adjacent ranks and
+    # thus intra-node (torchrun assigns rank = node_rank * nproc + local).
+    #
+    # Rank layout:
+    #   rank 0: data=0, pipe=0  (node 0, GPU 0)
+    #   rank 1: data=0, pipe=1  (node 0, GPU 1)
+    #   rank 2: data=0, pipe=2  (node 0, GPU 2)
+    #   rank 3: data=0, pipe=3  (node 0, GPU 3)
+    #   rank 4: data=1, pipe=0  (node 1, GPU 0)
+    #   rank 5: data=1, pipe=1  (node 1, GPU 1)
+    #   rank 6: data=1, pipe=2  (node 1, GPU 2)
+    #   rank 7: data=1, pipe=3  (node 1, GPU 3)
+    #
+    # PP groups (intra-node):  [0,1,2,3] and [4,5,6,7]
+    # DP groups (cross-node):  [0,4], [1,5], [2,6], [3,7]
+    # EP groups (cross-node):  [0,4], [1,5], [2,6], [3,7]  (pre-registered)
+    # -----------------------------------------------------------------------
+    topo = ProcessTopology(axes=['data', 'pipe'], dims=[args.dp, args.pp])
+
+    model = PipelineModule(
+        layers=layers,
+        topology=topo,
+        loss_fn=loss_fn,
+        partition_method="parameters",
+        activation_checkpoint_interval=0,
+    )
+    return model
+
+
+def parameter_counts(model_config: dict[str, int]) -> tuple[int, int]:
+    """Return total and active parameter counts for the local Qwen3 MoE model."""
+    vocab_size = int(model_config["vocab_size"])
+    dim = int(model_config["dim"])
+    n_heads = int(model_config["n_heads"])
+    n_kv_heads = int(model_config["n_kv_heads"])
+    head_dim = int(model_config["head_dim"])
+    n_layers = int(model_config["n_layers"])
+    hidden_dim = int(model_config["moe_inter_dim"])
+    num_experts = int(model_config["num_experts"])
+    top_k = int(model_config["top_k"])
+
+    embeddings_and_head = 2 * vocab_size * dim
+    attention = (
+        dim * (n_heads * head_dim)
+        + dim * (n_kv_heads * head_dim)
+        + dim * (n_kv_heads * head_dim)
+        + (n_heads * head_dim) * dim
+    )
+    norms = dim + head_dim + head_dim + dim
+    router = dim * num_experts
+    expert = 3 * dim * hidden_dim
+    total_block = attention + norms + router + num_experts * expert
+    active_block = attention + norms + router + top_k * expert
+    return embeddings_and_head + n_layers * total_block, embeddings_and_head + n_layers * active_block
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+def main():
+    parser = argparse.ArgumentParser(description="DeepSpeed Qwen3 MoE PP4+EP2")
+    parser.add_argument("--model", choices=tuple(MODEL_CONFIGS), default="qwen3_1b")
+    parser.add_argument("--pp", type=int, default=4)
+    parser.add_argument("--dp", type=int, default=2)
+    parser.add_argument("--ep", type=int, default=1)
+    parser.add_argument("--zero-stage", type=int, default=1, choices=(0, 1, 2, 3))
+    parser.add_argument("--schedule", choices=("1f1b",), default="1f1b")
+    parser.add_argument("--micro-bs", type=int, default=None)
+    parser.add_argument("--global-bs", type=int, default=None)
+    parser.add_argument("--seq-len", type=int, default=None)
+    parser.add_argument("--local_rank", type=int, default=-1)
+    parser.add_argument("--steps", type=int, default=8)
+    parser.add_argument("--seed", type=int, default=42)
+    parser = deepspeed.add_config_arguments(parser)
+    args = parser.parse_args()
+
+    # Initialize distributed
+    deepspeed.init_distributed()
+    torch.manual_seed(args.seed)
+
+    local_rank = int(os.environ.get("LOCAL_RANK", 0))
+    torch.cuda.set_device(local_rank)
+
+    rank = torch.distributed.get_rank()
+    world_size = torch.distributed.get_world_size()
+    if args.schedule != "1f1b":
+        raise ValueError("DeepSpeed runner currently supports only 1f1b schedule")
+    if args.pp > 1 and args.zero_stage > 1:
+        raise ValueError("DeepSpeed pipeline parallelism is not compatible with ZeRO-2/3")
+    if args.dp < args.ep or args.dp % args.ep != 0:
+        raise ValueError("--dp must be divisible by --ep and at least as large as --ep")
+    assert world_size == args.pp * args.dp, \
+        f"Expected {args.pp * args.dp} GPUs (PP={args.pp} x DP={args.dp}), got {world_size}"
+    model_config = dict(MODEL_CONFIGS[args.model])
+    model_config["ep_size"] = args.ep
+    if args.seq_len is not None:
+        model_config["seq_len"] = args.seq_len
+    if args.micro_bs is not None:
+        model_config["micro_batch_size"] = args.micro_bs
+    if args.global_bs is not None:
+        model_config["global_batch_size"] = args.global_bs
+    grad_accum_steps = int(model_config["global_batch_size"]) // (
+        int(model_config["micro_batch_size"]) * args.dp
+    )
+    if grad_accum_steps < 1:
+        raise ValueError("Derived gradient_accum_steps must be >= 1")
+
+    if rank == 0:
+        print(f"{'='*60}")
+        print(f"{args.model} — DeepSpeed")
+        print(f"  World size:   {world_size}")
+        print(f"  PP stages:    {args.pp}")
+        print(f"  EP size:      {args.ep}")
+        print(f"  DP size:      {args.dp}")
+        print(f"  ZeRO stage:   {args.zero_stage}")
+        print(f"  Seq len:      {model_config['seq_len']}")
+        print(f"  Global BS:    {model_config['global_batch_size']}")
+        print(f"  Micro BS:     {model_config['micro_batch_size']}")
+        print(f"  Grad accum:   {grad_accum_steps}")
+        print(f"  Num layers:   {model_config['n_layers']}")
+        print(f"  Num experts:  {model_config['num_experts']}")
+        print(f"  Top-K:        {model_config['top_k']}")
+        total_params, active_params = parameter_counts(model_config)
+        print(f"  Total params: {total_params:,}")
+        print(f"  Active params:{active_params:,}")
+        print(f"{'='*60}")
+
+    # Pre-register EP groups with correct cross-node topology BEFORE
+    # deepspeed.initialize() so that the automatic (wrong) creation is skipped.
+    if rank == 0:
+        print("\nPre-registering EP process groups (cross-node):")
+    pre_register_ep_groups(ep_size=args.ep, pp_size=args.pp, dp_size=args.dp)
+
+    # Build model
+    model = build_pipeline_model(model_config, args)
+
+    # Dataset: enough samples for all steps
+    # PipelineEngine handles batching internally
+    num_samples = int(model_config["global_batch_size"]) * (args.steps + 2)
+    dataset = MockTokenDataset(
+        vocab_size=int(model_config["vocab_size"]),
+        seq_len=int(model_config["seq_len"]),
+        num_samples=num_samples,
+        seed=args.seed,
+    )
+
+    ds_config = {
+        "train_micro_batch_size_per_gpu": int(model_config["micro_batch_size"]),
+        "train_batch_size": int(model_config["global_batch_size"]),
+        "gradient_accumulation_steps": grad_accum_steps,
+        "steps_per_print": 1,
+        "bf16": {"enabled": True},
+        "optimizer": {
+            "type": "Adam",
+            "params": {
+                "lr": 3.0e-4,
+                "betas": [0.9, 0.95],
+                "eps": 1.0e-8,
+                "weight_decay": 0.1,
+            },
+        },
+        "zero_optimization": {
+            "stage": args.zero_stage,
+        },
+    }
+
+    # Initialize DeepSpeed (auto-detects PipelineModule → PipelineEngine)
+    engine, _, _, _ = deepspeed.initialize(
+        args=args,
+        config=ds_config,
+        model=model,
+        model_parameters=[p for p in model.parameters() if p.requires_grad],
+        training_data=dataset,
+    )
+    torch.cuda.reset_peak_memory_stats()
+
+    if rank == 0:
+        print(f"\nPipeline stage: {engine.stage_id}, "
+              f"num micro-batches: {engine.micro_batches}")
+        print(f"Starting training for {args.steps} steps...\n")
+
+    # Training loop
+    start_time = time.time()
+    for step in range(1, args.steps + 1):
+        step_start = time.time()
+        loss = engine.train_batch()
+        step_time = time.time() - step_start
+
+        if rank == 0:
+            loss_val = loss.item() if torch.is_tensor(loss) else loss
+            print(f"[Step {step:3d}/{args.steps}]  loss={loss_val:.4f}  "
+                  f"step_time={step_time:.2f}s")
+
+    total_time = time.time() - start_time
+    torch.distributed.barrier()
+    peak_allocated_gb = torch.cuda.max_memory_allocated() / (1024 ** 3)
+    peak_reserved_gb = torch.cuda.max_memory_reserved() / (1024 ** 3)
+    local_peak = {
+        "rank": rank,
+        "peak_allocated_gb": peak_allocated_gb,
+        "peak_reserved_gb": peak_reserved_gb,
+    }
+    gathered_peaks = [None for _ in range(world_size)]
+    torch.distributed.all_gather_object(gathered_peaks, local_peak)
+    if rank == 0:
+        for peak in sorted(gathered_peaks, key=lambda item: int(item["rank"])):
+            print(
+                f"[rank{peak['rank']}] "
+                f"peak_memory_allocated_gb={float(peak['peak_allocated_gb']):.3f} "
+                f"peak_memory_reserved_gb={float(peak['peak_reserved_gb']):.3f}",
+                flush=True,
+            )
+    if rank == 0:
+        print(f"\nTraining complete. Total time: {total_time:.2f}s "
+              f"({total_time / args.steps:.2f}s/step)")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/artifact/run_megatron.py b/artifact/run_megatron.py
new file mode 100644
index 0000000..f5866bb
--- /dev/null
+++ b/artifact/run_megatron.py
@@ -0,0 +1,349 @@
+################ Below is training script for Megatron ################
+
+
+
+"""
+Train a Qwen3-30B-A3B style MoE model using mock (synthetic) data.
+No real dataset required — useful for debugging and benchmarking.
+
+Architecture: Qwen3-30B-A3B MoE
+  - 48 layers, hidden=2048, 32 attention heads (GQA: 4 query groups), kv_channels=128
+  - FFN: dense ffn_hidden_size=6144 (unused for MoE layers), moe_ffn_hidden_size=768
+  - 128 experts, top-8 routing, all layers are MoE (moe_layer_freq=1)
+  - RoPE: base=1000000, position_embedding_type=rope
+  - QK LayerNorm enabled
+
+Parallelism notes:
+  - EP (expert parallelism) is orthogonal to TP/PP/DP/CP.
+  - Total GPUs = tp * pp * dp * cp * ep.
+  - With alltoall dispatcher, EP tokens are exchanged across ep ranks within each dp group.
+  - Sequence parallelism (--sp) requires --tp > 1.
+
+Usage:
+    python examples/train_moe_30b_mock.py --nnodes N --nproc-per-node N
+        [--tensorboard-dir DIR]
+        [--tp TP] [--pp PP] [--dp DP] [--cp CP] [--ep EP] [--sp]
+        [--seq-length N] [--micro-bs N] [--global-bs N]
+        [--use-tp-pp-dp-mapping]
+        [--master-addr ADDR] [--master-port PORT]
+        [--disable-background-mode]
+
+Must be run from the Megatron-LM root directory.
+"""
+
+import argparse
+import os
+import subprocess
+import sys
+
+MODEL_CONFIGS = {
+    "qwen3_1b": {
+        "num_layers": 16,
+        "hidden_size": 1024,
+        "num_attention_heads": 16,
+        "num_query_groups": 8,
+        "kv_channels": 64,
+        "ffn_hidden_size": 3584,
+        "num_experts": 4,
+        "moe_router_topk": 2,
+        "moe_ffn_hidden_size": 3584,
+        "seq_length": 512,
+        "max_position_embeddings": 2048,
+        "vocab_size": 151936,
+    },
+    "qwen3_9b": {
+        "num_layers": 24,
+        "hidden_size": 2048,
+        "num_attention_heads": 32,
+        "num_query_groups": 8,
+        "kv_channels": 64,
+        "ffn_hidden_size": 7168,
+        "num_experts": 8,
+        "moe_router_topk": 2,
+        "moe_ffn_hidden_size": 7168,
+        "seq_length": 2048,
+        "max_position_embeddings": 2048,
+        "vocab_size": 151936,
+    },
+}
+
+MEGATRON_OPTIONAL_FLAGS = {
+    "--tensorboard-dir",
+    "--profile",
+    "--use-pytorch-profiler",
+    "--profile-step-start",
+    "--profile-step-end",
+    "--pytorch-profiler-collect-shapes",
+}
+
+
+def _detect_supported_megatron_flags() -> set[str]:
+    """Return optional CLI flags supported by this checkout's pretrain_gpt.py."""
+    try:
+        proc = subprocess.run(
+            [sys.executable, "pretrain_gpt.py", "--help"],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=True,
+            check=False,
+        )
+    except OSError:
+        return set()
+    help_text = f"{proc.stdout}\n{proc.stderr}"
+    return {flag for flag in MEGATRON_OPTIONAL_FLAGS if flag in help_text}
+
+def _setup_distributed(nnodes: int, master_addr: str, background_mode: bool):
+    env_node_rank = os.environ.get("NODE_RANK") or os.environ.get("GROUP_RANK") or "0"
+
+    if background_mode:
+        resolved_master = master_addr or os.environ.get("MASTER_ADDR") or "127.0.0.1"
+        return int(env_node_rank), resolved_master
+
+    if not master_addr:
+        raise ValueError("--master-addr is required when --disable-background-mode is set")
+    if nnodes == 1:
+        return 0, master_addr
+    return int(env_node_rank), master_addr
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--tensorboard-dir", default="tensorboard/moe_30b_mock",
+                        help="Directory for TensorBoard logs and profiler traces.")
+    parser.add_argument(
+        "--model",
+        choices=tuple(MODEL_CONFIGS),
+        default="qwen3_1b",
+        help="Qwen model preset to run.",
+    )
+    parser.add_argument("--micro-bs", type=int, default=16,
+                        help="Micro batch size.")
+    parser.add_argument("--global-bs", type=int, default=64,
+                        help="Global batch size.")
+    parser.add_argument("--train-iters", type=int, default=8,
+                        help="Training iterations.")
+    parser.add_argument("--tp", type=int, default=1,
+                        help="Tensor model parallel size.")
+    parser.add_argument("--pp", type=int, default=1,
+                        help="Pipeline model parallel size.")
+    parser.add_argument("--dp", type=int, default=1,
+                        help="Data parallel size.")
+    parser.add_argument("--cp", type=int, default=1,
+                        help="Context parallel size.")
+    parser.add_argument("--ep", type=int, default=1,
+                        help="Expert model parallel size.")
+    parser.add_argument("--seq-length", type=int, default=512,
+                        help="Sequence length (default: 512).")
+    parser.add_argument("--sp", action="store_true", default=False,
+                        help="Enable sequence parallelism (requires --tp > 1).")
+    parser.add_argument(
+        "--schedule",
+        choices=("1f1b", "interleaved1f1b"),
+        default="1f1b",
+        help="Pipeline schedule.",
+    )
+    parser.add_argument(
+        "--zero-level",
+        choices=("zero1", "zero2", "zero3"),
+        default="zero1",
+        help="Megatron data-parallel sharding mode.",
+    )
+    parser.add_argument("--use-tp-pp-dp-mapping", action="store_true", default=False,
+                        help="Use tp-cp-ep-pp-dp rank ordering (PP intra-node) "
+                             "instead of default tp-cp-ep-dp-pp (PP cross-node).")
+    parser.add_argument("--nnodes", type=int, required=True,
+                        help="Number of nodes.")
+    parser.add_argument("--nproc-per-node", type=int, required=True,
+                        help="GPUs per node.")
+    parser.add_argument("--master-addr", default=None,
+                        help="Master node address (required when --disable-background-mode).")
+    parser.add_argument("--master-port", default="6000",
+                        help="Master node port.")
+    parser.add_argument("--disable-background-mode", action="store_true", default=False,
+                        help="Disable hostfile-based rank discovery; requires --master-addr.")
+    parser.add_argument("--nsight", action="store_true", default=False,
+                        help="Enable Megatron/PyTorch profiler output under --tensorboard-dir.")
+    parser.add_argument("--rerun-mode", choices=("disabled", "validate_results", "report_stats"), default="disabled",
+                        help="Megatron rerun engine mode. Defaults to disabled for benchmark timing.")
+    args = parser.parse_args()
+    if "--seq-length" not in os.sys.argv:
+        args.seq_length = MODEL_CONFIGS[args.model]["seq_length"]
+    return args
+
+
+def build_training_args(
+    tensorboard_dir: str,
+    micro_bs: int,
+    global_bs: int,
+    seq_length: int,
+    tp: int,
+    pp: int,
+    dp: int,
+    cp: int,
+    ep: int,
+    sp: bool,
+    use_tp_pp_dp_mapping: bool,
+    model: str,
+    train_iters: int,
+    schedule: str,
+    zero_level: str,
+    enable_profiler: bool,
+    rerun_mode: str,
+    supported_flags: set[str] | None = None,
+) -> list:
+    if sp and tp == 1:
+        raise ValueError("--sp requires --tp > 1")
+
+    model_config = MODEL_CONFIGS[model]
+    supported_flags = supported_flags or set()
+    args = [
+        "--num-layers", str(model_config["num_layers"]),
+        "--hidden-size", str(model_config["hidden_size"]),
+        "--num-attention-heads", str(model_config["num_attention_heads"]),
+        "--kv-channels", str(model_config["kv_channels"]),
+        "--ffn-hidden-size", str(model_config["ffn_hidden_size"]),
+        "--seq-length", str(seq_length),
+        "--max-position-embeddings", str(model_config["max_position_embeddings"]),
+        "--normalization", "RMSNorm",
+        "--norm-epsilon", "1e-6",
+        "--position-embedding-type", "rope",
+        "--rotary-percent", "1.0",
+        "--rotary-base", "1000000",
+        "--use-rotary-position-embeddings",
+        "--swiglu",
+        "--disable-bias-linear",
+        "--untie-embeddings-and-output-weights",
+        "--group-query-attention",
+        "--num-query-groups", str(model_config["num_query_groups"]),
+        "--qk-layernorm",
+        "--attention-dropout", "0.0",
+        "--hidden-dropout", "0.0",
+        "--use-mcore-models",
+        "--transformer-impl", "transformer_engine",
+        "--use-flash-attn",
+        "--num-experts", str(model_config["num_experts"]),
+        "--moe-router-topk", str(model_config["moe_router_topk"]),
+        "--moe-ffn-hidden-size", str(model_config["moe_ffn_hidden_size"]),
+        "--moe-layer-freq", "1",
+        "--moe-router-load-balancing-type", "aux_loss",
+        "--moe-aux-loss-coeff", "0.001",
+        "--moe-token-dispatcher-type", "alltoall",
+        "--moe-grouped-gemm",
+        "--tensor-model-parallel-size", str(tp),
+        "--pipeline-model-parallel-size", str(pp),
+        "--context-parallel-size", str(cp),
+        "--expert-model-parallel-size", str(ep),
+        "--micro-batch-size", str(micro_bs),
+        "--global-batch-size", str(global_bs),
+        "--train-iters", str(train_iters),
+        "--weight-decay", "0.1",
+        "--adam-beta1", "0.9",
+        "--adam-beta2", "0.95",
+        "--init-method-std", "0.01",
+        "--clip-grad", "1.0",
+        "--bf16",
+        "--lr", "3.0e-4",
+        "--lr-decay-style", "cosine",
+        "--min-lr", "3.0e-5",
+        "--lr-warmup-iters", "0",
+        "--lr-decay-iters", "2",
+        "--mock-data",
+        "--vocab-size", str(model_config["vocab_size"]),
+        "--tokenizer-type", "NullTokenizer",
+        "--log-interval", "1",
+        "--eval-interval", "100000",
+        "--eval-iters", "0",
+        "--save-interval", "100000",
+        "--no-gradient-accumulation-fusion",
+        "--rerun-mode", rerun_mode,
+    ]
+    if "--tensorboard-dir" in supported_flags:
+        args.extend(["--tensorboard-dir", tensorboard_dir])
+    else:
+        print("[megatron] pretrain_gpt.py does not support --tensorboard-dir; skipping")
+    if enable_profiler:
+        profiler_args: list[str] = []
+        if "--profile" in supported_flags:
+            profiler_args.append("--profile")
+        if "--use-pytorch-profiler" in supported_flags:
+            profiler_args.append("--use-pytorch-profiler")
+        if "--profile-step-start" in supported_flags:
+            profiler_args.extend(["--profile-step-start", "5"])
+        if "--profile-step-end" in supported_flags:
+            profiler_args.extend(["--profile-step-end", "8"])
+        if "--pytorch-profiler-collect-shapes" in supported_flags:
+            profiler_args.append("--pytorch-profiler-collect-shapes")
+        if profiler_args:
+            args.extend(profiler_args)
+        else:
+            print("[megatron] profiler flags unsupported by pretrain_gpt.py; running without profiler flags")
+    if dp > 1:
+        args.extend([
+            "--use-distributed-optimizer",
+            "--overlap-grad-reduce",
+            "--overlap-param-gather",
+            "--data-parallel-sharding-strategy",
+            {
+                "zero1": "optim",
+                "zero2": "optim_grads",
+                "zero3": "optim_grads_params",
+            }[zero_level],
+        ])
+    if zero_level in ("zero2", "zero3"):
+        args.append("--use-megatron-fsdp")
+    if schedule == "interleaved1f1b":
+        args.extend(["--num-layers-per-virtual-pipeline-stage", "1"])
+    if sp:
+        args.append("--sequence-parallel")
+    if use_tp_pp_dp_mapping:
+        args.append("--use-tp-pp-dp-mapping")
+    return args
+
+
+def main():
+    # TODO: 
+    # os.environ["CUDA_DEVICE_MAX_CONNECTIONS"] = "1"
+
+    args = parse_args()
+    os.makedirs(args.tensorboard_dir, exist_ok=True)
+
+    background_mode = not args.disable_background_mode
+    node_rank, master_addr = _setup_distributed(
+        args.nnodes, args.master_addr, background_mode,
+    )
+
+    print("=" * 60)
+    print(f"Node rank:   {node_rank} / {args.nnodes}")
+    print(f"Master:      {master_addr}:{args.master_port}")
+    print(f"GPUs/node:   {args.nproc_per_node}")
+    print("=" * 60)
+
+    training_args = build_training_args(
+        args.tensorboard_dir, args.micro_bs, args.global_bs, args.seq_length,
+        args.tp, args.pp, args.dp, args.cp, args.ep, args.sp,
+        args.use_tp_pp_dp_mapping, args.model, args.train_iters, args.schedule, args.zero_level,
+        args.nsight, args.rerun_mode,
+        _detect_supported_megatron_flags(),
+    )
+
+    from torch.distributed.run import main as torchrun_main
+
+    # Use torchrun's static rendezvous path for multi-node runs so every node
+    # consistently connects to the fixed master_addr/master_port pair.
+    torchrun_args = [
+        f"--nproc_per_node={args.nproc_per_node}",
+        f"--nnodes={args.nnodes}",
+        f"--node_rank={node_rank}",
+        "--rdzv_backend=static", # TODO:
+        f"--master_addr={master_addr}",
+        f"--master_port={args.master_port}",
+    ]
+    torchrun_main([
+        *torchrun_args,
+        "pretrain_gpt.py",
+        *training_args,
+    ])
+
+
+if __name__ == "__main__":
+    main()
diff --git a/artifact/scripts/make_piper_base_schedule.py b/artifact/scripts/make_piper_base_schedule.py
new file mode 100755
index 0000000..5f0b8f3
--- /dev/null
+++ b/artifact/scripts/make_piper_base_schedule.py
@@ -0,0 +1,87 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Generate a Piper base schedule for Qwen e2e evals.")
+    parser.add_argument("--pp", type=int, required=True, help="Physical pipeline ranks.")
+    parser.add_argument("--dp", type=int, required=True, help="Data-parallel replicas.")
+    parser.add_argument("--virtual-stages", type=int, default=1)
+    parser.add_argument("--layout", choices=("linear", "v"), default="linear")
+    parser.add_argument("--zero-stage", type=int, default=1, choices=(0, 1, 2, 3))
+    parser.add_argument("--ep", action="store_true")
+    parser.add_argument("--bucket-size", type=float, default=None)
+    parser.add_argument("--output", type=Path, required=True)
+    return parser.parse_args()
+
+
+def devices_for_stage(stage: int, physical_pp: int, dp: int, layout: str) -> list[int]:
+    if layout == "v" and stage >= physical_pp:
+        physical_rank = (2 * physical_pp - 1 - stage) % physical_pp
+    else:
+        physical_rank = stage % physical_pp
+    return [physical_rank + replica * physical_pp for replica in range(dp)]
+
+
+def main() -> int:
+    args = parse_args()
+    if args.pp <= 0 or args.dp <= 0 or args.virtual_stages <= 0:
+        raise SystemExit("--pp, --dp, and --virtual-stages must be positive")
+
+    directives: list[dict] = []
+    stage_count = args.pp * args.virtual_stages
+    for stage in range(stage_count):
+        devices = devices_for_stage(stage, args.pp, args.dp, args.layout)
+        directives.append(
+            {
+                "op": "place",
+                "filter": {"PP": stage},
+                "devices": devices,
+                "stream": "pp_stream",
+            }
+        )
+
+    if args.dp > 1:
+        for stage in range(stage_count):
+            devices = devices_for_stage(stage, args.pp, args.dp, args.layout)
+            directive = {
+                "op": "replicate",
+                "filter": {"PP": stage},
+                "devices": devices,
+                "reduce_stream": "reduce_stream",
+            }
+            if args.zero_stage == 2:
+                directive["shard_grads"] = True
+            elif args.zero_stage == 3:
+                directive["gather_stream"] = "gather_stream"
+                directive["shard_grads"] = True
+                directive["shard_params"] = True
+                directive["bucket_size"] = int(args.bucket_size or 1000)
+            elif args.bucket_size is not None:
+                directive["bucket_size"] = int(args.bucket_size)
+            directives.append(directive)
+
+    if args.ep:
+        for stage in range(stage_count):
+            directives.append(
+                {
+                    "op": "shard",
+                    "filter": {"PP": stage, "EP": "*"},
+                    "devices": devices_for_stage(stage, args.pp, args.dp, args.layout),
+                    "stream": "ep_stream",
+                }
+            )
+
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    with args.output.open("w", encoding="utf-8") as handle:
+        json.dump(directives, handle, indent=2)
+        handle.write("\n")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/artifact/scripts/run_deepspeed.sh b/artifact/scripts/run_deepspeed.sh
new file mode 100755
index 0000000..d26590d
--- /dev/null
+++ b/artifact/scripts/run_deepspeed.sh
@@ -0,0 +1,37 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+NNODE=1
+NGPU=8
+NODE_RANK=0
+MASTER_ADDR=127.0.0.1
+MASTER_PORT=29501
+MODEL=qwen3_1b
+TRAIN_ARGS=()
+
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        --nnode) NNODE="$2"; shift 2 ;;
+        --ngpu) NGPU="$2"; shift 2 ;;
+        --node-rank) NODE_RANK="$2"; shift 2 ;;
+        --master-addr) MASTER_ADDR="$2"; shift 2 ;;
+        --master-port) MASTER_PORT="$2"; shift 2 ;;
+        --model) MODEL="$2"; shift 2 ;;
+        --) shift; TRAIN_ARGS=("$@"); break ;;
+        *) echo "Unknown argument: $1" >&2; exit 2 ;;
+    esac
+done
+
+cd /workspace/piper
+exec conda run -n deepspeed env \
+    NCCL_SOCKET_IFNAME="${NCCL_SOCKET_IFNAME:-}" \
+    GLOO_SOCKET_IFNAME="${GLOO_SOCKET_IFNAME:-}" \
+    torchrun \
+        "--nnodes=${NNODE}" \
+        "--nproc_per_node=${NGPU}" \
+        "--node_rank=${NODE_RANK}" \
+        "--master_addr=${MASTER_ADDR}" \
+        "--master_port=${MASTER_PORT}" \
+        /workspace/artifact/run_deepspeed.py \
+        --model "${MODEL}" \
+        "${TRAIN_ARGS[@]}"
diff --git a/artifact/scripts/run_megatron.sh b/artifact/scripts/run_megatron.sh
new file mode 100755
index 0000000..143ac4b
--- /dev/null
+++ b/artifact/scripts/run_megatron.sh
@@ -0,0 +1,57 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+NNODE=1
+NGPU=8
+NODE_RANK=0
+MASTER_ADDR=127.0.0.1
+MASTER_PORT=29500
+MODEL=qwen3_1b
+TP=1
+PP=1
+DP=1
+CP=1
+EP=1
+NSIGHT=false
+TRAIN_ARGS=()
+
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        --nnode) NNODE="$2"; shift 2 ;;
+        --ngpu) NGPU="$2"; shift 2 ;;
+        --node-rank) NODE_RANK="$2"; shift 2 ;;
+        --master-addr) MASTER_ADDR="$2"; shift 2 ;;
+        --master-port) MASTER_PORT="$2"; shift 2 ;;
+        --model) MODEL="$2"; shift 2 ;;
+        --tp) TP="$2"; shift 2 ;;
+        --pp) PP="$2"; shift 2 ;;
+        --dp) DP="$2"; shift 2 ;;
+        --cp) CP="$2"; shift 2 ;;
+        --ep) EP="$2"; shift 2 ;;
+        --nsight) NSIGHT=true; shift ;;
+        --) shift; TRAIN_ARGS=("$@"); break ;;
+        *) echo "Unknown argument: $1" >&2; exit 2 ;;
+    esac
+done
+
+NSIGHT_ARGS=()
+if $NSIGHT; then
+    NSIGHT_ARGS=(--nsight --tensorboard-dir "/tmp/megatron_profiler/${MODEL}_pp${PP}_dp${DP}_ep${EP}")
+fi
+
+cd /workspace/Megatron-LM
+exec conda run -n megatron env \
+    NODE_RANK="${NODE_RANK}" \
+    NCCL_P2P_DISABLE=1 \
+    NCCL_SOCKET_IFNAME="${NCCL_SOCKET_IFNAME:-}" \
+    GLOO_SOCKET_IFNAME="${GLOO_SOCKET_IFNAME:-}" \
+    python /workspace/artifact/run_megatron.py \
+        --model "${MODEL}" \
+        --nnodes "${NNODE}" \
+        --nproc-per-node "${NGPU}" \
+        --master-addr "${MASTER_ADDR}" \
+        --master-port "${MASTER_PORT}" \
+        --disable-background-mode \
+        --tp "${TP}" --pp "${PP}" --dp "${DP}" --cp "${CP}" --ep "${EP}" \
+        "${NSIGHT_ARGS[@]}" \
+        "${TRAIN_ARGS[@]}"
diff --git a/artifact/scripts/run_piper.sh b/artifact/scripts/run_piper.sh
new file mode 100755
index 0000000..dbed516
--- /dev/null
+++ b/artifact/scripts/run_piper.sh
@@ -0,0 +1,127 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+MODEL=1B
+SCHEDULE=1f1b
+PP=1
+DP=1
+EP=false
+ZERO_STAGE=1
+BUCKET_SIZE=""
+BATCH_SIZE=4
+SEQ_LEN=512
+MBS=1
+WARMUP=3
+ITERS=10
+ITERATION_SLEEP=0
+RAY_ADDRESS=""
+RAY_PORT=6379
+TEMP_DIR=/tmp/piper/ray_tmp
+METRICS_OUT=""
+NSIGHT=false
+USE_INDUCTOR_ARG=--use-inductor
+PP_OUTER_ARG=--no-pp-outer
+
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        --model) MODEL="$2"; shift 2 ;;
+        --schedule) SCHEDULE="$2"; shift 2 ;;
+        --pp) PP="$2"; shift 2 ;;
+        --dp) DP="$2"; shift 2 ;;
+        --ep) EP=true; shift ;;
+        --zero-stage) ZERO_STAGE="$2"; shift 2 ;;
+        --bucket-size) BUCKET_SIZE="$2"; shift 2 ;;
+        --batch-size) BATCH_SIZE="$2"; shift 2 ;;
+        --seq-len) SEQ_LEN="$2"; shift 2 ;;
+        --mbs) MBS="$2"; shift 2 ;;
+        --warmup) WARMUP="$2"; shift 2 ;;
+        --iters) ITERS="$2"; shift 2 ;;
+        --iteration-sleep) ITERATION_SLEEP="$2"; shift 2 ;;
+        --address) RAY_ADDRESS="$2"; shift 2 ;;
+        --port) RAY_PORT="$2"; shift 2 ;;
+        --temp-dir) TEMP_DIR="$2"; shift 2 ;;
+        --metrics-out) METRICS_OUT="$2"; shift 2 ;;
+        --nsight) NSIGHT=true; shift ;;
+        --use-inductor) USE_INDUCTOR_ARG=--use-inductor; shift ;;
+        --no-use-inductor) USE_INDUCTOR_ARG=--no-use-inductor; shift ;;
+        --gradient-accumulation|--no-gradient-accumulation) shift ;;
+        --ar-a2a-same-stream|--no-ar-a2a-same-stream) shift ;;
+        --overlap-chunks|--no-overlap-chunks) shift ;;
+        --pp-outer) PP_OUTER_ARG=--pp-outer; shift ;;
+        --no-pp-outer) PP_OUTER_ARG=--no-pp-outer; shift ;;
+        *) echo "Unknown argument: $1" >&2; exit 2 ;;
+    esac
+done
+
+case "$SCHEDULE" in
+    1f1b) HARNESS_SCHEDULE=1f1b; VIRTUAL_STAGES=1 ;;
+    interleaved1f1b|interleaved-1f1b|interleaved_1f1b) HARNESS_SCHEDULE=interleaved_1f1b; VIRTUAL_STAGES=2 ;;
+    zerobubble|interleaved-zerobubble|interleavedzerobubble) HARNESS_SCHEDULE=zerobubble; VIRTUAL_STAGES=1 ;;
+    dualpipe|dualpipev) HARNESS_SCHEDULE=dualpipev; VIRTUAL_STAGES=2 ;;
+    *) echo "Unsupported Piper schedule: $SCHEDULE" >&2; exit 2 ;;
+esac
+
+BASE_SCHEDULE="$(mktemp /tmp/piper-base-schedule.XXXXXX.json)"
+BASE_ARGS=(
+    --pp "$PP"
+    --dp "$DP"
+    --virtual-stages "$VIRTUAL_STAGES"
+    --layout "$([[ "$HARNESS_SCHEDULE" == "dualpipev" ]] && echo v || echo linear)"
+    --zero-stage "$ZERO_STAGE"
+    --output "$BASE_SCHEDULE"
+)
+if $EP; then
+    BASE_ARGS+=(--ep)
+fi
+if [[ -n "$BUCKET_SIZE" ]]; then
+    BASE_ARGS+=(--bucket-size "$BUCKET_SIZE")
+fi
+python /workspace/artifact/scripts/make_piper_base_schedule.py "${BASE_ARGS[@]}"
+
+HARNESS_ARGS=(
+    examples/test_harness.py
+    --test-file examples/test_qwen.py
+    --base-schedule "$BASE_SCHEDULE"
+    --schedule "$HARNESS_SCHEDULE"
+    --ranks "$PP"
+    --mbs "$MBS"
+    --temp-dir "$TEMP_DIR"
+)
+if [[ -n "$RAY_ADDRESS" ]]; then
+    HARNESS_ARGS+=(--address "$RAY_ADDRESS" --port "$RAY_PORT")
+fi
+
+TEST_ARGS=(
+    --model "$MODEL"
+    --batch-size "$BATCH_SIZE"
+    --seq-len "$SEQ_LEN"
+    --warmup "$WARMUP"
+    --iters "$ITERS"
+    --iteration-sleep "$ITERATION_SLEEP"
+    "$USE_INDUCTOR_ARG"
+    "$PP_OUTER_ARG"
+)
+if $NSIGHT; then
+    TEST_ARGS+=(--nsight)
+fi
+
+cd /workspace/piper
+export PYTHONPATH="/workspace/piper:/workspace/piper/examples:${PYTHONPATH:-}"
+set +e
+conda run -n piper python "${HARNESS_ARGS[@]}" "${TEST_ARGS[@]}"
+STATUS=$?
+set -e
+
+if [[ -n "$METRICS_OUT" ]]; then
+    mkdir -p "$(dirname "$METRICS_OUT")"
+    LATEST_RUN="$(ls -td out/* 2>/dev/null | head -1 || true)"
+    if [[ -n "$LATEST_RUN" && -f "$LATEST_RUN/results.csv" ]]; then
+        cp "$LATEST_RUN/results.csv" "$METRICS_OUT"
+        echo "Piper metrics copied to $METRICS_OUT"
+    else
+        echo "Piper metrics were not found under /workspace/piper/out" >&2
+    fi
+fi
+
+rm -f "$BASE_SCHEDULE"
+exit "$STATUS"
diff --git a/artifact/scripts/run_torchtitan.sh b/artifact/scripts/run_torchtitan.sh
new file mode 100755
index 0000000..6b17058
--- /dev/null
+++ b/artifact/scripts/run_torchtitan.sh
@@ -0,0 +1,62 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+NNODE=1
+NGPU=8
+NODE_RANK=0
+MASTER_ADDR=127.0.0.1
+MASTER_PORT=29500
+MODULE=qwen3
+CONFIG=qwen3_9b
+LOG_RANK=0
+NSIGHT=false
+USE_BMM_EXPERTS=false
+TRAIN_ARGS=()
+
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        --nnode) NNODE="$2"; shift 2 ;;
+        --ngpu) NGPU="$2"; shift 2 ;;
+        --node-rank) NODE_RANK="$2"; shift 2 ;;
+        --master-addr) MASTER_ADDR="$2"; shift 2 ;;
+        --master-port) MASTER_PORT="$2"; shift 2 ;;
+        --module) MODULE="$2"; shift 2 ;;
+        --config) CONFIG="$2"; shift 2 ;;
+        --log-rank) LOG_RANK="$2"; shift 2 ;;
+        --nsight) NSIGHT=true; shift ;;
+        --use-bmm-experts) USE_BMM_EXPERTS=true; shift ;;
+        --) shift; TRAIN_ARGS=("$@"); break ;;
+        *) echo "Unknown argument: $1" >&2; exit 2 ;;
+    esac
+done
+
+RUNNER=(
+    torchrun
+    "--nnodes=${NNODE}"
+    "--nproc_per_node=${NGPU}"
+    "--node_rank=${NODE_RANK}"
+    "--master_addr=${MASTER_ADDR}"
+    "--master_port=${MASTER_PORT}"
+    "--local-ranks-filter" "${LOG_RANK}"
+    "--role" "rank"
+    "--tee" "3"
+    -m torchtitan.train
+    --module "${MODULE}"
+    --config "${CONFIG}"
+    "${TRAIN_ARGS[@]}"
+)
+
+if $NSIGHT; then
+    mkdir -p /workspace/eval-out/nsight
+    RUNNER=(nsys profile --trace=cuda,nvtx,osrt --force-overwrite=true --output "/workspace/eval-out/nsight/torchtitan_node${NODE_RANK}" "${RUNNER[@]}")
+fi
+
+cd /workspace/torchtitan
+exec conda run -n torchtitan env \
+    PYTHONPATH="/workspace/artifact:${PYTHONPATH:-}" \
+    TORCHTITAN_USE_BMM_EXPERTS="$($USE_BMM_EXPERTS && echo 1 || echo 0)" \
+    PYTORCH_ALLOC_CONF=expandable_segments:True \
+    TORCHFT_LIGHTHOUSE=http://localhost:29510 \
+    NCCL_SOCKET_IFNAME="${NCCL_SOCKET_IFNAME:-}" \
+    GLOO_SOCKET_IFNAME="${GLOO_SOCKET_IFNAME:-}" \
+    "${RUNNER[@]}"
diff --git a/artifact/sitecustomize.py b/artifact/sitecustomize.py
new file mode 100644
index 0000000..1d6d302
--- /dev/null
+++ b/artifact/sitecustomize.py
@@ -0,0 +1,64 @@
+"""Runtime hooks for artifact-only TorchTitan experiments."""
+
+import os
+
+
+if os.environ.get("TORCHTITAN_USE_BMM_EXPERTS") == "1":
+    try:
+        import torch
+        import torch.nn.functional as F
+        from torch.distributed.tensor import DTensor
+
+        from torchtitan.models.common.moe import moe as moe_mod
+
+        def _run_experts_bmm(
+            w1: torch.Tensor,
+            w2: torch.Tensor,
+            w3: torch.Tensor,
+            x: torch.Tensor,
+            num_tokens_per_expert: torch.Tensor,
+        ) -> torch.Tensor:
+            dim = x.shape[-1]
+            num_experts = w1.shape[0]
+            counts = num_tokens_per_expert.to(device=x.device, dtype=torch.long)
+            capacity = int(counts.max().item()) if counts.numel() else 0
+            starts = torch.cumsum(counts, dim=0) - counts
+            positions = torch.arange(capacity, device=x.device, dtype=torch.long)
+            zero = x.new_zeros((capacity, dim))
+
+            packed_rows = []
+            for expert_idx in range(num_experts):
+                valid = positions < counts[expert_idx]
+                source_idx = starts[expert_idx] + positions
+                safe_idx = torch.where(valid, source_idx, positions.new_zeros(()))
+                packed_rows.append(torch.where(valid[:, None], x[safe_idx], zero))
+            packed = torch.stack(packed_rows, dim=0)
+
+            h = F.silu(torch.bmm(packed, w1.transpose(-2, -1)))
+            h = h * torch.bmm(packed, w3.transpose(-2, -1))
+            expert_out = torch.bmm(h, w2.transpose(-2, -1))
+
+            out = x.new_zeros(x.shape)
+            for expert_idx in range(num_experts):
+                valid = positions < counts[expert_idx]
+                dest_idx = starts[expert_idx] + positions
+                safe_idx = torch.where(valid, dest_idx, positions.new_zeros(()))
+                src = torch.where(valid[:, None], expert_out[expert_idx], zero)
+                out = out.scatter_add(0, safe_idx[:, None].expand(-1, dim), src)
+            return out
+
+        def _bmm_experts_forward(self, x: torch.Tensor, num_tokens_per_expert: torch.Tensor) -> torch.Tensor:
+            if isinstance(self.w1, DTensor):
+                w1 = self.w1.to_local()
+                w2 = self.w2.to_local()
+                w3 = self.w3.to_local()
+            else:
+                w1 = self.w1
+                w2 = self.w2
+                w3 = self.w3
+            return _run_experts_bmm(w1, w2, w3, x, num_tokens_per_expert)
+
+        moe_mod.GroupedExperts.forward = _bmm_experts_forward
+        print("[artifact] TorchTitan GroupedExperts.forward patched to BMM experts", flush=True)
+    except Exception as exc:
+        raise RuntimeError("Failed to install TorchTitan BMM experts artifact hook") from exc
diff --git a/examples/models/qwen3.py b/examples/models/qwen3.py
index 95832ef..b851293 100644
--- a/examples/models/qwen3.py
+++ b/examples/models/qwen3.py
@@ -13,6 +13,84 @@
 PP_TAG = "PP"
 EP_TAG = "EP"
 
+
+def _run_bmm_experts(
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    w3: torch.Tensor,
+    x: torch.Tensor,
+    num_tokens_per_expert: torch.Tensor,
+) -> torch.Tensor:
+    if x.is_meta:
+        return torch.empty_like(x)
+
+    dim = x.shape[-1]
+    num_experts = w1.shape[0]
+    counts = num_tokens_per_expert.to(device=x.device, dtype=torch.long)
+    capacity = int(counts.max().item()) if counts.numel() else 0
+    starts = torch.cumsum(counts, dim=0) - counts
+    positions = torch.arange(capacity, device=x.device, dtype=torch.long)
+    zero = x.new_zeros((capacity, dim))
+
+    packed_rows = []
+    for expert_idx in range(num_experts):
+        valid = positions < counts[expert_idx]
+        source_idx = starts[expert_idx] + positions
+        safe_idx = torch.where(valid, source_idx, positions.new_zeros(()))
+        packed_rows.append(torch.where(valid[:, None], x[safe_idx], zero))
+    packed = torch.stack(packed_rows, dim=0)
+
+    h = F.silu(torch.bmm(packed, w1.transpose(-2, -1)))
+    h = h * torch.bmm(packed, w3.transpose(-2, -1))
+    expert_out = torch.bmm(h, w2.transpose(-2, -1))
+
+    out = x.new_zeros(x.shape)
+    for expert_idx in range(num_experts):
+        valid = positions < counts[expert_idx]
+        dest_idx = starts[expert_idx] + positions
+        safe_idx = torch.where(valid, dest_idx, positions.new_zeros(()))
+        src = torch.where(valid[:, None], expert_out[expert_idx], zero)
+        out = out.scatter_add(0, safe_idx[:, None].expand(-1, dim), src)
+    return out
+
+
+_BMM_EXPERTS_LIBS = []
+
+
+def _register_bmm_experts_op() -> None:
+    if (
+        hasattr(torch.ops, "piper_artifact")
+        and hasattr(torch.ops.piper_artifact, "bmm_experts")
+    ):
+        return
+
+    def_lib = torch.library.Library("piper_artifact", "DEF")
+    def_lib.define(
+        "bmm_experts(Tensor w1, Tensor w2, Tensor w3, Tensor x, Tensor num_tokens_per_expert) -> Tensor"
+    )
+    impl_lib = torch.library.Library("piper_artifact", "IMPL")
+    impl_lib.impl("bmm_experts", _run_bmm_experts, "CPU")
+    impl_lib.impl("bmm_experts", _run_bmm_experts, "CUDA")
+    impl_lib.impl("bmm_experts", _run_bmm_experts, "Autograd")
+    impl_lib.impl("bmm_experts", _run_bmm_experts, "Meta")
+    _BMM_EXPERTS_LIBS.extend([def_lib, impl_lib])
+
+
+_register_bmm_experts_op()
+
+
+def bmm_experts(
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    w3: torch.Tensor,
+    x: torch.Tensor,
+    num_tokens_per_expert: torch.Tensor,
+) -> torch.Tensor:
+    return torch.ops.piper_artifact.bmm_experts(
+        w1, w2, w3, x, num_tokens_per_expert
+    )
+
+
 def create_qwen3_config(name: str) -> Qwen3ModelArgs:
     """Create Qwen3 model config based on name."""
     match name:
@@ -86,7 +164,7 @@ def create_qwen3_config(name: str) -> Qwen3ModelArgs:
             return Qwen3ModelArgs(
                 vocab_size=151936,
                 dim=2048,
-                n_layers=4, #24,
+                n_layers=24,
                 n_heads=32,
                 n_kv_heads=8,
                 head_dim=64,
@@ -224,13 +302,12 @@ def create_qwen3_config(name: str) -> Qwen3ModelArgs:
 
 
 class BmmExperts(nn.Module):
-    """Triton-free expert GEMM.
+    """Triton-free expert GEMM with GroupedExperts-compatible routing.
 
     Mirrors torchtitan ``GroupedExperts`` parameter shapes/names so the param
     registry is unchanged, but runs the per-expert SwiGLU via batched matmul
-    instead of ``torch._grouped_mm`` (which pulls in Triton permute-index
-    kernels). Tokens are assumed evenly distributed across experts, matching the
-    fixed-capacity reshape the caller performs before dispatch.
+    instead of ``torch._grouped_mm``. The input must be sorted by expert, and
+    ``num_tokens_per_expert`` gives each expert's dynamic token count.
     """
 
     def __init__(self, dim: int, hidden_dim: int, num_experts: int):
@@ -241,13 +318,8 @@ def __init__(self, dim: int, hidden_dim: int, num_experts: int):
         self.w3 = nn.Parameter(torch.empty(num_experts, hidden_dim, dim))
 
     def forward(self, x: torch.Tensor, num_tokens_per_expert: torch.Tensor) -> torch.Tensor:
-        dim = x.shape[-1]
-        # (num_experts * capacity, dim) -> (num_experts, capacity, dim)
-        x = x.reshape(self.num_experts, -1, dim)
-        h = F.silu(torch.bmm(x, self.w1.transpose(-2, -1)))
-        h = h * torch.bmm(x, self.w3.transpose(-2, -1))
-        out = torch.bmm(h, self.w2.transpose(-2, -1))
-        return out.reshape(-1, dim)
+        counts = num_tokens_per_expert.to(device=x.device, dtype=torch.long)
+        return bmm_experts(self.w1, self.w2, self.w3, x, counts)
 
 
 class AnnotatedMoE(MoE):
@@ -283,15 +355,10 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
                 * top_scores_experts_sorted.reshape(-1, 1)
             ).to(x.dtype)
 
-        routed_input = routed_input.reshape(
-            self.experts.num_experts, -1, dim)
-
         # expert component
         with annotate(EP_TAG):
             dispatched_input = routed_input.contiguous()
-            dispatched_input = dispatched_input.reshape(-1, dim)
             routed_output = self.experts(dispatched_input, num_tokens_per_expert)
-            routed_output = routed_output.reshape(self.experts.num_experts, -1, dim)
             gathered_output = routed_output.contiguous()
 
         gathered_output = gathered_output.reshape(-1, dim)
diff --git a/examples/test_qwen.py b/examples/test_qwen.py
index b087c86..cdbbbae 100644
--- a/examples/test_qwen.py
+++ b/examples/test_qwen.py
@@ -88,7 +88,8 @@ def main(args, pg):
     logger.info(f"Running {args.warmup} warmup iterations")
     for _ in range(args.warmup):
         piper_exec_dag(loss_fn)
-        time.sleep(1)
+        if args.iteration_sleep > 0:
+            time.sleep(args.iteration_sleep)
 
     logger.info(f"Running {args.iters} timed iterations")
     ray.get([actor.reset_peak_memory.remote() for actor in actors.values()])
@@ -98,7 +99,8 @@ def main(args, pg):
         piper_exec_dag(loss_fn, log_stats=True)
         end = time.perf_counter()
         iter_times.append(end - start)
-        time.sleep(1)
+        if args.iteration_sleep > 0:
+            time.sleep(args.iteration_sleep)
 
     peak_memory_stats = ray.get(
         [actor.get_and_reset_peak_memory_stats.remote() for actor in actors.values()]
@@ -111,7 +113,8 @@ def main(args, pg):
         ray.get([actor.start_pytorch_profiler.remote() for actor in actors.values()])
         for _ in range(args.pytorch_profiler_iters):
             piper_exec_dag(loss_fn)
-            time.sleep(1)
+            if args.iteration_sleep > 0:
+                time.sleep(args.iteration_sleep)
         ray.get([
             actor.stop_pytorch_profiler.remote(args.pytorch_profile_dir)
             for actor in actors.values()
@@ -151,6 +154,7 @@ def parse_args(argv=None):
     parser.add_argument("--seq-len", type=int, default=1024)
     parser.add_argument("--warmup", type=int, default=3)
     parser.add_argument("--iters", type=int, default=3)
+    parser.add_argument("--iteration-sleep", type=float, default=0.0)
     parser.add_argument('--activation-checkpointing', action='store_true', default=False)
     parser.add_argument("--nsight", action="store_true", default=False,
                         help="Whether to use Nsight Systems for tracing")
diff --git a/run_piper_remote.sh b/run_piper_remote.sh
deleted file mode 100644
index 98c74b0..0000000
--- a/run_piper_remote.sh
+++ /dev/null
@@ -1,83 +0,0 @@
-export PATH_TO_PIPER=/m-coriander/coriander/shubham/moe-scheduling/piper/piper
-export IMAGE=mfris/piper:latest
-# export SSH_KEY=/m-coriander/coriander/shubham/ray-autoscaler_us-east-2.pem
-export SSH_KEY=/home/shubham/.ssh/mfris-us-west-2.pem
-
-export HEAD_PUBLIC_IP=100.22.89.85
-export HEAD_PRIVATE_IP=10.0.79.139
-export WORKER1_PRIVATE_IP=10.0.71.178
-export WORKER2_PRIVATE_IP=10.0.70.34
-export WORKER3_PRIVATE_IP=10.0.69.189
-export WORKERS="$WORKER1_PRIVATE_IP $WORKER2_PRIVATE_IP $WORKER3_PRIVATE_IP"
-
-
-./scripts/resync.sh
-
-
-python scripts/run_qwen_e2e_eval.py \
-  --model 9B \
-  --sweeps zero \
-  --zero-stages 2 3 \
-  --zero-grad-acc-settings off \
-  --bucket-size 25 \
-  --batch-size 4 \
-  --zero-batch-sizes 2 4 8 12 16 24 28 32 \
-  --seq-len 128 \
-  --memory-profile \
-  --pp 2 \
-  --dp 4 \
-  --warmup 2 \
-  --iters 2
-
-# python scripts/run_qwen_e2e_eval.py \
-#   --model 9B \
-#   --sweeps zero \
-#   --zero-stages 2 \
-#   --zero-grad-acc-settings off \
-#   --bucket-size 25 \
-#   --batch-size 4 \
-#   --zero-batch-sizes 8 12 16 \
-#   --seq-len 128 \
-#   --memory-profile \
-#   --pp 2 \
-#   --dp 4 \
-#   --warmup 2 \
-#   --iters 2
-
-# LOCAL_DIR=/m-coriander/coriander/shubham/moe-scheduling/piper/piper/e2e-eval/20260419_235909/logs/zero-sched_1f1b-pp4-dp2-ep0-zero3-bucket25-bs32-sl512-mbs8-ga0-aras0-ozo1-och0/memory_snapshots
-# mkdir -p "$LOCAL_DIR"
-# REMOTE_DIR=/tmp/piper/out/zero-sched_1f1b-pp4-dp2-ep0-zero3-bucket25-bs32-sl512-mbs8-ga0-aras0-ozo1-och0/memory_snapshots
-
-# for ENTRY in "head:$HEAD_PUBLIC_IP" \
-#              "worker1:$WORKER1_PRIVATE_IP" \
-#              "worker2:$WORKER2_PRIVATE_IP" \
-#              "worker3:$WORKER3_PRIVATE_IP"; do
-#   LABEL=${ENTRY%%:*}; HOST=${ENTRY#*:}
-#   [[ -z "$HOST" ]] && continue
-
-#   if [[ "$LABEL" == "head" ]]; then
-#     SSH_OPTS=()
-#   else
-#     SSH_OPTS=(-o "ProxyCommand=ssh -i $SSH_KEY -o StrictHostKeyChecking=no -W %h:%p ubuntu@$HEAD_PUBLIC_IP")
-#   fi
-
-#   STAGE=/tmp/zero3_snaps_${LABEL}
-#   echo "=== $LABEL ($HOST) ==="
-#   ssh -i "$SSH_KEY" -o StrictHostKeyChecking=no "${SSH_OPTS[@]}" ubuntu@$HOST \
-#       "rm -rf $STAGE && mkdir -p $STAGE && \
-#        docker cp piper_ray:$REMOTE_DIR $STAGE/ 2>/dev/null; \
-#        ls $STAGE/memory_snapshots/ 2>/dev/null"
-
-#   scp -r -i "$SSH_KEY" -o StrictHostKeyChecking=no "${SSH_OPTS[@]}" \
-#       "ubuntu@$HOST:$STAGE/memory_snapshots/." "$LOCAL_DIR/" 2>/dev/null \
-#       && echo "  fetched from $LABEL" \
-#       || echo "  no snapshots on $LABEL (skip)"
-# done
-
-# echo "=== Final ==="
-# ls -la "$LOCAL_DIR"
-
-
-
-
-
diff --git a/scripts/copy-logs.sh b/scripts/copy-logs.sh
deleted file mode 100755
index 8edaf18..0000000
--- a/scripts/copy-logs.sh
+++ /dev/null
@@ -1,52 +0,0 @@
-#!/usr/bin/env bash
-# Copy training logs and/or Nsight profiles from the EC2 cluster.
-# Requires: SSH_KEY, HEAD_PUBLIC_IP, WORKER1_PRIVATE_IP, WORKER2_PRIVATE_IP, WORKER3_PRIVATE_IP
-
-set -euo pipefail
-
-NSIGHT=false
-NSIGHT_ONLY=false
-OUT_DIR="out/ec2"
-
-while [[ $# -gt 0 ]]; do
-    case "$1" in
-        --nsight)       NSIGHT=true; shift ;;
-        --nsight-only)  NSIGHT=true; NSIGHT_ONLY=true; shift ;;
-        --out-dir)      OUT_DIR="$2"; shift 2 ;;
-        *) echo "Unknown argument: $1" >&2; exit 1 ;;
-    esac
-done
-
-SSH="ssh -i $SSH_KEY -o StrictHostKeyChecking=no"
-PROXY="ProxyCommand=ssh -i $SSH_KEY -o StrictHostKeyChecking=no -W %h:%p ubuntu@$HEAD_PUBLIC_IP"
-
-mkdir -p "$OUT_DIR"
-
-if ! $NSIGHT_ONLY; then
-    # Copy training log files from head container.
-    $SSH ubuntu@$HEAD_PUBLIC_IP \
-      "rm -rf /tmp/piper-out && docker cp piper_ray:/tmp/piper/out/. /tmp/piper-out/"
-    scp -r -i "$SSH_KEY" ubuntu@$HEAD_PUBLIC_IP:/tmp/piper-out/. "$OUT_DIR/"
-fi
-
-if $NSIGHT; then
-    # Head node Nsight profile
-    mkdir -p "$OUT_DIR/nsight-head"
-    $SSH ubuntu@$HEAD_PUBLIC_IP \
-      "rm -rf /tmp/nsight-head && mkdir -p /tmp/nsight-head && \
-       docker cp piper_ray:/tmp/piper/ray_tmp/session_latest/logs/nsight/. /tmp/nsight-head/"
-    scp -r -i "$SSH_KEY" ubuntu@$HEAD_PUBLIC_IP:/tmp/nsight-head/. "$OUT_DIR/nsight-head/"
-
-    # Worker Nsight profiles
-    WORKER_IDX=1
-    for WORKER_IP in $WORKER1_PRIVATE_IP $WORKER2_PRIVATE_IP; do
-        mkdir -p "$OUT_DIR/nsight-worker${WORKER_IDX}"
-        $SSH -o "$PROXY" ubuntu@$WORKER_IP \
-          "rm -rf /tmp/nsight-worker && mkdir -p /tmp/nsight-worker && \
-           docker cp piper_ray:/tmp/piper/ray_tmp/session_latest/logs/nsight/. /tmp/nsight-worker/"
-        scp -r -i "$SSH_KEY" \
-          -o "ProxyCommand=ssh -i $SSH_KEY -o StrictHostKeyChecking=no -W %h:%p ubuntu@$HEAD_PUBLIC_IP" \
-          ubuntu@$WORKER_IP:/tmp/nsight-worker/. "$OUT_DIR/nsight-worker${WORKER_IDX}/"
-        WORKER_IDX=$((WORKER_IDX + 1))
-    done
-fi
diff --git a/scripts/export_qwen_sweep_results.py b/scripts/export_qwen_sweep_results.py
deleted file mode 100644
index 2fcef84..0000000
--- a/scripts/export_qwen_sweep_results.py
+++ /dev/null
@@ -1,310 +0,0 @@
-#!/usr/bin/env python3
-"""Export Qwen sweep results from out/ec2 metrics files to CSV.
-
-By default this targets the completed Piper sweep:
-  --model 9B
-  --schedules 1f1b
-  --pp-mbs 4:8 8:16
-  --dp-values 1 2 4
-  --zero-stages 0 2 3
-  --batch-sizes 8
-
-Each matching experiment produces one CSV row aggregated across dp_ranks.
-"""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-import os
-import re
-from collections import defaultdict
-from pathlib import Path
-
-
-DEFAULT_MODEL = "9B"
-DEFAULT_SCHEDULES = ("1f1b",)
-DEFAULT_PP_MBS = ("4:8", "8:16")
-DEFAULT_DP_VALUES = (1, 2, 4)
-DEFAULT_ZERO_STAGES = (0, 2, 3)
-DEFAULT_BATCH_SIZES = (8,)
-
-
-def _parse_pp_mbs(values: list[str]) -> dict[int, set[int]]:
-    result: dict[int, set[int]] = defaultdict(set)
-    for value in values:
-        pp_str, mbs_str = value.split(":", maxsplit=1)
-        result[int(pp_str)].add(int(mbs_str))
-    return result
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Export aggregated Qwen sweep results from out/ec2 metrics files."
-    )
-    parser.add_argument("--metrics-dir", default="out/ec2", help="Directory containing qwen metrics files")
-    parser.add_argument("--output-csv", default="out/ec2/qwen_sweep_results.csv", help="CSV output path")
-    parser.add_argument("--model", default=DEFAULT_MODEL, help="Model to include")
-    parser.add_argument(
-        "--schedules",
-        nargs="+",
-        default=list(DEFAULT_SCHEDULES),
-        help="Schedules to include. Default: 1f1b",
-    )
-    parser.add_argument(
-        "--pp-mbs",
-        nargs="+",
-        default=list(DEFAULT_PP_MBS),
-        metavar="PP:MBS",
-        help="PP/MBS pairs to include. Default: 4:8 8:16",
-    )
-    parser.add_argument(
-        "--dp-values",
-        nargs="+",
-        type=int,
-        default=list(DEFAULT_DP_VALUES),
-        help="DP values to include. Default: 1 2 4",
-    )
-    parser.add_argument(
-        "--zero-stages",
-        nargs="+",
-        type=int,
-        default=list(DEFAULT_ZERO_STAGES),
-        help="ZeRO stages to include. Default: 0 2 3",
-    )
-    parser.add_argument(
-        "--batch-sizes",
-        nargs="+",
-        type=int,
-        default=list(DEFAULT_BATCH_SIZES),
-        help="Batch sizes to include. Default: 8",
-    )
-    return parser.parse_args()
-
-
-def _iter_metrics_records(path: Path) -> list[dict]:
-    records: list[dict] = []
-    if not path.is_file():
-        return records
-    with open(path, "r", encoding="utf-8") as handle:
-        for line in handle:
-            marker = "metrics_json="
-            if marker not in line:
-                continue
-            payload = line.split(marker, 1)[1].strip()
-            try:
-                records.append(json.loads(payload))
-            except json.JSONDecodeError:
-                continue
-    return records
-
-
-_METRICS_NAME_RE = re.compile(
-    r"^qwen(?P<model>.+)-pp(?P<pp>\d+)-dp(?P<dp>\d+)"
-    r"-zero(?P<zero_stage>\d+)-bs(?P<batch_size>\d+)"
-    r"-sl(?P<seq_len>\d+)-ga(?P<gradient_accumulation>[01])"
-    r"-aras(?P<ar_a2a_same_stream>[01])"
-    r"(?:-ozo(?P<overlap_zero_ops>[01]))?"
-    r"-(?P<schedule>[^/]+)$"
-)
-
-
-def _parse_metrics_filename(path: Path) -> dict | None:
-    match = _METRICS_NAME_RE.match(path.name)
-    if match is None:
-        return None
-    data = match.groupdict()
-    return {
-        "model": data["model"],
-        "pp": int(data["pp"]),
-        "dp": int(data["dp"]),
-        "schedule": data["schedule"],
-        "zero_stage": int(data["zero_stage"]),
-        "batch_size": int(data["batch_size"]),
-        "seq_len": int(data["seq_len"]),
-        "gradient_accumulation": bool(int(data["gradient_accumulation"])),
-        "ar_a2a_same_stream": bool(int(data["ar_a2a_same_stream"])),
-        "overlap_zero_ops": bool(int(data["overlap_zero_ops"] or "0")),
-    }
-
-
-def _peak_memory_summary(peak_memory_by_rank: dict[str, dict]) -> str:
-    ordered = []
-    for rank in sorted(peak_memory_by_rank, key=lambda item: int(item)):
-        ordered.append(f"{peak_memory_by_rank[rank]['peak_memory_gb']:.3f}")
-    return "/".join(ordered)
-
-
-def main() -> int:
-    args = parse_args()
-    metrics_dir = Path(args.metrics_dir)
-    output_csv = Path(args.output_csv)
-    output_csv.parent.mkdir(parents=True, exist_ok=True)
-
-    pp_to_mbs = _parse_pp_mbs(args.pp_mbs)
-    schedules = set(args.schedules)
-    dp_values = set(args.dp_values)
-    zero_stages = set(args.zero_stages)
-    batch_sizes = set(args.batch_sizes)
-
-    candidate_files: dict[tuple, tuple[bool, Path]] = {}
-    for path in sorted(metrics_dir.glob("qwen*")):
-        meta = _parse_metrics_filename(path)
-        if meta is None:
-            continue
-        records = _iter_metrics_records(path)
-        if not records:
-            continue
-        first = records[0]
-        model = first.get("model", meta["model"])
-        schedule = first.get("schedule", meta["schedule"])
-        pp = int(first.get("pp", meta["pp"]))
-        dp = int(first.get("dp", meta["dp"]))
-        mbs = int(first.get("mbs"))
-        zero_stage = meta["zero_stage"]
-        batch_size = meta["batch_size"]
-        seq_len = int(first.get("seq_len", meta["seq_len"]))
-        gradient_accumulation = bool(
-            first.get("gradient_accumulation", meta["gradient_accumulation"])
-        )
-        ar_a2a_same_stream = bool(
-            first.get("ar_a2a_same_stream", meta["ar_a2a_same_stream"])
-        )
-        overlap_zero_ops = bool(
-            first.get("overlap_zero_ops", meta["overlap_zero_ops"])
-        )
-
-        if model != args.model:
-            continue
-        if schedule not in schedules:
-            continue
-        if pp not in pp_to_mbs or mbs not in pp_to_mbs[pp]:
-            continue
-        if dp not in dp_values:
-            continue
-        if zero_stage not in zero_stages:
-            continue
-        if batch_size not in batch_sizes:
-            continue
-
-        key = (
-            model,
-            schedule,
-            pp,
-            dp,
-            mbs,
-            zero_stage,
-            batch_size,
-            seq_len,
-            gradient_accumulation,
-            ar_a2a_same_stream,
-            overlap_zero_ops,
-        )
-        if key not in candidate_files:
-            candidate_files[key] = (True, path)
-
-    grouped: dict[tuple, list[dict]] = defaultdict(list)
-    for (
-        model,
-        schedule,
-        pp,
-        dp,
-        mbs,
-        zero_stage,
-        batch_size,
-        seq_len,
-        gradient_accumulation,
-        ar_a2a_same_stream,
-        overlap_zero_ops,
-    ), (_explicit, path) in sorted(candidate_files.items()):
-        records = _iter_metrics_records(path)
-        for record in records:
-            key = (
-                model,
-                schedule,
-                pp,
-                dp,
-                mbs,
-                zero_stage,
-                batch_size,
-                seq_len,
-                gradient_accumulation,
-                ar_a2a_same_stream,
-                overlap_zero_ops,
-            )
-            enriched = dict(record)
-            enriched["_path"] = str(path)
-            enriched["_zero_stage"] = zero_stage
-            grouped[key].append(enriched)
-
-    fieldnames = [
-        "schedule",
-        "pp",
-        "dp",
-        "zero_stage",
-        "gradient_accumulation",
-        "ar_a2a_same_stream",
-        "overlap_zero_ops",
-        "iter_time_mean",
-        "iter_time_stddev",
-        "peak_memory_gb_by_rank",
-    ]
-
-    rows: list[dict[str, object]] = []
-    for key in sorted(grouped):
-        latest_by_dp_rank: dict[int, dict] = {}
-        for record in grouped[key]:
-            latest_by_dp_rank[int(record["dp_rank"])] = record
-        records = [latest_by_dp_rank[rank] for rank in sorted(latest_by_dp_rank)]
-        (
-            model,
-            schedule,
-            pp,
-            dp,
-            mbs,
-            zero_stage,
-            batch_size,
-            seq_len,
-            gradient_accumulation,
-            ar_a2a_same_stream,
-            overlap_zero_ops,
-        ) = key
-        dp_ranks = [int(record["dp_rank"]) for record in records]
-        iter_means = [float(record["iter_time_mean_s"]) for record in records]
-        iter_stds = [float(record["iter_time_std_s"]) for record in records]
-
-        peak_memory_by_rank: dict[str, dict] = {}
-        for record in records:
-            peak_memory_by_rank.update(record.get("peak_memory_by_rank", {}))
-        peak_values = [
-            float(stats["peak_memory_gb"])
-            for rank, stats in sorted(peak_memory_by_rank.items(), key=lambda item: int(item[0]))
-        ]
-
-        rows.append(
-            {
-                "schedule": schedule,
-                "pp": pp,
-                "dp": dp,
-                "zero_stage": zero_stage,
-                "gradient_accumulation": int(gradient_accumulation),
-                "ar_a2a_same_stream": int(ar_a2a_same_stream),
-                "overlap_zero_ops": int(overlap_zero_ops),
-                "iter_time_mean": sum(iter_means) / len(iter_means),
-                "iter_time_stddev": sum(iter_stds) / len(iter_stds),
-                "peak_memory_gb_by_rank": _peak_memory_summary(peak_memory_by_rank),
-            }
-        )
-
-    with open(output_csv, "w", newline="", encoding="utf-8") as handle:
-        writer = csv.DictWriter(handle, fieldnames=fieldnames)
-        writer.writeheader()
-        writer.writerows(rows)
-
-    print(f"Wrote {len(rows)} experiments to {output_csv}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/scripts/nccl_benchmark.py b/scripts/nccl_benchmark.py
deleted file mode 100644
index 39b3ce3..0000000
--- a/scripts/nccl_benchmark.py
+++ /dev/null
@@ -1,223 +0,0 @@
-"""
-NCCL bandwidth benchmark for Qwen3-30B-A3B communication patterns.
-
-Qwen3-30B-A3B config:
-  hidden_size:           2048
-  moe_intermediate_size: 768    (per-expert FFN width)
-  num_experts:           128
-  num_experts_per_tok:   8      (top-k)
-  num_hidden_layers:     48
-  num_attention_heads:   32
-  num_key_value_heads:   4
-  head_dim:              128
-  vocab_size:            151936
-
-All-to-All:
-  Simulates MoE expert dispatch. Each GPU holds hidden states for BATCH_SIZE=64
-  tokens and sends them to every other GPU (each GPU holds num_experts/world_size
-  experts). Total send buffer per GPU: [world_size * BATCH_SIZE, hidden_size].
-
-All-Reduce:
-  Simulates gradient all-reduce of one MoE layer's expert weights:
-    128 experts * 3 projections (up, gate, down) * 2048 * 768 = 604M params
-    In bf16: ~1.15 GB — the dominant per-layer communication cost.
-
-GPU configurations:
-  4 GPUs  — within-node  (GPUs 0-3 on node 0)
-  8 GPUs  — within-node  (all 8 GPUs on node 0)
-  12 GPUs — cross-node   (6 GPUs on each of 2 nodes)
-  16 GPUs — cross-node   (8 GPUs on each of 2 nodes)
-
-Launch (run inside Docker container on head node):
-  # 4 GPUs, single node
-  torchrun --nproc_per_node=4 test/nccl_benchmark.py
-
-  # 8 GPUs, single node
-  torchrun --nproc_per_node=8 test/nccl_benchmark.py
-
-  # 12 GPUs, 2 nodes (run on head; worker connects back)
-  torchrun --nnodes=2 --nproc_per_node=6 \\
-    --rdzv_backend=c10d --rdzv_endpoint=$HEAD_PRIVATE_IP:29500 \\
-    test/nccl_benchmark.py
-
-  # 16 GPUs, 2 nodes
-  torchrun --nnodes=2 --nproc_per_node=8 \\
-    --rdzv_backend=c10d --rdzv_endpoint=$HEAD_PRIVATE_IP:29500 \\
-    test/nccl_benchmark.py
-
-  On the worker node (inside its Docker container), run the same torchrun
-  command for multi-node configurations.
-
-To test EFA interface count impact:
-  FI_EFA_NUM_NETWORK_CARDS=1 torchrun ...   # restrict to 1 EFA NIC
-  torchrun ...                               # use all 4 EFA NICs (default)
-"""
-
-import os
-import time
-
-import torch
-import torch.distributed as dist
-
-# ---------------------------------------------------------------------------
-# Qwen3-30B-A3B model config
-# ---------------------------------------------------------------------------
-HIDDEN_SIZE = 2048
-MOE_INTERMEDIATE_SIZE = 768
-NUM_EXPERTS = 128
-NUM_LAYERS = 48
-
-# ---------------------------------------------------------------------------
-# Benchmark config
-# ---------------------------------------------------------------------------
-BUCKET_BYTES = 25 * 1024 * 1024   # 25 MiB — tensor size for both kernels
-DTYPE = torch.bfloat16
-WARMUP = 10
-ITERS = 50
-
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-def gb(n_bytes: int) -> float:
-    return n_bytes / 1e9
-
-
-def separator(title: str, width: int = 64) -> str:
-    return f"\n{'─' * width}\n  {title}\n{'─' * width}"
-
-
-# ---------------------------------------------------------------------------
-# All-to-All benchmark
-# ---------------------------------------------------------------------------
-def benchmark_alltoall(rank: int, world_size: int, device: torch.device):
-    """
-    Fixed BUCKET_BYTES total send/recv tensor, split evenly across world_size
-    peers.  Matches the all-reduce tensor size for direct comparison.
-
-    Bandwidth reported:
-      algbw = (world_size - 1) * chunk_bytes / time
-              (data *sent* by one GPU per iteration, excluding self)
-      busbw = algbw * (world_size - 1) / world_size   (nccl-tests convention)
-    """
-    n_elems = BUCKET_BYTES // (torch.finfo(DTYPE).bits // 8)
-    # round down to a multiple of world_size so chunks are equal
-    n_elems = (n_elems // world_size) * world_size
-    send = torch.randn(n_elems, dtype=DTYPE, device=device)
-    recv = torch.empty_like(send)
-    send_chunks = list(send.chunk(world_size))
-    recv_chunks = list(recv.chunk(world_size))
-
-    for _ in range(WARMUP):
-        dist.all_to_all(recv_chunks, send_chunks)
-    torch.cuda.synchronize()
-    dist.barrier()
-    if rank == 0:
-        print(f"  [{time.strftime('%H:%M:%S')}] all-to-all: warmup done, timing {ITERS} iters...", flush=True)
-
-    start = time.perf_counter()
-    for _ in range(ITERS):
-        dist.all_to_all(recv_chunks, send_chunks)
-    torch.cuda.synchronize()
-    elapsed = (time.perf_counter() - start) / ITERS
-
-    total_bytes = send.numel() * send.element_size()
-    chunk_bytes = total_bytes // world_size
-    algbw = (world_size - 1) * chunk_bytes / elapsed
-    busbw = algbw * (world_size - 1) / world_size
-    return elapsed, total_bytes, algbw, busbw
-
-
-# ---------------------------------------------------------------------------
-# All-Reduce benchmark
-# ---------------------------------------------------------------------------
-def benchmark_allreduce(rank: int, world_size: int, device: torch.device):
-    """
-    Gradient of one MoE layer's expert weights:
-      128 experts * (up + gate + down) projections
-      = 128 * 3 * 2048 * 768 = 603,979,776 params  (~1.15 GB bf16)
-
-    Bandwidth reported:
-      algbw = tensor_bytes / time
-      busbw = algbw * 2 * (world_size - 1) / world_size   (ring all-reduce)
-    """
-    n_elems = BUCKET_BYTES // (torch.finfo(DTYPE).bits // 8)
-    tensor = torch.randn(n_elems, dtype=DTYPE, device=device)
-
-    for _ in range(WARMUP):
-        dist.all_reduce(tensor)
-    torch.cuda.synchronize()
-    dist.barrier()
-    if rank == 0:
-        print(f"  [{time.strftime('%H:%M:%S')}] all-reduce: warmup done, timing {ITERS} iters...", flush=True)
-
-    start = time.perf_counter()
-    for _ in range(ITERS):
-        dist.all_reduce(tensor)
-    torch.cuda.synchronize()
-    elapsed = (time.perf_counter() - start) / ITERS
-
-    total_bytes = tensor.numel() * tensor.element_size()
-    algbw = total_bytes / elapsed
-    busbw = algbw * 2 * (world_size - 1) / world_size
-    return elapsed, total_bytes, algbw, busbw
-
-
-# ---------------------------------------------------------------------------
-# Main
-# ---------------------------------------------------------------------------
-def log(rank, msg):
-    """Print a timestamped progress message from rank 0."""
-    if rank == 0:
-        print(f"  [{time.strftime('%H:%M:%S')}] {msg}", flush=True)
-
-
-def main():
-    rank = int(os.environ["RANK"])
-    local_rank = int(os.environ["LOCAL_RANK"])
-    world_size = int(os.environ["WORLD_SIZE"])
-
-    torch.cuda.set_device(local_rank)
-    device = torch.device(f"cuda:{local_rank}")
-
-    log(rank, f"rank {rank}/{world_size} initializing NCCL process group...")
-    dist.init_process_group("nccl", device_id=device)
-    log(rank, "process group ready")
-
-    topology = "within-node" if world_size <= 8 else "cross-node"
-
-    if rank == 0:
-        print(separator(f"NCCL Benchmark — {world_size} GPUs ({topology})"))
-        print(f"  dtype:       {DTYPE}")
-        print(f"  warmup/iters: {WARMUP}/{ITERS}")
-        efa_cards = os.environ.get("FI_EFA_NUM_NETWORK_CARDS", "default (all)")
-        print(f"  FI_EFA_NUM_NETWORK_CARDS: {efa_cards}")
-
-    dist.barrier()
-
-    # --- All-to-All ---
-    log(rank, "all-to-all: warming up...")
-    elapsed, total_bytes, algbw, _busbw = benchmark_alltoall(rank, world_size, device)
-    if rank == 0:
-        print(f"\n  All-to-All")
-        print(f"    tensor:    {total_bytes / 1024**2:.1f} MiB total  ({total_bytes / 1024**2 / world_size:.1f} MiB/peer)")
-        print(f"    time/iter: {elapsed * 1e3:.3f} ms")
-        print(f"    bandwidth: {gb(algbw):.2f} GB/s")
-
-    dist.barrier()
-
-    # --- All-Reduce ---
-    log(rank, "all-reduce: warming up...")
-    elapsed, total_bytes, algbw, _busbw = benchmark_allreduce(rank, world_size, device)
-    if rank == 0:
-        print(f"\n  All-Reduce")
-        print(f"    tensor:    {total_bytes / 1024**2:.1f} MiB")
-        print(f"    time/iter: {elapsed * 1e3:.3f} ms")
-        print(f"    bandwidth: {gb(algbw):.2f} GB/s")
-
-    log(rank, "done")
-    dist.destroy_process_group()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/plot_qwen_e2e_torchtitan_vs_piper.py b/scripts/plot_qwen_e2e_torchtitan_vs_piper.py
deleted file mode 100644
index 8c0fb7e..0000000
--- a/scripts/plot_qwen_e2e_torchtitan_vs_piper.py
+++ /dev/null
@@ -1,494 +0,0 @@
-#!/usr/bin/env python3
-from __future__ import annotations
-
-import argparse
-import csv
-import os
-from dataclasses import dataclass
-import logging
-from pathlib import Path
-from typing import Any
-
-os.environ.setdefault("MPLCONFIGDIR", "/tmp/matplotlib")
-
-import matplotlib
-
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-
-logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
-logger = logging.getLogger(__name__)
-
-TORCHTITAN_COLOR = "#4C72B0"
-PIPER_COLOR = "#DD8452"
-SWEEPS = ("scalability", "zero", "schedule")
-ZERO_MAP = {"none": 0, "zero1": 1, "zero2": 2, "zero3": 3}
-
-
-@dataclass(frozen=True)
-class CommonRow:
-    source: str
-    sweep: str
-    key: tuple[Any, ...]
-    label: str
-    pp: int
-    dp: int
-    zero_level: int
-    schedule: str
-    mb_size: int
-    seq_len: int
-    status: str
-    iter_time_mean: float | None
-    iter_time_std: float | None
-    peak_memory_values: list[float]
-
-
-@dataclass(frozen=True)
-class PlotSeries:
-    name: str
-    color: str
-    row: CommonRow
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Create unified torchtitan+piper iteration-time and memory plots for matched Qwen e2e sweeps."
-    )
-    parser.add_argument("--torchtitan-results", required=True, help="Path to torchtitan all_results.csv")
-    parser.add_argument("--piper-results", required=True, help="Path to piper all_results.csv")
-    parser.add_argument(
-        "--sweeps",
-        nargs="+",
-        choices=SWEEPS,
-        default=list(SWEEPS),
-        help="Subset of sweeps to compare. Default: scalability zero schedule",
-    )
-    parser.add_argument(
-        "--out-dir",
-        required=True,
-        help="Directory for combined plots and matched CSVs",
-    )
-    return parser.parse_args()
-
-
-
-def parse_torchtitan_peak_memory(summary: str) -> list[float]:
-    summary = summary.strip()
-    if not summary:
-        return []
-    values: list[float] = []
-    for part in summary.split("/"):
-        part = part.strip()
-        if not part:
-            continue
-        try:
-            values.append(float(part))
-        except ValueError:
-            continue
-    return values
-
-
-
-def parse_piper_peak_memory(summary: str) -> list[float]:
-    summary = summary.strip()
-    if not summary:
-        return []
-    values: list[float] = []
-    for part in summary.split(";"):
-        part = part.strip()
-        if not part or ":" not in part:
-            continue
-        _, value_text = part.split(":", maxsplit=1)
-        try:
-            values.append(float(value_text))
-        except ValueError:
-            continue
-    return values
-
-
-
-def torchtitan_status(raw: str) -> str:
-    if raw == "ok":
-        return "success"
-    return raw
-
-
-
-def torchtitan_key(row: dict[str, str]) -> tuple[Any, ...]:
-    sweep = row["sweep"]
-    if sweep == "scalability":
-        return (sweep, int(row["pp"]), int(row["dp"]))
-    if sweep == "zero":
-        return (sweep, ZERO_MAP[row["zero_level"]], int(row["mb_size"]))
-    return (sweep, row["schedule"])
-
-
-
-def torchtitan_label(row: dict[str, str]) -> str:
-    sweep = row["sweep"]
-    if sweep == "scalability":
-        return f"(pp={row['pp']}, dp={row['dp']})"
-    if sweep == "zero":
-        return f"(zero={ZERO_MAP[row['zero_level']]}, mb={row['mb_size']})"
-    return row["schedule"]
-
-
-
-def load_torchtitan_rows(path: Path) -> dict[tuple[Any, ...], CommonRow]:
-    rows: dict[tuple[Any, ...], CommonRow] = {}
-    with path.open("r", newline="", encoding="utf-8") as handle:
-        reader = csv.DictReader(handle)
-        for row in reader:
-            iter_mean_text = row.get("iter_time_mean", "").strip()
-            iter_std_text = row.get("iter_time_stddev", "").strip()
-            iter_time_mean = float(iter_mean_text) if iter_mean_text else None
-            iter_time_std = float(iter_std_text) if iter_std_text else None
-            common = CommonRow(
-                source="torchtitan",
-                sweep=row["sweep"],
-                key=torchtitan_key(row),
-                label=torchtitan_label(row),
-                pp=int(row["pp"]),
-                dp=int(row["dp"]),
-                zero_level=ZERO_MAP[row["zero_level"]],
-                schedule=row["schedule"],
-                mb_size=int(row["mb_size"]),
-                seq_len=int(row["seq_len"]),
-                status=torchtitan_status(row["status"]),
-                iter_time_mean=iter_time_mean,
-                iter_time_std=iter_time_std,
-                peak_memory_values=parse_torchtitan_peak_memory(row.get("peak_memory_gb_by_rank", "")),
-            )
-            rows[common.key] = common
-    return rows
-
-
-
-def piper_key(row: dict[str, str]) -> tuple[Any, ...]:
-    sweep = row["sweep"]
-    if sweep == "scalability":
-        return (sweep, int(row["pp"]), int(row["dp"]))
-    if sweep == "zero":
-        return (sweep, int(row["zero_stage"]), int(row["batch_size"]), int(row["gradient_accumulation"]))
-    return (sweep, row["schedule"])
-
-
-
-def load_piper_rows(path: Path) -> dict[tuple[Any, ...], CommonRow]:
-    rows: dict[tuple[Any, ...], CommonRow] = {}
-    with path.open("r", newline="", encoding="utf-8") as handle:
-        reader = csv.DictReader(handle)
-        for row in reader:
-            iter_time_mean = row.get("iter_time_mean_s", "").strip()
-            iter_time_std = row.get("iter_time_std_s", "").strip()
-            common = CommonRow(
-                source="piper",
-                sweep=row["sweep"],
-                key=piper_key(row),
-                label=row["label"],
-                pp=int(row["pp"]),
-                dp=int(row["dp"]),
-                zero_level=int(row["zero_stage"]),
-                schedule=row["schedule"],
-                mb_size=int(row["batch_size"]),
-                seq_len=int(row["seq_len"]),
-                status=row["status"],
-                iter_time_mean=float(iter_time_mean) if iter_time_mean else None,
-                iter_time_std=float(iter_time_std) if iter_time_std else None,
-                peak_memory_values=parse_piper_peak_memory(row.get("peak_memory_gb_by_rank", "")),
-            )
-            rows[common.key] = common
-    return rows
-
-
-
-def validate_pair_consistency(torchtitan_row: CommonRow, piper_row: CommonRow) -> None:
-    mismatches: list[str] = []
-    for field in ("pp", "dp", "zero_level", "schedule", "mb_size", "seq_len"):
-        tt_value = getattr(torchtitan_row, field)
-        piper_value = getattr(piper_row, field)
-        if tt_value != piper_value:
-            mismatches.append(f"{field}: torchtitan={tt_value!r} piper={piper_value!r}")
-    if mismatches:
-        raise ValueError(
-            "Inconsistent matched experiment for "
-            f"sweep={torchtitan_row.sweep} "
-            f"torchtitan_key={torchtitan_row.key!r} "
-            f"piper_key={piper_row.key!r}: "
-            + ", ".join(mismatches)
-        )
-
-
-
-def save_grouped_iter_time_plot(title: str, grouped_rows: list[list[PlotSeries]], output_path: Path) -> None:
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    labels = [group[0].row.label for group in grouped_rows]
-    xs = list(range(len(grouped_rows)))
-    width = 0.24 if max((len(group) for group in grouped_rows), default=0) >= 3 else 0.34
-
-    success_values = []
-    for group in grouped_rows:
-        for series in group:
-            row = series.row
-            if row.status == "success" and row.iter_time_mean is not None:
-                success_values.append(row.iter_time_mean)
-    marker_y = max(success_values, default=0.0) * 0.05 if success_values else 1.0
-
-    fig, ax = plt.subplots(figsize=(max(10, len(grouped_rows) * 0.9), 6))
-    legend_seen: set[str] = set()
-    for index, group in enumerate(grouped_rows):
-        offsets = centered_offsets(len(group), width)
-        for offset, series in zip(offsets, group):
-            row = series.row
-            xpos = index + offset
-            label = series.name if series.name not in legend_seen else None
-            if row.status == "success" and row.iter_time_mean is not None:
-                ax.bar(
-                    xpos,
-                    row.iter_time_mean,
-                    yerr=row.iter_time_std or 0.0,
-                    width=width * 0.9,
-                    color=series.color,
-                    ecolor="#2F2F2F",
-                    capsize=4,
-                    label=label,
-                )
-            else:
-                ax.scatter(
-                    xpos,
-                    marker_y,
-                    marker="x",
-                    s=120,
-                    color=series.color,
-                    linewidths=2.5,
-                    label=label,
-                )
-            legend_seen.add(series.name)
-
-    ax.set_title(title)
-    ax.set_ylabel("Iteration Time (s)")
-    ax.set_xticks(xs)
-    ax.set_xticklabels(labels, rotation=45, ha="right")
-    ax.grid(axis="y", linestyle="--", alpha=0.35)
-    ax.set_ylim(bottom=0)
-    ax.legend()
-    fig.tight_layout()
-    fig.savefig(output_path, dpi=200)
-    plt.close(fig)
-
-
-
-def save_grouped_memory_plot(title: str, grouped_rows: list[list[PlotSeries]], output_path: Path) -> None:
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    labels = [group[0].row.label for group in grouped_rows]
-    xs = list(range(len(grouped_rows)))
-    spacing = 0.16 if max((len(group) for group in grouped_rows), default=0) >= 3 else 0.18
-
-    success_values = [
-        value
-        for group in grouped_rows
-        for series in group
-        if series.row.status == "success"
-        for value in series.row.peak_memory_values
-    ]
-    marker_y = max(success_values, default=0.0) * 0.05 if success_values else 0.25
-
-    fig, ax = plt.subplots(figsize=(max(10, len(grouped_rows) * 0.9), 6))
-    legend_seen: set[str] = set()
-    for index, group in enumerate(grouped_rows):
-        offsets = centered_offsets(len(group), spacing)
-        for shift, series in zip(offsets, group):
-            row = series.row
-            xpos = index + shift
-            label = series.name if series.name not in legend_seen else None
-            if row.status == "success" and row.peak_memory_values:
-                ax.scatter(
-                    [xpos] * len(row.peak_memory_values),
-                    row.peak_memory_values,
-                    color=series.color,
-                    s=36,
-                    alpha=0.9,
-                    label=label,
-                )
-            else:
-                ax.scatter(
-                    xpos,
-                    marker_y,
-                    marker="x",
-                    s=120,
-                    color=series.color,
-                    linewidths=2.5,
-                    label=label,
-                )
-            legend_seen.add(series.name)
-
-    ax.set_title(title)
-    ax.set_ylabel("Peak Memory (GB)")
-    ax.set_xticks(xs)
-    ax.set_xticklabels(labels, rotation=45, ha="right")
-    ax.grid(axis="y", linestyle="--", alpha=0.35)
-    ax.set_ylim(bottom=0)
-    ax.legend()
-    fig.tight_layout()
-    fig.savefig(output_path, dpi=200)
-    plt.close(fig)
-
-
-
-def centered_offsets(count: int, spacing: float) -> list[float]:
-    midpoint = (count - 1) / 2
-    return [(index - midpoint) * spacing for index in range(count)]
-
-
-
-def build_plot_groups(
-    sweep: str,
-    sweep_pairs: list[tuple[CommonRow, CommonRow]],
-    piper_rows: dict[tuple[Any, ...], CommonRow],
-) -> list[list[PlotSeries]]:
-    if sweep != "zero":
-        return [
-            [
-                PlotSeries(name="torchtitan", color=TORCHTITAN_COLOR, row=tt),
-                PlotSeries(name="piper", color=PIPER_COLOR, row=pp),
-            ]
-            for tt, pp in sweep_pairs
-        ]
-
-    groups: list[list[PlotSeries]] = []
-    for tt, _ in sweep_pairs:
-        zero_stage = tt.key[1]
-        batch_size = tt.key[2]
-        piper_ga_on = piper_rows[(sweep, zero_stage, batch_size, 1)]
-        piper_ga_off = piper_rows[(sweep, zero_stage, batch_size, 0)]
-        groups.append(
-            [
-                PlotSeries(name="torchtitan", color=TORCHTITAN_COLOR, row=tt),
-                PlotSeries(name="piper ga=on", color=PIPER_COLOR, row=piper_ga_on),
-                PlotSeries(name="piper ga=off", color="#55A868", row=piper_ga_off),
-            ]
-        )
-    return groups
-
-
-
-def collect_standard_sweep_pairs(
-    sweep: str,
-    torchtitan_rows: dict[tuple[Any, ...], CommonRow],
-    piper_rows: dict[tuple[Any, ...], CommonRow],
-) -> list[tuple[CommonRow, CommonRow]]:
-    common_keys = sorted(set(torchtitan_rows) & set(piper_rows))
-    pairs: list[tuple[CommonRow, CommonRow]] = []
-    for key in common_keys:
-        torchtitan_row = torchtitan_rows[key]
-        piper_row = piper_rows[key]
-        if torchtitan_row.sweep != sweep or piper_row.sweep != sweep:
-            continue
-        validate_pair_consistency(torchtitan_row, piper_row)
-        pairs.append((torchtitan_row, piper_row))
-    return pairs
-
-
-
-def collect_zero_sweep_pairs(
-    torchtitan_rows: dict[tuple[Any, ...], CommonRow],
-    piper_rows: dict[tuple[Any, ...], CommonRow],
-) -> list[tuple[CommonRow, CommonRow]]:
-    pairs: list[tuple[CommonRow, CommonRow]] = []
-    for key in sorted(torchtitan_rows):
-        tt = torchtitan_rows[key]
-        if tt.sweep != "zero":
-            continue
-        zero_stage = tt.key[1]
-        batch_size = tt.key[2]
-        piper_ga_on_key = ("zero", zero_stage, batch_size, 1)
-        piper_ga_off_key = ("zero", zero_stage, batch_size, 0)
-        if piper_ga_on_key not in piper_rows:
-            logger.warning(
-                "Missing piper zero GA=on result for torchtitan experiment: label=%s key=%r",
-                tt.label,
-                key,
-            )
-            continue
-        if piper_ga_off_key not in piper_rows:
-            logger.warning(
-                "Missing piper zero GA=off result for torchtitan experiment: label=%s key=%r",
-                tt.label,
-                key,
-            )
-            continue
-        piper_ga_on = piper_rows[piper_ga_on_key]
-        piper_ga_off = piper_rows[piper_ga_off_key]
-        validate_pair_consistency(tt, piper_ga_on)
-        validate_pair_consistency(tt, piper_ga_off)
-        pairs.append((tt, piper_ga_on))
-    return pairs
-
-
-
-def write_matched_csv(path: Path, rows: list[tuple[CommonRow, CommonRow]]) -> None:
-    path.parent.mkdir(parents=True, exist_ok=True)
-    with path.open("w", newline="", encoding="utf-8") as handle:
-        writer = csv.writer(handle)
-        writer.writerow([
-            "sweep",
-            "label",
-            "key",
-            "torchtitan_status",
-            "torchtitan_iter_time_mean_s",
-            "torchtitan_iter_time_std_s",
-            "torchtitan_peak_memory_gb_by_rank",
-            "piper_status",
-            "piper_iter_time_mean_s",
-            "piper_iter_time_std_s",
-            "piper_peak_memory_gb_by_rank",
-        ])
-        for tt, pp in rows:
-            writer.writerow([
-                tt.sweep,
-                tt.label,
-                repr(tt.key),
-                tt.status,
-                tt.iter_time_mean,
-                tt.iter_time_std,
-                "/".join(str(v) for v in tt.peak_memory_values),
-                pp.status,
-                pp.iter_time_mean,
-                pp.iter_time_std,
-                "/".join(str(v) for v in pp.peak_memory_values),
-            ])
-
-
-
-def main() -> int:
-    args = parse_args()
-    torchtitan_rows = load_torchtitan_rows(Path(args.torchtitan_results))
-    piper_rows = load_piper_rows(Path(args.piper_results))
-    out_dir = Path(args.out_dir)
-    out_dir.mkdir(parents=True, exist_ok=True)
-
-    for sweep in args.sweeps:
-        if sweep == "zero":
-            sweep_pairs = collect_zero_sweep_pairs(torchtitan_rows, piper_rows)
-        else:
-            sweep_pairs = collect_standard_sweep_pairs(sweep, torchtitan_rows, piper_rows)
-        if not sweep_pairs:
-            continue
-        plot_groups = build_plot_groups(sweep, sweep_pairs, piper_rows)
-        write_matched_csv(out_dir / f"{sweep}_matched.csv", sweep_pairs)
-        save_grouped_iter_time_plot(
-            f"Qwen {sweep.title()} Iteration Time: Torchtitan vs Piper",
-            plot_groups,
-            out_dir / f"{sweep}_iter_time.png",
-        )
-        save_grouped_memory_plot(
-            f"Qwen {sweep.title()} Memory: Torchtitan vs Piper",
-            plot_groups,
-            out_dir / f"{sweep}_memory.png",
-        )
-        print(f"wrote {sweep} plots and matched csv")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/scripts/print_qwen3_9b_output_layer_size.py b/scripts/print_qwen3_9b_output_layer_size.py
deleted file mode 100644
index 68dd345..0000000
--- a/scripts/print_qwen3_9b_output_layer_size.py
+++ /dev/null
@@ -1,80 +0,0 @@
-#!/usr/bin/env python3
-"""Read the local Qwen3 9B config and print output-layer size in bf16."""
-
-from __future__ import annotations
-
-import sys
-import ast
-from pathlib import Path
-
-import torch
-from torch import nn
-
-
-def _add_repo_paths() -> None:
-    repo_root = Path(__file__).resolve().parents[1]
-    sibling_torchtitan = repo_root.parent / "torchtitan"
-
-    for path in (repo_root, sibling_torchtitan):
-        path_str = str(path)
-        if path.exists() and path_str not in sys.path:
-            sys.path.insert(0, path_str)
-
-
-_add_repo_paths()
-
-
-def _load_local_qwen3_9b_dims() -> tuple[int, int]:
-    repo_root = Path(__file__).resolve().parents[1]
-    config_path = repo_root / "test" / "models" / "qwen3.py"
-    module = ast.parse(config_path.read_text(encoding="utf-8"), filename=str(config_path))
-
-    in_9b_case = False
-    vocab_size = None
-    dim = None
-
-    for node in ast.walk(module):
-        if not isinstance(node, ast.Match):
-            continue
-        for case in node.cases:
-            pattern = case.pattern
-            if isinstance(pattern, ast.MatchValue) and isinstance(pattern.value, ast.Constant):
-                in_9b_case = pattern.value.value == "9B"
-            else:
-                in_9b_case = False
-            if not in_9b_case:
-                continue
-            for stmt in case.body:
-                if not isinstance(stmt, ast.Return) or not isinstance(stmt.value, ast.Call):
-                    continue
-                for kw in stmt.value.keywords:
-                    if kw.arg == "vocab_size" and isinstance(kw.value, ast.Constant):
-                        vocab_size = int(kw.value.value)
-                    elif kw.arg == "dim" and isinstance(kw.value, ast.Constant):
-                        dim = int(kw.value.value)
-            break
-
-    if vocab_size is None or dim is None:
-        raise RuntimeError(f"Failed to read vocab_size/dim for 9B from {config_path}")
-    return vocab_size, dim
-
-
-def main() -> None:
-    vocab_size, dim = _load_local_qwen3_9b_dims()
-    with torch.device("meta"):
-        output_layer = nn.Linear(dim, vocab_size, bias=False, dtype=torch.bfloat16)
-    weight = output_layer.weight
-    num_params = weight.numel()
-    size_bytes = num_params * torch.tensor([], dtype=torch.bfloat16).element_size()
-
-    print(f"model=Qwen3 9B dtype={torch.bfloat16}")
-    print("source=local-config")
-    print(f"output_layer_type={type(output_layer).__name__}")
-    print(f"output_weight_shape={tuple(weight.shape)}")
-    print(f"output_weight_numel={num_params}")
-    print(f"output_weight_size_bytes={size_bytes}")
-    print(f"output_weight_size_mib={size_bytes / (1024 ** 2):.2f}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/resync.sh b/scripts/resync.sh
deleted file mode 100755
index 24d2a7d..0000000
--- a/scripts/resync.sh
+++ /dev/null
@@ -1,24 +0,0 @@
-rsync -av --delete \
-  --exclude='.git' --exclude='__pycache__' --exclude='.venv' \
-  --exclude='venv' --exclude='out' --exclude='e2e-eval' --exclude='ray_tmp' --exclude='aws' \
-  -e "ssh -i $SSH_KEY -o StrictHostKeyChecking=no" \
-  . ubuntu@$HEAD_PUBLIC_IP:/tmp/piper/
-
-for WORKER_IP in $WORKER1_PRIVATE_IP $WORKER2_PRIVATE_IP $WORKER3_PRIVATE_IP; do
-  rsync -av --delete \
-    --exclude='.git' --exclude='__pycache__' --exclude='.venv' \
-    --exclude='venv' --exclude='out' --exclude='e2e-eval' --exclude='ray_tmp' --exclude='aws' \
-    -e "ssh -i $SSH_KEY -o StrictHostKeyChecking=no -o 'ProxyCommand=ssh -i $SSH_KEY -o StrictHostKeyChecking=no -W %h:%p ubuntu@$HEAD_PUBLIC_IP'" \
-    . ubuntu@$WORKER_IP:/tmp/piper/
-done
-
-ssh -i $SSH_KEY -o StrictHostKeyChecking=no ubuntu@$HEAD_PUBLIC_IP \
-  "docker cp /tmp/piper/. piper_ray:/tmp/piper/"
-
-for WORKER_IP in $WORKER1_PRIVATE_IP $WORKER2_PRIVATE_IP $WORKER3_PRIVATE_IP; do
-  ssh -i $SSH_KEY -o StrictHostKeyChecking=no \
-    -o "ProxyCommand=ssh -i $SSH_KEY -o StrictHostKeyChecking=no -W %h:%p ubuntu@$HEAD_PUBLIC_IP" \
-    ubuntu@$WORKER_IP \
-    "docker cp /tmp/piper/. piper_ray:/tmp/piper/" &
-done
-wait
diff --git a/scripts/run-qwen-ec2.sh b/scripts/run-qwen-ec2.sh
deleted file mode 100755
index de9359d..0000000
--- a/scripts/run-qwen-ec2.sh
+++ /dev/null
@@ -1,326 +0,0 @@
-#!/usr/bin/env bash
-# Run piper qwen training on EC2 cluster via SSH.
-#
-# Usage:
-#   ./scripts/run-qwen-ec2.sh [OPTIONS]
-#
-# All options can also be set as environment variables.
-#
-# Options:
-#   --model MODEL    Model size (default: $MODEL or 9B)
-#   --schedule NAME  Pipeline schedule (default: $SCHEDULE or 1f1b)
-#   --pp N           Pipeline parallel degree (default: $PP or 2)
-#   --dp N           Data parallel degree (default: $DP or 1)
-#   --ep             Enable expert parallel (default: off)
-#   --zero-stage N   ZeRO stage 0/1/2/3 (default: $ZERO_STAGE or 0)
-#   --bucket-size N  Bucket size in MB (default: $BUCKET_SIZE if set)
-#   --batch-size N   Global batch size (default: $BATCH_SIZE or 4)
-#   --seq-len N      Sequence length (default: $SEQ_LEN or 512)
-#   --mbs N          Microbatch size (default: $MBS or 4)
-#   --gradient-accumulation / --no-gradient-accumulation
-#                    Delay bucket reductions to the last occurrence (default: on)
-#   --ar-a2a-same-stream / --no-ar-a2a-same-stream
-#                    Run A2A ops on the same CUDA stream as AR ops (default: off)
-#   --overlap-zero-ops / --no-overlap-zero-ops
-#                    Run overlap_zero_ops on the per-rank DAGs (default: off)
-#   --overlap-chunks / --no-overlap-chunks
-#                    Run overlap_chunks on the per-rank DAGs (default: off)
-#   --pp-outer / --no-pp-outer
-#                    Use PP as the outer placement dim (one pipeline stage per
-#                    node, all DP replicas colocated). Makes per-stage EP/DP
-#                    collectives intra-node. Requires dp >= pp for sensible
-#                    packing. Default: off (legacy PP-inner layout).
-#   --warmup N       Warmup iterations (default: $WARMUP or 2)
-#   --iters N        Training iterations (default: $ITERS or 5)
-#   --ray-port PORT  Ray port (default: $RAY_PORT or 6379)
-#   --nsight         Enable Nsight profiling (saves to ray_tmp)
-#   --log-to-file    Tee all output to out/ec2/<experiment-name>_<timestamp>.log
-#   --out-dir DIR    Directory for log files (default: out/ec2)
-#   --remote-output-dir DIR
-#                    Directory inside the container for test_qwen outputs (default: /tmp/piper/out)
-#
-# Reads from environment: SSH_KEY, HEAD_PUBLIC_IP, HEAD_PRIVATE_IP
-
-set -euo pipefail
-
-MODEL="${MODEL:-9B}"
-SCHEDULE="${SCHEDULE:-1f1b}"
-PP="${PP:-2}"
-DP="${DP:-1}"
-ZERO_STAGE="${ZERO_STAGE:-0}"
-BUCKET_SIZE="${BUCKET_SIZE:-}"
-BATCH_SIZE="${BATCH_SIZE:-4}"
-SEQ_LEN="${SEQ_LEN:-512}"
-MBS="${MBS:-4}"
-WARMUP="${WARMUP:-2}"
-ITERS="${ITERS:-5}"
-RAY_PORT="${RAY_PORT:-6379}"
-NSIGHT=false
-EP=false
-LOG_TO_FILE=false
-OUT_DIR="out/ec2"
-REMOTE_OUTPUT_DIR="/tmp/piper/out"
-USE_INDUCTOR_ARG=""
-GRADIENT_ACCUMULATION_ARG="--gradient-accumulation"
-AR_A2A_SAME_STREAM_ARG="--no-ar-a2a-same-stream"
-OVERLAP_ZERO_OPS_ARG="--no-overlap-zero-ops"
-OVERLAP_CHUNKS_ARG="--no-overlap-chunks"
-PP_OUTER_ARG="--no-pp-outer"
-MEMORY_PROFILE=false
-MEMORY_PROFILE_DIR=""
-MEMORY_PROFILE_ARG=""
-
-SSH_BASE=(-i "$SSH_KEY" -o StrictHostKeyChecking=no)
-PROXY="ProxyCommand=ssh -i $SSH_KEY -o StrictHostKeyChecking=no -W %h:%p ubuntu@$HEAD_PUBLIC_IP"
-
-while [[ $# -gt 0 ]]; do
-    case "$1" in
-        --model)       MODEL="$2";       shift 2 ;;
-        --schedule)    SCHEDULE="$2";    shift 2 ;;
-        --pp)          PP="$2";          shift 2 ;;
-        --dp)          DP="$2";          shift 2 ;;
-        --ep)          EP=true;          shift ;;
-        --zero-stage)  ZERO_STAGE="$2";  shift 2 ;;
-        --bucket-size) BUCKET_SIZE="$2"; shift 2 ;;
-        --batch-size)  BATCH_SIZE="$2";  shift 2 ;;
-        --seq-len)     SEQ_LEN="$2";     shift 2 ;;
-        --mbs)         MBS="$2";         shift 2 ;;
-        --gradient-accumulation) GRADIENT_ACCUMULATION_ARG="--gradient-accumulation"; shift ;;
-        --no-gradient-accumulation) GRADIENT_ACCUMULATION_ARG="--no-gradient-accumulation"; shift ;;
-        --ar-a2a-same-stream) AR_A2A_SAME_STREAM_ARG="--ar-a2a-same-stream"; shift ;;
-        --no-ar-a2a-same-stream) AR_A2A_SAME_STREAM_ARG="--no-ar-a2a-same-stream"; shift ;;
-        --overlap-zero-ops) OVERLAP_ZERO_OPS_ARG="--overlap-zero-ops"; shift ;;
-        --no-overlap-zero-ops) OVERLAP_ZERO_OPS_ARG="--no-overlap-zero-ops"; shift ;;
-        --overlap-chunks) OVERLAP_CHUNKS_ARG="--overlap-chunks"; shift ;;
-        --no-overlap-chunks) OVERLAP_CHUNKS_ARG="--no-overlap-chunks"; shift ;;
-        --pp-outer) PP_OUTER_ARG="--pp-outer"; shift ;;
-        --no-pp-outer) PP_OUTER_ARG="--no-pp-outer"; shift ;;
-        --warmup)      WARMUP="$2";      shift 2 ;;
-        --iters)       ITERS="$2";       shift 2 ;;
-        --ray-port)    RAY_PORT="$2";    shift 2 ;;
-        --nsight)      NSIGHT=true;      shift ;;
-        --use-inductor) USE_INDUCTOR_ARG="--use-inductor"; shift ;;
-        --no-use-inductor) USE_INDUCTOR_ARG="--no-use-inductor"; shift ;;
-        --log-to-file) LOG_TO_FILE=true; shift ;;
-        --memory-profile) MEMORY_PROFILE=true; shift ;;
-        --memory-profile-dir) MEMORY_PROFILE_DIR="$2"; shift 2 ;;
-        --out-dir)     OUT_DIR="$2";     shift 2 ;;
-        --remote-output-dir) REMOTE_OUTPUT_DIR="$2"; shift 2 ;;
-        *) echo "Unknown argument: $1" >&2; exit 1 ;;
-    esac
-done
-
-run_head_ssh() {
-    ssh "${SSH_BASE[@]}" "ubuntu@$HEAD_PUBLIC_IP" "$@"
-}
-
-run_worker_ssh() {
-    local worker_ip="$1"
-    shift
-    ssh "${SSH_BASE[@]}" -o "$PROXY" "ubuntu@$worker_ip" "$@"
-}
-
-snapshot_ray_worker_logs() {
-    local node_kind="$1"
-    local node_host="${2:-}"
-    local snapshot_path="$3"
-    local remote_cmd="docker exec piper_ray bash -lc 'for f in /tmp/piper/ray_tmp/session_latest/logs/worker-*.out /tmp/piper/ray_tmp/session_latest/logs/worker-*.err; do [ -f \"\$f\" ] || continue; stat -c \"%n|%s\" \"\$f\"; done'"
-
-    if [[ "$node_kind" == "head" ]]; then
-        run_head_ssh "$remote_cmd" > "$snapshot_path" || true
-    else
-        run_worker_ssh "$node_host" "$remote_cmd" > "$snapshot_path" || true
-    fi
-}
-
-bundle_ray_worker_logs() {
-    local node_label="$1"
-    local node_kind="$2"
-    local node_host="${3:-}"
-    local before_snapshot="$4"
-    local bundle_dir="$5"
-    local after_snapshot
-    local remote_cmd
-
-    mkdir -p "$bundle_dir/$node_label"
-    after_snapshot="$(mktemp)"
-    snapshot_ray_worker_logs "$node_kind" "$node_host" "$after_snapshot"
-
-    declare -A before_sizes=()
-    while IFS='|' read -r path size; do
-        [[ -n "$path" ]] || continue
-        before_sizes["$path"]="$size"
-    done < "$before_snapshot"
-
-    while IFS='|' read -r path size; do
-        [[ -n "$path" ]] || continue
-        local before_size="${before_sizes[$path]:-0}"
-        if [[ "$size" == "$before_size" ]]; then
-            continue
-        fi
-        local fetch_from=1
-        if [[ "$size" =~ ^[0-9]+$ ]] && [[ "$before_size" =~ ^[0-9]+$ ]] && (( size > before_size )); then
-            fetch_from=$((before_size + 1))
-        fi
-
-        local dest="$bundle_dir/$node_label/$(basename "$path")"
-        remote_cmd="docker exec piper_ray bash -lc 'tail -c +$fetch_from \"${path}\"'"
-        if [[ "$node_kind" == "head" ]]; then
-            run_head_ssh "$remote_cmd" > "$dest" || true
-        else
-            run_worker_ssh "$node_host" "$remote_cmd" > "$dest" || true
-        fi
-    done < "$after_snapshot"
-
-    rm -f "$after_snapshot"
-}
-
-BUCKET_SIZE_LABEL=""
-if [[ -n "$BUCKET_SIZE" ]]; then
-    BUCKET_SIZE_LABEL="-bucket${BUCKET_SIZE}"
-fi
-
-NSIGHT_LABEL=""
-if $NSIGHT; then
-    NSIGHT_LABEL="-nsight1"
-fi
-
-GRADIENT_ACCUMULATION_LABEL=1
-if [[ "$GRADIENT_ACCUMULATION_ARG" == "--no-gradient-accumulation" ]]; then
-    GRADIENT_ACCUMULATION_LABEL=0
-fi
-
-AR_A2A_SAME_STREAM_LABEL=0
-if [[ "$AR_A2A_SAME_STREAM_ARG" == "--ar-a2a-same-stream" ]]; then
-    AR_A2A_SAME_STREAM_LABEL=1
-fi
-
-OVERLAP_ZERO_OPS_LABEL=0
-if [[ "$OVERLAP_ZERO_OPS_ARG" == "--overlap-zero-ops" ]]; then
-    OVERLAP_ZERO_OPS_LABEL=1
-fi
-
-OVERLAP_CHUNKS_LABEL=0
-if [[ "$OVERLAP_CHUNKS_ARG" == "--overlap-chunks" ]]; then
-    OVERLAP_CHUNKS_LABEL=1
-fi
-
-PP_OUTER_LABEL=0
-if [[ "$PP_OUTER_ARG" == "--pp-outer" ]]; then
-    PP_OUTER_LABEL=1
-fi
-
-EP_LABEL=0
-if $EP; then
-    EP_LABEL=1
-fi
-
-EXPERIMENT_NAME="qwen${MODEL}-sched_${SCHEDULE}-pp${PP}-dp${DP}-ep${EP_LABEL}-zero${ZERO_STAGE}${BUCKET_SIZE_LABEL}${NSIGHT_LABEL}-bs${BATCH_SIZE}-sl${SEQ_LEN}-mbs${MBS}-ga${GRADIENT_ACCUMULATION_LABEL}-aras${AR_A2A_SAME_STREAM_LABEL}-ozo${OVERLAP_ZERO_OPS_LABEL}-och${OVERLAP_CHUNKS_LABEL}-ppo${PP_OUTER_LABEL}"
-
-if $LOG_TO_FILE; then
-    mkdir -p "$OUT_DIR"
-    LOG_FILE="$OUT_DIR/${EXPERIMENT_NAME}_$(date +%Y%m%d_%H%M%S).log"
-    echo "Logging to $LOG_FILE"
-    exec > >(tee -a "$LOG_FILE") 2>&1
-fi
-
-ZERO_ARG=""
-if [[ "$ZERO_STAGE" -gt 0 ]]; then
-    ZERO_ARG="--zero-stage $ZERO_STAGE"
-fi
-
-BUCKET_SIZE_ARG=""
-if [[ -n "$BUCKET_SIZE" ]]; then
-    BUCKET_SIZE_ARG="--bucket-size $BUCKET_SIZE"
-fi
-
-NSIGHT_ARG=""
-if $NSIGHT; then
-    NSIGHT_ARG="--nsight"
-fi
-
-RUN_ID="$(date +%Y%m%d_%H%M%S)"
-REMOTE_RUN_DIR="${REMOTE_OUTPUT_DIR}/runner-fetch/${EXPERIMENT_NAME}_${RUN_ID}"
-REMOTE_LOG="${REMOTE_RUN_DIR}/test_qwen.log"
-LOCAL_CLUSTER_LOG="$OUT_DIR/${EXPERIMENT_NAME}_${RUN_ID}.cluster.log"
-LOCAL_RAY_LOG_BUNDLE_DIR="$OUT_DIR/${EXPERIMENT_NAME}_${RUN_ID}.ray-logs"
-SNAPSHOT_DIR="$(mktemp -d)"
-HEAD_SNAPSHOT="$SNAPSHOT_DIR/head.snapshot"
-WORKER_LOG_NODES=()
-
-if $MEMORY_PROFILE; then
-    MEM_DIR="${MEMORY_PROFILE_DIR:-$REMOTE_OUTPUT_DIR/memory_snapshots/${EXPERIMENT_NAME}_${RUN_ID}}"
-    MEMORY_PROFILE_ARG="--memory-profile --memory-profile-dir $MEM_DIR"
-fi
-
-for worker_ip in "${WORKER1_PRIVATE_IP:-}" "${WORKER2_PRIVATE_IP:-}" "${WORKER3_PRIVATE_IP:-}"; do
-    if [[ -n "$worker_ip" ]]; then
-        WORKER_LOG_NODES+=("$worker_ip")
-    fi
-done
-
-mkdir -p "$OUT_DIR"
-echo "Experiment name: $EXPERIMENT_NAME"
-echo "Remote run dir: $REMOTE_RUN_DIR"
-echo "Remote test_qwen log: $REMOTE_LOG"
-echo "Local fetched log: $LOCAL_CLUSTER_LOG"
-echo "Local Ray worker log bundle: $LOCAL_RAY_LOG_BUNDLE_DIR"
-
-snapshot_ray_worker_logs "head" "" "$HEAD_SNAPSHOT"
-for worker_idx in "${!WORKER_LOG_NODES[@]}"; do
-    snapshot_ray_worker_logs "worker" "${WORKER_LOG_NODES[$worker_idx]}" "$SNAPSHOT_DIR/worker$((worker_idx + 1)).snapshot"
-done
-
-set +e
-run_head_ssh \
-  "docker exec -e RAY_DEDUP_LOGS=0 -w /tmp/piper -e PYTHONPATH=/tmp/piper piper_ray \
-   bash -lc 'mkdir -p $REMOTE_OUTPUT_DIR $REMOTE_RUN_DIR && python3 -m test.test_qwen \
-     --model $MODEL \
-     --schedule $SCHEDULE \
-     --pp $PP \
-     --dp $DP \
-     $( $EP && echo --ep ) \
-     $ZERO_ARG \
-     $BUCKET_SIZE_ARG \
-     --batch-size $BATCH_SIZE \
-     --seq-len $SEQ_LEN \
-     --mbs $MBS \
-     $GRADIENT_ACCUMULATION_ARG \
-     $AR_A2A_SAME_STREAM_ARG \
-     $OVERLAP_ZERO_OPS_ARG \
-     $OVERLAP_CHUNKS_ARG \
-     $PP_OUTER_ARG \
-     --warmup $WARMUP \
-     --iters $ITERS \
-     --address $HEAD_PRIVATE_IP \
-     --port $RAY_PORT \
-     --output-dir $REMOTE_OUTPUT_DIR \
-     $USE_INDUCTOR_ARG \
-     $NSIGHT_ARG \
-     $MEMORY_PROFILE_ARG \
-     --temp-dir /tmp/piper/ray_tmp > $REMOTE_LOG 2>&1'"
-RUN_STATUS=$?
-
-run_head_ssh "docker exec piper_ray bash -lc 'test -f $REMOTE_LOG && cat $REMOTE_LOG'" > "$LOCAL_CLUSTER_LOG"
-FETCH_STATUS=$?
-
-bundle_ray_worker_logs "head" "head" "" "$HEAD_SNAPSHOT" "$LOCAL_RAY_LOG_BUNDLE_DIR"
-for worker_idx in "${!WORKER_LOG_NODES[@]}"; do
-    bundle_ray_worker_logs \
-        "worker$((worker_idx + 1))" \
-        "worker" \
-        "${WORKER_LOG_NODES[$worker_idx]}" \
-        "$SNAPSHOT_DIR/worker$((worker_idx + 1)).snapshot" \
-        "$LOCAL_RAY_LOG_BUNDLE_DIR"
-done
-set -e
-
-rm -rf "$SNAPSHOT_DIR"
-
-if [[ $FETCH_STATUS -ne 0 ]]; then
-    echo "Failed to fetch remote log from $REMOTE_LOG" >&2
-    exit $RUN_STATUS
-fi
-
-cat "$LOCAL_CLUSTER_LOG"
-exit $RUN_STATUS
diff --git a/scripts/run_ag_rs_bucket_benchmark.sh b/scripts/run_ag_rs_bucket_benchmark.sh
deleted file mode 100644
index a02da16..0000000
--- a/scripts/run_ag_rs_bucket_benchmark.sh
+++ /dev/null
@@ -1,77 +0,0 @@
-#!/usr/bin/env bash
-# Run the AR vs RS bucket microbenchmark across 2 nodes × 1 GPU.
-#
-# Requires:
-#   SSH_KEY, HEAD_PUBLIC_IP, HEAD_PRIVATE_IP, and either WORKER1_PRIVATE_IP or WORKER_PRIVATE_IP
-#
-# Usage:
-#   ./scripts/run_ag_rs_bucket_benchmark.sh [--warmup N] [--iters N] [--dtype DTYPE]
-
-set -euo pipefail
-
-: "${SSH_KEY:?SSH_KEY must be set}"
-: "${HEAD_PUBLIC_IP:?HEAD_PUBLIC_IP must be set}"
-: "${HEAD_PRIVATE_IP:?HEAD_PRIVATE_IP must be set}"
-
-WORKER_IP="${WORKER1_PRIVATE_IP:-${WORKER_PRIVATE_IP:-}}"
-: "${WORKER_IP:?WORKER1_PRIVATE_IP or WORKER_PRIVATE_IP must be set}"
-
-WARMUP=10
-ITERS=50
-DTYPE=bfloat16
-RDZV_PORT="${RDZV_PORT:-29500}"
-
-while [[ $# -gt 0 ]]; do
-    case "$1" in
-        --warmup) WARMUP="$2"; shift 2 ;;
-        --iters) ITERS="$2"; shift 2 ;;
-        --dtype) DTYPE="$2"; shift 2 ;;
-        *) echo "Unknown argument: $1" >&2; exit 1 ;;
-    esac
-done
-
-SSH_CFG=$(mktemp /tmp/ag-rs-ssh-cfg-XXXXXX)
-trap 'rm -f "$SSH_CFG"' EXIT
-cat > "$SSH_CFG" <<EOF
-Host head
-  HostName $HEAD_PUBLIC_IP
-  User ubuntu
-  IdentityFile $SSH_KEY
-  StrictHostKeyChecking no
-
-Host worker
-  HostName $WORKER_IP
-  User ubuntu
-  IdentityFile $SSH_KEY
-  StrictHostKeyChecking no
-  ProxyCommand ssh -F $SSH_CFG -W %h:%p head
-EOF
-
-ssh_head()   { ssh -F "$SSH_CFG" head "$@"; }
-ssh_worker() { ssh -F "$SSH_CFG" worker "$@"; }
-
-BASE_ENV="-e NCCL_SOCKET_IFNAME=ens32 -e GLOO_SOCKET_IFNAME=ens32"
-BENCH_CMD="docker exec $BASE_ENV -w /tmp/piper piper_ray bash -lc 'torchrun --nnodes=2 --nproc_per_node=1 --node_rank=%NODE_RANK% --master_addr=$HEAD_PRIVATE_IP --master_port=$RDZV_PORT test/allgather_reduce_scatter_bucket_bench.py --warmup $WARMUP --iters $ITERS --dtype $DTYPE'"
-
-tmpout=$(mktemp)
-trap 'rm -f "$SSH_CFG" "$tmpout"' EXIT
-
-echo "Starting AR/RS bucket benchmark on head=$HEAD_PUBLIC_IP worker=$WORKER_IP port=$RDZV_PORT"
-
-ssh_head "${BENCH_CMD//%NODE_RANK%/0}" 2>&1 | tee "$tmpout" &
-HEAD_PID=$!
-
-ssh_worker "${BENCH_CMD//%NODE_RANK%/1}" >/dev/null 2>&1 &
-WORKER_PID=$!
-
-set +e
-wait "$HEAD_PID"
-HEAD_STATUS=$?
-wait "$WORKER_PID"
-WORKER_STATUS=$?
-set -e
-
-if [[ $HEAD_STATUS -ne 0 || $WORKER_STATUS -ne 0 ]]; then
-    echo "Benchmark failed: head_status=$HEAD_STATUS worker_status=$WORKER_STATUS" >&2
-    exit 1
-fi
diff --git a/scripts/run_local.sh b/scripts/run_local.sh
deleted file mode 100755
index 3711777..0000000
--- a/scripts/run_local.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/bin/bash
-# Run piper inside Docker locally. Usage:
-#   ./run_local.sh test.test_env
-#   ./run_local.sh test.test_mixtral --model tiny --schedule 1f1b --pp 2 --dp 1 --mbs 4
-
-set -e
-
-MODULE="$1"
-shift
-
-docker run --rm \
-    --gpus all \
-    --shm-size=10g \
-    --network host \
-    -v "$(pwd)":/tmp/piper \
-    piper:latest \
-    bash -c "cd /tmp/piper && ray start --head --num-cpus=$(nproc) --num-gpus=$(nvidia-smi -L | wc -l) && python -m $MODULE $*"
diff --git a/scripts/run_nccl_benchmark.sh b/scripts/run_nccl_benchmark.sh
deleted file mode 100755
index 4319f45..0000000
--- a/scripts/run_nccl_benchmark.sh
+++ /dev/null
@@ -1,168 +0,0 @@
-#!/usr/bin/env bash
-# Run NCCL benchmark combinations and print a summary table.
-#
-# EFA experiments:    4 GPU configs × 2 NIC settings = 8 experiments
-# Socket experiments: 4 GPU configs × 1              = 4 experiments
-#   GPU counts:  4 (within-node), 8 (within-node), 12 (cross-node), 16 (cross-node)
-#   EFA NICs:    FI_EFA_NUM_NETWORK_CARDS = 1, 4  (ignored for socket)
-#
-# Requires these env vars (set from aws-manual-setup.md Variables section):
-#   SSH_KEY, HEAD_PUBLIC_IP, WORKER_PRIVATE_IP, HEAD_PRIVATE_IP
-#
-# Run from local machine:
-#   ./scripts/run_nccl_benchmark.sh
-
-set -euo pipefail
-
-: "${SSH_KEY:?SSH_KEY must be set}"
-: "${HEAD_PUBLIC_IP:?HEAD_PUBLIC_IP must be set}"
-: "${WORKER_PRIVATE_IP:?WORKER_PRIVATE_IP must be set}"
-: "${HEAD_PRIVATE_IP:?HEAD_PRIVATE_IP must be set}"
-
-RDZV_PORT=29500
-LOGFILE=$(mktemp /tmp/nccl_benchmark_XXXXXX.log)
-echo "Raw output → $LOGFILE"
-echo ""
-
-# Write an SSH config so ProxyCommand quoting is clean
-SSH_CFG=$(mktemp /tmp/ssh-cfg-XXXXXX)
-trap "rm -f $SSH_CFG" EXIT
-cat > "$SSH_CFG" <<EOF
-Host head
-  HostName $HEAD_PUBLIC_IP
-  User ubuntu
-  IdentityFile $SSH_KEY
-  StrictHostKeyChecking no
-
-Host worker
-  HostName $WORKER_PRIVATE_IP
-  User ubuntu
-  IdentityFile $SSH_KEY
-  StrictHostKeyChecking no
-  ProxyCommand ssh -F $SSH_CFG -W %h:%p head
-EOF
-
-ssh_head()   { ssh -F "$SSH_CFG" head "$@"; }
-ssh_worker() { ssh -F "$SSH_CFG" worker "$@"; }
-
-# Results: indexed by [gpus,efa_cards,transport] → "a2a_algbw a2a_busbw ar_algbw ar_busbw"
-declare -A RESULTS
-
-BASE_ENV="-e NCCL_SOCKET_IFNAME=ens32 -e GLOO_SOCKET_IFNAME=ens32 -e NCCL_PROTO=simple"
-
-# Build extra env vars for a given transport and NIC count.
-# transport: "efa" or "socket"
-# efa: NIC count (ignored for socket)
-extra_env() {
-    local transport=$1 efa=$2
-    if [[ $transport == socket ]]; then
-        echo "-e NCCL_NET=Socket"
-    else
-        echo "-e FI_EFA_NUM_NETWORK_CARDS=$efa"
-    fi
-}
-
-run_single_node() {
-    local gpus=$1 efa=$2 transport=$3
-    local nic_label; [[ $transport == socket ]] && nic_label="-" || nic_label="$efa"
-    printf "  %-16s %-7s NICs=%-2s  running..." "within-node ${gpus}G" "[$transport]" "$nic_label"
-    out=$(ssh_head "docker exec \
-        $BASE_ENV $(extra_env $transport $efa) \
-        -w /tmp/piper piper_ray \
-        torchrun --nproc_per_node=$gpus scripts/nccl_benchmark.py" 2>&1)
-    echo "$out" >> "$LOGFILE"
-    echo "  done"
-    parse_results "$out" "$gpus" "$efa" "$transport"
-}
-
-run_multi_node() {
-    local gpus_per_node=$1 efa=$2 transport=$3
-    local total=$((gpus_per_node * 2))
-    local nic_label; [[ $transport == socket ]] && nic_label="-" || nic_label="$efa"
-    printf "  %-16s %-7s NICs=%-2s  running..." "cross-node ${total}G" "[$transport]" "$nic_label"
-
-    local tmpout
-    tmpout=$(mktemp)
-    ssh_head "docker exec \
-        $BASE_ENV $(extra_env $transport $efa) \
-        -w /tmp/piper piper_ray \
-        torchrun --nnodes=2 --nproc_per_node=$gpus_per_node \
-        --node_rank=0 \
-        --master_addr=$HEAD_PRIVATE_IP --master_port=$RDZV_PORT \
-        scripts/nccl_benchmark.py" > "$tmpout" 2>&1 &
-    HEAD_PID=$!
-
-    ssh_worker "docker exec \
-        $BASE_ENV $(extra_env $transport $efa) \
-        -w /tmp/piper piper_ray \
-        torchrun --nnodes=2 --nproc_per_node=$gpus_per_node \
-        --node_rank=1 \
-        --master_addr=$HEAD_PRIVATE_IP --master_port=$RDZV_PORT \
-        scripts/nccl_benchmark.py" >/dev/null 2>&1 &
-    WORKER_PID=$!
-
-    wait $HEAD_PID
-    out=$(cat "$tmpout")
-    rm -f "$tmpout"
-    echo "$out" >> "$LOGFILE"
-    echo "  done"
-    wait $WORKER_PID
-
-    parse_results "$out" "$total" "$efa" "$transport"
-    RDZV_PORT=$((RDZV_PORT + 1))
-}
-
-parse_results() {
-    local out=$1 gpus=$2 efa=$3 transport=$4
-    local a2a_time a2a_bw ar_time ar_bw
-    a2a_time=$(echo "$out" | grep -A6 "All-to-All" | grep "time/iter" | awk '{print $2}')
-    a2a_bw=$(  echo "$out" | grep -A6 "All-to-All" | grep "bandwidth" | awk '{print $2}')
-    ar_time=$( echo "$out" | grep -A6 "All-Reduce"  | grep "time/iter" | awk '{print $2}')
-    ar_bw=$(   echo "$out" | grep -A6 "All-Reduce"  | grep "bandwidth" | awk '{print $2}')
-    RESULTS["${gpus},${efa},${transport}"]="${a2a_time:-N/A} ${a2a_bw:-N/A} ${ar_time:-N/A} ${ar_bw:-N/A}"
-}
-
-# ── Run all experiments ───────────────────────────────────────────────────────
-
-echo "── EFA ──────────────────────────────────────────────────────────────────"
-for efa in 1 4; do
-    run_single_node  4  $efa  efa
-    run_single_node  8  $efa  efa
-    run_multi_node   6  $efa  efa   # 12 GPUs total
-    run_multi_node   8  $efa  efa   # 16 GPUs total
-done
-
-echo ""
-echo "── Socket ───────────────────────────────────────────────────────────────"
-run_single_node  4  1  socket
-run_single_node  8  1  socket
-run_multi_node   6  1  socket   # 12 GPUs total
-run_multi_node   8  1  socket   # 16 GPUs total
-
-# ── Summary table ─────────────────────────────────────────────────────────────
-
-echo ""
-echo "═══════════════════════════════════════════════════════════════════════════════"
-echo "  NCCL Benchmark Summary (GB/s)"
-echo "═══════════════════════════════════════════════════════════════════════════════"
-printf "  %-18s  %-7s  %-5s  %-22s  %-22s\n" \
-    "config" "net" "NICs" "All-to-All (ms / GB/s)" "All-Reduce (ms / GB/s)"
-echo "  ──────────────────  ───────  ─────  ──────────────────────  ──────────────────────"
-
-for gpus in 4 8 12 16; do
-    if   [[ $gpus -le 8 ]]; then label="within-node ${gpus}G"
-    else                         label="cross-node  ${gpus}G"
-    fi
-    for efa in 1 4; do
-        key="${gpus},${efa},efa"
-        read -r a2a_time a2a_bw ar_time ar_bw <<< "${RESULTS[$key]:-N/A N/A N/A N/A}"
-        printf "  %-18s  %-7s  %-5s  %-10s %-10s  %-10s %-10s\n" \
-            "$label" "efa" "$efa" "$a2a_time" "$a2a_bw" "$ar_time" "$ar_bw"
-    done
-    key="${gpus},1,socket"
-    read -r a2a_time a2a_bw ar_time ar_bw <<< "${RESULTS[$key]:-N/A N/A N/A N/A}"
-    printf "  %-18s  %-7s  %-5s  %-10s %-10s  %-10s %-10s\n" \
-        "$label" "socket" "-" "$a2a_time" "$a2a_bw" "$ar_time" "$ar_bw"
-done
-echo "═══════════════════════════════════════════════════════════════════════════════"
-echo "  Full output: $LOGFILE"
diff --git a/scripts/run_qwen_e2e_eval.py b/scripts/run_qwen_e2e_eval.py
deleted file mode 100644
index 5c5c632..0000000
--- a/scripts/run_qwen_e2e_eval.py
+++ /dev/null
@@ -1,1634 +0,0 @@
-#!/usr/bin/env python3
-"""Run three end-to-end Qwen EC2 eval sweeps and generate plots/CSVs.
-
-This script wraps scripts/run-qwen-ec2.sh, stores per-experiment logs under
-e2e-eval/, copies the matching metrics file out of the remote piper_ray
-container, aggregates throughput statistics, and saves one CSV and one plot for
-each requested sweep.
-
-Defaults that were not fully specified in the request are made explicit here:
-  - model: 9B
-  - default batch size for non-ZeRO sweeps: 4
-  - seq len: 512
-  - warmup / iters: 3 / 10
-  - scalability sweep PP->MBS mapping: 4->8, 8->16
-  - base schedule for scalability and ZeRO sweeps: 1f1b
-  - schedule sweep ZeRO stage: 0
-  - schedule sweep MBS: 16
-  - ZeRO sweep batch sizes: 4, 8, 16
-  - ZeRO sweep num microbatches: 2 * pp
-"""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-import math
-import os
-import re
-import shutil
-import subprocess
-import sys
-from dataclasses import dataclass
-from pathlib import Path
-from datetime import datetime, timezone
-from typing import Iterable
-
-
-OOM_PATTERNS = (
-    "out of memory",
-    "torch.cuda.outofmemoryerror",
-    "cuda out of memory",
-    "oom-killed",
-    "std::bad_alloc",
-    "failed to cuda calloc async",
-)
-
-
-@dataclass(frozen=True)
-class Experiment:
-    sweep: str
-    schedule: str
-    pp: int
-    dp: int
-    ep: bool
-    zero_stage: int
-    bucket_size_mb: float | None
-    batch_size: int
-    seq_len: int
-    mbs: int
-    gradient_accumulation: bool
-    ar_a2a_same_stream: bool
-    overlap_zero_ops: bool
-    overlap_chunks: bool
-    nsight: bool
-
-    def slug(self) -> str:
-        bucket_part = (
-            f"-bucket{_format_bucket_size(self.bucket_size_mb)}"
-            if self.bucket_size_mb is not None
-            else ""
-        )
-        nsight_part = "-nsight1" if self.nsight else ""
-        return (
-            f"{self.sweep}-sched_{self.schedule}-pp{self.pp}-dp{self.dp}-"
-            f"ep{int(self.ep)}-zero{self.zero_stage}{bucket_part}{nsight_part}-bs{self.batch_size}-sl{self.seq_len}-"
-            f"mbs{self.mbs}-ga{int(self.gradient_accumulation)}-"
-            f"aras{int(self.ar_a2a_same_stream)}-ozo{int(self.overlap_zero_ops)}-"
-            f"och{int(self.overlap_chunks)}"
-        )
-
-    def label(self) -> str:
-        if self.sweep == "scalability":
-            return f"(pp={self.pp}, dp={self.dp})"
-        if self.sweep == "zero":
-            ga = "on" if self.gradient_accumulation else "off"
-            return f"(zero={self.zero_stage}, ga={ga}, batch_size={self.batch_size})"
-        return self.schedule
-
-    def key(self) -> tuple[object, ...]:
-        return (
-            self.sweep,
-            self.schedule,
-            self.pp,
-            self.dp,
-            int(self.ep),
-            self.zero_stage,
-            self.bucket_size_mb,
-            self.batch_size,
-            self.seq_len,
-            self.mbs,
-            int(self.gradient_accumulation),
-            int(self.ar_a2a_same_stream),
-            int(self.overlap_zero_ops),
-            int(self.overlap_chunks),
-            int(self.nsight),
-        )
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Run three EC2 Qwen end-to-end sweeps and save plots/CSVs."
-    )
-    parser.add_argument("--model", default="1B")
-    parser.add_argument("--pp", type=int, default=8, help="Default PP degree for non-scalability sweeps.")
-    parser.add_argument("--dp", type=int, default=1, help="Default DP degree for non-scalability sweeps.")
-    parser.add_argument("--batch-size", type=int, default=4)
-    parser.add_argument(
-        "--bucket-size",
-        type=float,
-        default=None,
-        help="Bucket size in MB for scalability and ZeRO sweeps. Default: unset (no bucketing).",
-    )
-    parser.add_argument("--seq-len", type=int, default=512)
-    parser.add_argument("--warmup", type=int, default=3)
-    parser.add_argument("--iters", type=int, default=10)
-    parser.add_argument("--ray-port", type=int, default=6379)
-    parser.add_argument(
-        "--remote-output-dir",
-        default="/tmp/piper/out",
-        help="Directory inside the remote piper_ray container where test_qwen writes outputs.",
-    )
-    parser.add_argument(
-        "--out-dir",
-        default="e2e-eval",
-        help="Base directory where timestamped eval run folders are created.",
-    )
-    parser.add_argument(
-        "--run-name",
-        help="Optional run directory name under --out-dir. Defaults to a UTC timestamp.",
-    )
-    parser.add_argument("--runner", default="scripts/run-qwen-ec2.sh")
-    parser.add_argument("--base-schedule", default="1f1b")
-    parser.add_argument(
-        "--scalability-pp-mbs",
-        nargs="+",
-        default=["4:8", "8:16"],
-        metavar="PP:MBS",
-        help="PP->MBS mapping for the scalability sweep.",
-    )
-    parser.add_argument(
-        "--zero-stages",
-        nargs="+",
-        type=int,
-        default=[1,2,3],
-    )
-    parser.add_argument(
-        "--zero-batch-sizes",
-        nargs="+",
-        type=int,
-        default=[16,32,64],
-    )
-    parser.add_argument(
-        "--zero-grad-acc-settings",
-        nargs="+",
-        choices=["on", "off"],
-        default=["on"],
-        help="Gradient accumulation settings to include in the zero sweep. "
-             "Examples: --zero-grad-acc-settings on | off | off on",
-    )
-    parser.add_argument(
-        "--schedule-values",
-        nargs="+",
-        default=["1f1b", "interleaved-1f1b", "interleaved-zerobubble", "dualpipev"],
-    )
-    parser.add_argument("--schedule-mbs", type=int, default=16)
-    parser.add_argument("--schedule-zero-stage", type=int, default=0)
-    parser.add_argument(
-        "--nsight",
-        action="store_true",
-        help="Run each experiment with Nsight enabled and fetch matching Nsight artifacts.",
-    )
-    parser.add_argument(
-        "--use-inductor",
-        dest="use_inductor",
-        action=argparse.BooleanOptionalAction,
-        default=None,
-        help="Override whether to pass --use-inductor through to test_qwen. "
-             "By default this is disabled for schedule sweeps and enabled for zero/scalability sweeps.",
-    )
-    parser.add_argument(
-        "--gradient-accumulation",
-        dest="gradient_accumulation",
-        action=argparse.BooleanOptionalAction,
-        default=True,
-        help="Whether to pass gradient accumulation through to test_qwen (default: true).",
-    )
-    parser.add_argument(
-        "--overlap-chunks",
-        dest="overlap_chunks",
-        action=argparse.BooleanOptionalAction,
-        default=False,
-        help="Whether to pass overlap_chunks through to test_qwen (default: false).",
-    )
-    parser.add_argument(
-        "--no-udse-inductor",
-        dest="use_inductor",
-        action="store_false",
-        help=argparse.SUPPRESS,
-    )
-    parser.add_argument(
-        "--no-resume",
-        action="store_true",
-        help="Do not reuse completed rows from an existing all_results.csv.",
-    )
-    parser.add_argument(
-        "--sweeps",
-        nargs="+",
-        choices=["scalability", "zero", "schedule"],
-        default=["scalability", "zero", "schedule"],
-        help="Subset of e2e sweeps to run. Default: scalability zero schedule",
-    )
-    parser.add_argument(
-        "--memory-profile",
-        action="store_true",
-        help="Pass --memory-profile through to run-qwen-ec2.sh / test_qwen.",
-    )
-    parser.add_argument(
-        "--memory-profile-dir",
-        default=None,
-        help="Optional directory on the remote for pickles; forwarded if set.",
-    )
-
-    parser.add_argument("--dry-run", action="store_true")
-    return parser.parse_args()
-
-
-def _format_bucket_size(bucket_size_mb: float | None) -> str:
-    if bucket_size_mb is None:
-        return "none"
-    return f"{bucket_size_mb:g}"
-
-
-def _parse_pp_mbs(values: list[str]) -> list[tuple[int, int]]:
-    parsed: list[tuple[int, int]] = []
-    for value in values:
-        pp_text, mbs_text = value.split(":", maxsplit=1)
-        parsed.append((int(pp_text), int(mbs_text)))
-    return parsed
-
-
-def _metrics_name(model: str, exp: Experiment) -> str:
-    return (
-        f"qwen{model}-pp{exp.pp}-dp{exp.dp}-zero{exp.zero_stage}-"
-        f"ep{int(exp.ep)}-"
-        f"bs{exp.batch_size}-sl{exp.seq_len}-ga{int(exp.gradient_accumulation)}-"
-        f"aras{int(exp.ar_a2a_same_stream)}-ozo{int(exp.overlap_zero_ops)}-"
-        f"och{int(exp.overlap_chunks)}-"
-        f"{exp.schedule}"
-    )
-
-
-def _runner_experiment_name(model: str, exp: Experiment) -> str:
-    bucket_part = (
-        f"-bucket{_format_bucket_size(exp.bucket_size_mb)}"
-        if exp.bucket_size_mb is not None
-        else ""
-    )
-    nsight_part = "-nsight1" if exp.nsight else ""
-    return (
-        f"qwen{model}-sched_{exp.schedule}-pp{exp.pp}-dp{exp.dp}-"
-        f"ep{int(exp.ep)}-zero{exp.zero_stage}{bucket_part}{nsight_part}-"
-        f"bs{exp.batch_size}-sl{exp.seq_len}-mbs{exp.mbs}-"
-        f"ga{int(exp.gradient_accumulation)}-aras{int(exp.ar_a2a_same_stream)}-"
-        f"ozo{int(exp.overlap_zero_ops)}-och{int(exp.overlap_chunks)}"
-    )
-
-
-def _remote_experiment_output_dir(args: argparse.Namespace, exp: Experiment) -> str:
-    return f"{args.remote_output_dir.rstrip('/')}/{exp.slug()}"
-
-
-def _metrics_remote_paths(
-    model: str,
-    exp: Experiment,
-    remote_output_dir: str,
-) -> list[str]:
-    return [
-        f"{remote_output_dir}/{_metrics_name(model, exp)}",
-        (
-            f"{remote_output_dir}/qwen{model}-pp{exp.pp}-dp{exp.dp}-zero{exp.zero_stage}-"
-            f"bs{exp.batch_size}-sl{exp.seq_len}-ga{int(exp.gradient_accumulation)}-"
-            f"aras{int(exp.ar_a2a_same_stream)}-ozo{int(exp.overlap_zero_ops)}-"
-            f"och{int(exp.overlap_chunks)}-"
-            f"{exp.schedule}"
-        ),
-    ]
-
-
-def _cluster_log_candidates(
-    model: str,
-    exp: Experiment,
-    fetch_dir: Path,
-) -> list[Path]:
-    pattern = f"{_runner_experiment_name(model, exp)}_*.cluster.log"
-    return sorted(fetch_dir.glob(pattern))
-
-
-def _runner_command(args: argparse.Namespace, exp: Experiment, fetch_dir: Path) -> list[str]:
-    remote_output_dir = _remote_experiment_output_dir(args, exp)
-    command = [
-        str(Path(args.runner).resolve()),
-        "--model", args.model,
-        "--schedule", exp.schedule,
-        "--pp", str(exp.pp),
-        "--dp", str(exp.dp),
-        "--zero-stage", str(exp.zero_stage),
-        "--batch-size", str(exp.batch_size),
-        "--seq-len", str(exp.seq_len),
-        "--mbs", str(exp.mbs),
-        "--gradient-accumulation" if exp.gradient_accumulation else "--no-gradient-accumulation",
-        "--ar-a2a-same-stream" if exp.ar_a2a_same_stream else "--no-ar-a2a-same-stream",
-        "--overlap-zero-ops" if exp.overlap_zero_ops else "--no-overlap-zero-ops",
-        "--overlap-chunks" if exp.overlap_chunks else "--no-overlap-chunks",
-        "--warmup", str(args.warmup),
-        "--iters", str(args.iters),
-        "--ray-port", str(args.ray_port),
-        "--out-dir", str(fetch_dir),
-        "--remote-output-dir", remote_output_dir,
-    ]
-    if exp.bucket_size_mb is not None:
-        command.extend(["--bucket-size", _format_bucket_size(exp.bucket_size_mb)])
-    if exp.ep:
-        command.append("--ep")
-    if exp.nsight:
-        command.append("--nsight")
-    use_inductor = args.use_inductor
-    if use_inductor is None:
-        use_inductor = exp.sweep != "schedule"
-    if use_inductor:
-        command.append("--use-inductor")
-    else:
-        command.append("--no-use-inductor")
-    
-    if args.memory_profile:
-        command.append("--memory-profile")
-        mem_dir = args.memory_profile_dir or f"{remote_output_dir}/memory_snapshots"
-        command.extend(["--memory-profile-dir", mem_dir])
-
-    return command
-
-
-def _ssh_head_command(remote_command: str) -> list[str]:
-    ssh_key = os.environ["SSH_KEY"]
-    head_public_ip = os.environ["HEAD_PUBLIC_IP"]
-    return [
-        "ssh",
-        "-i",
-        ssh_key,
-        "-o",
-        "StrictHostKeyChecking=no",
-        f"ubuntu@{head_public_ip}",
-        remote_command,
-    ]
-
-
-def _ssh_worker_command(worker_ip: str, remote_command: str) -> list[str]:
-    ssh_key = os.environ["SSH_KEY"]
-    head_public_ip = os.environ["HEAD_PUBLIC_IP"]
-    return [
-        "ssh",
-        "-i",
-        ssh_key,
-        "-o",
-        "StrictHostKeyChecking=no",
-        "-o",
-        (
-            "ProxyCommand="
-            f"ssh -i {ssh_key} -o StrictHostKeyChecking=no -W %h:%p ubuntu@{head_public_ip}"
-        ),
-        f"ubuntu@{worker_ip}",
-        remote_command,
-    ]
-
-
-def _fetch_remote_metrics(
-    model: str,
-    exp: Experiment,
-    destination: Path,
-    remote_output_dir: str,
-    remote_metrics_path: str | None = None,
-) -> bool:
-    remote_paths: list[str] = []
-    if remote_metrics_path:
-        remote_paths.append(remote_metrics_path)
-    remote_paths.extend(_metrics_remote_paths(model, exp, remote_output_dir))
-
-    seen: set[str] = set()
-    for remote_path in remote_paths:
-        if remote_path in seen:
-            continue
-        seen.add(remote_path)
-        remote_command = (
-            "docker exec piper_ray bash -lc "
-            + json.dumps(f"test -f {remote_path} && cat {remote_path}")
-        )
-        result = subprocess.run(
-            _ssh_head_command(remote_command),
-            check=False,
-            capture_output=True,
-            text=True,
-        )
-        if result.returncode != 0 or not result.stdout.strip():
-            continue
-
-        destination.parent.mkdir(parents=True, exist_ok=True)
-        destination.write_text(result.stdout, encoding="utf-8")
-        return True
-
-    return False
-
-
-def _experiment_fetch_dir(fetch_dir: Path, exp: Experiment) -> Path:
-    return fetch_dir / exp.slug()
-
-
-def _fetched_metrics_path(fetch_dir: Path, exp: Experiment, remote_metrics_path: str | None) -> Path:
-    if remote_metrics_path:
-        return _experiment_fetch_dir(fetch_dir, exp) / Path(remote_metrics_path).name
-    return _experiment_fetch_dir(fetch_dir, exp) / f"{exp.slug()}.metrics"
-
-
-def _copy_local_metrics(source: Path, destination: Path) -> bool:
-    if not source.is_file():
-        return False
-    destination.parent.mkdir(parents=True, exist_ok=True)
-    shutil.copyfile(source, destination)
-    return True
-
-
-def _fetch_remote_dag_order_logs(
-    args: argparse.Namespace,
-    exp: Experiment,
-    exp_logs_dir: Path,
-) -> list[Path]:
-    copied_paths: list[Path] = []
-    remote_base = _remote_experiment_output_dir(args, exp)
-    for rank in range(exp.pp):
-        remote_path = f"{remote_base}/dag_order_rank{rank}"
-        result = subprocess.run(
-            _ssh_head_command(
-                "docker exec piper_ray bash -lc "
-                + json.dumps(f"test -f {remote_path} && cat {remote_path}")
-            ),
-            check=False,
-            capture_output=True,
-            text=True,
-        )
-        if result.returncode != 0 or not result.stdout:
-            continue
-
-        destination = exp_logs_dir / f"dag-order-rank{rank}.log"
-        destination.write_text(result.stdout, encoding="utf-8")
-        copied_paths.append(destination)
-
-    return copied_paths
-
-def _fetch_remote_memory_snapshots(
-    args: argparse.Namespace,
-    exp: Experiment,
-    destination: Path,
-    log_path: Path,
-) -> bool:
-    """Copy memory_snapshots/ from piper_ray on every node (head + workers) into destination."""
-    if not args.memory_profile:
-        return False
-
-    remote_snap = f"{_remote_experiment_output_dir(args, exp)}/memory_snapshots"
-    destination.mkdir(parents=True, exist_ok=True)
-
-    any_ok = False
-    for node_label, node_kind, remote_host in _iter_remote_nodes(log_path):
-        stage = f"/tmp/piper_mem_stage_{exp.slug().replace('/', '_')}_{node_label}"
-        remote_command = (
-            f"rm -rf {stage} && mkdir -p {stage} && "
-            f"docker cp piper_ray:{remote_snap} {stage}/ 2>/dev/null || exit 0"
-        )
-        ssh_cmd = (
-            _ssh_head_command(remote_command)
-            if node_kind == "head"
-            else _ssh_worker_command(str(remote_host), remote_command)
-        )
-        subprocess.run(ssh_cmd, check=False, capture_output=True, text=True)
-
-        ok = _scp_from_remote(
-            node_kind=node_kind,
-            remote_host=remote_host,
-            remote_path=f"{stage}/memory_snapshots/.",
-            destination=destination,
-        )
-        any_ok = any_ok or ok
-
-        cleanup_cmd = (
-            _ssh_head_command(f"rm -rf {stage}")
-            if node_kind == "head"
-            else _ssh_worker_command(str(remote_host), f"rm -rf {stage}")
-        )
-        subprocess.run(cleanup_cmd, check=False, capture_output=True, text=True)
-
-    return any_ok
-
-def _strip_ansi(text: str) -> str:
-    return re.sub(r"\x1b\[[0-9;]*m", "", text)
-
-
-def _read_text_lossy(path: Path) -> str:
-    return path.read_text(encoding="utf-8", errors="replace")
-
-
-def _parse_head_private_ip(log_path: Path) -> str | None:
-    for line in _read_text_lossy(log_path).splitlines():
-        clean = _strip_ansi(line)
-        match = re.search(r"Namespace\(.*address='([^']+)'", clean)
-        if match:
-            return match.group(1)
-    return None
-
-
-def _node_ip_map_from_env(log_path: Path) -> dict[str, str]:
-    mapping: dict[str, str] = {}
-    head_private_ip = os.environ.get("HEAD_PRIVATE_IP") or _parse_head_private_ip(log_path)
-    if head_private_ip:
-        mapping[head_private_ip] = "head"
-    for idx in range(1, 4):
-        worker_ip = os.environ.get(f"WORKER{idx}_PRIVATE_IP")
-        if worker_ip:
-            mapping[worker_ip] = f"worker{idx}"
-    return mapping
-
-
-def _extract_experiment_pids_by_node(log_path: Path) -> dict[str, set[str]]:
-    pid_pattern = re.compile(r"pid=(\d+)(?:, ip=([0-9.]+))?")
-    ip_to_label = _node_ip_map_from_env(log_path)
-    pids_by_node: dict[str, set[str]] = {}
-
-    for line in _read_text_lossy(log_path).splitlines():
-        clean = _strip_ansi(line)
-        for pid, ip in pid_pattern.findall(clean):
-            if ip:
-                node_label = ip_to_label.get(ip, f"node-{ip}")
-            else:
-                node_label = "head"
-            pids_by_node.setdefault(node_label, set()).add(pid)
-
-    return pids_by_node
-
-
-def _iter_remote_nodes(log_path: Path) -> list[tuple[str, str, str | None]]:
-    nodes: list[tuple[str, str, str | None]] = [("head", "head", None)]
-    ip_to_label = _node_ip_map_from_env(log_path)
-    for ip, label in sorted(ip_to_label.items(), key=lambda item: item[1]):
-        if label == "head":
-            continue
-        nodes.append((label, "worker", ip))
-    return nodes
-
-
-def _scp_from_remote(
-    *,
-    node_kind: str,
-    remote_host: str | None,
-    remote_path: str,
-    destination: Path,
-) -> bool:
-    ssh_key = os.environ["SSH_KEY"]
-    head_public_ip = os.environ["HEAD_PUBLIC_IP"]
-    command = [
-        "scp",
-        "-r",
-        "-i",
-        ssh_key,
-        "-o",
-        "StrictHostKeyChecking=no",
-    ]
-    if node_kind == "head":
-        command.extend([f"ubuntu@{head_public_ip}:{remote_path}", str(destination)])
-    else:
-        command.extend(
-            [
-                "-o",
-                (
-                    "ProxyCommand="
-                    f"ssh -i {ssh_key} -o StrictHostKeyChecking=no -W %h:%p ubuntu@{head_public_ip}"
-                ),
-                f"ubuntu@{remote_host}:{remote_path}",
-                str(destination),
-            ]
-        )
-    result = subprocess.run(command, check=False, capture_output=True, text=True)
-    return result.returncode == 0
-
-
-def _stage_remote_nsight_dir(
-    *,
-    node_kind: str,
-    remote_host: str | None,
-    remote_tmp_dir: str,
-    destination: Path,
-) -> bool:
-    remote_command = (
-        "rm -rf "
-        + remote_tmp_dir
-        + " && mkdir -p "
-        + remote_tmp_dir
-        + " && docker cp piper_ray:/tmp/piper/ray_tmp/session_latest/logs/nsight/. "
-        + remote_tmp_dir
-        + "/"
-    )
-    run_command = (
-        _ssh_head_command(remote_command)
-        if node_kind == "head"
-        else _ssh_worker_command(str(remote_host), remote_command)
-    )
-    result = subprocess.run(run_command, check=False, capture_output=True, text=True)
-    if result.returncode != 0:
-        return False
-    destination.parent.mkdir(parents=True, exist_ok=True)
-    return _scp_from_remote(
-        node_kind=node_kind,
-        remote_host=remote_host,
-        remote_path=f"{remote_tmp_dir}/.",
-        destination=destination,
-    )
-
-
-def _matching_nsight_paths(paths: Iterable[str], pids: set[str]) -> list[str]:
-    matches: list[str] = []
-    for path in paths:
-        basename = Path(path).name
-        if any(pid in basename or pid in path for pid in pids):
-            matches.append(path)
-    return sorted(set(matches))
-
-
-def _copy_experiment_nsight_profiles(
-    exp: Experiment,
-    log_path: Path,
-    exp_logs_dir: Path,
-) -> list[Path]:
-    if not exp.nsight:
-        return []
-
-    pids_by_node = _extract_experiment_pids_by_node(log_path)
-    if not pids_by_node:
-        return []
-
-    copied_paths: list[Path] = []
-    exp_nsight_dir = exp_logs_dir / "nsight"
-    stage_root = exp_nsight_dir / ".stage"
-    shutil.rmtree(stage_root, ignore_errors=True)
-    for node_label, node_kind, remote_host in _iter_remote_nodes(log_path):
-        node_pids = pids_by_node.get(node_label, set())
-        if not node_pids:
-            continue
-        local_stage_dir = stage_root / node_label
-        remote_tmp_dir = f"/tmp/piper-nsight-{exp_logs_dir.name}-{node_label}"
-        if not _stage_remote_nsight_dir(
-            node_kind=node_kind,
-            remote_host=remote_host,
-            remote_tmp_dir=remote_tmp_dir,
-            destination=local_stage_dir,
-        ):
-            continue
-        staged_paths = [str(path.relative_to(local_stage_dir)) for path in local_stage_dir.rglob("*.nsys-rep")]
-        for relative_path in _matching_nsight_paths(staged_paths, node_pids):
-            source = local_stage_dir / relative_path
-            destination = exp_nsight_dir / node_label / Path(relative_path).name
-            destination.parent.mkdir(parents=True, exist_ok=True)
-            shutil.copy2(source, destination)
-            copied_paths.append(destination)
-
-    pid_manifest = {
-        node_label: sorted(pids)
-        for node_label, pids in sorted(pids_by_node.items())
-        if pids
-    }
-    if pid_manifest:
-        exp_nsight_dir.mkdir(parents=True, exist_ok=True)
-        (exp_nsight_dir / "matched_pids.json").write_text(
-            json.dumps(pid_manifest, indent=2, sort_keys=True),
-            encoding="utf-8",
-        )
-
-    shutil.rmtree(stage_root, ignore_errors=True)
-
-    return copied_paths
-
-
-def _recover_metrics_from_cluster_log(
-    exp: Experiment,
-    cluster_log_path: Path,
-    destination: Path,
-    expected_iters: int,
-) -> bool:
-    if not cluster_log_path.is_file():
-        return False
-
-    records: list[dict] = []
-    marker = "metrics_json="
-    for raw_line in cluster_log_path.read_text(encoding="utf-8", errors="replace").splitlines():
-        line = _strip_ansi(raw_line)
-        if marker not in line:
-            continue
-        payload = line.split(marker, maxsplit=1)[1].strip()
-        try:
-            records.append(json.loads(payload))
-        except json.JSONDecodeError:
-            continue
-
-    if records:
-        destination.parent.mkdir(parents=True, exist_ok=True)
-        with open(destination, "w", encoding="utf-8") as handle:
-            for record in records:
-                handle.write(f"rank {record['dp_rank']} metrics_json= {json.dumps(record, sort_keys=True)}\n")
-        return True
-
-    dp_rank_by_pid: dict[str, int] = {}
-    iter_times_by_pid: dict[str, list[float]] = {}
-    peak_memory_by_pid: dict[str, dict[str, dict[str, float | int]]] = {}
-
-    for raw_line in cluster_log_path.read_text(encoding="utf-8", errors="replace").splitlines():
-        line = _strip_ansi(raw_line)
-
-        dp_rank_match = re.search(r"run_dp_rank pid=(\d+).*DP rank (\d+) done\.", line)
-        if dp_rank_match:
-            dp_rank_by_pid[dp_rank_match.group(1)] = int(dp_rank_match.group(2))
-
-        step_match = re.search(r"run_dp_rank pid=(\d+).*step_time=([0-9.]+)s", line)
-        if step_match:
-            pid = step_match.group(1)
-            iter_times_by_pid.setdefault(pid, []).append(float(step_match.group(2)))
-
-            rank_memories = {
-                rank: {
-                    "peak_memory_gb": float(value),
-                    "peak_memory_bytes": int(float(value) * (1024 ** 3)),
-                }
-                for rank, value in re.findall(r"rank(\d+)_peak_mem=([0-9.]+)GB", line)
-            }
-            if rank_memories:
-                peak_memory_by_pid[pid] = rank_memories
-
-    if not iter_times_by_pid:
-        return False
-
-    for index, pid in enumerate(sorted(iter_times_by_pid, key=int)):
-        dp_rank_by_pid.setdefault(pid, index)
-
-    records: list[dict[str, object]] = []
-    for pid, iter_times in sorted(iter_times_by_pid.items(), key=lambda item: dp_rank_by_pid[item[0]]):
-        timed_iter_times = iter_times[-expected_iters:]
-        iter_time_mean = sum(timed_iter_times) / len(timed_iter_times)
-        iter_time_variance = sum((value - iter_time_mean) ** 2 for value in timed_iter_times) / len(timed_iter_times)
-        iter_time_std = math.sqrt(iter_time_variance)
-        throughput_mean = tokens_per_iter = exp.batch_size * exp.mbs * exp.seq_len
-        throughput_mean = tokens_per_iter / iter_time_mean
-        records.append(
-            {
-                "dp_rank": dp_rank_by_pid[pid],
-                "model": exp.batch_size,
-                "schedule": exp.schedule,
-                "pp": exp.pp,
-                "dp": exp.dp,
-                "ep": exp.ep,
-                "batch_size": exp.batch_size,
-                "mbs": exp.mbs,
-                "seq_len": exp.seq_len,
-                "gradient_accumulation": exp.gradient_accumulation,
-                "ar_a2a_same_stream": exp.ar_a2a_same_stream,
-                "overlap_zero_ops": exp.overlap_zero_ops,
-                "overlap_chunks": exp.overlap_chunks,
-                "samples": len(timed_iter_times),
-                "iter_time_mean_s": iter_time_mean,
-                "iter_time_std_s": iter_time_std,
-                "throughput_tokens_per_s": throughput_mean,
-                "iter_times_s": timed_iter_times,
-                "peak_memory_by_rank": peak_memory_by_pid.get(pid, {}),
-            }
-        )
-
-    destination.parent.mkdir(parents=True, exist_ok=True)
-    with open(destination, "w", encoding="utf-8") as handle:
-        for record in records:
-            handle.write(f"rank {record['dp_rank']} metrics_json= {json.dumps(record, sort_keys=True)}\n")
-    return True
-
-
-def _recover_metrics_from_fetch_logs(
-    args: argparse.Namespace,
-    exp: Experiment,
-    fetch_dir: Path,
-    destination: Path,
-) -> bool:
-    fetched_dir = _experiment_fetch_dir(fetch_dir, exp)
-    if fetched_dir.is_dir():
-        for candidate in sorted(fetched_dir.glob("*")):
-            if _copy_local_metrics(candidate, destination) and _parse_metrics_records(destination):
-                return True
-
-    candidates = _cluster_log_candidates(args.model, exp, fetch_dir)
-    if not candidates:
-        return False
-    return _recover_metrics_from_cluster_log(exp, candidates[-1], destination, args.iters)
-
-
-def _extract_remote_metrics_path(log_path: Path) -> str | None:
-    if not log_path.is_file():
-        return None
-
-    metrics_path: str | None = None
-    for raw_line in log_path.read_text(encoding="utf-8", errors="replace").splitlines():
-        line = _strip_ansi(raw_line)
-        for pattern in (
-            r"benchmark metrics will be saved by the driver to (\S+)",
-            r"Benchmark metrics saved to (\S+)",
-        ):
-            match = re.search(pattern, line)
-            if match:
-                metrics_path = match.group(1)
-    return metrics_path
-
-
-def _extract_remote_metrics_path_from_cluster_log(cluster_log_path: Path) -> str | None:
-    return _extract_remote_metrics_path(cluster_log_path)
-
-
-def _parse_metrics_records(metrics_path: Path) -> list[dict]:
-    records: list[dict] = []
-    if not metrics_path.is_file():
-        return records
-
-    marker = "metrics_json="
-    for line in metrics_path.read_text(encoding="utf-8").splitlines():
-        if marker not in line:
-            continue
-        payload = line.split(marker, maxsplit=1)[1].strip()
-        try:
-            records.append(json.loads(payload))
-        except json.JSONDecodeError:
-            continue
-    return records
-
-
-def _row_from_metrics(
-    exp: Experiment,
-    log_path: Path,
-    metrics_path: Path,
-) -> dict[str, object] | None:
-    records = _parse_metrics_records(metrics_path)
-    if not records:
-        return None
-
-    row: dict[str, object] = {
-        "sweep": exp.sweep,
-        "label": exp.label(),
-        "schedule": exp.schedule,
-        "pp": exp.pp,
-        "dp": exp.dp,
-        "ep": int(exp.ep),
-        "zero_stage": exp.zero_stage,
-        "bucket_size_mb": _format_bucket_size(exp.bucket_size_mb) if exp.bucket_size_mb is not None else "",
-        "batch_size": exp.batch_size,
-        "seq_len": exp.seq_len,
-        "mbs": exp.mbs,
-        "gradient_accumulation": int(exp.gradient_accumulation),
-        "ar_a2a_same_stream": int(exp.ar_a2a_same_stream),
-        "overlap_zero_ops": int(exp.overlap_zero_ops),
-        "overlap_chunks": int(exp.overlap_chunks),
-        "nsight": int(exp.nsight),
-        "status": "success",
-        "failure_reason": "",
-        "log_path": str(log_path),
-        "metrics_path": str(metrics_path),
-    }
-    row.update(_aggregate_metrics(records))
-    return row
-
-
-def _aggregate_metrics(records: list[dict]) -> dict[str, float | int | str]:
-    latest_by_dp_rank: dict[int, dict] = {}
-    for record in records:
-        latest_by_dp_rank[int(record["dp_rank"])] = record
-
-    selected = [latest_by_dp_rank[rank] for rank in sorted(latest_by_dp_rank)]
-    weighted_samples = 0
-    weighted_iter_time_mean = 0.0
-    weighted_iter_time_std = 0.0
-    weighted_throughput_mean = 0.0
-    weighted_throughput_std = 0.0
-    peak_memory_gb_max = 0.0
-    peak_memory_by_rank_summary: list[str] = []
-
-    for record in selected:
-        batch_size = int(record["batch_size"])
-        mbs = int(record["mbs"])
-        seq_len = int(record["seq_len"])
-        tokens_per_iter = batch_size * mbs * seq_len
-        samples = int(record.get("samples", len(record.get("iter_times_s", []))))
-        if samples <= 0:
-            continue
-
-        iter_time_mean = record.get("iter_time_mean_s")
-        if iter_time_mean is None:
-            iter_times = [float(iter_time) for iter_time in record.get("iter_times_s", [])]
-            if not iter_times:
-                continue
-            iter_time_mean = sum(iter_times) / len(iter_times)
-        iter_time_mean = float(iter_time_mean)
-
-        iter_time_std = record.get("iter_time_std_s")
-        if iter_time_std is None:
-            iter_times = [float(iter_time) for iter_time in record.get("iter_times_s", [])]
-            if iter_times:
-                variance = sum((value - iter_time_mean) ** 2 for value in iter_times) / len(iter_times)
-                iter_time_std = math.sqrt(variance)
-            else:
-                iter_time_std = 0.0
-        iter_time_std = float(iter_time_std)
-
-        throughput_mean = record.get("throughput_tokens_per_s")
-        if throughput_mean is None:
-            throughput_mean = tokens_per_iter / iter_time_mean
-        throughput_mean = float(throughput_mean)
-
-        # Use the reported iteration-time stddev from the metrics file rather than
-        # recomputing throughput variance from raw samples.
-        throughput_std = throughput_mean * (iter_time_std / iter_time_mean) if iter_time_mean > 0 else 0.0
-
-        weighted_samples += samples
-        weighted_iter_time_mean += samples * iter_time_mean
-        weighted_iter_time_std += samples * iter_time_std
-        weighted_throughput_mean += samples * throughput_mean
-        weighted_throughput_std += samples * throughput_std
-
-        peak_memory_by_rank = record.get("peak_memory_by_rank", {})
-        for rank, stats in sorted(peak_memory_by_rank.items(), key=lambda item: int(item[0])):
-            peak_gb = float(stats["peak_memory_gb"])
-            peak_memory_gb_max = max(peak_memory_gb_max, peak_gb)
-            peak_memory_by_rank_summary.append(f"{rank}:{peak_gb:.3f}")
-
-    if weighted_samples == 0:
-        return {
-            "samples": 0,
-            "iter_time_mean_s": 0.0,
-            "iter_time_std_s": 0.0,
-            "throughput_mean_tokens_per_s": 0.0,
-            "throughput_std_tokens_per_s": 0.0,
-            "peak_memory_gb_max": peak_memory_gb_max,
-            "peak_memory_gb_by_rank": ";".join(peak_memory_by_rank_summary),
-        }
-
-    return {
-        "samples": weighted_samples,
-        "iter_time_mean_s": weighted_iter_time_mean / weighted_samples,
-        "iter_time_std_s": weighted_iter_time_std / weighted_samples,
-        "throughput_mean_tokens_per_s": weighted_throughput_mean / weighted_samples,
-        "throughput_std_tokens_per_s": weighted_throughput_std / weighted_samples,
-        "peak_memory_gb_max": peak_memory_gb_max,
-        "peak_memory_gb_by_rank": ";".join(peak_memory_by_rank_summary),
-    }
-
-
-def _looks_like_oom(log_path: Path) -> bool:
-    log_text = log_path.read_text(encoding="utf-8", errors="replace").lower()
-    return any(pattern in log_text for pattern in OOM_PATTERNS)
-
-
-def _run_experiment(
-    args: argparse.Namespace,
-    exp: Experiment,
-    index: int,
-    total: int,
-    logs_dir: Path,
-    metrics_dir: Path,
-    fetch_dir: Path,
-) -> dict[str, object]:
-    exp_logs_dir = logs_dir / exp.slug()
-    exp_logs_dir.mkdir(parents=True, exist_ok=True)
-    exp_fetch_dir = _experiment_fetch_dir(fetch_dir, exp)
-    exp_fetch_dir.mkdir(parents=True, exist_ok=True)
-    log_path = exp_logs_dir / "run.log"
-    metrics_path = metrics_dir / f"{exp.slug()}.metrics"
-    command = _runner_command(args, exp, fetch_dir)
-
-    print(
-        f"[{index}/{total}] RUNNING {exp.sweep}: schedule={exp.schedule} pp={exp.pp} "
-        f"dp={exp.dp} zero={exp.zero_stage} bucket_size_mb={exp.bucket_size_mb} "
-        f"batch_size={exp.batch_size} mbs={exp.mbs} "
-        f"ga={'on' if exp.gradient_accumulation else 'off'} nsight={'on' if exp.nsight else 'off'}"
-    )
-    sys.stdout.flush()
-
-    if args.dry_run:
-        return {
-            "sweep": exp.sweep,
-            "label": exp.label(),
-            "schedule": exp.schedule,
-            "pp": exp.pp,
-            "dp": exp.dp,
-            "ep": int(exp.ep),
-            "zero_stage": exp.zero_stage,
-            "bucket_size_mb": _format_bucket_size(exp.bucket_size_mb) if exp.bucket_size_mb is not None else "",
-            "batch_size": exp.batch_size,
-            "seq_len": exp.seq_len,
-            "mbs": exp.mbs,
-            "gradient_accumulation": int(exp.gradient_accumulation),
-            "ar_a2a_same_stream": int(exp.ar_a2a_same_stream),
-            "overlap_zero_ops": int(exp.overlap_zero_ops),
-            "overlap_chunks": int(exp.overlap_chunks),
-            "nsight": int(exp.nsight),
-            "status": "dry-run",
-            "failure_reason": "",
-            "log_path": str(log_path),
-            "metrics_path": str(metrics_path),
-        }
-
-    with open(log_path, "w", encoding="utf-8") as handle:
-        result = subprocess.run(
-            command,
-            check=False,
-            stdout=handle,
-            stderr=subprocess.STDOUT,
-            text=True,
-        )
-
-    remote_metrics_path = _extract_remote_metrics_path(log_path)
-    if remote_metrics_path is None:
-        cluster_log_candidates = _cluster_log_candidates(args.model, exp, fetch_dir)
-        if cluster_log_candidates:
-            remote_metrics_path = _extract_remote_metrics_path_from_cluster_log(cluster_log_candidates[-1])
-
-    fetched_metrics_path = _fetched_metrics_path(fetch_dir, exp, remote_metrics_path)
-    metrics_found = False
-    if remote_metrics_path is not None:
-        metrics_found = _fetch_remote_metrics(
-            args.model,
-            exp,
-            fetched_metrics_path,
-            _remote_experiment_output_dir(args, exp),
-            remote_metrics_path=remote_metrics_path,
-        )
-        if metrics_found:
-            _copy_local_metrics(fetched_metrics_path, metrics_path)
-    if not metrics_found:
-        metrics_found = _fetch_remote_metrics(
-            args.model,
-            exp,
-            fetched_metrics_path,
-            _remote_experiment_output_dir(args, exp),
-            remote_metrics_path=None,
-        )
-        if metrics_found:
-            _copy_local_metrics(fetched_metrics_path, metrics_path)
-    dag_order_paths = _fetch_remote_dag_order_logs(args, exp, exp_logs_dir)
-    if args.memory_profile:
-        snap_dest = exp_logs_dir / "memory_snapshots"
-        _fetch_remote_memory_snapshots(args, exp, snap_dest, log_path)
-
-    nsight_paths = _copy_experiment_nsight_profiles(exp, log_path, exp_logs_dir)
-    if not metrics_found:
-        metrics_found = _recover_metrics_from_fetch_logs(args, exp, fetch_dir, metrics_path)
-    records = _parse_metrics_records(metrics_path) if metrics_found else []
-
-    status = "success"
-    failure_reason = ""
-    aggregated: dict[str, object] = {}
-    if records:
-        aggregated = _aggregate_metrics(records)
-        if result.returncode != 0 and _looks_like_oom(log_path):
-            status = "oom"
-            failure_reason = "oom"
-    elif result.returncode != 0:
-        if _looks_like_oom(log_path):
-            status = "oom"
-            failure_reason = "oom"
-        else:
-            status = "failed"
-            failure_reason = f"runner_exit_{result.returncode}"
-    else:
-        status = "failed"
-        failure_reason = "missing_metrics"
-
-    print(
-        f"[{index}/{total}] {status.upper()} {exp.sweep}: {exp.label()} "
-        f"log={log_path}"
-        + (
-            f" dag_logs={','.join(str(path.name) for path in dag_order_paths)}"
-            if dag_order_paths
-            else ""
-        )
-        + (
-            f" nsight_dir={exp_logs_dir / 'nsight'}"
-            if nsight_paths
-            else ""
-        )
-    )
-    sys.stdout.flush()
-
-    row: dict[str, object] = {
-        "sweep": exp.sweep,
-        "label": exp.label(),
-        "schedule": exp.schedule,
-        "pp": exp.pp,
-        "dp": exp.dp,
-        "ep": int(exp.ep),
-        "zero_stage": exp.zero_stage,
-        "bucket_size_mb": _format_bucket_size(exp.bucket_size_mb) if exp.bucket_size_mb is not None else "",
-        "batch_size": exp.batch_size,
-        "seq_len": exp.seq_len,
-        "mbs": exp.mbs,
-        "gradient_accumulation": int(exp.gradient_accumulation),
-        "ar_a2a_same_stream": int(exp.ar_a2a_same_stream),
-        "overlap_zero_ops": int(exp.overlap_zero_ops),
-        "overlap_chunks": int(exp.overlap_chunks),
-        "nsight": int(exp.nsight),
-        "status": status,
-        "failure_reason": failure_reason,
-        "log_path": str(log_path),
-        "metrics_path": str(metrics_path) if metrics_found else "",
-    }
-    row.update(aggregated)
-    return row
-
-
-def _write_csv(path: Path, rows: list[dict[str, object]]) -> None:
-    path.parent.mkdir(parents=True, exist_ok=True)
-    fieldnames = [
-        "sweep",
-        "label",
-        "schedule",
-        "pp",
-        "dp",
-        "ep",
-        "zero_stage",
-        "bucket_size_mb",
-        "batch_size",
-        "seq_len",
-        "mbs",
-        "gradient_accumulation",
-        "ar_a2a_same_stream",
-        "overlap_zero_ops",
-        "overlap_chunks",
-        "nsight",
-        "status",
-        "failure_reason",
-        "samples",
-        "iter_time_mean_s",
-        "iter_time_std_s",
-        "throughput_mean_tokens_per_s",
-        "throughput_std_tokens_per_s",
-        "peak_memory_gb_max",
-        "peak_memory_gb_by_rank",
-        "log_path",
-        "metrics_path",
-    ]
-    with open(path, "w", newline="", encoding="utf-8") as handle:
-        writer = csv.DictWriter(handle, fieldnames=fieldnames)
-        writer.writeheader()
-        writer.writerows(rows)
-
-
-def _row_key(row: dict[str, object]) -> tuple[object, ...]:
-    return (
-        row["sweep"],
-        row["schedule"],
-        int(row["pp"]),
-        int(row["dp"]),
-        int(row.get("ep", 0)),
-        int(row["zero_stage"]),
-        float(row["bucket_size_mb"]) if str(row.get("bucket_size_mb", "")).strip() else None,
-        int(row["batch_size"]),
-        int(row["seq_len"]),
-        int(row["mbs"]),
-        int(row["gradient_accumulation"]),
-        int(row["ar_a2a_same_stream"]),
-        int(row["overlap_zero_ops"]),
-        int(row.get("overlap_chunks", 0)),
-        int(row.get("nsight", 0)),
-    )
-
-
-def _load_existing_rows(path: Path) -> list[dict[str, object]]:
-    if not path.is_file():
-        return []
-    with open(path, "r", newline="", encoding="utf-8") as handle:
-        return list(csv.DictReader(handle))
-
-
-def _hydrate_existing_rows(existing_rows: list[dict[str, object]]) -> list[dict[str, object]]:
-    hydrated: list[dict[str, object]] = []
-    for row in existing_rows:
-        metrics_path_text = str(row.get("metrics_path", "")).strip()
-        if row.get("status") in {"success", "oom"} or not metrics_path_text:
-            hydrated.append(row)
-            continue
-
-        metrics_path = Path(metrics_path_text)
-        records = _parse_metrics_records(metrics_path)
-        if not records:
-            hydrated.append(row)
-            continue
-
-        aggregated = _aggregate_metrics(records)
-        updated = dict(row)
-        updated["status"] = "success"
-        updated["failure_reason"] = ""
-        updated.update(aggregated)
-        hydrated.append(updated)
-    return hydrated
-
-
-def _completed_keys_from_metrics(
-    experiments: list[Experiment],
-    metrics_dir: Path,
-    fetch_dir: Path,
-    existing_by_key: dict[tuple[object, ...], dict[str, object]],
-) -> set[tuple[object, ...]]:
-    completed: set[tuple[object, ...]] = set()
-    for exp in experiments:
-        existing_row = existing_by_key.get(exp.key())
-        if existing_row is not None and existing_row.get("status") not in {"success", "oom", "dry-run", ""}:
-            continue
-        metrics_path = metrics_dir / f"{exp.slug()}.metrics"
-        records = _parse_metrics_records(metrics_path)
-        if not records:
-            fetched_dir = _experiment_fetch_dir(fetch_dir, exp)
-            if fetched_dir.is_dir():
-                for candidate in sorted(fetched_dir.glob("*")):
-                    records = _parse_metrics_records(candidate)
-                    if records:
-                        _copy_local_metrics(candidate, metrics_path)
-                        break
-        if records:
-            completed.add(exp.key())
-    return completed
-
-
-def _save_bar_plot(
-    title: str,
-    xlabel: str,
-    rows: list[dict[str, object]],
-    output_path: Path,
-    *,
-    allow_oom_markers: bool = False,
-) -> None:
-    import matplotlib
-
-    matplotlib.use("Agg")
-    import matplotlib.pyplot as plt
-
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    labels = [str(row["label"]) for row in rows]
-    xs = list(range(len(rows)))
-
-    success_rows = [row for row in rows if row["status"] == "success"]
-    success_max = max(
-        (float(row["iter_time_mean_s"]) for row in success_rows),
-        default=0.0,
-    )
-    oom_marker_y = success_max * 0.05 if success_max > 0 else 1.0
-
-    fig, ax = plt.subplots(figsize=(max(10, len(rows) * 0.8), 6))
-    for index, row in enumerate(rows):
-        status = row["status"]
-        if status == "success":
-            ax.bar(
-                index,
-                float(row["iter_time_mean_s"]),
-                yerr=float(row["iter_time_std_s"]),
-                color="#4C72B0",
-                ecolor="#2F2F2F",
-                capsize=4,
-            )
-        elif allow_oom_markers and status == "oom":
-            ax.scatter(index, oom_marker_y, marker="x", s=120, color="red", linewidths=2.5)
-        else:
-            ax.scatter(index, oom_marker_y, marker="x", s=100, color="black", linewidths=2.0)
-
-    ax.set_title(title)
-    ax.set_xlabel(xlabel)
-    ax.set_ylabel("Iteration Time (s)")
-    ax.set_xticks(xs)
-    ax.set_xticklabels(labels, rotation=45, ha="right")
-    ax.grid(axis="y", linestyle="--", alpha=0.35)
-    ax.set_ylim(bottom=0)
-    fig.tight_layout()
-    fig.savefig(output_path, dpi=200)
-    plt.close(fig)
-
-
-def _parse_peak_memory_values(row: dict[str, object]) -> list[float]:
-    summary = str(row.get("peak_memory_gb_by_rank", "")).strip()
-    if not summary:
-        return []
-
-    values: list[float] = []
-    for item in summary.split(";"):
-        item = item.strip()
-        if not item or ":" not in item:
-            continue
-        _rank, value_text = item.split(":", maxsplit=1)
-        try:
-            values.append(float(value_text))
-        except ValueError:
-            continue
-    return values
-
-
-def _save_memory_plot(
-    title: str,
-    xlabel: str,
-    rows: list[dict[str, object]],
-    output_path: Path,
-    *,
-    allow_oom_markers: bool = False,
-) -> None:
-    import matplotlib
-
-    matplotlib.use("Agg")
-    import matplotlib.pyplot as plt
-
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    labels = [str(row["label"]) for row in rows]
-    xs = list(range(len(rows)))
-
-    success_values = [
-        value
-        for row in rows
-        if row["status"] == "success"
-        for value in _parse_peak_memory_values(row)
-    ]
-    marker_y = max(success_values, default=0.0) * 0.05 if success_values else 0.25
-
-    fig, ax = plt.subplots(figsize=(max(10, len(rows) * 0.8), 6))
-    for index, row in enumerate(rows):
-        status = row["status"]
-        if status == "success":
-            values = _parse_peak_memory_values(row)
-            if values:
-                ax.scatter(
-                    [index] * len(values),
-                    values,
-                    color="#DD8452",
-                    s=36,
-                    alpha=0.9,
-                )
-        elif allow_oom_markers and status == "oom":
-            ax.scatter(index, marker_y, marker="x", s=120, color="red", linewidths=2.5)
-        else:
-            ax.scatter(index, marker_y, marker="x", s=100, color="black", linewidths=2.0)
-
-    ax.set_title(title)
-    ax.set_xlabel(xlabel)
-    ax.set_ylabel("Peak Memory (GB)")
-    ax.set_xticks(xs)
-    ax.set_xticklabels(labels, rotation=45, ha="right")
-    ax.grid(axis="y", linestyle="--", alpha=0.35)
-    ax.set_ylim(bottom=0)
-    fig.tight_layout()
-    fig.savefig(output_path, dpi=200)
-    plt.close(fig)
-
-
-def _build_experiments(args: argparse.Namespace) -> list[Experiment]:
-    experiments: list[Experiment] = []
-    enabled_sweeps = set(args.sweeps)
-
-    if "scalability" in enabled_sweeps:
-        for pp, mbs in _parse_pp_mbs(args.scalability_pp_mbs):
-            for dp in (1, 2):
-                experiments.append(
-                    Experiment(
-                        sweep="scalability",
-                        schedule=args.base_schedule,
-                        pp=pp,
-                        dp=dp,
-                        ep=0,
-                        zero_stage=0,
-                        bucket_size_mb=args.bucket_size,
-                        batch_size=args.batch_size,
-                        seq_len=args.seq_len,
-                        mbs=mbs,
-                        gradient_accumulation=bool(args.gradient_accumulation),
-                        ar_a2a_same_stream=False,
-                        overlap_zero_ops=True,
-                        overlap_chunks=bool(args.overlap_chunks),
-                        nsight=bool(args.nsight),
-                    )
-                )
-
-    if "zero" in enabled_sweeps:
-        zero_grad_acc_settings = [setting == "on" for setting in args.zero_grad_acc_settings]
-        for zero_stage in args.zero_stages:
-            pp = args.pp
-            mbs = 2 * pp
-            for batch_size in args.zero_batch_sizes:
-                for grad_acc in zero_grad_acc_settings:
-                    experiments.append(
-                        Experiment(
-                            sweep="zero",
-                            schedule=args.base_schedule,
-                            pp=pp,
-                            dp=args.dp,
-                            ep=0,
-                            zero_stage=zero_stage,
-                            bucket_size_mb=args.bucket_size,
-                            batch_size=batch_size,
-                            seq_len=args.seq_len,
-                            mbs=mbs,
-                            gradient_accumulation=grad_acc,
-                            ar_a2a_same_stream=False,
-                            overlap_zero_ops=True,
-                            overlap_chunks=bool(args.overlap_chunks),
-                            nsight=bool(args.nsight),
-                        )
-                )
-
-    if "schedule" in enabled_sweeps:
-        for schedule in args.schedule_values:
-            experiments.append(
-                Experiment(
-                    sweep="schedule",
-                    schedule=schedule,
-                    pp=args.pp,
-                    dp=args.dp,
-                    ep=args.dp,
-                    zero_stage=args.schedule_zero_stage,
-                    bucket_size_mb=args.bucket_size,
-                    batch_size=args.batch_size,
-                    seq_len=args.seq_len,
-                    mbs=args.schedule_mbs,
-                    gradient_accumulation=bool(args.gradient_accumulation),
-                    ar_a2a_same_stream=False,
-                    overlap_zero_ops=True,
-                    overlap_chunks=bool(args.overlap_chunks),
-                    nsight=bool(args.nsight),
-                )
-            )
-
-    return experiments
-
-
-def _require_env_vars() -> None:
-    missing = [name for name in ("SSH_KEY", "HEAD_PUBLIC_IP") if not os.environ.get(name)]
-    if missing:
-        raise SystemExit(
-            "Missing required environment variables: " + ", ".join(missing)
-        )
-
-
-def _default_run_name() -> str:
-    return datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
-
-
-def main() -> int:
-    args = parse_args()
-    _require_env_vars()
-
-    runner_path = Path(args.runner).resolve()
-    if not runner_path.is_file():
-        raise SystemExit(f"Runner not found: {runner_path}")
-
-    out_root = Path(args.out_dir)
-    run_name = args.run_name or _default_run_name()
-    out_dir = out_root / run_name
-    logs_dir = out_dir / "logs"
-    metrics_dir = out_dir / "metrics"
-    fetch_dir = out_dir / "runner-fetch"
-    plots_dir = out_dir / "plots"
-    all_results_path = out_dir / "all_results.csv"
-    for directory in (logs_dir, metrics_dir, fetch_dir, plots_dir):
-        directory.mkdir(parents=True, exist_ok=True)
-
-    experiments = _build_experiments(args)
-    total = len(experiments)
-    existing_rows = [] if args.no_resume else _hydrate_existing_rows(_load_existing_rows(all_results_path))
-    rows: list[dict[str, object]] = list(existing_rows)
-    existing_by_key = {_row_key(row): row for row in existing_rows}
-    completed_keys = {
-        key
-        for key, row in existing_by_key.items()
-        if row.get("status") in {"success", "oom"}
-    }
-    completed_keys |= _completed_keys_from_metrics(experiments, metrics_dir, fetch_dir, existing_by_key)
-
-    for exp in experiments:
-        existing_row = existing_by_key.get(exp.key())
-        if existing_row is not None and existing_row.get("status") in {"success", "oom", "dry-run"}:
-            continue
-        metrics_path = metrics_dir / f"{exp.slug()}.metrics"
-        if not metrics_path.is_file():
-            recovered = _recover_metrics_from_fetch_logs(args, exp, fetch_dir, metrics_path)
-            if not recovered:
-                continue
-        cached_row = _row_from_metrics(exp, logs_dir / exp.slug() / "run.log", metrics_path)
-        if cached_row is None:
-            continue
-        existing_by_key[exp.key()] = cached_row
-
-    rows = list(existing_by_key.values())
-    completed_keys = {
-        key
-        for key, row in existing_by_key.items()
-        if row.get("status") in {"success", "oom", "dry-run"}
-    }
-
-    print(f"Starting e2e eval with {total} experiments")
-    print(f"Run directory: {out_dir}")
-    use_inductor_summary = (
-        "auto(schedule=0,zero/scalability=1)"
-        if args.use_inductor is None
-        else str(int(args.use_inductor))
-    )
-
-    print(
-        "Config: "
-        f"model={args.model} batch_size={args.batch_size} seq_len={args.seq_len} "
-        f"warmup={args.warmup} iters={args.iters} base_schedule={args.base_schedule} "
-        f"schedule_zero_stage={args.schedule_zero_stage} schedule_mbs={args.schedule_mbs} "
-        f"zero_batch_sizes={args.zero_batch_sizes} zero_grad_acc_settings={args.zero_grad_acc_settings} "
-        f"bucket_size_mb={_format_bucket_size(args.bucket_size)} "
-        f"zero_mbs=2*pp nsight={int(args.nsight)} use_inductor={use_inductor_summary} "
-        f"gradient_accumulation={int(args.gradient_accumulation)}"
-    )
-    if existing_rows and not args.no_resume:
-        print(
-            f"Resume: loaded {len(existing_rows)} existing rows, "
-            f"skipping {len(completed_keys)} completed experiments"
-        )
-    sys.stdout.flush()
-
-    failures = 0
-    oom_failures = 0
-    interrupted = False
-    for index, exp in enumerate(experiments, start=1):
-        if exp.key() in completed_keys:
-            print(f"[{index}/{total}] SKIP completed {exp.sweep}: {exp.label()}")
-            sys.stdout.flush()
-            continue
-        try:
-            row = _run_experiment(args, exp, index, total, logs_dir, metrics_dir, fetch_dir)
-        except KeyboardInterrupt:
-            interrupted = True
-            print(f"\nInterrupted during {exp.sweep}: {exp.label()}")
-            sys.stdout.flush()
-            break
-
-        existing_by_key[_row_key(row)] = row
-        rows = list(existing_by_key.values())
-        if row["status"] == "oom":
-            oom_failures += 1
-        elif row["status"] not in ("success", "dry-run"):
-            failures += 1
-        completed = len(
-            [experiment for experiment in experiments if experiment.key() in {_row_key(item) for item in rows}]
-        )
-        print(
-            f"Progress: completed={completed}/{total} "
-            f"success={sum(1 for item in rows if item['status'] == 'success')} "
-            f"oom={sum(1 for item in rows if item['status'] == 'oom')} "
-            f"failed={sum(1 for item in rows if item['status'] not in ('success', 'oom', 'dry-run'))}"
-        )
-        sys.stdout.flush()
-        _write_csv(all_results_path, rows)
-
-    _write_csv(all_results_path, rows)
-
-    for sweep in ("scalability", "zero", "schedule"):
-        sweep_rows = [row for row in rows if row["sweep"] == sweep]
-        _write_csv(out_dir / f"{sweep}_results.csv", sweep_rows)
-
-    if args.dry_run:
-        print(f"Dry run finished. Planned artifacts are rooted at {out_dir}")
-        return 0
-
-    _save_bar_plot(
-        "Scalability Sweep",
-        "(pp, dp)",
-        [row for row in rows if row["sweep"] == "scalability"],
-        plots_dir / "scalability.png",
-    )
-    _save_bar_plot(
-        "ZeRO Sweep",
-        "(zero, ga, batch_size)",
-        [row for row in rows if row["sweep"] == "zero"],
-        plots_dir / "zero.png",
-        allow_oom_markers=True,
-    )
-    _save_bar_plot(
-        "Schedule Sweep",
-        "schedule",
-        [row for row in rows if row["sweep"] == "schedule"],
-        plots_dir / "schedule.png",
-    )
-    _save_memory_plot(
-        "Scalability Sweep Memory",
-        "(pp, dp)",
-        [row for row in rows if row["sweep"] == "scalability"],
-        plots_dir / "scalability_memory.png",
-    )
-    _save_memory_plot(
-        "ZeRO Sweep Memory",
-        "(zero, ga, batch_size)",
-        [row for row in rows if row["sweep"] == "zero"],
-        plots_dir / "zero_memory.png",
-        allow_oom_markers=True,
-    )
-    _save_memory_plot(
-        "Schedule Sweep Memory",
-        "schedule",
-        [row for row in rows if row["sweep"] == "schedule"],
-        plots_dir / "schedule_memory.png",
-    )
-
-    if interrupted:
-        print(f"Interrupted. Partial results were saved to {out_dir}")
-        return 130
-
-    print(f"Finished. Results are in {out_dir}")
-    return 0 if failures == 0 else 1
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/scripts/run_qwen_ec2_sweep.py b/scripts/run_qwen_ec2_sweep.py
deleted file mode 100644
index 10418df..0000000
--- a/scripts/run_qwen_ec2_sweep.py
+++ /dev/null
@@ -1,377 +0,0 @@
-#!/usr/bin/env python3
-"""Run a configurable sweep over scripts/run-qwen-ec2.sh.
-
-Default sweep:
-  SCHEDULE in {1f1b}
-  (PP, MBS) in {(4, 8), (8, 16)}
-  DP in {2, 4}
-  ZERO_STAGE in {0, 1, 2, 3}
-  BATCH_SIZE in {8, 16, 32}
-  SEQ_LEN in {512}
-  GRAD_ACC in {on, off}
-  AR_A2A_SAME_STREAM in {on, off}
-  OVERLAP_ZERO_OPS in {on, off}
-  OVERLAP_CHUNKS in {off}
-
-Each dimension can be overridden from the command line.
-"""
-
-from __future__ import annotations
-
-import argparse
-import itertools
-import shlex
-import subprocess
-import sys
-import traceback
-from pathlib import Path
-
-
-DEFAULT_PP_MBS = ("4:8", "8:16")
-DEFAULT_SCHEDULES = ("1f1b",)
-DEFAULT_DP_VALUES = (1, 2, 4)
-DEFAULT_ZERO_STAGES = (0, 1, 2, 3)
-DEFAULT_BATCH_SIZES = (16, 32, 64)
-DEFAULT_SEQ_LENS = (512,)
-DEFAULT_GRADIENT_ACCUMULATION_VALUES = ("on", "off")
-DEFAULT_AR_A2A_SAME_STREAM_VALUES = ("on", "off")
-DEFAULT_OVERLAP_ZERO_OPS_VALUES = ("on", "off")
-DEFAULT_OVERLAP_CHUNKS_VALUES = ("off",)
-
-
-def _parse_pp_mbs(values: list[str]) -> list[tuple[int, int]]:
-    parsed: list[tuple[int, int]] = []
-    for value in values:
-        try:
-            pp_str, mbs_str = value.split(":", maxsplit=1)
-            parsed.append((int(pp_str), int(mbs_str)))
-        except ValueError as exc:
-            raise argparse.ArgumentTypeError(
-                f"Invalid --pp-mbs value '{value}'. Expected format PP:MBS, e.g. 4:8."
-            ) from exc
-    return parsed
-
-
-def _build_command(
-    script_path: Path,
-    *,
-    model: str,
-    schedule: str,
-    pp: int,
-    dp: int,
-    zero_stage: int,
-    batch_size: int,
-    seq_len: int,
-    mbs: int,
-    gradient_accumulation: bool,
-    ar_a2a_same_stream: bool,
-    overlap_zero_ops: bool,
-    overlap_chunks: bool,
-    warmup: int,
-    iters: int,
-    extra_args: list[str],
-) -> list[str]:
-    command = [
-        str(script_path),
-        "--model", model,
-        "--schedule", schedule,
-        "--pp", str(pp),
-        "--dp", str(dp),
-        "--zero-stage", str(zero_stage),
-        "--batch-size", str(batch_size),
-        "--seq-len", str(seq_len),
-        "--mbs", str(mbs),
-        "--gradient-accumulation" if gradient_accumulation else "--no-gradient-accumulation",
-        "--ar-a2a-same-stream" if ar_a2a_same_stream else "--no-ar-a2a-same-stream",
-        "--overlap-zero-ops" if overlap_zero_ops else "--no-overlap-zero-ops",
-        "--overlap-chunks" if overlap_chunks else "--no-overlap-chunks",
-        "--warmup", str(warmup),
-        "--iters", str(iters),
-    ]
-    command.extend(extra_args)
-    return command
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Sweep scripts/run-qwen-ec2.sh over configurable dimension values."
-    )
-    parser.add_argument("--model", default="1B", help="Model passed to run-qwen-ec2.sh")
-    parser.add_argument(
-        "--schedules",
-        nargs="+",
-        default=list(DEFAULT_SCHEDULES),
-        help="Pipeline schedules to sweep. Default: 1f1b",
-    )
-    parser.add_argument(
-        "--pp-mbs",
-        nargs="+",
-        default=list(DEFAULT_PP_MBS),
-        metavar="PP:MBS",
-        help="Pairs of pipeline degree and microbatch count. Default: 4:8 8:16",
-    )
-    parser.add_argument(
-        "--dp-values",
-        nargs="+",
-        type=int,
-        default=list(DEFAULT_DP_VALUES),
-        help="DP values to sweep. Default: 2 4",
-    )
-    parser.add_argument(
-        "--zero-stages",
-        nargs="+",
-        type=int,
-        default=list(DEFAULT_ZERO_STAGES),
-        help="ZeRO stages to sweep. Default: 0 1 2 3",
-    )
-    parser.add_argument(
-        "--batch-sizes",
-        nargs="+",
-        type=int,
-        default=list(DEFAULT_BATCH_SIZES),
-        help="Batch sizes to sweep. Default: 8 16 32",
-    )
-    parser.add_argument(
-        "--seq-lens",
-        nargs="+",
-        type=int,
-        default=list(DEFAULT_SEQ_LENS),
-        help="Sequence lengths to sweep. Default: 512",
-    )
-    parser.add_argument(
-        "--gradient-accumulation-values",
-        nargs="+",
-        choices=["on", "off"],
-        default=list(DEFAULT_GRADIENT_ACCUMULATION_VALUES),
-        help="Gradient accumulation settings to sweep. Default: on off",
-    )
-    parser.add_argument(
-        "--ar-a2a-same-stream-values",
-        nargs="+",
-        choices=["on", "off"],
-        default=list(DEFAULT_AR_A2A_SAME_STREAM_VALUES),
-        help="AR/A2A same-stream settings to sweep. Default: on off",
-    )
-    parser.add_argument(
-        "--overlap-zero-ops-values",
-        nargs="+",
-        choices=["on", "off"],
-        default=list(DEFAULT_OVERLAP_ZERO_OPS_VALUES),
-        help="overlap_zero_ops settings to sweep. Default: on off",
-    )
-    parser.add_argument(
-        "--overlap-chunks-values",
-        nargs="+",
-        choices=["on", "off"],
-        default=list(DEFAULT_OVERLAP_CHUNKS_VALUES),
-        help="overlap_chunks settings to sweep. Default: off",
-    )
-    parser.add_argument("--warmup", type=int, default=3, help="Warmup iterations")
-    parser.add_argument("--iters", type=int, default=10, help="Timed iterations")
-    parser.add_argument(
-        "--script",
-        default="scripts/run-qwen-ec2.sh",
-        help="Path to the underlying runner script",
-    )
-    parser.add_argument(
-        "--dry-run",
-        action="store_true",
-        help="Print commands without executing them",
-    )
-    parser.add_argument(
-        "--continue-on-error",
-        action="store_true",
-        default=True,
-        help="Deprecated: the sweep now always continues after a failed run",
-    )
-    parser.add_argument(
-        "extra_args",
-        nargs=argparse.REMAINDER,
-        help="Extra arguments forwarded to run-qwen-ec2.sh. Prefix with '--'.",
-    )
-    return parser.parse_args()
-
-
-def main() -> int:
-    args = parse_args()
-    pp_mbs_values = _parse_pp_mbs(args.pp_mbs)
-    script_path = Path(args.script).resolve()
-    if not script_path.is_file():
-        print(f"Runner script not found: {script_path}", file=sys.stderr)
-        return 1
-
-    extra_args = list(args.extra_args)
-    if extra_args and extra_args[0] == "--":
-        extra_args = extra_args[1:]
-
-    combinations: list[tuple[str, tuple[int, int], int, int, int, int, bool, bool, bool, bool]] = []
-    for schedule, (pp, mbs), dp, batch_size, seq_len, gradient_accumulation, ar_a2a_same_stream, overlap_zero_ops, overlap_chunks in itertools.product(
-        args.schedules,
-        pp_mbs_values,
-        args.dp_values,
-        args.batch_sizes,
-        args.seq_lens,
-        [value == "on" for value in args.gradient_accumulation_values],
-        [value == "on" for value in args.ar_a2a_same_stream_values],
-        [value == "on" for value in args.overlap_zero_ops_values],
-        [value == "on" for value in args.overlap_chunks_values],
-    ):
-        zero_stages = args.zero_stages if dp > 1 else [0]
-        for zero_stage in zero_stages:
-            combinations.append(
-                (
-                    schedule,
-                    (pp, mbs),
-                    dp,
-                    zero_stage,
-                    batch_size,
-                    seq_len,
-                    gradient_accumulation,
-                    ar_a2a_same_stream,
-                    overlap_zero_ops,
-                    overlap_chunks,
-                )
-            )
-
-    total = len(combinations)
-    failures: list[dict[str, object]] = []
-    for index, (
-        schedule,
-        (pp, mbs),
-        dp,
-        zero_stage,
-        batch_size,
-        seq_len,
-        gradient_accumulation,
-        ar_a2a_same_stream,
-        overlap_zero_ops,
-        overlap_chunks,
-    ) in enumerate(
-        combinations, start=1
-    ):
-        command = _build_command(
-            script_path,
-            model=args.model,
-            schedule=schedule,
-            pp=pp,
-            dp=dp,
-            zero_stage=zero_stage,
-            batch_size=batch_size,
-            seq_len=seq_len,
-            mbs=mbs,
-            gradient_accumulation=gradient_accumulation,
-            ar_a2a_same_stream=ar_a2a_same_stream,
-            overlap_zero_ops=overlap_zero_ops,
-            overlap_chunks=overlap_chunks,
-            warmup=args.warmup,
-            iters=args.iters,
-            extra_args=extra_args,
-        )
-        print(
-            f"[{index}/{total}] schedule={schedule} pp={pp} mbs={mbs} dp={dp} zero_stage={zero_stage} "
-            f"batch_size={batch_size} seq_len={seq_len} "
-            f"gradient_accumulation={'on' if gradient_accumulation else 'off'} "
-            f"ar_a2a_same_stream={'on' if ar_a2a_same_stream else 'off'} "
-            f"overlap_zero_ops={'on' if overlap_zero_ops else 'off'} "
-            f"overlap_chunks={'on' if overlap_chunks else 'off'}"
-        )
-        print("  " + " ".join(shlex.quote(part) for part in command))
-
-        if args.dry_run:
-            continue
-
-        try:
-            result = subprocess.run(command, check=False)
-            if result.returncode == 0:
-                continue
-            failures.append(
-                {
-                    "index": index,
-                    "schedule": schedule,
-                    "pp": pp,
-                    "mbs": mbs,
-                    "dp": dp,
-                    "zero_stage": zero_stage,
-                    "batch_size": batch_size,
-                    "seq_len": seq_len,
-                    "gradient_accumulation": gradient_accumulation,
-                    "ar_a2a_same_stream": ar_a2a_same_stream,
-                    "overlap_zero_ops": overlap_zero_ops,
-                    "overlap_chunks": overlap_chunks,
-                    "returncode": result.returncode,
-                    "command": command,
-                }
-            )
-            print(f"  command failed with exit code {result.returncode}, continuing", file=sys.stderr)
-        except KeyboardInterrupt:
-            print("\nSweep interrupted by user.", file=sys.stderr)
-            failures.append(
-                {
-                    "index": index,
-                    "schedule": schedule,
-                    "pp": pp,
-                    "mbs": mbs,
-                    "dp": dp,
-                    "zero_stage": zero_stage,
-                    "batch_size": batch_size,
-                    "seq_len": seq_len,
-                    "gradient_accumulation": gradient_accumulation,
-                    "ar_a2a_same_stream": ar_a2a_same_stream,
-                    "overlap_zero_ops": overlap_zero_ops,
-                    "overlap_chunks": overlap_chunks,
-                    "returncode": "interrupted",
-                    "command": command,
-                }
-            )
-            break
-        except Exception as exc:
-            failures.append(
-                {
-                    "index": index,
-                    "schedule": schedule,
-                    "pp": pp,
-                    "mbs": mbs,
-                    "dp": dp,
-                    "zero_stage": zero_stage,
-                    "batch_size": batch_size,
-                    "seq_len": seq_len,
-                    "gradient_accumulation": gradient_accumulation,
-                    "ar_a2a_same_stream": ar_a2a_same_stream,
-                    "overlap_zero_ops": overlap_zero_ops,
-                    "overlap_chunks": overlap_chunks,
-                    "returncode": "exception",
-                    "command": command,
-                    "error": repr(exc),
-                }
-            )
-            print(f"  command raised {type(exc).__name__}: {exc}", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
-
-    if failures:
-        print("\nSweep completed with failures:", file=sys.stderr)
-        for failure in failures:
-            print(
-                f"  [{failure['index']}/{total}] "
-                f"schedule={failure['schedule']} pp={failure['pp']} mbs={failure['mbs']} dp={failure['dp']} "
-                f"zero_stage={failure['zero_stage']} batch_size={failure['batch_size']} "
-                f"seq_len={failure['seq_len']} "
-                f"gradient_accumulation={'on' if failure['gradient_accumulation'] else 'off'} "
-                f"ar_a2a_same_stream={'on' if failure['ar_a2a_same_stream'] else 'off'} "
-                f"overlap_zero_ops={'on' if failure['overlap_zero_ops'] else 'off'} "
-                f"overlap_chunks={'on' if failure['overlap_chunks'] else 'off'} "
-                f"status={failure['returncode']}",
-                file=sys.stderr,
-            )
-            print(
-                "    " + " ".join(shlex.quote(part) for part in failure["command"]),
-                file=sys.stderr,
-            )
-            if "error" in failure:
-                print(f"    error={failure['error']}", file=sys.stderr)
-        return 1
-
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/scripts/test_env.py b/scripts/test_env.py
deleted file mode 100644
index 9a5d1bb..0000000
--- a/scripts/test_env.py
+++ /dev/null
@@ -1,161 +0,0 @@
-"""Smoke test to verify the Ray + CUDA environment is set up correctly."""
-import sys
-import signal
-
-def timeout_handler(signum, frame):
-    raise TimeoutError("timed out")
-
-def check(name, fn, timeout_sec=30):
-    sys.stdout.flush()
-    try:
-        signal.signal(signal.SIGALRM, timeout_handler)
-        signal.alarm(timeout_sec)
-        result = fn()
-        signal.alarm(0)
-        print(f"  [PASS] {name}: {result}", flush=True)
-        return True
-    except Exception as e:
-        signal.alarm(0)
-        print(f"  [FAIL] {name}: {e}", flush=True)
-        return False
-
-def main():
-    ok = True
-
-    print("Python", flush=True)
-    ok &= check("version", lambda: sys.version.split()[0])
-
-    print("PyTorch", flush=True)
-    import torch
-    ok &= check("version", lambda: torch.__version__)
-    ok &= check("CUDA available", lambda: torch.cuda.is_available())
-    ok &= check("CUDA version", lambda: torch.version.cuda)
-    ok &= check("GPU count", lambda: torch.cuda.device_count())
-    if torch.cuda.is_available():
-        ok &= check("GPU name", lambda: torch.cuda.get_device_name(0))
-        ok &= check("tensor on GPU", lambda: torch.zeros(1, device="cuda").device)
-
-    print("NCCL", flush=True)
-    ok &= check("available", lambda: torch.cuda.nccl.is_available(torch.randn(1, device="cuda")))
-    ok &= check("version", lambda: ".".join(str(v) for v in torch.cuda.nccl.version()))
-
-    print("CuPy", flush=True)
-    import cupy
-    ok &= check("version", lambda: cupy.__version__)
-
-    print("EFA", flush=True)
-    import os, subprocess
-    def check_efa_devices():
-        devs = os.listdir("/dev/infiniband") if os.path.isdir("/dev/infiniband") else []
-        if not devs:
-            raise RuntimeError("no devices under /dev/infiniband")
-        return devs
-    def check_fi_info():
-        result = subprocess.run(["fi_info", "-p", "efa"], capture_output=True, text=True)
-        if result.returncode != 0:
-            raise RuntimeError(result.stderr.strip() or "fi_info -p efa returned non-zero")
-        providers = [l for l in result.stdout.splitlines() if l.startswith("provider:")]
-        return f"{len(providers)} EFA provider(s) found"
-    ok &= check("devices", check_efa_devices)
-    try:
-        result = check_fi_info()
-        print(f"  [PASS] fi_info: {result}", flush=True)
-    except FileNotFoundError:
-        print("  [WARN] fi_info: not installed in container (host-side tool); skipping", flush=True)
-    except Exception as e:
-        print(f"  [FAIL] fi_info: {e}", flush=True)
-        ok = False
-    print("  [INFO] To confirm NCCL uses EFA: set NCCL_DEBUG=INFO before running a job and look for", flush=True)
-    print("         'NET/OFI Initializing aws-ofi-nccl' and 'Using EFA' in the output.", flush=True)
-
-    print("Piper imports", flush=True)
-    ok &= check("compile", lambda: (__import__("src.compile"), "ok")[1])
-    ok &= check("tasks", lambda: (__import__("src.tasks"), "ok")[1])
-    ok &= check("actor", lambda: (__import__("src.actor"), "ok")[1])
-
-    print("Ray", flush=True)
-    import ray
-    ok &= check("version", lambda: ray.__version__)
-    ok &= check("init", lambda: (ray.init(ignore_reinit_error=True), "connected")[1])
-    ok &= check("resources", lambda: {k: v for k, v in ray.cluster_resources().items() if k in ("CPU", "GPU")})
-
-    print("Multi-node", flush=True)
-    from ray.util.placement_group import placement_group, remove_placement_group
-    active_nodes = [n for n in ray.nodes() if n["Alive"]]
-    print(f"  [INFO] {len(active_nodes)} active node(s) in cluster", flush=True)
-    if len(active_nodes) < 2:
-        print("  [SKIP] strict_spread: need 2 nodes, only 1 available", flush=True)
-    else:
-        @ray.remote
-        def get_node_id():
-            return ray.get_runtime_context().get_node_id()
-
-        def test_strict_spread():
-            pg = placement_group([{"CPU": 1}, {"CPU": 1}], strategy="STRICT_SPREAD")
-            ray.get(pg.ready(), timeout=30)
-            ids = ray.get([
-                get_node_id.options(placement_group=pg, placement_group_bundle_index=0).remote(),
-                get_node_id.options(placement_group=pg, placement_group_bundle_index=1).remote(),
-            ])
-            remove_placement_group(pg)
-            if ids[0] == ids[1]:
-                raise RuntimeError(f"both actors landed on the same node")
-            return f"actors on 2 different nodes"
-
-        ok &= check("strict_spread", test_strict_spread, timeout_sec=60)
-
-        @ray.remote(num_gpus=1)
-        class NCCLWorker:
-            def get_ip(self):
-                return ray.util.get_node_ip_address()
-
-            def init_and_allreduce(self, rank, world_size, master_addr, master_port):
-                import torch
-                import torch.distributed as dist
-                import os
-                os.environ["MASTER_ADDR"] = master_addr
-                os.environ["MASTER_PORT"] = str(master_port)
-                os.environ.setdefault("NCCL_SOCKET_IFNAME", "ens32")
-                os.environ.setdefault("GLOO_SOCKET_IFNAME", "ens32")
-                os.environ.setdefault("NCCL_PROTO", "simple")
-                # Send value (rank+1) so rank 0 sends 1.0 and rank 1 sends 2.0;
-                # after all-reduce the sum should be 3.0 on both ranks.
-                dist.init_process_group(
-                    backend="nccl",
-                    init_method="env://",
-                    world_size=world_size,
-                    rank=rank,
-                )
-                t = torch.full((4,), float(rank + 1), device="cuda")
-                dist.all_reduce(t)
-                result = t[0].item()
-                dist.destroy_process_group()
-                return result
-
-        def test_cross_node_nccl():
-            pg = placement_group([{"CPU": 1, "GPU": 1}, {"CPU": 1, "GPU": 1}], strategy="STRICT_SPREAD")
-            ray.get(pg.ready(), timeout=30)
-            w0 = NCCLWorker.options(placement_group=pg, placement_group_bundle_index=0).remote()
-            w1 = NCCLWorker.options(placement_group=pg, placement_group_bundle_index=1).remote()
-            master_addr = ray.get(w0.get_ip.remote())
-            master_port = 29500
-            results = ray.get([
-                w0.init_and_allreduce.remote(0, 2, master_addr, master_port),
-                w1.init_and_allreduce.remote(1, 2, master_addr, master_port),
-            ], timeout=90)
-            remove_placement_group(pg)
-            expected = 3.0  # 1.0 + 2.0
-            if results[0] != expected or results[1] != expected:
-                raise RuntimeError(f"unexpected all-reduce results: {results}, expected {expected} on both ranks")
-            return f"all-reduce across 2 nodes: {results[0]} == {results[1]} == {expected}"
-
-        ok &= check("cross_node_nccl", test_cross_node_nccl, timeout_sec=120)
-
-    ray.shutdown()
-
-    print(flush=True)
-    print("ALL PASSED" if ok else "SOME CHECKS FAILED", flush=True)
-    sys.exit(0 if ok else 1)
-
-if __name__ == "__main__":
-    main()
diff --git a/src/fx.py b/src/fx.py
index d568d7b..5ab63d4 100644
--- a/src/fx.py
+++ b/src/fx.py
@@ -198,6 +198,14 @@ def _deserialize_target(payload):
     if kind == "torch_op":
         obj = torch.ops
         path = payload["path"]
+        if path == "piper_artifact.bmm_experts":
+            # The Qwen artifact model defines this model-specific custom op.
+            for module_name in ("models.qwen3", "examples.models.qwen3"):
+                try:
+                    importlib.import_module(module_name)
+                    break
+                except ImportError:
+                    pass
         try:
             for part in path.split("."):
                 obj = getattr(obj, part)

From 0c252c7a330f054410885f6ad8ba274efc38e2b5 Mon Sep 17 00:00:00 2001
From: meganfrisella <meganfrisella@gmail.com>
Date: Tue, 9 Jun 2026 05:13:49 +0000
Subject: [PATCH 100/100] initial release

---
 .github/workflows/test.yml                    |  30 ++
 .gitignore                                    |   7 -
 LICENSE                                       |  21 ++
 README.md                                     | 181 ++++++++++-
 examples/base-schedules/pp2.json              |  22 --
 examples/base-schedules/pp2_dp2.json          |  46 ---
 ...cket25.json => pp2_dp2_ep2_bucket100.json} |   4 +-
 .../base-schedules/pp2_dp2_ep2_zero2.json     |   4 +-
 .../base-schedules/pp2_dp2_ep2_zero3.json     |   6 +-
 examples/base-schedules/pp2_dp2_zero2.json    |  48 ---
 examples/base-schedules/pp2_dp2_zero3.json    |  54 ----
 examples/base-schedules/pp4_interleaved.json  |  42 ---
 examples/base-schedules/pp4_v_placement.json  |  42 ---
 examples/models/qwen3.py                      |  10 +-
 examples/run_qwen_examples.py                 | 172 ++++++++++
 examples/test_harness.py                      | 304 +++++++++++++-----
 examples/test_llama.py                        |  21 +-
 examples/test_qwen.py                         |  22 +-
 figs/architecture.jpg                         | Bin 0 -> 94306 bytes
 pyproject.toml                                |  10 +
 src/actor.py                                  |   9 +
 src/compile.py                                |   9 +-
 src/directives.py                             |  51 ++-
 src/executors.py                              |  71 +++-
 src/ordering.py                               |  18 +-
 src/piper.py                                  |   6 +-
 src/state.py                                  |   2 +-
 test/test_order_directive.py                  |  58 ++++
 28 files changed, 841 insertions(+), 429 deletions(-)
 create mode 100644 .github/workflows/test.yml
 create mode 100644 LICENSE
 delete mode 100644 examples/base-schedules/pp2.json
 delete mode 100644 examples/base-schedules/pp2_dp2.json
 rename examples/base-schedules/{pp2_dp2_ep2_bucket25.json => pp2_dp2_ep2_bucket100.json} (94%)
 delete mode 100644 examples/base-schedules/pp2_dp2_zero2.json
 delete mode 100644 examples/base-schedules/pp2_dp2_zero3.json
 delete mode 100644 examples/base-schedules/pp4_interleaved.json
 delete mode 100644 examples/base-schedules/pp4_v_placement.json
 create mode 100755 examples/run_qwen_examples.py
 create mode 100644 figs/architecture.jpg

diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
new file mode 100644
index 0000000..7410066
--- /dev/null
+++ b/.github/workflows/test.yml
@@ -0,0 +1,30 @@
+name: Tests
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install -e ".[dev]"
+
+      - name: Run CPU tests
+        run: python -m pytest -m "not gpu" -v test
diff --git a/.gitignore b/.gitignore
index 171b266..eac13f2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,13 +3,6 @@ __pycache__/
 .venv
 piper.egg-info/
 out/
-ray_tmp/
-aws/
-docs/
-figs/
-logs/
 .vscode/
-e2e-eval/
-artifact/ec2-scripts/
 .codex
 ec2-trust-policy.json
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..bd82f15
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Megan Frisella
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
\ No newline at end of file
diff --git a/README.md b/README.md
index 80fca53..8460243 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,41 @@
 # Piper
 
-Piper is a user-controllable distributed training system that decouples distributed training strategy from runtime implementation. Piper lets users express high-level placement and low-level scheduling intent through lightweight model annotations and scheduling directives. The compiler lowers this intent into a unified global training DAG that explicitly represents computation, communication, data dependencies, temporal dependencies, device placement, and GPU stream assignment. A centralized scheduler then decomposes the DAG into per-device execution plans, and a Ray-based distributed runtime executes those plans on GPU workers while managing streams, communicators, and memory. This design allows Piper to match existing general-purpose training systems on common strategies while making it easier to express and optimize composed strategies such as PP x EP/DP DualPipe-style schedules.
+[![arXiv](https://img.shields.io/badge/arXiv-TODO-b31b1b.svg)](TODO)
 
-![Piper architecture](figs/piper-architecture.png)
+New distributed training strategies should not require new distributed runtimes; Piper gives PyTorch users direct control over model placement and GPU scheduling with lightweight annotations and a small scheduling language.
 
-## Install
+## Updates
 
-The following installation has been tested with Python 3.10 on Linux with CUDA 12.8 drivers. 
+* 2026-06 - Blog post: [User-Controlled Distributed Training for PyTorch](TODO).
+* 2025-10 - Paper released on arXiv: [Piper: Towards Flexible Pipeline Parallelism for PyTorch](TODO).
 
-### venv
+## Introduction
+
+Large training jobs increasingly combine multiple parallelism strategies such as pipeline, data, and expert parallelism with ZeRO-style sharding, creating placement and GPU scheduling choices that current frameworks cannot express cleanly.
+Today, ML researchers and practitioners choose between one-off specialized systems that perform well but are hard to adapt, and general-purpose frameworks that are easier to use but expose limited control.
+
+Piper is a user-controllable distributed training system for PyTorch that separates model placement and GPU scheduling from model code and runtime implementation.
+With lightweight model annotations and a small scheduling language, Piper lets users express, visualize, profile, and run high-performance training schedules such as DualPipe-style pipeline- and expert-parallel overlap.
+
+## Architecture
+
+![Piper architecture](figs/architecture.jpg)
+
+Piper has two user-facing inputs:
+
+* An annotated PyTorch model: standard model code with lightweight tags for schedulable regions such as pipeline stages and MoE experts.
+* A schedule-directive program: instructions that tell the Piper compiler how to shard, replicate, order, and overlap those schedulable regions.
+
+The compiler traces the model with TorchDynamo, splits the graph by Piper annotations, builds a distributed training DAG IR, and applies DAG rewrites according to the schedule directives.
+The directive rewrites insert point-to-point pipeline communication, DP collectives, ZeRO gather/scatter collectives, EP all-to-all communication, temporal edges, device assignments, and logical stream assignments.
+
+The runtime decomposes the global DAG into per-device execution plans and runs them on Ray actors.
+Each actor manages local CUDA streams, process groups/communicators, model-state buffers, and intermediate tensors.
+
+## Installation
+
+Requires Python 3.10+ on Linux with CUDA GPUs.
+The current setup has been tested with Python 3.10 and CUDA 12.x.
 
 ```bash
 python3.10 -m venv .venv
@@ -17,9 +44,7 @@ python -m pip install --upgrade pip
 python -m pip install -e .
 ```
 
-### conda
-
-If you do not already have a local Python 3.10 interpreter, create one with conda:
+If you do not already have Python 3.10 locally:
 
 ```bash
 conda create -n piper python=3.10 -y
@@ -30,23 +55,149 @@ python -m pip install -e .
 
 ## Quickstart
 
-Run the test harness with the `pp2` base schedule and a generated 1F1B order schedule:
+From the repository root, run the Qwen example with a DualPipeV schedule:
 
 ```bash
 python examples/test_harness.py \
   --test-file examples/test_qwen.py \
-  --base-schedule examples/base-schedules/pp2.json \
+  --base-schedule examples/base-schedules/pp4_dp2_ep2_v_placement.json \
+  --schedule dualpipev \
+  --ranks 2 \
+  --mbs 4
+```
+
+The base schedule `pp4_dp2_ep2_v_placement.json` describes a PP x DP x EP placement with four annotated pipeline regions mapped onto two physical pipeline ranks in a V layout.
+Regions `PP=0` and `PP=3` run on device group `[0, 2]`, while `PP=1` and `PP=2` run on device group `[1, 3]`.
+Within each device group, Piper replicates non-expert regions for DP and shards expert regions for EP.
+This example expects four visible CUDA devices.
+The harness appends DualPipeV `split` and `order` directives for two physical pipeline ranks and four microbatches.
+
+Run the Qwen example with PP x ZeRO-3 1F1B schedule:
+
+```bash
+python examples/test_harness.py \
+  --test-file examples/test_qwen.py \
+  --base-schedule examples/base-schedules/pp2_dp2_ep2_zero3.json \
   --schedule 1f1b \
   --ranks 2 \
   --mbs 4
 ```
 
-Results and the generated full schedule are written under `out/<timestamp>/`. Add `--viz` to also render the generated schedule and per-rank TrainingDAGs.
+The base schedule `pp2_dp2_ep2_zero3.json` describes a PP x DP x EP placement with two pipeline stages, DP degree two, EP sharding, and ZeRO-3-style gradient and parameter sharding.
+Stage `PP=0` runs on device group `[0, 2]`, while `PP=1` runs on device group `[1, 3]`.
+This example also expects four visible CUDA devices.
+The harness appends 1F1B `split` and `order` directives for two pipeline ranks and four microbatches.
+Each run creates `out/<timestamp>/` with the complete generated schedule and metrics.
+
+## Overview of Inputs
+
+### Annotations
+
+`piper.annotate(tag)` is a context manager that attaches Piper metadata to all PyTorch operations traced inside the scope.
+The `tag` is a non-empty string naming a schedulable dimension of the model, such as `PP` for pipeline regions or `EP` for expert regions.
+For each tag name, Piper assigns integer indices in trace order, so repeated `piper.annotate("PP")` scopes become `PP=0`, `PP=1`, and so on.
+
+The schedule directives program uses these tag names and indices in filters to select regions of the traced model.
+For example, a filter can select one concrete region by its tag index, all regions with a tag, or regions that match a combination of tags.
+See the blog walkthrough section on [annotating a Qwen3 MoE model](TODO) for an example and further details.
+
+### Schedule Directives Program
+
+A schedule directive program is a JSON array of directive objects.
+Each directive has an `op` string naming the rewrite to apply to the distributed training DAG IR.
+Most directives use a `filter` object to select the model region to apply the directive to.
+The `order` directive uses `filters` to describe a sequence of filter groups.
+A `filter` is a JSON object whose keys are tag names and whose values are the tag indices to match.
+
+Filter keys can refer to annotation tags, new tags added by previous directives, or the compiler-provided `PASS` tag which supports `F` (forward), `B` (backward), `BI` (backward for inputs), and `BW` (backward for weights) for different training step stages.
+Filter values can be tag indices or the special value `"*"` to match any concrete index for the key.
+
+The empty filter `{}` matches the entire DAG IR.
+All key/value pairs in a filter are conjunctive, so `{"PP": 1, "EP": "*"}` matches nodes that are in pipeline region `PP=1` and have any `EP` index.
+
+Supported directives:
+
+* `place` assigns matched compute regions to device groups and names the stream used for inserted PP send/recv communication.
+  * `op`: `"place"`
+  * `filter`: Selects the model regions to place.
+  * `devices`: Non-empty list of CUDA device IDs for the placement group.
+  * `stream`: Optional logical stream name for inserted point-to-point communication.
+* `replicate` replicates matched regions across devices and inserts DP synchronization.
+  * `op`: `"replicate"`.
+  * `filter`: Selects the model regions to replicate.
+  * `devices`: Non-empty list of CUDA device IDs across which the region is replicated.
+  * `reduce_stream`: Optional logical stream name for gradient reduction collective communication.
+  * `gather_stream`: Optional logical stream name for ZeRO-3 parameter materialization collective communication.
+  * `bucket_size`: Optional parameter bucket size in MB for finer-grained synchronization.
+  * `shard_grads`: Optional boolean enabling ZeRO-2-style gradient sharding.
+  * `shard_params`: Optional boolean enabling ZeRO-3-style parameter sharding.
+* `shard` shards matched regions across devices and inserts all-to-all communication, typically for expert regions.
+  * `op`: `"shard"`
+  * `filter`: Selects the model regions to shard.
+  * `devices`: Non-empty list of CUDA device IDs across which the region is sharded.
+  * `stream`: Optional logical stream name for inserted all-to-all collective communication.
+* `split` duplicates the matched DAG by a named microbatch dimension.
+  * `op`: `"split"`
+  * `filter`: Selects the sub-DAG to duplicate.
+  * `dim_name`: Non-empty string naming the new split dimension.
+  * `num_microbatches`: Positive integer number of copies to create.
+* `order` adds temporal dependencies between filter groups.
+  * `op`: `"order"`
+  * `filters`: List of at least two non-empty filter groups.
+  * Each filter group is a list of filter objects that occupy the same ordering slot.
+  * Consecutive filter groups create temporal dependencies from one slot to the next.
+  * Multiple filters in the same group permit Piper to interleave those sub-DAGs.
+
+In practice, base schedules under `examples/base-schedules/` describe model placement and composed parallelism choices, while `examples/test_harness.py` appends generated `split` and `order` directives for schedule families such as `1f1b`, `interleaved_1f1b`, `zerobubble`, and `dualpipev`.
+For more detail, see the blog walkthrough sections on [PP x DP x EP placement](https://github.com/uw-syfi/uw-syfi.github.io/blob/piper-blog/_posts/2026-06-05-piper.md#scheduling-dualpipe-like-pp-x-dp-x-ep-model-placement), [DualPipe-like pipeline scheduling](https://github.com/uw-syfi/uw-syfi.github.io/blob/piper-blog/_posts/2026-06-05-piper.md#scheduling-a-dualpipe-like-pipeline-schedule), and [schedule builders](https://github.com/uw-syfi/uw-syfi.github.io/blob/piper-blog/_posts/2026-06-05-piper.md#generating-directives-with-schedule-builders).
+
+## Overview of Outputs
+
+Every harness run creates a timestamped directory under `out/`:
+
+```text
+out/<timestamp>/
+|-- <schedule>_pp<ranks>_mbs<mbs>.json
+`-- results.csv
+```
 
-## Citation
+`results.csv` contains a row for each SPMD rank (e.g., each DP rank) reporting the mean/std iteration time, training throughput in tokens/sec, and per-PP-rank peak memory in GB.
+
+Optional artifact generation flags:
 
-TBD
+* `--viz` renders the generated pipeline schedule and per-PP-rank DAG IRs under the run directory.
+* `--pytorch-profiler --pytorch-profiler-iters <n>` runs extra profiled iterations and writes combined Chrome trace files per SPMD rank; Piper annotates GPU events with DAG IR node labels.
 
-## Contact
+## Development
+
+The GitHub Actions workflow runs the CPU-only pytest suite on pushes and pull requests:
+
+```bash
+python -m pytest -m "not gpu" -v test
+```
 
-TBD
+There are currently no pytest tests marked `gpu` under `test/`.
+To run the full pytest suite, including any future GPU-marked tests, use:
+
+```bash
+python -m pytest -v test
+```
+
+For GPU end-to-end validation, run the quickstart examples above on a machine with four visible CUDA devices.
+To run all Qwen example schedules, use `examples/run_qwen_examples.py`.
+
+## Citation
+
+If you use Piper in your research, please cite:
+
+```bibtex
+@inproceedings{frisella2025piper,
+  title = {Piper: Towards Flexible Pipeline Parallelism for PyTorch},
+  author = {Frisella, Megan and Oentoro, Arvin and Gao, Xiangyu and Bernstein, Gilbert and Wang, Stephanie},
+  booktitle = {Proceedings of the 4th Workshop on Practical Adoption Challenges of ML for Systems},
+  year = {2025},
+  publisher = {Association for Computing Machinery},
+  doi = {10.1145/3766882.3767187},
+  url = {https://doi.org/10.1145/3766882.3767187}
+}
+```
diff --git a/examples/base-schedules/pp2.json b/examples/base-schedules/pp2.json
deleted file mode 100644
index 69c2aa0..0000000
--- a/examples/base-schedules/pp2.json
+++ /dev/null
@@ -1,22 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": {
-      "PP": 0
-    },
-    "devices": [
-      0
-    ],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": {
-      "PP": 1
-    },
-    "devices": [
-      1
-    ],
-    "stream": "pp_stream"
-  }
-]
diff --git a/examples/base-schedules/pp2_dp2.json b/examples/base-schedules/pp2_dp2.json
deleted file mode 100644
index 2a764ec..0000000
--- a/examples/base-schedules/pp2_dp2.json
+++ /dev/null
@@ -1,46 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": {
-      "PP": 0
-    },
-    "devices": [
-      0,
-      2
-    ],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": {
-      "PP": 1
-    },
-    "devices": [
-      1,
-      3
-    ],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": {
-      "PP": 0
-    },
-    "devices": [
-      0,
-      2
-    ],
-    "stream": "dp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": {
-      "PP": 1
-    },
-    "devices": [
-      1,
-      3
-    ],
-    "stream": "dp_stream"
-  }
-]
diff --git a/examples/base-schedules/pp2_dp2_ep2_bucket25.json b/examples/base-schedules/pp2_dp2_ep2_bucket100.json
similarity index 94%
rename from examples/base-schedules/pp2_dp2_ep2_bucket25.json
rename to examples/base-schedules/pp2_dp2_ep2_bucket100.json
index ed3d335..3956e3d 100644
--- a/examples/base-schedules/pp2_dp2_ep2_bucket25.json
+++ b/examples/base-schedules/pp2_dp2_ep2_bucket100.json
@@ -31,7 +31,7 @@
       2
     ],
     "reduce_stream": "dp_stream",
-    "bucket_size": 25
+    "bucket_size": 100
   },
   {
     "op": "replicate",
@@ -43,7 +43,7 @@
       3
     ],
     "reduce_stream": "dp_stream",
-    "bucket_size": 25
+    "bucket_size": 100
   },
   {
     "op": "shard",
diff --git a/examples/base-schedules/pp2_dp2_ep2_zero2.json b/examples/base-schedules/pp2_dp2_ep2_zero2.json
index 19f9430..be2379d 100644
--- a/examples/base-schedules/pp2_dp2_ep2_zero2.json
+++ b/examples/base-schedules/pp2_dp2_ep2_zero2.json
@@ -30,7 +30,7 @@
       0,
       2
     ],
-    "stream": "dp_stream",
+    "reduce_stream": "dp_stream",
     "shard_grads": true
   },
   {
@@ -42,7 +42,7 @@
       1,
       3
     ],
-    "stream": "dp_stream",
+    "reduce_stream": "dp_stream",
     "shard_grads": true
   },
   {
diff --git a/examples/base-schedules/pp2_dp2_ep2_zero3.json b/examples/base-schedules/pp2_dp2_ep2_zero3.json
index 3b9a217..1b0939c 100644
--- a/examples/base-schedules/pp2_dp2_ep2_zero3.json
+++ b/examples/base-schedules/pp2_dp2_ep2_zero3.json
@@ -30,7 +30,8 @@
       0,
       2
     ],
-    "stream": "dp_stream",
+    "gather_stream": "dp_stream",
+    "reduce_stream": "dp_stream",
     "shard_grads": true,
     "shard_params": true
   },
@@ -43,7 +44,8 @@
       1,
       3
     ],
-    "stream": "dp_stream",
+    "gather_stream": "dp_stream",
+    "reduce_stream": "dp_stream",
     "shard_grads": true,
     "shard_params": true
   },
diff --git a/examples/base-schedules/pp2_dp2_zero2.json b/examples/base-schedules/pp2_dp2_zero2.json
deleted file mode 100644
index 9b5d715..0000000
--- a/examples/base-schedules/pp2_dp2_zero2.json
+++ /dev/null
@@ -1,48 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": {
-      "PP": 0
-    },
-    "devices": [
-      0,
-      2
-    ],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": {
-      "PP": 1
-    },
-    "devices": [
-      1,
-      3
-    ],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": {
-      "PP": 0
-    },
-    "devices": [
-      0,
-      2
-    ],
-    "stream": "dp_stream",
-    "shard_grads": true
-  },
-  {
-    "op": "replicate",
-    "filter": {
-      "PP": 1
-    },
-    "devices": [
-      1,
-      3
-    ],
-    "stream": "dp_stream",
-    "shard_grads": true
-  }
-]
diff --git a/examples/base-schedules/pp2_dp2_zero3.json b/examples/base-schedules/pp2_dp2_zero3.json
deleted file mode 100644
index 4304f04..0000000
--- a/examples/base-schedules/pp2_dp2_zero3.json
+++ /dev/null
@@ -1,54 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": {
-      "PP": 0
-    },
-    "devices": [
-      0,
-      2
-    ],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": {
-      "PP": 1
-    },
-    "devices": [
-      1,
-      3
-    ],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "replicate",
-    "filter": {
-      "PP": 0
-    },
-    "devices": [
-      0,
-      2
-    ],
-    "gather_stream": "gather_stream",
-    "reduce_stream": "reduce_stream",
-    "shard_grads": true,
-    "shard_params": true,
-    "bucket_size": 1000
-  },
-  {
-    "op": "replicate",
-    "filter": {
-      "PP": 1
-    },
-    "devices": [
-      1,
-      3
-    ],
-    "gather_stream": "gather_stream",
-    "reduce_stream": "reduce_stream",
-    "shard_grads": true,
-    "shard_params": true,
-    "bucket_size": 1000
-  }
-]
diff --git a/examples/base-schedules/pp4_interleaved.json b/examples/base-schedules/pp4_interleaved.json
deleted file mode 100644
index 01e30c5..0000000
--- a/examples/base-schedules/pp4_interleaved.json
+++ /dev/null
@@ -1,42 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": {
-      "PP": 0
-    },
-    "devices": [
-      0
-    ],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": {
-      "PP": 1
-    },
-    "devices": [
-      1
-    ],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": {
-      "PP": 2
-    },
-    "devices": [
-      0
-    ],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": {
-      "PP": 3
-    },
-    "devices": [
-      1
-    ],
-    "stream": "pp_stream"
-  }
-]
diff --git a/examples/base-schedules/pp4_v_placement.json b/examples/base-schedules/pp4_v_placement.json
deleted file mode 100644
index 100e73d..0000000
--- a/examples/base-schedules/pp4_v_placement.json
+++ /dev/null
@@ -1,42 +0,0 @@
-[
-  {
-    "op": "place",
-    "filter": {
-      "PP": 0
-    },
-    "devices": [
-      0
-    ],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": {
-      "PP": 1
-    },
-    "devices": [
-      1
-    ],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": {
-      "PP": 2
-    },
-    "devices": [
-      1
-    ],
-    "stream": "pp_stream"
-  },
-  {
-    "op": "place",
-    "filter": {
-      "PP": 3
-    },
-    "devices": [
-      0
-    ],
-    "stream": "pp_stream"
-  }
-]
diff --git a/examples/models/qwen3.py b/examples/models/qwen3.py
index b851293..8ef8903 100644
--- a/examples/models/qwen3.py
+++ b/examples/models/qwen3.py
@@ -27,7 +27,9 @@ def _run_bmm_experts(
     dim = x.shape[-1]
     num_experts = w1.shape[0]
     counts = num_tokens_per_expert.to(device=x.device, dtype=torch.long)
-    capacity = int(counts.max().item()) if counts.numel() else 0
+    # Keep capacity shape-derived so torch.compile never has to materialize a
+    # routed-token count as a Python scalar.
+    capacity = x.shape[0]
     starts = torch.cumsum(counts, dim=0) - counts
     positions = torch.arange(capacity, device=x.device, dtype=torch.long)
     zero = x.new_zeros((capacity, dim))
@@ -164,7 +166,7 @@ def create_qwen3_config(name: str) -> Qwen3ModelArgs:
             return Qwen3ModelArgs(
                 vocab_size=151936,
                 dim=2048,
-                n_layers=24,
+                n_layers=4,
                 n_heads=32,
                 n_kv_heads=8,
                 head_dim=64,
@@ -357,9 +359,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
         # expert component
         with annotate(EP_TAG):
-            dispatched_input = routed_input.contiguous()
-            routed_output = self.experts(dispatched_input, num_tokens_per_expert)
-            gathered_output = routed_output.contiguous()
+            gathered_output = self.experts(routed_input, num_tokens_per_expert)
 
         gathered_output = gathered_output.reshape(-1, dim)
         out = self.shared_experts(x) if self.shared_experts is not None else None
diff --git a/examples/run_qwen_examples.py b/examples/run_qwen_examples.py
new file mode 100755
index 0000000..15bd1ac
--- /dev/null
+++ b/examples/run_qwen_examples.py
@@ -0,0 +1,172 @@
+#!/usr/bin/env python3
+"""Run the Qwen example schedule set with the normal execution path."""
+
+from __future__ import annotations
+
+import argparse
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+
+QWEN_MODELS = ("9M", "1B", "9B", "48B", "30B-A3B", "30-A3B-half", "72B")
+
+# name, base schedule, generated schedule, pp ranks, microbatches
+RUNS = (
+    (
+        "dualpipev",
+        "examples/base-schedules/pp4_dp2_ep2_v_placement.json",
+        "dualpipev",
+        2,
+        4,
+    ),
+    ("zero3_1f1b", "examples/base-schedules/pp2_dp2_ep2_zero3.json", "1f1b", 2, 4),
+    (
+        "bucket100_1f1b",
+        "examples/base-schedules/pp2_dp2_ep2_bucket100.json",
+        "1f1b",
+        2,
+        4,
+    ),
+    (
+        "interleaved_1f1b",
+        "examples/base-schedules/pp4_dp2_ep2_interleaved.json",
+        "interleaved_1f1b",
+        2,
+        4,
+    ),
+    ("zero2_1f1b", "examples/base-schedules/pp2_dp2_ep2_zero2.json", "1f1b", 2, 4),
+    (
+        "custom_order",
+        "examples/base-schedules/pp2_dp2_ep2_custom_order.json",
+        "custom",
+        2,
+        2,
+    ),
+    ("zerobubble", "examples/base-schedules/pp2_dp2_ep2.json", "zerobubble", 2, 4),
+)
+
+
+def main(argv: list[str]) -> int:
+    args = _parse_args(argv)
+    repo_root = Path(__file__).resolve().parent.parent
+    python_bin = _python_bin(repo_root)
+    forwarded_args = _forwarded_args(args)
+
+    print(f"Python: {python_bin}", flush=True)
+    if forwarded_args:
+        print(f"Forwarded args: {' '.join(forwarded_args)}", flush=True)
+
+    status = 0
+    for name, base_schedule, schedule, ranks, mbs in RUNS:
+        print(flush=True)
+        print(
+            f"==> {name}: {schedule} {base_schedule} --ranks {ranks} --mbs {mbs}",
+            flush=True,
+        )
+        cmd = [
+            python_bin,
+            "examples/test_harness.py",
+            "--test-file",
+            "examples/test_qwen.py",
+            "--base-schedule",
+            base_schedule,
+            "--schedule",
+            schedule,
+            "--ranks",
+            str(ranks),
+            "--mbs",
+            str(mbs),
+            *forwarded_args,
+        ]
+        result = subprocess.run(cmd, cwd=repo_root, check=False)
+        if result.returncode:
+            print(f"FAILED: {name}", file=sys.stderr, flush=True)
+            status = 1
+        else:
+            print(f"PASSED: {name}", flush=True)
+
+    return status
+
+
+def _parse_args(argv: list[str]) -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Run all Qwen example schedules with the normal execution path."
+    )
+    parser.add_argument("--model", choices=QWEN_MODELS)
+    parser.add_argument("--batch-size", type=int)
+    parser.add_argument("--seq-len", type=int)
+    parser.add_argument("--warmup", type=int)
+    parser.add_argument("--iters", type=int)
+    parser.add_argument("--iteration-sleep", type=float)
+    parser.add_argument("--activation-checkpointing", action="store_true")
+    parser.add_argument("--nsight", action="store_true")
+    parser.add_argument("--viz", action="store_true")
+    parser.add_argument(
+        "--use-inductor",
+        action=argparse.BooleanOptionalAction,
+        default=None,
+        help="Compile actor stage modules with torch.compile.",
+    )
+    parser.add_argument(
+        "--pp-outer",
+        action=argparse.BooleanOptionalAction,
+        default=None,
+        help="Use PP as the outer placement dimension.",
+    )
+    parser.add_argument("--pytorch-profiler", action="store_true")
+    parser.add_argument("--pytorch-profiler-iters", type=int)
+    parser.add_argument("--address")
+    parser.add_argument("--port", type=int)
+    parser.add_argument("--temp-dir", help="Ray temp directory.")
+    parser.add_argument("--ray-namespace")
+    return parser.parse_args(argv)
+
+
+def _forwarded_args(args: argparse.Namespace) -> list[str]:
+    forwarded: list[str] = []
+    _append_value(forwarded, "--model", args.model)
+    _append_value(forwarded, "--batch-size", args.batch_size)
+    _append_value(forwarded, "--seq-len", args.seq_len)
+    _append_value(forwarded, "--warmup", args.warmup)
+    _append_value(forwarded, "--iters", args.iters)
+    _append_value(forwarded, "--iteration-sleep", args.iteration_sleep)
+    if args.activation_checkpointing:
+        forwarded.append("--activation-checkpointing")
+    if args.nsight:
+        forwarded.append("--nsight")
+    if args.viz:
+        forwarded.append("--viz")
+    if args.use_inductor is not None:
+        forwarded.append("--use-inductor" if args.use_inductor else "--no-use-inductor")
+    if args.pp_outer is not None:
+        forwarded.append("--pp-outer" if args.pp_outer else "--no-pp-outer")
+    if args.pytorch_profiler:
+        forwarded.append("--pytorch-profiler")
+    _append_value(forwarded, "--pytorch-profiler-iters", args.pytorch_profiler_iters)
+    _append_value(forwarded, "--address", args.address)
+    _append_value(forwarded, "--port", args.port)
+    _append_value(forwarded, "--temp-dir", args.temp_dir)
+    _append_value(forwarded, "--ray-namespace", args.ray_namespace)
+    return forwarded
+
+
+def _append_value(out: list[str], flag: str, value: object | None) -> None:
+    if value is not None:
+        out.extend([flag, str(value)])
+
+
+def _python_bin(repo_root: Path) -> str:
+    if os.environ.get("PYTHON"):
+        return os.environ["PYTHON"]
+
+    venv_python = repo_root / ".venv" / "bin" / "python"
+    if venv_python.exists():
+        return str(venv_python)
+
+    return sys.executable
+
+
+if __name__ == "__main__":
+    raise SystemExit(main(sys.argv[1:]))
diff --git a/examples/test_harness.py b/examples/test_harness.py
index 6c60cf4..962906b 100644
--- a/examples/test_harness.py
+++ b/examples/test_harness.py
@@ -16,7 +16,6 @@
 import re
 import statistics
 import sys
-import tempfile
 from datetime import datetime
 from pathlib import Path
 
@@ -85,13 +84,6 @@ def main() -> None:
         default=None,
         help=argparse.SUPPRESS,
     )
-    parser.add_argument(
-        "--generated-schedule-file",
-        "--output",
-        type=Path,
-        default=None,
-        help="Where to write the generated full schedule JSON.",
-    )
     parser.add_argument(
         "--keep-existing-order",
         action="store_true",
@@ -102,12 +94,6 @@ def main() -> None:
         action="store_true",
         help="Write the generated schedule and print the test command without running it.",
     )
-    parser.add_argument(
-        "--schedule-viz-file",
-        type=Path,
-        default=None,
-        help="Where to render the generated schedule visualization (default: out/<schedule>.png).",
-    )
     parser.add_argument(
         "--viz",
         action="store_true",
@@ -119,13 +105,6 @@ def main() -> None:
         help="Run extra iterations under torch.profiler and combine the per-actor "
              "chrome traces into one trace per dp-rank under out/.",
     )
-    parser.add_argument(
-        "--pytorch-profile-dir",
-        type=Path,
-        default=None,
-        help="Shared directory where actors write per-actor chrome traces "
-             "(default: <run_dir>/pytorch_profiles). Removed after combining.",
-    )
     parser.add_argument(
         "--address",
         default="",
@@ -172,23 +151,24 @@ def main() -> None:
 
     generated_schedule, viz_path = build_schedule_file(args, run_dir)
     forwarded_args = _replace_schedule_arg(test_args, generated_schedule)
-    forwarded_args = _replace_arg(forwarded_args, "--output-dir", str(run_dir))
     if args.viz:
         forwarded_args.append("--viz")
 
-    profile_args: list[str] = []
     profile_dir: Path | None = None
-    if args.pytorch_profiler:
+    if args.pytorch_profiler and not args.dry_run:
         # Absolute path on the shared filesystem so remote actors (possibly on
         # other nodes) write to the same place the harness later reads.
-        profile_dir = (args.pytorch_profile_dir or (run_dir / "pytorch_profiles")).resolve()
+        profile_dir = (run_dir / "pytorch_profiles").resolve()
         if profile_dir.exists():
             for stale in profile_dir.glob("dp*_pp*.json"):
                 stale.unlink()
         profile_dir.mkdir(parents=True, exist_ok=True)
-        profile_args = ["--pytorch-profiler", "--pytorch-profile-dir", str(profile_dir)]
 
     if args.dry_run:
+        dry_run_args = list(forwarded_args)
+        if args.pytorch_profiler:
+            dry_run_args.append("--pytorch-profiler")
+        _validate_test_args(args.test_file, dry_run_args)
         cmd = [
             sys.executable,
             str(Path(__file__).resolve()),
@@ -203,41 +183,42 @@ def main() -> None:
             cmd.extend(["--ranks", str(args.ranks)])
         if args.mbs is not None:
             cmd.extend(["--mbs", str(args.mbs)])
-        cmd.extend([*forwarded_args, *profile_args])
+        cmd.extend(dry_run_args)
         print(" ".join(cmd))
         print(f"generated_schedule={generated_schedule}")
         if viz_path is not None:
             print(f"schedule_viz={viz_path}")
         return
 
-    with tempfile.TemporaryDirectory() as tmp:
-        metrics_json = Path(tmp) / "metrics.json"
-        forwarded_args = [
-            *forwarded_args,
-            *profile_args,
-            "--metrics-json-out",
-            str(metrics_json),
-        ]
-        dp_metrics = _run_test_module(args, forwarded_args)
-        if metrics_json.exists():
-            with metrics_json.open(encoding="utf-8") as f:
-                dp_metrics = json.load(f)
-        if dp_metrics:
-            rows = _metrics_rows(dp_metrics)
-            results_csv = run_dir / "results.csv"
-            _write_results_csv(results_csv, rows)
-            print(f"metrics written to {results_csv}")
-        if profile_dir is not None:
-            _combine_pytorch_profiles(
-                profile_dir,
-                run_dir,
-                schedule=args.schedule,
-                pp=args.ranks if args.ranks is not None else "X",
-                mbs=args.mbs if args.mbs is not None else "X",
-            )
+    if args.pytorch_profiler:
+        forwarded_args.append("--pytorch-profiler")
+    dp_metrics = _run_test_module(args, forwarded_args, profile_dir=profile_dir)
+    if dp_metrics:
+        rows = _metrics_rows(dp_metrics)
+        results_csv = run_dir / "results.csv"
+        _write_results_csv(results_csv, rows)
+        print(f"metrics written to {results_csv}")
+    if profile_dir is not None:
+        _combine_pytorch_profiles(
+            profile_dir,
+            run_dir,
+            schedule=args.schedule,
+            pp=args.ranks if args.ranks is not None else "X",
+            mbs=args.mbs if args.mbs is not None else "X",
+        )
 
 
-def _run_test_module(args: argparse.Namespace, test_args: list[str]) -> list[dict]:
+def _validate_test_args(test_file: str, test_args: list[str]) -> None:
+    _, test_module = _load_test_file(test_file)
+    test_module.parse_args(test_args)
+
+
+def _run_test_module(
+    args: argparse.Namespace,
+    test_args: list[str],
+    *,
+    profile_dir: Path | None = None,
+) -> list[dict]:
     logging.basicConfig(
         level=logging.INFO,
         format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
@@ -246,6 +227,7 @@ def _run_test_module(args: argparse.Namespace, test_args: list[str]) -> list[dic
     test_ns = test_module.parse_args(test_args)
     if hasattr(test_ns, "temp_dir"):
         test_ns.temp_dir = args.temp_dir
+    test_ns.profile_dir = str(profile_dir) if profile_dir is not None else ""
     if hasattr(test_module, "_derive_num_stages"):
         test_ns.num_stages = test_module._derive_num_stages(test_ns.schedule_directives_file)
 
@@ -268,13 +250,6 @@ def _run_test_module(args: argparse.Namespace, test_args: list[str]) -> list[dic
         )
         handles = coordinator.run_program.remote(test_module.main, pg, test_ns, pg)
         dp_metrics = ray.get(handles)
-        metrics_json_out = getattr(test_ns, "metrics_json_out", "")
-        if metrics_json_out:
-            metrics_path = Path(metrics_json_out)
-            metrics_path.parent.mkdir(parents=True, exist_ok=True)
-            with metrics_path.open("w", encoding="utf-8") as f:
-                json.dump(dp_metrics, f)
-            logging.getLogger(module_name).info("Raw metrics written to %s", metrics_path)
         return dp_metrics
     finally:
         ray.shutdown()
@@ -319,6 +294,7 @@ def _combine_pytorch_profiles(
             with path.open(encoding="utf-8") as f:
                 trace = json.load(f)
             events = trace.get("traceEvents", [])
+            annotated = _annotate_cuda_events_with_dag_labels(events)
             pid_map: dict = {}
             for ev in events:
                 opid = ev.get("pid")
@@ -333,6 +309,11 @@ def _combine_pytorch_profiles(
                     ev["args"]["name"] = f"dp{dp_rank} pp{pp_rank}: {pname}"
                 combined_events.append(ev)
             consumed.append(path)
+            if annotated:
+                print(
+                    f"pytorch profiler: annotated {annotated} CUDA event(s) "
+                    f"with DAG labels for dp{dp_rank} pp{pp_rank}"
+                )
         out_name = (
             f"pytorch_profile_{schedule}_pp{pp}_dp{dp_degree}"
             f"_mbs{mbs}_dprank{dp_rank}.json"
@@ -353,6 +334,193 @@ def _combine_pytorch_profiles(
         pass
 
 
+def _is_dag_node_label(name: object) -> bool:
+    return isinstance(name, str) and ":uid" in name and not name.startswith("##")
+
+
+def _annotate_cuda_events_with_dag_labels(events: list[dict]) -> int:
+    """Prefix GPU-side profiler events with their enclosing Piper DAG node.
+
+    ``torch.profiler`` emits Piper ``record_function`` ranges on the CPU thread,
+    while CUDA kernels and copies appear on GPU lanes with PyTorch/Inductor
+    names such as ``## Call CompiledFxGraph ...``.  CPU launch events and GPU
+    events share ``args["External id"]``; use that to carry the enclosing DAG
+    node label onto the GPU events in the combined Chrome trace.  Then rewrite
+    nested non-DAG GPU annotations so the visible annotation stack stays rooted
+    in Piper's TrainingDAG labels instead of Inductor's opaque graph hashes.
+    """
+    ranges_by_thread: dict[tuple[object, object], list[tuple[float, float, str]]] = {}
+    for ev in events:
+        if ev.get("ph") != "X" or ev.get("cat") != "user_annotation":
+            continue
+        label = ev.get("name")
+        if not _is_dag_node_label(label):
+            continue
+        ts = ev.get("ts")
+        dur = ev.get("dur")
+        if not isinstance(ts, (int, float)) or not isinstance(dur, (int, float)):
+            continue
+        ranges_by_thread.setdefault((ev.get("pid"), ev.get("tid")), []).append(
+            (float(ts), float(ts) + float(dur), label)
+        )
+    for ranges in ranges_by_thread.values():
+        ranges.sort(key=lambda item: (item[0], -item[1]))
+
+    external_id_to_label: dict[object, str] = {}
+    active: dict[tuple[object, object], list[tuple[float, str]]] = {}
+    cpu_events = sorted(
+        (
+            ev for ev in events
+            if ev.get("ph") == "X"
+            and ev.get("cat") != "kernel"
+            and str(ev.get("cat", "")).startswith(("cpu_op", "user_annotation"))
+        ),
+        key=lambda ev: float(ev.get("ts", 0.0) or 0.0),
+    )
+
+    range_idx = {key: 0 for key in ranges_by_thread}
+    for ev in cpu_events:
+        ts = ev.get("ts")
+        if not isinstance(ts, (int, float)):
+            continue
+        key = (ev.get("pid"), ev.get("tid"))
+        ranges = ranges_by_thread.get(key)
+        if not ranges:
+            continue
+        stack = active.setdefault(key, [])
+        while stack and stack[-1][0] < float(ts):
+            stack.pop()
+        idx = range_idx[key]
+        while idx < len(ranges) and ranges[idx][0] <= float(ts):
+            _start, end, label = ranges[idx]
+            if end >= float(ts):
+                stack.append((end, label))
+            idx += 1
+        range_idx[key] = idx
+        if not stack:
+            continue
+        ext_id = (ev.get("args") or {}).get("External id")
+        if ext_id is not None:
+            external_id_to_label[ext_id] = stack[-1][1]
+
+    annotated = 0
+    cuda_event_cats = {"kernel", "gpu_memcpy", "gpu_memset"}
+    for ev in events:
+        if ev.get("ph") != "X" or ev.get("cat") not in cuda_event_cats:
+            continue
+        args = ev.setdefault("args", {})
+        ext_id = args.get("External id")
+        label = external_id_to_label.get(ext_id)
+        if not label:
+            continue
+        name = ev.get("name", "")
+        prefix = f"{label}::"
+        if isinstance(name, str) and name.startswith(prefix):
+            continue
+        args.setdefault("piper_dag_node", label)
+        args.setdefault("original_cuda_name", name)
+        ev["name"] = f"{prefix}{name}"
+        annotated += 1
+    annotated += _rewrite_nested_gpu_annotations(events)
+    return annotated
+
+
+def _rewrite_nested_gpu_annotations(events: list[dict]) -> int:
+    gpu_annotations = [
+        ev for ev in events
+        if ev.get("ph") == "X" and ev.get("cat") == "gpu_user_annotation"
+    ]
+    dag_ranges_by_lane: dict[tuple[object, object], list[tuple[float, float, str]]] = {}
+    for ev in gpu_annotations:
+        label = ev.get("name")
+        if not _is_dag_node_label(label):
+            continue
+        ts = ev.get("ts")
+        dur = ev.get("dur")
+        if not isinstance(ts, (int, float)) or not isinstance(dur, (int, float)):
+            continue
+        dag_ranges_by_lane.setdefault((ev.get("pid"), ev.get("tid")), []).append(
+            (float(ts), float(ts) + float(dur), label)
+        )
+    for ranges in dag_ranges_by_lane.values():
+        ranges.sort(key=lambda item: (item[0], item[1] - item[0]))
+
+    cuda_events_by_lane: dict[tuple[object, object], list[dict]] = {}
+    for ev in events:
+        if ev.get("ph") == "X" and ev.get("cat") in {"kernel", "gpu_memcpy", "gpu_memset"}:
+            cuda_events_by_lane.setdefault((ev.get("pid"), ev.get("tid")), []).append(ev)
+    for lane_events in cuda_events_by_lane.values():
+        lane_events.sort(key=lambda ev: float(ev.get("ts", 0.0) or 0.0))
+
+    rewritten = 0
+    for ev in gpu_annotations:
+        name = ev.get("name")
+        if _is_dag_node_label(name):
+            continue
+        ts = ev.get("ts")
+        dur = ev.get("dur")
+        if not isinstance(ts, (int, float)) or not isinstance(dur, (int, float)):
+            continue
+        start = float(ts)
+        end = start + float(dur)
+        lane = (ev.get("pid"), ev.get("tid"))
+        label = _enclosing_dag_gpu_label(dag_ranges_by_lane.get(lane, []), start, end)
+        if label is None:
+            label = _contained_cuda_dag_label(
+                cuda_events_by_lane.get(lane, []), start, end
+            )
+        if label is None:
+            continue
+        args = ev.setdefault("args", {})
+        args.setdefault("original_gpu_annotation", name)
+        args.setdefault("piper_dag_node", label)
+        ev["name"] = label
+        rewritten += 1
+    return rewritten
+
+
+def _enclosing_dag_gpu_label(
+    dag_ranges: list[tuple[float, float, str]], start: float, end: float
+) -> str | None:
+    label = None
+    best_duration = None
+    for dag_start, dag_end, dag_label in dag_ranges:
+        if dag_start > start:
+            break
+        if dag_end < end:
+            continue
+        duration = dag_end - dag_start
+        if best_duration is None or duration < best_duration:
+            label = dag_label
+            best_duration = duration
+    return label
+
+
+def _contained_cuda_dag_label(
+    cuda_events: list[dict], start: float, end: float
+) -> str | None:
+    labels: dict[str, float] = {}
+    for ev in cuda_events:
+        ev_start = ev.get("ts")
+        ev_dur = ev.get("dur")
+        if not isinstance(ev_start, (int, float)) or not isinstance(ev_dur, (int, float)):
+            continue
+        ev_start = float(ev_start)
+        if ev_start > end:
+            break
+        ev_end = ev_start + float(ev_dur)
+        if ev_end < start:
+            continue
+        label = (ev.get("args") or {}).get("piper_dag_node")
+        if not label:
+            continue
+        overlap = max(0.0, min(end, ev_end) - max(start, ev_start))
+        labels[label] = labels.get(label, 0.0) + overlap
+    if not labels:
+        return None
+    return max(labels.items(), key=lambda item: item[1])[0]
+
+
 def _metrics_rows(dp_metrics: list[dict]) -> list[dict]:
     """Summarize the raw metrics into one CSV row per DP rank (no averaging
     across DP ranks).
@@ -460,25 +628,15 @@ def build_schedule_file(args: argparse.Namespace, run_dir: Path) -> tuple[Path,
         base_schedule=args.base_schedule,
     )
     run_schedule = run_dir / f"{schedule_stem}.json"
-    output = args.generated_schedule_file or run_schedule
-    output.parent.mkdir(parents=True, exist_ok=True)
-    with output.open("w", encoding="utf-8") as f:
+    with run_schedule.open("w", encoding="utf-8") as f:
         json.dump(schedule, f, indent=2)
         f.write("\n")
 
-    # Drop a copy of the complete schedule into the run directory so the exact
-    # directives used (base + appended order) are visible alongside results.csv,
-    # profile traces, and the schedule viz.
-    if run_schedule.resolve() != output.resolve():
-        with run_schedule.open("w", encoding="utf-8") as f:
-            json.dump(schedule, f, indent=2)
-            f.write("\n")
-
     viz_path = None
     if args.viz:
-        viz_output = args.schedule_viz_file or run_dir / f"{schedule_stem}.png"
+        viz_output = run_dir / f"{schedule_stem}.png"
         viz_path = visualize_order_directives(order_directives, viz_output)
-    return output, viz_path
+    return run_schedule, viz_path
 
 
 def _schedule_attr_stem(
diff --git a/examples/test_llama.py b/examples/test_llama.py
index f764952..d024c63 100644
--- a/examples/test_llama.py
+++ b/examples/test_llama.py
@@ -100,7 +100,6 @@ def main(args, pg):
         nsight=args.nsight,
         temp_dir=args.temp_dir,
         visualize_dag=args.viz,
-        output_dir=args.output_dir,
         const_attrs={"freqs_cis": freqs_cis, "mask": mask},
         use_inductor=args.use_inductor,
         pp_outer=args.pp_outer,
@@ -130,13 +129,16 @@ def main(args, pg):
     metrics = _raw_metrics(args, iter_times, peak_memory_stats)
 
     if args.pytorch_profiler:
+        profile_dir = getattr(args, "profile_dir", "") or os.path.join(
+            "out", "pytorch_profiles"
+        )
         logger.info(f"Running {args.pytorch_profiler_iters} PyTorch-profiled iterations")
         ray.get([actor.start_pytorch_profiler.remote() for actor in actors.values()])
         for _ in range(args.pytorch_profiler_iters):
             piper_exec_dag(loss_fn)
             time.sleep(1)
         ray.get([
-            actor.stop_pytorch_profiler.remote(args.pytorch_profile_dir)
+            actor.stop_pytorch_profiler.remote(profile_dir)
             for actor in actors.values()
         ])
 
@@ -166,7 +168,6 @@ def parse_args(argv=None):
     parser.add_argument("--nsight", action="store_true", default=False)
     parser.add_argument("--viz", action="store_true", default=False)
     parser.add_argument("--temp-dir", default="/tmp/piper/ray_tmp")
-    parser.add_argument("--output-dir", default="out")
     parser.add_argument("--use-inductor", action=argparse.BooleanOptionalAction, default=True)
     parser.add_argument("--pp-outer", action=argparse.BooleanOptionalAction, default=False)
     parser.add_argument(
@@ -175,13 +176,6 @@ def parse_args(argv=None):
         default="examples/base-schedules/pp2.json",
         help="JSON file containing TrainingDAG schedule directives",
     )
-    parser.add_argument(
-        "--metrics-json-out",
-        type=str,
-        default="",
-        help="If set, write the raw per-dp-rank metrics list as JSON to this path "
-             "(consumed by test_harness to build the metrics CSV).",
-    )
     parser.add_argument(
         "--pytorch-profiler",
         action="store_true",
@@ -189,13 +183,6 @@ def parse_args(argv=None):
         help="Run extra iterations under torch.profiler on every actor and write "
              "per-actor chrome traces (combined per dp-rank by test_harness).",
     )
-    parser.add_argument(
-        "--pytorch-profile-dir",
-        type=str,
-        default="",
-        help="Directory where actors write per-actor chrome traces "
-             "(dp{dp}_pp{pp}.json). Set by test_harness.",
-    )
     parser.add_argument(
         "--pytorch-profiler-iters",
         type=int,
diff --git a/examples/test_qwen.py b/examples/test_qwen.py
index cdbbbae..acae165 100644
--- a/examples/test_qwen.py
+++ b/examples/test_qwen.py
@@ -74,7 +74,6 @@ def main(args, pg):
         nsight=args.nsight,
         temp_dir=args.temp_dir,
         visualize_dag=args.viz,
-        output_dir=args.output_dir,
         const_attrs={"rope_cache": rope_cache},
         use_inductor=args.use_inductor,
         pp_outer=args.pp_outer,
@@ -109,6 +108,9 @@ def main(args, pg):
     metrics = _raw_metrics(args, iter_times, peak_memory_stats)
 
     if args.pytorch_profiler:
+        profile_dir = getattr(args, "profile_dir", "") or os.path.join(
+            "out", "pytorch_profiles"
+        )
         logger.info(f"Running {args.pytorch_profiler_iters} PyTorch-profiled iterations")
         ray.get([actor.start_pytorch_profiler.remote() for actor in actors.values()])
         for _ in range(args.pytorch_profiler_iters):
@@ -116,7 +118,7 @@ def main(args, pg):
             if args.iteration_sleep > 0:
                 time.sleep(args.iteration_sleep)
         ray.get([
-            actor.stop_pytorch_profiler.remote(args.pytorch_profile_dir)
+            actor.stop_pytorch_profiler.remote(profile_dir)
             for actor in actors.values()
         ])
 
@@ -162,8 +164,6 @@ def parse_args(argv=None):
                         help="Save schedule and per-rank DAG visualizations")
     parser.add_argument("--temp-dir", default="/tmp/piper/ray_tmp",
                         help="Ray temp directory (default: /tmp/piper/ray_tmp)")
-    parser.add_argument("--output-dir", default="out",
-                        help="Directory for benchmark metrics output (default: out)")
     parser.add_argument(
         "--use-inductor",
         action=argparse.BooleanOptionalAction,
@@ -187,13 +187,6 @@ def parse_args(argv=None):
         default="examples/base-schedules/pp2.json",
         help="JSON file containing schedule directives for the piper backend",
     )
-    parser.add_argument(
-        "--metrics-json-out",
-        type=str,
-        default="",
-        help="If set, write the raw per-dp-rank metrics list as JSON to this path "
-             "(consumed by test_harness to build the metrics CSV).",
-    )
     parser.add_argument(
         "--pytorch-profiler",
         action="store_true",
@@ -201,13 +194,6 @@ def parse_args(argv=None):
         help="Run extra iterations under torch.profiler on every actor and write "
              "per-actor chrome traces (combined per dp-rank by test_harness).",
     )
-    parser.add_argument(
-        "--pytorch-profile-dir",
-        type=str,
-        default="",
-        help="Directory where actors write per-actor chrome traces "
-             "(dp{dp}_pp{pp}.json). Set by test_harness.",
-    )
     parser.add_argument(
         "--pytorch-profiler-iters",
         type=int,
diff --git a/figs/architecture.jpg b/figs/architecture.jpg
new file mode 100644
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BUcLYT

literal 0
HcmV?d00001

diff --git a/pyproject.toml b/pyproject.toml
index 859cf31..62d361d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,6 +8,7 @@ version = "0.1.0"
 description = "User-controllable distributed training schedules for large-scale ML models."
 readme = "README.md"
 requires-python = ">=3.10"
+license = "MIT"
 dependencies = [
   "torchtitan",
   "numpy",
@@ -25,9 +26,18 @@ dev = [
   "pytest",
 ]
 
+[project.urls]
+Repository = "https://github.com/uw-syfi/piper"
+Issues = "https://github.com/uw-syfi/piper/issues"
+Paper = "TODO"
+Blog = "TODO"
+
 [tool.setuptools.packages.find]
 where = ["."]
 include = ["src", "src.*", "test", "test.*", "examples", "examples.*"]
 
 [tool.pytest.ini_options]
 testpaths = ["test"]
+markers = [
+  "gpu: tests requiring CUDA GPUs",
+]
diff --git a/src/actor.py b/src/actor.py
index 5e1a1df..4619e86 100644
--- a/src/actor.py
+++ b/src/actor.py
@@ -24,6 +24,13 @@
 
 logger = create_logger("actor", LOG_LEVEL)
 
+def _disable_functorch_donated_buffers() -> None:
+    import importlib
+
+    config = importlib.import_module("torch._functorch.config")
+    config.donated_buffer = False
+
+
 def _get_rank(pp_rank, dp_rank, pp_degree):
     return pp_rank + dp_rank * pp_degree
 
@@ -111,6 +118,8 @@ def __init__(
 
         self.optim_class = optim_class
         self.use_inductor = bool(use_inductor)
+        if self.use_inductor:
+            _disable_functorch_donated_buffers()
         self.runtime = RuntimeState(
             pp_rank=pp_rank,
             dp_rank=dp_rank,
diff --git a/src/compile.py b/src/compile.py
index 22616de..8280fdd 100644
--- a/src/compile.py
+++ b/src/compile.py
@@ -3,6 +3,7 @@
 import copy
 import os
 import time
+from pathlib import Path
 
 from .actor import _create_actors
 from .state import (
@@ -107,6 +108,11 @@ def _resolve_model_constructor_value(value, schedule_info: dict):
     return value
 
 
+def _artifact_dir_for_schedule(schedule_directives_file: str) -> str:
+    parent = Path(schedule_directives_file).parent
+    return str(parent if str(parent) else Path("out"))
+
+
 def piper_setup(
     model_class,
     model_args=(),
@@ -123,7 +129,6 @@ def piper_setup(
     nsight=False,
     temp_dir: str = None,
     visualize_dag: bool = False,
-    output_dir: str = "out",
     const_attrs: dict = None,
     pp_outer: bool = False,
     schedule_directives_file: str | None = None,
@@ -159,7 +164,7 @@ def piper_setup(
         raise ValueError("piper_setup requires schedule_directives_file")
 
     piper_metadata.visualize_dag = visualize_dag
-    piper_metadata.output_dir = output_dir
+    piper_metadata.artifact_dir = _artifact_dir_for_schedule(schedule_directives_file)
     piper_metadata.schedule_directives = list(schedule_directives or [])
     piper_metadata.schedule_directives_file = schedule_directives_file
     piper_metadata.schedule_info = dict(schedule_info)
diff --git a/src/directives.py b/src/directives.py
index cca1cfb..b6cf5c2 100644
--- a/src/directives.py
+++ b/src/directives.py
@@ -9,6 +9,7 @@
     TrainingDAG,
     TrainingDAGEdge,
     TrainingDAGNode,
+    _has_path,
     _iter_data_edges,
     _remove_edge,
     _remove_node_and_incident_edges,
@@ -1074,6 +1075,7 @@ def _a2a_boundary_for_edge(src_node: TrainingDAGNode, dst_node: TrainingDAGNode)
                     )
             _remove_node_and_incident_edges(dag, comm_uid)
         if removable_comm_uids:
+            node.node_meta.pop("zero_alloc_full_grads_before", None)
             node.node_meta.pop("zero_free_full_params_after", None)
         if node.compute_subkind == "BWD_W":
             continue
@@ -1467,7 +1469,48 @@ def _add_order_dummy_node(
     return uid
 
 
-def _add_temporal_order_edge(dag: TrainingDAG, src_uid: str, dst_uid: str) -> None:
+def _device_key_for_order_edge(dag: TrainingDAG, uid: str) -> tuple[int, ...] | None:
+    device = dag.nodes[uid].device
+    if device is None:
+        return None
+    return tuple(sorted(int(d) for d in device))
+
+
+def _validate_order_edge(
+    dag: TrainingDAG,
+    src_uid: str,
+    dst_uid: str,
+    *,
+    directive_idx: int,
+) -> None:
+    if _has_path(dag, dst_uid, src_uid):
+        raise ValueError(
+            f"order directive[{directive_idx}] violates model dataflow: "
+            f"adding temporal edge {src_uid} -> {dst_uid} would create a cycle"
+        )
+
+    src_device = _device_key_for_order_edge(dag, src_uid)
+    dst_device = _device_key_for_order_edge(dag, dst_uid)
+    if src_device is None or dst_device is None:
+        raise ValueError(
+            f"order directive[{directive_idx}] requires placed nodes with a single device set: "
+            f"{src_uid} device={src_device}, {dst_uid} device={dst_device}"
+        )
+    if src_device != dst_device:
+        raise ValueError(
+            f"order directive[{directive_idx}] crosses device placement: "
+            f"{src_uid} device={src_device}, {dst_uid} device={dst_device}"
+        )
+
+
+def _add_temporal_order_edge(
+    dag: TrainingDAG,
+    src_uid: str,
+    dst_uid: str,
+    *,
+    directive_idx: int,
+) -> None:
+    _validate_order_edge(dag, src_uid, dst_uid, directive_idx=directive_idx)
     logger.debug("Adding temporal dependency edge for order directive: %s -> %s", src_uid, dst_uid)
     dag.add_edge(
         TrainingDAGEdge(
@@ -1527,16 +1570,16 @@ def _apply_order_directive(
         )
         for csrcs in group_sources:
             for v in csrcs:
-                _add_temporal_order_edge(dag, src_uid, v)
+                _add_temporal_order_edge(dag, src_uid, v, directive_idx=directive_idx)
         for csnks in group_sinks:
             for u in csnks:
-                _add_temporal_order_edge(dag, u, sink_uid)
+                _add_temporal_order_edge(dag, u, sink_uid, directive_idx=directive_idx)
         segments.append(_OrderSegment(source_uids={src_uid}, sink_uids={sink_uid}))
 
     for i in range(len(segments) - 1):
         for u in segments[i].sink_uids:
             for v in segments[i + 1].source_uids:
-                _add_temporal_order_edge(dag, u, v)
+                _add_temporal_order_edge(dag, u, v, directive_idx=directive_idx)
 
 
 def apply_schedule_directives(training_dag: TrainingDAG, directives: list[Any] | None) -> None:
diff --git a/src/executors.py b/src/executors.py
index c0383f1..c5d0c59 100644
--- a/src/executors.py
+++ b/src/executors.py
@@ -308,6 +308,14 @@ def fused_backward(self, stage_outputs_or_loss: list, output_grads: Any, weights
                     w.grad = self.grad_with_param_layout(w, w.grad)
                 w.grad += dw
 
+    def backward_weight_from_outputs(
+        self,
+        stage_outputs_or_loss: list,
+        output_grads: Any,
+        weights: Any,
+    ) -> None:
+        self.fused_backward(stage_outputs_or_loss, output_grads, list(weights))
+
     def bucket_backward_input(
         self,
         stage_outputs_or_loss: list,
@@ -326,7 +334,7 @@ def bucket_backward_input(
             for i, t in enumerate(stage_outputs_or_loss):
                 if isinstance(t, torch.Tensor):
                     stage_outputs_or_loss[i] = t.detach()
-            return (), []
+            return (), [], None
 
         stage_output_grad_fns = list(filter(None, map(_get_grad_fn_or_grad_acc, stage_outputs_or_loss)))
         stage_input_grad_fns = list(filter(None, map(_get_grad_fn_or_grad_acc, input_values)))
@@ -334,7 +342,6 @@ def bucket_backward_input(
 
         reverse_edges_dict = construct_reverse_graph(stage_output_grad_fns)
         param_groups = get_param_groups(stage_input_grad_fns, weight_grad_fns, reverse_edges_dict)
-
         handles = []
         for param_group in param_groups:
             for i, intermediate in enumerate(param_group["intermediates"]):
@@ -359,14 +366,15 @@ def hook(grad_inputs):
             else:
                 inp.grad += dinput
 
-        for i, t in enumerate(stage_outputs_or_loss):
-            if isinstance(t, torch.Tensor):
-                stage_outputs_or_loss[i] = t.detach()
+        output_backward_ctx = {
+            "stage_outputs_or_loss": list(stage_outputs_or_loss),
+            "output_grads": output_grads,
+        }
 
         for handle in handles:
             handle.remove()
 
-        return dinputs, param_groups
+        return dinputs, param_groups, output_backward_ctx
 
     def bucket_backward_weight(
         self,
@@ -503,6 +511,26 @@ def _wait_for_all_gather(self, compute_node: Any) -> None:
                 if ag_evt is not None:
                     compute_stream.wait_event(ag_evt)
 
+    def _all_to_all_ep_boundary(
+        self,
+        node: Any,
+        tensor: torch.Tensor,
+        stream: torch.cuda.Stream,
+    ) -> torch.Tensor:
+        direction = self._node_meta(node).get("direction")
+        if direction == "outgoing":
+            tensor = tensor.contiguous()
+        elif direction != "incoming":
+            raise ValueError(
+                f"A2A node uid={getattr(node, 'uid', '<unknown>')} has invalid "
+                f"direction={direction!r}; expected 'incoming' or 'outgoing'"
+            )
+
+        tensor = self.communication.all_to_all(tensor, stream=stream)
+        if direction == "incoming":
+            tensor = tensor.contiguous()
+        return tensor
+
     def run(
         self,
         dag: Any,
@@ -593,8 +621,8 @@ def run(
                     fwd_buf = dict(self.buffers.task[fwd_pred.uid])
                     self.buffers.release(fwd_pred.uid)
                     detached_outs = list(fwd_buf["detached_outs"])
-                    detached_outs[tensor_idx] = self.communication.all_to_all(
-                        detached_outs[tensor_idx], stream=node_stream
+                    detached_outs[tensor_idx] = self._all_to_all_ep_boundary(
+                        node, detached_outs[tensor_idx], node_stream
                     ).requires_grad_(True)
                     fwd_buf["detached_outs"] = detached_outs
                     self.buffers.task[node.uid] = fwd_buf
@@ -616,10 +644,8 @@ def run(
                     assert grad_a2a_out is not None, (
                         f"BWD_A2A tag={node_tag}: grad at a2a_tensor_idx={tensor_idx} is None"
                     )
-                    if not grad_a2a_out.is_contiguous():
-                        grad_a2a_out = grad_a2a_out.contiguous()
-                    inp_grads[tensor_idx] = self.communication.all_to_all(
-                        grad_a2a_out, stream=node_stream
+                    inp_grads[tensor_idx] = self._all_to_all_ep_boundary(
+                        node, grad_a2a_out, node_stream
                     )
                     bwd_buf["inp_grads"] = inp_grads
                     self.buffers.task[node.uid] = bwd_buf
@@ -868,7 +894,7 @@ def run(
                     weights = self.stages.bucket(ubid).weights()
 
                     with torch.cuda.stream(node_stream):
-                        dinputs, param_groups = self.compute.bucket_backward_input(
+                        dinputs, param_groups, output_backward_ctx = self.compute.bucket_backward_input(
                             stage_outputs_or_loss, output_grads, input_values, iter(weights)
                         )
                     fwd_inputs_full = fwd_out.get("fwd_inputs")
@@ -883,6 +909,8 @@ def run(
                         "inp_grads": inp_grads_full,
                         "param_groups": param_groups,
                     }
+                    if output_backward_ctx is not None:
+                        self.buffers.task[node.uid]["output_backward_ctx"] = output_backward_ctx
                     if dinputs:
                         self.buffers.task[node.uid]["send_output"] = list(dinputs)
                     fwd_out.clear()
@@ -901,12 +929,21 @@ def run(
                     if self._node_meta(node).get("zero_alloc_full_grads_before"):
                         self.params.alloc_full_grads(ubid, node_stream)
                     bwdi_node = next(p for p in node.data_preds if p.task_type == TaskType.BWD_I)
-                    param_groups = self.buffers.task[bwdi_node.uid]["param_groups"]
+                    bwdi_buf = self.buffers.task[bwdi_node.uid]
+                    param_groups = bwdi_buf["param_groups"]
                     weights = self.stages.bucket(ubid).weights()
                     with torch.cuda.stream(node_stream):
-                        self.compute.bucket_backward_weight(
-                            iter(weights), param_groups, ubid=ubid, mb_idx=mb_idx
-                        )
+                        output_backward_ctx = bwdi_buf.get("output_backward_ctx")
+                        if output_backward_ctx is not None:
+                            self.compute.backward_weight_from_outputs(
+                                output_backward_ctx["stage_outputs_or_loss"],
+                                output_backward_ctx["output_grads"],
+                                iter(weights),
+                            )
+                        else:
+                            self.compute.bucket_backward_weight(
+                                iter(weights), param_groups, ubid=ubid, mb_idx=mb_idx
+                            )
                     self.params.accumulate_zero_param_grads_to_flat(ubid, node_stream)
                     self.buffers.task[node.uid] = {}
                     self.buffers.release(bwdi_node.uid)
diff --git a/src/ordering.py b/src/ordering.py
index 88bea28..f61c4e4 100644
--- a/src/ordering.py
+++ b/src/ordering.py
@@ -7,12 +7,14 @@
 )
 
 _DEFAULT_STREAM = "default_stream"
-_OTHER_COMM_KINDS = {
-    "REDUCE_COMM",
+_CRITICAL_PATH_COMM_KINDS = {
     "ALL_GATHER_COMM",
-    "REDUCE_SCATTER_COMM",
     "A2A_COMM",
 }
+_REDUCTION_COMM_KINDS = {
+    "REDUCE_COMM",
+    "REDUCE_SCATTER_COMM",
+}
 
 
 def _serial_topological_order(
@@ -22,7 +24,7 @@ def _serial_topological_order(
     """Serialize topological levels into a deterministic dispatch order.
 
     Nodes with lower topological levels always come first. Within a level,
-    priority order is: SEND > other comm > compute > RECV.
+    priority order is: SEND > critical-path comm > reduction comm > compute/other > RECV.
     """
     if topo_levels is None:
         topo_levels = _topological_levels(dag)
@@ -34,11 +36,13 @@ def node_priority(uid: str) -> int:
         kind = dag.nodes[uid].node_kind
         if kind == "SEND_COMM":
             return 0
-        if kind in _OTHER_COMM_KINDS:
+        if kind in _CRITICAL_PATH_COMM_KINDS:
             return 1
+        if kind in _REDUCTION_COMM_KINDS:
+            return 2
         if kind == "RECV_COMM":
-            return 3
-        return 2
+            return 4
+        return 3
 
     return sorted(
         dag.nodes,
diff --git a/src/piper.py b/src/piper.py
index df32a51..92ace4a 100644
--- a/src/piper.py
+++ b/src/piper.py
@@ -189,15 +189,15 @@ def piper(gm, example_inputs, **kwargs):
     )
     piper_metadata.training_dag = training_dag
     per_pp_training_dags = _split_global_training_dag_by_pp_rank(training_dag)
-    output_dir = getattr(piper_metadata, "output_dir", "out")
+    artifact_dir = getattr(piper_metadata, "artifact_dir", "out")
     for i, subdag in enumerate(per_pp_training_dags):
         zero_chains = _prune_zero_lifetime_metadata(subdag)
         resolve_total_order_per_stream(subdag)
         _add_inter_chain_temporal_edges(subdag, zero_chains)
         if getattr(piper_metadata, "visualize_dag", False):
             log_training_dag_dependencies(subdag)
-            print_training_dag_order(subdag, label=f"pp{i}", rank=i, out_dir=output_dir)
-            render_training_dag(subdag, output_path=f"{output_dir}/training_dag_pp{i}")
+            print_training_dag_order(subdag, label=f"pp{i}", rank=i, out_dir=artifact_dir)
+            render_training_dag(subdag, output_path=f"{artifact_dir}/training_dag_pp{i}")
     piper_metadata.per_pp_training_dags = per_pp_training_dags
 
     logger.info(
diff --git a/src/state.py b/src/state.py
index 9a923ce..815bdc5 100644
--- a/src/state.py
+++ b/src/state.py
@@ -41,7 +41,7 @@ def create_logger(name: str, log_level: str):
 class PiperMetadata:
     actors = dict()
     visualize_dag: bool = False  # Whether to render per-rank DAG PNGs after compilation
-    output_dir: str = "out"  # Base directory for debug artifacts emitted during runs
+    artifact_dir: str = "out"  # Directory for debug artifacts emitted during runs
     training_dag = None  # DAG of annotated model segments and transform-inserted nodes
     per_pp_training_dags = None  # Per-PP-rank DAGs built by the TrainingDAG backend
     compiled_data_store = None  # Ray actor used to share compiled DAGs across DP ranks
diff --git a/test/test_order_directive.py b/test/test_order_directive.py
index 54223b9..6a8dc80 100644
--- a/test/test_order_directive.py
+++ b/test/test_order_directive.py
@@ -217,6 +217,64 @@ def test_apply_order_directive_groups_nested_subdags_with_dummy_source_sink() ->
     assert topo.index(sink_uid) < topo.index("next")
 
 
+def test_apply_order_directive_rejects_reverse_model_dataflow() -> None:
+    dag = TrainingDAG()
+    dag.add_node(_compute("producer", {"slot": 0}))
+    dag.add_node(_compute("consumer", {"slot": 1}))
+    dag.add_edge(TrainingDAGEdge("producer", "consumer", "data"))
+
+    with pytest.raises(ValueError, match="violates model dataflow"):
+        _apply_order_directive(
+            dag,
+            [
+                [{"slot": 1}],
+                [{"slot": 0}],
+            ],
+            directive_idx=3,
+        )
+
+
+def test_apply_order_directive_rejects_cross_device_order_edge() -> None:
+    dag = TrainingDAG()
+    dag.add_node(_compute("rank0", {"slot": 0}))
+    dag.add_node(_compute("rank1", {"slot": 1}))
+    dag.nodes["rank1"].device = [1]
+
+    with pytest.raises(ValueError, match="crosses device placement"):
+        _apply_order_directive(
+            dag,
+            [
+                [{"slot": 0}],
+                [{"slot": 1}],
+            ],
+            directive_idx=4,
+        )
+
+
+def test_shard_removes_zero_grad_lifetime_metadata_with_reduce_scatter() -> None:
+    dag = TrainingDAG()
+    node = TrainingDAGNode(
+        uid="expert.bwd",
+        node_kind="COMPUTE",
+        compute_subkind="BWD",
+        tag={"PP": 0, "EP": 0, "PASS": "B"},
+        device=[0, 2],
+        stream="default_stream",
+        node_meta={"bucket_key": "expert", "fwd_uid": "expert.fwd"},
+    )
+    dag.add_node(node)
+
+    directives._insert_reduce_scatter_comm_nodes(dag, [{"PP": 0, "EP": 0}], [0, 2])
+
+    assert node.node_meta["zero_alloc_full_grads_before"] is True
+    assert any(n.node_kind == "REDUCE_SCATTER_COMM" for n in dag.nodes.values())
+
+    directives._insert_shard_a2a_comm_nodes(dag, [{"PP": 0, "EP": 0}], [0, 2])
+
+    assert "zero_alloc_full_grads_before" not in node.node_meta
+    assert not any(n.node_kind == "REDUCE_SCATTER_COMM" for n in dag.nodes.values())
+
+
 def test_validate_schedule_tags_exist_rejects_tags_missing_from_model() -> None:
     dag = TrainingDAG()
     dag.add_node(_compute("fwd", {"PP": 0, "PASS": "F"}))